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Program analysis is a problem area concerned with methodical extraction of information from programs. It has attracted a great deal of attention from computer scientists since the inception of computer science as an academic discipline. Earlier research efforts were mostly motivated by problems encountered in compiler construction (Aho and Ullman, 1973). Subsequently, the problem area was expanded to include those that arise from development of computer-aided software engineering tools, such as the question of how to detect certain programming errors through static analysis (Fosdick and Osterweil, 1976).
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The analysis and research of power system necessitates the current computing. However, the bottleneck of current computing lies in the limited computing capacity in power system. Cloud computing's service-oriented characteristics advance a new way of service provisioning called utility based computing, which could provide powerful computing capability for current computing. However, toward the deployment of practical current computing Cloud, we encounter one challenge that the existing job scheduling algorithms under utility based computing do not take hardware/software failure and recovery in the Cloud into account. In an attempt to address this challenge, we introduce the failure and recovery scenario in the current Cloud computing entities and propose a Reinforcement Learning (RL) based algorithm to make job scheduling in the current computing Cloud fault tolerant. We carry out experimental comparison with Resource-constrained Utility Accrual algorithm (RUA), Utility Accrual Packet scheduling algorithm (UPA) and LBESA to demonstrate the feasibility of our proposed approach.
This paper describes a methodology for software testing and validation. By recognizing that there are several major error types, this methodology uses different test strategies to expose a particular type of error. To facilitate these strategies, specific tools are needed. This paper not only identifies the desired tools, but also discusses the design concepts behind various tools as they have been built at International Software Systems, Inc. (ISSI).
The current limitations of user–product interaction with smart speakers have spurred the proposal of a model to circumvent these challenges. We used the ViP design principle to redefine the user’s approach to interacting with the product. Throughout the deconstruction and design stages, we explored the structure and function of the conventional product across three layers: the product layer, interaction layer, and context layer using three models. We used the hierarchical analysis method to effectively quantify the design factors affecting user experience and identify the key design factors. This approach enabled us to contextualize the smart audio system, explore the interaction dynamics between the product and the user, and provide valuable insights on designing new products. A questionnaire method was used to survey 67 users, and a reliability test was conducted to ensure the validity of the questionnaire v (Cronbach’s coefficient α = 0.868). A pairwise comparison of factors was conducted on a 1–9 scale, with weights determined through the analytic hierarchy process (AHP). The combination of the ViP design principle and hierarchical analysis presents a novel and objective paradigm to guide designers to customize product characteristics (design attributes) to enhance user human–computer interaction experience. We validated the feasibility of the innovative design approach using the smart speaker model, offering insights for research on designing similar products.
We propose an approach to detect an unknown quantum state of the atom(s) by measuring the phase shifts of the transmitted photons through a dispersively-coupled cavity. In the framework of the input-output theory, we derive the relations between the phase shifts of the transmitted photons and the states of the atom(s) in the cavity. It is shown that due to the dispersive interaction between the cavity and the atom(s), information about the atomic state can then be extracted by measuring the phase shifts of the transmitted photons through the cavity. The feasibility of the proposal is also discussed with the experimental parameters by numerical method.
Quantum routing in a T-bulge-shaped waveguide system coupled with a driven cyclic three-level atom and a two-level atom is investigated theoretically. By employing the discrete-coordinate scattering method, exact expressions of the transport coefficients along three ports of the waveguide channels are derived. Our results show that bidirectional high transfer-rate single-photon routing between two channels can be effectively implemented, with the help of the effective potential generated by two atoms and the external driving. Moreover, multiple band zero-transmission emerges in the scattering spectra, arising from the quantum interferences among photons scattered by the boundary and the bulged resonators. The proposed system may suggest an efficient duplex router with filtering functions.
Interpersonal short video forwarding is currently one of the most popular activities of Internet users. One of the key factors that affects this online interpersonal behavior is forwarding intention. In this study, a quality perception → benefit expectation → intention model was proposed to explain the formation of video forwarding intention. To test the model data were collected from 210 undergraduate students. The results showed that perceptions of the quality of the content and empathy affect 3 benefit expectations of control, inclusion, and affection. In addition, control, inclusion, and affection affect forwarding intention.
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