Miracle-dz/UniFAR-FTUs huggingface dataset:
Scaffolding the Construction of Scientific Arguments by Prospective Teachers Using Inquiry-Empowering Technologies

This study investigated the influence of scaffolds on prospective teachers'(PTs') construction of scientific arguments. Participants were enrolled in an innovative science course aimed at providing PTs with experiences learning science using inquiry empowering technologies. PTs' arguments were constructed within the context of a light unit in which they collected evidence about the properties/behaviors of light from 10 investigation stations. The study examined the nature and development of arguments PTs constructed about light using Progress Portfolio (a generalized software tool for articulation and reflection), noting how scaffolds in Progress Portfolio influenced the articulation and revision of PTs' arguments about light. Data collection involved reviewing students' electronic journals and examining videotapes of students working at the investigation stations and interacting with Progress Portfolio. Results indicated that the computer-based scaffolding supported articulation and reflection of evidence-based explanations. The PTs showed increasing sophistication in their explanations, and the prompts within the Progress Portfolio seemed to stimulate PTs to become more precise in their explanations, to offer justifications, and to connect evidence with claims. An appendix presents content of light unit investigation stations. (Contains 41 references.) (SM) Reproductions supplied by EDRS are the best that can be made from the original document.
Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics

Many modern data sets can be represented in high dimensional vector spaces and have benefited from computational methods that utilize advanced techniques from numerical linear algebra and optimization. Visual analytics approaches have contributed greatly to data understanding and analysis due to utilization of both automated algorithms and human's quick visual perception and interaction. However, visual analytics targeting high dimensional large-scale data has been challenging due to low dimensional screen space with limited pixels to represent data. Among various computational techniques supporting visual analytics, dimension reduction and clustering have played essential roles by reducing the dimension and volume to visually manageable scales. In this talk, we present some of the key foundational methods for supervised dimension reduction such as linear discriminant analysis (LDA), dimension reduction and clustering/topic discovery by nonnegative matrix factorization (NMF), and visual spatial alignment for effective fusion and comparisons by Orthogonal Procrustes. We demonstrate how these methods can effectively support interactive visual analytic tasks that involve large-scale document and image data sets. Bio Prof. Haesun Park received her B.S. degree in Mathematics from Seoul National University, Seoul Korea, in 1981 with summa cum laude and the University President's Medal for the top graduate, and her M.S. and Ph.D. degrees in Computer Science from Cornell University, Ithaca, NY, in 1985 and 1987, respectively. She has been a professor in the School of Computational Science and Engineering at the Georgia Institute of Technology, Atlanta, Georgia since 2005. Before joining Georgia Tech, she was on faculty at University of Minnesota, Twin Cities, and program director at the National Science Foundation, Arlington, VA. She has published extensively in the areas including numerical algorithms, data analysis, visual analytics, text mining, and parallel computing. She has been the director of the NSF/DHS FODAVA-Lead (Foundations of Data and Visual Analytics) center and executive director of Center for Data Analytics at Georgia Tech. She has served on numerous editorial boards including IEEE Transactions on Pattern Analysis and Machine Intelligence, SIAM Journal on Matrix Analysis and Applications, SIAM Journal on Scientific Computing, and has served as a conference co-chair for SIAM International Conference on Data Mining in 2008 and 2009. In 2013, she was elected as a SIAM Fellow. Copyright is held by the author. IDEA’13, August 11, 2013, Chicago, IL, USA. ACM 978-1-4503-2329-1.
Research Ideas for Artificial Intelligence in Auditing : The Formalization of Audit and Workforce Supplementation

After decades of frustration with long ‘‘AI Winters,’’ various business industries are witnessing the arrival of AI’s ‘‘Spring,’’ with its massive and compelling benefits. Auditing will also evolve with the application of AI. Recently, there has been a progressive evolution of technology aimed at creating ‘‘artificially intelligent’’ devices. Although this evolution has been permeated with false starts and exaggerated claims, there is some convergence on the fact that substantive progress has been obtained in the last few years with the adoption of deep learning in conjunction with much faster machines and dimensionally larger storage spaces (and samples). The area of auditing has lagged business adoption in the past (Oldhouser 2016), but is prime for partial automation due to its labor intensiveness and range of decision structures. Several accounting firms have disclosed substantive investments in the AI fields. This paper proposes various areas of AI-related research to examine where this emerging technology is most promising. Moreover, this paper raises a series of methodological and evolutionary research questions aiming to study the AI-driven transformation of today’s world of audit into the assurance of the future.
Future Generation Computer Systems

The Internet of Things (IoT) is becoming an attractive system paradigm, in which physical perceptions, cyber interactions, social correlations, and even cognitive thinking can be intertwined in the ubiquitous things’ interconnections. It realizes a perfect integration of a new cyber–physical–social–thinking (CPST) hyperspace, which has profound implications for the future IoT. In this article
Retrieval of Relevant Visual Data in Selected Machine Vision Tasks: Examples of Hardware-based and Software-based Solutions

To illustrate diversity of methods used to extract relevant (where the concept of relevance can be differently defined for different applications) visual data, the paper discusses three groups of such methods. They have been selected from a range of alternatives to highlight how hardware and software tools can be complementarily used in order to achieve various functionalities in case of different specifications of “relevant data”. First, principles of gated imaging are presented (where relevance is determined by the range). The second methodology is intended for intelligent intrusion detection, while the last one is used for content-based image matching and retrieval. All methods have been developed within projects supervised by the author. Keywords—Relevant visual data, gated imaging, intrusion detection, image matching.
Strategies for Translation of English Commercial Advertisements from the Intercultural Perspective

With the development of economic globalization, advertising industry has been developing at a speed no one could imagine twenty years ago. This paper focuses on the research of advertising translation with the directions of intercultural interaction and western translation theories. What is more, it provides several effective strategies and vivid instances in advertising practice. The research indicates that according to the features of ads, proper methods can be utilized in the process of translation.
NimbRo AdultSize Team Description Paper 2018

This paper describes the RoboCup Humanoid League team NimbRo AdultSize of Rheinische Friedrich-Wilhelms-Universität Bonn, Germany, as required by the RoboCup qualification procedure for the competition held from June 16–21, 2018 in Montreal, Canada. Our team uses self-constructed robots for playing soccer. This paper describes the software and hardware aspects of the robots, and highlights some of our scientific achievements, including in particular in areas of perception, motion, and localization without a compass on a soccer field of symmetric appearance.
Visual Grounding via Accumulated Attention

Visual Grounding (VG) aims to locate the most relevant object or region in an image, based on a natural language query. The query can be a phrase, a sentence or even a multi-round dialogue. There are three main challenges in VG: 1) what is the main focus in a query; 2) how to understand an image; 3) how to locate an object. Most existing methods combine all the information curtly, which may suffer from the problem of information redundancy (i.e. ambiguous query, complicated image and a large number of objects). In this paper, we formulate these challenges as three attention problems and propose an accumulated attention (A-ATT) mechanism to reason among them jointly. Our A-ATT mechanism can circularly accumulate the attention for useful information in image, query, and objects, while the noises are ignored gradually. We evaluate the performance of A-ATT on four popular datasets (namely Refer-COCO, ReferCOCO+, ReferCOCOg, and Guesswhat? ), and the experimental results show the superiority of the proposed method in term of accuracy.
Voodoo Dolls : Seamless Interaction at Multiple Scales in Virtual Environments

The Voodoo Dolls technique is a two-handed interacti technique for manipulating objects at a distance in immers virtual environments. This technique addresses some limitation existing techniques: they do not provide a lightweight method interacting with objects of widely varying sizes, and many limit th objects that can be selected and the manipulations possible making a selection. With the Voodoo Dolls technique, the us dynamically creates dolls: transient, hand held copies of object whose effects on the objects they represent are determined by hand holding them. For simplicity, we assume a right-handed u in the following discussion. When a user holds a doll in his rig hand and moves it relative to a doll in his left hand, the obj represented by the doll in his right hand moves to the same pos and orientation relative to the object represented by the doll in left hand. The system scales the dolls so that the doll in the left h is half a meter along its longest dimension and the other d maintain the same relative size; this allows the user to w seamlessly at multiple scales. The Voodoo Dolls technique a allows both visible and occluded objects to be selected, a provides a stationary frame of reference for working relative moving objects.
Smart Health

Acoustic signals generated by the human body have often been used as biomarkers to diagnose and monitor diseases. As the pathogenesis of COVID-19 indicates impairments in the respiratory system, digital acoustic biomarkers of COVID-19 are under investigation. In this paper, we explore an accurate and explainable COVID-19 diagnosis approach based on human speech, cough, and breath data using the power of machine learning. We first analyze our design space considerations from the data aspect and model aspect. Then, we perform data augmentation, Mel-spectrogram transformation, and develop a deep residual architecture-based model for prediction. Experimental results show that our system outperforms the baseline, with the ROC-AUC result increased by 5.47%. Finally, we perform an interpretation analysis based on the visualization of the activation map to further validate the model.
Automating Production of Cross Media Content for Multi-channel Distribution www.AXMEDIS.org

This document describes the guidelines for setting up and maintaining AXMEDIS framework. Repositories and validation and acceptance mechanisms will be used in order to provide support to the project partners, and other affiliated partners in the future. It is also described how some tools of the AXMEDIS framework will be tested, including some indications on how data can be created to do so. Keyword List: AXMEDIS Framework, validation, integration DE5.2. 1 – AXMEDIS Framework and Validation AXMEDIS project CONFIDENTIAL 2 Table of
A Readability Checker with Supervised Learning using Deep Syntactic and Semantic Indicators

Checking for readability or simplicity of texts is importan t for many institutional and individual users. Formulas for approximately measuring text readability have a long tradition. Usually, they exploit surface-oriented indicators like sentence le ngth, word length, word frequency, etc. However, in many cases, this informati on is not adequate to realistically approximate the cogniti ve difficulties a person can have to understand a text. Therefore we use deep sy ntactic and semantic indicators in addition. The syntactic information is represented by a dependency tree, the semantic informati n by a semantic network. Both representations are automati cally generated by a deep syntactico-semantic analysis. A global readabili ty score is determined by applying a nearest neighbor algori thm on 3,000 ratings of 300 test persons. The evaluation showed, that the deep syntactic and semantic indicators lead to quite compar able esults to most surface-based indicators. Finally, a graphical user i nt face has been developed which highlights difficult-toread text passages, depending on the individual indicator values, and displays a global readability score.
Kasner solutions , climbing scalars and bigbang singularity CPHT-RR 032 . 0513 ROM 2 F / 2013 / 03

We elaborate on a recently discovered phenomenon where a scalar field close to big-bang is forced to climb a steep potential by its dynamics. We analyze the phenomenon in more general terms by writing the leading order equations of motion near the singularity. We formulate the conditions for climbing to exist in the case of several scalars and after inclusion of higher-derivative corrections and we apply our results to some models of moduli stabilization. We analyze an example with steep stabilizing potential and notice again a related critical behavior: for a potential steepness above a critical value, going backwards towards big-bang, the scalar undergoes wilder oscillations, with the steep potential pushing it back at every passage and not allowing the scalar to escape to infinity. Whereas it was pointed out earlier that there are possible implications of the climbing phase to CMB, we point out here another potential application, to the issue of initial conditions in inflation. ar X iv :1 30 6. 09 11 v2 [ he pth ] 2 7 Ju n 20 13
A Review of Modular Construction Shipbuilding in Malaysian Shipyard

The shipbuilding and ship repair industry in Malays i comprises of designing, building and assembling, repairing an d maintaining, transforming and advancement of vessels as well as m rine equipment . It is getting larger and always evolve in line with the development of technology such as with the latest t chnology which is embedded in modular construction. The modu lar construction approach in building a vessel is a com m n method nowadays. The latest, Boustead Naval Shipyard (BNS) were entrusted to build 6 Littoral Combat Ship (LCS) for Royal Malaysian Navy (RMN) in BNS in Lumut, Malaysia. One of the advantages for this contract is the technology tran sfer which also includes the modular construction. Thus, this paper shows the contribution to the modular construction resulting i Malaysia Shipyard. The result can be used for the better u nderstanding of modular construction and its impact on production p rocess in Malaysia, and also improve the technology and metho d of modular construction.
Bayesian Learning for Neural Dependency Parsing

While neural dependency parsers provide state-of-the-art accuracy for several languages, they still rely on large amounts of costly labeled training data. We demonstrate that in the small data regime, where uncertainty around parameter estimation and model prediction matters the most, Bayesian neural modeling is very effective. In order to overcome the computational and statistical costs of the approximate inference step in this framework, we utilize an efficient sampling procedure via stochastic gradient Langevin dynamics to generate samples from the approximated posterior. Moreover, we show that our Bayesian neural parser can be further improved when integrated into a multi-task parsing and POS tagging framework, designed to minimize task interference via an adversarial procedure. When trained and tested on 6 languages with less than 5k training instances, our parser consistently outperforms the strong bilstm baseline (Kiperwasser and Goldberg, 2016). Compared with the biaffine parser (Dozat et al., 2017) our model achieves an improvement of up to 3% for Vietnames and Irish, while our multi-task model achieves an improvement of up to 9% across five languages: Farsi, Russian, Turkish, Vietnamese, and Irish.
Modeling the evolution of religious institutions

Alam, S.J., Geller, A. , Meyer, R. , & Werth, B. (2011). Modelling contextualized reasoning in complex societies with ‘‘endorsements. ’’ Journal of Artificial Societies and Social Simulation, 13(4). Retrieved from http://jasss.soc. surrey.ac. uk/13/4/6.html Axtell, R. (2000). Why agents? On the varied motivations for agent computing in the social sciences. Center on Social and Economic Dynamics (Working Paper No. 17). Washington, DC: Brookings Institution. Axtell, R.L. , & Epstein, J.M. (1994). Agent-based models: Understanding our creations. Bulletin of the Santa Fe Institute, 28! 32. Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(3), 7280! 7287. Corbin, J., & Strauss, A. (1990). Grounded theory research: Procedures, canons, and evaluative criteria. Qualitative Sociology, 13(1), 3! 21. Danzig, R. (2011). Driving in the dark: Ten propositions about prediction and national security. Washington, DC: Center for a New American Security. Geller, A. , Latek, M.M. , Mussavi Rizi, S.M. , Fink, C. , MacKerrow, E. , Bos, N.D. , & Thies, C. (2012). Data acquisition protocols for modeling and simulation of human emergency: A proposal for a workshop. Unpublished document. Latek, M.M. , & Mussavi Rizi, S.J. (2011). Robust decision making under strategic uncertainty in multiagent environments. In Proceedings ofWorkshop on Applied Adversarial Reasoning and Risk Modeling (pp. 34! 41) Association for the Advancement of Artificial Intelligence. Merton, R.K. (1949). On sociological theories of the middle range. In R.K. Merton (Ed.), On theoretical sociology: Five essays, old and new (pp. 39! 53). New York: Free Press. Moss, S. (2002). Policy analysis from first principles. Proceedings of the National Academy of Sciences, 99(3), 7267! 7274. Moss, S. , & Edmonds, B. (2005). Towards good social science. Journal of Artificial Societies and Social Simulation, 8(4). Retrieved from http://jasss.soc. surrey.ac. uk/8/4/13.html Newell, A. , & Simon, H. (1961). Computer simulation of human thinking. Science, 124(3495), 2011! 2017. Polhill, J.G. , Parker, D. , Brown, D. , & Grimm, V. (2008). Using the ODD protocol for describing three agent-based social simulation models of land-use change. Journal of Artificial Societies and Social Simulation, 11(2). Retrieved from http://jasss.soc. surrey.ac. uk/11/2/3.html Popper, S.W. , Lempert, R.J., & Bankes, S.C. (2005). Shaping the future. Scientific American, April, 66! 71. Sun, R. (2008). Prolegomena to integrating cognitive modeling and social simulation. In R. Sun (Ed.), Cognition and multi-agent interaction (pp. 1! 26). Cambridge: Cambridge University Press.
BIOINFORMATICS ORIGINAL

Motivation: We consider the problem of multiple locus linkage analysis for expression traits of genes in a pathway or a network. To capitalize on co-expression of functionally related genes, we propose a penalized regression method that maps multiple expression quantitative trait loci (eQTLs) for all related genes simultaneously while accounting for their shared functions as speciﬁed a priori by a gene pathway or network. Results: An analysis of a mouse dataset and simulation studies clearly demonstrate the advantage of the proposed method over a standard approach that ignores biological knowledge of gene networks.
Real-time Computation: a Formal Deenition and Its Applications

There are few formal de nitions of real-time problems, and the currently available de nitions do not capture all the relevant aspects of such computations. We propose a new de nition, and we believe that it allows a uni ed treatment of all practically meaningful variants of real-time computations. In order to support our thesis, we present some important features of real-time algorithms, namely the presence of deadlines and the continuous, real-time, arrival of input data, together with their corresponding models in our formalism. Moreover, in order to illustrate the expressive power of our formalism, we also model aspects from the practical areas of real-time database systems and ad hoc networks. We believe that the proposed model is a rst step towards a uni ed and realistic complexity theory for real-time computation.
Stochastic Learning Algorithms for Modeling Human Category Learning

learning use a gradient-based learning method, which assumes that locally-optimal changes are made to model parameters on each learning trial. This method tends to underpredict variability in individual-level cognitive processes. In addition many recent models of human category learning have been criticized for not being able to replicate rapid changes in categorization accuracy and attention processes observed in empirical studies. In this paper we introduce stochastic learning algorithms for NN models of human category learning and show that use of the algorithms can result in (a) rapid changes in accuracy and attention allocation, and (b) different learning trajectories and more realistic variability at the individual-level.
Self-Disclosure and SNS Platforms: The Impact of SNS Transparency

Self-disclosure on social networking platforms has attracted attention in Information Systems (IS) research. While studies have connected individual beliefs such as perceived privacy, perceived benefits, and cost to Social Network Sites (SNS) use, less research has examined how characteristics of the social media platform itself shape SNS self-disclosure behaviors. This study conceptually develops and operationalizes a new construct—SNS transparency—that helps researchers to gain a better understanding of SNS-based self-disclosure and offers insights into how to integrate transparency into social media platforms. We examine how SNS transparency affects user's self-disclosure of personal information. This study extends the literature by building a comprehensive, integrative model that explains the impact of the contextual factors on self-disclosure behaviors. and operationalize the two types of SNS transparency: information transparency and network transparency. Future researchers could adopt our conceptualization and operationalization of these two constructs and investigate related SNS phenomenon. Our study also has