Malay Bhattacharyya

Auroshikha Mandal, Mehul Agarwal, Malay Bhattacharyya

Collaborative creativity is the approach of employing crowd to accomplish creative tasks. In this paper, we present a collaborative crowdsourcing platform for writing stories by means of connecting a series of `images'. These connected images are termed as Image Chains, reflecting successive scenarios. Users can either start or extend an Image Chain by uploading their own image or choosing from the available ones. These users are allowed to pen their stories from the Image Chains. Finally, stories get published based on the number of votes obtained. This provides an organized framework of story writing unlike most of the state-of-the-art collaborative editing platforms. Our experiments on 25 contributors highlight their interest in growing shorter Image Chains but voting longer Image Chains.

Sujoy Chatterjee, Anirban Mukhopadhyay, Malay Bhattacharyya

Expertise of annotators has a major role in crowdsourcing based opinion aggregation models. In such frameworks, accuracy and biasness of annotators are occasionally taken as important features and based on them priority of the annotators are assigned. But instead of relying on a single feature, multiple features can be considered and separate rankings can be produced to judge the annotators properly. Finally, the aggregation of those rankings with perfect weightage can be done with an aim to produce better ground truth prediction. Here, we propose a novel weighted rank aggregation method and its efficacy with respect to other existing approaches is shown on artificial dataset. The effectiveness of weighted rank aggregation to enhance quality prediction is also shown by applying it on an Amazon Mechanical Turk (AMT) dataset.

Sankar Kumar Mridha, Braznev Sarkar, Sujoy Chatterjee et al.

Due to the significant growth of social networking and human activities through the web in recent years, attention to analyzing big data using real-time crowdsourcing has increased. This data may appear in the form of streaming images, audio or videos. In this paper, we address the problem of deciding the appropriateness of streaming videos in public media with the help of crowdsourcing in real-time.

Abhisek Dash, Sujoy Chatterjee, Tripti Prasad et al.

Cluster analysis has become one of the most exercised research areas over the past few decades in computer science. As a consequence, numerous clustering algorithms have already been developed to find appropriate partitions of a set of objects. Given multiple such clustering solutions, it is a challenging task to obtain an ensemble of these solutions. This becomes more challenging when the ground truth about the number of clusters is unavailable. In this paper, we introduce a crowd-powered model to collect solutions of image clustering from the general crowd and pose it as a clustering ensemble problem with variable number of clusters. The varying number of clusters basically reflects the crowd workers' perspective toward a particular set of objects. We allow a set of crowd workers to independently cluster the images as per their perceptions. We address the problem by finding out centroid of the clusters using an appropriate distance measure and prioritize the likelihood of similarity of the individual cluster sets. The effectiveness of the proposed method is demonstrated by applying it on multiple artificial datasets obtained from crowd.

Malay Bhattacharyya

Crowdsourcing environments have shown promise in solving diverse tasks in limited cost and time. This type of business model involves both the expert and non-expert workers. Interestingly, the success of such models depends on the volume of the total number of workers. But, the survival of the fittest controls the stability of these workers. Here, we show that the crowd workers who fail to win jobs successively loose interest and might dropout over time. Therefore, dropout prediction in such environments is a promising task. In this paper, we establish that it is possible to predict the dropouts in a crowdsourcing market from the success rate based on the arrival pattern of workers.

Sujoy Chatterjee, Anirban Mukhopadhyay, Malay Bhattacharyya

Providing opinions through labeling of images, tweets, etc. have drawn immense interest in crowdsourcing markets. This invokes a major challenge of aggregating multiple opinions received from different crowd workers for deriving the final judgment. Generally, opinion aggregation models deal with independent opinions, which are given unanimously and are not visible to all. However, in many real-life cases, it is required to make the opinions public as soon as they are received. This makes the opinions dependent and might incorporate some bias. In this paper, we address a novel problem, hereafter denoted as dependent judgment analysis, and discuss the requirements for developing an appropriate model to deal with this problem. The challenge remains to be improving the consensus by revealing true opinions.