C-Graph

Students learn best when they are active and engaged.Even when we combine asynchronous and synchronous communication, it is challenging to get the optimal levels of engagement and communication.A number of different ways of creating communication and interactivity have been tested out including mandatory on-campus seminars, small size groups of students, video conferencing with chat and teaching in real time.This paper presents a holistic approach to increase communication and engagement in online teaching by using the blended learning concept.The paper presents three different types of network models that combine synchronous and asynchronous communication.The methodological approach is mainly qualitative.The empirical data is mainly interviews and observations.The respondents are lectures who are teaching online.The theory this research rests on is theory on communication and relevant theory about online teaching.Findings and results are from Lervik's PhD work.The results show that the following conditions promote communication, dialogue and engagement in online teaching: compulsory campus meetings before starting online teaching, having small groups of students online, communicating through video conferencing with chat and teaching in true time.Blended learning will in this case be a combination of synchronous and asynchronous communication.The main findings of this research are that these lecturers conduct online education through three different network models.These three web network models vary with the aim of facilitating the flexibility of online students that combine work and study.A critical factor is to conduct on-campus seminars at the start of the education as this enables the teacher and students to get to know each other and develop the trust required to have the required involvement, engagement and communication.A high level of involvement and engagement will increase the learning outcome from the subsequent online teaching.The blend of learning opportunities provides the students with different approaches that can support different learning styles.

Online learning is available in various ways and methods. To find out the difference between online communication and material presented, it must be appropriate so that it can be well received by students. For that, every university must prepare well. Learning online communication that is not ready can automatically disrupt the course of teaching and learning activities. Differences in Synchronous and Asynchronous Online Learning Communication. Synchronous communication is a learning communication carried out in a real time communication system. Lecturers can deliver material in class directly to students. Synchronous online, lecturers can still do virtual learning by online class media. Asynchronous communication is learning communication that is not limited in time. In the meaning lecturer provides material that can be used as a reference for students. The material can be learn by students in the form of PPT files, PDFs, and videos. The Covid-19 pandemic has affected almost all aspects of human life, to break the chain of virus transmission, Indonesian government take of health protocol regulations. The condition of education has huge impact. However, education must continue. Learning process during in emergency is carried out by Synchronous and Asynchronous learning

Historically, online instructors have primarily, if not solely, used asynchronous text-based communication to interact and communicate with students in online courses. However, despite this use, online instructors and students have expressed concerns and limitations with asynchronous communication. Research suggests that synchronous communication technology may address some of the limitations of asynchronous communication. Further, advances in synchronous video-based communication technology make it easier than ever to incorporate synchronous communication technology into online courses. However, comparatively, little is known about online instructors’ experiences and perceptions of using synchronous communication technologies in online courses and how they think they can help with community development. The purpose of this study was to explore instructors’ experiences and perceptions of synchronous communication technology. Thematic analysis of semi-structured interviews with 18 online instructors resulted in the following themes: (a) instructors use synchronous communication technology in multiple and various ways in online courses; (b) the perceived benefits of real-time visual communication outweigh the drawbacks identified; (c) the benefits of nonverbal communication depend on situational factors and how synchronous features are used; (d) productive and meaningful interaction requires intentional yet flexible facilitation during synchronous sessions; (e) synchronous sessions can provide a place for community to build and grow but they are not required for community development. Altogether, findings suggest that real-time visual communication may aid in community building in online courses but that its effectiveness depends on several situational factors, and that synchronous lecturing may be less conducive to developing classroom community in online courses. Results and future research directions are discussed.

Graph algorithms such as graph traversal have been gaining ever-increasing importance in the era of big data. However, graph processing on traditional architectures issues many random and irregular memory accesses, leading to a huge number of data movements and the consumption of very large amounts of energy. To minimize the waste of memory bandwidth, we investigate utilizing processing-in-memory (PIM), combined with non-volatile metal-oxide resistive random access memory (ReRAM), to improve both computation and I/O performance. We propose a new ReRAM-based processing-in-memory architecture called RPBFS, in which graph data can be persistently stored and processed in place. We study the problem of graph traversal, and we design an efficient graph traversal algorithm in RPBFS. Benefiting from low data movement overhead and high bank-level parallel computation, RPBFS shows a significant performance improvement compared with both the CPU-based and the GPU-based BFS implementations. On a suite of real-world graphs, our architecture yields a speedup in graph traversal performance of up to 33.8×, and achieves a reduction in energy over conventional systems of up to 142.8×.

Modular High-Level Nets (MHLN) is now in widespread use for many different practical purposes. However the modular analysis methods developed so far for MHLNs, allow dealing only with one way of communication, thus forcing the designer to choose in an earlier stage of the design phase between synchronous or asynchronous communication. This problem arises particularly in MHLNs like ECATNets (Extended Concurrent Algebraic Term Nets) where both synchronous and asynchronous communication can coexist within a modular net. The objective of this paper is to propose an algorithm (and prove its correctness) allowing transforming a given MHLN (namely ECATNet) mixing the two ways of communication into a MHLN behaviorally equivalent but making use of only one way of communication. This approach must be done in such a way that algebraic specifications of generated modules are well defined and data models are preserved.

Higher-order process calculi are calculi in which processes can be communicated. We study the expressiveness of strictly higher-order process calculi, and focus on two issues well-understood for first-order calculi but not in the higher-order setting: synchronous vs. asynchronous communication and polyadic vs. monadic communication. First, and similarly to the first-order setting, synchronous process-passing is shown to be encodable into asynchronous processpassing. Then, the absence of name-passing is shown to induce a hierarchy of higher-order process calculi based on the arity of polyadic communication, thus revealing a striking point of contrast with respect to first-order calculi. Finally, the passing of abstractions (i.e., functions from processes to processes) is shown to be more expressive than process-passing alone.

Compatibility is a crucial problem that is encountered while constructing new software by reusing and composing existing components. A set of software components is called compatible if their composition preserves certain properties, such as deadlock freedom. However, checking compatibility for systems communicating asynchronously is an undecidable problem, and asynchronous communication is a common interaction mechanism used in building software systems. A typical approach in analyzing such systems is to bound the state space. In this paper, we take a different approach and do not impose any bounds on the number of participants or the sizes of the message buffers. Instead, we present a sufficient condition for checking compatibility of a set of asynchronously communicating components. Our approach relies on the synchronizability property which identifies systems for which interaction behavior remains the same when asynchronous communication is replaced with synchronous communication. Using the synchronizability property, we can check the compatibility of systems with unbounded message buffers by analyzing only a finite part of their behavior. We have implemented a prototype tool to automate our approach and we have applied it to many examples.

The paper describes graph algorithms performance when using different types of data structures. To achieve that, we developed a multi-level graph processing system, which allows to create graph applications independently of any implementation details such as graph data structure or underlying computational architecture. We measure the performance of breadth-first search, max flow and random graph building algorithms when using compressed sparse row and adjacency matrix data structures. Experiments reveal different graph processing rates for different data structures, which indicates the need of using specific data structures for specific algorithms to achieve highest performance.

This article presents MST, a communication-efficient message library for fast graph traversal on exascale clusters. The key idea is to follow the multi-level network topology to perform topology-aware message aggregation, where small messages are gathered and scattered at each level of domain. To facilitate message aggregation, we equip MST with flexible buffer management including active buffer switching and dynamic buffer expansion. We implement MST on the newest-generation Tianhe supercomputer and evaluated its performance using various traversal-centric algorithms on both synthetic trillion-scale graphs and real-world big graphs. The results show that MST-based graph traversal is orders of magnitude faster than that based on Active Messages Library (AML). For the Graph500-BFS benchmark, MST-based Tianhe (with 77.2 K nodes) outperforms the Fugaku supercomputer (with 148.5 K nodes) by 18.53%, while Fugaku is ranked No. 1 in the latest Graph500-BFS ranking (June 2023). MST also greatly improves graph processing performance on other commercial large-scale computing systems at the National Supercomputing Center in Changsha (NSCC) and WuzhenLight.

The purpose of this paper is to develop a specialized language for processing graph data and its software implementation. The proposed solution ensures versatility, usability, and efficiency, enabling the execution of both basic graph operations and more complex procedures. The tool supports classical graph algorithms, including shortest path search, graph traversal, and minimum spanning tree construction, as well as applications in modeling and analyzing message transition processes and processing graph-based representations of textual data. A common drawback of many existing graph analysis tools—often implemented as libraries of general-purpose programming languages—is their limited usability. This limitation arises from the fact that the description of graph analysis procedures relies on data structure operations defined in terms of these general-purpose languages, which complicates perception and reduces the clarity of the mathematical methods being implemented. Developing a specialized domain-specific language based on high-level abstractions can address these shortcomings. Such a language will provide a formalized description of methods for analyzing and processing graph models, improving their comprehensibility and accessibility to users. Its software implementation will deliver ready-to-use solutions for executing graph analysis methods. Composing such methods will facilitate solving a wide range of tasks, including the analysis of natural language messages and the study of information publication and dissemination processes in online environments.

A key issue in process-aware e-commerce collaboration is to orchestrate business processes of multiple business partners throughout a supply chain network in an automated and seamless way. Since each partner has its own internal processes with different control flow structures and message interfaces, the real challenge lies in verifying the correctness of process collaboration, and reconciling conflicts in an automated manner to make collaboration successful. The purpose of business process adaptation is to mediate the communication between independent processes to overcome their mismatches and incompatibilities. The goal of this article is to develop and compare efficient approaches of optimal adapter (i.e. one that minimizes the number of messages to be adapted) creation for multiple interacting processes under both synchronous and asynchronous communication. We start with an analysis of interactions of each message pair, and show how to identify incompatible cases and their adaptation elements for both types of communication. Then, we show how to extend this analysis into more general cases involving M messages and N processes ( M, N > 2). Further, we present optimal adapter creation algorithms for both scenarios based on our analysis technique. The algorithms were implemented in a Java-based prototype system, and results of two experiments are reported. We compare and discuss the insights gained about adapter creation in these two scenarios.

Honda and Tokoro provide a formal system for communicating systems developed from Milner's π-calculus. Unlike other formalisms, their work is based on asynchronous communication primitives.This paper proposes some minor but practically significant extensions to a model based on asynchronous communication and shows how the resulting system may be mapped very directly onto a graph rewriting system.While the model based on asynchronous communication permits the most direct translation, a related model using synchronous communication may be implemented in a similar manner.KeywordsProcess NetworkAsynchronous CommunicationSynchronous CommunicationLambda CalculusGraph Transformation RuleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Low- and middle-income countries (LMICs) struggle with the highest burden of mental health disorders, along with an often unequal distribution of resources, including specialist care.1 This situation can translate into unequal access to mental health care in different regions within these countries. Also, and due to a multitude of reasons, referral patterns in LMICs are often a barrier to the timely provision of care for people with a mental disorder.2 The early identification and initial management of a mental disorder plays a significant role in determining its prognosis; hence, it is vital to establish prompt access to specialist care whenever necessary. In this letter, we would like to emphasize the importance of establishing a strong referral system for people with a mental disorder and the pivotal role that telecare could play in this regard. Telecare, in particular telepsychiatry, could assist with addressing existing mental healthcare access inequities in LMICs and prove useful in reducing time delays in the referral process. For example, eConsults or asynchronous electronic communications could allow specialized care to be relayed through the local healthcare provider (LHP) to the service user via electronic means. In an eConsult, the LHP sends the psychiatric provider a focused clinical question or a prerecorded video of an interview via email, which is then responded with a clear recommendation. This asynchronous electronic communication could reduce the gap between health professionals, assisting the LHP in providing mental healthcare locally, and expediting the referral process when necessary. Moreover, since patients tend to seek mental health care from their LHP, eConsults could improve the levels of adherence to this care.3 In situations where the psychiatric provider must lead the provision of care, interactive videoconferencing—or synchronous communications—can further complement local healthcare services. Interactive videoconferencing can enable the provision of specialized mental health care at a distance. It could be provided to clinically supervised settings—like mental health units or primary care services—or not clinically supervised settings—like a service user's home—without the need for the service user to move great distances to access the needed care. This service, along with the eConsults, could expedite access to specialized care when required while also allowing service users to continue accessing their local support networks. In summary, telepsychiatry could easily complement the existing network of mental healthcare delivery in LMICs and prove vital in reducing healthcare access inequities.4 There are, however, some challenges that may need to be addressed first, such as confidentiality and security limitations. In addition, not everybody will have the necessary resources and digital literacy to liaise with a psychiatric provider.5 It will be important to ensure the necessary technical conditions (e.g., internet, computer, or smartphone) are present. Funding also remains a challenge in LMICs, with many countries allocating less than 1% of their overall health budget to mental health.1 Also, providing mental health care that caters to the service user's cultural needs, particularly to the most vulnerable sectors of these countries, is of the utmost importance, and may require further training.6 Thus, access to the necessary resources, including human and material resources, and appropriate formal training should first be ensured to avoid contributing to existing inequities. To conclude, early diagnosis and treatment of mental health disorders is pivotal in mental health care. In contexts where there is unequal access to specialist care, as is often the case in LMICs, we recommend the adoption of telepsychiatry. Telepsychiatry, whether in the form of asynchronous or synchronous communications, could ensure easy access to specialist care and a swift referral process, thus securing timely access to specialized mental health care and reducing healthcare access inequities in LMICs. In LMICs, a high burden of mental health disorders is often met with an unequal distribution of resources, including psychiatric specialist care, a situation that translates into inequitable access to mental healthcare. Timely access to care has a substantial impact on the prognosis of a mental health disorder, and thus, it is imperative to address these inequities. Telepsychiatry could prove a valuable tool in this task, as it could help optimize resource allocation, enable access to specialist care at a distance, and allow a swift referral process. There may be some barriers ahead, such as access to funding and a need for further training in the use of telecommunication technologies and cultural responsiveness in the provision of telecare. Still, the integration of telepsychiatry in advanced psychiatric nursing practice could assist in reducing healthcare access inequities and improving clinical outcomes for the population. The authors declare that there are no conflict of interests.

This paper presents a semi-Markov decision process (SMDP) formulation of the satellite task scheduling problem. This formulation can consider multiple operational objectives simultaneously and plan transitions between distinct functional modes. We consider the problem of scheduling image collections, ground contacts, sun-pointed periods for battery recharging, and data recorder management for an agile, resource-constrained Earth-observing spacecraft. By considering multiple mission objectives simultaneously, the algorithm is able to find optimized task schedule that satisfies all operational constraints in a single planning step, thus reducing the operational complexity and number of steps involved in mission planning. We present two solution approaches based on forward search and Monte Carlo Tree search. We baseline against rule-based, graph search, and mixed-integer-linear programming approaches. The SMDP formulation is evaluated in both single-objective and multi-objective scenarios. The SMDP solution is found to perform comparably with the baseline methods at greatly increased speed in single-objective scenarios and greater schedule reward in multi-objective.

This paper proposes a serialized multi-layer multi-head attention for neural speaker embedding in text-independent speaker verification. In prior works, frame-level features from one layer are aggregated to form an utterance-level representation. Inspired by the Transformer network, our proposed method utilizes the hierarchical architecture of stacked self-attention mechanisms to derive refined features that are more correlated with speakers. Serialized attention mechanism contains a stack of self-attention modules to create fixed-dimensional representations of speakers. Instead of utilizing multi-head attention in parallel, the proposed serialized multi-layer multi-head attention is designed to aggregate and propagate attentive statistics from one layer to the next in a serialized manner. In addition, we employ an input-aware query for each utterance with the statistics pooling. With more layers stacked, the neural network can learn more discriminative speaker embeddings. Experiment results on VoxCeleb1 dataset and SITW dataset show that our proposed method outperforms other baseline methods, including x-vectors and other x-vectors + conventional attentive pooling approaches by 9.7% in EER and 8.1% in DCF0.01.