In recent years, Graph Neural Networks (GNNs) have made significant advances in processing structured data. However, most of them primarily adopted a model-centric approach, which simplifies graphs by converting them into undirected formats and emphasizes model designs. This approach is inherently limited in real-world applications due to the unavoidable information loss in simple undirected graphs and the model optimization challenges that arise when exceeding the upper bounds of this sub-optimal data representational capacity. As a result, there has been a shift toward data-centric methods that prioritize improving graph quality and representation. Specifically, various types of graphs can be derived from naturally structured data, including heterogeneous graphs, hypergraphs, and directed graphs. Among these, directed graphs offer distinct advantages in topological systems by modeling causal relationships, and directed GNNs have been extensively studied in recent years. However, a comprehensive survey of this emerging topic is still lacking. Therefore, we aim to provide a comprehensive review of directed graph learning, with a particular focus on a data-centric perspective. Specifically, we first introduce a novel taxonomy for existing studies. Subsequently, we re-examine these methods from the data-centric perspective, with an emphasis on understanding and improving data representation. It demonstrates that a deep understanding of directed graphs and their quality plays a crucial role in model performance. Additionally, we explore the diverse applications of directed GNNs across 10+ domains, highlighting their broad applicability. Finally, we identify key opportunities and challenges within the field, offering insights that can guide future research and development in directed graph learning.

While many inkjet printers employ only four types of ink (i.e. CKMY) to produce a wide range of colors, numerous technical challenges still exist for contemporary 3D printers to fabricate various materials and generate composite products such as electric devices. Conversely, there have been attempts and endeavors to make things through self-assembly of parts, analogous to the autonomous and decentralized development process of the human body from just 20 types of amino acids. In our previous work, we proposed a method for the rapid production of 3D objects using the centimeter-sized modules (referred to as Roblets) capable of generating a 2D structure and subsequently self-folding themselves into a 3D configuration, akin to origami. To further leverage the capability of generating a wide variety of different types of structures by combining different modules, this research studies a method of automatically selecting and supplying modules using environmental cues. More precisely, we developed a mechanism to couple different modules corresponding to three different environments (on a flat surface, on low-dense saline, and on saturated saline) and yielded different module configurations. The process of self-assembly necessitated the application of perturbation, which was realized by imparting magnetic torque originating from an external magnetic field onto the magnets embedded in the modules.

Inspired by human proteins that are synthesized from only 20 types of amino acids, the development of self-assembly methods that allow robots to be built simply by randomly stirring the parts has been explored for many years. The key challenges include how to synthesize parts in pieces into a three-dimensional functional structure in a practical time, and subsequently, achieve a controlled robotic motion, all with minimal human intervention. This study proposes a method of self-assembling a 3D robot by first self-assembling random parts into a 2D structure and then self-folding it into a 3D shape. Once self-folded, the robot, whose compositional parts contain magnets, becomes capable of performing basic tasks such as block-pushing upon an application of an external magnetic field. Self-assembly from parts into a two-dimensional structure was performed by repeatedly colliding the parts with each other, and combining them with complementary-shaped parts, like matching jigsaw puzzle pieces. Self-folding was performed by shrinking a heat-responsive film attached across the hinge of each assembly part in hot water, causing the entire 2D structure to self-fold. The experiment demonstrated a series of 13 parts self-assembling into the shape of a 3D beetle, then walking and pushing an object in 13 minutes. The self-assembly process is programmed (mechanically) to generate the same geometry even if the number of parts is greater than the necessary number for the structure, thus is capable of generating multiple structures simultaneously.

Due to the growing needs in decision-making under uncertainty, existing studies introduced consistency and consensus-driven algorithms for group decision-making (GDM) problems with hesitant fuzzy linguistic preference relations (HFLPRs). A decision support system (DSS) that can host these GDM algorithms to provide decision-support services or tools for practical use is urgently needed. However, the state-of-the-art architectures cannot organize these algorithms and related data to run within one framework. This is mainly due to the running environments for these GDM algorithms are different since these algorithms were not originally designed to be compatible. Given the multilingual consistency and consensus-based decision support algorithms, how to design and implement a cloud-based DSS in a multilingual environment is still an open question. To fill this gap, this paper provides a web-based and cloud-based DSS with a novel architecture that utilizes the advantages of microservices. The proposed system implements a multilingual support framework to dynamically upload, manage and run multilingual consistency and consensus-based decision support algorithms. An algorithm recommendation module is developed to help users choose suitable decision support algorithms. Tokenization is applied to deal with regulatory issues of knowledge protection, data privacy, and security while storing, analyzing, and transforming data into different algorithms for effective decision-making. An expert feedback study verified that our web and cloud-based DSS is a right artifact to fulfill the objective claimed in this paper.

Abstract With the increasing demand for dynamic and customised resource provisioning for computational experiments in e-Science, solutions are required to mediate different participants’ varied demands for such resource provision. This paper presents a novel negotiation protocol based on a new collaboration model. The protocol allows e-Scientists, the manager of an e-Scientist’s collaboration, and resource providers to reach resource provisioning agreements. By considering the manager of an e-Scientist collaboration for negotiation decisions, the protocol enables fine-grained accountable resource provision on a per job basis for e-Scientist collaborations, without binding the e-Scientist collaboration to resource providers. A testbed built with the protocol is also presented, making use of a production e-Science gateway, use cases, and infrastructures. The testbed is experimentally evaluated, via designed scenarios and comparison with existing production tools. It demonstrates that the proposed negotiation protocol can facilitate accountable resource provision per job, based on resource sharing rules defined and managed by e-Scientist collaborations.

The concept that scientists and engineers should be able to monitor and control simulations running on supercomputers has been discussed and implemented since the late 1980s. The recent explosion in the variety and capabilities of mobile devices allows this access to be taken to a new level, since the simulations running on supercomputers can be accessed as users are moving around in their normal work activities. This means that they have to be able to connect and disconnect at will from the simulation running on the supercomputer, without disturbing its execution. We present a general framework for such “mobile supercomputing” within the framework of Web services standards. To illustrate the potential of our method, we present a particular application of this framework to provide a rich mobile interface to a lattice-Boltzmann simulation of complex fluids running on an IBM Blue Gene/Q supercomputer.