Computer science's multidisciplinary importance is becoming widely recognized, and few fields are seeing this change more rapidly than agriculture. One example is the advent of precision agriculture, which leverages advancements in technology to monitor crops and livestock and precisely apply nutrients, herbicides, etc. for the overall health of the crop. Such innovations will upset the status quo, creating an opportunity for greater equity in emerging Computer Science job markets. However, opportunities in Computer Science are not equitably distributed both socioeconomically and geographically, with most opportunities existing in wealthier metropolitan areas. We surveyed and interviewed K-12 agriculture teachers from Rural Title 1 schools in North Carolina and interviewed them about their experiences, visions of the future of agriculture, emerging AgTech economies, and difficulties they had adjusting to these shifting agricultural domains in their teaching. These teachers form the first phase of the Rural CS+Agriculture Alliance, which will focus on the creation of curriculum to support the integration of computer science topics into rural agriculture classrooms.

The ever increasing computing demands in embedded systems is driving the adoption of hardware accelerators such as GPUs , which offer powerful platforms that can compute parallel workloads efficiently. Relevant critical applications that benefit from such platforms, for instance autonomous driving , usually impose additional real-time requirements that must be met to guarantee the correctness of the systems. In this paper, we propose exploiting readily available and extensively validated techniques to model and analyze real-time systems with GPUs . Specifically, we propose a methodology to employ the MAST model to characterize such systems, and different variants of the Offset-Based Response-Time Analysis techniques to validate the real-time requirements. We verify our approach with a real industrial application sourced from the railway industry . Through a comprehensive evaluation involving synthetic and real task-sets, we characterize the applicability of the approach, and we also show how estimated worst-case response times are aligned with real measurements up to 87.2%.

Chips pack ever more, ever smaller transistors. Fault rates increase in turn and become more concerning, particularly at the scale ofHigh-Performance Computing(HPC) systems: on one hand, hardware fault protection is costly - more than 10% silicon area for floating-point units; on the other, HPC users expect correct application output after the anticipated time of computation, but workloads are seldom bit-reproducible and tolerances in output are allowed for. Benign hardware faults causing errors within these tolerances are therefore acceptable: however, with abstract reliability targets such as ’undetected failures per time,’ current HPC system design does not allow for pursuing trade-offs between reliability and performance with respect to faults. To address the above, we propose a user-centric reliability benchmark to specify HPC system reliability targets, allowing for better performance optimizations in hardware design, while meeting HPC user expectations. Our open-sourceHardware Design Fault Injection Toolkit(HDFIT) enables - for the first time - end-to-end hardware design reliability experiments: from netlist-level fault injection to application output error. In a proof of concept we present an HPCgeneral matrix multiply(GEMM) reliability study, targeting a series of popular applications, and using HDFIT to benchmark an open-source GEMM accelerator.

Summary Safety‐critical systems are evolving into complex, networked, and distributed systems. As a result of the high interconnectivity among all networked systems and of potential security threats, security countermeasures need to be incorporated. Nonetheless, albeit cutting‐edge security measures are adopted and incorporated during the system development, such as latest recommended encryption algorithms, these protection mechanisms may turn out obsolete because of the long operational periods. New security flaws and bugs are continuously detected. Software updates are then essential to restore the security level of the system. However, system shutdowns may not be acceptable when high availability is required. As expressed by the European Union Agency for Network and Information Security (ENISA) “the research in the area of patching and updating equipment without disruption of service and tools” is needed. In this article, a novel live updating approach for zero downtime safety‐critical systems named Cetratus is presented. Cetratus, which is based on a quarantine‐mode execution and monitoring, enables the update of non‐safety‐critical software components while running, without compromising the safety integrity level of the system. The focus of this work lies on the incorporation of leading‐edge security mechanisms while safety‐related software components will remain untouched. Other non‐safety‐related software components could also be updated.

In this paper, we propose a motion-path based gesture recognition technique and show its application in a smart home environment. Users hand gestures are recognized by capturing the motion-path while they draw different symbols in the air. In order to capture the motion-path, we use infra-red camera's IR sensing capability. The IR camera tracks the infra-red emitter attached to the user's hand gloves and produces a sequence of motion-points, which are then analyzed syntactically to recognize the intended hand gesture. The recognized gesture is used to interact with the intelligent environment for accessing various services. Toggling a lamp switch, changing the light intensity, and playing/pausing a movie are few examples where we have integrated the gesture-based interaction. Our experiment shows that the proposed gesture recognition technique is robust and its use in the smart home environment is interesting and appealing to the people.