Informatik

The routing simulations over ad hoc networks indicate that network capacity is poorly utilized in terms of throughput and packet delay when the 802.11 MAC protocol is integrated with routing algorithms. Also, since wireless network access point is open to anyone, problem of security is inherent in wireless scenario. In this paper we aim to study the characteristics & performance of MAC Layer with regard to IEEE 802.11 MAC protocol and 802.3 MAC protocol from the point of view of Campus Area Network. We conducted some simulation for the same using NS-2 and concluded an adaptive performance model best suited for University Campus Area for networking in terms of throughput and fairness. We created a performance model of Wireless local area network to show what happens when large no. of mobile nodes take part, move and communicate with one another in a WLAN and simulated our model taking varying slot time from 20 to 15, 12 & 10 micro sec. for getting optimum key point for such WLANs.

Vo rtragsaufzeichnungen digital präsentierter Inhalte erfreuen sich bei Studenten und Dozenten heute gleichermaßen großer Beliebtheit. Systeme zur Präsentation und Aufzeichnung vonInhalten sind bisher entweder Infrastruktur-oder Individuallösungen. Eine Infrastruktur-Lösung bietet für Dozenten ein System, auf dem man mit minimaler Vo rbereitung eigene Inhalte präsentieren und aufzeichnen lassen kann, jedoch wird der Dozent in der Wahl der Inhalte durch das System beschränkt. Im Gegensatz dazu erlaubt eine individuelle Lösung dem einzelnen Dozenten, beliebige Inhalte zu präsentieren und aufzuzeichnen, wasj edoch technisches Knowhowe rfordert und einen jeweils hohen individuellen Aufwand für Wartung, Vo r-sowie Nachbereitung für den Einzelnen nach sich zieht. Im Rahmen dieses Papers zeigen wir anhand bestehender Präsentations-und Aufzeichnungssysteme Ve rtreter dieser Klassen und formulieren Anforderungen an eine neue, alternative Klasse. Wirskizzieren die Funktionsweise eines solchen Systems und präsentieren einen Prototypen sowie Erfahrungen, die im Rahmen einer Lehrveranstaltungen mit diesem System gemacht wurden. Das Paper schließt mit einer Reihe von Erweiterungen, welche sich aus dem bisherigen Erfahrungen heraus anbieten. 1E inleitung Bildschirmpräsentationen zählen zu den häufigsten Formen der elektronisch unterstützten Präsenzlehre. Moderne Präsentationswerkzeuge wie Microsoft Powerpoint, OpenOffice Impress oder Apple Keynote ermöglichen die einfache Erstellung auch komplexerP r asentationen. In Kombination mit Werkzeugen wie dem Digitalen Hörsaal [RTM + 04] oder Lecturnity [IMC] können die elektronischen Inhalte präsentiert und samt Bild und Tondes vortragenden Dozenten aufgezeichnet werden. Diese Vo rlesungsaufzeichnungen erfreuen sich bei Studenten zur Nachbereitung möglicherweise verpasster Ve ranstaltungen sowie zur Vo rbereitung für abschließende Prüfungen großer Beliebtheit [Phi02].

Recent intensive theoretical and experimental studies shed light on possible new physics beyond the standard model of particle physics, which can be probed with sub-eV energy experiments. In the second run of the OSQAR photon regeneration experiment, which looks for the conversion of photon to axion (or Axion-Like Particle), two spare superconducting dipole magnets of the Large Hadron Collider (LHC) have been used. In this paper we report on first results obtained from a light beam propagating in vacuum within the 9 T field of two LHC dipole magnets. No excess of events above the background was detected and the two-photon couplings of possible new scalar and pseudo-scalar particles could be constrained.

The OSQAR photon regeneration experiment searches for pseudoscalar and scalar axionlike particles by the method of "Light Shining Through a Wall", based on the assumption that these weakly interacting sub-eV particles couple to two photons to give rise to quantum oscillations with optical photons in strong magnetic field. No excess of events has been observed, which constrains the di-photon coupling strength of both pseudoscalar and scalar particles down to 5.7 · 10 −8 GeV −1 in the massless limit. This result is the most stringent constraint on the di-photon coupling strength ever achieved in laboratory experiments.

We present a sentiment classification system that participated in the SemEval 2014 shared task on sentiment analysis in Twitter. Our system expands tokens in a tweet with semantically similar expressions using a large novel distributional thesaurus and calculates the semantic relatedness of the expanded tweets to word lists representing positive and negative sentiment. This approach helps to assess the polarity of tweets that do not directly contain polarity cues. Moreover, we incorporate syntactic, lexical and surface sentiment features. On the message level, our system achieved the 8th place in terms of macroaveraged F-score among 50 systems, with particularly good performance on the Life-Journal corpus (F 1 =71.92) and the Twitter sarcasm (F 1 =54.59) dataset. On the expression level, our system ranked 14 out of 27 systems, based on macro-averaged F-score.

Online, social media communication is often ambiguous, and it can encourage speed and inattentiveness. We investigated whether Actively Open Minded Thinking (AOT), a dispositional willingness to seek out new or potentially threatening information, may help users avoid these pitfalls. In Study 1, we determined that correctly assessing social media authors’ traits was positively predicted by raters’ AOT. In Study 2, we used data-driven methods to devise a three-dimensional picture of online behaviors of people high or low in AOT, finding that AOT is associated with thoughtful, nuanced, idiosyncratic actions and with resisting the typically fast pace of online interactions. AOT may be an important factor in accurate, socially responsible online behavior.

Scientific reasoning and argumentation (SRA) is a complex process. Thus, analyzing the quality of learners’ SRA and presenting SRA outcomes are important research problems. This study attempts to account for these problems by developing a user interface that facilitates learning scientists to analyze SRA, enabling them to evaluate the performance of an automated coding algorithm and supporting them to recognize patterns in the coded data using flexible visualizations. The usability and the comprehensibility of the user interface have been tested by means of a user study in an online performance test. The results indicate that the user interface effectively supports the investigation of SRA.

Recent intensive theoretical and experimental studies shed light on possible new physics beyond the standard model of particle physics, which can be probed with sub-eV energy experiments. In the second run of the OSQAR photon regeneration experiment, which looks for the conversion of photon to axion (or Axion-Like Particle), two spare superconducting dipole magnets of the Large Hadron Collider (LHC) have been used. In this paper we report on first results obtained from a light beam propagating in vacuum within the 9 T field of two LHC dipole magnets. No excess of events above the background was detected and the two-photon couplings of possible new scalar and pseudo-scalar particles could be constrained.

Obtaining insights into the style and content characteristics of a novel can provide a benefit to a large number of users. Parents and teachers may be interested in finding appropriate books for children. Booksellers may want to assess the fit of a candidate’s artwork into their portfolio or determine the target audience for their promotion activities. Literature scholars might discover particular stylistic similarities in writing patterns of different authors. For all of the above, manually reviewing the textual content of the books is a tedious and time-consuming task which can be achieved only to a limited level of detail. The combination of automated data analysis of literature and computer-based visualization techniques proves to be powerful in giving a quick overview as well as providing details of the visualized data. In this chapter we define the umbrella term Story Complexity, and outline the text data analysis required to describe properties of literature contributing to t...

There are many applications of text classification such as gender attribution in market research or the identification of forged product reviews on e-commerce sites. Although several automatic methods provide satisfying performance in most application cases, we see a gap in supporting the analyst to understand the results and derive knowledge for future application scenarios. In this paper, we present a visualization driven application that allows analysts to gain insight in text classification tasks such as sentiment detection or authorship attribution on feature level, built with a practitioner’s way of reasoning in mind, the Text Classification Analysis Process.

People associate certain behaviors with certain social groups. These stereotypical beliefs consist of both accurate and inaccurate associations. Using large-scale, data-driven methods with social media as a context, we isolate stereotypes by using verbal expression. Across four social categories—gender, age, education level, and political orientation—we identify words and phrases that lead people to incorrectly guess the social category of the writer. Although raters often correctly categorize authors, they overestimate the importance of some stereotype-congruent signal. Findings suggest that data-driven approaches might be a valuable and ecologically valid tool for identifying even subtle aspects of stereotypes and highlighting the facets that are exaggerated or misapplied.

This dissertation is concerned with the applicability of knowledge, contained in lexical-semantic resources, to text classification tasks. Lexical-semantic resources aim at systematically encoding various types of information about the meaning of words and their relations. Text classification is the task of sorting a set of documents into categories from a predefined set, for example, “spam” and “not spam”. With the increasing amount of digitized text, as well as the increased availability of the computing power, the techniques to automate text classification have witnessed a booming interest. The early techniques classified documents using a set of rules, manually defined by experts, e.g. computational linguists. The rise of big data led to the increased popularity of distributional hypothesis - i.e., ``a meaning of word comes from its context'' - and to the criticism of lexical-semantic resources as too academic for real-world NLP applications. For long, it was assumed tha...

Social media allows any user to express themselves to the public through posting content. Using a crowdsourcing experiment, we aim to quantify and analyze which human attributes lead to better perceptions of the true identity of others. Using tweet content from a set of users with known age and gender information, we ask workers to rate their perception of these traits and we analyze those results in relation to the crowdsourcing workers’ age and gender. Results show that female workers are both more confident and more accurate at reporting gender, and workers in their thirties were most accurate but least confident for rating age. Our study is a first step in identifying the worker traits which contribute to a better understanding of others through their posted text content. Our findings help to identify the types of workers best suited for certain tasks.

This paper discusses the potential for creating academic resources (tools, data, and evaluation approaches) to support research in conversational search, by focusing on realistic information needs and conversational interactions. Specifically, we propose to develop and operate a prototype conversational search system for scholarly activities. This Scholarly Conversational Assistant would serve as a useful tool, a means to create datasets, and a platform for running evaluation challenges by groups across the community. This article results from discussions of a working group at Dagstuhl Seminar 19461 on Conversational Search.

Would you target your audience differently, knowing the real age and gender of the text authors on your website forum? This paper examines hundreds of thousands of online documents, e.g. chat lines or blog posts, showing that computers are capable to address this task better than humans, without relying on content stereotypes. Pointing out that age and gender profiling are not independent problems, we approach the task as a multiclass classification problem, combining the age and gender information to define six classes. Utilizing a wide range of stylistic and content features and a large number of readability measures we demonstrate the high predictive abilities of the parts of speech, the punctuation and the amount of emotions and slang used in the text, independently of the topic discussed.

In this paper, we aim to reveal the impact of lexical-semantic resources, used in particular for word sense disambiguation and sense-level semantic categorization, on automatic personality classification task. While stylistic features (e.g., part-of-speech counts) have been shown their power in this task, the impact of semantics beyond targeted word lists is relatively unexplored. We propose and extract three types of lexical-semantic features, which capture high-level concepts and emotions, overcoming the lexical gap of word n-grams. Our experimental results are comparable to state-of-the-art methods, while no personality-specific resources are required.

Social media allows any user to express themselves to the public through posting content. Using a crowdsourcing experiment, we aim to quantify and analyze which human attributes lead to better perceptions of the true identity of others. Using tweet content from a set of users with known age and gender information, we ask workers to rate their perception of these traits and we analyze those results in relation to the crowdsourcing workers' age and gender. Results show that female workers are both more confident and more accurate at reporting gender, and workers in their thirties were most accurate but least confident for rating age. Our study is a first step in identifying the worker traits which contribute to a better understanding of others through their posted text content. Our findings help to identify the types of workers best suited for certain tasks.