Dept. CS

distributed system is a network of sites that exchange informa-< I A tion with each other by message passing. A site consists of computing and storage facilities and an interface to local is a constant users and to a communication network. A primary motivation for using distributed problem, often offsetting systems is the possibility of resource shar-the advantages of resource sharing.

Framework: Groups are a fascinating interdisciplinary phenomenon. They can be defined as bounded and structured entities that emerge from the purposive, interdependent actions of individuals. One of the current open challenges on automated groups' analysis is to provide computational models of higher level concepts called emergent states, that is states emerging as results of affective, behavioral and cognitive interactions among the members of a group. Cohesion is one of these states. It is a dynamic process that is reflected in the tendency for a group to stick together and remain united in the pursuit of its instrumental objectives and/or for the satisfaction of members' affective needs. Cohesion is considered as a highly valued group property serving crucial roles for group effectiveness and performance. Scholars proposed theoretical models of cohesion having from one to five dimensions. Among these dimensions, the task and social ones were the most investigated. The task dimension concerns the extent to which group members are united to achieve the group's goals and objectives; the social dimension refers to the social relationships within the group (e.g. the extent to which group members like each other). The internship will focus on the development of a computational model of cohesion among humans, able to integrate its task and social dimensions and also accounting for their relationship and their development over time. This work will be conducted in the framework of the ANR JCJC French national project GRACE (Groups' Analysis for automated Cohesion Estimation). Intended tasks:-State-of-the-art on cohesion to identify which are its most suitable and frequent multimodal behavioral descriptors. State-of-the-art will span several research fields, including sociology, psychology, and computer science-Computation of multimodal behavioral descriptors of cohesion-Designing, implementing, and evaluating a computational model of cohesion

We study fairness in classification, where individuals are classified, e.g., admitted to a university, and the goal is to prevent discrimination against individuals based on their membership in some group, while maintaining utility for the classifier (the university). The main conceptual contribution of this paper is a framework for fair classification comprising (1) a (hypothetical) task-specific metric for determining the degree to which individuals are similar with respect to the classification task at hand; (2) an algorithm for maximizing utility subject to the fairness constraint, that similar individuals are treated similarly. We also present an adaptation of our approach to achieve the complementary goal of "fair affirmative action," which guarantees statistical parity (i.e., the demographics of the set of individuals receiving any classification are the same as the demographics of the underlying population), while treating similar individuals as similarly as possible. Finally, we discuss the relationship of fairness to privacy: when fairness implies privacy, and how tools developed in the context of differential privacy may be applied to fairness.

Upon film premiere, a major form of speculation concerns the relative success of the film. This relativity is in particular regards to the film’s original budget – as many a time have “big-budget blockbusters” been met with exceptional success as met with abject failure. So how does one predict the success of an upcoming film? In this paper, we explored a vast array of film data in an attempt to develop a model that could predict the expected return of an upcoming film. The approach to this development is as follows: First, we began with the MovieLens dataset [2] having common movie attributes alongwith genome tags per each film. Genome tags give insight into what particular characteristics of the film are most salient. We then included additional features regarding film content, cast/crew, audience perception, budget, and earnings from TMDB, IMDB, and Metacritic websites. Next, we performed exploratory data analysis and engineered awide range of new features capturing historical in...

Techniques for making future predictions based upon the present and past data, has always been an area with direct application to various real life problems. We are discussing a similar problem in this paper. The problem statement is provided by Kaggle, which also serves as an ongoing competition on the Kaggle platform. In this project, we worked with a challenging time-series dataset consisting of daily sales data, kindly provided by one of the largest Russian software firms - 1C Company. The objective is to predict the total sales for every product and store in the next month given the past data. In order to perform forecasting for next month, we have deployed eXtreme Gradient Boosting (XGBoost) and Long Short Term Memory (LSTM) based network architecture to perform learning task. Root mean squared error (RMSE) between the actual and predicted target values is used to evaluate the performance, and make comparisons between the deployed algorithms. It has been found that XGBoost far...