14,755 research outputs found

    A visual analytics platform for competitive intelligence

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    Silva, D., & Bação, F. (2023). MapIntel: A visual analytics platform for competitive intelligence. Expert Systems, [e13445]. https://doi.org/https://www.authorea.com/doi/full/10.22541/au.166785335.50477185, https://doi.org/10.1111/exsy.13445 --- Funding Information: This work was supported by the (research grant under the DSAIPA/DS/0116/2019 project). Fundação para a Ciência e Tecnologia of Ministério da Ciência e Tecnologia e Ensino SuperiorCompetitive Intelligence allows an organization to keep up with market trends and foresee business opportunities. This practice is mainly performed by analysts scanning for any piece of valuable information in a myriad of dispersed and unstructured sources. Here we present MapIntel, a system for acquiring intelligence from vast collections of text data by representing each document as a multidimensional vector that captures its own semantics. The system is designed to handle complex Natural Language queries and visual exploration of the corpus, potentially aiding overburdened analysts in finding meaningful insights to help decision-making. The system searching module uses a retriever and re-ranker engine that first finds the closest neighbours to the query embedding and then sifts the results through a cross-encoder model that identifies the most relevant documents. The browsing or visualization module also leverages the embeddings by projecting them onto two dimensions while preserving the multidimensional landscape, resulting in a map where semantically related documents form topical clusters which we capture using topic modelling. This map aims at promoting a fast overview of the corpus while allowing a more detailed exploration and interactive information encountering process. We evaluate the system and its components on the 20 newsgroups data set, using the semantic document labels provided, and demonstrate the superiority of Transformer-based components. Finally, we present a prototype of the system in Python and show how some of its features can be used to acquire intelligence from a news article corpus we collected during a period of 8 months.preprintauthorsversionepub_ahead_of_prin

    Measuring the Effects of Stack Overflow Code Snippet Evolution on Open-Source Software Security

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    This paper assesses the effects of Stack Overflow code snippet evolution on the security of open-source projects. Users on Stack Overflow actively revise posted code snippets, sometimes addressing bugs and vulnerabilities. Accordingly, developers that reuse code from Stack Overflow should treat it like any other evolving code dependency and be vigilant about updates. It is unclear whether developers are doing so, to what extent outdated code snippets from Stack Overflow are present in GitHub projects, and whether developers miss security-relevant updates to reused snippets. To shed light on those questions, we devised a method to 1) detect outdated code snippets versions from 1.5M Stack Overflow snippets in 11,479 popular GitHub projects and 2) detect security-relevant updates to those Stack Overflow code snippets not reflected in those GitHub projects. Our results show that developers do not update dependent code snippets when those evolved on Stack Overflow. We found that 2,405 code snippet versions reused in 2,109 GitHub projects were outdated, with 43 projects missing fixes to bugs and vulnerabilities on Stack Overflow. Those 43 projects containing outdated, insecure snippets were forked on average 1,085 times (max. 16,121), indicating that our results are likely a lower bound for affected code bases. An important insight from our work is that treating Stack Overflow code as purely static code impedes holistic solutions to the problem of copying insecure code from Stack Overflow. Instead, our results suggest that developers need tools that continuously monitor Stack Overflow for security warnings and code fixes to reused code snippets and not only warn during copy-pasting

    Deep generative models for network data synthesis and monitoring

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    Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network. Although networks inherently have abundant amounts of monitoring data, its access and effective measurement is another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset without leaking commercial sensitive information. Second, it could be very expensive to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources in the network element that can be applied to support the measurement function are too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex structure. Various emerging optimization-based solutions (e.g., compressive sensing) or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet meet the current network requirements. The contributions made in this thesis significantly advance the state of the art in the domain of network measurement and monitoring techniques. Overall, we leverage cutting-edge machine learning technology, deep generative modeling, throughout the entire thesis. First, we design and realize APPSHOT , an efficient city-scale network traffic sharing with a conditional generative model, which only requires open-source contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system — GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time network telemetry system with latent GANs and spectral-temporal networks. Finally, we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through this research are summarized, and interesting topics are discussed for future work in this domain. All proposed solutions have been evaluated with real-world datasets and applied to support different applications in real systems

    Enhancing healthcare services through cloud service: a systematic review

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    Although cloud-based healthcare services are booming, in-depth research has not yet been conducted in this field. This study aims to address the shortcomings of previous research by analyzing all journal articles from the last five years using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) systematic literature review methodology. The findings of this study highlight the benefits of cloud-based healthcare services for healthcare providers and patients, including enhanced healthcare services, data security, privacy issues, and innovative information technology (IT) service delivery models. However, this study also identifies challenges associated with using cloud services in healthcare, such as security and privacy concerns, and proposes solutions to address these issues. This study concludes by discussing future research directions and the need for a complete solution that addresses the conflicting requirements of the security, privacy, efficiency, and scalability of cloud technologies in healthcare

    Determinants of embryonic and foetal growth

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    The main aims of this thesis were:1. To investigate whether there are associations between determinants related to the living environment (in particular neighbourhood deprivation and air pollution) and embryonic growth, foetal growth and pregnancy outcomes;2. To assess the associations between maternal cardiometabolic determinants in pregnancy (lipid status and the presence of hypertensive disorders of pregnancy)and embryonic growth, foetal growth and childhood outcomes;3. To investigate the impact of neighbourhood deprivation on the effectiveness ofthe mHealth “Smarter Pregnancy” program, aimed at improving nutrition and lifestyle behaviours;<br/

    Predicting depression and suicidal tendencies by analyzing online activities using machine learning in android devices

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    Artificial Intelligence (AI) has brought about a profound transformation in the realm of technology, with Machine Learning (ML) within AI playing a crucial role in today's healthcare systems. Advanced systems with intellectual abilities resembling those of humans are being created and utilized to carry out intricate tasks. Applications like Object recognition, classification, Optical Character Recognition (OCR), Natural Language processing (NLP), among others, have started producing magnificent results with algorithms trained on humongous data readily available these days. Keeping in view the socio-economic implications of the pandemic threat posed to the world by COVID-19, this research aims at improving the quality of life of people suffering from mild depression by timely diagnosing the symptoms using AI in android devices, especially phones. In cases of severe depression, which is highly likely to lead to suicide, valuable lives can also be saved if adequate help can be dispatched to such patients within time. This can be achieved using automatic analysis of users’ data including text messages, emails, voice calls and internet search history, among other mobile phone activities, using Text mining/ text analytics which is the process of deriving meaningful information from natural language text. Machine Learning models analyse the users’ behaviour continuously from text and voice communications and data, thereby identifying if there are any negative tendencies in the behaviour over a certain period of time, and by using this information make inferences about the mental health state of the patient and instantly request appropriate healthcare before it is too late. In this research, an android application capable of performing the aforementioned tasks in real-time has been developed and tested for various performance features with an average accuracy of 95%

    Development of an internet of things-based weather station device embedded with O2, CO2, and CO sensor readings

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    Weather station devices are used to monitor weather parameter conditions, such as wind direction, speed, rainfall, solar radiation level, temperature, and humidity. This article discusses the design of a customized weather station embedded with gas concentration readings, whereby the gas concentration measurement includes oxygen (O2), carbon dioxide (CO2), and carbon monoxide (CO). The measurements and data processing of input sensors were transmitted to an Arduino Uno microcontroller, and the input data were then remitted to Wemos D1 Mini to be uploaded to a cloud server. Furthermore, the gas sensors' characterization methods were also considered to reveal the obtained results of accuracy, precision, linearity, and hysteresis. An android-based mobile application was also designed for monitoring purposes. The system in our experiment utilized an internet connection with a field station, base station, and database server

    InGAME international pathway to collaboration: Collaboration in Games UK-China

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    In 2019 the Arts &amp; Humanities Research Council (AHRC) funded a series of projects as part of its UK-China Creative Partnerships Programme. Led by Abertay University in partnership with academic and industry partners across the UK and China, InGAME International was funded through this AHRC programme with the aim of studying the potential for UK-China cooperation and collaboration in the computer games sector. The project is linked to the AHRC Creative Industries Cluster, InGAME: Innovation for Games and Media Enterprise, which is also led by Abertay University in partnership with the University of Dundee and University of St Andrews. The games industry is one of the largest and fastest growing sectors in both the UK and the Chinese creative economies. In 2023, China was the largest gaming market globally with revenue forecast at 82.064billioncomparedwith82.064 billion compared with 7.94 billion in the UK (Statista, 2023). The growth in China’s market has long been the source of appeal for UK game developers and publishers seeking new routes to market. However, the divergence between the UK and China in terms of market profile, consumption patterns, leading companies, technologies, regulation, licensing, management, and business culture has presented ongoing difficulties for any UK based developer interested in engagement in- or with- China. It is from this basis that the current study sought to consolidate industry, legal, and regulatory knowhow with a view to providing a valuable resource to games professionals and researchers who have interests in UK-China collaboration. This Pathway to Collaboration report curates the cumulative knowledge and insight generated during the InGAME International programme, with an intended audience of games industry professionals and researchers interested in UK-China collaboration. At the heart of the research is an unprecedented qualitative study that involved in-depth interviews with 47 leading experts from the UK, China and other territories and with knowledge of games development, business, publishing, marketing, localisation, IP, copyright, regulation, markets, and sales. This report is the first comprehensive qualitative study to investigate the intersection between the UK and China games industries and markets at this scale and depth, providing readers with an invaluable, interactive resource that will support professionals and researchers to initiate new collaborations between the two nations.</p
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