Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing user behavior in Online Social Networks: Study of seasonal changes in the Moroccan community on Facebook

Online Social Networks (OSNs) considers user as a central axis, its crucial role has allowed it to benefit from communication technologies and to remain connected with communities. The aims of this work is to provide an overview of the use of social networks based on the seasonal changes’ analysis linked to the Moroccan community behavior on Facebook. Measuring user’s behavior is an important approach to establish a knowledge extraction process based on human uses. The descriptive study based on a monthly representation showed the influence of interactions by the seasons of the year and that users interact with each other according to a distributed information dissemination model according to comments and topics covered in the form of interlinked discussions, posts deal with current social, political and sports issues are the global interests of the targeted community. This work made it possible to model the seasonal component and to deduce the mathematical model coefficients based on trigonometric function using the least squares method.
The Use of Computer Based Test Battery for the Assessment of Cognitive Functions in Elite-Level Strength Training

The aim of this study was to propose a standard test battery consisting of necessary tools for measuring and comparing the various aspects of cognitive outcomes. The battery was used to determine whether adolescent women performing regular elite-level strength training differed from their sedentary peers in terms of cognition, and also to determine how a single session of strength training affects cognition in highly trained adolescents. Motor functions, ability of sustaining attention and executive functions of 25 elite female weightlifters and 22 sedentary females were evaluated through finger tapping performance, visual reaction time (VRT) and recognition visual reaction time (R-VRT) data. Weightlifters were tested before and after a training session, sedentary controls were tested only during resting. There was a significant increase in mean complex R-VRT of weightlifters after training (p<0.01). In R-VRT tests, rate of false answers increased significantly after training (p<0.05). Mean VRT of weightlifters (during rest) and sedentary peers were not different in any of the tests (p>0.05). Total number of taps and mean inter-tap intervals did not show any difference in the weightlifter group before and after training, also between weightlifters and sedentary controls (p>0.05). Elite level strength training does not improve cognition in adolescence. Adolescent weightlifters' executive functions are deteriorated following a single training session however, this effect is temporary.
New Lithium Battery Balancing Circuit Design Using Isolated Converter

In order to pursue small size, high efficiency and low cost, this paper proposes a method of balancing the battery pack using an isolated bidirectional converter. The proposed converter adopts active clamping technology and synchronous rectification technology, combined with switch control, and can achieve zero voltage switching (ZVS) effect and improve efficiency. In order to improve the accuracy of the state of charge (SOC) estimation, the average SOC is calculated by a lookup table method and a Coulomb integration method to determine the equilibrium state of the battery pack.
Hardware-In-the-Loop Simulation of UAV for Fault Injection

Unmanned aerial vehicles (UAVs) are widely used in military and civilian applications. The safety of UAV has been paid more and more attention. Prognostic and Health Management (PHM) of UAV can realize the fault prediction during flight and make an appropriate response according to potential faults. Therefore, the safety and reliability of UAV are improved. However, the existing PHM research on UAVs has the following two challenges: a) the number of fault samples in historical flight data is small, and it is impossible to cover multiple fault modes of UAVs to meet the demand of modeling and verification, b) the actual flight verification of the UAV PHM technology cannot be carried out directly through actual flight test. Aiming at the above problems, this paper proposes a fault data generation method based on the Hardware-In-the-Loop Simulation (HILS) technology. We construct a UAV model and its corresponding fault models by analyzing fault features of UAV flight control system. It can simulate the actual faults and generate flight data with multiple fault modes, thereby available data are provided to support PHM research. Besides, PHM algorithms implemented on airborne PHM modules can be verified via this platform in real-time. The experiment results show that the HILS platform has a good performance for fault injection.
Bearing Performance Degradation Assessment based on A Combination of Multi-Scale Entropy and K-medoids Clustering

Bearing performance degradation assessment (PDA) underlies the residual useful life prediction and maintenance decision-making. In bearing fault diagnosis and PDA, a commonly used method is clustering analysis, among which K-medoids clustering is not susceptible to extreme data. The amalgamation of K-medoids clustering and Degree of Membership (DOM) is expected to give a health indicator with a determined value range for PDA. In this study a diagnostic model for bearing PDA based on Multi-scale entropy (MSE) and K-medoids clustering is proposed. Extracting the multi-scale entropy of the rolling bearing vibration signal to construct the K-medoids clustering model. The tested data are input into the model to obtain the Degree of Membership as the index to evaluate the current bearing operating state. Resultant health indicator is used to depict bearing health condition with the help of an adaptive threshold. Results on artificially induced faults and bearing run-to-failure data demonstrate the proposed method is able to track the progress of bearing faults and detect them at incipient stage.
Accelerating Random Network Coding using 512-bit SIMD Instructions

Random network coding (RNC) can be used to increase bandwidth, to reduce delay, and to improve reliability of networked systems. This paper presents experimental results on various Single Instruction Multiple Data (SIMD) instruction sets used to increase the operation speed of Galois field arithmetic in the encoding/decoding process of random network coding. The decoding performances of RNC using 512-bit, 256bit and 128-bit wide SIMD instruction sets are compared to show how much improvement can be achieved with the SIMD instruction set width when the generation size, the block size, and the number of threads are varied. According to our results, a RNC decoder with 512-bit SIMD shows approximately 58% and 26%, higher throughput compared to those with 128-bit and 256-bit SIMD, respectively.
Design of Embedded Voice Conversion System Based on MEMS Digital Microphone

This paper studied the voice conversion algorithms based on Gaussian mixture model and non-negative matrix factorization, and selected non-negative matrix factorization algorithm to implement voice conversion on embedded system. The whole system is divided into two phases: the training phase and the conversion phase. The training phase is completed on the PC and the conversion phase is completed on the STM32F407 embedded development board. Experiments are conducted with datasets acquired by MEMS microphones and a suitable evaluation system is established. The results show that the embedded voice conversion system works effectively.
The Impact of Architecture on the Deep Neural Networks Training

Deep neural networks are able to solve much more complex and nonlinear problems than very popular but shallow technologies such as ELM, SVR or SLP. Despite of their power deep neural networks are difficult to apply due to problems with effective and successful training caused by ‘vanishing’ problem. The paper shows that these problems can be reduced by using appropriate network architecture. The paper presents the influence of the neural network architecture on the training effectiveness and the training time. Selected network architectures such as BMPL, FCC and MLPL are described. Presented experimental results of research confirming the significant influence of architecture on the success of network training.
FRI0205 Gender and previous treatment influence outcomes from abatacept in a 5-year rheumatoid arthritis cohort

Background Abatacept is a novel biological T-cell modulation drug used in RA. Data on patient characteristics, diagnosis, preceding treatment and outcomes of abatacept had been collected since approval in the national Swedish Rheumatology Quality register (SRQ). Objectives To evaluate the effects of abatacept in clinical practice five years after approval in a cohort of rheumatoid arthritis patients. Methods Observational data from the SRQ was collected for the period from April 1st, 2006 to September 30th, 2011.Survival analysis (Kaplan Meier curves) with right censoring and log-rank test of equality across strata was performed. Comparisons between all pairs of curves with Tukey-Kramer multiple-comparison adjustments were applied. Results There were 789 patients who started abatacept treatment between April 2006 and September 2011. 614 patients (487 females, 79.3%) had RA diagnosis with median (min-max) “age at start of abatacept” of 60 years (23-89) and median “disease duration” of 11 years (0.1-67). Median “time since first given biological treatment to abatacept start” was 3.4 years (0-11). Median “abatacept treatment time” was 0.9 years (0-5). Abatacept was used as a first biological treatment in 68 patients (11%), after one or two previous and unique biological drugs in 341 (56%) patients, after three or more unique biological drugs in 205 (33%) patients. After 1, 2, 3 and 4 years drug survival was 80%, 73%, 63% and 63% respectively in bio-naïve patients, 64%, 51%, 49% and 46% in patients with 1-2 previous biological treatments and 57%, 41%, 30% and 30% in patients with three or more previous biological treatments. Bio-naïve patients had significantly longer drug survival time than patients with three or more previous treatments (p=0.006), and patients with 1-2 previous biological treatments had longer drug survival time than patients with three or more previous treatments. Disease activity outcomes were measured as DAS28 before treatment and after 3, 6 and 12 months of treatment. A per-protocol analysis was used. There was significant reduction of DAS28 at all time points compared with baseline. Median DAS28 at baseline was 5.5, at 3 months 4.5, at 6 months 4.3 and at 12 months 3.9. The effect of abatacept expressed by DAS28 was significantly better in men than in women at all time points except for 6 months. A longer drug survival was found also in men compared to women (p=0.05). At 1, 2, 3 and 4 years respectively drug survival was 68%, 59%, 54% and 51% in men and 62%, 47%, 40% and 39% in women. Conclusions In the cohort of 614 RApatients starting abatacept treatment during 2006 – 2011, drug survival time was longer for bio-naïve patients compared to patients treated with previous biological drugs. A gender difference in favour of men was seen in the reduction of disease activity as well as in abatacept drug survival. Disclosure of Interest S. Lindblad Grant/Research support from: BristolMyersSquibb as an unrestricted research grant, L. Stawiarz: None Declared
Ambient Mode: A Novel Service and Intelligent Control based on User Awareness using BLE and Wi-Fi

In this paper, we introduce a novel service, Ambient Mode, which delivers a functional value to customers when TV is off. Instead of showing a meaningless black screen, the Ambient Mode provides meaningful and emotional background experiences. To mitigate users' concern about the cost of using the Ambient feature, the TV intelligently detects the presence of a person in the room from the registered mobile's BLE (Bluetooth Low Energy) signal and Wi-Fi connection to AP (Access Point). If there is no user nearby, the TV intelligently turns off, to save energy (a manual-off timer is available too). To interact with both Android phone and iPhone, we design a general BLE advertisement for Android phone and an iBeacon format for iPhone. On the BLE proximity-based service, the Ambient Mode can be turned off/on based on the BLE signal presence. On the Wi-Fi connection based service for long-range detection, TV can detect mobile's AP connection using ARP (Address Resolution Protocol). The proposed service and intelligent controls have been implemented as real products and launched the service of Ambient Mode in the market.
Analysis of Automation Systems and Virtual Reality Applications in Smart Libraries

With the rapid development of information technology, automation systems and virtual reality technology have become important pillars of economic and social development. As a key institution to store books and information materials, develop information resources and provide knowledge support, libraries are constructing and optimizing their systems. Based on the literature review about library automation systems and virtual reality application, this paper analyzes their expectable influence to promote more concern from scholars and technical personnel, and thereby, enhance the overall management and service level of libraries, especially for smart libraries, which represent the development trend of this "smart+" era.
Adaptation and Evaluation of Deep Learning Techniques for Skin Segmentation on Novel Abdominal Dataset

Skin segmentation plays an important role in a wide variety of biomedical image processing applications, such as skin cancer identification, skin lesion detection, and wound isolation. However, contemporary research has been mainly based on facial and hand skin datasets, with no other body regions considered for skin pixels sampling. Segmenting skin specifically in the abdominal region can aid in robotic abdominal surgeries and treatment procedures, such as robot-assisted laparoscopic surgeries and abdominal ultrasounds. A robust and highly accurate abdominal skin detection technique thus becomes imperative. To this end, we compiled a novel dataset of 1,400 segmented abdominal pictures and adapted and compared four abdominal skin segmentation techniques: one based on thresholding and three deep learning techniques, namely a fully connected neural network for pixel-level classification, and two convolution-based networks, U-Net and Mask-RCNN. We show that the U-Net model outperforms the other segmentation techniques, resulting in a pixel-to-pixel mean cross-validation accuracy of 95.51% on our Abdominal dataset. The incorporation of the Abdominal dataset in the training helped improve the abdominal skin segmentation accuracy by 10.19%. The U-Net model proved to be computationally the fastest, enabling real time skin segmentation with a processing rate of 37 frames per second.
Integration of Common-Path Heterodyne Interferometry and Half-Wave Plates to Measure the Optical Activity of Water in Small Polarization Rotation

This study adopted common-path heterodyne interferometry and half-wave plates with high stability to propose a novel method for measuring small polarization rotation. Under an azimuth angle of 22.5°, evident phase changes are observed in a half-wave plate. Therefore, half-wave plates can be used to distinguish different types of materials with optical activities (e.g., various types of water) by observing the phase changes in these materials. In addition, a look-in amplifier can be used to determine the extent of phase changes, and related data can be recorded in a tabular form to derive equations for the relationship between the polarization rotation and different water types.
Klm-PPSA: Klm-based profiling and preventing security attacks for cloud environments: Invited Paper

Cloud computing is the newly emerged technology adopted by many organizations due to its different benefits. Unfortunately, despite all the benefits offered by the cloud, there are certain concerns regarding the security issues related to the cloud platform which can threaten its widespread adoption. In this study, we suggest a scalable model to profile and prevent security attacks in the application layer of a cloud environment using an accurate and interpretable machine learning algorithm called regularized class association rules. The proposed model is based, first, on three additional security factors $(k, l$ and $m)$, second, on the traditional authentication methods such as passwords and biometrics including keystroke to grant access to the cloud services/resources for an authorized user. Moreover, a case study of the proposal is given in order to validate the model and its usefulness. Eventually, a simulation was done to test the model performances.
Degradation of PhotoVoltaic (PV) Panel Performance due to Shading Effect- Case of Pakistan

Pakistan is geographically suitable for the installation of Photovoltaic (PV) cells to produce electricity. Pakistan covers the maximum of its land where sunny days prevail throughout the year. The energy produced from these panels can be affectively distributed throughout and excess of it can be provided to the nearby grid. The population of Pakistan is tremendously enhancing due to which construction of buildings is growing up causing shading that harmfully affects the performance of PV panel. The production of electricity from the renewable energy sources is surely considerate and friendly. No detrimental chemicals are given out making the environment pollution free. The performance of the PV cells solely depends upon many factors i.e. intensity of light, shading, temperature, dust and cells configuration. In this paper the shading effect over the PV cells and how to nullify its substandard effect on the performance of PV panel is overviewed. It was accounted that the depletion in the maximum output power & short circuit current was 34% and 40% respectively due to the 30% shading strength in mono crystalline PV panel. For the better efficiency of the PV array, modules are coupled in the series and parallel configuration to enhance the voltage & current respectively. Bypass diodes are instigated to overwhelm the shading effect.
A Hybrid Solution for Human Activity Recognition: Application to Wrist-Worn Accelerometry

This paper presents a systematic approach that is able to classify activities of daily living using a wrist-worn triaxial accelerometer. The strategy, which divides the multi-class problem into several binary classification sub-problems, is a mixture of both feature thresholding method and machine learning. The idea behind our design is to separate cyclic activities from transient ones, then to provide explicit recognition in these two categories separately. Experimental results have successfully proven the effectiveness of the proposed activity recognition process.
Dual Input Converter Fed Transformerless Multilevel Inverter for Standalone PV Application

Most of the transformers less photovoltaic inverters fail to extract maximum power from each panel especially during partial shading conditions. In this regard, a dual input based dual stage transformer less multilevel inverter for maximum power extraction from each panel is proposed in this paper. The proposed dual input structure extracts the maximum power form each panel at all operating scenarios. The T-type multilevel inverter is used for DC/AC conversion. The proposed structure offers advantages of simplicity, reliability and control flexibility. The key advantage of the transformerless topology presented is that without any complex modulation strategies nor is any additional components/complex circuitry, the leakage currents in the permissible limits as per German standards. The proposed topology is comprehensively analyzed for different operating scenarios in MATLAB/Simulink environment.
Use Case Specification for Upper-limb Rehabilitation with FES and Exoskeleton

The goal of this research is to support the disabled who want to restore upper limb motor function lost by stroke, injury or other causes. These days, existing companies, utilizing traditional technologies for sensing and actuating devices, are facing a new market trend which is integration of artificial intelligence technology. Our work aims to facilitate AT-HOME automated rehabilitation which demands smart and safe adaptation of rehabilitative systems. This paper presents the scope of our project, an application scenario including activities of daily living (ADL), and extracted use cases for this project.
Research on Prognosis for Engines by LSTM Deep Learning Method

Prognostics and health management (PHM) technology has been successfully applied in many complex equipment. However, with the equipment becoming more and more complex, the working conditions changing with time, and the equipment status information increasing, it is difficult by traditional technologies to cope with the new situation and new application scenarios. The application of deep learning method in many fields proves the ability of this method to deal with massive and complex data. In this paper, the special recurrent neural networks (RNN) called long-short term memory (LSTM) network are used to estimate the remaining life of engines with the data of PHM08 Challenge Competition. First, standardize the original data and add life labels in the data preprocessing stage. Then the influences of different data input methods on the prediction results are studied, and the results show that proper method is to input all the time series information at one time. The over-fitting phenomenon can be reduced to some extent by reducing the complexity of the neural network. Thus, a remaining life prediction method based on multi-dimensional data is obtained. The final result was uploaded to the competition’s scoring system and got good results, which confirmed the accuracy of this method. Therefore, the article summarizes a highly accurate LSTM-based multidimensional data failure prediction method.
Effect of Colored Content on Depth Perception of S3D Images

This paper has mainly focused on the depth perception effects and the influence factor of hue for colored content of stereoscopic 3D (S3D) images. The subjective evaluation experiment was designed with different color combinations of foreground object and background. Through the data analysis, the hue of background was proved to be a significant indicator for certain colored foreground content with negative disparity. For most of the contents designed with positive disparity, the effect of background hue was not obvious. To get better effect of space sense, for all of the foreground colors with either positive or negative disparity, black can get the higher rating results than any other colors. Besides, appropriate color combination could bring idea stereo experience for observers. Our studies have practical significance and reference value to improve viewing experience and relative objective models for the S3D image and videos.
Effectiveness of Beam Steering for Non-Coherent OFDM-DBPSK for Summer and Winter Hydrology

Sea and ocean hydrology has a huge impact on sound propagation processes and underwater acoustic communication consequently. It is not constant, so underwater acoustic communication systems are better to be able to adapt to it. This paper presents a study of the feasibility of phased array with active beam steering implementation in non-coherent OFDM underwater acoustic communication systems with differential binary phase-shift keying of subcarriers. Results of the experimental assessment of this approach in both winter and summer conditions of real aquatic areas of Sea of Japan (Peter the Great Bay) are shown.