Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimum Classification Error based Kiss Metric Learning for Personal Reidentification

In recent years, person reidentification has received growing attention with the increasing popularity of intelligent video surveillance. This is because person reidentification is critical for human tracking with multiple cameras. Recently, keep it simple and straightforward (KISS) metric learning has been regarded as a top level algorithm for person reidentification. The covariance matrices of KISS are estimated by maximum likelihood (ML) estimation. It is known that discriminative learning based on the minimum classification error (MCE) is more reliable than classical ML estimation with the increasing of the number of training samples. When considering a small sample size problem, direct MCE KISS does not work well, because of the estimate error of small eigenvalues. Therefore, we further introduce the smoothing technique to improve the estimates of the small eigenvalues of a covariance matrix. Our new scheme is termed the minimum classification error-KISS (MCE-KISS). We conduct thorough validation experiments on the VIPeR dataset, which demonstrate the robustness and effectiveness of MCE-KISS for person reidentification.
Discretization of Unlabeled Data using RST & Clustering

An algorithm can be applied on numerical or continuous attributes as well as on nominal or discrete value. If input to an algorithm required only attributes of nominal or discrete type then continuous attributes of the dataset need to be discretize before applying such algorithm. Discretization method can be of two types namely supervised and unsupervised. Supervised methods of dicretization utilize class labels of the dataset while in unsupervised method class labels are totally disregarded. In many literatures it has been shown that supervised methods gives good discretization result. Supervised algorithms cannot apply if dataset is unlabeled. In real life, many dataset do not have class (label) attribute and only unsupervised discretization methods are applicable in such cases. This paper presents discretization schemes for unlabeled data based on RST (Rough Set Theory) and clustering. The experiments have been performed to compare the proposed technique with other discretization methods for labeled data on two benchmark datasets. Two parameters Class-Attribute Interdependence Redundancy and the total number of intervals have been used to compare the proposed techniques with other existing techniques. The results display a satisfactory tradeoff between the information loss and number of intervals for the proposed method.
An optimization approach for black and white structural topology designs under multiple load cases

The gray problem of displacement constrained topology volume minimization under multiple load cases still is an opening topic of research. In this paper, a novel displacement constrained optimization approach for black and white structural topology designs under multiple load cases, is proposed to obtain a series of topologies with clear profiles. Firstly, a distribution feature of constraint displacement derivatives is investigated. Secondly, an adaptive adjusting approach of design variable bounds is proposed, and an approximate model with varied constraint limits and a volume penalty objective function are constructed. Thirdly, an improved density-based optimization method is proposed for the displacement constrained topology volume minimization under multiple load cases. Finally, several examples are given to demonstrate that the results obtained by the proposed method provide a series of topologies with clear profiles during an optimization process. It is concluded from examples that the proposed method is effective and robust for generating an optimal topology.
Analysis of Changes in Muscle Architecture and Explosive Ability in NCAA Division I Volleyball Players

INTRODUCTION: Volleyball is a sport characterized by intermittent bouts of jumping, short sprints, diving, blocking, and hitting. The average work to rest ratio during a volleyball match ranges from 1:1-1:3 with rallies lasting 6-10s interspersed with 11-15s rest periods (Reilly, Secher, Snell, & Williams, 1990). Depending on the number of sets played, matches can last 2-3 hours (Lecompte & Rivet, 1979; Reilly et al., 1990; Viitasalo et al., 1987). Based on these observations it is clear that volleyball athletes must possess the ability to sustain high power outputs over long time periods. Previous research has also demonstrated a positive relationship between volleyball-specific fitness characteristics (countermovement jump height and take-off velocity, maximal strength, and motor coordination) and performance indicators (spike velocity, spike jump reach, impact height, and level of achievement) (Forthomme, Croisier, Ciccarone, Crielaard, & Cloes, 2005; Pion et al., 2015; Sheppard et al., 2008; Stamm, Stamm, & Thomson, 2005). Ultrasonography has commonly been used to assess changes in an athlete’s muscle architectural properties following training. Moderate to strong correlations have been observed between vastus lateralis muscle thickness (MT), fascicle length (FL), squat and countermovement jump height, isometric mid-thigh pull peak force, 1-RM back squat and sprint performance in various athletic groups (Alegre, Lara, Elvira, & Aguado, 2009; Brechue & Abe, 2002; Nimphius, McGuigan, & Newton, 2012; Secomb et al., 2015). Considering these findings, leg extensor muscle architecture may play an important role in the performance of fitness characteristics specific to volleyball. Therefore, the purpose of this study was to examine changes in muscle architecture and jumping performance in NCAA division I (DI) volleyball athletes over 1 year of training.
School Uniforms

The need for education reform is evident in public schools across the nation. Researchers, school officials, parents, and teachers have been looking at different ways to promote a better learning environment that will allow students to feel safe, equal, and free to express themselves in different ways within the classroom. In recent years, many of these same people have contended that implementing school uniform policies within schools is the way to create that kind of learning environment. Within this paper, a closer look at the issues surrounding school uniform policies will be examined. Both sides of the issues will be weighed to see if school unifom1 policies are what public school systems need in order to meet the changing needs of the education world. Additionally, research based conclusions on how to successfully implement a unifon11 policy within a school district are presented.
New Record of the Species Synthesiomyia Nudiseta (van der Wulp, 1883) (Diptera, Muscidae) with Forensic Study from Kerbala City, Iraq

The present study aimed to investigate Synthesiomyia nudiseta (van der Wulp, 1883) in the carcasses of dogs and rats in four different localities of Kerbala governorate. during four seasons. The results indicated the appearance of S. nudiseta only in the spring and autumn seasons within the urban and agricultural areas, as well as the appearance of this species on the bodies of dogs only without rats. Taxonomy and morphological futures were described. S. nudiseta is belonging to the family Muscidae (Order: Diptera) is described as a first time recorded in Iraqi entomofuna. The specimens wear collected from carcasses of dogs and rats at agriculture and urban regions of Kerbala city. The diagnostic characters and mean morphological features were photoed.
Signature based Detection of Dropping Attacks in a Wireless Ad Hoc Networks

In a multi-hop wireless ad hoc network link error and malicious packet dropping are two sources for packet losses. It is very important to find whether the losses are due to link errors only or is due to both link error and malicious packet drop i.e. the cause of packet loss. In this work the importance is given to the insider attack case where malicious nodes drops packets to degrade the network performance. Packet dropping rate in the insider attack case is approximately equal to normal link error because of which existing algorithms cannot find the exact cause of the packet loss. Thus truthful detection of packet dropping attack is very important. Thus in here the correlation between lost packets are found and to ensure that these correlations are correct an accurate public auditing mechanism called Homomorphic Linear Authenticator (HLA) is used which allows the detector to verify whether the packet loss information reported by various nodes are true or not. The proposed mechanisms achieve more detection accuracy than conventional methods. As an enhancement dynamic routing to provide secure communication between nodes and classification of packets to classify the packets is proposed to be implemented. Keywords—Malicious packet drop;homomorphic linear authenticator;auditing
Tell: An Elastic Database System for Mixed Workloads

It is an exciting time to do database research. Two movements dominated the field for the last few years: Big Data and NoSQL. Both movements arose out of necessity, as cloud computing imposes new requirements on database systems. Cloud computing makes scalability and elasticity more important than ever. A user does not want to pay for computing and storage resources she does not use, but she expects to be able to get these resources as soon as they are needed. Traditional database management systems, however, are not able to meet these requirements. Early NoSQL systems provided elasticity and scalability by massively simplifying the provided consistency guarantees and the underlying data model. Most notably key value stores can scale to thousands of machines and allow resizing their cluster at runtime. However, their simplicity is also their greatest weakness: The lack of transactions makes it difficult to reason about concurrency, and the simple data model makes them difficult to use. Key value stores push most of their complexity into the application. As a result, more recent solutions try not only to add transactions, but they also implement complex operations in a layer above the underlying NoSQL storage. This layering is often referred to as SQL over NoSQL. Big Data, on the other hand, is about the analytical processing of massive amounts of data in the cloud. The Hadoop ecosystem and, more recently, Spark are the most prominent systems that play in this field. These systems allow for massive parallelization of complex analytical queries and are elastic and scalable. They achieve this by implementing a shared data architecture which decouples computing resources from storage resources. However, these Big Data platforms still have a problem: bringing the data from the online NoSQL (or SQL) database into Hadoop is a complex issue. Traditionally, this is solved like traditional data warehousing which is a heavy weight solution.
A geostatistical model for estimating edge effects and cumulative human disturbance in wetlands and coastal waters

ABSTRACT The modification of landscapes for agriculture, residential housing, and other human uses has the potential to alter ecosystem function, reduce native biodiversity, and to diminish the capacity of natural systems to provide essential goods and services. Resultantly, methods aimed at quantifying disturbance intensity at the intersection of anthropogenic and natural lands are important to applied aspects of landscape planning and natural resource management, as well as theoretical facets of spatial ecology. Tools to appraise human disturbance in wetlands and coastal waters are especially needed because the topographic settings of surface waters make them particularly susceptible to pollutant accumulation, hydrologic alteration, and other influences from the surrounding landscape. Assuming a water resources focus, we developed a novel geostatistical method to quantify and visualize edge effects and cumulative human disturbance for all wetlands and coastal waters in the State of Florida (USA). Model validation revealed that estimated disturbance intensity was strongly correlated to species richness, levels of heavy metal contamination, and persistent organic pollutant concentration. The presented modeling framework offers a flexible means to assess human disturbance across spatial scales and could be readily applied to prioritize conservation land acquisition and to anticipate future impacts from the proposed development.
Study of Behaviour Dynamic of the Pedestrian Loading on a Footbridge

Abstract— This paper presents the study of the movement between a passerby and the concrete walkway of a type armor footbridge, located in the Pachuca de Soto, Hidalgo, Mexico. In which readings were taken people walking with a portable equipment. The human body modeled as a dynamic system consisting of a mass concentrated in its gravity center and two docks representing damped passage. The movement of the mass simulated in a scheme that consists vertical and lateral displacement representation in the footbridge. The influence of the wind or other type of dynamic excitation on the footbridge is not examined here. The scheme idealized in a mathematical model matrixed coupling with the motion equation, through Lagrangian mechanics. It considers the temporary movement of the mass, and the dynamic response of the catwalk. This displacement is the result obtained by the restoration of the concrete footbridge and the energy contribution of the model when walking of six proposed cases, corresponding to different frequencies of passage and human mass, obtaining values that help to identify that the structural performance of the bridge, maintains optimal service conditions.
Method for Restoring Functional Ovarian Activity in Cows

| The article describes the study on the efficacy of recombinant interferons in the restoration of functional activity of the ovaries. The search for rational means of restoring gonadal hormonal function is relevant given the high prevalence of hypofunctional disorders in highly productive cows. Ovarian hypofunction was found to occur in 28.943. 9% of cows examined and increase with higher milk productivity. In first-calf heifers, ovarian hypofunction was detected 1.3 to 2.0 times more frequently than in second-lactating cows and 1.3 to 2.8 times more frequently than in thirdor later-lactating cows. Complex use of bovine recombinant interferons -α, -γ twice with a 24-hour interval at a dose of 5.0 ml and interferon-tau 8-9 days after the second injection of interferons -α, -γ twice with an interval of 24 hours at a dose of 10.0 and 5.0 ml is accompanied by an increase in therapeutic efficacy for cows with ovarian hypofunction by 10.0-20.0% (90.0%). After the complex use of bovine recombinant interferons -α, -γ and interferontau on days 18-20 after the treatment onset, the content of glucose increased by 55.5%, that of vitamin A by 67.2%, vitamin C by 14.1 %, serum bacterial activity by 5.0%, serum lysozyme activity by 13.3%, phagocytic activity of leukocytes by 6.2%, estradiol by 36.7%, progesterone by 5.5 times, dehydroepiandrosterone sulfate by 73.1 %. The dynamics of the blood anti-Müllerian hormone increase in cows on days 18-20th after the treatment onset slows down (25.2%) as follicles reach preovulatory condition. Apart from restoring functional ovarian activity, recombinant interferons facilitate the stabilization of energy metabolism, the weakening of lipid peroxidation and endogenous poisoning, and the activation of antioxidant protection.
Classification and Review of the Charging Strategies for Commercial Lithium-Ion Batteries

The growing demand for lithium-ion (Li-ion) battery in electric vehicles has expedited the need for new optimal charging approaches to improve the speed and reliability of the charging process without deteriorating battery performances. Many efforts have been deployed to develop optimal charging strategies for commercial Li-ion batteries over the last decade. The active optimal charging strategies have great potential to meet the requirement. This paper is a review of the studies on constructing the optimal charging algorithms for Li-ion batteries. The battery models on which these protocols rest are stated, the generalized structures are examined, the advantages and the drawbacks of the mathematical controller algorithms are discussed, and their applications are presented. Suggestions for overcoming the shortcomings of the proposed strategies are proposed. Challenges and future directions in the development of optimal charging strategies for commercial Li-ion batteries are also discussed.
Scalable Bayesian Inference for Coupled Hidden Markov and Semi-Markov Models

Abstract Bayesian inference for coupled hidden Markov models frequently relies on data augmentation techniques for imputation of the hidden state processes. Considerable progress has been made on developing such techniques, mainly using Markov chain Monte Carlo (MCMC) methods. However, as the dimensionality and complexity of the hidden processes increase some of these methods become inefficient, either because they produce MCMC chains with high autocorrelation or because they become computationally intractable. Motivated by this fact we developed a novel MCMC algorithm, which is a modification of the forward filtering backward sampling algorithm, that achieves a good balance between computation and mixing properties, and thus can be used to analyze models with large numbers of hidden chains. Even though our approach is developed under the assumption of a Markovian model, we show how this assumption can be relaxed leading to minor modifications in the algorithm. Our approach is particularly well suited to epidemic models, where the hidden Markov chains represent the infection status of an individual through time. The performance of our method is assessed on simulated data on epidemic models for the spread of Escherichia coli O157:H7 in cattle. Supplementary materials for this article are available online.
New Approach of Inter-Cross: An Efficient Multilevel Cache Management Policy

Cache performance has been critical for large scale systems. Until now, many multilevel cache management policies LRU-K, PROMOTE, DEMOTE have been developed but still there is performance issue. Many approaches have been proposed to reduce the gap between different levels such as hint-based multilevel cache. Some approaches like demote or promote are based on the latest cache history information, which is inadequate for applications where there are regular demote and promote operations occur. The major drawback of these policies is selecting a victim. In this paper, the new multilevel cache replacement policy called Inter-cross is implemented to improve the cache performance of a system. The decision of promotion and demotion is based on the block's previous N-step promotion or demotion history and the size and resident time of the block in the cache. Comparative study between inter-cross and existing multilevel policies shows that, existing keeps track on last K references of the block within a last cache level, while inter-cross keeps track of the information of the last K movements of blocks among all the cache levels. Inter-cross algorithms are designed that can efficiently describe the activeness of any blocks in any cache level. Experimental results show that inter-cross achieves better performance compared to existing multilevel cache replacement policies such as LRU-K, PROMOTE, and DEMOTE under different workloads.
Proficient classification of packed and polymorphic malware Using malwise

String based signatures have remained popular in commercial systems due to their high efficiency, but can be ineffective in detecting malware variants. T o classify the packed and polymorphic malware, this p aper proposes a novel system named Malwise. Classificati on is performed using a fast application level emulato r to reverse the code packing transformation and three d ata mining algorithms to perform classification. We use real and synthetic malware to demonstrate the effectiven ess and efficiency of Malwise. Polymorphism describes related malware sharing a common history of code. C ode sharing among variants can be derived from autonomously self mutating malware, or manually cop ied by the malware creator to reuse previously authored code. Both static and dynamic analysis will be performed for effective classification.
Development and Evaluation of Digital ID card as a Portfolio Portal

Physical ID card has been use throughout history and are still widely use today. However, suchphysical characteristic leads to three drawbacks. First, it is not environmentally friendly due to printed materials. Second, Physical ID card requires the need for management and physical storage, prone to damage and lost. Third, information inside a physical ID card limited and cannot be updated. This paper proposes the use of digital ID card in the form of a mobile application as a substitute to physical ID card. A survey is conducted to evaluate the use of these digital ID card. It is then concluded that digital ID card can counter the downside of physical ID card and even provides features to enhance its role such as being a portal for information. Keywords—Digital ID, Information Portal, Mobile Application, Android.
STUDY OF THE POTENTIAL DISTRIBUTION OF RARE AND ENDANGERED SPECIES OF THE EUPHORBIACEAE FAMILY OF CÔTE D’IVOIRE

The main objective of this study is to contribute to the knowledge of the species of the Euphorbiaceae family from Côte d'Ivoire. Specifically, this involves carrying out a floristic analysis of Euphorbiaceae from Côte d'Ivoire, determining the species with special statuses of the Euphorbiaceae family from Côte d'Ivoire and analyzing their potential distribution. The endangered species database (Aké Assi, 1988) and the IUCN Red List (2019) were used to identify species with special status. Quantum GIS software and the Maxent model were used for the realization of the potential distribution maps. Four special status species have been listed: Croton membranaceus Müll. Arg. , Croton aubrevillei J. Léonard, Macaranga beillei Pan. and Sapium caterinum J. Léonard. The potential distribution map of Sclerocroton carterianus (J.Léonard) Kruijt & Roebers, was not produced due to insufficient number of samples. Floristic analysis revealed that 75% of species are microphanerophytes, 50% are endemic to the forest block west of Togo, including Ghana, Côte d’Ivoire, Gambia and Senegal.