Matrix Factorization Techniques for Recommender Systems

In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly’s daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.

Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey

Errors and Complex Systems: Introduction Errors, complex systems, accidents, and investigations. Antecedents: Equipment The operator The company and the regulator Maintenance and inspection Multi-operator systems Culture. Data and Data Analysis: System recorders Written documentation Interviews Analysis. Issues: Situation awareness and decision making Automation. Applying the Data: Case study Final thoughts.

Investigating Human Error: Incidents, Accidents, and Complex Systems : Incidents, Accidents and Complex Systems

Smart device industry allows developers and designers to embed different sensors, processors, and memories in small-size electronic devices. Sensors are added to enhance the usability of these devices and improve the quality of experience through data collection and analysis. However, with the era of big data and machine learning, sensors’ data may be processed by different techniques to infer various hidden information. The extracted information may be beneficial to device users, developers, and designers to enhance the management, operation, and development of these devices. However, the extracted information may be used to compromise the security and the privacy of humans in the era of Internet of Everything (IoE). In this work, we attempt to review the process of inferring meaningful data from smart devices’ sensors, especially, smartphones. In addition, different useful machine learning applications based on smartphones’ sensors data are shown. Moreover, different side channel attacks utilizing the same sensors and the same machine learning algorithms are overviewed.

/pdf/sensors-of-smart-devices-in-the-internet-of-everything-ioe-4d8rk1rvan.pdf

Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

Real-time activity monitoring with a wristband and a smartphone

CONTEXT: Intelligent Software Engineering (ISE) refers to the application of intelligent techniques to software engineering. We define an “intelligent technique” as a technique that explores data (from digital artifacts or domain experts) for knowledge discovery, reasoning, learning, planning, natural language processing, perception or supporting decision-making. OBJECTIVE: The purpose of this study is to synthesize and analyze the state of the art of the field of applying intelligent techniques to Agile Software Development (ASD). Furthermore, we assess its maturity and identify adoption risks. METHOD: Using a systematic literature review, we identified 104 primary studies, resulting in 93 unique studies. RESULTS: We identified that there is a positive trend in the number of studies applying intelligent techniques to ASD. Also, we determined that reasoning under uncertainty (mainly, Bayesian network), search-based solutions, and machine learning are the most popular intelligent techniques in the context of ASD. In terms of purposes, the most popular ones are effort estimation, requirements prioritization, resource allocation, requirements selection, and requirements management. Furthermore, we discovered that the primary goal of applying intelligent techniques is to support decision making. As a consequence, the adoption risks in terms of the safety of the current solutions are low. Finally, we highlight the trend of using explainable intelligent techniques. CONCLUSION: Overall, although the topic area is up-and-coming, for many areas of application, it is still in its infancy. So, this means that there is a need for more empirical studies, and there are a plethora of new opportunities for researchers.

Intelligent software engineering in the context of agile software development: A systematic literature review

DOI reference number: 10.18293/SEKE2016-040 Abstract—Activity recognition has been widely studied in ubiquitous computing since it can be used in several application domains, such as fall detection and gesture recognition. Initially, works in this area were based on research-only devices (bodyworn sensors). However, with advances in mobile computing, current research focuses on mobile devices, mainly, smartphones. These devices provide Internet access, processing, and various sensors, such as accelerometer and gyroscope, which are useful resources for activity recognition. Therefore, many studies use smartphones as data source. Additionally, some works have already considered the use of wristbands and specially-designed watches, but fewer investigate the latest marketable wearable devices, such as smartwatches, which are less intrusive and can provide new opportunities to complement smartphone data. Moreover, for the best of our knowledge, no previous work experimentally evaluates the impact caused by the combination of sensor data from smartwatches and smartphones on the accuracy of activity recognition approaches. Therefore, the main goal of this experimental evaluation is to compare the use of data from smartphones as well as the combination of data from smartphones and smartwatches for activity recognition. We evidenced that the use of smartphone and smartwatch data combined can increase the accuracy of activity recognition.

/pdf/combining-smartphone-and-smartwatch-sensor-data-in-activity-2vk2cmvcjk.pdf

Combining Smartphone and Smartwatch Sensor Data in Activity Recognition Approaches: an Experimental Evaluation.

Felipe Ramos§, Alexandre Costa§, Mirko Perkusich¶, Hyggo Almeida§ and Angelo Perkusich§ §Intelligent Software Engineering (ISE) Group, Federal University of Campina Grande, Campina Grande, Paraı́ba, Brazil, 58429-140 {feliperamos, antonioalexandre}@copin.ufcg.edu.br, hyggo@dsc.ufcg.edu.br, perkusic@dee.ufcg.edu.br ¶Federal Institute of Paraı́ba, Monteiro, Paraı́ba, Brazil, 58500-000 mirko.perkusich@ifpb.edu.br

/pdf/a-non-functional-requirements-recommendation-system-for-2yz08ruoq7.pdf

A Non-Functional Requirements Recommendation System for Scrum-based Projects.

Context: Software team formation is an important project management activity. However, forming appropriate teams is a challenge for most of the companies. Objective: To analyze and synthesize the state of the art on the software team formation research. Additionally, we aim to organize the identified body of knowledge in software team formation as a taxonomy. Method: Using a Snowballing-based systematic mapping study, 51 primary studies, out of 2516, were identified and analyzed. We classified the studies considering the research methods used, their overall quality, and the characteristics of the formed teams and the proposed solutions. Results: The majority of the studies use search and optimization techniques in their approaches. Also, technical attributes are the most frequent type considered to build individuals’ profiles during the team formation process. Furthermore, we proposed a taxonomy on software team formation. Conclusion: There is a predominant use of search-based approaches that combine search and optimization techniques with technical attributes. However, the adoption of non-technical attributes as complementary information is a tendency. Regarding the research gaps, we highlight the level of subjectivity in software team formation and the lack of scalability of the proposed solutions.

/pdf/team-formation-in-software-engineering-a-systematic-mapping-1qrb1v1fsv.pdf

Team Formation in Software Engineering: A Systematic Mapping Study

Context - Agile Software Development (ASD) and Reuse-Driven Software Engineering (RDSE) are well-accepted strategies to improve the efficiency of software processes. A challenge to integrate both approaches is that ASD relies mostly on tacit knowledge, hampering the reuse of software development assets. An opportunity to enable RDSE for ASD is by improving the traceability between user stories (USs), the most used notation to register product requirements in ASD. Having enough link semantics between USs could enable defining similarity between them and, consequently, promote RDSE for ASD. However, this is an open challenge. Objective - To propose a taxonomy for adding link semantics between USs, focusing on easing the task of identifying similar ones. Such links, with support of traceability tools, enable the reuse of USs and their related assets. Method : We constructed a taxonomy for types of US focusing on Web Information Systems. The taxonomy is used to classify the US, given two facets: module and operation. Such information is used to infer the similarity between USs using link rules. We developed the taxonomy based on an empirical analysis of five product backlogs, containing a total of 118 USs. Afterward, we validated the taxonomy in terms of its potential to enable the reuse of US-related assets. First, we executed an offline validation by applying it to classify 530 USs from 26 already ended projects. Finally, we applied the taxonomy in a case study with two ongoing projects (59 USs). Results : The proposed taxonomy for USs is composed of two sub-facets, namely, module and operation, which have, respectively, three and 18 terms. In terms of coverage, for the offline study and case study, we classified 90.17% of the USs with the proposed taxonomy. For the case study, we classified all the USs analyzed. Conclusion : We concluded that it is possible to use our approach to compare USs and, consequently, retrieve their related assets. Our results regarding its practical utility have shown that users considered the taxonomy a useful approach to ease the process of assessing the similarity between user stories.

/pdf/enabling-the-reuse-of-software-development-assets-through-a-31l41hdwp9.pdf

Felipe Barbosa Araújo Ramos

Papers

Intelligent software engineering in the context of agile software development: A systematic literature review

Combining Smartphone and Smartwatch Sensor Data in Activity Recognition Approaches: an Experimental Evaluation.

A Non-Functional Requirements Recommendation System for Scrum-based Projects.

Team Formation in Software Engineering: A Systematic Mapping Study

Enabling the Reuse of Software Development Assets Through a Taxonomy for User Stories