Miracle-dz/UniFAR-FTUs huggingface dataset:
Scaling Analytics via Approximate and Distributed Computing

The amount data generated from different sources everyday is increasing exponentially and for businesses to generate actionable insights, it is paramount that the analyses complete within a reasonable amount of time. Compute capacity, unfortunately, has not increased at a comparable rate. In recent past, researchers thus have focused on alternate approaches to scaling analytics to large datasets. Two of the most popular techniques used are (i) approximate computing techniques and (ii) distributed computing techniques for analytics execution speedup. Approximate computing involves either a reduction in data size (usually through sampling and/or dimensionality reduction) or a reduction in algorithmic complexity. This results in significant gains in performance (in terms of execution time), however at a cost in reduction of accuracy for many analytics tasks. The biggest challenge in this space is understanding the tradeoff between performance and accuracy. Most of these approximate computing techniques do have some theoretical guarantees: for instance, the extremely popular principal component analysis (PCA) dimensionality reduction technique minimizes the data reconstruction error. However, a user typically interprets quality in terms of a few popular metrics such as recall and precision. A theoretical guarantee in terms of reconstruction error is not very useful from the context of a particular application of interest. Distributed computing, on the other hand, tries to use many servers to process the data instead of a single server. Typical case is to partition the data across many servers process
Natural Language Processing for Classification of Acute, Communicable Findings on Unstructured Head CT Reports: Comparison of Neural Network and Non-Neural Machine Learning Techniques

Background and Purpose To evaluate the accuracy of non-neural and neural network models to classify five categories (classes) of acute and communicable findings on unstructured head computed tomography (CT) reports. Materials and Methods Three radiologists annotated 1,400 head CT reports for language indicating the presence or absence of acute communicable findings (hemorrhage, stroke, hydrocephalus, and mass effect). This set was used to train, develop, and evaluate a non-neural classifier, support vector machine (SVM), in comparisons to two neural network models using convolutional neural networks (CNN) and neural attention model (NAM) Inter-rater agreement was computed using kappa statistics. Accuracy, receiver operated curves, and area under the curve were calculated and tabulated. P-values < 0.05 was significant and 95% confidence intervals were computed. Results Radiologist agreement was 86-94% and Cohen’s kappa was 0.667-0.762 (substantial agreement). Accuracies of the CNN and NAM (range 0.90-0.94) were higher than SVM (range 0.88-0.92). NAM showed relatively equal accuracy with CNN for three classes, severity, mass effect, and hydrocephalus, higher accuracy for the acute bleed class, and lower accuracy for the acute stroke class. AUCs of all methods for all classes were above 0.92. Conclusions Neural network models (CNN & NAM) generally had higher accuracies compared to the non-neural models (SVM) and have a range of accuracies that comparable to the inter-annotator agreement of three neuroradiologists. The NAM method adds ability to hold the algorithm accountable for its classification via heat map generation, thereby adding an auditing feature to this neural network. Abbreviations NLP Natural Language Processing CNN Convolutional Neural Network NAM Neural Attention Model HER Electronic Health Record
Quantitative Investing and Market Instability

The May 2010 Flash Crash and August 2007 Quant Meltdown raised concerns about the impact of quantitative investment strategies on market stability. Theory is split on whether quantitative investing dampens or exacerbates market instability. To test the theory we focus on mutual fund fire sales. We find that quantitative fund fire sales have a much larger impact on market instability than fire sales by traditional mutual funds. For the same magnitude fire sale, quantitative funds’ impact is over five times as large. The evidence suggests this is due to quantitative funds’ reliance on similar trading signals and sensitivity to the time-series of returns.
SYNTHESIS OF FexZn1-xCr2O4 BROWN CERAMIC PIGMENT BY STARCH ASSISTED SOL-GEL PROCESS

In the present paper, the ceramic pigments of Fe<sub>x</sub>Zn<sub>1-x</sub>Cr<sub>2</sub>O<sub>4 </sub>(x = 0 ¸ 1) were synthesized by the starch assisted sol-gel method. The resulting pigments were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), CIE L*a*b* color measurement. The results showed that the preparation pigments of Fe<sub>x</sub>Zn<sub>1-x</sub>Cr<sub>2</sub>O<sub>4</sub>, calcinated at 1100<sup>o</sup>C for 60 minutes, obtained phases of spinel ACr<sub>2</sub>O<sub>4</sub> (A: Zn, Fe) and perovskite FeCrO<sub>3</sub> with excellent crystallinity. The brown color intensity was gradually increased as a function of the amount of substituted Fe<sup>2+</sup> ion. The achieved pigments met industrial requirements in terms of physicochemical characteristics.
Representation of morality in children: A qualitative approach

ABSTRACT Previous research on children’s moral reasoning usually used a quantitative approach and a pre-determined set of methods in order to establish early moral landmarks. We proposed a qualitative perspective on the basis of which we have formulated three main objectives: 1) to identify the main categories of behaviors that children spontaneously associate with the notion of morality, in line with Turiel’s Domain Theory; 2) to investigate children’s conceptions of moral and social-conventional rules and 3) to assess the gender differences in the use of care and justice reasoning, in line with Gilligan’s assertion that females are care-oriented and that males consistently use the justice orientation and infrequently use the care orientation. Ninety-two children (57% boys, mean age was 8.19 years) were required to describe morality in behavioral terms without being guided by specific indications from the researchers. Our results reflected a tendency towards morality perceived through behaviors directed towards others, namely through altruism, and also the domination of moral norms as compared to social ones, partially confirming Gilligan’s theory concerning gender specificities regarding moral reasoning in the questions of care and justice.
Gesture Recognition for Human-Robot Interaction for Service Robots

Robots are quickly becoming an intrinsic part of our daily lives and it is becoming important to provide the users a simple and intuitive way to interact with them. In this thesis, we present a multimodal Human-Robot interface for an existing service robot, mostly addressed to people with reduced mobility on the shopping process in dynamic and crowded environments ( eg. supermarkets). This interface was created in order to recognize the "Start", "Stop" and "Pause" commands.The proposed Human-Robot Interface includes two types of interaction: verbal and non-verbal. Regarding verbal interaction, four state of the art implementations (Google Speech Recognition, Houndify, Microsoft Bing Voice Recognition, CMUsphinx) were tested and compared. The Houndify proved to be the more suitable for our project.Relatively to the non-verbal interaction, a novel method for hand gesture recognition based on depth information was implemented and tested. The software was developed to be used by a robot equipped with a RGB-D camera. This camera captures images in real time where the robot user's position is obtained. Taking as input the information already processed by the robot, the arm/hand is obtained by a depth based segmentation approach. A principal component analysis is then computed to each object where its center of mass and eigen vectors are calculated in order to extract the hand's tip and orientation. A Kalman filter is then applied for tracking the hand and get its position through time. Given this information and based on finite state machines that were implemented to describe gestures (start, stop, pause) we perform gesture recognition.
Dynamics of gravity-driven viscoelastic films on wavy walls

The linear stability and nonlinear dynamics of viscoelastic liquid films flowing down inclined surfaces with sinusoidal topography are investigated. The Oldroyd-B constitutive model is used, and numerical solutions of a long-wave nonlinear evolution equation for the film thickness (introduced by Davalos-Orozco[1]) provide insight into the influence of elasticity and wall topography on the nonlinear film dynamics, while Floquet analysis of the linearized evolution equation is used to study the onset of linear instability. Focusing initially on inertialess films (with zero Reynolds number), linear stability results are organized into three regimes based on the wall wavelength. For su ciently short and su ciently long wall wavelengths, the onset of instability is not tangibly a↵ected by the topography. There is, however, an intermediate range of wavelengths where, as the wall wavelength is increased, the critical Deborah number for the onset of instability first decreases (topography is destabilizing) and then increases su ciently for topography to be stabilizing (relative to the flat wall). Solutions to a perturbation amplitude equation indicate that the character of the instability changes substantially within this intermediate range; topography induces streamwise variations in the base-state velocity at the free surface which couple with perturbations and substantially influence the instability growth rate. Very similar trends are observed for Newtonian films and variations in the critical Reynolds number. Simulations of the full nonlinear evolution equation produce a broad range of nonlinear states including traveling waves, time-periodic waves, and chaos. Perturbations to the film generally saturate at higher amplitudes for cases with larger linear growth rates (e.g. with increasing Deborah number or for a destabilizing wall wavelength), and topography introduces finer temporal scales in the dynamics. The qualitative influences of inclination and inertia on the nonlinear dynamics are shown to be simply related to the influence of elasticity using analytical linear stability results for the flat-wall case. 1
Uji Metode Klasifikasi Data Dalam Proses Seleksi Penerima Beasiswa SMK PGRI Ploso

Abstrak Proses seleksi beasiswa melibatkan banyak variabel untuk indikator penilaian kelayakan penerima beasiswa. Terdapat dua jenis variabel penilaian, yaitu variabel Nominal dan variabel Numerik. Kedua variabel ini memiliki kesetaraan perhitungan dan pertimbangan dalam proses seleksi. Terdapat berbagai metode yang bisa dipergunakan untuk melakukan proses klasifikasi dan pengenalan pola data yang dipakai dalam seleksi beasiswa. Didalam penelitian ini dilakukan pengujian terhadap dua metode klasifikasi dan pengenalan pola data, yaitu menggunakan implementasi logika fuzzy dan integrasi logika fuzzy dengan algoritma C4.5. Pengujian dilakukan untuk menguji keakuratan dari metode yang diperhitungkan dengan menggunakan metode pengujian 10-folds cross validation. Dari hasil penelitian diperoleh Root Mean Square Error (RMSE) sebesar 0,216 dan Mean Absolut Error (MAE) sebesar 0,0941 untuk implementasi logika fuzzy. Sedangkan untuk integrasi logika fuzzy dengan algoritma C4.5 diperoleh hasil RMSE sebesar 0,2714 dan MAE sebesar 0,132. Dari penelitian ini disimpulkan bahwa implementasi logika fuzzy memberikan hasil pengujian yang lebih baik. Kata Kunci : Seleksi Beasiswa, Variabel Nominal, Variabel Numerik, Implementasi Logika Fuzzy, Algoritma C4.5. Abstract The scholarship selection process involves many variables for feasibility assessment indicators recipients. There are two types of assessment variables, namely Nominal variables and Numeric variables. Both of these variables have equal calculation and consideration in the selection process. There are various methods that can be used to make the process of classification and pattern recognition data used in the selection of scholarship. In this study tested the two methods of data classification and pattern recognition, which uses fuzzy logic implementation and integration of fuzzy logic with C4.5 algorithm. Tests conducted to test the accuracy of the method were calculated using the method of testing 10-folds cross validation. The results were obtained Root Mean Square Error (RMSE) of 0.216 and Mean Absolute Error (MAE) of 0.0941 to the fuzzy logic implementation. As for the integration of fuzzy logic and C4.5 algorithm’s resulting RMSE value 0.2714 and MAE value 0.132. This study suggests that the fuzzy logic implementation provides better test results. Keywords: Scholarship Selection, Nominal Variables, Numeric Variables, Fuzzy Logic Implementation, C4.5 Algorithm.
A hybridized semantic trust-based framework for personalized web page recommendation

ABSTRACT The World Wide Web is constantly evolving and is the most dynamic information repository in the world that has ever existed. Since the information on the web is changing continuously and owing to the presence of a large number of similar web pages, it is very challenging to retrieve the most relevant information. With a large number of malicious and fake web pages, it is required to retrieve Web Pages that are trustworthy. Personalization of the recommendation of web pages is certainly necessary to estimate the user interests for suggesting web pages as per their choices. Moreover, the Web is tending towards a more organized Semantic Web which primarily requires semantic techniques for recommending the Web Pages. In this paper, a framework for personalized web page recommendation based on a hybridized strategy is proposed. Web Pages are recommended based on the user query by analyzing the Web Usage Data of the users. An array of strategies is intelligently integrated together to achieve an efficient Web Page Recommendation system. Latent Semantic Analysis is applied to the User-Term Matrix and the Term-Frequency Matrix that are built from the Web Usage Information to form a Term Prioritization Vector. Further, techniques like Latent Dirichlet Allocation for Topic-based Segregation of the URLs and Normalized Pointwise Mutual Information strategies are used for recommending web pages based on users’ queries. The Personalization is achieved by prioritizing the Web pages based on the Prioritization Vector. Also, a unique methodology is incorporated into the system to retrieve trustworthy websites. An overall Accuracy of 0.84 is achieved which is better than the existing strategies.
An integrated framework for adaptive local ramp-metering control

Abstract In order to increase the operational capability of transportation systems, nowadays, freeway networks are equipped with ramp-metering systems, which aim to regulate, in an appropriate way, the inflow from on-ramps to the main carriageways. The effectiveness of a rampmetering system in improving traffic conditions, e.g. by minimizing traffic congestion or journey times, depends highly upon the efficiency of its operational strategy. In this paper we introduce an adaptive framework for local ramp-metering control based on a coupling of the continuum model of traffic flow introduced in [15], [21] with the reinforcement learning approach [23], [25].
Research on the Simulation Models of Knowledge Emergence Based on CAS Theory

This paper firstly gives the definition of knowledge emergence. Afterwards, based on CAS theory, this paper studies the microcosmic model of multi-Agent and its logic construction of simulation system of macro-level knowledge emergence. This paper reveals evolution rules between the microscopic and macroscopic systems of the knowledge system taking time as a parameter, and gives the applicable conditions of knowledge emergence with CAS simulation methods. Keywords-knowledge emergence; simulation modeling; CAS theory
Towards a Multi-Religious Topology of Islam: The Global Circulation of a Mutable Mobile

Narratives of the origins, the history, and the present state of Islam always entail spatial claims. Accordingly, Islam emerged in the Arabian Peninsula, spread over its so-called heartlands, and became a world religion. A common understanding inscribes Islam onto the Orient and opposes it to Europe, the Occident, or the West. Such spatial claims are faced with fundamental challenges and epistemological shortcomings because neither Islam nor space are naturally given, bounded entities. Rather, different historical actors and observers produce spatialized Islam. In this chapter, I challenge the notion that “Muslim space” is a useful analytical concept, and scrutinize the ways in which academic discourses inscribe Islam onto space and history. As an alternative, I propose a topology that understands the production of space as a multi-dimensional social process, including Muslim and non-Muslim perspectives at the same time. Thus, I delineate the topology of Islam as variegated, dynamic, and multi-religious from its inception. My argument is that Islam’s trans-regional spread turned it into a polycentric, mutable mobile characterized by internal and external diversity. I further argue that images of Islam are an integral, yet often concealed part of European and Western knowledge production and self-understanding. Epistemologically, this perspective argues that the “Islamization of Islam” is nowhere better visible than in the spatial ramifications of discourses that marginalize, exclude, or obfuscate both the multi-religious experiences in Islamic contexts and the continuous presence of Islam in European history.
An Interaction Approach Between Services for Extracting Relevant Data From Tweets Corpora

We present a system based on the need of special infrastructure adequate to software agents to operate, to compose and make sense from the contents of the Web resources through the development of a multi-agent system oriented services interactions. Our method follows the different construction ontology techniques and updates them by extracting new terms and integrate them to the ontology.It is based on the detection phrases via the ontological database DBPedia. The system treats each syntagme extracted from the corpus of messages and verifies whether it is possible to associate them directly to a DBPedia knowledge. In case of failure, these service agents interact with each other in order to find the best possible answer to the problem, by operating directly in the phrase, trying to semantically modify it, until the association with ontological knowledge becomes possible. The advantage of our approach is its modularity : it is both possible to add / modify / delete a service or define a new one, and then influence the outcome product. We could compare the results extracted from a heterogeneous body of messages from the Twitter social network with Tagme method, based mainly on storage and annotation of encyclopaedic corpus.
Achieving Sustainable Development Through Employees’ Innovative Behaviour and Knowledge Management

Recently, sustainable development has become an important factor for all companies and manufactures. From previous research reviews, it is understood that sustainable development (IISD) could be improved through effect of innovation. Past researches also indicated that increase in knowledge management (KM) can enhance innovation. This study investigated the relationship between knowledge management, innovation, and sustainable development in Malaysiaâ€™s manufacturing sector. For this purpose, a survey was conducted to and addressed to a total of 123 employees of Proton Holdings Berhad. Findings from this study revealed that there is a significant relationship between the three variables mentioned; and the mediating role of innovative behaviour (IB) is accepted. This study contributes to the body of sustainable development literature. A particular implication for managers to take note is to invest in sustainable development through knowledge management and innovative behaviour.
Events in Social Networks: A Stochastic Actor-oriented Framework for Dynamic Event Processes in Social Networks

Interactions between people are ubiquitous. When people make phone calls, transfer money, connect on social network sites, or visit each other, these actions can be collected as dyadic, directed, relational events. Each of those events can be understood as driven by multiple individual decisions that at least partially involve rational considerations. This book aims at developing models that allow to understand individual event decisions in the context of large social networks.
Drug safety decision support model to reduce medication errors

The steps involved in the medication process for instance prescribing, dispensing and administering medicines (the "medication process") have always posed some risk to individual patient health. Such "medication errors" were sufficiently common in developed countries by the 1960s to warrant formal research. Moreover, the frequency of errors appears not to have declined with the introduction of electronic systems. Several technological approaches has been used to reduce medication errors such as Computerized Physician Order Entry (CPOE), Electronic Patient Record (EPR), drug record system in Denmark and PRESGUID project in France, based on models and theories of errors in healthcare and electronic systems that were drawn from different countries including the UK. Examples of the systems and their evaluation methodology are examined, some of them have already been implemented and others are still being assessed. Consequently, an addition of Clinical Decision Support Systems in current prescribing systems (CDSSs) is necessary. The research considers the development of a medication management model including all the steps of the medication process that involves a clinical decision support system starting from medication prescribing to administration. A necessary preliminary step is to understand the types and contexts of the risks involved. The design of the model was based on the systematic review and meta-analysis outcomes that analysed journal papers, addressing individual themes from definitions, the source of risks and the consequences of errors to comparisons of the medication errors rates between simple prescribing systems and electronic prescribing systems that include CDSSs. The English National Programme For IT is also given prominence as it is a test case for many developed countries and the context for much of my practical work. The new model demonstrates a notable reduction in the frequency of medication errors and the number of patients with Adverse Drug Events. A system dynamics methodology is used to design the model; the first step is the development of Causal Loop Diagrams (CLDs) which are used as an alternative summary of systematic review finding. They show connections between human and technological factors in the medication process and suggest points for support and intervention potentially addressed by the model that includes CDSSs. The second step is the development of system dynamic models of hospital medication flow in order to detect the effect of CDSSs in reducing the rate of patients with medication errors and application of the effect data of CDSSs to one large hospital to identify the consequence of the model with CDSSs on patient’s rate. An evaluation is performed based on focus groups and discussions with clinical pharmacists to demonstrate how the drug safety model can be used. Furthermore an exploitation of pharmacist’s opinions on CLDs and drug safety model by using a short survey has been presented to improve the consequences of medication errors.