Miracle-dz/UniFAR-FTUs huggingface dataset:
Review of Decision Tree Based Classification Algorithms in Medical Data

— Classification problem in data mining is widely used to discover the potential information hidden in the data. Clinical, microarray data or image data related to medical field consists of high dimensions which pose difficulties for biomedical researchers in acquiring and analyzing data. Three principal challenges related to high dimensional data are Volume, Velocity and Variety. Various dimensionality reduction techniques are been used to remove irrelevant features to make the task easier and efficient. Also, using dimensionality techniques result in improved classification performance of the classifiers. This paper presents a review on the supervised machine learning algorithms for classification and prediction of various diseases. It also discusses various splitting criterion to determine the best attributes. Decision Tree algorithms are easy to understand and easy to use among all the classifiers.
Textbook of Cosmetic Dermatology

1. Influences on Skin Parameters 2. Pharmacology of Cosmetic Products 3. Cutaneous Absorption and Cosmetology 4. Cosmetology for Normal Skin 5. Cosmetology for Special Locations 6. Cosmetic Treatment for Special Conditions 7. Skin Care for Special Groups 8. Non-Invasive Assessment Techniques in Cosmetology 9. Techniques in Cosmetological Treatment 10. Side-Effects and Social Aspects of Cosmetology
A study of the pastoral care needs of the ICU/ED staff in a general hospital from the pastoral care department / [thesis]

In a world that is in tremendous transition where the need to re-evaluate where we are socially, economically and personally is an ever present challenge, the concept of spirituality to our overall well-being and health is been focused on in a way we may never have expected. Pastoral care has a role now more than it ever had before. However if you ask some one what is pastoral care they will pause to think. There is no definitive answer to this question. Some say it is spiritual accompaniment while other says it is just simply being present with people on a life journey. The economic reality for this generation has impacted on the pastoral care teams as the posts in hospitals are not filled due to the fiscal policy in the respective Health Boards. This means the service is not as effective as it should be. The fact is that chaplains are needed more now than ever, especially by the staff who often work in difficult circumstances. This study as to the needs of the ICU/ED staff from the Pastoral Care team reflects the importance of this service in the hospitals that they are present in. Staff in these two departments work in a high stress atmosphere and need support from within to help them cope with this stress. We can see that the needs are great and the greater need to be heard is vital. The Pastoral Care team play a significant role in giving this support and my research and the results of the audit of staff in these two departments validate this. The motivation behind this work is to demonstrate that pastoral care should be treated as an equal partner in a multidisciplinary approach to patient care in hospital. The conclusions reached I believe reinforces this motivation.
Biometric authentication on the basis of lectroencephalograms parameters

Static biometric patterns such as fingerprint, iris and face are difficult to keep secret. Since the open pattern has a little potential replacement options stealing a strange open biometrics provides great opportunities for compromising systems. Authentication on the basis of electroencephalogram pattern (EEG) is the most secure kind of biometric security. The present study aims to develop a method of biometric authentication by the EEG data with high accuracy. Several neural network EEG pattern verification algorithms have been tested. A method for verification of the human EEG pattern based on a modified Bayes hypothesis formula has been developed. The following error indicators FAR <10-4 with FRR = 0.062 were achieved. This work was supported by the Russian Foundation for Basic Research (RFBR) and the Government of the Omsk Region (grant 18-41-550002).
Computation-in-Memory based on Memristive Devices

In-memory computing is a promising computing paradigm due to its capability to alleviate the memory bottleneck. It has even higher potential when implemented usingmemristive devices ormemristors with various beneficial characteristics such as nonvolatility, high scalability, near-zero standby power consumption, high density, and CMOS compatibility. Exploring in-memory computing architectures in the combination withmemristor technology is still in its infancy phase. Therefore, it faces challenges with respect to the development of the devices, circuits, architectures, compilers and applications. This thesis focuses on exploring and developing in-memory computing in terms of architectures (including classification, limited schemes of instruction set,micro-architecture, communication and controller, as well as automation and simulator), and circuits (including logic synthesis flow and interconnect network schemes).
Lecture notes : respiratory medicine

Part 1 Structure and Function. 1 Anatomy and physiology of the lungs. Part 2 History Taking, Examination and Investigations. 2 History taking and examination. 3 Pulmonary function tests. 4 Radiology of the chest. Part 3 Respiratory Diseases. 5 Upper respiratory tract infections and influenza. 6 Pneumonia. 7 Tuberculosis. 8 Respiratory disease in HIV and immunocompromised patients. 9 Bronchiectasis and lung abscess. 10 Cystic fibrosis. 11 Asthma. 12 Chronic obstructive pulmonary disease. 13 Carcinoma of the lung. 14 Interstitial lung disease. 15 Occupational lung disease. 16 Pulmonary vascular disease. 17 Pneumothorax and pleural effusion. 18 Acute respiratory distress syndrome. 19 Sleep-related breathing disorders. 20 Smoking and smoking cessation. 21 Lung transplantation
Deep generative modelling for amortised variational inference

Probabilistic and statistical modelling are the fundamental frameworks that underlie a large proportion of the modern machine learning (ML) techniques. These frameworks allow for the practitioners to develop tailor-made models for their problems that may include their expert knowledge and can learn from data. Learning from data in the Bayesian framework is referred as inference. In general, model-specific inference methods are hard to derive as they require high level of mathematical and statistical dexterity on the practitioner’s part. As a result, there is a large industry of researchers in ML and statistics that work towards developing automatic methods of inference (Carpenter et al., 2017; Tran et al., 2016; Kucukelbir et al., 2016; Ge et al., 2018; Salvatier et al., 2016; Uber, 2017; Lintusaari et al., 2018). These methods are generally model agnostic and are therefore called black-box inference. Recent work has shown that use of deep learning techniques (Rezende and Mohamed, 2015b; Kingma et al., 2016; Srivastava and Sutton, 2017; Mescheder et al., 2017a) within the framework of variational inference (Jordan et al., 1999) not only allows for automatic and accurate inference but does so in a drastically efficient way. The added efficiency comes from the amortisation of the learning cost by using deep neural networks to leverage the smoothness between data points and their posterior parameters. The field of deep learning based amortised variational inference is relatively new and therefore has numerous challenges and issues to be tackled before it can be established as a standard method of inference. To this end, this thesis presents four pieces of original work in the domain of automatic amortised variational inference in statistical models. We first introduce two sets of techniques for amortising variational inference in Bayesian generative models such as the Latent Dirichlet Allocation (Blei et al., 2003) and Pachinko Allocation Machine (Li and McCallum, 2006). These techniques use deep neural networks and stochastic gradient based first order optimisers for inference and can be generically applied for inference in a large number of Bayesian generative models. Similarly, we also introduce a novel variational framework for implicit generative models of data, called VEEGAN. This framework allows for doing inference in statistical models where unlike the Bayesian generative models, a prescribed likelihood function is not available. It makes use of a discriminator based density ratio estimator (Sugiyama et al., 2012) to deal with the intractability of the likelihood function. Implicit generative models such as the generative adversarial networks (Goodfellow et al., 2014) suffer from learning issues like mode collapse (Srivastava et al., 2017) and training instability (Arjovsky et al., 2017). We tackle the mode collapse in GANs using VEE-
The Impact of 4 X 4 Block Scheduling at an Urban High School

xiv CHAPTER ONE: INTRODUCTION AND BACKGROUND TO THE STUDY Introduction. 1 Purpose of the Study. 5 Research Questions and Hypotheses. 5 Definition of Terms 6 Significance of the Study . 9 Limitations and Delimitations of the Study 9 Organization of the Study. 11
Revocable Identity Based Signature Scheme with Outsourced Cloud Revocation Authority

In last few years, many aspects of Identity based Cryptosystems (IBC) were studied because it has a critical feature of avoiding high overheads linked with management of public key certificate. But the one issue associated with it how to revoke defiant user remains open. Many schemes were proposed to overcome this problem. One move towards revocation in identity based settings was taken by introducing the key generation centre (KGC) which updates users’ private key periodically. But with this move, computation work on KGC will increase quickly with increasing number of users. We have proposed an ID based signature scheme (IBS) with outsourced cloud revocation authority (CRA). In this scheme, the revocation functionality is outsourced to a CRA. We have described the system framework of the scheme as well as its security model with complete security analysis.
A Survey on Digital Forensics in Internet of Things

Internet of Things (IoT) is increasingly permeating peoples’ lives, gradually revolutionizing our way of life. Due to the tight connection between people and IoT, now civil and criminal investigations or internal probes must take IoT into account. From the forensic perspective, the IoT environment contains a rich set of artifacts that could benefit investigations, while the forensic investigation in IoT paradigm may have to alter to accommodate characteristics of IoT. Therefore, in this article, we analyze the impact of IoT on digital forensics and systematize the research efforts made by previous researchers from 2010 to 2018. We sketch the landscape of IoT forensics and examine the state of IoT forensics under a 3-D framework. The 3-D framework consists of a temporal dimension, a spatial dimension, and a technical dimension. The temporal dimension walks through the standard digital forensic process while the spatial dimension explores where to identify sources of evidence in IoT environment. These two dimensions attempt to provide principles and guidelines for standardizing digital investigations in the context of IoT. The technical dimension guides a way to the exploration of tools and techniques to ensure the enforcement of digital forensics in the ever-evolving IoT environment. Put together, we present a holistic overview of digital forensics in IoT. We also highlight open issues and outline promising suggestions to inspire future study.
ABC of Monitoring Drug Therapy

Reading is a hobby to open the knowledge windows. Besides, it can provide the inspiration and spirit to face this life. By this way, concomitant with the technology development, many companies serve the e-book or book in soft file. The system of this book of course will be much easier. No worry to forget bringing the abc of monitoring drug therapy book. You can open the device and get the book by on-line.
Low Voter Turnout in Kepulauan Riau Province Gubernatorial Election

The purpose of this study is to examine the factors behind the large number of absentees among voters in regional elections (Pilkada) in Riau Islands province, Indonesia. Since 2005, people have their right to vote for their own local leader that was previously selected by the parliament to become a candidate. However, this democratic reform was not accompanied by voters’ enthusiasm to cast their vote. In 2015, the Election Education Network for the People found that there were still more than 50% voters who did not use their rights to elect their governor candidate in Batam. Lower voter turnout was affected by Golput (Golongan Putih) or non-voting which was driven by various reasons, but the most popular causes were protests against misconducts in the implementation of elections, loss of expectation to and trust in candidates, and loss of trust in the political system. For this paper, we conducted a quantitative survey to 400 respondents of those who did not cast their vote during the gubernatorial election of Kepulauan Riau Province in 2015. This paper examines non-voting behavior using psychological and rational choice approaches, particularly on three main variables that may have provided the motive behind the non-voting behavior of the eligible voters, i.e. candidate orientation, issue orientation, and media orientation. It argues that despite the fact that the voters know the two candidates running for governor office as both were incumbents (governor and vice-governor were both run for the governor office), the voters chose to not to cast their votes for several reasons; i.e. the lack of accessible information about the vision of the candidates, and the lack of clarity of programs offered by the candidates. Keywords—election; voting-behavior; turnout; psychological approach; rational choice approach
Image Fusion for Scene Classification Using Machine Learning

: Image fusion is the mechanism of gathering all important information. It is not only reduce data but also more appropriate and understandable for human and machine. Scene classification is widely used in day to day lifecycle. Their importance is increasing gradually. Scene classification is a classification which classify the image according to their area of importance. In this paper, Image is segmented, features of image are extracted and information is stored in database about image. Lastly, image is classified by machine learning and output comes in the text format. We use machine learning based support vector machine for classification which is more accurate than KNN classifier. The main aim of this study is to improve the accuracy and to reduce the delay of computation for the system.
Deep Trajectory Recovery with Fine-Grained Calibration using Kalman Filter

With the development of location-acquisition technologies, there are a huge number of mobile trajectories generated and accumulated in a variety of domains. However, due to the constraints of device and environment, many trajectories are recorded at low sampling rate, which increases the uncertainty between two consecutive sampled points in the trajectories. Our task is to recover a high-sampled trajectory based on the irregular low-sampled trajectory in free space, i.e., without road network information. There are two major problems with traditional solutions. First, many of these methods rely on heuristic search algorithms or simple probabilistic models. They cannot well capture complex sequential dependencies or global data correlations. Second, for reducing the predictive complexity of the unconstrained numerical coordinates, most of the previous studies have adopted a common preprocessing strategy by mapping the space into discrete units. As a side effect, using discrete units is likely to bring noise or inaccurate information. Hence, a principled post-calibration step is required to produce accurate results, which has been seldom studied by existing methods. To address the above difficulties, we propose a novel Deep Hybrid Trajectory Recovery model, named DHTR. Our recovery model extends the classic sequence-to-sequence generation framework by implementing a subsequence-to-sequence recovery model tailored for the current task, named subseq2seq. In order to effectively capture spatiotemporal correlations, we adopt both spatial and temporal attentions for enhancing the model performance. With the attention mechanisms, our model is able to characterize long-range correlations among trajectory points. Furthermore, we integrate the subseq2seq with a calibration component of Kalman filter (KF) for reducing the predictive uncertainty. At each timestep, the noisy predictions from the subseq2seq component will be fed into the KF component for calibration, and then the refined predictions will be forwarded to the subseq2seq component for the computation of the next timestep. Extensive results on real-world datasets have shown the superiority of the proposed model in both performance and interpretability.
A review of the Multiple Object Detection, Tracking and Recognition in Video Surveillance Systems using different approaches

Moving item location in any video scene and its detection are the 2 significant testing errands in the moving video investigation system which is dynamic in nature. It is trying because of impediment, nearness of shadows, jumbling, dynamic foundation, clamor etc. Detection of moving objects, following, object coordinating crosswise over multi-camera, and reidentification are the fundamental strides of multi camera video investigation system. Multiple object location and following in savvy city video investigation can be created by propriety of society, for example, shrewd observation, savvy stopping, traffic observing, vehicle route, savvy human services and so forth. The objective of this paper is to examine and audit different methodologies towards such different types of moving object detection, its segmentation, histogram equalization, filtering, classification etc… Also, this survey paper gives a brief idea about the work done by various authors till date in the field of video image processing so that this review papers serves as a base paper for many of the researchers who want to do research in this exciting field of video processing. Some of the drawbacks or the disadvantages of the works that were carried out by the earlier researchers are considered in our research work, studied in brief & problem is being defined along with the objectives in order to overcome some of the deficiencies of the existing algos.
Ectc-Docd: An End-to-End Structure with CTC Encoder and OCD Decoder for Speech Recognition

Real-time streaming speech recognition is required by most applications for a nice interactive experience. To naturally support online recognition, a common strategy used in recently proposed end-to-end models is to introduce a blank label to the label set and instead output alignments. However, generating the alignment means decoding much longer than the length of the linguistic sequence. Besides, there exist several blank labels between two output units in the alignment, which hinders models from learning the adjacent dependency of units in the target sequence. In this work, we propose an innovative encoder-decoder structure, called ECTC-DOCD, for online speech recognition which directly predicts the linguistic sequence without blank labels. Apart from the encoder and decoder structures, ECTC-DOCD contains an additional shrinking layer to drop the redundant acoustic information. This layer serves as a bridge connecting acoustic representation and linguistic modelling parts. Through experiments, we confirm that ECTC-DOCD can obtain better performance than a strong CTC model in online ASR tasks. We also show that ECTC-DOCD can achieve promising results on both Mandarin and English ASR datasets with first and second pass decoding.
Framework for CDN Interconnection

This document presents a framework for Content Distribution Network Interconnection (CDNI). The purpose of the framework is to provide an overall picture of the problem space of CDNI and to describe the relationships among the various components necessary to interconnect CDNs. CDN Interconnection requires the specification of several interfaces and mechanisms to address issues such as request routing, metadata exchange, and the acquisition of content by one CDN from another. The intent of this document is to outline what each interface needs to accomplish, and to describe how these interfaces and mechanisms fit together, while leaving their detailed specification to other documents.
The ‘volunteer tourist gaze’: commercial volunteer tourists’ interactions with, and perceptions of, the host community in Cusco, Peru

ABSTRACT This paper presents the commodified volunteer tourist gaze through the use of a case study which contextualizes commercial volunteer tourism. Interviews undertaken with volunteer tourists in Cusco, Peru, and on-the-ground participant observation, provide insights into what we term a ‘volunteer tourist gaze’ underpinned by neo-colonial tendencies. The findings demonstrate that volunteer tourists are not passive consumers of a destination, but actively engage in a multi-sensory, embodied experience. This is evidenced in the way they describe their interactions with local people, and their views and perceptions of poverty in Cusco. However, the findings suggest that the volunteer-host interactions and experiences do little to foster cross-cultural understanding, particularly given the limitations to these interactions imposed by a significant language barrier. Instead, the commodified volunteer tourist gaze perpetuates neo-colonial discourses by emphasizing the differences between volunteer tourists from the developed world (the haves) and host communities in the Global South (the have nots).
Feasibility of a powder coating process inside indian paint’s company through VSM approach

Paint manufacturing industries in common are facing yield loss and poor quality of the products that has led to low productivity, longer production lead time, rework and rejection. The focus is on the efficiency of the plant, and these factors will reduce the overall efficiency of the plant. The purpose of the study is to implement a lean tool that is Value Stream Mapping. The objective is to identify the waste and eliminate them in the plant. For collecting the information required to complete the project, the researcher will take a tour to the shop floor which will enable the researcher to be familiar with the activities that are taking place, and this will help in getting a vague idea about the process and the production flow. The result obtained was observed, and suitable suggestions were proposed to overcome the problems that the industry is facing
Speech Emotion Recognition in Dyadic Dialogues with Attentive Interaction Modeling

In dyadic human-human interactions, a more complex interaction scenario, a person’s emotional state can be influenced by both self emotional evolution and the interlocutor’s behaviors. However, previous speech emotion recognition studies infer the speaker’s emotional state mainly based on the targeted speech segment without considering the above two contextual factors. In this paper, we propose an Attentive Interaction Model (AIM) to capture both selfand interlocutor-context to enhance the speech emotion recognition in the dyadic dialog. The model learns to dynamically focus on long-term relevant contexts of the speaker and the interlocutor via the self-attention mechanism and fuse the adaptive context with the present behavior to predict the current emotional state. We carry out extensive experiments on the IEMOCAP corpus for dimensional emotion recognition in arousal and valence. Our model achieves on par performance with baselines for arousal recognition and significantly outperforms baselines for valence recognition, which demonstrates the effectiveness of the model to select useful contexts for emotion recognition in dyadic interactions.