Miracle-dz/UniFAR-FTUs huggingface dataset:
'In the eye of the beholder': Knowledge and skills requirements for data professionals

Introduction. The professionals required to in data science, data librarianship and data management are a new breed for whom the knowledge and skills are just emerging. Academic institutions are putting together courses to address this shortage without fully understanding what the knowledge and skills requirements are for data professionals in different types of organisation types. This paper aims to increase that understanding. Method. Interviews were conducted with thirty-six currently practicing data professionals and their employers about current knowledge and skills requirements. Participants were purposefully selected to achieve a cross section of data roles and employing institutions and interviews ceased once saturation was achieved. Analysis. Two types of coding were employed: initial coding to establish categories, followed by more focused coding, for analytical depth. Every effort is made to accurately present the viewpoint of participants. Results. All participants reported the importance of high level communication and personal learning skills and characteristics around curiosity, flexibility, and comfort with change. In universities and scientific research organisations the required knowledge and skills are in areas which might be classified as data management and curation; and in business and government organisations, as data science and management. Conclusion. While there are still uncertainties about knowledge and skills requirements and role ambiguities in different data roles in different organisational types, knowledge and skills related to particular roles begin to emerge and are discussed.
Human and Mobile Robot Tracking in Environments with Different Scales

In the near future, we envision many mobile robots working for or interacting with humans in various scenarios such as guided shopping, policing, and senior care. In these scenarios, tracking humans and mobile robots is a crucial enabling technology as it provides their continuous locations and identities. We study each kind of tracking individually as humans and mobile robots differ in appearance, mobility, capability of computation and sensing, and feasibility of cooperation with tracking systems. In addition to these distinctions, the scale of a tracking system’s environment is another key factor that determines the appropriate tracking algorithm. In small scale environments such as halls and rooms, tracking systems need to be lightweight with real-time performance. In large scale environments such as campuses and entire buildings, tracking systems face complex and noisy backgrounds. Tracking systems need to be robust and scalable. This dissertation examines problems for human and mobile robot tracking in both small and large scale environments in order to enable future human-robot applications. First, we study human tracking. In small scale environments like halls, multiple cameras are typically deployed to monitor most areas. We need to track multiple humans across multiple cameras accurately in real-time. We present VM-Tracking, a tracking system that achieves these goals. VM-Tracking aggregates motion sensor information from humans’ mobile devices, which accompany them everywhere, and
E-Learning to m-Learning: framework for data protection and security in cloud infrastructure ( output prize)

In recent years, the advancement in internet technologies has greatly altered the learning landscape, thus, a shift from traditional methods of learning to internet based learning platforms. E-learning, m-learning and cloud are some of the most powerful responses to these growing technological shift by the education sectors. Their impact and benefits cannot be over-emphasized with regard to making learning accessible, affordable, available and convenient. In addition, the use of cloud technology has made the world of education more integrated, networked and composite. This makes elearning and m-learning as highly effective as the conventional method of learning delivery. However, despite these advantages, the security and the protection of learners’ data on this cloud platform have been some of the major challenges to m-learning effective implementation and use. This paper discusses the various benefits of the using m-learning platform and cloud infrastructure in higher education. It also examines the vulnerabilities of the platform as well as other security and privacy challenges regarding the effective implementation of m-learning in cloud infrastructure environment. Finally, it proposes a detailed data protection and security framework that is needed for addressing these issues. It is expected that the proposed framework when fully implemented, will bring about necessary solution to issues relating to the security and data protection of m-learners in cloud computing environment, increase trust in the use of the system as well as enhance the m-learning platforms.
Ensuring QoS in Wireless Body Area Network: A Review

Objectives: Wireless Body Area Networks (WBANs) have gained immense popularity among researchers and this field has also touched the health sector. In this paper we have analyzed the various methods that are used to ensure the QoS in body nodes in WBANs. Methods/Statistical Analysis: The better functioning nodes with longevity of the battery nodes help to exchange the messages between the nodes uninterruptedly. Various works are presented each for improving the quality of one part or another of the network. The importance of the various methods and how these have benefitted in the better management of the sensor nodes has also been presented. The review of the researches that are undergoing in this field has been given. Findings: The body nodes work more efficiently if the interrupted energy supply is provided to them. Energy can be collected from the body movements also and can prove to be more beneficially limiting the health constraints of removing batteries again and again. Some scheduling algorithms can reduce the interference between the nodes and the routing protocols can manage the data traffic among the WBAN. The power optimizing techniques reduce the power consumption and the data loss. So many techniques provide the much needed quality of service in WBAN. Novelty/Improvement: The paper brings out various techniques which assist in better WBAN thus reinforces the use of wireless sensors in the health domain with better quality.
Maskulinitas Dalam Akun Instagram Influencer Laki-Laki

Dalam kata-kata tersohor dari Umberto Eco, “I speak through my cloth” (aku berbicara lewat pakaianku)—pakaian yang kita kenakan membuat pernyataan tentang diri kita. Pakaian yang dirancang dengan sedemikian rupa biasanya kita sebut sebagai mode atau fashion. Fashion memberikan estetika pada penampilan mulai dari ujung kepala sampai ujung kaki, dan demi kepuasan dan kepentingan pribadi, penampilan fashion sering diabadikan ke dalam sebuah gambar atau foto. Penelitian ini fokus pada perkembangan mode dalam kaitannya dengan nilai-nilai maskulinitas yang ditunjukkan pada objek foto yang ditampilkan pada akun Instagram seorang fashion influencer yang berada di Indonesia. Foto-foto tersebut menampilkan cara berbusana sesuai dengan identitas fashion yang ingin direpresentasikan oleh ketiga objek fashion influencer yang dipilih yaitu @joviadhiguna, @andyyanata dan @alvinsuryanto. Selain itu dengan adanya identitas fashion yang ditampilkan ketiga objek penelitian, peneliti juga akan mengupas identitas gender yang dimiliki ketiga objek serta mengupas nilai-nilai maskulinitas yang ditunjukkan berdasarkan identitas tersebut. Simbol-simbol yang ditampilkan dalam foto-foto tersebut merepresentasikan identitas diri yang dikonstruksi melalui foto fashion dalam Instagram. Penelitian ini menggunakan pendekatan kualitatif tipe deskriptif dengan metode analisis semiotika Roland Barthes, yang memaknai sebuah komunikasi visual secara denotasi, konotasi dan mitos. Melalui metode tersebut peneliti berusaha menganalisis kode/simbol/penanda dalam foto-foto fashion secara lengkap dan terperinci serta mengaitkannya dengan jenis nilai maskulin yang melekat dalam foto fashion influencer tersebut. Penelitian ini juga akan mengupas tentang gambaran new masculinity fashion yang tidak hanya menjadi sebuah tren, melainkan sebuah tanggapan akan perubahan cara berpikir yang sedang terjadi. Hal itu menunjukkan bahwa perkembangan fashion di Indonesia menjadi semakin terbuka dan tidak kaku pada aturan gender yang melingkupinya. Dengan adanya gaya fashion dari Jovi, Andy dan Alvin menunjukkan bahwa fashion has no gender mulai berkembang dan berterima di kalangan masyarakat urban dilihat dari banyaknya followers di media sosial Instagram mereka.
Overview of AO calibration strategies in the ELT context

The scientific potential of the ELT will rely on the performance of its AO systems that will require to be perfectly calibrated before and during the operations. The actual design of the ELT will provide a constraining environment for the calibration and new strategies have to be developed to overcome these constraints. This will be particularly true concerning the Interaction Matrix of the system with no calibration source upward M4 and moving elements in the telescope. After a brief presentation of the ELT specificities for the calibration, this communication focuses on the different strategies that have already been developed to get/measure the Interaction Matrix of the system, either based on synthetic models or using on-sky measurements. First tests of these methods have been done using numerical simulations for a simple AO system and a proposition for a calibration strategy of the ELT will be presented.
Color Image Enhancement Using Multiscale Retinex Based on Particle Swarm Optimization Method

This paper introduces, a novel method for the image enhancement using multiscale retinex and practical swarm optimization. Multiscale retinex is widely used image enhancement technique which intemperately pertains on parameters such as Gaussian scales, gain and offset, etc. To achieve the privileged effect, the parameters need to be tuned manually according to the image. In order to handle this matter, a developed retinex algorithm based on PSO has been used. The PSO method adjusted the parameters for multiscale retinex with chromaticity preservation (MSRCP) attains better outcome to compare with other existing methods. The experimental result indicates that the proposed algorithm is an efficient one and not only provides true color loyalty in low light conditions but also avoid color distortion at the same time.
Mobile phone-based health interventions for improved management of non-communicable diseases in Sub-Saharan Africa: challenges and opportunities for Ghana

xii Chapter 1: Introduction.1 Chapter 2: A systematic review of randomized controlled trials of mHealth interventions against non-communicable diseases in developing countries.9 Chapter 3: Self-management of diabetes in Sub-Saharan Africa: a systematic review.20 Chapter 4: A realist review of mobile phone-based health interventions for noncommunicable disease management in sub-Saharan Africa.32 Chapter 5: A mixed-methods study to review non-communicable disease management in Ghana.45 Chapter 6: Determining the potential of mobile-phone based health interventions in subSaharan Africa: A study from Kumasi, Ghana.78 Chapter 7: Achieving sustainability and scale-up of mHealth interventions in sub-Saharan Africa: The views of policy-makers in Ghana.91 Chapter 8: Conclusion.105
Disfluency Detection for Spoken Learner English

One of the challenges for computer aided language learning (CALL) is providing high quality feedback to learners. An obstacle to improving feedback is the lack of labelled training data for tasks such as spoken ”grammatical” error detection and correction. One approach to addressing this lack of data is to convert the output of an automatic speech recognition (ASR) system into a form that is closer to text data, for which there is signiﬁcantly more labelled data available. Disﬂuency detection, locating regions of the speech where for example false starts and repetitions occur, and subsequent removal of the associated words, helps to make speech transcriptions more text-like. Ad-ditionally, ASR systems do not usually generate sentence-like units, the output is simply a sequence of words associated with the particular speech segmentation used for coding. This motivates the needs for automated systems for sentence segmentation. By combining these approaches, advanced text processing techniques should perform signiﬁcantly better on the output from spoken language processing systems. Unfortunately labelled data for these tasks is not available for learners of English. In this work disﬂuency detection and ”sentence” segmentation systems trained on data from native speakers are applied to spoken grammatical error detection and correction tasks for learners of English. Performance gains using these approaches are shown on a free speaking test.
Consideration of Insider Based Collusion Attacks on Cyber Systems

—Insider threats are a growing concern to both public and private entities. Like other threats within the cyber realm, these threats tend to be dynamic in nature. Organizational structures and varying motivating factors of threat actors can impact the effectiveness of mitigating technologies and strategies. This paper explores the gaps that result when organizations try to segment logical and physical assets, which in turn creates the opportunity for collusion or coordinated attacks. In this paper, various data sources have been examined to identify organizational patterns that can potentially contribute to the successful outcome of these types of attacks. A motivating example is then presented, in junction with the data, to hypothesis what percentage of organizations would be able to identify the actions of insiders working in tandem based on the current state of the industry.
FPGA Implementation of Adaptive Noise Canceller

This paper presents hardware implementation of least mean square (LMS) adaptive filter based Adaptive Noise Canceller (ANC) structure on FPGA using VHDL hardware description language. First, the adaptive parameters are obtained by simulating ANC on MATLAB. Second, the data, processed by FPGA, such as step size, input and output signals, desired signal, and coefficients of ANC, are exactly expressed into fixed-point data representation. Finally, the functions of FPGA-based system structure for such LMS algorithm in time sequence are synthesized, simulated, and implemented on Xilinx XC3S500E FPGA using Xilinx ISE 9.2i developing tool. The research results show that it is feasible to implement, on chip train, and use adaptive LMS filter based ANC in a single FPGA chip.
Finding an optimum in service granularity

In the past 10 years a new paradigm called Service Oriented Architecture (SOA) has emerged that is based on the development, deployment and reuse of (web) services which can easily be assembled in different ways allowing organizations to quickly adapt to changing business needs (Cox and Kreger, 2005). However while SOA has a large potential for business one of the most complex issues in any SOA project is to define the right granularity of the services. While Steghuis (2006) and others state the importance of optimum service granularity our research tries to answer the HOW question by using the Design & Engineering Methodology for Organizations (DEMO) (Dietz, 2006). Our research consists of a literature study and the execution of two essential case studies at organizations in the Netherlands. Validated by interviews that were held at the organizations the case studies learned that the applied methodology is directive in obtaining a functional optimum in service granularity for business services and information services.
A State of the Art Review on Emotional Speech Databases

Thirty-two emotional speech databases are reviewed. Each database consists of a corpus of human speech pronounced under different emotional conditions. A basic description of each database and its applications is provided. The conclusion of this study is that automated emotion recognition on these databases cannot achieve a correct classification that exceeds 50% for the four basic emotions, i.e., twice as much as random selection. Second, natural emotions cannot be easily classified as simulated ones (i.e., acting) can be. Third, the most common emotions searched for in decreasing frequency of appearance are anger, sadness, happiness, fear, disgust, joy, surprise, and boredom.
Optimally Tuned PID Controller of A DC Motor System using Bacterial Foraging Algorithm

This paper presents an exercise in applying the bacterial foraging algorithm (BFA) optimisation method on a proportional—integral-derivative controller (PID) of a DC motor circuit. The paper presents the system description of the DC motor transfer function and the simulation of the close loop system using MATLAB. The BFA algorithm is described and discussed with the simulation results presented to illustrate the enhancement of the system response that in result enhances the operation of the DC motor system
The Complementary Strip-Slot: Analysis and Antenna Applications

In this Ph.D. thesis, a novel radiating element, the so-called complementary strip-slot, has been proposed for avoiding the intrinsic resonant nature of the microstrip-fed slot. The all-pass behaviour of the structure is explained through electromagnetic analysis, and an equivalent circuit is proposed, which is capable not only of explaining its properties but also of offering the required analytical tools to state the design methodology. After a comprehensive study of the element behaviour, its potential for building series-fed arrays with novel functionalities is explored through different antenna configurations: linear travelling-wave, log-periodic and sequentially rotated arrays. It is highlighted that the broad impedance matching of the proposed element brings about an enhancement in performance in the arrays with respect to those based on its narrow-band counterpart. Thus, since this thesis provides a solution for the narrow impedance bandwidth of microstrip radiators, it broadens the horizon of the antennas in this technology to non-resonant elements.
Model for Automation of Software Testing using Genetic Algorithm to improve Software Quality

: Test case generation is the most effort consuming part of software testing. Once the test cases were generated these were used to test the software for quality. These inputs were fed as inputs to the software to compare the observed and expected results. This comparison need to be done automatically. In this paper, a model is proposed to improve the process of software testing to improve the overall quality of software
Low-light-level image super-resolution reconstruction based on iterative projection photon localization algorithm

Abstract. The intensified charge-coupled device (ICCD) is widely used in the field of low-light-level (LLL) imaging. The LLL images captured by ICCD suffer from low spatial resolution and contrast, and the target details can hardly be recognized. Super-resolution (SR) reconstruction of LLL images captured by ICCDs is a challenging issue. The dispersion in the double-proximity-focused image intensifier is the main factor that leads to a reduction in image resolution and contrast. We divide the integration time into subintervals that are short enough to get photon images, so the overlapping effect and overstacking effect of dispersion can be eliminated. We propose an SR reconstruction algorithm based on iterative projection photon localization. In the iterative process, the photon image is sliced by projection planes, and photons are screened under the constraints of regularity. The accurate position information of the incident photons in the reconstructed SR image is obtained by the weighted centroids calculation. The experimental results show that the spatial resolution and contrast of our SR image are significantly improved.
Influence of Information Collection Strategy on Designer’s Mental Stress

Information collection may affect the design quality and designer's performance through changing the structure of information and the way how information is searched and organized. Based on the theoretical analysis conducted by Wang et al., the present work continues to investigate the influence of designer's natural choice of information collection strategy on his/her mental stress both theoretically and empirically. Designers’ stresses are quantified from HRV data and are compared under different information collection strategies.
Feature extraction for document text using Latent Dirichlet Allocation

Feature extraction is one of stages in the information retrieval system that used to extract the unique feature values of a text document. The process of feature extraction can be done by several methods, one of which is Latent Dirichlet Allocation. However, researches related to text feature extraction using Latent Dirichlet Allocation method are rarely found for Indonesian text. Therefore, through this research, a text feature extraction will be implemented for Indonesian text. The research method consists of data acquisition, text pre-processing, initialization, topic sampling and evaluation. The evaluation is done by comparing Precision, Recall and F-Measure value between Latent Dirichlet Allocation and Term Frequency Inverse Document Frequency KMeans which commonly used for feature extraction. The evaluation results show that Precision, Recall and F-Measure value of Latent Dirichlet Allocation method is higher than Term Frequency Inverse Document Frequency KMeans method. This shows that Latent Dirichlet Allocation method is able to extract features and cluster Indonesian text better than Term Frequency Inverse Document Frequency KMeans method.
Bayesian causal inference of cell signal transduction from proteomics experiments

Ness, Robert D. O. PhD, Purdue University, December 2016. Bayesian Causal Inference of Cell Signal Transduction from Proteomics Experiments . Major Professor: Hyonho Chun. Cell signal transduction describes how a cell senses and processes signals from the environment using networks of interacting proteins. In computational systems biology, investigators apply machine learning methods for causal inference to develop causal Bayesian network models of signal transduction from experimental data. Directed edges in the network represent causal regulatory relationships, and the model can be used to predict the e↵ects of interventions to signal transduction. Causal inference approaches applied to proteomics experiments use statistical associations between observed signaling protein concentrations to infer a causal Bayesian network model, but there is no experimental and analysis framework for applying these methods to this experimental context. The goal of this dissertation is to provide a Bayesian experimental design and modeling framework for causal inference of signal transduction. We evaluate how different high-throughput experimental settings a↵ect the performance of algorithms that detect conditional dependence relationships between proteins. We present a Bayesian active learning approach for designing intervention experiments that reveal the direction of causal influence between proteins. Finally, we present a Bayesian model for inferring the parameters of the conditional probability density functions in a causal Bayesian network. The parameters are directly interpretable as a function of the rate constants in the biochemical reactions between interacting proteins. The work pays special attention to analysis of single-cell “snapshot” data such as mass cytometry, where each cell is a multivariate cell-level replicate of signal trans-