Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Performance Memory Snapshotting for Real-Time, Consistent, Hypervisor-Based Monitors

This paper presents a concurrent-computing approach—high-performance memory snapshotting—to improving security-introspection of virtual machine guest memory. Efficient introspection improves security monitoring in existing hypervisor systems with real-time, consistent memory introspection capabilities. Efficient introspection has three requirements that each must be met to provide protection against evasive threats: native memory introspection performance, accpetable guest performance, and consistent introspection view of guest memory. Existing introspection systems have provided one or two of these properties but not all three at once. High-performance memory snapshots are evaluated as a solution for meeting all three efficient introspection requirements. In this work we describe how existing system performance can be improved with high-performance snapshotting, present an efficient introspection prototype that has been released as an element of the open-source LibVMI introspection library1, evaluate the efficient introspection prototype on both applications and microbenchmarks, provide demonstrations of introspection application modules enabled by efficient introspection, and provide performance guidance for developing introspection applications utilizing efficient introspection.
Fault Classification of Bearings Using Machine Learning Algorithms

In a world where machinery has a primary role in the productivity of most industrial companies, predictive maintenance is crucial. Also, considering that bearings are present in the majority of industrial machinery, most of the monitoring, in one way or another, has a central focus on these elements. At the same time, we find ourselves undergoing an industrial revolution – Industry 4.0 –, where low cost distributed monitoring and data transfer are vital. Considering these goals, which directly translate to a large amount of data generated, it is no surprise that the implementation of Artificial Intelligence, more specifically Machine Learning algorithms, is gaining growing importance when it comes to data analysis in real-time. With this in mind, the development of this dissertation tries to present a practical case where both, monitoring of bearings and Artificial Intelligence, can be applied, always considering the main theoretical concepts of each topic: the range of frequencies to study when analyzing the bearings, the kind of equipment to measure and acquire data, as well as the algorithm to use in order to train the Machine Learning model – in this case, an Artificial Neural Network. This project uses bearings of anemometers during data acquisition, both inside a wind tunnel as well as in an acoustically-isolated environment. Relative to the data analysis, most of the work was done using LabVIEW in junction with MATLAB scripts. The Virtual Instruments created enabled the data analysis and visualization, which allowed proper parameter selection in order to implement an Artificial Neural Network. The final result was a trained Artificial Neural Network model capable to distinguish faulty bearings from healthy ones, under certain limitations due to the small size of the available knowledge. It was left for future works the use of different data acquisition methods for real-time classification on the field, as well as the application of this model to Acoustic Emission data, which allows for better predictive maintenance. It is also expected that this model has the ability to classify different flaws, given better acquisition conditions.
Bankruptcy Analysis with Support Vector Machines

This paper describes the rating methodology that is based on the non-parametric non-linear classification method, the support vector machine, and a proposed non-parametric technique for mapping rating scores into probabilities of default. We give an introduction to underlying statistical models and introduce the results of testing our approach on the German Bundesbank (Central Bank) data. A comparison with more traditional approaches such as discriminant analysis and logit regression is given. The selection of variables and graphical data representation are discussed.
The Impact of Input Enrichment in Long Text vs. Short Texts on Grammatical Accuracy in Writing Among Elementary Language Learners

This study was conducted to investigate the influence of teaching accurate grammar in writing via enriched long text and short text for the elementary students at Shokouhe_Farhang institute. The homogenized subjects were divided into two groups of 18 and 17 participants. Using a writing exam as a pretest in order to check the students’ knowledge in English past tense. The control group received the instruction through short texts and the experimental group received instruction through long text activity. Short and long texts enriched with past tense and past progressive tense presented in ten sessions of instruction and at least learners took part in a writing exam as posttest . Comparing the result through statistical analysis showed that there is a significant difference between the performances of both groups. It was concluded that using long text instruction is more influential in using accurate grammar in writing than short texts.
Some Antecedents and Effects of using Mobile Apps in Tourism Marketing

: The purpose of this paper is to provide an improved understanding of the effect of Perceived Ubiquity, Perceived Informativeness, Perceived Personalization, and Perceived Enjoyment towards Mobile App Usefulness, as well as Mobile App Usefulness towards Revisit Intention. A quantitative approach has been employed. The gathered data is obtained using questionnaire with itemized rating scale resulting in 100 respondents. For the purpose of data analysis, Structured Equation Model (SEM) method has been adopted. The causal model was validated using Smart-PLS 2.0. The results indicate that Mobile Application is an important antecedent of Revisit Intention and mediates the influence of Perceived Ubiquity, Perceived Informativeness, Perceived Personalization, and Perceived Enjoyment. The study provides a comprehensive framework of the antecedents and outcome of a mobile application’s usefulness for tourism marketing. The findings provide insight for marketing managers and governments in developing a successful mobile application for tourism marketing.
A New Approach for Train Driver Tracking in Real Time

Find the main driver in the train cab and keep tracking him is the primary step for driver activity analysis in real time. But long-term robust human tracking is still a challenging task due to many practical factors. Most template-based tracking algorithms of templates often fail when encountering occlusion, pose variation and motion blur. In this paper, we propose a real-time algorithm based on correlation filters for long-term human tracking. To restore the false model caused by occlusion, we construct a conditional target re-detection mechanism. We adopted a high-confidence model update strategy to ensure the correct detection and avoid model drift problem. The experimental results indicate that the proposed algorithm is robust and accurate to variations in pose and heavy occlusion while runs at speed in real-time.
Seeking Theoretical Common Ground: An Integrative Framework of Understanding Accountability for Service Delivery in the Community School in Nepal

Existing literature provides ample evidence on how people understand accountability in different contexts. However, little attention has been paid on the integration of various theoretical perspectives about understanding accountability for education service delivery. Discussing theoretical premises against the empirical evidence from the community schools of Nepal, this article explores common ground of various theoretical perspectives about understanding school actors' accountability. It provides a framework for understanding accountability by integrating the ancient principle of 'karma-yoga' and modern theoretical ingredients of structure-agency and knowledge-power. The article argues that both ancient and modern streams of theoretical perspectives are not mutually exclusive rather they share a common ground of process-based accountability which is embedded in broader concept of responsibility. Finally, the paper contributes to existing literature of accountability in education arguing that the school head’s agency rather than working structure of the school played a significant role in creating an accountable space between the school head and other actors in the community school.
Project Management Success in a Troubled Environment: The Mobile Telecommunications Sector in Mogadishu, Somalia

This paper considers the nature of project management success in Somalia in the mobile telecommunications sector during a period of warfare. The principal form of research was personal interviews of 52 business owners and managers in Mogadishu, the capital city of the country. It was found that most businesses were operated on the basis of international best practice despite the fact that the ongoing violence caused people to behave in different ways and to have different priorities. Compromises were made by managers to the conditions they faced but a commitment to ultimate profitability was maintained.
Study on Modeling and Simulation of the Electronic Virtual Laboratory

This paper proposes a model structure based on “mathematical model and solid model” and a simulation algorithm based on SPICE for electronic virtual laboratory. In this model, the virtual experiment simulates real experiment via abstracting experiment scenes, instrument objects and component objects from actual experiment and constructing mathematical and solid model with object-oriented method. And familiar modeling methods and technologies are discussed in detail. In the simulation algorithm, SPICE is redeveloped and imbedded in the multimedia interface as simulating kernel, which succeeds in solving integration of simulation model and solid model. Finally, an application example “the virtual laboratory for electronic circuit experiment” is also given. The system provides the users with realistic environment, convenient operation and powerful simulation capability, and has been successfully applied in the experimental teaching.
Inside the Closed World: User and Device Profile Analytics for SCADA Security

Attacks that use sophisticated and complex methods in-creased recently, aiming to infiltrate the Supervisory Control and Data Acquisition (SCADA) system and stay undetected. Therefore, attackers often get access to authorized permissions of SCADA and bring catastrophic damages by sending ‘legitimate’ control commands. Furthermore, insiders may also misuse or abuse their permissions to damage SCADA system, which is difficult to predict and protect against them. Most existing security systems employ standard signature-based or anomaly-based approaches, which are not able to identify this type of malicious activities. In this paper, we use machine learning algorithms based on Singular Values Decomposition (SVD) to create profiles of users and devices. The major contribution of this paper is providing a general process to detect anomalies, independent of specific use-cases. Suspicious actions are altered to analysts with relevant contextual information for further investigation and action. We provide detailed description of algorithms, methodology, processing of profiling and anomaly detection. Having profiles of different users and devices can provide us a baseline of normal behavior to compare against unusual behaviors. To demonstrate the proposed method, attack scenarios have been simulated at a Compressed Natural Gas (CNG) system in our lab. Experimental results illustrate that the proposed method is effective for abnormal behaviors in SCADA system.
Target recognition of SAR images based on multi-resolution representation

ABSTRACT This letter presents a target recognition method for synthetic aperture radar (SAR) images by exploiting the multi-resolution information. For the training samples, images with lower resolutions are generated to construct a multi-resolution dictionary for the sparse representation-based classification (SRC). Then the test sample will be classified based on the augmented dictionary. The multi-resolution representation of the training samples can not only promote the representation capability of the dictionary but also enhance the robustness to the resolution variance of the test sample due to the sensor variation. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset to demonstrate the validity of the proposed method.
An efficient method for web page classification based on text

—According to Google index , the number of web pages now exceeds 50 billion, and is increasing by millions per day. The global population of internet users is also growing rapidly and then a web page classiﬁcation problem arises. According to that, automatic classiﬁcation method is required to deal with this problem of the World Wide Web (WWW). The traditional methods that use text determine the class of the document, but usually retrieve unrelated web pages. In order to e ﬀ ectively classify web pages, we apply di ﬀ erent feature extraction techniques with di ﬀ erent web page classiﬁcation methods to ﬁnd an e ﬃ cient method for web page classiﬁcation. The three feature extraction methods used in the study are Term Occurrence (TO), Term Frequency (TF), Term Frequency-Inverse Document Frequency (TF-IDF) and the three classiﬁers used in the study are K-nearest neighbor (K-NN), Naive Bayes (NB) and Decision Tree (DT). Each web page is represented by the three feature extraction methods. The principal component analysis (PCA) is used to select the most relevant features for the classiﬁcation as the number of unique words in the collection set is big. The ﬁnal output of the PCA is sent to the three di ﬀ erent classiﬁers to ﬁnd the best method for web page classiﬁcation. The experimental evaluation used demonstrates that the combination of Naive Bayes (NB) and Term Frequency-Inverse Document Frequency provides an e ﬃ cient classiﬁcation accuracy compared to other methods
Corporate Social Responsibility: Assessing the Alignment of Theory and Practice Utilizing Case Studies

A research of scholarly literature and corporate websites reveal an esteem for the framework of corporate social responsibility (CSR). Business leaders have integrated corporate social responsibility into their operational plans and overall business strategy, as exhibited on their public disclosure statements, to improve customer demand, reduce cost, and spur innovation. Although some scholars have argued in the past that these CSR activities may be symbolic rather than practical, there are reasons to believe the latter as corporate leaders have shown interest in engaging in aspects of CSR. The theory of CSR may be broader than its practice which may be limited in scope and without a clear implementation paradigm. The practice of CSR by service corporations that are broad in scope and influence in terms of revenue and market capitalization may epitomize the nature of CSR implementation within corporations. The author used case study method to assess the CSR programs of Amazon and Apple to create an implementation paradigm of CSR worthy of emulation by corporate leaders and decision makers. Findings revealed CSR implementation is based on the philanthropic, employee wellbeing and development, and innovative programs of corporate leaders to improve the sustainability of their businesses.
Improved detection model for DCT coefficient quantization

A detection model is developed to predict visibility thresholds for discrete cosine transform coefficient quantization error, based on the luminance and chrominance of the error. The model is an extension of a previously proposed luminance-based model, and is based on new experimental data. In addition to the luminance-only predictions of the previous model, the new model predicts the detectability of quantization error in color space directions in which chrominance error plays a major role. This more complete model allows DCT coefficient quantization matrices to be designed for display conditions other than those of the experimental measurements: other display luminances, other veiling luminances, other spatial frequencies (different pixel sizes, viewing distances, and aspect ratios), and other color directions.
On the parallelization of network diffusion models

In this thesis, we investigate methods by which discrete event network diffusion simulators may execute without the restriction of lockstep or near lockstep synchronicity. We develop a discrete event simulator that allows free clock drift between threads, develop a differential equations model to approximate communication cost of such a simulator, and propose an algorithm by which we leverage information gathered in the natural course of simulation to redistribute agents to parallel threads such that the burden of communication is lowered during future replicates.
Piccola introduzione alla cartografia delle controversie Introducing the cartography of controversies

The cartography of controversies is a collection of techniques to observe and describe social issues developed by Bruno Latour as an applied version of Actor-Network Theory. Originally, it was used to guide college students in the exploration of scientific and technological debates. The scope and interest of such cartography, however, exceed its didactic origin. Adopted in several universities and developed by a number of international projects, the cartography of controversies is today a full research methodology. In this article, we draw on our experience as Latour's teaching assistant, to introduce some of the main ideas and techniques of the cartography of controversies. The purpose of this article is tracing the intellectual genealogy of this methodology, sketching its present articulation (in particular by comparing it with ethnographic methods) and to envisioning its future developments. La cartografia delle controversie nell'opera di Bruno Latour A uno sguardo superficiale, l'opera di Bruno Latour sembra seguire una traiettoria che va dall'osservazione piu minuziosa e diretta delle pratiche sociali fino a una riflessione piu teorica e astratta sullo spirito della modernita in una specie di metamorfosi dall'etnografia verso filosofia sociale. Per smentire quest'interpretazione basterebbe, in effetti, considerare con attenzione la bibliografia del pensatore francese. Se e vero che Latour ha cominciato la sua carriera con un lavoro strettamente etnografico sulle routine scientifiche (Laboratory Life, 1979) e che alcune delle sue ultime opere hanno un taglio decisamente piu speculativo (Politics of Nature, 1999a, Reassembling the Social, 2005), e altrettanto vero che gia nel 1984 egli pubblica una serie di riflessioni squisitamente teoriche (Irreductions) e che nel 2002 riprende i metodi etnografici applicandoli al Consiglio di Stato francese (La Fabrique du droit). Piu ancora della cronologia delle pubblicazioni, e il senso stesso dell'opera latouriana a mettere in discussione la distinzione tra 'osservazione' e 'riflessione'. Tutte le opere di Latour nascono dall'idea che le grandi domande della filosofia occidentale trovino risposta che nell'osservazione delle pratiche sociali: cosi Latour esplora il lavoro dei laboratori di ricerca
Research on Perfecting Government Non-Tax Revenue Management System

At present, China’s fiscal revenue system consists of two important components. One is tax revenue and the other is non-tax revenue. With the continuous expansion of the scale of non-tax revenue and the phenomenon of irregular management, some non-tax revenue in China has become the main source of dismembering financial functions, reducing the efficiency of capital use, disrupting economic order and breeding corruption. Since the reform of non-tax revenue touches on the interests of those with vested interests and all parties have different views on the reform, it is difficult to reform non-tax revenue. As we all know, economy is related to a country’s life and death. Economy is the top priority of the country. The development of the country depends on economic growth. However, many people ignore the impact of non-tax revenue on the economy, resulting in the country not making full use of non-tax revenue to achieve economic growth. Therefore, it is extremely important to study the non-tax revenue management system from the perspective of the impact of non-tax revenue on the economy. This paper focuses on the promotion and suppression of non-tax revenue to economic growth and puts forward some constructive suggestions on improving the management system of non-tax revenue.
Modeling and adaptive compensation of tooth surface contour error for internal gearing power honing

The machining of high precision gears requires a strict and accurate co-movement relationship controlled by the electronic gearbox between the moving axes of the gear machine tools. This article proposes a tooth surface contour error modeling method and an adaptive electronic gearbox cross-coupling controller for internal gearing power honing. First, the electronic gearbox model is structured according to the generative machining principle of internal gearing power honing and the tooth surface contour error is established by means of homogeneous coordinate transformation and meshing principle. Then, the adaptive electronic gearbox cross-coupling controller is designed, which comprises the electronic gearbox cross-coupling controller and the fuzzy proportional–integral–derivative controllers whose universes of membership functions in fuzzy rules are optimized by particle swarm optimization to improve the adaptability and robustness to disturbance fluctuation and model uncertainty of the system. Finally, experiments are carried out on a self-developed gear numerical control system. The results have demonstrated that the estimated tooth surface contour error using the proposed method is very close to the actual one, and the proposed adaptive electronic gearbox cross-coupling controller can effectively reduce the tracking error and the tooth surface contour error when compared to the electronic gearbox cross-coupling controller and non–electronic gearbox cross-coupling controller (electronic gearbox controller without cross-coupling and adaptive compensation).
Does Efficiency Contribute to Market Valuation of Banks? Evidence from Turkish Banking System

Purpose- Operational efficiency is one of the strategic concerns for bank management. The aim of our study is to investigate the effect of operational efficiency on banks’ market valuation. Methodology- We empirically examine the way in which the financial market evaluates different efficiency measures of banks, using a panel data of listed banks at Borsa Istanbul for the years 2010 – 2017. We employ three different efficiency measures as proxies. Those are i) operating expenses compared to total net income, ii) total net income per branch, and iii) total net income per employee. Findings- We find that cost efficiency, interest income as return on equity (ROE) and loan loss provisions (LLPs) are found to be statistically significant drivers of market valuation of banks. We use risk-weighted assets to total assets as a measure for risk-taking behavior of banks. The results suggest tentative findings for risk-taking behavior of banks. The findings also suggest that bank size and non-interest income as ROE do not significantly influence bank market valuation. Conclusion- We conclude that operating expenses compared to total net income as an efficiency measure is a determinant of market valuation of banks while income per branch and income per employee as efficiency measures are not related to market valuation of banks. Thus, we recommend bank managers boost net interest income while avoiding credit losses, and drop off operating costs compared to revenue in order to maximize bank’s market valuation.