Anish Anil Patankar

Evolving the User Graph: From unsupervised topic models to knowledge assisted networks

The next generation intelligent devices need to understand and evolve with the user. Towards this goal, we present a User Graph generation framework that models users level of interest and knowledge across a set of categories. The user graph is built through an unsupervised and semi-supervised topic modeling process, using latent semantic analysis technology. The self-evolving framework utilizes in-device user data, is built and managed within a local mobile device, thereby ensuring user privacy without the need for additional network based infrastructure. We present and analyze our trial results, aimed at optimizing model accuracy and execution efficiency. In addition to native application adaptation use cases, we also present three new services: Graph Clusters, Graph Shares and Graph Nets that utilize the framework.

Attention Sensitive Web Browsing

The attention level is an important indicator of the users level of interest while viewing any content. The web browser is one of the most popular means to access information, and the usage of browsers in mobile devices is increasing. In this paper we analyze the use of attention as an input for web browsers. Attention can be measured easily in real time using cheap commercially available wearable EEG sensors, such as NeuroSkys MindWave. We use the measured level of attention in the following ways: as an input mechanism for navigating through the controls on the web browser such as buttons, menus and hyperlinks, to correlate the attention with the section of the webpage being browsed and make the web browser responsive to the users attention level in real time, and as an input that is fed back to the web server enabling the web content developer to make attention sensitive websites. For each of these, we provide the implementation details and some results obtained. We also provide some pointers how the input attention level event obtained from the EEG sensors can be standardized in the W3C specification.

Bias Based Navigation for News Articles and Media

In existing news related services, readers cannot decide if there is another side of a news story, unless they actually come across an article representing a different perspective. However, it is possible to determine the bias in a given article using NLP related tools. In this paper we determine the bias in media content such as news articles, and use this determined bias in two ways. First, we generate the topic/bias index for one or more news articles, positioning each article within the index for a given topic or attribute. We then provide a user interface to display how much bias is present in the currently read article, along with a slider to enable the reader to change the bias. Upon the user changing the bias value, the system loads a different news article on the same topic with a different bias. The system can be extended for a variety of media such as songs, provided the lyrics are known. We test our system on a few news articles on different topics, reconfirming the detected bias manually.

Enabling Multi-Topic and Cross-Language Browsing Using Web-Semantics Service

Mobile web interaction using multiple modalities such as speech, gesture and touch is a well-researched area with examples of multiple framework deployments within the domain. Here, we explore usage of semantics of web content as a new modality for mobile web content discovery and navigation. In addition to semantics-based interactivity, the framework enables dynamic cross-language switching between web pages represented in different languages representing same topics through use of their inherent semantic relations. The approach uses transforming natural web content representations to an intermediate index of latent topic distributions. As the system is semantically indexed, no translation or transliteration techniques are required for cross language switch. The multi-dimensional topic index is built using content-topic distributions which in turn are inferred using supervised topic models. The end-to-end system has been developed as an industrial application with enhanced navigational capability provided as an extension to Samsung Browser v4.0 for Android platform. The service also allows standard browsers to query limited services using the web services RESTful API. The current system provides semantic navigation along with cross-language support for English, Hindi, and Spanish. The semantics based navigation modality, benchmarked against state of the art semantic browsers on objective and usability metrics by users demonstrate the superiority of this system. The cross-language system has been evaluated by language experts for both cross-mapping accuracy as well as relevance order mapping resulting in a subjective cumulative gain score of over 72%.

Semantics-Based Browsing Using Latent Topic Warped Indexes

Mobile web browsing has inherent usability and content discovery problems that warrants alternate browsing paradigms. To provision improved web usability, a server hosted multi-topic content indexing system and a client-based topic cluster solution has been developed that allows the user to browse the web based on content semantics alone. Semantic connections between content are established based on mono-topic and multi-topic indexes built by our service framework. Topic indexing is performed through latent analysis of web content and semantic relations between content is established through distance based relational mining. From user perspective, once an anchor page is loaded in mobile web browser, the user can browse semantically related pages with respect to the anchor page using intuitive vertical and horizontal swipe actions. The pages can be ordered temporally which adds a longitudinal (temporal) dimension to browsing. We present our experimental results based on subjective and objective evaluations via user trials validating semantics-based browsing paradigm.