An integrated ranking algorithm for efficient information computing in social networks

Social networks have ensured the expanding disproportion between the face of WWW stored traditionally in search engine repositories and the actual ever changing face of Web. Exponential growth of web users and the ease with which they can upload contents on web highlights the need of content controls on material published on the web. As definition of search is changing, socially-enhanced interactive search methodologies are the need of the hour. Ranking is pivotal for efficient web search as the search performance mainly depends upon the ranking results. In this paper new integrated ranking model based on fused rank of web object based on popularity factor earned over only valid interlinks from multiple social forums is proposed. This model identifies relationships between web objects in separate social networks based on the object inheritance graph. Experimental study indicates the effectiveness of proposed Fusion based ranking algorithm in terms of better search results.Comment: 14 pages, International Journal on Web Service Computing (IJWSC), Vol.3, No.1, March 201

Erich Fromm and the Critical Theory of Communication

Erich Fromm (1900-1980) was a Marxist psychoanalyst, philosopher and socialist humanist. This paper asks: How can Fromm’s critical theory of communication be used and updated to provide a critical perspective in the age of digital and communicative capitalism? In order to provide an answer, the article discusses elements from Fromm’s work that allow us to better understand the human communication process. The focus is on communication (section 2), ideology (section 3), and technology (section 4). Fromm’s approach can inform a critical theory of communication in multiple respects: His notion of the social character allows to underpin such a theory with foundations from critical psychology. Fromm’s distinction between the authoritarian and the humanistic character can be used for discerning among authoritarian and humanistic communication. Fromm’s work can also inform ideology critique: The ideology of having shapes life, thought, language and social action in capitalism. In capitalism, technology (including computing) is fetishized and the logic of quantification shapes social relations. Fromm’s quest for humanist technology and participatory computing can inform contemporary debates about digital capitalism and its alternatives

Knowledge-rich Image Gist Understanding Beyond Literal Meaning

We investigate the problem of understanding the message (gist) conveyed by images and their captions as found, for instance, on websites or news articles. To this end, we propose a methodology to capture the meaning of image-caption pairs on the basis of large amounts of machine-readable knowledge that has previously been shown to be highly effective for text understanding. Our method identifies the connotation of objects beyond their denotation: where most approaches to image understanding focus on the denotation of objects, i.e., their literal meaning, our work addresses the identification of connotations, i.e., iconic meanings of objects, to understand the message of images. We view image understanding as the task of representing an image-caption pair on the basis of a wide-coverage vocabulary of concepts such as the one provided by Wikipedia, and cast gist detection as a concept-ranking problem with image-caption pairs as queries. To enable a thorough investigation of the problem of gist understanding, we produce a gold standard of over 300 image-caption pairs and over 8,000 gist annotations covering a wide variety of topics at different levels of abstraction. We use this dataset to experimentally benchmark the contribution of signals from heterogeneous sources, namely image and text. The best result with a Mean Average Precision (MAP) of 0.69 indicate that by combining both dimensions we are able to better understand the meaning of our image-caption pairs than when using language or vision information alone. We test the robustness of our gist detection approach when receiving automatically generated input, i.e., using automatically generated image tags or generated captions, and prove the feasibility of an end-to-end automated process