Comparative analysis of Active Learning strategies in Twitter domain

Since its launch in the year 2006, Twitter has been One of the most popular social media platforms where users are free to share opinions, ideas and feelings. Latest statistics tell us that nearly 350,000 tweets are being posted every minute On Twitter. Also twitter is the first place to track the response to any important incident or events in the world. For this reason, Twitter has attracted the researchers from many fields, including Sentiment Analysis which deals with opinion mining from text. Twitter data is rich in containing the sentiments but is inherent with the problem of being very informal and unstructured which makes it very difficult to convert this data information. Labeling this large amount of data build classifiers for supervised learning is next to impossible. So we make use of Active Learning which is a subfield of Machine Learning and concerns with the selection of most informative instances to train the classifiers thus saving labeling efforts. This thesis deals with the comparative analysis of selected Active learning sampling strategies with twitter domain. The results show Uncertainty Sampling beats Random Satnpling and (Query by Committee consistently An analysis of agreelllent levels among annotators for twitter data has also been presented

Whistleblowing in the Software Industry: a Survey:[Proceedings]

Background: Wrongdoings occurring within or in relation to software can have big implications on individuals, groups of people, or society as a whole. Whistleblowing is considered an effective tool to reveal and stop wrongdoing but is still a controversial topic that has been researched sparsely in the software industry. Aim: In this study we address this gap and research the current environment for whistleblowing (reporting wrongdoing) in the software industry. Method: We surveyed 147 software practitioners about their views on whistleblowing, the current means they have to report software-related wrongdoing, and the enabling and obstructing factors to whistleblow. Results: Our study shows that software practitioners have a positive view towards whistleblowing. However, in practice whistleblowing is obstructed by the difficulty of proving the actual harm and fear of retaliation. Practitioners with more years of experience report more comfort using readily established mechanisms and procedures in their organization, are more willing to speak up and have more confidence that their report will lead to action than their less experienced peers. These differences are statistically significant. Conclusion: Through our results we conclude that the software industry needs to improve the environment for whistleblowers by providing more external reporting mechanisms, anonymity, and confidentiality, as well as support practitioners with less years of experience.</p

A Systematic Approach for Interfacing Component-Based Software with an Active Automata Learning Tool

Applying Model-Driven Engineering can improve development efficiency. But gaining such benefits for legacy software requires models, and creating them manually is both laborious and error prone. Active automata learning has the potential to make it cost-effective, but practitioners face practical challenges applying it to software components of industrial cyber-physical systems. To overcome these challenges, we present a framework to learn the behavior of component-based software with a client/server architecture, focusing on interfacing isolated component code with an active learning tool. An essential part of the framework is an interfacing protocol that provides a structured way of handling the (a)synchronous communications between the component and learning tool. Our main contribution is the systematic derivation of such interfacing protocols for component-based software, which we demonstrate on the software architecture of ASML, a leading company in developing lithography machines. Through several practical case studies we show that our semi-automatic approach enables setting up a learning environment to learn component behaviors within hours. The protocol’s responsibilities and the way it handles different communication types apply to component-based software in general. Our framework could thus be adapted for companies with similar software architectures

Collaborative Model-Driven Software Engineering — A systematic survey of practices and needs in industry

The engineering of modern software-intensive systems is carried out in collaboration among stakeholders with specialized expertise. The complexity of such systems often also necessitates employing more rigorous approaches, such as Model-Driven Software Engineering (MDSE). Collaborative MDSE is the combination of the two disciplines, with its specific opportunities and challenges. The rapid expansion and maturation of the field started attracting tool builders from outside of academia. However, available systematic studies on collaborative MDSE focus exclusively on mapping academic research and fail to identify how academic research aligns with industry practices and needs. To address this shortcoming, we have carried out a mixed-method survey on the practices and needs concerning collaborative MDSE. First, we carried out a qualitative survey in two focus group sessions, interviewing seven industry experts. Second, based on the results of the interviews, we constructed a questionnaire and carried out a questionnaire survey with 41 industry expert participants. In this paper, we report the results of our study, investigate the alignment of academic research with the needs of practitioners, and suggest directions on research and development of the supporting techniques of collaborative MDSE

Interface protocol inference to aid understanding legacy software components

High-tech companies are struggling today with the maintenance of legacy software. Legacy software is vital to many organizations as it contains the important business logic. To facilitate maintenance of legacy software, a comprehensive understanding of the software’s behavior is essential. In terms of component-based software engineering, it is necessary to completely understand the behavior of components in relation to their interfaces, i.e., their interface protocols, and to preserve this behavior during the maintenance activities of the components. For this purpose, we present an approach to infer the interface protocols of software components from the behavioral models of those components, learned by a blackbox technique called active (automata) learning. To validate the learned results, we applied our approach to the software components developed with model-based engineering so that equivalence can be checked between the learned models and the reference models, ensuring the behavioral relations are preserved. Experimenting with components having reference models and performing equivalence checking builds confidence that applying active learning technique to reverse engineer legacy software components, for which no reference models are available, will also yield correct results. To apply our approach in practice, we present an automated framework for conducting active learning on a large set of components and deriving their interface protocols. Using the framework, we validated our methodology by applying active learning on 202 industrial software components, out of which, interface protocols could be successfully derived for 156 components within our given time bound of 1 h for each component

Emotional and behavioral problems and coping strategies among adolescent orphans

Background and objective: Rising incidence of mental health problems is a serious issue all over the world. Adolescents living in orphanages are at a particular risk as they have numerous challenges in their life and coping with them requires adequate life skills. This study aims to assess the emotional and behavioral issues and the coping strategies adopted by adolescent orphans in Pakistan.Methods: This cross-sectional study comprises 109 adolescent orphans living in different orphanages. The Strengths and Difficulties Questionnaire for evaluating the emotional and behavioral problems (EBPs) was used while KIDCOPE scale was adopted to assess the coping strategies.Results: About 34.9% of orphans fell in an abnormal range of EBP and 22.9% were in the borderline zone. The most prevalent problem was conduct (25.7%) followed by peer problems (24.8%), emotional instability (18.3%), hyperactivity (17.4%), and prosocial behavior (11%). A significant and positive correlation was observed between peer problems and maladaptive strategies (r = 0.191, p = 0.047) and between prosocial behavior and adaptive strategies (r = 0.294, p = 0.002).Conclusion: Orphans residing in orphanages suffer from behavioral and emotional problems and are using maladaptive coping strategies. It is highly suggestive to monitor and maintain an optimal psychological health of this vulnerable population in our country.&nbsp;</p

Energy Consumption Forecasting for University Sector Buildings

Reliable energy forecasting helps managers to prepare future budgets for their buildings. Therefore, a simple, easier, less time consuming and reliable forecasting model which could be used for different types of buildings is desired. In this paper, we have presented a forecasting model based on five years of real data sets for one dependent variable (the daily electricity consumption) and six explanatory variables (ambient temperature, solar radiation, relative humidity, wind speed, weekday index and building type). A single mathematical equation for forecasting daily electricity usage of university buildings has been developed using the Multiple Regression (MR) technique. Data of two such buildings, located at the Southwark Campus of London South Bank University in London, have been used for this study. The predicted test results of MR model are examined and judged against real electricity consumption data of both buildings for year 2011. The results demonstrate that out of six explanatory variables, three variables; surrounding temperature, weekday index and building type have significant influence on buildings energy consumption. The results of this model are associated with a Normalized Root Mean Square Error (NRMSE) of 12% for the administrative building and 13% for the academic building. Finally, some limitations of this study have also been discussed