RICIS Symposium 1992: Mission and Safety Critical Systems Research and Applications

This conference deals with computer systems which control systems whose failure to operate correctly could produce the loss of life and or property, mission and safety critical systems. Topics covered are: the work of standards groups, computer systems design and architecture, software reliability, process control systems, knowledge based expert systems, and computer and telecommunication protocols

Pushing the Limits of Machine Design: Automated CPU Design with AI

Design activity -- constructing an artifact description satisfying given goals and constraints -- distinguishes humanity from other animals and traditional machines, and endowing machines with design abilities at the human level or beyond has been a long-term pursuit. Though machines have already demonstrated their abilities in designing new materials, proteins, and computer programs with advanced artificial intelligence (AI) techniques, the search space for designing such objects is relatively small, and thus, "Can machines design like humans?" remains an open question. To explore the boundary of machine design, here we present a new AI approach to automatically design a central processing unit (CPU), the brain of a computer, and one of the world's most intricate devices humanity have ever designed. This approach generates the circuit logic, which is represented by a graph structure called Binary Speculation Diagram (BSD), of the CPU design from only external input-output observations instead of formal program code. During the generation of BSD, Monte Carlo-based expansion and the distance of Boolean functions are used to guarantee accuracy and efficiency, respectively. By efficiently exploring a search space of unprecedented size 10^{10^{540}}, which is the largest one of all machine-designed objects to our best knowledge, and thus pushing the limits of machine design, our approach generates an industrial-scale RISC-V CPU within only 5 hours. The taped-out CPU successfully runs the Linux operating system and performs comparably against the human-designed Intel 80486SX CPU. In addition to learning the world's first CPU only from input-output observations, which may reform the semiconductor industry by significantly reducing the design cycle, our approach even autonomously discovers human knowledge of the von Neumann architecture.Comment: 28 page

A REVIEW ON REUSE OF SOFTWARE COMPONENTS FOR SUSTAINABLE SOLUTIONS IN DEVELOPMENT PROCESS

Effective reuse of a software product will increase the productivity, reliability and maintainability. It saves the development and verification time and reduces the risk and the cost involved in the software development. From the literature in this field, it is noticed that very few attempts had been made to identify or measure the software reuse process level.      Also planning for reuse and determining the suitable component for reuse in a system development process have some significant challenges. To overcome these challenges reuse engineers must apply effective methods to identify high potential and quality reusable software components

A REVIEW ON REUSE OF SOFTWARE COMPONENTS FOR SUSTAINABLE SOLUTIONS IN DEVELOPMENT PROCESS

Effective reuse of a software product will increase the productivity, reliability and maintainability. It saves the development and verification time and reduces the risk and the cost involved in the software development. From the literature in this field, it is noticed that very few attempts had been made to identify or measure the software reuse process level.      Also planning for reuse and determining the suitable component for reuse in a system development process have some significant challenges. To overcome these challenges reuse engineers must apply effective methods to identify high potential and quality reusable software components

Chronic-Pain Protective Behavior Detection with Deep Learning

In chronic pain rehabilitation, physiotherapists adapt physical activity to patients' performance based on their expression of protective behavior, gradually exposing them to feared but harmless and essential everyday activities. As rehabilitation moves outside the clinic, technology should automatically detect such behavior to provide similar support. Previous works have shown the feasibility of automatic protective behavior detection (PBD) within a specific activity. In this paper, we investigate the use of deep learning for PBD across activity types, using wearable motion capture and surface electromyography data collected from healthy participants and people with chronic pain. We approach the problem by continuously detecting protective behavior within an activity rather than estimating its overall presence. The best performance reaches mean F1 score of 0.82 with leave-one-subject-out cross validation. When protective behavior is modelled per activity type, performance is mean F1 score of 0.77 for bend-down, 0.81 for one-leg-stand, 0.72 for sit-to-stand, 0.83 for stand-to-sit, and 0.67 for reach-forward. This performance reaches excellent level of agreement with the average experts' rating performance suggesting potential for personalized chronic pain management at home. We analyze various parameters characterizing our approach to understand how the results could generalize to other PBD datasets and different levels of ground truth granularity.Comment: 24 pages, 12 figures, 7 tables. Accepted by ACM Transactions on Computing for Healthcar