With the rapid growth of surveillance cameras to monitor the human activity demands such system which recognize the violence and suspicious events automatically. Abnormal and violence action detection has become an active research area of computer vision and image processing to attract new researchers. The relevant literature presents different techniques for detection of such activities from the video proposed in the recent years. This paper reviews various state-of-the-art techniques of violence detection. In this paper, the methods of detection are divided into three categories that is based on classification techniques used: violence detection using traditional machine learning, using support vector machine (SVM), and using deep learning. The feature extraction techniques and object detection techniques of the each single method are also presented. Moreover, datasets and video features that used in the techniques, which play a vital role in recognition process are also discussed. For better understanding, the steps of the research approaches have been presented in an architecture diagram. The overall research findings have been discussed which may be helpful for finding the potential future work in this research domain.

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A Review on State-of-the-Art Violence Detection Techniques

Context: User stories have been widely accepted as artifacts to capture the user requirements in agile software development. They are short pieces of texts in a semi-structured format that express requirements. Natural language processing (NLP) techniques offer a potential advantage in user story applications. Objective: Conduct a systematic literature review to capture the current state-of-the-art of NLP research on user stories. Method: The search strategy is used to obtain relevant papers from SCOPUS, ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, and Google Scholar. Inclusion and exclusion criteria are applied to filter the search results. We also use the forward and backward snowballing techniques to obtain more comprehensive results. Results: The search results identified 718 papers published between January 2009 to December 2020. After applying the inclusion/exclusion criteria and the snowballing technique, we identified 38 primary studies that discuss NLP techniques in user stories. Most studies used NLP techniques to extract aspects of who, what, and why from user stories. The purpose of NLP studies in user stories is broad, ranging from discovering defects, generating software artifacts, identifying the key abstraction of user stories, and tracing links between model and user stories. Conclusion: NLP can help system analysts manage user stories. Implementing NLP in user stories has many opportunities and challenges. Considering the exploration of NLP techniques and rigorous evaluation methods is required to obtain quality research. As with NLP research in general, the ability to understand a sentence’s context continues to be a challenge.

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User Stories and Natural Language Processing: A Systematic Literature Review

Business intelligence helps gather essential information from a wide variety of unstructured data and convert them into actionable information that allows firms to make informed policy decisions and improve business efficiency and productivity. The challenges facing any organization in business intelligence and decision-making include plan failure, lack of preparation, resource failure, and risk-taking capability.This paper proposes a framework of Optimized Data Management using Big Data Analytics (ODM-BDA) to increase the intelligent organizational effectiveness and decision-making analysis in organizations. A backtracking method is introduced in business intelligence and decision-making environments to enhance plan failure and risk-taking ability. The steep optimized strategy is incorporated in the ODM-BDA framework to improve the training plan and manage money. The research data provided an essential set of acceptance research for these models in increasing the relative level of effectiveness and performance.The simulation analysis is performed based on true positive analysis, performance analysis, error analysis, and accuracy analysis, proving the proposed framework's reliability.

Organizational business intelligence and decision making using big data analytics

During the last decade, the use social media has provided a virtual community where the users express their intention, opinion and communicate with others. Organizations and researchers are very much interested to investigate the polarity of the user's opinions for making predictions and planning of future plans. The use and influence of social media in politics have been recognized by many researchers and political parties in making electoral predictions and devising future campaign strategies. As the literature on making electoral predictions is increasing day by day, this study aims to give an overview of the current state of approaches being utilized, social media used for data collection and the outcomes of the proposed approaches, either succeeded in making accurate electoral predictions using social media data and approaches used or not. This study gives a quick overview and guides researchers in understanding the approaches previously adopted and data sources utilized in predicting electoral results.

Predicting Elections: Social Media Data and Techniques

Developers always wish to ensure that their latest changes to the code base do not break existing functionality. If test cases fail, they expect these failures to be connected to the submitted changes. Unfortunately, a flaky test can be the reason for a test failure. Developers spend time to relate possible test failures to the submitted changes only to find out that the cause for these failures is test flakiness. The dilemma of an identification of the real failures or flaky test failures affects developers' perceptions about what is test flakiness. Prior research on test flakiness has been limited to test smells and tools to detect test flakiness. In this paper, we have conducted a multiple case study with four different industries in Scandinavia to understand practitioners' perceptions about test flakiness and how this varies between industries. We observed that there are little differences in how the practitioners perceive test flakiness. We identified 23 factors that are perceived to affect test flakiness. These perceived factors are categorized as 1) Software test quality, 2) Software Quality, 3) Actual Flaky test and 4) Company-specific factors. We have studied the nature of effects such as whether factors increase, decrease or affect the ability to detect test flakiness. We validated our findings with different participants of the 4 companies to avoid biases. The average agreement rate of the identified factors and their effects are 86% and 86% respectively, among participants.

Empirical Analysis of Factors and their Effect on Test Flakiness - Practitioners' Perceptions.

Software Requirements Engineering has paved its roots in both industry and academia, as today’s complex systems are programmed to provide efficient user-centric functionalities. This also refers to the emergence of challenges in Requirements Elicitation techniques, approaches, and tools while performing them. Particularly, in the area of Requirements Engineering for software development, a number of techniques and approaches have been observed in literature but for mobile application development, which is different from the traditional software development, has not been discussed much in past studies. Short development cycle, device limitations, and less development time for mobile application development are some of the issues to which there is no ‘silver bullet’ available. Therefore, the Requirement Analysts are in dire need of defined guidelines for Requirement Elicitation in mobile application development. With this study, we aim to provide a detailed overview of Requirements Elicitation techniques and its challenges. We have conducted a systematic literature review by surveying 4507 initial and 36 primary studies. A comprehensive set of 22 elicitation techniques were measured based on quality assessment criteria, including time and cost factors, resource effectiveness, and domain understanding. Furthermore, the challenges in Requirements Elicitation were also grouped into eight different categories based on their applicability. Our study effectively contributes in highlighting Software Requirements Elicitation Techniques and its challenges in mobile application development.

A Systematic Study on Software Requirements Elicitation Techniques and its Challenges in Mobile Application Development

A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these opinions is the primary concern of Sentiment analysis or opinion mining. Efficiently capturing, gathering, and analyzing sentiments have been challenging for researchers. To deal with these challenges, in this research work, we propose a highly accurate approach for SA of fake news on COVID-19. The fake news dataset contains fake news on COVID-19; we started by data preprocessing (replace the missing value, noise removal, tokenization, and stemming). We applied a semantic model with term frequency and inverse document frequency weighting for data representation. In the measuring and evaluation step, we applied eight machine-learning algorithms such as Naive Bayesian, Adaboost, K-nearest neighbors, random forest, logistic regression, decision tree, neural networks, and support vector machine and four deep learning CNN, LSTM, RNN, and GRU. Afterward, based on the results, we boiled a highly efficient prediction model with python, and we trained and evaluated the classification model according to the performance measures (confusion matrix, classification rate, true positives rate...), then tested the model on a set of unclassified fake news on COVID-19, to predict the sentiment class of each fake news on COVID-19. Obtained results demonstrate a high accuracy compared to the other models. Finally, a set of recommendations is provided with future directions for this research to help researchers select an efficient sentiment analysis model on Twitter data.

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Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches.

The overall quality and success of software highly depends on the involvement of stakeholders. Requirements elicitation supports RE analyst to gather requirements from the stakeholders based on their needs. There are multiple elicitation techniques present in literature and used by the practitioners. Some of them are questionnaires, interviews, prototyping, and user stories etc. However, these techniques are based on textual representation of requirements. These techniques are quite common among the requirement engineers yet problems of ambiguity, inconsistency, incompleteness still exist mostly due to their textual nature and lack of stakeholder involvement. Lack of clarity about the system increases the ambiguity of what exactly are the system requirements. Since elicitation is carried at an early stage of development the users are not sure of what they want, as requirements tend to evolve with the help of discussions and interactions among various stakeholders and technical team. Furthermore, the conventional elicitation methods are limited when it comes to stakeholders’ participation and involvement, thus leaving a space for more ambiguous and incomplete requirements. In this work, Gamification, a game-based context will be used in non-gaming context for user involvement in fun ways. During elicitation, gamification would help to involve and interact with the stakeholders, with an intention to develop their interest in eliciting and finalizing system requirements. The goal of this paper is to reduce ambiguity during requirements elicitation. This would help in reducing the cost and time of development. Furthermore, we will elicit software requirements using gamification by developing a gamification tool with a focus to elicit unambiguous requirements by ensuring users’ participation and maintaining interest. The validation of tool would be done using multiple confirmatory case studies from software industry.

Reducing Ambiguity in Requirements Elicitation via Gamification

Sentiment analysis is the study area in Natural language processing (NLP) that is concerned to identify the mood or opinion with in the text. This paper emphasizes on the different methods utilized for classifying the natural language text reviews in accordance with opinions expressed in text to analyze whether the extensive behavior is negative, positive or neutral. The abundance of discussion platforms, Weblogs, product reviews sites, e-commerce and social networking sites have encouraged stream of thoughts and articulation of opinions. Social media is considered to be a big platform of sentiments, reviews and opinion evaluation. Data used in this study are online product reviews collected from twitter and used to rank the best classifier for sentiments. The method of analysis on polarity classification was discussed in experimental work by using well known classifiers including Naive byes, Support vector machine and Logistic regression for predicting the user reviews.

Sentiment Analysis of Polarity in Product Reviews In Social Media

A Natural Language Interface (NLI) facilitates users to pose queries to retrieve information from a database without using any artificial language such as the Structured Query Language (SQL). Several applications in various domains including healthcare, customer support and search engines, require elaborating structured data having information on text. Moreover, many issues have been explored including configuration complexity, processing of intensive algorithms, and popularity of relational databases, due to which translating natural language to database query has become a secondary area of investigation. The emerging trend of querying systems and speech-enabled interfaces revived natural language to database queries research area., The last survey published on this topic was six years ago in 2013. To best of our knowledge, there is no recent study found which discusses the current state of the art translations frameworks for natural language for structured and non-structured query languages. In this paper, we have reviewed 47 frameworks from 2008 to 2018. Out of 47, 35 were closely relevant to our work. SQL based frameworks have been categorized as statistical, symbolic and connectionist approaches. Whereas, NoSQL based frameworks have been categorized as semantic matching and pattern matching. These frameworks are then reviewed based on their supporting language, scheme of their heuristic rule, interoperability support, dataset scope and their overall performance score. The findings stated that 70% of the work in natural language to database querying has been carried out for SQL, and NoSQL share 15%, 10% and 5% of languages like SPAROL, CYPHER and GREMLIN respectively. It has also been observed that most of the frameworks support English language only.

Hafsa Dar

Papers

A Systematic Study on Software Requirements Elicitation Techniques and its Challenges in Mobile Application Development

Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches.

Reducing Ambiguity in Requirements Elicitation via Gamification

Sentiment Analysis of Polarity in Product Reviews In Social Media

Frameworks for Querying Databases Using Natural Language: A Literature Review