Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploratory interaction and extended cognition : redesigning decision-making support in healthcare

This thesis presents practice-based research on redesigning decision support within the area of prostate cancer screening. More fundamentally, this research is specifically interested in a politics and practice of supporting exploratory interactivity in healthcare decision-making by non-experts. The motivation for this research stems partly from insights gained from my own empirical research into algorithmic web-search results personalisation as well as issues underlying the biopolitical logic of cancer screening practices, the individualisation of risk and the profound uncertainties inherent in evidencebased medicine which are largely underarticulated in the design of decisionsupport tools for individuals. Taking these conditions as the driver for this research, the first and theoretical part of the thesis analyses models of interactivity and the politics of statistically-derived medical knowledge in evidence-based medicine as well as problematising dominant but narrow conceptions of human decision-making. Following such analysis are the insights that the statistical nature of epidemiological risk information is of limited applicability for individual decision-making and, thus, patient preferences matter to guide decision-making under uncertainty. By introducing cognition understood as socially distributed and extended into and performed through the environment, this research proposes to rethink how to design for exploratory information interaction in medical decision support. Following a research-through-design method and developing a minimal reserved design approach, a number of prototypes were developed to investigate the potentials hypothesised in the theoretical part of the thesis. The prototypes assumed a probing function in shedding further light on the thinking and practices of medical professionals, which leads me to suggest repositioning them as probotypes, occupying a middle ground between prototypes and cultural probes. Ultimately, the contribution of this research lies in critically and practically exploring conceptual and methodological potentials for redesigning exploratory interactions in shared medical decision-making processes.
PENGARUH KNOWLEDGE MANAGEMENT, SKILL DAN ATTITUDE TERHADAP KINERJA KARYAWAN(STUDI PADA PT. BANK SULUTGO KANTOR PUSAT DI MANADO) THE INFLUENCE OFKNOWLEDGE MANAGEMENT , SKILL, AND ATTITUDEON EMPLOYEE PERFORMANCE (STUDY AT BANK SULUT GO OFFICE IN MANADO)

The study aims to determine the effect of Knowledge Management, Skills and Attitude simultaneously and partially on Employee Performanceat PT. Bank SulutGo Manado. Th e study was conductedin PT. Bank SulutGo Manado in Jalan Sam Ratulangi . The data was carried out in a month. 75 customers PT. Bank SulutGo Manado were surveyed as respondents, rondomly . The F test found that the research model consisting of management knowledge, skills, and attitude has a positive and significant effect on work performance. Then, the results shows knowledge management, skill and attitude have a significant and positive influence partially on the performance of e mployees of PT. Bank SulutGo Manado. Skill is the most dominant variable influence on the performance of employees of PT. Bank SulutGo Manado, whereas knowledge management and attitude are second and third variables that affect the performance of employees of PT. Bank SulutGo Manado.
PENGEMBANGAN MOBILE LEARNING BERBASIS ANDROID MENGGUNAKAN ADOBE FLASH CS6 PADA MATA PELAJARAN BAHASA INGGRIS UNTUK MENINGKATKAN HASIL BELAJAR SISWA KELAS X TKJ SMK HIDAYAH SEMARANG

Berdasarkan hasil observasi di SMK Hidayah Semarang, terdapat beberapa kendala dalam pembelajaran Bahasa Inggris di kelas X TKJ. Nilai Bahasa Inggris siswa yang masih dibawah standar ketuntasan hasil belajar, minat belajar siswa rendah, kurangnya jam pelajaran, dan keterbatasan kemampuan guru dalam mengembangkan media pembelajaran menjadi pokok permasalahan dalam penelitian ini. Mengatasi kendala tersebut dibutuhkan sebuah media pembelajaran yang dapat merangsang kreatifitas siswa, menumbuhkan minat belajar, serta membantu guru dalam memberikan materi pembelajaran, salah satunya yaitu mobile learning berbasis android. Penelitian ini bertujuan untuk mengetahui bagaimana memproduksi mobile learning berbasis android dan seberapa efektif pengembangan mobile learning berbasis android pada pembelajaran Bahasa Inggris kelas X. Metode yang digunakan adalah model penelitian dan pengembangan ADDIE. Tahapan dalam penelitian ini diawali dengan mengumpulkan informasi, menganalisis kebutuhan, mengembangkan instrumen, merancang dan membuat mobile learning, penerapan pada pembelajaran, serta dilakukan tes hasil belajar. Hasil penelitian menunjukkan bahwa mobile learning berbasis android yang dikembangkan dinyatakan layak digunakan sebagai media pembelajaran Bahasa Inggris kelas X. Hasil uji efektifitas dengan uji t satu sampel diperoleh hasil perhitungan pada α = 5% dengan db = 52 diperoleh ttabel = 2,007 dan thitung = 2, 337, sehingga ttabel = 2,007 ttabel , maka Ha diterima. Kesimpulannya bahwa mobile learning berbasis android efektif digunakan dalam pembelajaran. Guru perlu melakukan pemanfaatan dan pengembangan media pembelajaran guna menumbuhkan minat belajar siswa dan memudahkan dalam penyampaian materi pembelajaran.
AN INTRODUCTION FOR ECONOMETRICIANS

The literature on stochastic limit theory has a long history, but during the second half of this century it has undergone impressive development. The works of probabilists such as Billingsley, Doob, Ibragimov, McLeish, Parthasarathy, Prokhorov, and Skorokod, among many others, are today the cornerstones of modern stochastic limit theory. They allow one to develop a powerful framework by the introduction of martingales and its use as a convenient tool, the characterization of different forms of dependence for general possibly nonstationary time series, and the application of weak topology results. This framework has been used by econometricians such as Andrews, Bierens, Gallant, Phillips, White, and others to study the asymptotic properties of different kinds of estimators in various setups (nonlinear and linear). The framework is elaborate and relies on the simultaneous use of different mathematical notions that are not usually taught to students in econometrics. Davidson's book aims at providing econometricians (students or researchers) with a detailed description of these ideas and at gathering the asymptotic results available in the most recent literature in econometrics among which are some of his own works. According to the preface, "the first objective (of this book) has been to provide a coherent and unified account of modern asymptotic theory" (Preface, p. XV). A unified theory of asymptotic stochastic theory seems today still beyond reach, but a collection of the various definitions of dependence for stochastic processes and of the basic ideas of the asymptotic results in a unified framework is a very useful and practical tool. The approach followed by the author is constructive. Each element is introduced in succession. A proof is provided for nearly all the results. In this respect, it is quite different from the main textbooks already available and
Feeling the Pulse of Global Value Chains: Air Cargo and COVID-19

July 2021 Abstract: This paper focuses on air cargo market development, with special attention to the connections between countries in Asia, the European Union, and the United States. Before the coronavirus disease (COVID-19) crisis, we show that participation in global value chains played a crucial role in how countries in Asia increased their exposure to the European Union market, which was hit hardest by the COVID-19 crisis. Analysing the effects of the crisis in 2020 – using a fuzzy set complexity approach and recent high frequency data on air cargo transport – we show that such demand effects, together with domestic contraction conditions, explain a large share of the variation in air cargo dynamics across countries in Asia. However, we also show that implementing best practices in pandemic control positively impacts air cargo recovery for countries that cannot rely on export market rebounds. After reviewing the convergence in air cargo business models since 2010, the paper continues to assess recovery options. The main conclusion is that business models will converge on long haul point to point models that combine passengers and cargo, moving away from the current hub and spoke system.
Subsidizing Compliance: A Multi-Unit Price List Mechanism for Legal Fishing Nets at Lake Victoria

10 Like many common-pool resources, the Lake Victoria fisheries are characterized by poor compliance with production input regulations that are intended to reduce overexploitation. To explore the use of input subsidies to increase compliance, we determine the subsidy level required to induce demand for legal fishing nets, thereby compensating fishermen for loss of productivity net of enforcement risk. Our study 15 additionally tests the subsidy-enhancing effect of a norm-nudge. A new multiple price list mechanism for eliciting revealed willingness to pay for multiple units of a production input is developed, adapted to the demands of a challenging field setting, and implemented with 462 fishermen at 20 landings sites on the Tanzanian lakeshore. Consistent with the high prevalence of illegal fishing gear at our sites, we 20 find a zero median demand for legal net panels at local market prices. The subsidy required to shift median demand to at least one legal net panel is a 21% discount. Norm-nudging generates no policy-relevant enhancement of the subsidy. JEL Classification: C93, D04, H23, Q28 25
PROFILE OF STRUMA PATIENTS WHO RECEIVED SURGICAL TREATMENT AT DR. SOETOMO GENERAL HOSPITAL FROM 2016 TO 2020

Goiter is an enlargement of the thyroid gland. The etiology of goiter varies by country. The purpose of the study is to examine the medical records of all patients with goiter who had surgery at Dr. Soetomo Hospital Surabaya Indonesia from January 2016 to December 2020. 78% of the 274 cases were female. The illness was more prevalent in the >40 age group than the other two, at 68%. Non-toxic multinodular goiter (47%) was the most common, followed by non-toxic unimodular goiter (31%) and toxic multinodular goiter (5%). Retrosternal goiter was 1%, toxic uninodular goiter was 1%, non-toxic diffuse goiter was 6%, and intrathoracic goiter was 3%. Almost half of the study's participants had a total thyroidectomy. This trial's outcome and issues were both good, with minor difficulties.
Interactions of light and mirrors: advanced techniques for modelling future gravitational wave detectors

The second generation of ground based interferometric gravitational wave detectors, offering ten times the sensitivity of their predecessors, are now just beginning to come online. With these fully operational the community expects that within the next few years we will finally observe the first direct detection of a gravitational wave. The effectiveness of numerical modelling of realistic systems for design and improvements is in many cases limited by the computation time. I have investigated the use of a reduced-order quadrature technique to reduce the computational cost of calculating spatial overlap integrals of Hermite-Gaussian mode. This significant reduction allows for new parameter spaces to be efficiently explored. One of the challenges in the design of future gravitational wave detectors are unstable mechanical oscillations of the test masses: also known as parametric instabilities. I have shown a new method to reduce these parametric instabilities by using purely optical means. Finally, the susceptibility of waveguide grating mirrors to lateral displacement phase shifts coupling in to the reflected beam was investigated. It was demonstrated using a finite-difference time-domain model to solve Maxwell’s equations that such a coupling does not affect waveguide grating mirrors.
Visio-Linguistic Brain Encoding

Brain encoding aims at reconstructing fMRI brain activity given a stimulus. There exists a plethora of neural encoding models which study brain encoding for single mode stimuli: visual (pretrained CNNs) or text (pretrained language models). Few recent papers have also obtained separate visual and text representation models and performed late-fusion using simple heuristics. However, previous work has failed to explore the co-attentive multi-modal modeling for visual and text reasoning. In this paper, we systematically explore the efficacy of image and multi-modal Transformers for brain encoding. Extensive experiments on two popular datasets, BOLD5000 and Pereira, provide the following insights. (1) We find that VisualBERT, a multi-modal Transformer, significantly outperforms previously proposed single-mode CNNs, image Transformers as well as other previously proposed multi-modal models, thereby establishing new state-of-the-art. (2) The regions such as LPTG, LMTG, LIFG, and STS which have dual functionalities for language and vision, have higher correlation with multi-modal models which reinforces the fact that these models are good at mimicing the human brain behavior. (3) The supremacy of visio-linguistic models raises the question of whether the responses elicited in the visual regions are affected implicitly by linguistic processing even when passively viewing images. Future fMRI tasks can verify this computational insight in an appropriate experimental setting. We make our code publicly available.
Lightweight Single Image Super-Resolution by Channel Split Residual Convolution

In recent years, deep convolutional neural networks have made significant progress in the research of single image super-resolution. However, it is difficult to be applied in practical computing terminals or embedded devices due to a large number of parameters and computational effort. To balance these problems, we propose CSRNet, a lightweight neural network based on channel split residual learning structure, to reconstruct high-resolution images from low-resolution images. Lightweight refers to designing a neural network with fewer parameters and a simplified structure for lower memory consumption and faster inference speed. At the same time, it is ensured that the performance of recovering high-resolution images is not degraded. In CSRNet, we reduce the parameters and computation by channel split residual learning. Simultaneously, we propose a double-upsampling network structure to improve the performance of the lightweight super-resolution network and make it easy to train. Finally, we propose a new evaluation metric for the lightweight approaches named 100_FPS. Experiments show that our proposed CSRNet not only speeds up the inference of the neural network and reduces memory consumption, but also performs well on single image super-resolution.
Investigating Students’ Adoption of MOOCs during COVID-19 Pandemic: Students’ Academic Self-Efficacy, Learning Engagement, and Learning Persistence

Students’ learning environments are significantly influenced by massive open online courses (MOOCs). To better understand how students could implement learning technology for educational purposes, this study creates a structural equation model and tests confirmatory factor analysis. Therefore, the aim of this study was to develop a model through investigating observability (OB), complexity (CO), trialability (TR), and perceived usefulness (PU) with perceived ease-of-use (PEU) of MOOCs adoption by university students to measure their academic self-efficacy (ASE), learning engagement (LE), and learning persistence (LP). As a result, the study used an expanded variant of the innovation diffusion theory (IDT) and the technology acceptance model (TAM) as the research model. Structural Equation Modeling (SEM) with Smart-PLS was applied to quantitative data collection and analysis of 540 university students as respondents. Student responses were grouped into nine factors and evaluated to decide the students’ ASE, LE, and LP. The findings revealed a clear correlation between OB, CO, and TR, all of which were important predictors of PU and PEU. Students’ ASE, LE, and LP were affected by PEU and PU. This study’s established model was effective in explaining students’ ASE, LE, and LP on MOOC adoption. These findings suggest implications for designing and developing effective instructional and learning strategies in MOOCs in terms of learners’ perceptions of themselves, their instructors, and learning support systems.
Detection of pump cavitation by vibration signature

Abstract Cavitation in pumps causes destructive consequences and occurs at certain operating circumstances. Therefore, it must be detected and prevented. The main thrust of the present work is detection and investigation of cavitation in a centrifugal pump experimentally, by monitoring the flow characteristics as well as the vibration spectrum. It is an attempt to declare the discrete signature of cavitation at various strengths on the vibration spectrum. The experimental results on a centrifugal pump at a rotational speed of 2850 rpm and absolute values of suction pressures from 10 to 78 kPa, showed that the inception of cavitation is declared by sudden deterioration in the hydraulic characteristics, decrease in the efficiency and increase in the overall vibration level. As the cavitation strength increases, the vibration level decreases and then increases sharply. Cavitation can be detected by online monitoring of the vibration amplitude of the discrete frequencies corresponding to the pump rotational speed and the first blade passing. As the pump operation moves from no-cavitation to cavitation case, the vibration amplitude of the rotational speed frequency increases while it decreases for the first passing frequency. Also, the cavitation in pumps signs the vibration spectrum by appearance of high energy at high frequencies.
Interactive comment on “ Characterization of total ecosystem scale biogenic VOC exchange at a Mediterranean oak-hornbeam forest ” by S .

The main strengths of the manuscript include a thorough description of methods used for flux calculation (both manual and automated data processing approaches), the observation of deposition of methanol due to dew (very interesting, although not a novel result), confirming evidence for the bidirectional exchange of MVK+MAC, and providing new information on fluxes observable by PTR-TOF-MS in an oak dominated system
Causation and the Silly Norm Effect

In many spheres, the law takes the legal concept of causation to correspond to the folk concept (the correspondence assumption). Courts, including the US Supreme Court, tend to insist on the “common understanding” and that which is “natural to say” (Burrage v. United States) when it comes to expressions relating to causation, and frequently refuse to clarify the expression to juries. As recent work in psychology and experimental philosophy has uncovered, lay attributions of causation are susceptible to a great number of unexpected factors, some of which seem rather peripheral to causation. One of those is the norm effect (Knobe & Fraser, 2008): Agents who, in acting as they do, break a salient norm, are more likely to be considered as having caused a certain consequence than when they do not violate a norm. According to some (e.g., Alicke, 1992) this constitutes a bias. According to others (e.g., Sytsma, 2021), the folk concept of causation is sensitive to normative factors, and there’s nothing wrong with that. In this paper, we explore the question whether the norm effect should be considered a bias from the legal perspective on the one hand, and from the psychological perspective on the other. To do this, we test whether norms which are nonpertinent to the consequences or outright silly also impact causation judgements. The data from two preregistered experiments (total N=593) clearly show they do. This, we argue, makes the bias interpretation plausible from the psychological perspective, and both plausible and problematic from the legal perspective. It also shows that the law should abstain from unreflectively assuming conceptual correspondence between legal and ordinary language concepts.
Causal Discovery for Linear Mixed Data

Discovery of causal relationships from observational data, especially from mixed data that consist of both continuous and discrete variables, is a fundamental yet challenging problem. Traditional methods focus on polishing the data type processing policy, which may lose data information. Compared with such methods, the constraint-based and score-based methods for mixed data derive certain conditional independence tests or score functions from the data’s characteristics. However, they may return the Markov equivalence class due to the lack of identiﬁability guarantees, which may limit their applicability or hinder their interpretability of causal graphs. Thus, in this paper, based on the structural causal models of continuous and discrete variables, we provide sufﬁcient identiﬁability conditions in bivariate as well as multivariate cases. We show that if the data follow our proposed restricted Linear Mixed causal model (LiM), such a model is identiﬁable. In addition, we proposed a two-step hybrid method to discover the causal structure for mixed data. Experiments on both synthetic and real-world data empirically demonstrate the identiﬁability and efﬁcacy of our proposed LiM model.
Classification of heart valve condition using acoustic measurements

Prosthetic heart valves and the many great strides in valve design have been responsible for extending the life spans of many people with serious heart conditions. Even though the prosthetic valves are extremely reliable, they are eventually susceptible to long-term fatigue and structural failure effects expected from mechanical devices operating over long periods of time. The purpose of our work is to classify the condition of in vivo Bjork-Shiley Convexo-Concave (BSCC) heart valves by processing acoustic measurements of heart valve sounds. The structural failures of interest for Bscc valves is called single leg separation (SLS). SLS can occur if the outlet strut cracks and separates from the main structure of the valve. We measure acoustic opening and closing sounds (waveforms) using high sensitivity contact microphones on the patient`s thorax. For our analysis, we focus our processing and classification efforts on the opening sounds because they yield direct information about outlet strut condition with minimal distortion caused by energy radiated from the valve disc.
Teaching in the Collaborative Virtual Learning Environment of Second Life: Design Considerations For Virtual World Developers

Teaching in the Collaborative Virtual Learning Environment of Second Life: Design Considerations for Virtual World Developers. (December 2011) Daniel Lee Pogue, B.S., Texas A&M University Chair of Advisory Committee: Dr. Richard Furuta Educators are seeking ways to better engage their students including the use of collaborative virtual learning environments (CVLEs). Some virtual worlds can serve as CVLEs as the advent of Second Life has created particular interest within the education community. Second Life, however, was not initially designed to facilitate education alone. I propose that as a CVLE, Second Life may be failing educators’ expectations of its initial, ongoing, and future use as a system for supporting education. In order to determine how Second Life may be failing educators, I conducted a case study with a group of university-level educators that examined their reasons for and against adopting Second Life as a CVLE, the affordances they explored, the barriers they encountered, and how these affordances and barriers affected student learning and the participant’s future use of Second Life and future virtual worlds in education. I then compare their use of Second Life to that of traditional groupware systems. As a result, I propose and detail the development of a rich integrated development environment, application programming interface, more flexible privacy policy, and more robust community tools for educators based on these comparisons.