Accelerated growth of urban population in the world put incremental stresses on metropolitan cities. Smart city centric strategies are expected to comprise solutions to sustainable environment and urban life. Acting as an indispensable role in smart city, IoT (Internet of Things) connects the executive ability of the physical world and the intelligence of the computational world, aiming to enlarge the capabilities of things in real city and strengthen the practicality of functions in cyber world. One of the important application areas of IoT in cities is food industry. Municipality governors are withstanding all kinds of food safety issues and enduring the hardest time ever due to the lack of sufficient guidance and supervision. IoT systems help to monitor, analyze, and manage the real food industry in cities. In this paper, a smart sensor data collection strategy for IoT is proposed, which would improve the efficiency and accuracy of provenance with the minimized size of data set at the same time. We then present algorithms of tracing contamination source and back tracking potential infected food in the markets. Our strategy and algorithms are evaluated with a comprehensive evaluation case of this IoT system, which shows that this system performs well even with big data as well.

Quantum(-inspired) annealers show promise in solving combinatorial optimisation problems in practice. There has been extensive researches demonstrating the utility of D-Wave quantum annealer and quantum-inspired annealer, i.e., Fujitsu Digital Annealer on various applications, but few works are comparing these platforms. In this paper, we benchmark quantum(-inspired) annealers with three combinatorial optimisation problems ranging from generic scientific problems to complex problems in practical use. In the case where the problem size goes beyond the capacity of a quantum(-inspired) computer, we evaluate them in the context of decomposition. Experiments suggest that both annealers are effective on problems with small size and simple settings, but lose their utility when facing problems in practical size and settings. Decomposition methods extend the scalability of annealers, but they are still far away from practical use. Based on the experiments and comparison, we discuss the advantages and limitations of quantum(-inspired) annealers, as well as the research directions that may improve the utility and scalability of the these emerging computing technologies.

One of the most attractive things about cloud computing from the perspective of business people is that it provides an effective means to outsource IT. The behaviors of business applications on cloud are constantly evolving due to technical upgrading, cloud migration as well as social outbreaks. These changes bring the challenge of detecting anomalies during the change of applications on cloud. LOF (Local Outlier Factor) algorithm has already been proven as the most promising outlier detection method for detecting network intrusions. To improve the performance of detection, LOF needs a complete set of normal behaviors of business applications, which is usually not available in cloud computing. We present an adaptive anomaly detection scheme for cloud computing based on LOF. Our scheme learns behaviors of applications both in training and detecting phase. It is adaptive to the change during detecting. The adaptability of our scheme reduces demand of efforts on collecting training data before detecting. It also enables the ability to detect contextual anomalies. Experimental results show that our scheme can effectively detect contextual anomalies with relatively low computational overhead.

Brain-inspired spiking neural networks (SNNs) replace the multiply-accumulate operations of traditional neural networks by integrate-and-fire neurons, with the goal of achieving greater energy efficiency. Specialized hardware implementations of those neurons clearly have advantages over general-purpose devices in terms of power and performance, but exhibit poor scalability when it comes to accelerating large neural networks. DeepFire2 introduces a hardware architecture which can map large network layers efficiently across multiple super logic regions in a multi-die FPGA. That gives more control over resource allocation and parallelism, benefiting both throughput and energy consumption. Avoiding the use of lookup tables to implement the AND operations of an SNN, prevents the layer size to be limited by logic resources. A deep pipeline does not only lead to an increased clock speed of up to 600 MHz. We double the throughput and power efficiency compared to our previous version of DeepFire, which equates to an almost 10-fold improvement over other previous implementations. Importantly, we are able to deploy a large ImageNet model, while maintaining a throughput of over 1500 frames per second.