Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Illumination and Pose Invariant Face Recognition System using Support Vector Machines

The fundamental objective behind the denoising is to get rid of the noise while recollecting the significant signal features to the maximum possible extent. This issue seems to be very simple against the backdrop of realistic scenarios, where the category and quantity of noise, along with the noise and the kind of images all are variable parameters, and a solitary technique or approach is incompetent to yield reasonable results. There is a host of methods employed to eliminate the noise in images and carryout the classification procedure efficiently. In the innovative approach, at the outset, the images are shortlisted from the database, and thereafter the technique flows through the following three phases such as the pre-processing procedure, feature extraction procedure and the classification procedure by means of the SupportVector Machine (SVM). In the feature extraction procedure the Gray Level Co-occurrence Matrix(GLCM) traits like the autocorrelation, contrast, cluster prominence, cluster shade, dissimilarity, energy, area, homogeneity, perimeter, circularity and entropy are extracted. Subsequently, SVM is employed for the purpose of the face recognition, because the optimal separating hyper plane can be achieved easily after ascertaining the thinner product between feature vectors, which constitutes an exemplary quality of the SVM. The kernel functions are able to achieve only the inner product value in the feature space being unaware of the nonlinear mapping.
IMPLEMENTATION OF ELECTRONIC PROCUREMENT IN COUNTY GOVERNMENTS IN KENYA : A CASE STUDY OF NAIROBI COUNTY GOVERNMENT

The interest of this study is to determine the reasons for unsuccessful implementation of EProcurement in the county governments in Kenya. It focused on the Nairobi county government, which is guided by the Public procurement procedures. With the use of a short form questionnaire, the data was collected from a population sample of the Top managers of the institution and middle level operations staff. This was used to establish the pertinent factors that affect successful implementation of electronic procurement process in public institution. The business environment within which the county government operates is very volatile; the political anxiety, cartels, competition from the private sector, technological advancement, and global changes are some of the challenges that have greatly affected the success of the council’s operations. Whereas the people of Nairobi and Kenya at large expect nothing but efficient and reliable services from the county government, smooth process management is a subject of interest in order to achieve this goal and enhance accountability The main objective of this study was to investigate why implementation of electronic procurement system has not been successful in spite the attempts that have been made in the past and also the benefits that the public institutions will enjoy if electronic procurement is implemented. The study was done through data collected using a semistructured questionnaire and analyzed based on descriptive statistics. The collected data was analyzed in tables, pie charts and graphs. The study found out that majority of the respondents agreed that Technological infrastructure, organization culture and professionalism contributed to the success of E-Procurement in the organization. A majority of the respondents felt that IT cost doesn’t hinder success of E-procurement however there should be budget allocated to that venture as an investment. International Journal of Human Resource and Procurement Vol.4, Issue 5, 2015 Htttp://www.ijsse. org ISSN 2307-6305 Page | 21
Macro microscopic fingerprints of panchanga of Ishwari- Aristolochia indica Linn.

The drug Ishwari or Nakuli is botanically equated to Aristolochia indica Linn. The drug has been mentioned in Samhitha Granthas in different form for the condition like sheetha jwara (fever with rigor), sarpa visha (snake bite poisoning), vrana (wound), ekanga shopha (local oedema), unmade (schizophrenia), apasmara (epilepsy) etc. Substitution and adulteration due to similar morphological features, same classical vernacular name, and presence of similar active principles affect the therapeutic activity of medicinal products. Systematic macromicroscopy identity of herbal raw drug is becoming increasingly important to produce standardized herbal products. In this study, macro-microscopic and powder characters were recorded for five different parts (panchanga) of raw herb Ishwari using standard methodology. Both root and stem showed the presence of oil globules, stone cells, and starch grain. The midrib region showed a prominent vascular bundle, while there are few trace bundles distributed in the mesophyll tissue. The portion of perianth enlarged showed lower mesophyll tissue formed by parenchyma with intercellular space having a little chlorophyll in it. TS of fruit stalk is circular in outline, shows an epidermis covered with a cuticle. TS of fruit show an epicarp having 8 to 10 layers of parenchyma forming mesocarp. In the parenchyma cells below the endocarp there is a seed having testa, endosperm parenchyma. The powder microscopy showed the important cells of individual part of the plant. These diagnostic features can be used as a fingerprint for the identification and differentiation of their substitute and adulterants of the plant.
Can behavioural ecologists help establish protected areas?

Protecting wild places is conservation's most pressing task given rapid contemporary declines in biodiversity and massive land use changes. We suggest that behavioural ecology has a valuable, albeit limited, role to play in this agenda. Behaviourally based empiricism and modelling, especially of animal movements and habitat preferences have enjoyed wide applicability in delineating reserve boundaries. In protected areas that sanction exploitation, it may also be important to understand individuals' behavioural and life-history responses to management decisions. We also argue, however, that the in-depth studies of behavioural ecologists may have an important role in conservation by elevating species’ status from mundane to charismatic and often sparking public empathy, and their mere presence in field generates local (or broader) intrigue. More generally behavioural ecologists will only be listened to, and their contributions considered of conservation importance, if they become more involved in decision-making processes as witnessed by several prominent examples that have supported the establishment of protected areas. This article is part of the theme issue ‘Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation’.
In situ predator conditioning of naive prey prior to reintroduction

Many translocations and introductions to recover threatened populations fail because predators kill prey soon after release; a problem exacerbated for predator-naive prey. While pre-release training has been shown to work in some situations, it is time consuming and relies on using inferred predator cues and treating small groups. We review a relatively new and very promising management tool: in situ, pre-release predator conditioning. Here, the goal is to allow prey in large enclosures to live with low densities of predators to accelerate selection for antipredator traits (in an evolutionary sense) or provide prey essential experience with predators that they will later encounter. We review the published results of a large-scale, controlled experiment where we have permitted burrowing bettongs (Bettongia lesueur) and greater bilblies (Macrotis lagotis) to live with low densities of feral cats (Felis catus), a species implicated in their widespread decline and localized extinction. We found that both species could persist with cats, suggesting that future work should define coexistence thresholds—which will require knowledge of prey behaviour as well as the structure of the ecological community. Compared to control populations, predator-naive prey exposed to cats has a suite of morphological and behavioural responses that seemingly have increased their antipredator abilities. Results suggest that predator-conditioned bilbies survive better when released into a large enclosure with an established cat population; future work will determine whether this increased survival extends to the wild. This article is part of the theme issue ‘Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation’.
Social amoebae establish a protective interface with their bacterial associates by lectin agglutination

Dictyostelium social amoebae agglutinate bacteria with a secreted lectin to optimize their survival during feeding. Both animals and amoebae use phagocytosis and DNA-based extracellular traps as anti-bacterial defense mechanisms. Whether, like animals, amoebae also use tissue-level barriers to reduce direct contact with bacteria has remained unclear. We have explored this question in the social amoeba Dictyostelium discoideum, which forms plaques on lawns of bacteria that expand as amoebae divide and bacteria are consumed. We show that CadA, a cell adhesion protein that functions in D. discoideum development, is also a bacterial agglutinin that forms a protective interface at the plaque edge that limits exposure of vegetative amoebae to bacteria. This interface is important for amoebal survival when bacteria-to-amoebae ratios are high, optimizing amoebal feeding behavior, and protecting amoebae from oxidative stress. Lectins also control bacterial access to the gut epithelium of mammals to limit inflammatory processes; thus, this strategy of antibacterial defense is shared across a broad spectrum of eukaryotic taxa.
The genome of Peromyscus leucopus, natural host for Lyme disease and other emerging infections

The genome of the white-footed deer mouse will accelerate our understanding of several tick-borne infections. The rodent Peromyscus leucopus is the natural reservoir of several tick-borne infections, including Lyme disease. To expand the knowledge base for this key species in life cycles of several pathogens, we assembled and scaffolded the P. leucopus genome. The resulting assembly was 2.45 Gb in total length, with 24 chromosome-length scaffolds harboring 97% of predicted genes. RNA sequencing following infection of P. leucopus with Borreliella burgdorferi, a Lyme disease agent, shows that, unlike blood, the skin is actively responding to the infection after several weeks. P. leucopus has a high level of segregating nucleotide variation, suggesting that natural resistance alleles to Crispr gene targeting constructs are likely segregating in wild populations. The reference genome will allow for experiments aimed at elucidating the mechanisms by which this widely distributed rodent serves as natural reservoir for several infectious diseases of public health importance, potentially enabling intervention strategies.
Facebook Disaster Maps: Aggregate Insights for Crisis Response & Recovery

After a natural disaster or other crisis, humanitarian organizations need to know where affected people are located and what resources they need. While this information is difficult to capture quickly through conventional methods, aggregate usage patterns of social media apps like Facebook can help fill these information gaps. This talk will describe the data and methodology that power Facebook Disaster Maps. These maps utilize information about Facebook usage in areas impacted by natural hazards, producing insights into how the population is affected by and responding to the hazard. In addition to methodology details, including efforts taken to ensure the security and privacy of Facebook users, I'll also discuss how we worked with humanitarian partners to develop the maps, which are actively used in disaster response today. I'll give examples of insights generated from the maps and I'll also discuss some limitations of the current methodologies, challenges, and opportunities for improvement.
PressLight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network

Traffic signal control is essential for transportation efficiency in road networks. It has been a challenging problem because of the complexity in traffic dynamics. Conventional transportation research suffers from the incompetency to adapt to dynamic traffic situations. Recent studies propose to use reinforcement learning (RL) to search for more efficient traffic signal plans. However, most existing RL-based studies design the key elements - reward and state - in a heuristic way. This results in highly sensitive performances and a long learning process. To avoid the heuristic design of RL elements, we propose to connect RL with recent studies in transportation research. Our method is inspired by the state-of-the-art method max pressure (MP) in the transportation field. The reward design of our method is well supported by the theory in MP, which can be proved to be maximizing the throughput of the traffic network, i.e., minimizing the overall network travel time. We also show that our concise state representation can fully support the optimization of the proposed reward function. Through comprehensive experiments, we demonstrate that our method outperforms both conventional transportation approaches and existing learning-based methods.
Tutorial: Are You My Neighbor?: Bringing Order to Neighbor Computing Problems.

Finding nearest neighbors is an important topic that has attracted much attention over the years and has applications in many fields, such as market basket analysis, plagiarism and anomaly detection, community detection, ligand-based virtual screening, etc. As data are easier and easier to collect, finding neighbors has become a potential bottleneck in analysis pipelines. Performing pairwise comparisons given the massive datasets of today is no longer feasible. The high computational complexity of the task has led researchers to develop approximate methods, which find many but not all of the nearest neighbors. Yet, for some types of data, efficient exact solutions have been found by carefully partitioning or filtering the search space in a way that avoids most unnecessary comparisons. In recent years, there have been several fundamental advances in our ability to efficiently identify appropriate neighbors, especially in non-traditional data, such as graphs or document collections. In this tutorial, we provide an in-depth overview of recent methods for finding (nearest) neighbors, focusing on the intuition behind choices made in the design of those algorithms and on the utility of the methods in real-world applications. Our tutorial aims to provide a unifying view of "neighbor computing" problems, spanning from numerical data to graph data, from categorical data to sequential data, and related application scenarios. For each type of data, we will review the current state-of-the-art approaches used to identify neighbors and discuss how neighbor search methods are used to solve important problems.
Tutorial: Data Mining Methods for Drug Discovery and Development

In silico modeling of medicine refers to the direct use of computational methods in support of drug discovery and development. Machine learning and data mining methods have become an integral part of in silico modeling and demonstrated promising performance at various phases of the drug discovery and development process. In this tutorial we will introduce data analytic methods in drug discovery and development. For the first half, we will provide an overview about related data and analytic tasks, and then present the enabling data analytic methods for these tasks. For the second half, we will describe concrete applications of each of those tasks. The tutorial will be concluded with open problems and a Q&A session.
GeneratingNaturalLanguageDescriptionsfromOWLOntologies:A DetailedPresentationoftheNaturalOWLSystem

We present Naturalowl, an open-source natural language generation system that produces texts describing individuals (e.g., products, museum exhibits) or classes of individuals from owl ontologies optionally associated with linguistic and user modeling resources. Unlike simpler owl verbalizers, which typically express a single axiom of the ontology at a time in controlled and often not entirely uent natural language mostly for the benet of domain experts, we aim to generate uent, coherent, and interesting multi-sentence texts appropriate for end-users (e.g., customers, museum visitors). A key benet of using a system like Natural owl on the Semantic Web is that it becomes possible to publish information in the form of owl ontologies, and rely on natural language generation to automatically produce personalized texts in multiple languages from the ontologies, thus making the information easily accessible not only to computer programs and domain experts, but also to end-users. We discuss Naturalowl’s processing stages of generating texts, the optional domain-specic linguistic and user modeling resources that can be used at each stage, why such resources are useful, and how they can be created and represented. We also report on trials we performed to measure the eort that is required to congure Natural owl for new ontologies, and the quality of the resulting texts.
Chesson's coexistence theory: Comment.

Recently, Barabas et al (2018) tried to explain some of my contributions to species coexistence theory. They include many laudatory comments, and it might seem churlish to complain, but unfortunately, I found many mistakes in their account. I was not shown a copy of Barabas et al (2018) before it was accepted and appeared online, precluding my commenting before publication. Fortunately, my recent publication (Chesson 2018) provides detailed discussions of many of my concerns. Here, I focus on a key part of Barabas et al (2018) that receives scant attention in Chesson (2018), namely, the development of multispecies coexistence theory in Chesson (1994), which has been the foundation of much other work. This article is protected by copyright. All rights reserved.
A Real-Time Method to Estimate Speed of Object Based on Object Detection and Optical Flow Calculation

In recent years Convolutional Neural Network (CNN) has been widely used in computer vision field and makes great progress in lots of contents like object detection and classification. Even so, combining Convolutional Neural Network, which means making multiple CNN frameworks working synchronously and sharing their output information, could figure out useful message that each of them cannot provide singly. Here we introduce a method to real-time estimate speed of object by combining two CNN: YOLOv2 and FlowNet. In every frame, YOLOv2 provides object size; object location and object type while FlowNet providing the optical flow of whole image. On one hand, object size and object location help to select out the object part of optical flow image thus calculating out the average optical flow of every object. On the other hand, object type and object size help to figure out the relationship between optical flow and true speed by means of optics theory and priori knowledge. Therefore, with these two key information, speed of object can be estimated. This method manages to estimate multiple objects at real-time speed by only using a normal camera even in moving status, whose error is acceptable in most application fields like manless driving or robot vision.
ICT Literacy in the Information Age

The development of homosapien man can be classified into ages. These ages fundamentally affect the patterns of how humans work, play and interact. Some social anthropologists (e.g., Toffler & Toffler, 1994) have identified three such ages: agricultural, industrial, and information. Definitions of these ages are complex, and a detailed discussion of them lies outside the scope of this chapter, however, they can usefully be typified as follows:
Stability and change of postchemotherapy symptoms in relation to optimism and subjective stress: A prospective study of breast cancer survivors

Inconsistent findings were reported about the course of postchemotherapy symptoms; specifically, the effect of changes in optimism and perception of subjective stress on the evolution of symptoms remains understudied. This prospective study aimed (a) to examine the course of postchemotherapy symptoms of emotional distress, fatigue, and cognitive difficulties among breast cancer survivors and (2) to assess the effect of changes in optimism and subjective stress on the trajectory of these symptoms.
Lung Cancer Detection using Probabilistic Neural Network with modified Crow-Search Algorithm

Objective: Lung cancer is a type of malignancy that occurs most commonly among men and the third most common type of malignancy among women. The timely recognition of lung cancer is necessary for decreasing the effect of death rate worldwide. Since the symptoms of lung cancer are identified only at an advanced stage, it is essential to predict the disease at its earlier stage using any medical imaging techniques. This work aims to propose a classification methodology for lung cancer automatically at the initial stage. Methods: The work adopts computed tomography (CT) imaging modality of lungs for the examination and probabilistic neural network (PNN) for the classification task. After pre-processing of the input lung images, feature extraction for the work is carried out based on the Gray-Level Co-Occurrence Matrix (GLCM) and chaotic crow search algorithm (CCSA) based feature selection is proposed. Results: Specificity, Sensitivity, Positive and Negative Predictive Values, Accuracy are the computation metrics used. The results indicate that the CCSA based feature selection effectively provides an accuracy of 90%. Conclusion: The strategy for the selection of appropriate extracted features is employed to improve the efficiency of classification and the work shows that the PNN with CCSA based feature selection gives an improved classification than without using CCSA for feature selection.