Miracle-dz/UniFAR-FTUs huggingface dataset:
GAIT RECOGNITION BASED ON GEI & GABOR FILTER

The field of Human Gait Recognition is one of the active research fields. The number of studies has increased which indicates that this area is important. The present study has many benefits as it enables us to recognize a person through his gait in front of a camera even if the gait picture is taken from a long distance with a low-quality camera. Moreover, many other biometrics require special equipment; and some of them require taking the picture from a short distance. This study aims at finding a solution with high quality and efficiency in the process of recognizing human gait. This is achieved by providing a strong training set. For this purpose, we created two training sets. The first depends on figuring out human features by using Gait Energy Image (GEI). The second set depends upon figuring out human features by using Gait Energy Image (GEI), and then filtering it by using Gabor Filter. This study was conducted as an experimental research to gain ideal parameter values for Gabor Filter that contributes to enhancing the training sets samples that leads to increasing the system performance efficiency, and we found the best values for Gabor filter parameters for our system is kernel =3 ,theta = 225 , 𝛾 =1.25, 𝜓 =0, 𝜆 =3.5, and a bandwidth =2.8. The training and testing were conducted by using (SVM) Algorithm, the study uses the gait database (CASIA-B) to evaluate our system, Then, we used (CCR) for Performance measurement . The study reached good ratios for all views with an average of 76.50% for (GEI+GABOR FILTER) and 76.42 for (GEI). Moreover, the study achieved an ideal percentage of 78.10% for (GEI+GABOR FILTER) and 77.66 for (GEI), as compared to previous studies of the positions of normal-walking, wearing-coat, and carrying-bags at a view of 90.
OPTIMIZED CONTROLLER BASED VOLTAGE QUALITY ASSESSMENT IN GRID CONNECTED HYBRID MICROGRID

Growing distributed generation based on renewable energy sources pushed for the improvement of power quality. The variable nature of interconnected renewable energy sources such as wind and solar generators, which are reliant on weather and climate change, has an impact on the power quality of micro grids. Power quality assessment involves a number of metrics; including voltage quality, voltage imbalance, sag score, and current score (current THD). Good power quality assessment reduces the energy losses in a power system resulting in high profitability. In this research; voltage quality assessment in hybrid microgrid using a novel krill herd optimization (NKHO) technique is described. Use of NKHO in voltage quality assessment for hybrid microgrids offers a powerful and efficient means of optimizing the control and operation of the microgrid, ensuring its reliability and minimizing its impact on the grid. The proposed technique enables the identification of sensitive buses and the optimal sizing of energy storage systems to mitigate the impact of voltage sags. This research assessed the application of a fractional order proportional, integral and derivative (FOPID) controller for microgrid voltage regulation. The proposed hybrid microgrid is developed using Matlab/Simulink environment.
Elma+ : An Ensemble Learning Based Model For Accurate Prediction Of Software Defects

: The recent developments made in the software engineering technologies has introduced the growth of the data. In order to handle the explosive growth of the data, many software quality assessments are introduced to validate designed software. Software defects assessment is one of the finest qualities of software engineering models. Software defects classification is the basis for effective management of software defects. This paper presents a novel ELMA+ technique to predict the classes of software defects. Here, an ensemble learning approach is taken to do the prediction systems. Initially, the defect data is collected from the public repository. A simpler exploratory data analysis is done to know the count of presence and absence of software defects. SMOTE technique is applied to preprocess the collected dataset. The presence of oversampling data has lowered the participation of minority classes during the training process. In order to leverage the minority classes and the presence of data ambiguity issues, the oversampling data are aligned with the synthetic data creation. The generated synthetic examples in alignment to the real-time data behave like feature space instead of data space. The minority of each class combines with the line segments of the nearest data points. Once the majority and minority classes are defined properly, then the oversampled data are sorted out. The scaled features are then fed into the ensemble of classifiers, namely, k-nearest neighbors (k-NN), Adaboost and Bagging. These three classifiers take the feature scaled data as input to classify the defects of the softwares. The proposed framework is simulated and has shown the efficacy of the proposed ensemble classifiers in terms of accuracy, sensitivity, specificity and the precision. The comparative analysis done from the perspective of before and after SMOTE application. It is clearly understood from the achieved
Prospective relationships of sedentary behaviors with incident depression and potential biological mediators

Sedentary behavior (SB) is associated with depression, yet little is known about the relationship between different types of SB (e.g., passive versus mentally-active) with depression and potential biological mediators of these associations. We used cohort data from the 1958 National Child Development Study (n = 4,607;50.4% women), employing the 44y wave as baseline, and the 46y, 50y and 55y waves as follow-up. TV-viewing and watching videos (i.e., passive SB), and time sitting while doing light activities such as deskwork or driving a car during worktime (i.e., mentally-active SB) were self-reported by participants. Depression diagnosis was self-reported during the initial and follow-up waves and the participants answered the CIS-R at 44y. Waist circumference, C-reactive protein, and glycated hemoglobin were assessed at baseline and examined as potential mediators. We created Cox proportional hazards regression models, adjusting for gender, education, employment status, physical activity, tobacco smoking, and alcohol consumption at baseline. There were 636 cases of incident depression over 47,184 person-years. Passive SB was associated with higher hazards of incident depression (HR: 1.43; 95%CI: 1.19–1.71), while there was no association with mentally-active SB. Waist circumference (coe�cient: -0.03; 95%CI: -0.05; -0.01; 9.2%) and C-reactive protein (coe�cient: -0.03; 95%CI: -0.04; -0.01; 8.3%), but not glycated hemoglobin, partly mediated the association with passive SB. Higher durations of time spent in mentally-passive SB can increase risk of incident depression. The mediating contributions of waist circumference and C-reactive protein point to possible inammatory-related mechanisms and suggest potential bene�ts of jointly addressing metabolic and mental health.
Optimal cognitive distance and absorptive capacity

This paper tests hypotheses that in inter-firm alliances innovative performance is an inverted-U shaped function of cognitive distance, that the resulting optimal cognitive distance is higher for exploratory than for exploitative learning, and that optimal cognitive distance depends on absorptive capacity. Most hypotheses are confirmed for 994 alliances in several industries, in the period 1986-1996. The results indicate a new hypothesis that with more knowledge one needs larger cognitive distances to find novelty.
A HYBRID PLACESNET-LSTM MODEL FOR MOVIE TRAILER GENRE CLASSIFICATION

Video analysis technology has always been an essential branch of computer vision. Analyzing the movie genres is beneficial to pushing relevant and exciting content to target customer groups to achieve precision marketing. There are some researches on movie trailers to classify movie genres. However, most of them are based on movies' auditory and visual content using various machine learning models or neural network models for classification. This paper considers the features learned using scene-based neural network models in movie genre classification. This paper proposes a hybrid PlacesNet-LSTM (long short-term memory) model for movie trailer genre classification. To compare the performance, the paper also studies two schemes using various video and audio features based on multiple machine learning models and LSTM, respectively. The experimental results show that the PlacesNet-LSTM model on scene recognition achieves the best classification performance in various combinations.
Faster Access Of Credit Cards Through Data Cube Technology

From the past years, it is observed that Online Analytical Processing (OLAP) became more popular among the people for accurate analysis of data through the complex structure of servers. It is due to fast growing of internet and increasing the size of data files. Therefore, it is necessary to represent the data in a simple form because it consists of multidimensional, which can be very simple through the traditional table representation. In the current scenario, the size of database is increasing and many organizations are storing the data over on the cloud servers. The present work is an attempt to store the complex database into the data cube technology, thereafter extracted the desired database within fraction of time. Further, correlation coefficient is obtained for analysis of data. The proposed technique is efficient and less time consuming for the long database, considered in the form of credit card information. That proposed technique can be used for decision-making process in banking industry.
Scaling and Rounding Periodic Event Scheduling Instances to Different Period Times

The Periodic Event Scheduling Problem (PESP) is a notoriously hard combinatorial optimization problem, essential for the design of periodic timetables in public transportation. The coefficients of the integer variables in the standard mixed integer linear programming formulations of PESP are the period time, e.g., 60 for a horizon of one hour with a resolution of one minute. In many application scenarios, lines with different frequencies have to be scheduled, leading to period times with many divisors. It then seems natural to consider derived instances, where the period time is a divisor of the original one, thereby smaller, and bounds are scaled and rounded accordingly. To this end, we identify two rounding schemes: wide and tight. We then discuss the approximation performance of both strategies, in theory and practice.
ESH: Design and Implementation of an Optimal Hashing Scheme for Persistent Memory

Recent advancements in memory technology have opened up a wealth of possibilities for innovation in data structures. The emergence of byte-addressable persistent memory (PM) with its impressive capacity and low latency has accelerated the adoption of PM in existing hashing-based indexes. As a result, several new hashing schemes utilizing emulators have been proposed. However, these schemes have proven to be suboptimal, lacking scalability when implemented on real devices. Only a handful of hash table designs have successfully addressed critical properties such as load factor, scalability, efficient memory utilization, and recovery. One of the main challenges in redesigning data structures for an effective hashing scheme in PM is minimizing the overhead associated with dynamic hashing operations in the hash table. To tackle this challenge, this paper introduces ESH, an efficient and scalable hashing scheme that significantly improves memory efficiency, scalability, and overall performance on PM. The ESH scheme maximizes the utilization of the hash table’s available space, thus reducing the frequency of full-table rehashing and improving performance. Consequently, this scheme achieves a high load factor while minimizing the need for rehashing. To evaluate the effectiveness of the ESH scheme, we compare it to widely used dynamic hashing schemes employing similar techniques on Intel Optane® DC persistent memory (DCPMM). The experimental results demonstrate that ESH outperforms CCEH and Dash in terms of data insertion performance, exhibiting a 30% improvement over CCEH and a 4% improvement over Dash. Furthermore, ESH improves the lookup operation by nearly 10% compared to Dash, while achieving a load factor of up to 91%, surpassing its competitors.
Cryopreservation of ovarian tissue Opportunities beyond fertility preservation and a positive view into the future

therapy (cHRT) by the use of stored ovarian tissue could be one such option comprising both induction of puberty in prepubertal POI girls, treatment of POI and premature menopause, and as primary prevention at the onset of menopause. In the current review, we explore known and entirely new applications for the potential utilization of OTC including cHRT, social freezing, culture of immature oocytes, and a modern ovarian resection for women with polycystic ovaries, and discuss the indications hereof.
Embedded One-Class Classifiers for media data classification. Pattern Recognition, 60, 585-595

This paper introduces the Graph Embedded One-Class Support Vector Machine and Graph Embedded Support Vector Data Description methods. These methods constitute novel extensions of the One-Class Support Vectors Machines and Support Vector Data Description, incorporating generic graph structures that express geometric data relationships of interest in their optimization process. Local or global relationships between the training patterns can be expressed with single graphs or combinations of fully connected and k NN graphs. We show that the adoption of generic geometric class information acts as a regularizer to the solu-tion of the original methods. Moreover, we prove that the regularized solutions for both One-Class Support Vector Machine and Support Vector Data Description are equivalent to applying the original methods in a transformed (and shared) feature space. Qualitative and quantitative evaluation of the proposed methods shows that they compare favorably to the standard OC-SVM and SVDD classiﬁers, respectively.
Well Constraints in Full Waveform Inversion and its Application in Time-lapse Seismic

Full waveform inversion (FWI) has become an integral part of the velocity model building flow due to its resolution, especially in the shallow regimes. Most of the times the wide angle acquisition is not available because of its expense and we still would like to use the power of FWI by utilizing the vintage data. In these older prospects since there is a lot more geological information available besides just seismic data, we can use information from wells for example, to constrain the FWI iterations. Even though well information is at our disposal it is difficult to incorporate it into the traditional FWI which is represented by data misfit objective function. We incorporate wells in the model is to build an apriori model from them and take this into account in the objective function. The objective function can be extended by a model misfit term alongside the data misfit one. We demonstrate how the new objective function works on real data. Furthermore we introduce two workflows to recover model difference between the base-line and the monitor or monitor surveys by FWI in the time-lapse seismic world.
Contextualizing Object Detection and Classification

We investigate how to iteratively and mutually boost object classification and detection performance by taking the outputs from one task as the context of the other one. While context models have been quite popular, previous works mainly concentrate on co-occurrence relationship within classes and few of them focus on contextualization from a top-down perspective, i.e. high-level task context. In this paper, our system adopts a new method for adaptive context modeling and iterative boosting. First, the contextualized support vector machine (Context-SVM) is proposed, where the context takes the role of dynamically adjusting the classification score based on the sample ambiguity, and thus the context-adaptive classifier is achieved. Then, an iterative training procedure is presented. In each step, Context-SVM, associated with the output context from one task (object classification or detection), is instantiated to boost the performance for the other task, whose augmented outputs are then further used to improve the former task by Context-SVM. The proposed solution is evaluated on the object classification and detection tasks of PASCAL Visual Object Classes Challenge (VOC) 2007, 2010 and SUN09 data sets, and achieves the state-of-the-art performance.
Building cyber knowledge through online portals - a case study

Online learning has become a key role player in facilitating the growth of the knowledge economy in the past few years with Massive Open Online Courses (MOOCs) becoming a hot topic of debate. Although there is still much speculation about how these online courses may transform academic institutions in the future, the ease of access to information and learning is undeniably convenient. In certain instances, however, end users are not necessarily looking to complete a course or receive formal training – they merely require on-demand access to single pieces of information that are of some relevance at a specific time. This presents an opportunity for web portals to address an individual user’s needs by providing them with quick access to core pieces of information. These portals can also address a bigger audience by providing information that is relevant to various levels of expertise as opposed to MOOCs that are often level-dependent. In this paper we discuss the development of such a web portal, the University of Johannesburg (UJ) Centre for Cyber Security (www.cybersecurity.org.za), by looking at the changes that the portal had undergone from 2013 to 2016, and also list some of the lessons learnt. We conclude the paper by reiterating important aspects to consider when developing such a portal also noting that portals and MOOCs may be used in a complementary fashion to further enforce or encourage user awareness and knowledge gathering.
THE IMPACT OF FACTORS THAT CAUSE ISSUES FOR NON-ICT BACKGROUND WORKERS IN ICT JOBS ON THEIR JOB SATISFACTION

It is not uncommon to see non-ICT educated workers doing ICT-based roles and jobs. Through a preliminary assessment, it is discovered that workers with non-ICT background that work in ICT-based roles often face issues. The identified issues consist of four factors: the lack of information and documentation, insufficient training, insufficient mentorship, and the lack of prior IT knowledge and competencies. These issues cause effects and behaviour that indicate dissatisfaction in their work life. Working performance and person job fit were also thought to act as the bridge between these issue-causing factors and the worker’s job satisfaction. Prior studies regarding job satisfaction, working performance, and person-job fit in various fields have delved in these aspects for various fields, but not in the ICT area – especially in Indonesia. Hence, this study aims to verify if these factors contribute to job satisfaction overall, as well as its mediating variables. It is discovered that prior ICT knowledge and competencies have a significant impact towards job satisfaction through working performance and person-job fit – and greatly impacts both factors. Working performance is also impacted by the information and documentation regarding ICT technologies. However, the lack of information and documentation, training, and mentorship do not affect job satisfaction significantly. Consistent with other research in various fields, working performance and person-job fit have great impact towards job satisfaction. From this study, it is discovered that the prior ICT knowledge and competencies of the worker are key aspects that can contribute to their performance, fit, and job satisfaction overall. These findings may be used as input for various recruitment and preparation strategies for companies that seek to hire ICT workers from other educational backgrounds.
PRELIMINARY STUDY OF TRACTIVE EFFECT OF LATERAL ROOTS OF PINUS YUNNANENSIS ON SHALLOW SOIL MASS

Lateral roots of plants are provided with mechanical stabilizing effect on the rooted soil, through their tractive effect. In contrast to the effect of vertically extending roots, the traction effect reinforced the soil by enhancing the tensile strength of the rooted soil zone. To verify whether or not a tractive effect exists in the roots system of Yunnan pine forest ( Pinus yunnanensis ) in the Hutiaoxia Gorge, SW China, a direct in situ test was conducted at two sites in the gorge. The results show that, in the topmost soil (0～20cm), the lateral roots are able to provide a tractive force of up to 421 19～561 05N over a vertical cross section area of 20cm×50cm,or an increase in the pulling resistance of the rooted soil by 33 2～36 8%. This is equivalent to a tensile strength increase of the shallow rooted soil by 4 2～5 6kPa. The results imply that, together with the pine's vertical roots, the lateral roots of the pine trees, through their traction effect, are able to mitigate against shallow instability in the forested slopes, such as shallow slide and creep, at a certain degree.
Subarctic Thermokarst Ponds: Investigating Recent Landscape Evolution and Sediment Dynamics in Thawed Permafrost of Northern Québec (Canada)

Abstract Although widely distributed throughout Arctic and subarctic regions, thermokarst ponds and lakes remain relatively unexplored regarding geomorphological changes in their catchments and their internal properties in relation to climate change over the past decades. This study synthesizes recent landscape evolution and modern sedimentology of limnologically diverse thermokarst ponds near southeastern Hudson Bay, Canada. Spatio-temporal analysis of permafrost mounds, thermokarst ponds, and vegetation surface areas over the past five decades revealed that the recent climate-induced decrease of permafrost-affected areas was not primarily compensated by thermokarst pond development, but rather by a remarkable increase in vegetation cover. These changes appeared to be modulated by topographical and hydrological gradients at the study site, which are associated with east-ward increasing thickness of postglacial marine deposits. At a more contemporary timescale, physico-chemical measurements made on sedimenting materials (sediment traps) and freshly deposited lacustrine sediments of selected thermokarst ponds revealed striking differences both among ponds and between the oxic epilimnion and the oxygen-depleted hypolimnion. These findings underscore the major influence of local landscape properties and oxycline development on pond sedimentology and geochemistry, such as the transport of detritic particles and the concentration of redox-sensitive elements.
MALWARE ANALYSIS BASED ON SMART AGENTS AND IMAGE CLASSIFICATION

Windows-based systems and operating systems in general are significantly damaged, affecting infrastructures. At present, Malware analysis is performed in laboratories that use high costs and resources; so there are few methods of classification of Malware, based on artificial intelligence that consumes few resources. This article provides a system that was developed for the dynamic analysis of malware in Windows and classified using SIFT, SURF, and Bayesian networks. This involves the transformation of infected files into image files that allows the identification and classification of Malware. The samples of malicious software that allows generating a contingency plan were identified. The system was developed using intelligent agents. The analysis of Postal worm malware is presented as an example. When comparing with other malware detection and classification systems, it is observed that the multi-agent-based system is competitive.