Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Relationship B40 against Purchasing Behaviour Non-Basic Needs using Loans and Intention to Increase debt

Nowadays, the culture of debt has been considered synonymous with low-income, middle-income, and high-income households. The behavioral of owing among these groups of households can be distinguished through the types of needs based on what they want to fulfil. In view of the requirement hierarchy introduced by Maslow (1954), there are five levels of need to be met, including the intrinsic need i.e. social needs. Individuals with low income or group B40 are expected to loan for their basic needs and to fund their daily basic costs. However, due to time and environment circulation, there may be other requirements that they want to fulfil. Therefore, this research is to study the effect of social relationships on the intention of increasing the loan and the purchase of tahsiniyyat goods (such as comfort and beauty requirements) through loans carried out among B40 households in Felda Sabah, Malaysia. This research uses a questionnaire instrument and analyzed data using Smart PLS software. The findings show that there is a significant relationships between social relationship and the purchase of non-basic goods through loans. Some basic implications and suggestions are also discussed in this study.
Radio-over-Fibre transmission of multiple wireless standards for digital cities: exploiting the new tramway infrastructure

The present paper aims at the specific application of Radio-over-Fibre to provide dedicated Wi-Fi access to tramway passengers. The challenge here is to provide wireless services in densely populated city areas based on a real-case scenario targeting public transport. The proposed system targets 2.4 GHz Wi-Fi access at 1 Mbps per user inside a tram, with a wireless bridge at 5 GHz along the track. A chosen design will finally be implemented and evaluated in terms of WiFi coverage, BER and power consumption. Lessons learned will be interpolated for 60 GHz RoF for Wi-Fi access. Replacing the 5-GHz track site solution by a 60 GHz wireless link will bring a significant increase in data throughput (>1 Gbps). We report here performances of running experiments on 2.4 and 5 GHz RoF systems and results on photonic 60 GHz generation for existing fibre backbone between the stations.
Context-Aware Sensor Fusion For Securing Cyber-Physical Systems

The goal of this dissertation is to provide detection and estimation techniques in order to ensure the safety and security of modern Cyber-Physical Systems (CPS) even in the presence of arbitrary sensors faults and attacks. We leverage the fact that modern CPS are equipped with various sensors that provide redundant information about the system's state. In such a setting, the system can limit its dependence on any individual sensor, thereby providing guarantees about its safety even in the presence of arbitrary faults and attacks. In order to address the problem of safety detection, we develop sensor fusion techniques that make use of the sensor redundancy available in modern CPS. First of all, we develop a multidimensional sensor fusion algorithm that outputs a bounded fusion set which is guaranteed to contain the true state even in the presence of attacks and faults. Furthermore, we provide two approaches for strengthening sensor fusion's worst-case guarantees: 1) incorporating historical measurements as well as 2) analyzing sensor transmission schedules (e.g., in a time-triggered system using a shared bus) in order to minimize the attacker's available information and impact on the system. In addition, we modify the sensor fusion algorithm in order to provide guarantees even when sensors might experience transient faults in addition to attacks. Finally, we develop an attack detection technique (also in the presence of transient faults) in order to discard attacked sensors. In addition to standard plant sensors, we note that modern CPS also have access to multiple environment sensors that provide information about the system's context (e.g., a camera recognizing a nearby building). Since these context measurements are related to the system's state, they can be used for estimation and detection purposes, similar to standard measurements. In this dissertation, we first develop a nominal contextaware filter (i.e., with no faults or attacks) for binary context measurements (e.g., a building detection). Finally, we develop a technique for incorporating context measurements into sensor fusion, thus providing guarantees about system safety even in cases where more than half of standard sensors might be under attack. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Computer and Information Science First Advisor Insup Lee Second Advisor James Weimer
ONTOLOGY-BASED CONSTRUCTING AND MAINTAINING OF WIKI-SYSTEMS

The paper discusses the problems of the development and maintenance of information systems on the basis of ontology and Wiki-technology. In particular, an approach including the method of constructing information systems using Wikitechnology and ontology of subject domain and the method of ontology extraction from the already existing Wiki-systems is suggested. An implementation of these methods is described in detail.
Trigger Warnings: When Is Goodwill Impairment Disclosure Informative?

MARIA NYKYFOROVYCH: Trigger Warnings: When Is Goodwill Impairment Disclosure Informative? (Under the direction of Jeffery Abarbanell) This paper examines the information content of financial statement disclosures related to goodwill impairment testing after the implementation of the Statement of Financial Accounting Standards (SFAS) 142. I hand-collect a sample of triggering events that firms disclose at the time of a goodwill impairment announcement. Factor analysis reveals that impairment reasons group into three categories: firm-, industryor economy-related. I find significant price and volume market reactions to a firm’s decision to impair goodwill, but only if a firm discloses firmspecific triggering events. This result may explain previous mixed evidence on market reactions to goodwill impairment announcements that do not account for the triggering event cited by the firm. Additional findings indicate that consistent with the predictions of Kim and Verrecchia (1994), firm-specific triggering events increase the post-announcement information asymmetry, and predict future goodwill impairments when a firm records multiple impairments. Overall, these results indicate that financial statement users require more detailed firm-specific disclosures related to goodwill impairment testing. The SEC and FASB might consider this finding while developing future disclosure guidance for financial statement filers.
Inference attacks and control on database structures

Today’s databases store information with sensitivity levels that range from public to highly sensitive, hence ensuring confidentiality can be highly important, but also requires costly control. This paper focuses on the inference problem on different database structures. It presents possible treats on privacy with relation to the inference, and control methods for mitigating these treats. The paper shows that using only access control, without any inference control is inadequate, since these models are unable to protect against indirect data access. Furthermore, it covers new inference problems which rise from the dimensions of new technologies like XML, semantics, etc.
Effective Heart Disease Prediction usingFrequent Feature Selection Method

The healthcare environment is generally perceived as being ‘information rich’ yet ‘knowledge poor’. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. Knowledge discovery and data mining have found numerous applications in business and scientific domain. Valuable knowledge can be discovered from application of data mining techniques in healthcare system. The diagnosis of heart disease is a significant and tedious task in medicine. This research paper proposed a frequent feature selection method for Heart Disease Prediction. Good performance of this method comes from the use of the fuzzy measure and the relevant nonlinear integral. The none additively of the fuzzy measure reflects the importance of the feature attributes as well as their interactions. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. Clustering the objects which have similar meaning, the proposed approach improves the accuracy and reduces the computational time.
ASSOCIATION BETWEEN CORPORATE FINANCIAL PERFORMANCE AND CORPORATE SOCIAL RESPONSIBILITY IN A CRISIS: FOCUSING ON THE MARCH 11 DISASTER

This study investigates how firms damaged by the Great East Japan Earthquake and Tsunami in 2011 (March 11 Disaster) proceeded toward a turnaround by focusing on corporate social responsibility (CSR). We define CSR as the responsibility that a firm has to pursue as a public institution for the benefit of the community and its environment. Ota and Nakashima (2015) proposed the Ota-Nakashima CSR model that measures CSR by applying the Ota turnaround model (2013) to the Kanji-Chopra CSR model. We compute CSR using the Ota and Nakashima CSR models (2015), and analyze the association between CSR and corporate financial performance (CFP). By examining the association between CSR and CFP, we investigate how firms damaged by the March 11 disaster consider planning and implementing CSR to proceed toward a turnaround.
Monopoly behavior of China’s manufacturing enterprises

Purpose With the increase of state capital, corporate total factor productivity (TFP) has a tendency to jump up at first and then slowly decrease. Generally, no significant “productivity paradox” can be observed in China’s manufacturing industry. With the increase of export density, corporate TFP also shows a trend of initial jump growth and subsequent slow decline. This paper aims to discuss these issues. Design/methodology/approach Using the 1996–2013 China Industrial Enterprise Database, this paper studies the monopolistic behavior of Chinese manufacturing enterprises through the measurement of TFP and corporate monopoly power. Findings Results show that China’s manufacturing monopoly enterprises are generally innovation-oriented rather than rent-seeking. However, there are certain differences between diversified types of monopoly enterprises: the ones with state capital are more inclined to innovate than those without, whereas the ones with export delivery value are more inclined to seek rent than those without. Originality/value Therefore, the government should implement differentiated policies for diversified types of monopoly enterprises, and do so in a targeted manner fully reflecting the containment of rent-seeking and the encouragement of innovation.
Telescope Pupil Tracking using a Pyramid WFS

During an AO observation the Lyot stop of an infrared instrument fed by the AO system must remain aligned with the image of the telescope pupil. Uncertainties and drifts of the pupil image position must be accommodated by under-sizing the Lyot stop, which, especially for AO corrected observations, reduces very significantly the science productivity of the instrument. In the case of IRIS, the first client instrument of NFIRAOS on the TMT, there is a requirement to limit the under-sizing of the IRIS Lyot stop to 1% of the pupil diameter, which therefore means to tightly stabilize the image of the telescope pupil. In this paper, we show how this can be accomplished by finely aligning the IRIS Lyot stop to NFIRAOS during calibration (sighting actuators pokes on the NFIRAOS high-altitude DM with the IRIS pupil viewing camera), and, during observation, by processing the images obtained by the NFIRAOS Truth WFS to detect drifts and feeding this drift information to the telescope control system so it can repoint the beam fed to NFIRAOS. We show that this measurement of the pupil position with the Truth WFS can be accomplished in nominal observing conditions with an RMS error of less than 0.02% of the pupil diameter in bright time, and less than 0.03% of the pupil diameter in dark time. We include in this evaluation noise sources on the Truth WFS, as well as uncertainties on the reflectivity of the primary mirror segments due to their recoating schedule. This work directly feeds into the work presented in reference [1], which describes the full tolerance stack-up required to reach the 1% under-sizing of the Lyot stop.
Implementing the Type System for a Typed Javascript and its IDE

Implementing a programming language with IDE tooling features poses several challenges even when using language workbenches like Xtext that provides Eclipse integration. A complex type system with powerful type inference mechanisms requires focusing carefully on performance issues that might undermine the effective usability of the IDE: the editor must be responsive even when type inference takes place in the background, otherwise the programmer will experience too many lags. In this paper, we will present a real-world case study: N4JS, a JavaScript dialect with a full-featured Java-like static type system (including generics) and present some evaluation results. We will concentrate on techniques to make the type system implementation of N4JS integrate efficiently with Eclipse. For the implementation of such a type system we use Xsemantics, a DSL for writing type systems, reduction rules and in general relation rules for languages implemented in Xtext. Xsemantics uses a syntax that resembles formal type system specifications, so that the implementation of formally defined type rules can be implemented easier and more directly than in Java. Keywords–DSL; Type System; Implementation; Eclipse.
Towards Explainability of UAV-Based Convolutional Neural Networks for Object Classification

Establishing a basis for certification of autonomous systems using trust and trustworthiness is the focus of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR), a new NASA Convergent Aeronautical Solutions (CAS) Project. One critical research element of ATTRACTOR is explainability of the decision-making across relevant subsystems of an autonomous system. The ability to explain why an autonomous system makes a decision is needed to establish a basis of trustworthiness to safely complete a mission. Convolutional Neural Networks (CNNs) are popular visual object classifiers that have achieved high levels of classification performances without clear insight into the mechanisms of the internal layers and features. To explore the explainability of the internal components of CNNs, we reviewed three feature visualization methods in a layer-by-layer approach using aviation related images as inputs. Our approach to this is to analyze the key components of a classification event in order to generate component labels for features of the classified image at different layers of depths. For example, an airplane has wings, engines, and landing gear. These could possibly be identified somewhere in the hidden layers from the classification and these descriptive labels could be provided to a human or machine teammate while conducting a shared mission and to engender trust. Each descriptive feature may also be decomposed to a combination of primitives such as shapes and lines. We expect that knowing the combination of shapes and parts that create a classification will enable trust in the system and insight into creating better structures for the CNN.
The Influence of Economic Factors on the Value Relevance of Accounting Information in Jordan

The paper examines the influence of economic factors namely foreign ownership and trading volume on the value relevance of earnings, book value and cash flows relative to three share price proxies namely average annual share price, annual closing share price and share price after a three-month period following the financial year-end (hereafter ATM-share price), after controlling company’s size and leverage for Jordanian services and industrial companies within the period 2004-2009. Since the paper is the first, as the researcher believes, that examines the influence of foreign ownership and trading volume on the value relevance of earnings, book value and cash flows in an emerging exchange, it aims to provide evidence regarding the influence of these factors on the value relevance of accounting information. The paper found that the value relevance of earnings and book value is greater for companies having foreign ownership and larger trading volume. With the effect of these factors, closing share price shows more response than the other share price proxies. The findings suggest that market participants might be able to extract the firm value via these economic factors. The paper extends the valuation model by including cash flows together with earnings and book value. The findings demonstrate that there is a shift away from earnings towards book value as the basis of firm valuation.
Generating Grey Lists from Input Libraries

Many cell lines and tumour samples show anomalous signal in the input or control sample in some regions. These regions also show high signal in the corresponding ChIPs. Peak callers are not, in general, well-behaved in these regions, tending to call many spurious peaks. The purpose of this package is to identify those regions, so that reads in those regions may be removed prior to peak calling, allowing for more accurate insert size estimation and reducing the number of false-positive peaks. As part of the ENCODE project, Anshul Kundaje identified regions that show enrichment in ChIP experiments independent of what factor is being ChIPped, or what cell line the sample comes from[1, 2]. He called those regions ”signal artefact” regions, or colloquially ”black lists”. We call our lists of high signal grey lists, to distinguish them from ENCODE’s black lists, because they are not universal, but rather cell line (or sample) specific, and because they can be tuned depending on the stringency required, and so that we can make jokes about having 50 shades of grey lists[3]. This vignette summarizes the construction of grey lists.
MonArch: Scalable Monitoring and Analytics for Visibility and Insights in Virtualized Heterogeneous Cloud Infrastructure

MonArch: Scalable Monitoring and Analytics for Visibility and Insights in Virtualized Heterogeneous Cloud Infrastructure Jieyu Lin Master of Applied Science Department of Electrical and Computer Engineering University of Toronto 2015 With rapid development of cloud computing technologies, cloud data centers continue to grow in scale and complexity. Monitoring and analytics are the key enablers to provide visibility and insights in a large-scale infrastructure. In this thesis, we present a monitoring and analytics system, called MonArch, designed for monitoring data collection, storage and analytics in a virtualized heterogeneous cloud environment. MonArch provides high scalability and flexibility to enable multi-layer heterogeneous resources monitoring. Scalable anomaly detection, root cause analysis, and graph processing capabilities are offered in MonArch to simplify resource monitoring and support real-time identification of system anomalies. MonArch supports a wide variety of use cases including security detection, system diagnosis, resource allocation, and IoT sensor data storage and processing. MonArch is implemented and deployed in the SAVI Testbed. The evaluation results demonstrate MonArch’s scalability and efficiency in data monitoring, as well as its effectiveness in anomaly detection and root cause analysis.
Design and Development of a Cartesian Robot for PCB Drilling: A Systematic Mechatronic Approach

Design of mechatronic products is a complex process which involves various engineering domains [1] [2] [3]. Choice of an appropriate systematic procedure for product design and development is important in mechatronic system design. The V model (VDI 2206) for mechatronic system design and the Pahl and Beirtz design methodology was suggested for the development of a Cartesian robot for Printed circuit board (PCB) drilling [2]. The system was subdivided into, mechanical, electronics and software engineering domains. Methods suitable for design in each domain were used, and sub systems were developed which then were integrated to develop the whole system. Software that extracts the coordinates of the holes from the gabber file was developed. The file of coordinates will then be transferred to the control unit which will then coordinated the actuators to move to the desired Cartesian coordinates and perform the drilling operation. A graphical user interface was developed also to input the coordinates manually. The designed Cartesian robot is a portable desk machine that can be used to perform the drilling operations. It can also be used as a teaching aid in the computer aided manufacturing courses to demonstrate the tool motion activities.
The Procedural Issues after the Implementation of Development Charge in Malaysia

The development charge is levied to developers if the project is approved for either the conversion of land use, the increment of plot ratios or density, which increases the land value. To date, seven (7) states have implemented the development charges at their local authorities. Each state has a different interpretation of the policy, framework and development charges practices. An interview using a focus group was conducted to identify issues after the implementation of the development charges. Eight (8) local authorities were selected as research samples. Findings from the focus group have discovered five (5) main issues; namely duration, the collection of data, individual application, payment method, and unstandardized rate. Keywords: work procedure, development charge, local governments, issues and challenges.Keywords: Procedural Issues; Development ChargeeISSN: 2398-4287 © 2019. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.DOI: https://doi.org/10.21834/e-bpj.v4i11. 1668
The Influence of Information Technology and Systems on Managerial Performance: An Empirical Study at a Regional Bank in North Sulawesi Province

This study aims to determine the influence of information technology and systems on managerial performance in a regional bank in Indonesia. The novelty of this study lies in the relationship between information technology and managerial performance, and the research object of this study which was a regional bank which generally operating in the districts resulting from the expansion after the era of regional autonomy in Indonesia. The data of this study were collected using a questionnaire to 242 Bank Sulutgo employees as the respondents, operating in 10 cities and districts in North Sulawesi province. Structural equation model (SEM) was used to analyse the data.