Mapping the Dropout Phenomenon for the New Digital Context

Open, distance, and digital education (ODDE) has a long history concerning dropout. With the new reality of the digital age, the terminologies and concepts that have been long adopted are inevitably subject to change. To understand dropout in the digital transformation, it is important to take an evidence-based approach and examine the literature. Therefore, this study applies the steps of a systematic literature review to retrieve relevant articles on dropout since 2010 and conducts a content analysis via text mining. The results indicate that student dropout and retention declined in publication during the pandemic despite remaining one of the critical issues in higher education. The main concepts indicate that most of the dropout studies focus on comparisons between face-to-face and online modalities; the learning environment and engagement; and support and motivation

A Coordination Language for Databases

We present a coordination language for the modeling of distributed database applications. The language, baptized Klaim-DB, borrows the concepts of localities and nets of the coordination language Klaim but re-incarnates the tuple spaces of Klaim as databases. It provides high-level abstractions and primitives for the access and manipulation of structured data, with integrity and atomicity considerations. We present the formal semantics of Klaim-DB and develop a type system that avoids potential runtime errors such as certain evaluation errors and mismatches of data format in tables, which are monitored in the semantics. The use of the language is illustrated in a scenario where the sales from different branches of a chain of department stores are aggregated from their local databases. Raising the abstraction level and encapsulating integrity checks in the language primitives have benefited the modeling task considerably

Learning to Generalize Provably in Learning to Optimize

Learning to optimize (L2O) has gained increasing popularity, which automates the design of optimizers by data-driven approaches. However, current L2O methods often suffer from poor generalization performance in at least two folds: (i) applying the L2O-learned optimizer to unseen optimizees, in terms of lowering their loss function values (optimizer generalization, or ``generalizable learning of optimizers"); and (ii) the test performance of an optimizee (itself as a machine learning model), trained by the optimizer, in terms of the accuracy over unseen data (optimizee generalization, or ``learning to generalize"). While the optimizer generalization has been recently studied, the optimizee generalization (or learning to generalize) has not been rigorously studied in the L2O context, which is the aim of this paper. We first theoretically establish an implicit connection between the local entropy and the Hessian, and hence unify their roles in the handcrafted design of generalizable optimizers as equivalent metrics of the landscape flatness of loss functions. We then propose to incorporate these two metrics as flatness-aware regularizers into the L2O framework in order to meta-train optimizers to learn to generalize, and theoretically show that such generalization ability can be learned during the L2O meta-training process and then transformed to the optimizee loss function. Extensive experiments consistently validate the effectiveness of our proposals with substantially improved generalization on multiple sophisticated L2O models and diverse optimizees. Our code is available at: https://github.com/VITA-Group/Open-L2O/tree/main/Model_Free_L2O/L2O-Entropy.Comment: This paper is accepted in AISTATS 202

An experimental study on blurred margins between architecture and landscape

Günümüzde, yapılı çevrenin disiplinleri aşan yaklaşımlarla elde edildiği görülmektedir. Bu yaklaşımla ortaya çıkan ürün, tek bir disiplin bilgisi ile üretilemeyecek ve kavranamayacak düzeyde karmaşıklığa, melezliğe sahiptir. Uygulamalar, mimarlığın ve peyzajın teorik çerçevesini ve klasik terminolojilerine ait kavramları çoktan aşmıştır. Genel geçer içerik, teknoloji ve yapılı çevrenin yeni ürünleri, tanıdık bilginin keşişimlerinde değildir. Ne mimarlığın ne de peyzajın konvansiyonel kavramları yeni durumu anlamaya yeterli gelmeyecektir. Ayrıca, bütün ürünler kentin görünümünü oluşturan peyzaj elemanı olarak nitelendirilebilir. Kenti ve kentte yer alan ürünleri peyzaj ve mimarlık arakesitinde kavramaya çalışmak mimarlık disiplinin sınırlarını da genişletecektir. Bu bağlamda tezde "habitus" olarak görülen mimarlığa göre "dışarı" olarak nitelendiren peyzajın içselleştirilmesi hedeflenmiştir. Bu tezin çalışma alanını oluşturan iki disiplinin etkileşim düzeyleri incelenmiş ve ortaya çıkan ürünün niteliğine göre üç ana başlıkta sınıflandırılmıştır. Bunlar "taklit, kombinasyon ve yeninin yaratımı/dönüşümü" olarak belirlenmiş ve bu sınıflandırma tezin kurgusunu oluşturmuştur. Peyzaj mimarlığının ortaya çıkışıyla başlayan sürecin evrimsel dönüşümü, daha doğrusu jenealojisi, belirlenen kırılma noktalarıyla ortaya koyulmuştur. Bu kırılma noktaları, sonuçlarını ortaya çıkaran disipliner durumlarla ifade edilmiştir. Multidisipliner yaklaşımla başlayan birlikteliğin, tarihsel süreçte yerini disiplinlerin ötesinde bir kavrayışa bıraktığı görülmektedir. Bu noktada "hem o hem bu, ne o ne bu" olarak ifade edilen yeni bir mekansal durumun -üçüncü tür- ortaya çıktığı iddia edilmektedir. Mimarlık ve peyzaj ayrımını yok eden, kavranamayan bu yeni durumda iki disiplinin bir biri içine geçtiği görülmektedir. Sonuçta, mekânsal üretimin yeni durumu, herhangi bir sınıflandırma altında değerlendirilemeyecek bir tekilliği gündeme getirmektedir. Böylece, her ürün ya da müdahalenin disiplinlerin ötesinde, yeni ve özgün, kendi kavramlarını ürettiği görülmektedir. Bu kavramlara örnek olarak, melezlik, karmaşıklık, füzyon verilmiştir.The contemporary built environment has many examples that utilize transdisciplinary approaches. However, the products/ outcomes that are generated with this approach have complexity and hybridity, which will not be produced and cannot be comprehended with a single discipline knowledge. The products/ outcomes have already exceeded the classical terminology and theoretical framework of architecture and landscape. The discursive content, techniques and the production of new territory of the built environment are not at the intersection of distinct knowledge-basis any more. Neither architectural nor conventional concepts of landscape are not adequate to comprehend the new circumstances. Furthermore, all products in the city form the "scape" of it. Working on the concept of urban and urban products in the context of landscape and architecture will broaden the boundaries of architecture discipline. In this sense, the aim is to internalize the term landscape, which is described as "outside" according to the architecture seen as "habitus" in the thesis. The interaction levels of the two disciplines that constituties the study area of this thesis were examined and were classified in three main categories according to the qualification of products/ outcomes. These have been designated as "reproduction, combination and invention/ innovation" and they have formed the thesis structure. The evolutionary transformation, rather that of genealogy, of the process that started with the emergence of landscape architecture was revealed by the determined breaking points. These breaking points are expressed with disciplinary situations that reveal their consequences. The association that started with a multidisciplinary approach seems to have left its place to supra-disciplinary comprehending in the historical process. With these factors in mind, it can be claimed that a new spatial production that is expressed as "neither this nor that or that it is both this and that" –third genus- emerged. It was observed that the two disciplines interpenetrate each other in this uncomprehended new circumstance, which destroys the distinction between architecture and landscape. Ultimately, the contemporary modes of spatial production bring a singularity that cannot be understood under any current classification. Thus, it seemed that every production or intervention produces or derives new and authentic concepts of its own that are supra disciplinary. Hybridity, complexity, fusion were given as examples of these concepts

Using a Dynamic Domain-Specific Modeling Language for the Model-Driven Development of Cross-Platform Mobile Applications

There has been a gradual but steady convergence of dynamic programming languages with modeling languages. One area that can benefit from this convergence is modeldriven development (MDD) especially in the domain of mobile application development. By using a dynamic language to construct a domain-specific modeling language (DSML), it is possible to create models that are executable, exhibit flexible type checking, and provide a smaller cognitive gap between business users, modelers and developers than more traditional model-driven approaches. Dynamic languages have found strong adoption by practitioners of Agile development processes. These processes often rely on developers to rapidly produce working code that meets business needs and to do so in an iterative and incremental way. Such methodologies tend to eschew “throwaway” artifacts and models as being wasteful except as a communication vehicle to produce executable code. These approaches are not readily supported with traditional heavyweight approaches to model-driven development such as the Object Management Group’s Model-Driven Architecture approach. This research asks whether it is possible for a domain-specific modeling language written in a dynamic programming language to define a cross-platform model that can produce native code and do so in a way that developer productivity and code quality are at least as effective as hand-written code produced using native tools. Using a prototype modeling tool, AXIOM (Agile eXecutable and Incremental Objectoriented Modeling), we examine this question through small- and mid-scale experiments and find that the AXIOM approach improved developer productivity by almost 400%, albeit only after some up-front investment. We also find that the generated code can be of equal if not better quality than the equivalent hand-written code. Finally, we find that there are significant challenges in the synthesis of a DSML that can be used to model applications across platforms as diverse as today’s mobile operating systems, which point to intriguing avenues of subsequent research

Designing evolving cyber-physical-social systems: computational research opportunities

In the context of the theme for this special issue, namely, challenges and opportunities in computing research to enable next generation engineering applications, our intent in writing this paper is to seed the dialog on furthering computing research associated with the design of cyber-physical-social systems. Cyber-Physical-Social Systems (CPSS's) are natural extensions of Cyber-Physical Systems (CPS's) that add the consideration of human interactions and cooperation with cyber systems and physical systems. CPSS's are becoming increasingly important as we face challenges such as regulating our impact on the environment, eradicating disease, transitioning to digital and sustainable manufacturing, and improving healthcare. Human stakeholders in these systems are integral to the effectiveness of these systems. One of the key features of CPSS is that the form, structure, and interactions constantly evolve to meet changes in the environment. Design of evolving CPSS include making tradeoffs amongst the cyber, the physical, and the social systems. Advances in computing and information science have given us opportunities to ask difficult, and important questions, especially those related to cyber-physical-social systems. In this paper we identify research opportunities worth investigating. We start with theoretical and mathematical frameworks for identifying and framing the problem – specifically, problem identification and formulation, data management, CPSS modeling and CPSS in action. Then we discuss issues related to the design of CPSS including decision making, computational platform support, and verification and validation. Building on this foundation, we suggest a way forward

Convolutional Neural Operators for robust and accurate learning of PDEs

Although very successfully used in conventional machine learning, convolution based neural network architectures -- believed to be inconsistent in function space -- have been largely ignored in the context of learning solution operators of PDEs. Here, we present novel adaptations for convolutional neural networks to demonstrate that they are indeed able to process functions as inputs and outputs. The resulting architecture, termed as convolutional neural operators (CNOs), is designed specifically to preserve its underlying continuous nature, even when implemented in a discretized form on a computer. We prove a universality theorem to show that CNOs can approximate operators arising in PDEs to desired accuracy. CNOs are tested on a novel suite of benchmarks, encompassing a diverse set of PDEs with possibly multi-scale solutions and are observed to significantly outperform baselines, paving the way for an alternative framework for robust and accurate operator learning

Application of Model-driven engineering to multi-agent systems: a language to model behaviors of reactive agents

Many users of multi-agent systems (MAS) are very commonly disinclined to model and simulate using current MAS platforms. More specifically, modeling the dynamics of a system (in particular the agents' behaviors) is very often a challenge to MAS users. This issue is more often observed in the domain of socio-ecological systems (SES), because SES domain experts are rarely programmers. Indeed, the majority of MAS platforms were not conceived taking into consideration domain-experts who are non-programmers. Most current MAS tools are not dedicated to SES, or nor do they possess an easily understandable formalism to represent the behaviors of agents. Moreover, because it is platform-dependent, a model realized in a given MAS platform cannot be properly used on another platform due to incompatibility between MAS platforms. To overcome these limitations, we propose a domain-specific language (DSL) to describe the behaviors of reactive agents, regardless of the MAS platform used for simulation. To achieve this result, we used model-driven engineering (MDE), an approach that provides tools to develop DSLs from a meta-model (abstract syntax), textual editors with syntax highlighting (for the concrete syntax) and code generation capabilities (for source-code generation of a model). As a result, we implemented a language and a textual editor that allow SES domain experts to describe behaviors in three different ways that are close to their natural expression: as equations when they are familiar with these, as a sequence of activities close to natural language or as an activity diagram to represent decisions and a sequence of behaviors using a graphic formalism. To demonstrate interoperability, we also developed code generators targeting two different MAS platforms (Cormas and Netlogo). We tested the code generators by implementing two SES models with the developed DSL. The generated code was targeted to both MAS platforms (Cormas and Netlogo), and successfully simulated in one of them. We conclude that the MDE approach provides adequate tools to develop DSL and code generators to facilitate MAS modeling and simulation by non-programmers. Concerning the DSL developed, although the behavioral aspect of MAS simulation is part of the complexity of modeling in MAS, there are still other essential aspects of model and simulation of MAS that are yet to be explored, such as model initialization and points of view on the model simulated worl