Instantiation Of Framework Research Articles

A framework to foster accessibility in post-pandemic virtual higher education

The SARS-CoV-2 Coronavirus pandemic (COVID-19) forced educational institutions to move their programmes to the virtual world. Several tech-based solutions –including virtual training and tutoring, discussion forums, access to content and information, collaborative platforms, and Open Educational Resources (OER)– were implemented to address this shift and continue to be used in the post-pandemic era due to the advantages they offer, especially for hybrid and blended learning. However, the implementation of these tech-based solutions also revealed several accessibility issues that need to be addressed to fully leverage the technological benefits. This study aims to provide a framework to facilitate the adoption of good practices related to technological accessibility in virtual Higher Education. The implementation of the framework is divided into four basic actions, each of which should be tailored to the constraints and needs for improving accessibility in Higher Education Institutions (HEIs). The framework's instantiation in four HEIs serves as a proof-of-concept in real-world scenarios. The preliminary results suggest that the proposal is promising, as it was adaptable to the specific needs of each HEI fostering accessibility and inclusion through technological alternatives that align with their organisational structures and current levels of attention to accessibility.

MQT Predictor: Automatic Device Selection with Device-Specific Circuit Compilation for Quantum Computing

Fueled by recent accomplishments in quantum computing hardware and software, an increasing number of problems from various application domains are being explored as potential use cases for this new technology. Similarly to classical computing, realizing an application on a particular quantum device requires the corresponding (quantum) circuit to be compiled so that it can be executed on the device. With a steadily growing number of available devices—each with their own advantages and disadvantages—and a wide variety of different compilation tools, the number of choices to consider when trying to realize an application is quickly exploding. Due to missing tool support and automation, especially end-users who are not quantum computing experts are easily left unsupported and overwhelmed. In this work, we propose a methodology that allows one to automatically select a suitable quantum device for a particular application and provides an optimized compiler for the selected device. The resulting framework—called the MQT Predictor —not only supports end-users in navigating the vast landscape of choices, it also allows mixing and matching compiler passes from various tools to create optimized compilers that transcend the individual tools. Evaluations of an exemplary framework instantiation based on more than 500 quantum circuits and seven devices have shown that—compared to both Qiskit’s and TKET’s most optimized compilation flows for all devices—the MQT Predictor produces circuits within the top-3 out of 14 baselines in more than \(98\% \) of cases while frequently outperforming any tested combination by up to \(53\% \) when optimizing for expected fidelity . Additionally, the framework is trained and evaluated for critical depth as another figure of merit to showcase its flexibility and generalizability—producing circuits within the top-3 in \(89\% \) of cases while frequently outperforming any tested combination by up to \(400\% \) . MQT Predictor is part of the Munich Quantum Toolkit (MQT) and publicly available as open-source on GitHub (https://github.com/cda-tum/mqt-predictor) and as an easy-to-use Python package (https://pypi.org/p/mqt.predictor).

A generalizable framework for low-rank tensor completion with numerical priors

Low-Rank Tensor Completion, a method which exploits the inherent structure of tensors, has been studied extensively as an effective approach to tensor completion. Whilst such methods attained great success, none have systematically considered exploiting the numerical priors of tensor elements. Ignoring numerical priors causes loss of important information regarding the data, and therefore prevents the algorithms from reaching optimal accuracy. Despite the existence of some individual works which consider ad hoc numerical priors for specific tasks, no generalizable frameworks for incorporating numerical priors have appeared. We present the Generalized CP Decomposition Tensor Completion (GCDTC) framework, the first generalizable framework for low-rank tensor completion that takes numerical priors of the data into account. We test GCDTC by further proposing the Smooth Poisson Tensor Completion (SPTC) algorithm, an instantiation of the GCDTC framework, whose performance exceeds current state-of-the-arts by considerable margins in the task of non-negative tensor completion, exemplifying GCDTC’s effectiveness. Our code is open-source.

Transformer-Empowered Invariant Grounding for Video Question Answering.

Video Question Answering (VideoQA) is the task of answering questions about a video. At its core is the understanding of the alignments between video scenes and question semantics to yield the answer. In leading VideoQA models, the typical learning objective, empirical risk minimization (ERM), tends to over-exploit the spurious correlations between question-irrelevant scenes and answers, instead of inspecting the causal effect of question-critical scenes, which undermines the prediction with unreliable reasoning. In this work, we take a causal look at VideoQA and propose a modal-agnostic learning framework, named Invariant Grounding for VideoQA (IGV), to ground the question-critical scene, whose causal relations with answers are invariant across different interventions on the complement. With IGV, leading VideoQA models are forced to shield the answering from the negative influence of spurious correlations, which significantly improves their reasoning ability. To unleash the potential of this framework, we further provide a Transformer-Empowered Invariant Grounding for VideoQA (TIGV), a substantial instantiation of IGV framework that naturally integrates the idea of invariant grounding into a transformer-style backbone. Experiments on four benchmark datasets validate our design in terms of accuracy, visual explainability, and generalization ability over the leading baselines. Our code is available at https://github.com/yl3800/TIGV.

From Speech Acoustics to Communicative Participation in Dysarthria: Toward a Causal Framework.

We proposed and tested a causal instantiation of the World Health Organization's International Classification of Functioning, Disability and Health (ICF) framework, linking acoustics, intelligibility, and communicative participation in the context of dysarthria. Speech samples and communicative participation scores were collected from individuals with dysarthria (n = 32). Speech was analyzed for two acoustic metrics (i.e., articulatory precision and speech rate), and an objective measure of intelligibility was generated from listener transcripts. Mediation analysis was used to evaluate pathways of effect between acoustics, intelligibility, and communicative participation. We observed a strong relationship between articulatory precision and intelligibility and a moderate relationship between intelligibility and communicative participation. Collectively, data supported a significant relationship between articulatory precision and communicative participation, which was almost entirely mediated through intelligibility. These relationships were not significant when speech rate was specified as the acoustic variable of interest. The statistical corroboration of our causal instantiation of the ICF framework with articulatory acoustics affords important support toward the development of a comprehensive causal framework to understand and, ultimately, address restricted communicative participation in dysarthria.

An Automated Approach for Constructing Framework Instantiation Documentation

A substantial effort, in general, is required for understanding APIs of application frameworks. High-quality API documentation may alleviate the effort, but the production of such documentation still poses a major challenge for modern frameworks. To facilitate the production of framework instantiation documentation, we hypothesize that the framework code itself and the code of existing instantiations provide useful information. However, given the size and complexity of existent code, automated approaches are required to assist the documentation production. Our goal is to assess an automated approach for constructing relevant documentation for framework instantiation based on source code analysis of the framework itself and of existing instantiations. The criterion for defining whether documentation is relevant would be to compare the documentation with an traditional framework documentation, considering the time spent and correctness during instantiation activities, information usefulness, complexity of the activity, navigation, satisfaction, information localization and clarity. We propose an automated approach for constructing relevant documentation for framework instantiation based on source code analysis of the framework itself and of existing instantiations. The proposed approach generates documentation in a cookbook style, where the recipes are programming activities using the necessary API elements driven by the framework features. We performed an empirical study, consisting of three experiments with 44 human subjects executing real framework instantiations aimed at comparing the use of the proposed cookbooks to traditional manual framework documentation (baseline). Our empirical assessment shows that the generated cookbooks performed better or, at least, with non-significant difference when compared to the traditional documentation, evidencing the effectiveness of the approach.

Hyper-parameter optimization in classification: To-do or not-to-do

Hyper-parameter optimization is a process to find suitable hyper-parameters for predictive models. It typically incurs highly demanding computational costs due to the need of the time-consuming model training process to determine the effectiveness of each set of candidate hyper-parameter values. A priori, there is no guarantee that hyper-parameter optimization leads to improved performance. In this work, we propose a framework to address the problem of whether one should apply hyper-parameter optimization or use the default hyper-parameter settings for traditional classification algorithms. We implemented a prototype of the framework, which we use a basis for a three-fold evaluation with 486 datasets and 4 algorithms. The results indicate that our framework is effective at supporting modeling tasks in avoiding adverse effects of using ineffective optimizations. The results also demonstrate that incrementally adding training datasets improves the predictive performance of framework instantiations and hence enables “life-long learning.”

Two‐stage algorithms for covering array construction

AbstractModern software systems often consist of many different components, each with a number of options. Although unit tests may reveal faulty options for individual components, functionally correct components may interact in unforeseen ways to cause a fault. Covering arrays are used to test for interactions among components systematically. A two‐stage framework, providing a number of concrete algorithms, is developed for the efficient construction of covering arrays. In the first stage, a time and memory efficient randomized algorithm covers most of the interactions. In the second stage, a more sophisticated search covers the remainder in relatively few tests. In this way, the storage limitations of the sophisticated search algorithms are avoided; hence, the range of the number of components for which the algorithm can be applied is extended, without increasing the number of tests. Many of the framework instantiations can be tuned to optimize a memory‐quality trade‐off, so that fewer tests can be achieved using more memory.

Model-based Framework for Change Management and Integrated Devlopment of Information Security

This paper introduces a business process-based goal-oriented framework which consists of generic and specific model repositories, and of methodology for integrated change management of business and IT evolutions. Sets of generic models of ISO/IEC 27001 and 27002 standards for information security support developers and decision makers in MDE process. The techniques and tools used are from the User Requirements Notation technologies for model compositions and traceability assessments of goal-oriented and scenario-based models. An example is given from the instantiation of framework for B2B change management with empirical validation within a commercial SME. The framework supports MDE process of enterprise architecture re-engineering integrating the development of information security.

Temporal Segment Networks for Action Recognition in Videos.

We present a general and flexible video-level framework for learning action models in videos. This method, called temporal segment network (TSN), aims to model long-range temporal structure with a new segment-based sampling and aggregation scheme. This unique design enables the TSN framework to efficiently learn action models by using the whole video. The learned models could be easily deployed for action recognition in both trimmed and untrimmed videos with simple average pooling and multi-scale temporal window integration, respectively. We also study a series of good practices for the implementation of the TSN framework given limited training samples. Our approach obtains the state-the-of-art performance on five challenging action recognition benchmarks: HMDB51 (71.0 percent), UCF101 (94.9 percent), THUMOS14 (80.1 percent), ActivityNet v1.2 (89.6 percent), and Kinetics400 (75.7 percent). In addition, using the proposed RGB difference as a simple motion representation, our method can still achieve competitive accuracy on UCF101 (91.0 percent) while running at 340 FPS. Furthermore, based on the proposed TSN framework, we won the video classification track at the ActivityNet challenge 2016 among 24 teams.

Marginal process framework: A model reduction tool for Markov jump processes.

Markov jump process models have many applications across science. Often these models are defined on a state space of product form and only one of the components of the process is of direct interest. In this paper we extend the marginal process framework, which provides a marginal description of the component of interest, to the case of fully coupled processes. We use entropic matching to obtain a finite-dimensional approximation of the filtering equation, which governs the transition rates of the marginal process. The resulting equations can be seen as a combination of two projection operations applied to the full master equation so that we obtain a principled model reduction framework. We demonstrate the resulting reduced description on the totally asymmetric exclusion process. An important class of Markov jump processes are stochastic reaction networks, which have applications in chemical and biomolecular kinetics, ecological models, and models of social networks. We obtain a particularly simple instantiation of the marginal process framework for mass-action systems by using product Poisson distributions for the approximate solution of the filtering equation. We investigate the resulting approximate marginal process analytically and numerically.

Graph-Based Slice-to-Volume Deformable Registration

Deformable image registration is a fundamental problem in computer vision and medical image computing. In this paper we investigate the use of graphical models in the context of a particular type of image registration problem, known as slice-to-volume registration. We introduce a scal-able, modular and flexible formulation that can accommodate low-rank and high order terms, that simultaneously selects the plane and estimates the in-plane deformation through a single shot optimization approach. The proposed framework is instantiated into different variants seeking either a compromise between computational efficiency (soft plane selection constraints and approximate definition of the data similarity terms through pair-wise components) or exact definition of the data terms and the constraints on the plane selection. Simulated and real-data in the context of ultrasound and magnetic resonance registration (where both framework instantiations as well as different optimization strategies are considered) demonstrate the potentials of our method.

Logic-based argumentation with existential rules

In this paper we are interested in the use of argumentation for handling inconsistency in inconsistent knowledge bases expressed with existential rules. We propose an instantiation of an argumentation framework and demonstrate it is coherent, relatively grounded and non-trivial, therefore satisfying the rationality postulates from the literature. We demonstrate how argumentation semantics relate to the state of the art of handling inconsistency in this setting, allowing us to propose the first dialectical proof in the literature for a given semantics.

Customer engagement within multiple new media and broader business ecosystem – a holistic perspective

PurposeCustomer engagement (customers’ behavioral manifestations going beyond customer-firm purchase transactions) has been regarded as strategic imperatives for generating enhanced corporate performance. The plethora of new media has provided customers with different options to interact with firms and other customers. However, the primacy of value-laden interactive customer relationships and value co-creation raises challenges for firms and customers, especially in the context of broader business ecosystems such as brand partnership for extending value co-creation. This study aims to explore how customer engagement with well-designed choreograph of various new media’s channels can increase the value co-creation extent in the context of broader business ecosystems, resulting in higher levels service offerings, experiences and innovation.Design/methodology/approachThis exploratory study presents a new framework of customer engagement that holistically integrates the elements of multiple new media and broader business ecosystem, stimulating a virtuous circle of realizing customer engagement toward superior results or innovations. The framework considers new media’s different information service and technologies (e.g. search engine, social recommender, social media) that can be properly choreographed to achieve a virtuous customer engagement circle.FindingsThis paper uses an exemplar framework's instantiation – an information technology enabled engagement platform (called iEngagement) – that can demonstrate how to empower the central companies together with their eco-stakeholders to holistically perform customer engagement utilizing new media toward fruitful customer engagement.Originality/valueThis exploratory study is among the first that addresses the theory and practice of customer engagement within multiple new media and broader business ecosystem. This paper presents a customer engagement framework and an exemplified engagement platform that holistically integrate the elements of multiple new media and broader business ecosystem, for stimulating a virtuous circle of realizing customer engagement toward superior results or innovations.

Créolité, Creolization, and Contemporary Caribbean Culture

Taking the publication of Éloge de la créolité as a starting point, a number of key questions can be posed: What was the discursive and thematic goal of the Éloge? How does it distinguish itself from creolization? And how do we assess its political stance and its sociocultural accomplishments? One key predecessor, Kamau Brathwaite, suggested that the binarisms undergirding the principle of cultural distinctness on which much of the historical definition of the region was drawn be abandoned in favor of an increasing recognition of its intrinsic cultural heterogeneity. For many, the programmatic nature of the tenets of créolité—highlighting specificities of periodization and linguistic and cultural practice—appeared to mark the opposite of an open approach to ethnocultural representation. But perhaps créolité's ultimate value lay in its instantiation of an enunciative framework for cultural identity, also visible in Caribbean carnivals and in regional musical rhythms such as zouk.

CollabRDL: A language to coordinate collaborative reuse

Coordinating software reuse activities is a complex problem when considering collaborative software development. This is mainly motivated due to the difficulty in specifying how the artifacts and the knowledge produced in previous projects can be applied in future ones. In addition, modern software systems are developed in group working in separate geographical locations. Therefore, techniques to enrich collaboration on software development are important to improve quality and reduce costs. Unfortunately, the current literature fails to address this problem by overlooking existing reuse techniques. There are many reuse approaches proposed in academia and industry, including Framework Instantiation, Software Product Line, Transformation Chains, and Staged Configuration. But, the current approaches do not support the representation and implementation of collaborative instantiations that involve individual and group roles, the simultaneous performance of multiple activities, restrictions related to concurrency and synchronization of activities, and allocation of activities to reuse actors as a coordination mechanism. These limitations are the main reasons why the Reuse Description Language (RDL) is unable to promote collaborative reuse, i.e., those related to reuse activities in collaborative software development. To overcome these shortcomings, this work, therefore, proposes CollabRDL, a language to coordinate collaborative reuse by providing essential concepts and constructs for allowing group-based reuse activities. For this purpose, we extend RDL by introducing three new commands, including role, parallel, and doparallel. To evaluate CollabRDL we have conducted a case study in which developer groups performed reuse activities collaboratively to instantiate a mainstream Java framework. The results indicated that CollabRDL was able to represent critical workflow patterns, including parallel split pattern, synchronization pattern, multiple-choice pattern, role-based distribution pattern, and multiple instances with decision at runtime. Overall, we believe that the provision of a new language that supports group-based activities in framework instantiation can help enable software organizations to document their coordinated efforts and achieve the benefits of software mass customization with significantly less development time and effort.

Dual-Server Public-Key Encryption With Keyword Search for Secure Cloud Storage

Searchable encryption is of increasing interest for protecting the data privacy in secure searchable cloud storage. In this paper, we investigate the security of a well-known cryptographic primitive, namely, public key encryption with keyword search (PEKS) which is very useful in many applications of cloud storage. Unfortunately, it has been shown that the traditional PEKS framework suffers from an inherent insecurity called inside keyword guessing attack (KGA) launched by the malicious server. To address this security vulnerability, we propose a new PEKS framework named dual-server PEKS (DS-PEKS). As another main contribution, we define a new variant of the smooth projective hash functions (SPHFs) referred to as linear and homomorphic SPHF (LH-SPHF). We then show a generic construction of secure DS-PEKS from LH-SPHF. To illustrate the feasibility of our new framework, we provide an efficient instantiation of the general framework from a Decision Diffie-Hellman-based LH-SPHF and show that it can achieve the strong security against inside the KGA.

SIGHTED: A Framework for Semantic Integration of Heterogeneous Sensor Data on the Internet of Things

Sensors are embedded nowadays in a growing number of everyday life objects. Smartphones, wearables, and sensor networks together play an important role in bridging the gap between physical and cyber worlds, a fundamental aspect of the Internet of Things vision. The ability to reuse sensor data integrated from multiple heterogeneous sources is a step towards building innovative applications and services. In this paper SIGHTED, a sensor data integration framework, is proposed exploiting semantic web technologies and linked data principles. It provides a layered structure as a guideline for integrating sensor data from various sources supporting accessibility and usability. DotThing, a demo platform, is implemented as an instantiation of SIGHTED framework and evaluated. Smartphones and sensor nodes are connected to DotThing showing the ability to query and reuse integrated sensor data from multiple sources to create more flexible horizontal applications. DotThing implementation also demonstrates the need for adding a semantic layer to existing IoT cloud-based platforms, like Xively, that generally lack such layer resulting in proprietary vertical solutions with limited data integration and discovery capabilities. DotThing makes use of vocabularies from existing ontologies on the linked data cloud providing a unified model to annotate data and link it to existing resources on the web.

Certifying execution time in multicores

This article presents a semantics-based program verification framework for critical embedded real-time systems using the worst-case execution time (WCET) as the safety parameter. The verification algorithm is designed to run on devices with limited computational resources where efficient resource usage is a requirement. For this purpose, the framework of abstract-carrying code (ACC) is extended with an additional verification mechanism for linear programming (LP) by applying the certifying properties of duality theory to check the optimality of WCET estimates. Further, the WCET verification approach preserves feasibility and scalability when applied to multicore architectural models.The certifying WCET algorithm is targeted to architectural models based on the ARM instruction set and is presented as a particular instantiation of a compositional data-flow framework supported on the theoretic foundations of denotational semantics and abstract interpretation. The data-flow framework has algebraic properties that provide algorithmic transformations to increase verification efficiency, mainly in terms of verification time. The WCET analysis/verification on multicore architectures applies the formalism of latency-rate (LR) servers, and proves its correctness in the context of abstract interpretation, in order to ease WCET estimation of programs sharing resources.

Towards Competence-Based Learning Design Driven Remote and Virtual Labs Recommendations for Science Teachers

Remote and virtual labs (RVLs) are widely used by science education teachers in their daily teaching practice. This has led to a plethora of RVLs that are offered with or without cost. In order to organise them and facilitate their search and findability, several RVL web-based repositories have been operated. As a result, a key open challenge is to design and implement recommender systems that support science teachers in finding and selecting appropriate RVLs based on core pedagogical elements of their learning designs and their own information and communication technology (ICT)-supported teaching competence profiles. In response to this challenge, we propose a recommender system for supporting learning design driven RVLs recommendations taking into consideration teachers’ profiling elements related to the ICT-supported teaching competences for using RVLs. To achieve this, we propose an instantiation of the UNESCO ICT competency framework for teachers for the context of using RVL and capitalise on our previously proposed metadata schema for describing RVLs, which includes core elements of a learning design and teachers’ required ICT-supported teaching competences. Our evaluation results based on a user study, highlight the high level of ranking accuracy of our proposed recommender system and support its capacity to facilitate science teachers in selecting appropriate RVLs.