Anomaly Detection in Small-Scale Industrial and Household Appliances

Anomaly detection is concerned with identifying rare events/ observations that differ substantially from the majority of the data. It is considered an important task in the energy sector to enable the identification of non-standard device conditions. The use of anomaly detection techniques in small-scale residential and industrial settings can provide useful insights about device health, maintenance requirements, and downtime, which in turn can lead to lower operating costs. There are numerous approaches for detecting anomalies in a range of application scenarios such as prescriptive appliance maintenance. This work reports on anomaly detection using a data set of fridge power consumption that operates on a near zero energy building scenario. We implement a variety of machine and deep learning algorithms and evaluate performances using multiple metrics. In the light of the present state of the art, the contribution of this work is the development of a inference pipeline that incorporates numerous methodologies and algorithms capable of producing high accuracy results for detecting appliance failures

Scoring and summarising gene product clusters using the Gene Ontology

We propose an approach for quantifying the biological relatedness between gene products, based on their properties, and measure their similarities using exclusively statistical NLP techniques and Gene Ontology (GO) annotations. We also present a novel similarity figure of merit, based on the vector space model, which assesses gene expression analysis results and scores gene product clusters’ biological coherency, making sole use of their annotation terms and textual descriptions. We define query profiles which rapidly detect a gene product cluster’s dominant biological properties. Experimental results validate our approach, and illustrate a strong correlation between our coherency score and gene expression patterns

Using Data Mining to Assess Sofwtare Reliability

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Backtracking Incremental Continuous Integration

Failing integration builds are show stoppers. Development activity is stalled because developers have to wait with integrating new changes until the problem is fixed and a successful build has been run. We show how backtracking can be used to mitigate the impact of build failures in the context of component-based software development. This way, even in the face of failure, development may continue and a working version is always available

A model for selecting CSCW technologies for distributed software maintenance teams in virtual organisations

Software maintenance, just like any other software engineering activity, is being conducted in an increasingly distributed manner by teams which are often virtual. This paper critically reviews existing models for Virtual Organisations, investigates issues affecting Distributed Software Maintenance Teams (DSMT) and proposes a model for selecting the appropriate Computer Supported Cooperative Work (CSCW) and Groupware tools and technologies in order to facilitate communication and resource allocation for DSMT. This model builds on current theories, classifications and major concepts in the area of CSCW and advances the way DSMT are perceived. This theoretical model is yet to be empirically evaluated and enriched so that it includes Workflow management systems

A Data Science approach analysing the Impact of Injuries on Basketball Player and Team Performance

The sports industry utilizes science to improve short to long-term team and player management regarding budget, health, tactics, training, and most importantly performance. Data Science (DS) and Sports Analytics play key roles in supporting teams, players and experts to improve performance. This paper reviews the literature to identify important attributes correlated with injuries and attempts to quantify their impact on player and team performance, using analytics in the National Basketball Association (NBA) from 2010 up to 2020. It also provides an overview of Machine Learning (ML) and DS techniques and algorithms used to study injuries. Additionally, it provides information for coaches, sports and health scientists, managers and decision makers to recognize the most common injuries and investigate possible injury patterns during competitions. We identify teams and players who suffered the most, and the type of injuries requiring more attention. We found a high impact from injuries and pathologies on performance; musculoskeletal impairments are the most common ones that lead to decreased performance. Finally, we conclude that there is a weak positive relationship between performance and injuries based on a holistic multivariate model that describes player and team performance. © 2021 Elsevier Lt