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In order to realize highly-reliable volunteer computing (VC), this paper develops a VC system with credibility-based voting. Credibility-based voting is known as an efficient technique for eliminating incorrect calculation results returned by participant (worker) nodes. Although its theoretical performance has been studied in detail, its implementation and practical performance have not been fully studied yet. Our VC system consists of a management server and a number of worker nodes. In the management server, each process that includes credibility-based voting is multithreaded, and all information is managed in a database for high performance and stable operation. Through performance evaluations, we found that the most time-consuming processes are the credibility-based voting in a network environment with a small delay (e.g., Intranet), and the process for sending jobs in a network environment with a moderate delay (e.g., domestic Internet). Moreover, multithreading is shown to be effective for those performance bottlenecks, and feasible system configurations are also revealed for a variety of request rates.

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We propose call-trail dependent inline caching to improve the method dispatch performance of Method Seals. Method Seals is a class extension mechanism that allows users to manually control the effective range of class extensions. It provides better safety than existing class extension mechanisms. However, the absence of inline method cache renders Method Seals' runtime performance unsatisfactory. To enable inline caching on Method Seals, we added call-trail dependency to the conventional inline caching mechanism. To that end, we introduced the notion of call-trails which represent sets of classes along a call path. We use fixed-length bitsets for representing the current call-trail and a method definition's unsealed package list. Also, we relaxed Method Seals' semantic constraints accordingly in order to implement our proposal. We also implemented the proposed call-trail dependent inline caching on top of Method Seals and benchmarked its performance.

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CPU flush instruction-based cache side-channel attacks (cache instruction attacks) target a wide range of machines. For instance, Meltdown / Spectre combined with FLUSH+RELOAD gain read access to arbitrary data in operating system kernel and user processes, which work on cloud virtual machines, laptops, desktops, and mobile devices. Additionally, fault injection attacks use a CPU cache. For instance, Rowhammer, is a cache instruction attack that attempts to obtain write access to arbitrary data in physical memory, and affects machines that have DDR3. To protect against existing cache instruction attacks, various existing mechanisms have been proposed to modify hardware and software aspects; however, when latest cache instruction attacks are disclosed, these mechanisms cannot prevent these. Moreover, additional countermeasure requires long time for the designing and developing process. This paper proposes a novel mechanism termed FlushBlocker to protect against all types of cache instruction attacks and mitigate against cache instruction attacks employ latest side-channel vulnerability until the releasing of additional countermeasures. FlushBlocker employs an approach that restricts the issuing of cache flush instructions and the attacks that lead to failure by limiting control of the CPU cache. To demonstrate the effectiveness of this study, FlushBlocker was implemented in the latest Linux kernel, and its security and performance were evaluated. Results show that FlushBlocker successfully prevents existing cache instruction attacks (e.g., Meltdown, Spectre, and Rowhammer), the performance overhead was zero, and it was transparent in real-world applications.

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We propose Roadscape-based Route Recommender System (R3), which provides diversified roadscape-based routes. Given starting and destination points, R3 provides four types of roadscape-based routes: rural-, mountainous-, waterside-, and urban-prior routes. To reduce the computational cost, we propose a coarse-to-fine route search approach that consists of a roadscape-based clustering method, roadscape cluster graph, coarse-grained route search, and fine-grained route search. We evaluated the performance of R3 using network data for real roads. The experimental results qualitatively show the validity of the generated roadscape clusters by comparing them with Google satellite maps and Google Street View images. The results also show the validity of the roadscape-based route recommendations. Furthermore, the results show that using a coarse-grained route search can significantly reduce the route search time. Finally, we quantitatively evaluate R3 from the perspective of users. The results show that R3 can appropriately recommend roadscape-based routes for given scenarios.

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This paper describes an automatic karaoke generation system, which can suppress the singing voice in audio music signals, and can also change the pitch of the song. Furthermore, this system accepts the streaming input, and it works in real-time. To the best of our knowledge, there have been no real-time audio-to-audio karaoke system that has the two functions above. This paper particularly describes the two technical components, as well as some comments on the implementation. In this system, the authors employed two signal processing techniques: singing voice suppression that is based on two-stage HPSS, a vocal enhancement technique that the authors proposed previously, and a pitch shift technique that is based on the spectrogram stretch and phase vocoder. The attached video file shows that the system works in real-time, and the sound quality may be practically acceptable.

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In this paper, a novel stochastic optimal control method based on a stochastic model predictive control framework is proposed. The proposed method is formulated as mixed integer linear programming using statistical information and binary variables, which allows us to obtain the deterministic optimization problem from the stochastic optimization problem. Moreover, it does not need to assume a class of stochastic process such as white noise. This paper also shows that the present method can be applied to real stochastic systems that have only low computation specifications, through an example problem on online optimal mode management for a Plug-in Hybrid Vehicle. The usefulness of the method is demonstrated via a detailed numerical simulator named ADVISOR, and the results show that the amount of the fuel consumption is reduced and computation time is small enough for the problem.

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Fog-Cloud computing system has recently attracted much attention. This system has a simple three-tiered hierarchy: Network edge, Fog, and Cloud. Network edge includes IoT (Internet of Things) devices. Fog is localized computing facilities including network devices such as an edge router, a switch and any other. In this system, Fog handles various data processing, which are sensitive to delay, to shorten WAN propagation latency arising from the location of Cloud. However, if the data processing is concentrated on Fog, the delay of the processing and power consumption of the network edge may increase rather than Cloud computing. The reason why is because Fog has more limited resources than Cloud. Therefore, we should decide an appropriate allocation of roles between Fog and Cloud taking the latency of the processing and the power consumption of the network edge into account. In this paper, we consider the design of Fog-Cloud computing taking the latency and cost resulted from the power consumption into account by using a mixed integer linear programming.

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