The Six Components of Social Interactions: Actor, Partner, Relation, Activities, Context, and Evaluation

Social interactions are essential aspects of social relationships. Despite their centrality, there is a lack of a standardized approach to systematize social interactions. The present research developed (Study 1) and tested (Study 2) a taxonomy of social interactions. In Study 1 (5,676 descriptions of social interactions from N = 708 participants, age range 18–83 years), we combined a bottom-up approach based on the grounded theory with a top-down approach integrating existing empirical and theoretical literature to develop the taxonomy. The resulting taxonomy (APRACE) comprises the components Actor, Partner, Relation, Activities, Context, and Evaluation, each specified by features on three levels of abstraction. A social situation can be described by a combination of the components and their features on the respective abstraction level. Study 2 tested the APRACE using another dataset (N = 303, age range 18–88 years) with 1,899 descriptions of social interactions. The index scores of the six components, the frequencies of the features on the most abstract level, and their correlations were largely consistent across both studies, which supports the generalizability of the APRACE. The APRACE offers a generalizable tool for the comprehensive, parsimonious, and systematic description of social interactions and, thus, enables networked research on social interactions and application in a number of practical fields.

HW-FlowQ: A Multi-Abstraction Level HW-CNN Co-design Quantization Methodology

Model compression through quantization is commonly applied to convolutional neural networks (CNNs) deployed on compute and memory-constrained embedded platforms. Different layers of the CNN can have varying degrees of numerical precision for both weights and activations, resulting in a large search space. Together with the hardware (HW) design space, the challenge of finding the globally optimal HW-CNN combination for a given application becomes daunting. To this end, we propose HW-FlowQ, a systematic approach that enables the co-design of the target hardware platform and the compressed CNN model through quantization. The search space is viewed at three levels of abstraction, allowing for an iterative approach for narrowing down the solution space before reaching a high-fidelity CNN hardware modeling tool, capable of capturing the effects of mixed-precision quantization strategies on different hardware architectures (processing unit counts, memory levels, cost models, dataflows) and two types of computation engines (bit-parallel vectorized, bit-serial). To combine both worlds, a multi-objective non-dominated sorting genetic algorithm (NSGA-II) is leveraged to establish a Pareto-optimal set of quantization strategies for the target HW-metrics at each abstraction level. HW-FlowQ detects optima in a discrete search space and maximizes the task-related accuracy of the underlying CNN while minimizing hardware-related costs. The Pareto-front approach keeps the design space open to a range of non-dominated solutions before refining the design to a more detailed level of abstraction. With equivalent prediction accuracy, we improve the energy and latency by 20% and 45% respectively for ResNet56 compared to existing mixed-precision search methods.

A Service-Oriented Component-Based Framework for Dynamic Reconfiguration Modeling Targeting SystemC/TLM

To deal with the complex design issues of Dynamically Reconfigurable Systems-on-Chip (DRSoCs), it is extremely relevant to raise the abstraction level in which models are expressed. A high abstraction level allows great flexibility and reusability while bypassing low-level implementation details. In this context, model-driven engineering (MDE) provides support to build and transform precise and structured models for a particular purpose at different levels of abstraction. Indeed, high-level models are successively refined to low-level models until reaching the executable ones. Thus, this paper presents an MDE-based framework for DRSoCs design enabling the transformation of UML/MARTE specifications to SystemC/TLM implementation. To achieve a high degree of expressiveness for modeling dynamic reconfiguration, we use a suitable software engineering approach based on service-oriented component architecture. Since MARTE does not cover the common features of dynamic reconfiguration domain and service orientation concepts, new stereotypes are created by refinement to add missing capabilities to the profile. Likewise, SystemC does not provide native support for dynamic reconfiguration, thus leading us to adopt a design pattern based solution for DRSoCs implementation in compliance with standards. The proposed framework is validated through a reconfigurable active 3-way crossover case study in which we demonstrate the practicability of the approach by gradual model transformations with reduced implementation effort and significant design productivity gain.

A Retargetable Model-Driven Framework for the Development of Mobile User Interfaces

Since the emergence of mobile devices, the architecture of mobile applications has been transformed significantly. In mobile applications, the User Interface (UI) is one of the major elements, but its development is complex and time-consuming. Existing practices do not support various presentation issues of the UI at a higher abstraction level, in a retargetable fashion, with complete tool support. Therefore, it is critical to develop a simple and automated framework for the development of mobile UIs by exploiting model-driven engineering concepts. In this paper, a Unified Modeling language (UML) profile for Mobile User Interfaces (UMMUI) has been proposed, which employs some standard UML notations for representing the mobile UI requirements at a higher abstraction level. Subsequently, a complete open-source transformation engine has been developed to automatically transform the high-level source models (in UMMUI) into the target low-level React Native implementation. Finally, the applicability of the proposed framework is validated through two benchmark case studies, i.e., Patient Management System and Library Application. The results verify that the proposed framework allows the modeling of UIs with simplicity and generates the target code automatically with minimum transformation losses.

MAPPING AND ENHANCING DESIGN STUDIES WITH PSI META-THEORETIC DESIGN FRAMEWORK

Design research as a field has been studied from diverse perspectives starting from product inception to their disposal. The product of these studies includes knowledge, tools, methods, processes, frameworks, approaches, and theories. The contexts of these studies are innumerable. The unit of these studies varies from individuals to organizations, using a variety of theoretical tools and methods that have fragmented the field, making it difficult to understand the map of this corpus of knowledge across this diversity.In this paper, we propose a model-based approach that on the one hand, does not delve into the details of the design object itself, but on the other hand, unifies the description of design problem at another abstraction level. The use of this abstract framework allows for describing and comparing underlying models of published design studies using the same language to place them in the right context in which design takes place and to enable to inter-relate them, to understand the wholes and the parts of design studies.Patterns of successful studies could be generated and used by researchers to improve the design of new studies, understand the outcome of existing studies, and plan follow-up studies.

Flow-Based Programming For Machine Learning

Machine Learning (ML) has gained prominence and has tremendous applications in fields like medicine, biology, geography and astrophysics, to name a few. Arguably, in such areas it is used by domain experts, who are not necessarily skilled-programmers. Thus, it presents a steep learning curve for such domain experts in programming ML applications. To overcome this and foster widespread adoption of ML techniques, we propose to equip them with domain-specific graphical tools. Such tools, based on the principles of flow-based programming paradigm, would support the graphical composition of ML applications at a higher level of abstraction and auto-generation of target code. Accordingly, (i) we have modelled ML algorithms as composable components; (ii) described an approach to parse a flow created by connecting several such composable components and use an API-based code generation technique to generate the ML application. To demonstrate the feasibility of our conceptual approach, we have modelled the APIs of Apache Spark ML as composable components and validated it in three use-cases. The use-cases are designed to capture the ease of program specification at a higher abstraction level, easy parametrisation of ML APIs, auto-generation of the ML application and auto-validation of the generated model for better prediction accuracy.

Modul Berbasis Logika Pembuktian untuk Mengurangi Level Abstraksi Topik Grup dan Sifat-Sifatnya

The most essential thing in mathematics is proof, it makes mathematics being different with other subjects. One of subject in mathematics that always need prove to understand the concept is abstract algebra. In studying abstract algebra, student need various abstract concepts to include in its concepts. It is hard for student to understand the structures in abstract algebra and prove some of mathematical object that satisfy the structures. Group and its properties is the first structure in abstract algebra that has an abstract concept. It is hard for student to understand some objects, that is proven satisfy a structure and why the proof steps just flow. By giving explanation and reason in every proofing step, we try to increase student proving level and reduce the abstraction level of the concepts. To see how this module reduces the abstraction level in teaching group, this module is applied to university students and evaluated by interviewing and questionnaires to the students. Base on student response and by some perspectives, student proving ability increase and the abstraction level of the concept is diminished in some aspects.

Proving Abstract Algebra Skills with Problem-Based Learning Integrated with Videos and Worksheets

Previous research showed students faced difficulties in solving the given problems in the Abstract Algebra course. The research aimed to describe the effect of the method of problem-based learning integrated with videos and worksheets to improve the proving skills of mathematics education students in one of the universities in Central Kalimantan, Indonesia. The researcher developed and uploaded the videos on YouTube. The research design was an experimental study. The researcher implemented the method in an experimental class. The control class students learned by using the usual method of the past three years that emphasized acquiring the Abstract Algebra concepts. The researcher selected the experimental class randomly. The numbers of students in the experimental and control classes were 32 and 28, respectively. The students of both classes solved the same problems in the post-test at end of the implementation. The post-test contained five problems to prove. The research results showed that the transactive reasoning activities in the experimental class enabled the students to prove at an appropriate abstraction level. The students’ scores in the Abstract Algebra for the experimental class were greater than those in the control class. Therefore, the method affected students’ ability to solve Abstract Algebra problems.