Abstract: A display panel includes an active area and a peripheral area surrounding the active area, and the display panel further includes: a substrate and a plurality of light emitting devices arranged on the substrate in array, wherein the plurality of light emitting devices are located at least in the active area; a conducting layer comprising a cathode ring and cathodes of the plurality of light emitting devices, wherein the cathode ring is located in the peripheral area, and the cathode ring surrounds the active area; and a lens layer located at a side of the light emitting devices away from the substrate, wherein the lens layer extends from the active area to the peripheral area; wherein an orthographic projection of the lens layer on the substrate is located within an area delineated by an outer contour of an orthographic projection of the cathode ring on the substrate.

Abstract: A system for training and deploying an artificial conversational entity using an artificial intelligence (AI) based communications system is disclosed. The system may comprise a memory storing machine readable instructions. The system may also comprise a processor to execute the machine readable instructions to receive a request via an artificial conversational entity. The processor may also transmit a response to the request based on a dialog tree generated from at least a model-based action generator and a memory-based action generator. The processor may further provide a training option to a user in the event the response is suboptimal. The processor may additionally receive a selection from the user via the training option. The selection may be associated with an optimal response.

Abstract: A system for training and deploying an artificial conversational entity using an artificial intelligence (AI) based communications system is disclosed. The system may comprise a memory storing machine readable instructions. The system may also comprise a processor to execute the machine readable instructions to receive a request via an artificial conversational entity. The processor may also transmit a response to the request based on a dialog tree generated from at least a model-based action generator and a memory-based action generator. The processor may further provide a training option to a user in the event the response is suboptimal. The processor may additionally receive a selection from the user via the training option. The selection may be associated with an optimal response.

Abstract: Generally discussed herein are devices, systems, and methods for detecting a conversation with a virtual agent is offtrack and responding appropriately. A method can include receiving a prompt, expected responses to the prompt, and a response of an interaction session with the virtual agent, the interaction session to solve a problem of a user, determining whether the response indicates the interaction session is in an offtrack state based on the prompt, expected responses, and response, in response to a determination that the interaction session is in the offtrack state, determining a taxonomy of the offtrack state, and providing, based on the determined taxonomy, a next prompt to the interaction session.

Abstract: Generally discussed herein are devices, systems, and methods for virtual agent selection of an option not expressly selected by a user. A method can include receiving, from a virtual agent interface device of the virtual agent device, a response regarding a problem, wherein the response is responsive to a prompt, and wherein the prompt is associated with one or more expected responses, determining whether the response is a match to one of the expected answers by performing one or more of (a) an ordinal match, (b) an inclusive match, (c) an entity match, and (d) a model match, and providing, responsive to a determination that the response is a match, a next prompt, or provide a solution to the problem, the next prompt associated with expected responses to the next prompt.

Abstract: Computer-readable media, computer systems, and computing devices facilitate enhancing a web index with uniform resource locator (URL)/non-encoding character (NEC) word pairs to facilitate relevance ranking of search results provided in response to a search query that includes NEC words. URLs are received from web pages and substrings extracted therefrom. Additional elements are received from the web page, word-broken into sequences of NEC words, and the NEC words are converted into encoding-language representations which are matched against the URL substrings to identify candidate URL/NEC pairs for utilization in relevance ranking.

Abstract: This document describes tools for adjusting anchor text weight to provide more relevant search engine results. Specifically, these tools take advantage of a site-relationship model to consider relationships not only between an anchor text source site and a destination page but also relationships between multiple anchor text source sites to improve web searches. Consideration of these relationships aids in determining a new an anchor text weight, which in turn results in more relevant search results.

Abstract: A method using a RankBoost-based algorithm to automatically select features for further ranking model training is provided. The method reiteratively applies a set of ranking candidates to a training data set comprising a plurality of ranking objects having a known pairwise ranking order. Each round of iteration applies a weight distribution of ranking object pairs, yields a ranking result by each ranking candidate, identifies a favored ranking candidate for the round based on the ranking results, and updates the weight distribution to be used in next iteration round by increasing weights of ranking object pairs that are poorly ranked by the favored ranking candidate. The method then infers a target feature set from the favored ranking candidates identified in the iterations.

Abstract: Computer-readable media, computer systems, and computing devices facilitate enhancing a web index with uniform resource locator (URL)/non-encoding character (NEC) word pairs to facilitate relevance ranking of search results provided in response to a search query that includes NEC words. URLs are received from web pages and substrings extracted therefrom. Additional elements are received from the web page, word-broken into sequences of NEC words, and the NEC words are converted into encoding-language representations which are matched against the URL substrings to identify candidate URL/NEC pairs for utilization in relevance ranking.

Abstract: This document describes tools for adjusting anchor text weight to provide more relevant search engine results. Specifically, these tools take advantage of a site-relationship model to consider relationships not only between an anchor text source site and a destination page but also relationships between multiple anchor text source sites to improve web searches. Consideration of these relationships aids in determining a new an anchor text weight, which in turn results in more relevant search results.

Abstract: A method using a RankBoost-based algorithm to automatically select features for further ranking model training is provided. The method reiteratively applies a set of ranking candidates to a training data set comprising a plurality of ranking objects having a known pairwise ranking order. Each round of iteration applies a weight distribution of ranking object pairs, yields a ranking result by each ranking candidate, identifies a favored ranking candidate for the round based on the ranking results, and updates the weight distribution to be used in next iteration round by increasing weights of ranking object pairs that are poorly ranked by the favored ranking candidate. The method then infers a target feature set from the favored ranking candidates identified in the iterations.

Abstract: Search results provided by a search engine (e.g., for the Internet) are improved and/or made more accurate by addressing the limited availability of human labeled training data for certain domains (e.g., languages other than English, within certain date ranges, corresponding to queries over a certain length, etc.). More particularly, a ranking model trained on in-domain data, for which a small amount of human labeled training data (e.g., query/URL pairs) is available (e.g., languages other than English) is adjusted based upon out-domain data, for which a large amount of human labeled training data (e.g., query/URL pairs) is available (e.g., English). Thus, even though the resulting adapted in-domain ranking model is used in the context of in-domain data (e.g., non-English) to provide search results, the search results are improved because they are influenced by an abundance of, albeit out-domain, human labeled training data.