Analysis of local online review systems as digital word-of-mouth

Using a large dataset of Yelp's online reviews for local busi- nesses, we investigate how Word-of-Mouth research can in- form the design of local online review systems and how these systems' data can extend our understanding of digital WOM in a local context. In this paper, we analyze how visual cues currently present in Yelp map to WOM concepts. We also show that these concepts are highly related to the perceived usefulness of the local reviews, which is aligned with prior WOM literature. Additionally, we found that local exper- Tise, measured at the level of the neighborhood, strongly correlates with the perceived usefulness of reviews. Our findings augment the understanding of local online WOM and have design implications for local review systems

My friends are here! Why talk to "Strangers"?

Many online communities face the challenge of incorporating a stable inux of newcomers into the community. Research on socialization in offine organizations suggest that newcomers who join an organization with a cohort of other newcomers are more likely to succeed in the organization. We have studied the effect of socializing a cohort of newcomers who share common offine identity into an online community. Our results suggest that cohort support can improve newcomers' performance but it might hinder communication with existing members of the community

Designing for neighborhoods: Lessons learned from paper-based bulletin boards

Many local information systems struggle to remain viable over time. The low volume of new content that is generated each day in a local community places burdens on the sustainability of such systems [2]. To shed light on designing for local communities, we investigated the content, design and significance of paper-based bulletin boards as sustainable local information systems. We found that their viability is built upon several design strategies such as announcing information about local services and small-scale events; a dual strategy of supporting sense of community and information discovery; and using a flexible, but strategic definition of the communities' geographical boundaries. Future work will investigate these design strategies in online settings

Periodic orbits, pair nucleation, and unbinding of active nematic defects on cones

Geometric confinement and topological constraints present promising means of controlling active materials. By combining analytical arguments derived from the Born-Oppenheimer approximation with numerical simulations, we investigate the simultaneous impact of confinement together with curvature singularity by characterizing the dynamics of an active nematic on a cone. Here, the Born-Oppenheimer approximation means that textures can follow defect positions rapidly on the time scales of interest. Upon imposing strong anchoring boundary conditions at the base of a cone, we find a a rich phase diagram of multi-defect dynamics including exotic periodic orbits of one or two $+1/2$ flank defects, depending on activity and non-quantized geometric charge at the cone apex. By characterizing the transitions between these ordered dynamical states, we can understand (i) defect unbinding, (ii) defect absorption and (iii) defect pair nucleation at the apex. Numerical simulations confirm theoretical predictions of not only the nature of the circular orbits but also defect unbinding from the apex.Comment: 17 pages, 13 figures, 5 table

Progressor: Social navigation support through open social student modeling

The increased volumes of online learning content have produced two problems: how to help students to find the most appropriate resources and how to engage them in using these resources. Personalized and social learning have been suggested as potential ways to address these problems. Our work presented in this paper combines the ideas of personalized and social learning in the context of educational hypermedia. We introduce Progressor, an innovative Web-based tool based on the concepts of social navigation and open student modeling that helps students to find the most relevant resources in a large collection of parameterized self-assessment questions on Java programming. We have evaluated Progressor in a semester-long classroom study, the results of which are presented in this paper. The study confirmed the impact of personalized social navigation support provided by the system in the target context. The interface encouraged students to explore more topics attempting more questions and achieving higher success rates in answering them. A deeper analysis of the social navigation support mechanism revealed that the top students successfully led the way to discovering most relevant resources by creating clear pathways for weaker students. © 2013 Taylor and Francis Group, LLC

Fuzziness in LGBT Non-Profit ICT Use

This note reports on the use of ICTs by a small nonprofit organization that serves LGBT youth. Our work centers on a reective evaluation of the use of online communities for LGBT community through qualitative interviews with the organization. Perceived issues around ICT use in the organi- zation were shaped by the blurred lines between professional and personal interactions online, the small size of the com- munity and ubiquity of social media use, and ambivalence of members toward online communication. The project models one way for researchers in ICT4D to work within communi- ties to develop an understanding of self-identified issues in vulnerable populations

Malware Detection using Artificial Bee Colony Algorithm

Malware detection has become a challenging task due to the increase in the number of malware families. Universal malware detection algorithms that can detect all the malware families are needed to make the whole process feasible. However, the more universal an algorithm is, the higher number of feature dimensions it needs to work with, and that inevitably causes the emerging problem of Curse of Dimensionality (CoD). Besides, it is also difficult to make this solution work due to the real-time behavior of malware analysis. In this paper, we address this problem and aim to propose a feature selection based malware detection algorithm using an evolutionary algorithm that is referred to as Artificial Bee Colony (ABC). The proposed algorithm enables researchers to decrease the feature dimension and as a result, boost the process of malware detection. The experimental results reveal that the proposed method outperforms the state-of-the-art

Abstract Interpretation with Unfoldings

We present and evaluate a technique for computing path-sensitive interference conditions during abstract interpretation of concurrent programs. In lieu of fixed point computation, we use prime event structures to compactly represent causal dependence and interference between sequences of transformers. Our main contribution is an unfolding algorithm that uses a new notion of independence to avoid redundant transformer application, thread-local fixed points to reduce the size of the unfolding, and a novel cutoff criterion based on subsumption to guarantee termination of the analysis. Our experiments show that the abstract unfolding produces an order of magnitude fewer false alarms than a mature abstract interpreter, while being several orders of magnitude faster than solver-based tools that have the same precision.Comment: Extended version of the paper (with the same title and authors) to appear at CAV 201