Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous robot navigation through a crowded and dynamic environment : using a novel form of path planning to demonstrate consideration towards pedestrians and other robots

This thesis presents a novel path planning algorithm for robotic crowd navigation through a pedestrian environment. The robot is designed to negotiate its way through the crowd using considerate movements. Unlike many other path planning algorithms, which assume cooperation with other pedestrians, this algorithm is completely independent and requires only observation. A considerate navigation strategy has been developed in this thesis, which utilises consideration as an directs an autonomous mobile robot. Using simple methods of predicting pedestrian movements, as well as simple relative distance and trajectory measurements between the robot and pedestrians, the robot can navigate through a crowd without causing disruption to pedestrian trajectories. Dynamic pedestrian positions are predicted using uncertainty ellipses. A novel Voronoi diagram-visibility graph hybrid roadmap is implemented so that the path planner can exploit any available gaps in between pedestrians, and plan considerate paths. The aim of the considerate path planner is to have the robot behave in specific ways when moving through the crowd. By predicting pedestrian trajectories, the robot can avoid interfering with them. Following preferences to move behind pedestrians, when cutting across their trajectories; to move in the same direction of the crowd when possible; and to slow down in crowded areas, will prevent any interference to individual pedestrians, as well as preventing an increase in congestion to the crowd as a whole. The effectiveness of the considerate navigation strategy is evaluated using simulated pedestrians, multiple mobile robots loaded with the path planning algorithm, as well as a real-life pedestrian dataset. The algorithm will highlight its ability to move with the aforementioned consideration towards each individual dynamic agent.
PENERAPAN TECHNOLOGY ACCEPTANCE MODEL (TAM) PADA RADIO FREQUENCY IDENTIFICATION (RFID) DI FTI UKSW (STUDI KASUS: DOSEN FTI UKSW)

FTI UKSW saat ini sudah menerapkan teknologi RFID sebagai sistem kunci yang baru, sebelumnya sistem kunci di FTI menggunakan sistem kunci manual dimana hal ini dianggap kurang efisien karena dosen harus melakukan pemesanan penggunaan ruangan dan meminjam kunci ruangan pada bagian laboran. Penelitian ini menggunakan metode Technology Acceptance Model (TAM) untuk menganalisis tingkat penerimaan dosen terhadap sistem kunci baru yang ada di FTI UKSW. Penelitian ini dilakukan dari tahap persiapan, tahap perumusan model penelitian, tahap pengumpulan data, tahap pengolahan dan analisis data, sampai pada tahap pembuatan hasil dan kesimpulan. Hasil yang dapat disimpulkan dalam penelitian ini yaitu masih terdapat dosen yang keberatan dengan adanya sistem kunci yang baru dengan menggunakan RFID di FTI UKSW. Dosen yang menolak adanya sistem kunci RFID ini yaitu dengan alasan sistem kunci RFID tidak memiliki korelasinya dengan peningkatan kinerja dosen, dan untuk menggunakan sistem kunci tersebut dibutuhkan banyak usaha serta teknologi RFID yang diterapkan di FTI UKSW belum maksimal jika hanya sebatas untuk sistem kunci saja.
A Spatio-Temporal-Contour-Consistency-Based Shape Tracking Framework from Multi-View Video

The Spatio-temporal coherence of time-varying model sequence is an important and highly-desirable property in varies of applications. We propose an efficient framework that reconstructs a Spatio-temporally coherent point cloud sequence for dynamic objects from multiview video. The main distinction of the framework is that it does not complicatedly and strictly match between any two contours on the same model or the neighboring model. The point cloud between the discrete frames is obtained by transporting the neighboring models or by reconstructing the in-between model based on interpolation silhouette. The time-varying and quasi-dense point cloud of such dynamic objects shape can be tracked successfully thanks to Spatio-Temporal-Contour consistency and distance field interpolation employed in our shape tracking framework. Compared to existing approaches, the resulting point cloud sequence has better Spatio-temporal coherence. Experimental results demonstrate that the framework obtains the promise.
THE EMPLOYEE ENGAGEMENT OF MILLENNIAL EMPLOYEES

Millennials have an important role in the growth of the company nowadays and in the future where the workforce will be majority by the millennial generation. Employee engagement has become a potential factor for organizational performance. Many leaders or practitioners of human resource management still using the old method and have poorly understanding on how to engage their millennials which lead them to many difficulties in managing millennial which results in organizations failing to keep them working within the desired time period and also failed to engage them for their best contribution to the company. The purpose of this case study is to understand the strategies and methods used by a company located in Jakarta, the largest metropolitan city in Indonesia, in order to engage its millennial employees to get more contribution of organizational productivity. This case study uses triangulation which is a multimethod approach when collecting and analyzing data, to ensure the correctness of data or information obtained from a variety of different perspectives. In this study data was obtained from interviews, online data searches through the internet regarding the company and 2018 quarterly reports from the HR Department. The findings revealed that the HR Department used specific strategies for career growth opportunities, good communication between superiors and subordinates, pleasant work environment, positive image of the company in the community. The results obtained are very beneficial to the organization growth and company’s success.
Experimental evaluation of wall shear stress in elastic cerebral aneurysm model

Pulsating flow within an elastic cerebral aneu rysm model was visualized by particle image velocimetry (PIV) to clarify the effect of wall deformation on wall shear stress (WSS). In the experiment, we used a full-scale patient-specific cerebral aneurysm phantom model with a thin transparent silicone wall fabricated by soaking a plaster mold. The experiment was performed in two planes, one in the middle plane and the other perpendicular to it. The results showed that in the middle plane, the wall deforma tion reduced the maximum WSS in systole by about 20% and the averaged WSS by about 20% in comparison with the less-deformation model. On the other hand, for planes perpendicular to the aforementioned planes, the averaged WSS increased by about 10% in comparison with the less-deformation model. In addition, it was observed that the local WSS changed in intensity according to the plane and phase, because of movement of the separation point of the wall jet. This finding suggests that the wall deformation of cerebral aneurysm affects the magnitude of WSS.
Automatic Optimization of Granularity Control Algorithms for Parallel Programs

In the last two decades, processors have changed from a single-core to a multi-core design, due to physical constrains in chip manufacturing. Furthermore, GPUs have become suitable targets for general purpose programming. This change in hardware design has had an impact on software development, resulting in a growing investment in parallel programming and parallelization tools. Writing parallel programs is difficult and error prone. Two of the main problems in parallelization are the identification of which sections of the code can be safely parallelized and how to efficiently partition work. Automatic parallelization techniques can save programmers time identifying parallelism. In parallelization, each parallelizable section is denoted as a task, and a program is comprised of several tasks with dependencies among them. Work partition consists in deciding how many tasks will be created for a given parallel workload, thus defining the task granularity. Current techniques focus solely on loop and recursive parallelization, neglecting possible fine-grained task-level parallelism. However, if the granularity is too fine, penalizing scheduling overheads may be incurred. On the other hand, if the granularity is too coarse, there may not be enough parallelism in the program to occupy all processor cores. The ideal granularity of a program is influenced by its nature and the available resources. Our experiments have shown that a program that terminates within seconds with the correct granularity may execute for days with an unsuitable granularity. Finding the best granularity is not trivial, more so in the case of automatic parallelization, in which there is no knowledge of the program domain. The current approach consists in empirically evaluating several alternatives to find the optimal granularity. This thesis proposes a more efficient model for automatic parallelization, in which parallelism is identified at the Abstract Syntax Tree (AST) node level. Static analysis is used to obtain access permissions, representations of how an AST node interacts with others in terms of memory accesses and control-flow. Parallelism at the AST node level is very fine grained and may generate more tasks than those that can be executed simultaneously, resulting in scheduling overheads. In order to reduce these overheads, tasks may be merged in coarser tasks, thus reducing parallelism. A cost-model is proposed to dynamically adjust granularity according to the complexity of tasks, resulting in programs more efficient than the best existing alternative. Because the automatic parallelization model can generate programs that can execute either on the CPU or the GPU, it is important to automatically decide if a program should execute on the CPU with a coarse granularity, or on the GPU with a finer granularity. To perform this decision, a Machine Learning approach was built, based on static compiler-obtained and runtime features. This model performed
Advanced detection of spam and email filtering using natural language processing algorithms

Unsolicited bulk emails from random email addresses sent to a user's inbox are generally called junk or spam emails. 45% of all emails sent are spam and 14.5 billion spam emails are sent every single day. Around 36% of spam emails is content related to sales, advertising, and promotions that the recipient explicitly did not opt to receive. However, not all spam emails are used for this purpose. Spam emails are also sent for phishing purposes that deceive users and lead the recipients to malicious websites with unethical intentions. Numerous techniques have been developed to block such spam emails but a majority of users still receive them. This is because of the ability of the spammers to manipulate the filters. Spam costs businesses a whopping $20.5 billion every year. Even worse is that the cost of spam is likely to continue rising. Data indicates that losses to business will grow to $257 billion annually within a few years if the current rate of spam email is not decreased. To curb this problem, we present a method based on Natural Language Processing (NLP) for the filtration of spam emails in order to enhance online security. The technique proposed in this research paper is an approach which stepwise blocks spam mail based on the sender's email address along with the content of the email. This paper presents a proposed NLP system using N-gram model, Word Stemming algorithm and Bayesian Classification algorithm for detection of spam content and effectively filtering it.
The Study of Fraud Detection in Financial and Credit Institutions with Real Data

This paper presents a review of data mining techniques for the fraud detection. Development of information systems such as data due to it has become a source of important organizations. Method and techniques are required for efficient access to data, sharing the data, extracting information from data and using this information. In recent years, data mining technology is an important method that it has changed to extract concepts from the data set. Scientific data mining and business intelligence technology is as a valuable and some what hidden to provide large volumes of data. This research studies using service analyzes software annual transactions related to 20000 account number of financial institutions in the country.The main data mining techniques used for financial fraud detection (FFD) are logistic models, neural networks and decision trees, all of which provide primarysolutions to the problems inherent in the detection and classification of fraudulent data. The proposed method is clustering clients based on client type. An appropriate rule for each cluster is determined by the behavior of group members in case of deviation from specified behavior will be known among suspected cases. The rules of the C5 have been applied in decision tree algorithm. Model is able to extract about a lot of the rules related to client behavior.
Between shifts and alternations: Ditransitive constructions

In this paper I present two classes of double object constructions in Modern Greek, i.e., the genitive, as well as the double accusative, ditransitive constructions. I show that these two classes differ from one another in that not both of them permit derivational processes such as the formation of adjectival passives. I also look at the case properties associated with the verbs which head Modern Greek genitive and double accusative ditransitive constructions. Finally, the analysis I propose for these constructions in Modern Greek are formalized using the Minimal Recursion Semantics (MRS) framework of Copestake et al. (2001) and Copestake et al. (2003).
Examining approaches to internet regulation in Ethiopia

ABSTRACT This article examines the current internet content regulatory regime in Ethiopia from the perspectives of the extensively discussed and broadly accepted regulatory models namely statutory regulation, self-regulation, and co-regulation. It argues that although statutory regulation has been a potentially useful tool of regulation in Ethiopia, self-regulation and co-regulation models appear to be neither practised nor feasible in the Ethiopian context. A closer look at the regulatory patterns in the last decade – and emerging initiatives – suggest that a hybrid of five regulatory models namely statutory regulation, informal regulation, gatekeeping, self-censorship, and regulation through ‘Social Media Army’ are currently common in Ethiopia. This article then weighs sustainability and propriety of these approaches pursued by the Ethiopian government in regulating internet content.
Implications of Equity Capital Financing on Corporate Financial Performance of Deposit Money Banks in Nigeria

This study examines the implications of equity capital financing on the corporate financial performance of deposit money banks in Nigeria, as such, 14 banks listed on Nigerian Stock Exchange for a period of 11 years (2006-2016) was selected. The data used is secondary in nature; extracted from the Annual Reports and Accounts of the various Banks and employed ex-post facto research design, and Pooled Ordinary Least Square Method in the analysis. It made use of panel data structure and the data was analysed with E-View package (version 13). Also, the P and T values assisted in the analysis of both magnitude and direction of the relationship between the independent and dependent variables. It also revealed that both ROE AND EVA has a positive effect on the corporate financial performance of Deposit Money Banks in Nigeria. The study concludes that Equity financing has positive effect on corporate financial performance of Deposit Money Bank in Nigeria; therefore, Increasing this variable will bring a positive effect on the corporate financial performance of Deposit Money Banks in Nigeria. It recommends that the implications of scheduling banks capital into equity financing, short-term debt and long-term debt by managers should be closely supervised and monitored by both shareholders and bondholders’ so as to avoid the company adding negative value to them who are contributors of finance. Keywords : Return on Equity, Deposit Money Banks, Economic Value Added, Equity Capital Financing DOI : 10.7176/RJFA/10-12-11 Publication date :June 30 th 2019
Analysis of surface X-ray scattering from synthetic quartz mirror

Results and Discussion Fig. 1 shows experimental results of specular and nonspecular scattering for samples, “20-2” and “1-3”. The maximum reflectivity below the critical angle, around 1.9 mrad, was 93.6 % and 81.8 %, respectively. No oscillating structures were observed in the whole specular reflectivity curve, indicating that significant surface layers do not exist. Non-specular scattering, as shown in the inset, exhibits Yoneda peaks at around 1.8 mrad and 15.7 mrad. Those features of the data were more or less common for all the samples. During previous studies [3], it was found that the intensity ratio of the specular and non-specular scattering is strongly correlated to the surface morphology. In the present case, the intensity at Yoneda peak (1.8 mrad) is 20.5%, 14.2%, 15.2%, and 11.8% to the specular scattering at 8.75 mrad, for “1-3”, “4-5”, “10-5” and “202”, respectively. One can see that the order agrees well with that of flatness as certified by the laser-light method. The data were analyzed based on the self-affine surface model [2], of which the height-to-height correlation function for the distance ρ is given as C(ρ) = σ exp[(ρ/ξ)], where σ, ξ and h are the surface roughness, the correlation length in horizontal directions, and Hurst parameter, respectively. In the present study, simultaneous fitting has been attempted for specular and non-specular (rocking and detector scans) data. The results are summarized in Table 1. The rms roughness σ was different, but quite close for the four samples, around 1.3 nm. This is not so surprising considering that their specular reflectivity did not show such big differences even in the higher-angle region (7~14 mrad). On the other hand, ξ and h are quite different. One can see that density factor also exhibits differences. It has been found that “20-2” is quite normal and has a moderate surface, while “1-3” has an extraordinarily jagged surface. The authors would like to thank Prof. S. Kishimoto for his assistance during the experiment.
Measurement of Creep Deformation in Weldments

This PhD project has developed a new high temperature strain measurement system using Digital Image Correlation (DIC) in order to investigate spatially varying and time dependent deformation during high temperature creep tests of engineering materials. Several challenges associated with measuring creep deformation at elevated temperature have been overcome including the choice of specimen design, specimen oxidation, furnace temperature uniformity, image distortion caused by thermal currents and sufficient illumination. It is demonstrated that the system created can produce reliable measurement data over a period of several months with a spatial resolution of 0.6 mm for temperatures up to 650°C, but in principle higher resolution and temperatures should be achievable. The research aim of this project, funded by EDF Energy (formerly British Energy), was to attempt to measure spatially varying creep deformation properties across thick and thin section weldments that operate at high temperatures in UK advanced gas cooled reactor power plant (AGR). The new measurement system is applied to examine the creep behaviour of a thick section multi-pass welded joint made from Type 316H austenitic stainless steel which was supplied by EDF Energy. Specifically the local creep deformation properties across the weldment in the parent material, heat affected zone (HAZ) and multipass weld layers are investigated in medium term creep tests (>2300 hours). This is achieved by cutting samples from three different locations of the thick section joint, that is from top, middle and bottom positions, and subjecting them to tensile and creep testing at a temperature of 545°C. Spatially resolved stress-strain (tensile) and strain-time (creep) results were obtained transversely across the whole section of the multi-pass weldment and across the thickness direction. The DIC in-situ measurements also provided strain information in the transverse to loading direction from which the reduction of area of the specimen and true stress and strain distribution were calculated. The weld metal showed faster creep rates than HAZ and parent materials and this is attributed to the observed introduction of substantial plastic strain in the parent material on initial loading. Locally the creep strain distribution in the weld metal appears to correlate with individual weld passes. The full-field measurement results allowed the development of creep deformation leading to ultimate rupture to be observed. The high temperature tensile and creep results presented in this thesis demonstrate the capability of the new DIC based system created for full field measurements of displacement and strain at high temperature during creep tests enduring several thousand hours. The system opens a new horizon for studying deformation and rupture behaviour of complex structures at elevated temperature.
Musical Instrument Identification and Tone Detection Using Feature Learning

In music information retrieval (MIR) field, most of the tasks have been heavily relying on the hand-crafted features which are highly useful to measure and quantify the certain target characteristics of the sound such as pitch, roughness, and brightness. However, there are an increasing number of attempts on applying feature learning technique, which has shown superior performance across the research fields, in MIR tasks especially when the goal is the identification of the sound. The aim of this thesis is to advance the state-of-the-art in musical instrument identification and tone detection tasks with feature learning approaches, which can be used for various music-related applications, including but not limited to, music searching, browsing, recommendation, and education. We utilize sparse feature learning and convolutional neural network to learn a feature from input data, and propose the network architecture and data processing framework that is suitable for music signal. We present experimental results on MIR tasks such as fingering detection of overblown flute sound, instrument identification in monophonic sound, and predominant instrument recognition in a real-world polyphonic music. In addition, we conducted an extensive experiment to find the optimal data processing techniques and parameters such as input frame sampling, frequency scaling, activation pooling, window/hop size, output aggregation method, and network training hyperparameters.
Machine learning methods in predicting the student academic motivation

Academic motivation is closely related to academic performance. For educators,it is equally important to detect early students with a lack of academic motivation as it is to detect those with a high level of academic motivation. In endeavouring to develop a classification model for predicting student academic motivation based on their behaviour in learning management system (LMS) courses, this paper intends to establish links between the predicted student academic motivation and their behaviour in the LMS course. Students from all years at the Faculty of Education in Osijek participated in this research. Three machine learning classifiers (neural networks, decision trees, and support vector machines) were used. To establish whether a significant difference in the performance of models exists, a t-test of the difference in proportions was used. Although, all classifiers were successful, the neural network model was shown to be the most successful in detecting the student academic motivation based on their behaviour in LMS course.