Miracle-dz/UniFAR-FTUs huggingface dataset:
Tool support for automated multi-device GUI generation from discourse-based communication models

Automated generation of graphical user interfaces (GUIs) from models is possible, but their usability is often not good enough for real-world use, in particular not for small devices. Also automated tailoring of GUIs for different devices is still an issue. Our tools provide such tailoring for different devices through automatic optimization of corresponding optimization objectives under given constraints. Currently, two different optimization strategies are implemented, with their focus on tapping and vertical scrolling on touchscreen, respectively. The constraints (relevant properties such as screen size and resolution) are to be provided by the users of our tools in device specifications. Through our tool support, WIMP (window, icon, menu, pointer) GUIs can be generated at a decent level of usability nearly automatically, in particular for small devices. This is important due to the more and more widespread use of smartphones.
Guided Self Training for Sentiment Classification

The application of machine learning techniques to classify text documents into sentiment categories has become an increasingly popular area of research. These techniques rely upon the availability of labelled data, but in certain circumstances the availability of pre-classified documents may be limited. Limited labelled data can impact the performance of the model induced from it. There are a number of strategies which can compensate for the lack of labelled data, however these techniques may be suboptimal if the initial labelled data selection does not contain a sufficient cross section of the total document collection. This paper proposes a variant of self-training as a strategy to this problem. The proposed technique uses a high precision classifier (linguistic rules) to influence the selection of training candidates which are labelled by the base learner in an iterative self-training process. The linguistic knowledge encoded in the high precision classifier corrects highconfidence errors made by the base classifier in a preprocessing step. This step is followed by a standard self training cycle. The technique was evaluated in three domains: user generated reviews for (1) airline meals, (2) university professors and (3) music against: (1) constrained learning strategies (voting and veto), (2) induction and (3) standard self-training. The evaluation measure was by estimated F-Measure. The results demonstrate clear advantage for the proposed method for classifying text documents into sentiment categories in domains where there is limited amounts of training data.
Combining Quantitative and Qualitative Measures of Uncertainty in Model‐Based Environmental Assessment: The NUSAP System

This article discusses recent experiences with the Numeral Unit Spread Assessment Pedigree (NUSAP) system for multidimensional uncertainty assessment, based on four case studies that vary in complexity. We show that the NUSAP method is applicable not only to relatively simple calculation schemes but also to complex models in a meaningful way and that NUSAP is useful to assess not only parameter uncertainty but also (model) assumptions. A diagnostic diagram can be used to synthesize results of quantitative analysis of parameter sensitivity and qualitative review (pedigree analysis) of parameter strength. It provides an analytic tool to prioritize uncertainties according to quantitative and qualitative insights in the limitations of available knowledge. We show that extension of the pedigree scheme to include societal dimensions of uncertainty, such as problem framing and value‐laden assumptions, further promotes reflexivity and collective learning. When used in a deliberative setting, NUSAP pedigree assessment has the potential to foster a deeper social debate and a negotiated management of complex environmental problems.
Architecture-Based Software Reliability Estimation : Problem Space , Challenges , and Strategies

In modern software-intensive systems, reliability is considered to be one of the most critical non-functional properties. To build software in a cost-efficient manner, reliability should be analyzed at architecture design time. In this paper, we consider the problem space of, challenges in, and strategies for architecture-based estimation of a software system’s reliability. Architecture-based reliability estimation is challenging –– during early design phases, architects lack information necessary for precisely determining a system’s reliability, such as the system’s operational profile and failure and recovery information. Thus, we explore how such information can be obtained from alternative sources. Finally, we present a critical overview of existing approaches to architecture-based reliability estimation, in order to indicate directions for future research.
Natriuretic peptides in embryonic stem cell-derived cardiomyocytes and their receptors in the CNS.

The natriuretic peptides (NPs) are a family of related hormones that play important roles in the cardiovascular homeostasis, cell growth and neuroendocrine functions. Recently, they have emerged as potentially important clinical biomarkers in heart failure. The heart secretes two major natriuretic peptides: atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP), while C-type natriuretic peptide (CNP) is mainly secreted from the brain and blood vessels. The physiological effects of NPs are initiated by binding to natriuretic peptide receptors (NPRs), which are widely distributed in several organs. This review describes: the expression of natriuretic peptides in the cardiomyocytes differentiated from ES cells and their role in the cardiomyocyte development. We also describe the detailed distribution of NPRs in the central nervous system and their possible functions in various brain regions.
Intraoperative radiotherapy with low energy photons in recurrent colorectal cancer: a single centre retrospective study

Aim of the study Intraoperative radiotherapy (IORT) may improve outcome of surgical treatment of recurrent colorectal cancer (CRC). The aim of this study is to determine the feasibility, safety and long-term results of surgical treatment of recurrent CRC with orthovolt IORT. Material and methods Fifty-nine consecutive CRC patients with local recurrence (LR), undergoing surgery, were included in the retrospective analysis of prospectively collected data. The modified Wanebo classification was used to stage LR (Tr). Twenty-five (43%) patients received IORT using INTRABEAM® PRS 500. The complications were classified according to the Clavien-Dindo classification. Results There were 32 males and 27 females, with a median age of 63 years. Multi-visceral resections were performed in 37 (63%) patients. Median hospitalization time after surgery with IORT was 7 days. One (1.7%) in-hospital postoperative death was reported. Grade 3/4 postoperative complications were found in 11 (19%) patients. Intraoperative radiotherapy had no effect on the postoperative hospitalization time, morbidity and mortality. Median survival after R0 resection was 32 months. Complete resection (R0), no synchronous liver metastases (M0), and no lateral and posterior pelvic wall involvement, were significant predictors of improved survival. Stage of LR was found to be an independent prognostic factor in the multivariate analysis (p = 0.03); Cox regression model). In patients with LR stage < Tr5, a 3-year overall survival (OS) rate was 52%. Conclusions Combination of surgical resection and orthovolt IORT is a safe and feasible procedure that does not increase the risk of postoperative complications or prolongs the hospital stay. Despite aggressive surgery supported by IORT, the advanced stage of LR is a limiting factor of long-term survival.
Crowdsourcing Intelligent Research Participants: A Student versus MTurk Comparison

The use of online workers as research participants has grown in recent years, prompting interest in how online workers compare to traditional accounting research participants. To date, no study has compared the relative intelligence of online workers to student subjects. Such a comparison may be important to behavioral accounting researchers given the homogeneity of accounting students relative to online subject pools and given prior research suggesting accounting students have relatively high analytic ability. While graduate students outperform both undergraduate and MTurk participants on common intelligence tests and across two reasonably complex tasks, we find that MTurk participants perform similarly to undergraduate accounting students. Overall, our results provide incremental assurance that MTurk participants are suitable subjects when accounting expertise is not explicitly required. We also provide evidence that screening MTurk workers on intelligence scores may benefit researchers who require participants with relatively high cognitive ability.
On SSK and Conversing with Scientists: Eugénia Rodrigues Talks with Michael J. Mulkay

Mike Mulkay takes Eugénia Rodrigues through a journey that revisits his involvement in the Sociology of Scientific Knowledge and STS. They talk dates, career moves, foundational work, books, and how it all started with a denied visa application from the US. Running under this formalistic level, though, other themes emerge: the notion of a “field” and its meaning in STS; the observed lack of epistemological and methodological rigor in research practice; Mulkay’s unsuccessful (on his own account) project on the literary forms or his unfazed take on the work produced by the “Edinburgh School. ” It is a fascinating expedition and one that reveals some very present-day questions for STS, not least about the meanings of engagement and reflexivity. A reflection by Eugénia Rodrigues follows the interview.
Efficient Analysis of Metallic and Dielectric Posts in Parallel-Plate Waveguide Structures

A mode-matching method is proposed for the accurate and fast analysis of structures composed by metallic and dielectric posts in a parallel-plate waveguide environment. The incident and scattered fields on each post are expressed with a cylindrical mode expansion. After enforcing the appropriate boundary conditions, a set of matrix equations is derived. The corresponding solution are the coefficients of the field vector expansion. The method is validated with several examples found in the literature and compared with the results obtained with the commercial software Ansys HFSS. The proposed method can be used for a very fast electromagnetic analysis of substrate integrated waveguides and substrate integrated slab waveguides.
Ieee Transactions on Visualization and Computer Graphics Uncertainty-aware Multidimensional Ensemble Data Visualization and Exploration

—This paper presents an efficient visualization and exploration approach for modeling and characterizing the relationships and uncertainties in the context of multidimensional ensemble datasets. Its core is a novel dissimilarity-preserving projection technique that characterizes not only the relationships among the mean values of the ensemble data objects but also the relationships among the distributions of ensemble members. This uncertainty-aware projection scheme leads to an improved understanding of the intrinsic structure in an ensemble dataset. The analysis of the ensemble dataset is further augmented by a suite of visual encoding and exploration tools. Experimental results on both artificial and real-world datasets demonstrate the effectiveness of our approach.
Crystallographic and Spectroscopic Insights into Heme Degradation by Mycobacterium tuberculosis MhuD

Mycobacterium heme utilization degrader (MhuD) is a heme-degrading protein from Mycobacterium tuberculosis responsible for extracting the essential nutrient iron from host-derived heme. MhuD has been previously shown to produce unique organic products compared to those of canonical heme oxygenases (HOs) as well as those of the IsdG/I heme-degrading enzymes from Staphylococcus aureus. Here, we report the X-ray crystal structure of cyanide-inhibited MhuD (MhuD–heme–CN) as well as detailed 1H nuclear magnetic resonance (NMR), UV/vis absorption, and magnetic circular dichroism (MCD) spectroscopic characterization of this species. There is no evidence for an ordered network of water molecules on the distal side of the heme substrate in the X-ray crystal structure, as was previously reported for canonical HOs. The degree of heme ruffling in the crystal structure of MhuD is greater than that observed for HO and less than that observed for IsdI. As a consequence, the Fe 3dxz-, 3dyz-, and 3dxy-based MOs are very close in energy, and the room-temperature 1H NMR spectrum of MhuD–heme–CN is consistent with population of both a 2Eg electronic state with a (dxy)2(dxz,dyz)3 electron configuration, similar to the ground state of canonical HOs, and a 2B2g state with a (dxz,dyz)4(dxy)1 electron configuration, similar to the ground state of cyanide-inhibited IsdI. Variable temperature, variable field MCD saturation magnetization data establishes that MhuD–heme–CN has a 2B2g electronic ground state with a low-lying 2Eg excited state. Our crystallographic and spectroscopic data suggest that there are both structural and electronic contributions to the α-meso regioselectivity of MhuD-catalyzed heme cleavage. The structural distortion of the heme substrate observed in the X-ray crystal structure of MhuD–heme–CN is likely to favor cleavage at the α- and γ-meso carbons, whereas the spin density distribution may favor selective oxygenation of the α-meso carbon.
Observer-Based Leader-Following Formation Control Using Onboard Sensor Information

In this paper, leader-following formation control for mobile multiagent systems with limited sensor information is studied. The control algorithms developed require information available from onboard sensors only, and in particular, the measurement of the leader (neighbor) speed is not needed. Instead, an observer is designed for the estimation of this speed. With the proposed control algorithms as building blocks, many complex formations can be obtained.
OBDDs and (Almost) $k$-wise Independent Random Variables

OBDD-based graph algorithms deal with the characteristic function of the edge set E of a graph $G = (V,E)$ which is represented by an OBDD and solve optimization problems by mainly using functional operations. We present an OBDD-based algorithm which uses randomization for the first time. In particular, we give a maximal matching algorithm with $O(\log^3 \vert V \vert)$ functional operations in expectation. This algorithm may be of independent interest. The experimental evaluation shows that this algorithm outperforms known OBDD-based algorithms for the maximal matching problem. In order to use randomization, we investigate the OBDD complexity of $2^n$ (almost) $k$-wise independent binary random variables. We give a OBDD construction of size $O(n)$ for $3$-wise independent random variables and show a lower bound of $2^{\Omega(n)}$ on the OBDD size for $k \geq 4$. The best known lower bound was $\Omega(2^n/n)$ for $k \approx \log n$ due to Kabanets. We also give a very simple construction of $2^n$ $(\varepsilon, k)$-wise independent binary random variables by constructing a random OBDD of width $O(n k^2/\varepsilon)$.
A Multicenter Study of Pertussis Infection in Adults with Coughing in Korea: PCR-Based Study

Background Limited data on the incidence and clinical characteristics of adult pertussis infections are available in Korea. Methods Thirty-one hospitals and the Korean Centers for Disease Control and Prevention collaborated to investigate the incidence and clinical characteristics of pertussis infections among adults with a bothersome cough in non-outbreak, ordinary outpatient settings. Nasopharyngeal aspirates or nasopharyngeal swabs were collected for polymerase chain reaction (PCR) and culture tests. Results The study enrolled 934 patients between September 2009 and April 2011. Five patients were diagnosed as confirmed cases, satisfying both clinical and laboratory criteria (five positive PCR and one concurrent positive culture). Among 607 patients with cough duration of at least 2 weeks, 504 satisfied the clinical criteria of the US Centers for Disease Control and Prevention (i.e., probable case). The clinical pertussis cases (i.e., both probable and confirmed cases) had a wide age distribution (45.7±15.5 years) and cough duration (median, 30 days; interquartile range, 18.0~50.0 days). In addition, sputum, rhinorrhea, and myalgia were less common and dyspnea was more common in the clinical cases, compared to the others (p=0.037, p=0.006, p=0.005, and p=0.030, respectively). Conclusion The positive rate of pertussis infection may be low in non-outbreak, ordinary clinical settings if a PCR-based method is used. However, further prospective, well-designed, multicenter studies are needed.
Spot price dynamics in deregulated power markets

Modelling spot price behavior plays a key role in the electric- ity market, since this is the breeding engine for the activity in the corre- sponding forward and futures market: developers and generators (as well as traders) need to know how electricity prices behave, as their profitabil- ity depends on them. Additionally, credit rating agencies need to monitor the exposure of different players in the market to price fluctuations and risks. Starting from those considerations, this work is intended to offer a comparative analysis of the statistical properties of hourly prices in the day–ahead electricity markets of several countries, in order to fix some features which a good model should have to fit day–ahead prices. A number of stochastic processes will be then examined as perspective candidate to generate sample paths with explanatory power respect on the real time–series, and results will be discussed.
Hierarchical Credit Allocation in a Classifier System

Learning systems which engage in sequential activity face the problem of properly allocating credit to steps or actions which make possible later steps that result in environmental payoff. In the classifier systems studied by Holland and others, credit is allocated by means of a "bucket-brigade" algorithm through which, over time, environmental payoff in effect Bows back to classifiers which take early, stage-setting actions. The algorithm has advantages of simplicity and locality, but may not adequately reinforce long action sequences. We suggest an alternative form for the algorithm and the system's operating principles designed to induce behavioral hierarchies in which modularity of the hierarchy would keep all bucket-brigade chains short, thus more reinforceable and more rapidly learned, but overall action sequences could be long.
Thirty years of research on online learning

Summary This paper presents a personal account of developments in research on online learning over the past 30 years. Research on how to design online instruction represents an example of applying the science of learning to education. It contributes to the science of learning (as exemplified by developments in cognitive load theory, the cognitive theory of multimedia learning, and incorporating metacognitive, motivational, and affective aspects of learning), the science of instruction (as exemplified by the continuing development of research ‐ based principles of instructional design), and the science of assessment (as exemplified by supplementing self ‐ report surveys and retention tests with multilevel transfer tests, log file data during learning, and cognitive neuroscience measures of cognitive processing during learning). Some recurring themes are that learning is caused by instructional methods rather than instructional media, so research should focus on features that are uniquely afforded by digital learning environments; instructional practice should be grounded in rigorous and systematic research, including value ‐ added experiments aimed at pinpointing the active ingredients in online instruction; research in online learning should identify boundary conditions under which instructional techniques are most effective; and research in online learning should test and contribute to learning theory.
New Solutions to the Firing Squad Synchronization Problems for Neural and Hyperdag P Systems

We propose two uniform solutions to an open question: the Firing Squad Synchronization Problem (FSSP), for hyperdag and symmetric neural P systems, with anonymous cells. Our solutions take e_c+5 and 6e_c+7 steps, respectively, where e_c is the eccentricity of the commander cell of the dag or digraph underlying these P systems. The first and fast solution is based on a novel proposal, which dynamically extends P systems with mobile channels. The second solution is substantially longer, but is solely based on classical rules and static channels. In contrast to the previous solutions, which work for tree-based P systems, our solutions synchronize to any subset of the underlying digraph; and do not require membrane polarizations or conditional rules, but require states, as typically used in hyperdag and neural P systems.
Application of Integrated Central and Distributed Decisions to Path Tracking Control of a Vision-based Intelligent Vehicle

Vision-based intelligent vehicle is widely used for its advantages in acquiring more information, higher flexibility in path setting, etc. The control architecture, which is the foundation of the intelligent vehicle system, must be appropriately built up before the vehicle system is designed. By analyzing the characteristics of the existing architectures, an architecture based on central and distributed decisions is presented. A specific vehicle system is designed based on path tracking. The vehicle system acquires the path information by machine vision and automatically tracks the recognized path. In order to recognize the path image robustly, the methods of segmental optimal thresholds and adaptable thresholds are presented. Furthermore, a preview controller, which can adjust the vehicle speed and its preview distance, is designed for improving the path-tracking accuracy. Finally, experiments are performed under different scenarios to verify the proposed theories and methods.
Awase-E: Recognition-based Image Authentication Scheme Using Users' Personal Photographs

In this paper, we propose a recognition-based image authentication system, named "Awase-E". There are two unique features in the system. One is to use users' personal photographs as their secret information. The other is that the system introduces a "no answer" case in a verification of an authentication trial. We developed an Web-base prototype system and conducted exploratory experiments to determine the memorability of this secret information. As a result of the experiments, we determined that the memorability of the secret information in Awase-E is almost the same as the memorability of a PIN number, even over a long period of time with infrequent use. Moreover, even after users are forced to update their secret information, they can pass the authentication without forgetting the secret information or confusing the old and new secret information. We also present a comparison between Awase-E and PIN authentication regarding both security and usability, and we indicate that the Awase-E has potential to become a reasonable candidate as an alternative to PIN authentication