Abstract We present a verifier of quantum programs called AutoQ 2.0. Quantum programs extend quantum circuits (the domain of AutoQ 1.0) by classical control flow constructs, which enable users to describe advanced quantum algorithms in a formal and precise manner. The extension is highly non-trivial, as we needed to tackle both theoretical challenges (such as the treatment of measurement, the normalization problem, and lifting techniques for verification of classical programs with loops to the quantum world), and engineering issues (such as extending the input format with a support for specifying loop invariants). We have successfully used AutoQ 2.0 to verify two types of advanced quantum programs that cannot be expressed using only quantum circuits: the repeat-until-success (RUS) algorithm and the weak-measurement-based version of Grover’s search algorithm. AutoQ 2.0 can efficiently verify all our benchmarks: all RUS algorithms were verified instantly and, for the weak-measurement-based version of Grover’s search, we were able to handle the case of 100 qubits in $$\sim $$ ∼ 20 minutes.

In this paper, we propose the Interest Meter (IM), a system making the computer conscious of user's reactions to measure user's interest and thus use it to conduct video summarization. The IM takes account of users' spontaneous reactions when they view videos. To estimate user's viewing interest, quantitative interest measures are devised based on the perspectives of attention and emotion. For estimating attention states, variations of user's eye movement, blink, and head motion are considered. For estimating emotion states, facial expression is recognized as positive or neural emotion. By combining characteristics of attention and emotion by a fuzzy fusion scheme, we transform users' viewing behaviors into quantitative interest scores, determine interesting parts of videos, and finally concatenate them as video summaries. Experimental results show that the proposed concept “editing by viewing” works well and may provide a promising direction to consider the human factor in video summarization.

In this paper, we propose the Interest Meter (IM), a system making computer conscious of user's reactions, to measure user's interest in real time. The Interest Meter takes account of users' spontaneous reactions when users interact with computers. In this work, we analyze variations of user's eye movement, blink, head motion, and facial expression. Furthermore, we propose an algorithm to combine those signals into interest score and determine important parts of video shots when people watch raw home videos. Experimental result shows that this new type of editing mechanism can effectively generate home video summaries.

Hierarchical Mobile IPv6 management(HMIPv6) which is drawn up by the IETF (Internet Engineering Task Force) utilizes the Mobile Anchor Point (MAP) to reduce the considerable number of binding update messages among the mobile node, correspondent node, and the mobile node's home agent. According to the HMIPv6 mechanism, the MAP of higher layer can efficiently reduce the frequency of binding update messages; it will have higher loading of service and become the bottleneck of the whole network. Because the MAP's bandwidth of services is finite and if MAP serves as the gateway at the same time, the whole network will be crashed due to overloading. This research proposes a MAP selection mechanism that takes the mobile node's particular characteristics which include the mobility velocity and quantity of communication services into consideration, the research can manage MAPs efficiently. Besides, the research also designs the MAP Load Balance mechanism to avoid the network crash due to the overloaded MAP.