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The invention discloses a horrible video scene recognition method based on multi-view and multi-instance learning. The horrible video scene recognition method comprises the steps of: extracting video shots from videos in a training video set, and selecting an emotion representative frame and an emotion abrupt frame for each video shot; extracting audio and vision characteristics of each video shot in the training video set, wherein the vision characteristic is extracted on the basis of the extracted emotion representative frame and the extracted emotion abrupt frame; extracting four view characteristic vectors for each video to form a multi-view characteristic set of the training video set; carrying out sparse reconstruction on the obtained multi-view characteristic set corresponding to the training video set and the multi-view characteristic vectors of the videos to be recognized to obtain a sparse reconstruction coefficient; calculating reconstruction errors of the multi-view characteristic vectors of the videos to be recognized and multi-view characteristic sets respectively corresponding to a horrible video set and a non-horrible video set in the training video set according to the sparse reconstruction coefficient, and further determining whether the videos to be recognized are horrible videos.

A method for detecting a phishing website includes extracting a domain name from a target URL of a web page under investigation, and querying PageRank and / or Alexa ranking of the domain name; extracting a title character string from the title of the web page and matching the character string to phishing sensitive words; using the title of web page as a keyword to search on a search engine; querying whether Target URL and the web page in the search result with a same title as the web page under investigation have the same domain name server NS and server IP address to determine whether the website is a phishing website. The disclosed method utilizes common features of phishing websites and public resources on the Internet, and overcomes the difficulty in collecting phishing website samples, and is well adapted to detecting phishing aimed at new target websites.

A device for measuring a mechanical property of a tissue includes a probe configured to perturb the tissue with movement relative to a surface of the tissue, an actuator coupled to the probe to move the probe, a detector configured to measure a response of the tissue to the perturbation, and a controller coupled to the actuator and the detector. The controller drives the actuator using a stochastic sequence and determines the mechanical property of the tissue using the measured response received from the detector. The probe can be coupled to the tissue surface. The device can include a reference surface configured to contact the tissue surface. The probe may include a set of interchangeable heads, the set including a head for lateral movement of the probe and a head for perpendicular movement of the probe. The perturbation can include extension of the tissue with the probe or sliding the probe across the tissue surface and may also include indentation of the tissue with the probe. In some embodiments, the actuator includes a Lorentz force linear actuator. The mechanical property may be determined using non-linear stochastic system identification. The mechanical property may be indicative of, for example, tissue compliance and tissue elasticity. The device can further include a handle for manual application of the probe to the surface of the tissue and may include an accelerometer detecting an orientation of the probe. The device can be used to test skin tissue of an animal, plant tissue, such as fruit and vegetables, or any other biological tissue.

A system for real-time sizing of fluid-borne particles is disclosed. The system further determines, in real time, whether the detected particles are biological or non-biological. As the fluid is being tested, it is exposed to a microbe collection filter which is cultured to determine the type of microbes present in the fluid being tested.

A catalyst for purifying exhaust gases includes a substrate, and a catalytic layer. The catalytic layer includes a lower catalytic layer, a first upper catalytic layer, and a second upper catalytic layer. The lower catalytic layer being loaded with Pd and / or Pt is formed on the substrate. The first upper catalytic layer being loaded with Pd covers an upstream side of the lower catalytic layer, and exhibits a concentration of loaded Pd that falls in a range of from 4.5 to 12% by mass when the entirety of the first upper catalytic layer is taken as 100% by mass. The second upper catalytic layer being loaded with Rh covers a downstream side of the lower catalytic layer.

A method is provided which measures PCB trace characteristics from measurements of a PCB trace structure.