An Application of Sentiment Analysis Techniques to Determine Public Opinion in Social Media

This paper describes a prototype application that gathers textual data from the microblogging platform Twitter and carries out sentiment analysis to determine the polarity and subjectivity in relation to Brexit, the UK´ s exit from the European Union. The design, implementation and testing of the developed prototype will be discussed and an experimental evaluation of the product described. Specifically we provide insight into how events affect public opinion and how sentiment and public mood may be gathered from textual twitter data and propose this as an alternative to opinion polls. Traditional approaches to opinion polling face growing challenges in capturing the public mood. Small sample response and the time it takes to capture swings in public opinion make it difficult to provide accurate data for the political process. With over 500 million daily messages posted worldwide, the social media platform Twitter is an untapped resource of information. Users post short real time messages views and opinions on many topics, often signed with a ‘#hashtag’ to classify and document the subject matter in discussion. In this paper we apply automated sentiment analysis methods to tweets giving a measure of public support or hostility to a topic (‘Brexit’). The data were collected during several periods to determine changes in opinion. Using machine learning techniques we show that changes in opinion were also related to external events. Limitations of the method are that age, location and education are confounding factors where Twitter users over represent a young, urban public. However, the economic advantage of the method over real-time telephone polling are considerable

Automated Blood Cell Detection and Counting via Deep Learning for Microfluidic Point-of-Care Medical Devices

Automated in-vitro cell detection and counting have been a key theme for artificial and intelligent biological analysis such as biopsy, drug analysis and decease diagnosis. Along with the rapid development of microfluidics and lab-on-chip technologies, in-vitro live cell analysis has been one of the critical tasks for both research and industry communities. However, it is a great challenge to obtain and then predict the precise information of live cells from numerous microscopic videos and images. In this paper, we investigated in-vitro detection of white blood cells using deep neural networks, and discussed how state-of-the-art machine learning techniques could fulfil the needs of medical diagnosis. The approach we used in this study was based on Faster Region-based Convolutional Neural Networks (Faster RCNNs), and a transfer learning process was applied to apply this technique to the microscopic detection of blood cells. Our experimental results demonstrated that fast and efficient analysis of blood cells via automated microscopic imaging can achieve much better accuracy and faster speed than the conventionally applied methods, implying a promising future of this technology to be applied to the microfluidic point-of-care medical devices

Live video transmission over data distribution service with existing low-power platforms

This paper investigates video transmission over a middleware layer based on the Object Management Group’s Data-Distribution Service (DDS) standard, with a focus on low power platforms. Low power platforms are being widely utilised to implement IoT devices. One important type of IoT application is live video sharing which requires higher bandwidth than current typical applications. However, only limited research has been carried out on quality of services of data distribution utilising low end platforms. This paper discusses the development of prototypes that consist of both a Raspberry Pi 2 and an Android smartphone with client applications using Prismtech’s Vortex line of DDS middleware. Experiments have yielded interesting performance results: DDS middleware implementations that run on low power hardware with native code can provide sufficient performance. They are efficient enough to consistently handle high bandwidth live video with the network performance proving to be the bottleneck rather than the processing power of the devices. However, virtual machine implementations on an Android device did not achieve similar performance levels. These research findings will provide recommendations on adopting low power devices for sharing live video distribution in IoT over DDS middleware

Adoption of Cloud Computing in Hotel Industry as Emerging Services

The hotel industry is experiencing forces of change as a result of data explosion, social media, increased individualized expectations by customers. It is thus appealing to study the cloud computing adoption in the hotel industry to respond such changes. This paper reported an investigation on such topic by identifying the cloud computing services and summarising their benefits and challenges in organization, management and operation. The research findings were comparatively studied in reference to the results appeared in the literature. In addition, recommendations were made for both cloud service providers and hotels in strategic planning, investment, and management of cloud-oriented services

Concept Drift Detection by Tracking Weighted Prediction Confidence of Incremental Learning

Data stream mining is great significant in many real-world scenarios, especially in the big data area. However, conventional machine learning algorithms are incapable to process because of its two characteristics (1) potential unlimited number of data is generated in real-time way, it is impossible to store all the data (2) evolving over time, namely, concept drift, will influence the performance of predictor trained on previous data. Concept drift detection method could detect and locate the concept drift in data stream. However, existing methods only utilize the prediction result as indicator. In this article, we propose a weighted concept drift indicator based on incremental ensemble learning to detect the concept. The indicator not only considers the prediction result, but the change of prediction stability of predictor with occurs of concept drift. Also, an incremental ensemble learning based on vote mechanism is especially used to get constantly updated value of indicator. Based on the experiment result on both benchmark and real-world dataset, our method could effectively detect concept drift and outperform other existing methods

Superconductivity in the vicinity of antiferromagnetic order in CrAs

One of the common features of unconventional, magnetically mediated superconductivity as found in the heavy-fermions, high-transition-temperature (high-Tc) cuprates, and iron pnictides superconductors is that the superconductivity emerges in the vicinity of long-range antiferromagnetically ordered state.[1] In addition to doping charge carriers, the application of external physical pressure has been taken as an effective and clean approach to induce the unconventional superconductivity near a magnetic quantum critical point (QCP).[2,3] Superconductivity has been observed in a majority of 3d transition-metal compounds,[4-9] except for the Cr- and Mn-based compounds in the sense that the low-lying states near Fermi level are dominated by their 3d electrons. Herein, we report on the discovery of superconductivity on the verge of antiferromagnetic order in CrAs via the application of external high pressure. Bulk superconductivity with Tc ~ 2 K emerges at the critical pressure Pc ~ 8 kbar, where the first-order antiferromagnetic transition at TN = 265 K under ambient pressure is completely suppressed. Abnormal normal-state properties associated with a magnetic QCP have been observed nearby Pc. The close proximity of superconductivity to an antiferromagnetic order suggests an unconventional pairing mechanism for the superconducting state of CrAs. The present finding opens a new avenue for searching novel superconductors in the Cr and other transitional-metal based systems

Detection and forecasting of shallow landslides: lessons from a natural laboratory

Shallow-rapid landslides are a significant hillslope erosion mechanism and limited understanding of their initiation and development results in persistent risk to infrastructure. Here, we analyse the slope above the strategic A83 Rest and be Thankful road in the west of Scotland. An inventory of 70 landslides (2003-2020) shows three types of shallow landslide, debris flows, creep deformation and debris falls. Debris flows dominate and account for 5,350m3 (98 ) of shallow-landslide source volume across the site. We use novel time-lapse vector tracking to detect and quantify slope instabilities, whilst seismometers demonstrate the potential for live detection and location of debris flows. Using on-slope rainfall data, we show that shallow-landslides are typically triggered by abrupt changes in the rainfall trend, characterised by high-intensity, long duration rainstorms, sometimes part of larger seasonal rainfall changes. We derive empirical antecedent precipitation (>62mm) and intensity-duration (>10 hours) thresholds over which shallow-landslides occur. Analysis shows the new thresholds are more effective at raising hazard alerts than the current management plan.The low-cost sensors provide vital notification of increasing hazard, the initiation of movement, and final failure. This approach offers considerable advances to support operational decision-making for infrastructure threatened by complex slope hazards

Anticipating and managing future trade-offs and complementarities between ecosystem services

Peer reviewedPostprin

Constraint-based co-evolutionary genetic programming for bargaining problems

EThOS - Electronic Theses Online ServiceGBUnited Kingdo