Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncertainty Sampling Based Posterior Probability Extreme Learning Machine for Human Activity Recognition

Human activity recognition is a main research area of context-aware computing, and is widely used in many applications, such as smart home and elderly care. Smartphone-based human activity recognition is very popular by making use of the embedded inertial sensors. However, there exists the problems of misclassification activities, and how to effectively apply the model trained by known users to new users. To solve these two problems, in this paper, we proposed a novel approach, Uncertainty Sampling based posterior Probability Extreme Learning Machine (USP-ELM), by introducing two strategies: first, we transfer the actual outputs of ELM to posterior probabilities for each instances, and then use uncertainty sampling strategy for confidence level assignment to adapt the training model and improve the classification accuracy. Experimental results show that the proposed approach is more efficient, compared with the existing ELMs.
Optimal Investment and Risk Control Problem for an Insurer: Expected Utility Maximization

Motivated by the AIG bailout case in the financial crisis of 2007-2008, we consider an insurer who wants to maximize the expected utility of the terminal wealth by selecting optimal investment and risk control strategies. The insurer's risk process is modelled by a jump-diffusion process and is negatively correlated with the capital gains in the financial market. We obtain explicit solution to optimal strategies for various utility functions.
Autonomic Fault Tolerant Scheduling for Multiple Workflows in Cloud Environment

Cloud Computing is becoming an increasingly admired paradigm that owns the characteristics of existing paradigms through strong support for virtualization along with various additional features such as on demand resource provisioning, reduced cost, computing flexibility etc. Most of the scientific communities employ workflow technologies to cope up with the complexity and heterogeneity of large scale scientific applications. As, the scientific workflows need a suitable paradigm for deployment and execution in conjunction with high availability of Cloud services. Thus, Cloud is a current benchmark for effective facilitation of the execution of scientific workflows through flexibility of accessible services such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) without allusion to the infrastructure on which these applications are hosted. For the successful execution of the scientific workflows on Clouds, Cloud platform should be able to manage the faults through autonomic fault tolerant approaches during the scheduling of workflow tasks on Cloud resources. Cloud providers also entail efficient scheduling algorithms to schedule these workflows along with autonomic fault tolerant approaches. Although, Cloud Computing technology has evolved but still some of the key challenges like autonomic fault tolerance and workflow scheduling need to be achieved. To achieve the set of challenges for the fault tolerant workflow scheduling, a comprehensive study of workflow scheduling algorithms along with the required set of Quality of Service(QoS) parameters is carried out. In addition, Cloud platforms and workflow engines are extensively explored and metrics relevant to the Cloud services are ascertained. Furthermore, a thorough study of failure prediction approaches, fault tolerant techniques and fault tolerant scheduling has been performed. Based on the literature survey, it is evident that the key challenge in scheduling workflow applications on Cloud that needs to be addressed is fault tolerant scheduling in autonomic way. Hence, to address the assorted challenges, autonomic fault tolerant scheduling for multiple workflows is the main focus of this research work.
On The Risk-Return Relationship in European and MENA Major Stock Markets During the 2008 Financial Crisis

The purpose of this study is to examine the relationship between risk and return in financial markets. In particular, a comparative study is conducted to shed light on such association by using stock market data from Middle East and North Africa (MENA) and Europe. The exponential generalized autoregressive conditionally heteroskedastic in the mean (EGARCH-M) methodology is adopted to investigate the return generating process in financial markets under study during the 2008 financial crisis. Empirical findings show evidence that some MENA region financial markets generated more risk reward than European stock markets.
Contemporary Drawing Key Concepts And Techniques

contemporary drawing key concepts and techniques is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the contemporary drawing key concepts and techniques is universally compatible with any devices to read.
Foreign Direct Investment and the Correlation between Cost and Revenue

This paper studies irreversible investment in the presence of uncertain revenue and uncertain cost of production. Using methodology of real options, we find the threshold markup of price over cost that triggers investment. When the processes for revenue and cost are negatively correlated, the standard result that uncertainty delays investment always holds. However, when these two processes are positively correlated, greater uncertainty of revenue or cost might accelerate investment. As less correlated cost and revenue, vertical FDI is less desirable than producing at home, but horizontal FDI that brings production to the output market is an advantage.
Prototyping low-cost and flexible vehicle diagnostic systems

Diagnostic systems are software and hardware-based equipment that interoperate with an external monitored system. Traditionally, they have been expensive equipment running test algorithms to monitor physical properties of, e.g., vehicles, or civil infrastructure equipment, among others. As computer hardware is increasingly powerful (whereas its cost and size is decreasing) and communication software becomes easier to program and more run-time efficient, new scenarios are enabled that yield to lower cost monitoring solutions. This paper presents a low cost approach towards the development of a diagnostic systems relying on a modular component-based approach and running on a resource limited embedded computer. Results on a prototype implementation are shown that validate the presented design, its flexibility, performance, and communication latency.
Evaluating multiparty gaze-based turn taking behavior for a robot in situated dialog

Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration.
The Impact of Podcasts on English Vocabulary Development in a Blended Educational Model

This experimental study attempts to see whether incorporating supplemental podcasts into the blended module of second language (L2) vocabulary teaching and learning leads to better learning outcomes in comparison with other common teaching and learning methods as self-study and conventional. To that end, undergraduate students from Ahvaz Jundishapur University of Medical Sciences were summoned up via an announcement to take part in the study. Volunteers were homogenized via Vocabulary Levels Test (VLT) and were then randomly divided into three groups to learn English vocabulary items via three different scenarios during 32 sessions. The collected data from the participants’ answers to the attitude questionnaire and interview as well as the data from assessing their performance throughout the course were analyzed both descriptively and inferentially. The analysis of the data revealed that the podcast-mediated blended L2 learning scenario appeared as the most successful scenario in L2 vocabulary learning. Consequently, it could be concluded that providing miscellaneous practicing opportunities for students would facilitate learning process and contribute to learning improvement. Persian Abstract: در این پژوهش تجربی، با پیوند پادپخش‌های آموزشی به شیوه‌ی یادگیری ترکیبی واژگان انگلیسی، تأثیر این شیوه با سایر شیوه‌های رایج آموزشی همچون سنتی و خود‌خوان مورد قیاس قرار گرفت. به این منظور، دانشجویان کارشناسی دانشگاه علوم پزشکی جندی‌شاپور اهواز طی یک فراخوان جهت شرکت در آزمون تعیین سطح واژگان و همگون‌سازی دعوت شدند. در نتیجه 132 دانشجو به‌عنوان شرکت کننده انتخاب شدند تا واژگان انگلیسی را طی 32 جلسه و به شیوه‌های خودخوان، سنتی و ترکیبی مبتنی بر پادپخش فراگیرند. داده‌های گردآوری شده از پاسخ شرکت کنندگان به پرسشنامه‌ی نگرش، عمل‌کرد و نیز پاسخ‌های آن‌ها به مصاحبه پایانی، به شیوه‌های توصیفی و استنباطی مورد تحلیل قرار گرفت. نتایج حاکی از اثر‌بخش بودن شیوه‌ی یادگیری ترکیبی مبتنی بر پادپخش در ارتقای دانش واژگان انگلیسی شرکت کنندگان بود. از این‌رو، می‌توان گفت شیوه‌های یادگیری به‌کمک فناوری از جمله یادگیری ترکیبی مبتنی بر پادپخش با فراهم نمودن محیط‌های متنوع یادگیری و تمرین نقش به‌سزایی در ارتقای دانش واژگان انگلیسی فراگیران ایفا می‌کند. واژگان کلیدی: شیوه‌ ی یادگیری، واژگان انگلیسی، سامانه مدیریت یادگیری الکترونیکی، پادپخش
Why and How Blockchain?

We discuss the current state of blockchain in the technical industry and discuss blockchain's decentralization roots and its inclusion in the world of mainstream corporate technology. We include some technical background and issues in the context of the industry, feasibility, and future directions. We offer speculation as to how blockchain technology may serve various competing agendas. The purpose of this paper is to raise questions more than provide answers - to stimulate thought and discussion - in the context of the technology and its positioning in the centralization vs. decentralization spectrum of interests.
SELF- DEVELOPMENT PROGRAM BASED ON HYBRIDLEARNING TO INCREASE CHEMISTRY TEACHER ABILITY OF RESEARCH AND SCIENTIFIC PAPERS WRITING

This research aims to design a self-development program based on hybrid learning to increase the ability in research and write scientific papers for high school chemistry teacher. The designed include the structure of the program, media for supporting program in the form of training modules and web-based media. The method used is the exploratory mixed methods design with instrument development models. Research procedure includes four stages, namely the qualitative development of instruments, quantitative and interpretation. Qualitative phase is done with need assessment and literature. Instrument development is done by designing the program structure in the form of training activities and preparation of modules and web-based media for program implementation. The research use three instruments, i.e. a sheet of expert validation, assessment sheets of module and web based media. Assessment sheets module contains of 58 items covering three aspects of assessment, namely instructional design, content and design of technical feasibility. Web-based assessment sheets consists of 31 items statements include continuities aspects, modalities, redundancies, coherence, interactivity, personalization, simplicity and specificity. The data were analyzed descriptively. The first results is the self-development program structure based on learning hybrid with characteristics; follow the pattern In-On-In; with two training strategy -face to face and online- that conducted for 18 weeks; training materials include basic research, action research, and scientific writings. Media for supporting program include Tinular module and Tinular website. Tinular training modules consist of six chapters and further reading material. Tinular website for training can be accessed at http://dikkitinular.wix. com/titinular. Quality of Tinular modul and Tinular website are in good categories based on peer reviewers assessment.
Looking for peace in the national curriculum of Mexico

Schools are places where we can learn ways of being, seeing and living. They are transmission belts – social institutions that can engender values and attitudes from both how we learn and what we learn. Using content analysis, this mixed methods study assesses the national curriculum of Mexico – the Plan de Estudios Educación Básica, 2011 – for three components found in peace education programmes: recognizing violence (direct, structural or cultural); addressing conflict nonviolently; and creating the conditions of positive peace. These three components contribute to the analysis of the Sustainable Development Goals (SDGs): quality education (SDG 4); gender equality (SDG 5); reduced inequalities (SDG 10); responsible consumption and production (SDG 12); and peace, justice and strong institutions (SDG 16). This component of the Peace Education Curriculum Analysis (PECA) Project finds that the Plan de Estudios contains limited content that recognizes violence, some evidence of techniques used in transforming conflict nonviolently and only select content that is concerned with contributing to positive peace.
Study on 3D body scanning,reconstruction and measurement techniques based on Kinect

A kind of depth camera named Microsoft Kinect is used to scan the body.Firstly,the body depth data are captured,and the 3D body point cloud data are calculated.Secondly,the body model is reconstructed by using Pro/E.At last,the characteristic data of the body surface are measured.Compared with conventional 3D scanners,depth image can be captured by the device at video rate with little effect of light or texture condition.
EMAP: EXPEDITE MESSAGE AUTHENTICATION PROTOCOL FOR VEHICULAR AD HOC NETWORKS

Dynamic Public Key Infrastructure (PKI) assumes essential part in Vehicular Ad hoc Networks (VANETs). In this framework; affirmation of got message should be possible by checking the sender's testament is incorporated into the Certificate Revocation Lists (CRLs), which means checking its renouncement status, then, substantiating the sender's authentication, lastly accepting the sender's mark. Since the CRL size is required to be extensive, the postponement of checking the renouncement status of a declaration incorporate into a got message is relied upon to be expansive. Keeping in mind the end goal to lessen the deferral it uses Hasten confirmation process for Vehicular Ad hoc Networks, which uses Hash Key strategy with upgraded and non streamlined calculations which checks the renouncement status of the sender in a CRL. Besides, it is impervious to basic assaults while adopting so as to perform verification method and utilizing secure correspondence computerized declaration for correspondence by trade the information just between non repeal vehicles. Hurry can extensively decrease the time
An econometric analysis of the transition to retirement in Europe: results from SHARE data

Pensions and, in general, social security are one of the most significant expenditure items of the government budget, especially in Europe. The constant ageing process and the decreasing fertility rate in developed countries in the recent years urge policy makers to implement strategies to avoid an explosive rise of government spending. Policies, in turn, may influence individuals’ decisions about consumption, saving but also their choice about retirement. The aim of this research is to find which factors are significant for the transition to retirement, and how they differ across European countries, namely in Western Europe. The study uses data from the 5th and 6th Wave of the SHARE survey. The work will develop an econometric analysis of the transition between labor force and retirement.
Design of DTN Routing Algorithm Based on Probabilistic Delay

In DTN,the current routing forwarding strategies include flooding method based on copy and forwarding method based on knowledge.In order to deliver more messages with less knowledge oracle,a new routing algorithm which calculates the delivery predictability using the history contact information is proposed.The algorithm combines copy strategy and probabilistic routing.When one node meets the other node,a message is transferred to the other node if the delivery predictability to destination is higher at the other node.Binary mode copy strategy is used to distribute a message to a number of nodes to increase transmission rate and an active mechanism is used to delete redundancy messages.Simulation result shows this algorithm has a higher message delivery ratio than Epidemic,PRoPHET and BSW.In addition,this superiority scales better with the buffer size and the number of nodes.
Investigating the drivers of mobile learning acceptance among young adults in the World Heritage town of Luang Prabang, Laos

This study is motivated by the initiative to implement mobile learning to promote world heritage site awareness among young adults living in the world heritage town of Luang Prabang in the Lao People’s Democratic Republic (PDR). Mobile learning is still in its infant stage in Laos. The objective of this study is to investigate the drivers of mobile learning acceptance among young adults in Luang Prabang. Structural equation modeling analysis based on 349 students of higher education indicates that perceived usefulness, perceived ease of use, and perceived enjoyment directly affected their behavioral intention to use mobile learning. Social influence and self-efficacy indirectly affected mobile learning acceptance through perceived usefulness and perceived ease of use respectively. Findings from this study yield insights for policy recommendations for mobile learning implementation in Luang Prabang, and also contribute to the understanding of mobile learning acceptance from the perspectives of a least developed country.
Future Regional Contributions for Climate Change Mitigation: Insights from Energy Investment Gap and Policy Cost

Mitigating climate change and ensuring regional equity development is equitable are matters of global concern. Systematic and in-depth research into theseissues is seldom conducted. In this research we combine qualitative and quantitative studies and use six state-of-the-art energy-economy analysis models and four long term scenarios to explore the distribution of regional contributions for climate change mitigation in the future. We focus on the energy investment gap and policy cost. The study’s conclusion is that, under the assumption of carbon tax as a source of energy investment from 2025, the global positive energy investment gap in the climate change mitigation scenario will not appear until around 2035–2040. Asia and OECD90+EU (Countries from the OECD 1990, EU and its candidates) are the regions that will have a significant direct impact on the global energy investment gap under climate policies in the future. However, from the perspective of the relative value (the percentage of the energy investment gap relative to the energy investment in the Current Policies (CPol) scenario), Asia will contribute the most to the global energy investment gap under the climate stability policies. Under the Nationally Determined Contributions (NDC) scenario, Asia will contribute the most in the near term and REF will contribute the most in the medium term.The findings show that OECD90+EUwill bear more cost in the pledges scenario, and Asia will bear more cost in the climate stability scenarios in the medium term. Contrary to the common sense expectation, the developed regions will contribute the least in terms of the proportion of the policy cost to the respective economic aggregates under the climate stability scenarios in the medium and long term, but the opposite is true in the developing regions. By and large, from the perspective of the current climate change mitigation policies, the developed regions and developing regions will achieve a win-win situation in the long run, but the relative contribution of the developed regions is not as great as was previously expected. These novel findings should prove to be useful to policy makers when developing transition strategies for climate change mitigation.
Incomplete Clustering for Large Scale Short Texts

Clustering is an unsupervised classification of patterns(observations,data items,or feature vectors) into groups(clusters). So far,many clustering algorithms have been proposed.With the rapid development of internet,short texts such as query logs and Twitter messages play a more and more important role in our daily life.Most existing clustering methods are hard to be applied in dealing with this kind of information due to the huge scale of data.This paper reveals the long tail distribution of this kind of information,and proposes an incomplete clustering algorithm.The experimental results show that the proposed method can cluster the short texts effectively and efficiently.
Global Low-Resolution CT Scan Regulated Tomo-synthesis

Tomosynthesis reconstructs a 3D object from a scan consisting of a limited number of projections. Hence, tomosynthesis requires much less radiation dosage as compared to computed tomography (CT). A major problem with tomosynthesis is image artifacts associated with incompleteness of data. In this paper, we propose a tomosynthesis approach to achieve higher image quality in a region of interest (ROI) than competing techniques. First, a low-resolution global CT scan is acquired. Then, a high-resolution local scan is performed with respect to the ROI. Finally, images of the ROI are reconstructed from these two datasets. Our numerical simulation results show that images of the ROI obtained by our approach are significantly better than the counterparts without using the global scan information.
WNNP-Based Clustering Algorithm for Ad Hoc Networks

According to the dynamic characteristics of ad hoc network topology,a wavelet neural network prediction(WNNP) model was used to predict the geometrical location of the nodes.Comparing the predicted total holding time with the threshold,the stabilization of a cluster in next time can be measured.If the cluster tends to be unstable in next time,a routing pre-repair mechanism can be initiated before the link failure to avoid frequent breaks of links.Thus the network performance is significantly improved.Simulation results show that compared with the lowest-identifier(lowest ID) algorithm and location-based WCA(LWCA) which has no prediction model,WNNP-LWCA can improve by 7% and 5% of the packet delivery rate,reduce by 63% and 50% of the broken routing number,and maintain the stabilization of the cluster.
INSPECTING STUDENTS' ACADEMIC REALITIES USING SOCIAL MEDIA PLATFORM

The informal conversation of the students used on social media has been studied vastly now-a-days to understand their experiences. Twitter has been examined in which the hashtag #EnggProblems is mined to extract tweets and to classify the problems of the students into categories: Heavy study load, sleep problems, lack of social engagement, diversity issues, negative emotion and there is one more category that is ‘others’ . many classification algorithms were applied on the tweets extracted from the said hashtag, out of which naive bayes multi label classifier gives the best results. It has been observed that most tweets get classified into others category as a result of which students’ problem cannot be identified clearly. This paper proposes an approach where SentiWordNet has been used to assign scores to each tweet and classify them as positive, negative or neutral. This helps to understand the sentiment of the student tweeting and identify at risk students