Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Blockchain for smart communities: Applications, challenges and opportunities

Big data analytics: Computational intelligence techniques and application areas

The social media network phenomenon creates massive amounts of valuable data that is available online and easy to access. Many users share images, videos, comments, reviews, news and opinions on different social networks sites, with Twitter being one of the most popular ones. Data collected from Twitter is highly unstructured, and extracting useful information from tweets is a challenging task. Twitter has a huge number of Arabic users who mostly post and write their tweets using the Arabic language. While there has been a lot of research on sentiment analysis in English, the amount of researches and datasets in Arabic language is limited. This paper introduces an Arabic language dataset, which is about opinions on health services and has been collected from Twitter. The paper will first detail the process of collecting the data from Twitter and also the process of filtering, pre-processing and annotating the Arabic text in order to build a big sentiment analysis dataset in Arabic. Several Machine Learning algorithms (Naive Bayes, Support Vector Machine and Logistic Regression) alongside Deep and Convolutional Neural Networks were utilized in our experiments of sentiment analysis on our health dataset.

Arabic language sentiment analysis on health services

Muriel Lederman claims “that science, like any other discipline, is a social enterprise” [1]. While the now traditional distinction of science as fact-driven and engineering as artifact-driven remains, the notion of engineering as an isolated profession of social misfits housed in cubicles, much like those depicted in the popular US comic strip “Dilbert,” is slowly being dismantled. Carol Tenopir and Donald W. King’s Communication Patterns of Engineers is a strong testament to how important communication is to engineers and the work they do.

Communication Patterns of Engineers

Task-specific information retrieval systems for software engineers

A novel Big Data analytics and intelligent technique to predict driver's intent

The Internet provides an easy and uniform way for businesses to make their brands and products visible to their customers. Due to the vast number of companies that take advantage of the Internet to conduct their business, it is very challenging for them to increase their sales and market awareness. In this paper, we investigate two techniques which are used to improve the visibility of an e-business in order to generate more traffic and sales. We describe a process of capturing the Return on Investment (ROI) from Search Engine Marketing (SEM) and focus on two main techniques: Search Engine Optimization (SEO) and Pay Per Click campaign (PPC). The investigation is carried out based on two UK-based small and medium sized enterprises (SME). We describe the results of optimization and its impact on the companies’ future strategy in this paper.

E-Marketing Strategy for Businesses

The importance of information retrieval systems is unquestionable in the modern society and both individuals as well as enterprises recognise the benefits of being able to find information effectively. Current code-focused information retrieval systems such as Google Code Search, Codeplex or Koders produce results based on specific keywords. However, these systems do not take into account developers’ context such as development language, technology framework, goal of the project, project complexity and developer’s domain expertise. They also impose additional cognitive burden on users in switching between different interfaces and clicking through to find the relevant code. Hence, they are not used by software developers. In this paper, we discuss how software engineers interact with information and general-purpose information retrieval systems e.g. Google, Yahoo! and investigate to what extent domain-specific search and recommendation utilities can be developed in order to support their work-related activities. In order to investigate this, we conducted a user study and found that software engineers followed many identifiable and repeatable work tasks and behaviours. These behaviours can be used to develop implicit relevance feedback-based systems based on the observed retention actions. Moreover, we discuss the implications for the development of task-specific search and collaborative recommendation utilities embedded with the Google standard search engine and Microsoft IntelliSense for retrieval and re-engineering of code. Based on implicit relevance feedback, we have implemented a prototype of the proposed collaborative recommendation system, which was evaluated in a controlled environment simulating the real-world situation of professional software engineers. The evaluation has achieved promising initial results on the precision and recall performance of the system.

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Design implications for task-specific search utilities for retrieval and re-engineering of code

A predictive modelling system for predicting location data from user textual data comprising: an input for receiving user data, the user data comprising user textual data and location data; a pre-processing module arranged to correlate user textual data with location data to form a set of correlated data; a training module arranged to use the set of correlated data to train a machine learning algorithm such that the algorithm is arranged to output predicted location data from an input textual query.

Adam Grzywaczewski

Papers

Task-specific information retrieval systems for software engineers

A novel Big Data analytics and intelligent technique to predict driver's intent

E-Marketing Strategy for Businesses

Design implications for task-specific search utilities for retrieval and re-engineering of code

User text content correlation with location