This paper addresses the scope for more integrated general analysis of diversity in science, technology and society. It proposes a framework recognizing three necessary but individually insufficient properties of diversity. Based on 10 quality criteria, it suggests a general quantitative non-parametric diversity heuristic. This allows the systematic exploration of diversity under different perspectives, including divergent conceptions of relevant attributes and contrasting weightings on different diversity properties. It is shown how this heuristic may be used to explore different possible trade-offs between diversity and other aspects of interest, including portfolio interactions. The resulting approach offers a way to be more systematic and transparent in the treatment of scientific and technological diversity in a range of fields, including conservation management, research governance, energy policy and sustainable innovation.

A General Framework for Analysing Diversity in Science, Technology and Society

Global Networks: Computers and International Communication

Road transportation is the backbone of modern economies despite costing annually millions of human deaths and injuries and trillions of dollars. Twitter is a powerful information source for transportation but major challenges in big data management and Twitter analytics need addressing. We propose Iktishaf, developed over Apache Spark, a big data tool for traffic-related event detection from Twitter data in Saudi Arabia. It uses three machine learning (ML) algorithms to build multiple classifiers to detect eight event types. The classifiers are validated using widely used criteria and against external sources. Iktishaf Stemmer improves text preprocessing, event detection and feature space. Using 2.5 million tweets, we detect events without prior knowledge including the KSA national day, a fire in Riyadh, rains in Makkah and Taif, and the inauguration of Al-Haramain train. We are not aware of any work, apart from ours, that uses big data technologies for event detection of road traffic events from tweets in Arabic. Iktishaf provides hybrid human-ML methods and is a prime example of bringing together AI theory, big data processing, and human cognition applied to a practical problem.

Iktishaf: a Big Data Road-Traffic Event Detection Tool Using Twitter and Spark Machine Learning

A survey on the Artificial Bee Colony algorithm variants for binary, integer and mixed integer programming problems

Context To improve the quality and correctness of a software product it is necessary to test different aspects of the software system. Among different approaches for software testing, combinatorial testing along with covering array is a proper testing method. The most challenging problem in combinatorial testing strategies like t-way, is the combinatorial explosion which considers all combinations of input parameters. Many evolutionary and meta-heuristic strategies have been proposed to address and mitigate this problem. Objective Genetic Algorithm (GA) is an evolutionary search-based technique that has been used in t-way interaction testing by different approaches. Although useful, all of these approaches can produce test suite with small interaction strengths (i.e. t ≤ 6). Additionally, most of them suffer from expensive computations. Even though there are other strategies which use different meta-heuristic algorithms to solve these problems, in this paper, we propose an efficient uniform and variable t-way minimal test suite generation approach to address these problems using GA, called Genetic Strategy (GS). Method By changing the bit structure and accessing test cases quickly, GS improves performance of the fitness function. These adjustments and reduction of the complexities of GA in the proposed GS decreases the test suite size and increases the speed of test suite generation up to t = 20 . Results To evaluate the efficiency and performance of the proposed GS, various experiments are performed on different set of benchmarks. Experimental results show that not only GS supports higher interaction strengths in comparison with the existing GA-based strategies, but also its supported interaction strength is higher than most of other AI-based and computational-based strategies. Conclusion Furthermore, experimental results show that GS can compete against the existing (both AI-based and computational-based) strategies in terms of efficiency and performance in most of the case studies.

A tuned version of genetic algorithm for efficient test suite generation in interactive t-way testing strategy

The low participation by women authors in research is an important equity issue in Computer Science Research (CSR). There are various parameters and methodologies that can be used to measure the gender imbalance. In this paper, we present a study on gender gap, imbalance and women participation in CSR. We conduct our experiments on DBLP bibliographical database and analyze several years of publication dataset across various domains of CSR. We perform Exploratory Data Analysis on the bibliographical dataset and study the trend of gender imbalance over several years. We propose eight research questions across various facets and our results shows a significant gender imbalance in different sub-fields within CSR and low rate of women participation across various regions of world.

Women in computer science research: what is the bibliography data telling us?

Research shows that many public service agencies use Twitter to share information and reach out to the public. Recently, Twitter is also being used as a platform to collect complaints from citizens and resolve them in an efficient time and manner. However, due to the dynamic nature of the website and presence of free-form-text, manual identification of complaint posts is overwhelmingly impractical. We formulate the problem of complaint identification as an ensemble classification problem. We perform several text enrichment processes such as hashtag expansion, spell correction and slang conversion on raw tweets for identifying linguistic features. We implement a one-class SVM classification and evaluate the performance of various kernel functions for identifying complaint tweets. Our result shows that linear kernel SVM outperforms polynomial and RBF kernel functions and the proposed approach classifies the complaint tweets with an overall precision of \(76\,\%\). We boost the accuracy of our approach by performing an ensemble on all three kernels. Result shows that one-class parallel ensemble SVM classifier outperforms cascaded ensemble learning with a margin of approximately \(20\,\%\). By comparing the performance of each kernel against ensemble classifier, we provide an efficient method to classify complaint reports.

Got a Complaint?- Keep Calm and Tweet It!

ACM Special Interest Group on Hypertext and the Web (ACM SIGWEB) is a community of researchers and supporting organization of seven conferences primarily focusing on the World Wide Web, document engineering, digital libraries and linked media oriented researches. ACM SIGWEB group covers a variety of topics related to hypertext, hypermedia, social networks, information retrieval and web science. In this paper, we present a bibliometric analysis of the scientific publications and corresponding ACM metadata (also published as a research output) of seven conferences sponsored by ACM SIGWEB. We perform an exploratory data analysis on bibliography database and analyze several years of publication data (since the year of conference started until September 2015) of these conferences. We propose 5 research questions across various facets such as number of publications, authors and affiliation participation and conduct a statistical analysis of DBLP records and ACM metadata to answer those questions. Our results reveal that new SIGWEB conferences (started in or after year 2000) are growing much faster in terms of number of publications, authors and affiliation participation across various regions of world. We also find that HT, despite being the oldest SIGWEB conference the rate of number submissions received are relatively less in comparison to WSDM (new SIGWEB conference). Our analysis also reveals that CIKM is the most leading SIGWEB conference.

A glance at seven ACM SIGWEB series of conferences

Research shows that Twitter is being used as a platform to not only share and disseminate the information but also collecting complaints from citizens. However, due to the presence of high volume and large stream data, real-time manual identification of those complaints is overwhelmingly impractical. In this paper, we identify the complaints and grievances posted on bad road conditions causing life risks, discomfort and poor road experience to the citizens. We formulate the problem of killer road complaints identification as a multiclass text classification problem. We address the challenge of keyword based flagging methods and identify several linguistic features that are unique for the killer road complaints tweets such as the issue reported in the complaint, pinpoint location of the issue, city or region location information. Our results reveal that not all complaint reports posted to Public agencies contain the sufficient information and are not useful. Therefore, we further propose a mechanism to enrich the nearly-useful tweets and convert them into useful reports. We present our results using information visualization and gain actionable insights from them. Our results show that the proposed features are discriminatory and able to classify killer roads complaints with an accuracy of 67% and a recall of 65%.

Potholes and bad road conditions: mining Twitter to extract information on killer roads

In a component based system, interaction among components may give rise to interaction faults. Faults can be caused by the interactions of two or more than two components. Since, it is not feasible to test all possible interactions combinatorial interaction testing is being employed to generate covering arrays (CAs) that covers all t-way interactions between components. In this paper, we generalize our previous work, that uses a greedy based genetic algorithm, to generate CA from 2-way to t-way testing (2 ≤ t ≤ 4). We implement the proposed strategy by extending an open source tool PWiseGen. The main drawback of PWiseGen is the need to input the size of CA in advance. To overcome this restriction of PWiseGen, we propose a variation of binary search algorithm that generates optimal CA without knowing its size in advance. Experiments are conducted to evaluate the effectiveness of the proposed approach and it has been observed that the proposed approach performs well as compared to existing state-of-the-art algorithms.

Nitish Mittal

Papers

Women in computer science research: what is the bibliography data telling us?

Got a Complaint?- Keep Calm and Tweet It!

A glance at seven ACM SIGWEB series of conferences

Potholes and bad road conditions: mining Twitter to extract information on killer roads

Construction of t-way covering arrays using genetic algorithm