KnowledgeZoom for Java: A Concept-Based Exam Study Tool with a Zoomable Open Student Model

Research efforts focused on developing active are currently underway. Active reports are designed to foster communication among teachers, students, and parents by listening to all stakeholders, using assessment information to guide teaching and learning, and to reconcile potential conflicts. Open student models can handle different views of the student. These views support different kinds of assessment information coming from a variety of sources (e.g. results from summative and formative assessments). As teachers, students, and parents interact with open student models, assessment claims (e.g. students' self-assessment, systems' and teachers' assessment of student knowledge) may evolve from unsupported claims to evidence-based arguments. Paths on this evidentiary argument space are characterized by rich interactions among sources of evidence that may challenge personal claims and improve metacognitive skills. This paper (a) presents an evidence-based approach to interacting with open student models that builds on existing research on open student models, evidentiary arguments and evidence-centered design, (b) illustrates this approach in the context of an assessment-based learning environment called Math Intervention Module (MIM), and (c) reports on a study carried out with Algebra teachers/assessment specialists aimed at evaluating the feasibility of implementing this approach in real settings.

The increased volumes of online learning content have produced two problems: how to help students to find the most appropriate resources and how to engage them in using these resources. Personalized and social learning have been suggested as potential ways to address these problems. Our work presented in this paper combines the ideas of personalized and social learning in the context of educational hypermedia. We introduce Progressor, an innovative Web-based tool based on the concepts of social navigation and open student modeling that helps students to find the most relevant resources in a large collection of parameterized self-assessment questions on Java programming. We have evaluated Progressor in a semester-long classroom study, the results of which are presented in this paper. The study confirmed the impact of personalized social navigation support provided by the system in the target context. The interface encouraged students to explore more topics attempting more questions and achieving higher success rates in answering them. A deeper analysis of the social navigation support mechanism revealed that the top students successfully led the way to discovering most relevant resources by creating clear pathways for weaker students.

In these recent years, several metaheuristic optimization algorithms have been developed to solve complex real-world problems. However, according to the no free lunch theorem, there is no metaheuristic algorithm which has the best suited for solving all optimization problems. Therefore, developing new metaheuristic algorithms is an open problem. Two major categories of metaheuristics are swarm intelligence and evolutionary algorithms. Both of them have their own advantages. In this paper, we present a naturally swarm and evolutionary algorithm in which its particles have memory, collaboration, information sharing, and competition inherited from one specific analogy. So, it has the advantages of both swarm and evolutionary algorithms without complications of hybridizing them. We formulate a new metaheuristic optimization algorithm called open source development model algorithm (ODMA) inspired by open source development model and communities, in such a way that each potential solution is considered as a software, and by evolution of the softwares, better solutions of the function that should be optimized are searched. The main operations of the algorithm include employing features and methods of leading softwares, evolution of leading softwares based on their history and forking from leading softwares. After a detailed formulation and explanation of its implementation, the algorithm is evaluated by $$17$$17 well-known benchmark functions, and the results are compared with PSO, ICA, and GA. The results show that ODMA outperforms GA, PSO, and ICA in terms of finding the global optimum and convergence.

ABSTRACTWith the wide adoption of online platforms in education, having the content organised and readily available for the learners to self-assess their progress is crucial to ensure academic success. However, without the support of an active mentor, it might be difficult for the learners to guide themselves on how to accurately evaluate their learning outcome. This article focuses on how students benefited from self-assessing their programming course progress using an adaptive platform, namely QuizIT. We discuss the self-assessment procedure and development as it progresses into an open student model learning platform. The initial release was designated to assist programming novices as they encounter the programming learning journey. It was then followed by an extension to open the possibilities to the learner by having access to the learning model and gaining more control over the assessment experience. Quiz Adapt, was the set of features that provided an adaptive recommender based on an open learner model. The data collected from four study setups are evaluated to show the impact on the learning gain from interacting with the system. In addition, this paper sheds light on the effectiveness of daily learning opportunities in the introductory phase of education.

Using deep learning models on small scale datasets would result in overfitting. To overcome this problem, the process of pre-training a model and fine-tuning it to the small scale dataset has been used extensively in domains such as image processing. Similarly for question answering, pre-training and fine-tuning can be done in several ways. Commonly reading comprehension models are used for pre-training, but we show that other types of pre-training can work better. We compare two pre-training models based on reading comprehension and open domain question answering models and determine the performance when fine-tuned and tested over BIOASQ question answering dataset. We find open domain question answering model to be a better fit for this task rather than reading comprehension model.

First we give an overview of the heterogeneous networks that are typically based on optical transport, then we focus onto open problems, constraints, models and algorithms for distributed - but still as optimal as possible - use of resources while routing the demands

FinTech and its interaction with banking is widely discussed today as a new phenomenon notwithstanding the relationship between technology and financial services is not a new topic. Most of the research focuses on innovations and determinants of their adoptions including among other innovations in the payment system. The studies dedicated directly to PayTechs as a special kind of a FinTech entity and its market activity are a relatively new field of research. This paper aims to fill this gap. The multidimensional character of this exploratory research causes the necessity to apply various research methods, including both inductive and deductive methods, together with comparative analysis. The theoretical analysis conducted in the paper for defining PayTechs from the perspective of business model and market behavior was based on an in-depth literature review. In this section, the inductive method and comparative analysis were mostly applied. The empirical part of the paper includes the analysis of quantitative data published by the National Bank of Poland (NBP), Central Statistical Office (GUS), and Bank for International Settlements (BIS). The subject of the case is the Polish Payment Standard referred to as BLIK implemented in Poland in 2015 for mobile payments. The BLIK diffusion is measured by the number of entrants and acceptants as well as the scope of transactions while the adoption by the number of customers using BLIK in everyday transactions. The results present the market behavior of BLIK as an open business model and the key success factors of BLIK adoption and diffusion and the determinants for further open payment innovations’ development. The newly developed definition of PayTechs, the identification of the major components of the PayTech open business model, as well as the indication of the key success factors of adoption and diffusion of m-payments, constitute the original contribution of the paper.

Multi-input functional encryption, MIFE, is a powerful generalization of functional encryption that allows computation on encrypted data coming from multiple different data sources. In a recent work, Agrawal, Goyal, and Tomida (CRYPTO 2021) constructed MIFE for the class of quadratic functions. This was the first MIFE construction from bilinear maps that went beyond inner product computation. We advance the state-of-the-art in MIFE, and propose new constructions with stronger security and broader functionality. Stronger Security: In the typical formulation of MIFE security, an attacker is allowed to either corrupt all or none of the users who can encrypt the data. In this work, we study MIFE security in a stronger and more natural model where we allow an attacker to corrupt any subset of the users, instead of only permitting all-or-nothing corruption. We formalize the model by providing each user a unique encryption key, and letting the attacker corrupt all non-trivial subsets of the encryption keys, while still maintaining the MIFE security for ciphertexts generated using honest keys. We construct a secure MIFE system for quadratic functions in this fine-grained corruption model from bilinear maps. Our construction departs significantly from the existing MIFE schemes as we need to tackle a more general class of attackers. Broader Functionality: The notion of multi-client functional encryption, MCFE, is a useful extension of MIFE. In MCFE, each encryptor can additionally tag each ciphertext with appropriate metadata such that ciphertexts with only matching metadata can be decrypted together. In more detail, each ciphertext is now annotated with a unique label such that ciphertexts encrypted for different slots can now only be combined together during decryption as long as the associated labels are an exact match for all individual ciphertexts. In this work, we upgrade our MIFE scheme to also support ciphertext labelling. While the functionality of our scheme matches that of MCFE for quadratic functions, our security guarantee falls short of the general corruption model studied for MCFE. In our model, all encryptors share a secret key, therefore this yields a secret-key version of quadratic MCFE, which we denote by SK-MCFE. We leave the problem of proving security in the general corruption model as an important open problem.

Performance of automatic target recognition (ATR) systems depends on numerous factors including the mission description, operating conditions, sensor modality, and ATR algorithm itself. Performance prediction models sensitive to these factors could be applied to ATR algorithm design, mission planning, sensor resource management, and data collection design for algorithm verification. Ideally, such a model would return measures of performance (MOPs) such as probability of detection (Pd), correct classification (Pc), and false alarm (Pfa), all as a function of the relevant predictor variables. Here we discuss the challenges of model-based and data-based approaches to performance prediction, concentrating especially on the synthetic aperture radar (SAR) modality. Our principal conclusion for model-based performance models (predictive models derived from fundamental physics- and statistics-based considerations) is that analytical progress can be made for performance of ATR system components, but that performance prediction for an entire ATR system under realistic conditions will likely require the combined use of Monte Carlo simulations, analytical development, and careful comparison to MOPs from real experiments. The latter are valuable for their high-fidelity, but have a limited range of applicability. Our principal conclusion for data-based performance models (that fit empirically derived MOPs) offer a potentially important means for extending the utility of empirical results. However, great care must be taken in their construction due to the necessarily sparse sampling of operating conditions, the high-dimensionality of the input space, and the diverse character of the predictor variables. Also the applicability of such models for extrapolation is an open question.

The increasing complexity of Earth system models has inspired efforts to quantitatively assess model fidelity through rigorous comparison with best available measurements and observational data products. Earth system models exhibit a high degree of spread in predictions of land biogeochemistry, biogeophysics, and hydrology, which are sensitive to forcing from other model components. Based on insights from prior land model evaluation studies and community workshops, the authors developed an open source model benchmarking software package that generates graphical diagnostics and scores model performance in support of the International Land Model Benchmarking (ILAMB) project. Employing a suite of in situ, remote sensing, and reanalysis data sets, the ILAMB package performs comprehensive model assessment across a wide range of land variables and generates a hierarchical set of web pages containing statistical analyses and figures designed to provide the user insights into strengths and weaknesses of multiple models or model versions. Described here is the benchmarking philosophy and mathematical methodology embodied in the most recent implementation of the ILAMB package. Comparison methods unique to a few specific data sets are presented, and guidelines for configuring an ILAMB analysis and interpreting resulting model performance scores are discussed. ILAMB is being adopted by modeling teams and centers during model development and for model intercomparison projects, and community engagement is sought for extending evaluation metrics and adding new observational data sets to the benchmarking framework.

Abstract. With recent government initiatives for smart cities, 3D virtual city models are in demand for managing and monitoring the urban infrastructure. 3D building models forms an important component of 3D virtual city model. LiDAR remote sensing has revolutionized the way the third dimension can be precisely mapped and proved to be an important source of data for 3D models. The model thus generated should be in an open data format to be used across various applications. CityGML is an open data model framework that enables storage and exchange of 3D models which can be used for diversified applications. The main objective of this research is to develop a methodological workflow to create 3D building models in CityGML standard from airborne LiDAR point cloud. Initially building points were isolated from the airborne LiDAR data using point cloud processing algorithms. 3D building models with levels of detail (LoD1 and LoD2) were generated from the building points in CityGML standard using commercial (ArcGISPro) well as open source packages (3dfier, Citygml4j). Results prove that the models developed using open source packages are comparable to that provided by commercial packages.

Portfolio theory has been an important component of open economy macroeconomic models. In those models, it is essential to distinguish among several categories of assets, both foreign and domestic, and to specify the demands and supplies. This framework has become increasingly relevant. Movements of capital across regional and national boundaries, and across currencies, have exploded in volume, thanks to the dismantling of currency and exchange controls and other financial regulations and to revolutionary economies in technologies of communication and transactions. The globalization of financial markets was stimulated by the floating exchange rate regime established in 1973.(This abstract was borrowed from another version of this item.)

We created conceptual models that might be used to analyze and forecast the dynamic effects of open innovation. We then applied these models to the smartphone sector using a model-based analysis approach. In addition, we built an open innovation simulation model for the smartphone sector. The dynamic model of open innovation will link logic and concepts relating open innovation, complex adaptive systems, and evolutionary change. The model was used to analyze the dynamic effects of open innovation strategies and open innovation simulation for the selection of future strategies.

An iterative improvement approach for the nonpreemptive open shop scheduling problem

We propose a new open question answering framework for question answering over a knowledge base (KB). Our system uses both a curated KB, Freebase, and one that is extracted automatically by an open information extraction model, IE KB. Our system consists of only one layer of paraphrase, compared to the three layers used in a previous open question answering system (Fader et al., 2014). However, because of the more accurately extracted relation triples in IE KB, combined with linked entities from IE KB to Freebase, our system achieves a 7% absolute gain in F1 score over the previous system.