Miracle-dz/UniFAR-FTUs huggingface dataset:
Encryption of Digitized Humans: Chasing Privacy in the Internet of Things

: - Video surveillance will greatly increase in the era of the Internet of Things, raising severe concerns about people’s privacy. Since video surveillance systems are invasive, it is very challenging to balance between privacy and system’s functionalities. Traditional algorithms for protection of visual privacy usually encrypt and decrypt the whole video, without considering video objects. However this approach has several drawbacks including lack of scalability of access and security. In this paper a digitized human encryption scheme is proposed which overcomes the aforementioned shortcomings. In particular initially a fast face detection algorithm is adopted which provides initial information to the proposed body detection module. Afterwards a chaotic encryption scheme is designed to provide security and fast scrambling of the extracted digitized humans. Experimental results on real world videos exhibit the promising performance of the proposed scheme.
RELATIONSHIP BETWEEN GEOGRAPHIC PROXIMITY AND GENETIC SIMILARITY AMONG THE NATURAL POPULATIONS OF PINUS BRUTIA TEN.: ITS IMPLICATION ON GENETIC CONSERVATION

Genetic variation in five natural populations of Pinus brutia Ten. , was determined with isoenzyme analyses. Isozymes from nine enzyme systems extracted from haploid female gametophytes of the seeds were separated by horizontal starch gel electrophoresis. In the nine enzyme systems, 14 loci and 32 alleles were observed. The average proportion of polymorphic loci for the populations ranged from 64.3% to 78.6%. The average number of alleles per locus per populations was estimated 2.08 (± 0.2). The mean estimated expected-heterozygosity (He) of the populations was 0.276. A rather high proportion of genetic variation (95.8%) was due to within-population variation and the remaining (4.2%) was due to variation among populations. The level of gene flow (Nm) was 5.75 per generation. According to genetic variation parameters, although there is no significant differentiation among population, the populations in the western and central parts of Mediterranean region (Muğla, Isparta and Mersin) have relatively more genetic variation than populations in the eastern part. Therefore, these populations should be given a high priority in forest tree breeding, selection and for in situ conservation studies in the region.
Copula-Monte Carlo Based Probabilistic Oscillatory Stability Analysis of Microgrid

This paper investigates effects of wind speed and solar irradiance uncertainties on oscillatory stability of Microgrid (MG). Novel estimation technique based on Student’s t-Copula is applied to estimate the probability distribution function (PDF) of wind speed and solar irradiance by taking into account the stochastic dependencies between those energy resources. A detailed MG system comprising of Wind Energy Conversion System (WECS), PV system and Diesel Engine (DE) based distributed generation (DG) units is considered to provide a complete dynamic response of the investigated MG. Distribution of critical modes damping ratio is thoroughly investigated by means of Monte Carlo Simulation (MCS). Probabilistic study of small signal stability in MG implies that the renewable energy resources (RES) uncertainties result in a dynamic change of power-sharing strategies and introduce adverse effects on small signal stability. Moreover, it was monitored that wind speed fluctuation brings more severe impact on system damping than irradiance variation. Consequently, the MG experienced more oscillatory conditions and even lead to unstable situation at high wind speed circumstances. While under solar irradiance change, the investigated MG system can maintain its stable operation.
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition

In Section 4.3, we validated our proposed disentangled multi-scale aggregation scheme by contrasting its effectiveness on the individual pathways of the STGC blocks. Table S1 shows the individual pathway model architectures used for producing the results in Table 1 of the main paper. (For MS-GCN, MS-TCN, and MS-G3D block definitions, refer to Fig. 3 of the main paper.) The factorized pathway models were referred to as “GCN” and “G3D” in Table 1, respectively. Keeping consistent notations, we use T , N , C to denote the number of frames, the number of nodes in the skeleton graph, and the number of feature channels, respectively. N is datasetdependent. The blue highlighted blocks in Table S1 indicate the modules where the number of scales, K, to aggregate from skeleton graphs are adjusted as in Table 1 of the main paper. This applies to both the spatial graph G and the spatial-temporal graph G(τ). S1.2. Visualizing Table 1 & Further Discussions
Investigation and Evaluation of Program Code Optimization Algorithms for Reducing Power Consumption by Combining Genetic and K-means Algorithms

ince 1990, power has been one of the major restrictions in computer systems. Due to lower energy reserves, the importance of saving power is increasingly felt. Today this issue is much more evident in computer science, especially computer programs. Further savings in power is more acceptable in different software aspects because power saving in software aspects is more cost effective than the hardware aspects. Power consumption is a major challenge in programs, thus code optimization algorithms accelerate application's navigation and optimizes reduced power consumption. In the applications, by reducing the consumed power, the processor will have free time and its waste of time is prevented. For example, when a program runs in a CPU with slow clock, it consumes less energy than is the case when it runs with higher clock speed. The purpose of this paper is to reduce power consumption by applications. The power consumption has become a major challenge in computer systems and knowledge and access to parts of the program that have higher power consumption is very important because, by having access to these parts, it is possible to examine their improvement methods. Here, genetic and K-means algorithms are combined and an algorithm is proposed for code optimization that uses the loop interchange optimization technique which can reduce the power consumption by the program. The implementation results indicate the reduced power consumption in implementation of various matrix sizes. Program processing. The purpose of code optimization algorithm is to improve the performance of programs to reduce their power consumption.
Vision Guided Servoing using Neural Networks

| In a closed loop control system, a six-degree-of-freedom robot with a CCD-camera mounted on its end-e®ector, is operated. An object moves freely in 3D space (translation + rotation). The aim is to servo the camera on the object, so that the image of the object is always close to the \reference image", de¯ned by a given reference position of the object with respect to the camera. Classical kinematics equations are ¯rst studied in order to determine the signi¯cant parameters of the problem. Two neural approaches are then proposed: in the ¯rst solution, a Multi-Layer Perceptron (MLP) is fed with the image coordinates of feature points and with previous robot commands. In the second solution, di®er-ent neural input parameters are used, that are based on a±ne transformations between suc-ceeding image coordinates. Results and comparisons with the classical approach (uniform and non-uniform translations of the object) are presented, and show the interest of the approach vs. classical methods.
ANALYSIS OF DATABASE REPLICATION PROTOCOLS

, Informix, Paradox, Sybase, Dbase, etc. This paper has as aim to analyze the general database replication protocols. So to reach this aim, the armature of this document is structured as follows: the first section has serving as introduction to present the context under which this investigation is running, the second section will present the basic methods of database replication, the third will show some deficiencies and proposed solutions and finally the fourth section will conclude this study
Macroeconomic Performance and Banking Industry Performance Nexus: A Perceptual Evidence from Nigeria

The main objective of this study is to through perceptual econometric analysis explore the relationship between macroeconomic performance proxied by macroeconomic stability and financial performance of the Nigerian banking Industry. The study employed Generalised Method of Moments technique in a firm-level analysis of 120 bank branches and 2400 customers in Nigeria. It was revealed that at the 1% significance level, macroeconomic performance positively impacts on the financial performance of the Nigerian banking industry. It was also found that managers sex and employment status significantly predict performance. Female managers seemed to be more profitable than their male counterparts and the higher the employment status of the bank managers, the more their abilities to drive profitability of their banks. Policies aimed at stabilizing the economy would invariably enhance Nigerian banking industry’s growth and stability.
Increasing the Efficiency of Lung Cancer Detection by Improving Local Magnification Operations of the FPR Network

Lately, lung cancer has become a terminal disease increasing the mortality rate due to the late diagnosis of the ailment. Early diagnosis can help reduce the death rate abundantly. The prediction of abnormalities from the given input images is a crucial factor. Deep learning has played an important role in early cancer detection by training networks to detect abnormalities via the given image. Convolution Neural Network (CNN) are most commonly used for cancer detection. In this paper, we propose a CNN with the concept of down-sample in the Region of Interest (RoI) of the Computed Tomography (CT) images where the RoI will be subjected to magnification. Here, the magnification operation will first identify a spot from the upper region and then travel downwards towards the end of the CT image. However, every RoI will undergo local magnification process before the network could detect the next lesion. Detecting lesion are more effective as the lesions are disrupted structures in the human tissues that projects anomalies in the section viewed. Therefore, these anomalies can be useful in detecting lung cancer efficiently.
MAP3Kδ4, an Arabidopsis Raf-like MAP3K, regulates plant growth and shoot branching

MAP3Kδ4 (At4g23050) is a Raf-like mitogen-activated protein kinase kinase kinase (MAP3K) in Arabidopsis thaliana, classified as subgroup B2. The B2 subgroup MAP3Ks have a kinase domain in the C-terminus and a PAS domain in the N-terminal region. PAS domains are found in a variety of proteins and are reported to mediate protein–protein interactions and to sense environmental stimuli. However, the function of MAP3Kδ4 has not yet been determined. We generated transgenic plants constitutively expressing MAP3Kδ4 or its kinase-negative mutant (MAP3Kδ4KN) and characterized their physiological traits. The transgenic plants overexpressing MAP3Kδ4 showed earlier bolting than wild-type plants. They also showed more vigorous growth by both fresh weight and stem length. In contrast, the transgenic plants overexpressing MAP3Kδ4KN showed a highly branched phenotype; MAP3Kδ4 overexpression had no effect on branch number. These results indicated that MAP3Kδ4 is crucial to regulating both plant growth and shoot branching. In addition, MAP3Kδ4 transcripts were found in all Arabidopsis tissues examined and upregulated by auxin treatment in seedlings, suggesting that MAP3Kδ4 functions in an auxin-dependent manner.
UNER: Universal Named-Entity Recognition Framework

We introduce the Universal Named-Entity Recognition (UNER)framework, a 4-level classification hierarchy, and the methodology that isbeing adopted to create the first multilingual UNER corpus: the SETimesparallel corpus annotated for named-entities. First, the English SETimescorpus will be annotated using existing tools and knowledge bases. Afterevaluating the resulting annotations through crowdsourcing campaigns,they will be propagated automatically to other languages within the SE-Times corpora. Finally, as an extrinsic evaluation, the UNER multilin-gual dataset will be used to train and test available NER tools. As part offuture research directions, we aim to increase the number of languages inthe UNER corpus and to investigate possible ways of integrating UNERwith available knowledge graphs to improve named-entity recognition.
Removing Noise During the Filtering Images

In the problems of image recognition, various approaches used when the image is noisy and there is a small sample of observations. The article discusses the issue of noise filtering in image processing. The lack of a priori information complicates the processing of data, as a result of which it is necessary to rely on some statistical models of signals and noise. The use of known filters does not always give the desired result. A Gaussian filter can be used for additive noise, a modified Kalman filter eliminates a wider range of noise
Egg production in a whitefish ( Coregonus shinzi palea ) brood stock: effects of photoperiod on the timing of spawning and the quality of eggs

The results of preliminary experiments in the development of a hatchery brood stock of whitefish of Lake Geneva, Coregonus shinzi palea Cuv. and Val. (lavaretus), are described. Factors likely to influence the date of spawning and the quality of eggs were studied by reference to the performance of wild spawners from the Lake Geneva. Fish were reared in tanks and the ovulation of females occurred spontaneously at the same spawning time as in wild spawners from the lake. Reared females have produced 30 000 eggs/kg against 45 000 in wild fish. The survival rate of a domestic egg was about 10% less than that of wild females. Since the overripening of the ova was rapid it was necessary to check the ripe females twice a week. The exposure of fish to long days in autumn delayed spawning by two months and permitted the synchronization of hatching under optimal conditions of growth. The ovulations were spread over a period of 3 months when the fish were subjected to long days until spawning. In contrast, termination of the long day regime in December induced a synchronization of the ovulations over just one month.
Identifying factors affecting placement status of engineering students using explainable machine learning

Machine learning (ML) models are increasing their penetration in different problem domains due to their increasing accuracy. Majority of the advanced ML-based stand high in predictive accuracy but are wanting in terms of explaining their outcomes. This research study has two objectives. First, to develop predictive models that can predict the placement status of undergraduate engineering students studying in India. Second, to understand the behavior of these ML-based models in terms of most important features affecting the prediction outcome. The outcomes of such study are of importance to any university as ability to anticipate placement status of students can help in planning timely interventions. It is concluded that ‘CurrentCGPA’ and ‘AttendancePercentage’ of a student plays an important role towards getting placed. Moreover, understanding of the behavior of a model help building trust in the model and provide multiple insights of importance to different stake holders.
Nonlinear model predictive control of a Stewart platform based on improved dynamic model

This paper presents a nonlinear model predictive control (NMPC) for six-degree-of-freedom (DOF) Stewart Platform based on parallel mechanisms. Nowadays, NMPC has been used in many applications in industry. First, nonlinear equations related to Stewart platform dynamic are extracted using Lagrange method. The advantages of this dynamic model are improved and are highly accurate because we take into account rational velocity and acceleration of pods around their longitudinal axis and accurately model the friction of joint. In this controller, outputs are anticipated at any time. The main advantage of the proposed controller is that constraints can be applied to inputs and outputs. Another benefit is its high precision. If the weights are added to inputs and outputs, the errors of tracking will reduce in outputs. In current work, three different trajectories were used in simulation to verify the performance of designed and proposed NMPC. Also, we extracted PID controller results to compare and validate the NMPC. Key-Words: Nonlinear Model Predictive Control, Stewart Platform, parallel mechanisms, Lagrange method
Big Data in Telecom Industry: Eﬀective Predictive Techniques on CDRs

Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since the mobile network operators have considered a source of big data traditional techniques are not e ﬀ ective with new era big data, internet of things (IoT) and 5G, as a result handling e ﬀ ectively di ﬀ erent big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution(LTE) to 5G therefore, there is an urgent need for su ﬃ cient big data analytic to predict future demands, tra ﬃ c, and network performance to fulﬁll the requirements of the ﬁfth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the auto-regressive integrated moving average(ARIMA) Bayesian-based curve ﬁtting, and recurrent neural network(RNN) is employed for a data-driven application to mobile network operators. The main framework included in models is an identiﬁcation parameter of each model, estimation, prediction, and ﬁnal data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italian Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using speciﬁc well-known evaluation criteria that show that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset as the RNN (deep learning model).
Human-Computer Interaction with Special Emphasis on Converting Brain Signals to Speech

Huge hurdle neuro engineers face on the road to effective brain-computer interfaces is attempting to translate the big selection of signals made by our brain into words pictures which may be simply communicable. The science-fiction plan of having the ability to manage devices or communicate with others simply by thinking is slowly but surely, obtaining nearer to reality. Translating brainwaves into words has been another large challenge for researchers, but again with the help of machine learning algorithms, superb advances are seen in recent years. The exploitation of deep learning and acceptable machine learning algorithms, the management signals from the brain will regenerate to some actions or some speech or text. For this, a neural network is created for the brain and conjointly a mapping is completed to catch all the brain signals in which neural network will be additionally used for changing these signals into actions. From the past literature, it is being concluded that the Deep Neural Networks are one of the main algorithms that are being placed into use for this research. This review article majorly focuses on studying the behavioral patterns generated by the brain signals and how they can be converted into actions effectively so that people suffering from semi or full paralysis can use this technology to live a normal life if not completely but to a certain extent. Also, it focuses on analyzing and drawing a comparison between linear and non-linear models and to conclude the best-suited model for the same currently available to the researchers.
Machine Learning in Agriculture Application: Algorithms and Techniques

Machine learning techniques with high performance computing technologies can create various new opportunities in the agriculture domain. This paper does comprehensivereview of various papers which are concentrating on machine learning (ML) and deep learning application in agriculture. This paper is categorized into three sections a) Yield prediction using machine learning technique b) Price prediction c) Leaf disease detection using neural networks. In this paper we study the comparison of neural network models with existing models. The findings of this survey paper indicate Deep learning models give high accuracy and outperform traditional image processing technique and ML techniques outperforms various traditional techniques in prediction.
Do mycorrhizas improve tropical tree seedling performance under water stress and low light conditions? A case study with Dicorynia guianensis (Caesalpiniaceae)

We tested the response of seedlings of Dicorynia guianensis, a major timber tree species of French Guiana, to mycorrhizal symbiosis and water limitation in a semi-controlled experiment under natural light conditions. Under well-watered conditions, mycorrhizal colonization resulted in an increase of net photosynthesis, growth and phosphorus uptake. When submitted to water stress, no growth reduction of mycorrhizal seedlings was observed. Mycorrhizal seedlings were more sensitive to drought than non-mycorrhizal ones in terms of carbon assimilation, but not with regard to stomatal closure. In contrast to previous studies on temperate tree seedlings, this result precludes a mycorrhizal effect on the hydraulic properties of this species. Furthermore, our results suggest that below a specific threshold of soil moisture, carbon assimilation of D. guianensis seedlings was decreased by the mycorrhizal symbiosis. This is probably related to the competition between the plant and its host fungus for carbon allocation under low light intensity, even though it did not seem to have a significant effect on mortality in our experiment.