Unveiling the frontiers of deep learning: innovations shaping diverse domains

Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

Health Misinformation in Search and Social Media

People increasingly rely on the Internet in order to search for and share health-related information. Indeed, searching for and sharing information about medical treatments are among the most frequent uses of online data. While this is a convenient and fast method to collect information, online sources may contain incorrect information that has the potential to cause harm, especially if people believe what they read without further research or professional medical advice. The goal of this thesis is to address the misinformation problem in two of the most commonly used online services: search engines and social media platforms. We examined how people use these platforms to search for and share health information. To achieve this, we designed controlled laboratory user studies and employed large-scale social media data analysis tools. The solutions proposed in this thesis can be used to build systems that better support people's health-related decisions. The techniques described in this thesis addressed online searching and social media sharing in the following manner. First, with respect to search engines, we aimed to determine the extent to which people can be influenced by search engine results when trying to learn about the efficacy of various medical treatments. We conducted a controlled laboratory study wherein we biased the search results towards either correct or incorrect information. We then asked participants to determine the efficacy of different medical treatments. Results showed that people were significantly influenced both positively and negatively by search results bias. More importantly, when the subjects were exposed to incorrect information, they made more incorrect decisions than when they had no interaction with the search results. Following from this work, we extended the study to gain insights into strategies people use during this decision-making process, via the think-aloud method. We found that, even with verbalization, people were strongly influenced by the search results bias. We also noted that people paid attention to what the majority states, authoritativeness, and content quality when evaluating online content. Understanding the effects of cognitive biases that can arise during online search is a complex undertaking because of the presence of unconscious biases (such as the search results ranking) that the think-aloud method fails to show. Moving to social media, we first proposed a solution to detect and track misinformation in social media. Using Zika as a case study, we developed a tool for tracking misinformation on Twitter. We collected 13 million tweets regarding the Zika outbreak and tracked rumors outlined by the World Health Organization and the Snopes fact-checking website. We incorporated health professionals, crowdsourcing, and machine learning to capture health-related rumors as well as clarification communications. In this way, we illustrated insights that the proposed tools provide into potentially harmful information on social media, allowing public health researchers and practitioners to respond with targeted and timely action. From identifying rumor-bearing tweets, we examined individuals on social media who are posting questionable health-related information, in particular those promoting cancer treatments that have been shown to be ineffective. Specifically, we studied 4,212 Twitter users who have posted about one of 139 ineffective ``treatments'' and compared them to a baseline of users generally interested in cancer. Considering features that capture user attributes, writing style, and sentiment, we built a classifier that is able to identify users prone to propagating such misinformation. This classifier achieved an accuracy of over 90%, providing a potential tool for public health officials to identify such individuals for preventive intervention

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

A Sociology of Gab: A Computational Analysis of a Far-Right Social Network

This dissertation examines the racial discourse circulated on Gab, a microblogging and social networking platform, by the far right to proliferate hate speech, and how the far-right discourse has evolved on the platform. Gab was created 2016 in response to mainstream social media’s increase of content moderation and deplatforming of extremist users to curtail hate speech and harassment. The platform gained a substantial number of new users after the Charlotteville incident of 2017. In this thesis, I examine the creation of Gab, as an alternative social media platform, as a strategic site of socio-technical innovation, as well as the important part far-right discourse on Gab plays in the asymmetric polarization phenomenon of the media ecosystem. This project asks: How has Gab developed and what discourses about race circulate on Gab? To answer these questions, I draw on a large dataset of digital trace data of the entire Gab platform of approximately 10 million posts on Gab from 2016 to 2019. This constitutes an archive of the far-right activities on an important alternative social media platform. I use computational methodologies including structural topic modeling, word embeddings and qualitative analysis to examine the materials, and form conclusions about the impacts of asymmetric polarization of the far right on social media. I argue that Gab occupies an important position in the social media ecosystem in the context of mainstream social media platforms’ deplatforming post- Charlottesville, and the absence of legislation that regulates hate speech. Gab, as an opportunistic innovation, is emblematic of an alternative social media ecosystem that flourishes online, drawing in users who are rejecting, and rejected by, mainstream social media platforms and searching for platforms with looser content moderation that reflect their absolutist freedom-of-speech stance. This environment is conducive to asymmetric polarization, spreading anti-liberal, anti-mainstream media, anti-Semitic, and anti-immigration discourses. These ideological strings are not new but consistent with earlier articulations of the ideology of white supremacy ideology, just taking place on a newer platform that itself may enable new variations of the same theme

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

14th Conference on DATA ANALYSIS METHODS for Software Systems

DAMSS-2023 is the 14th International Conference on Data Analysis Methods for Software Systems, held in Druskininkai, Lithuania. Every year at the same venue and time. The exception was in 2020, when the world was gripped by the Covid-19 pandemic and the movement of people was severely restricted. After a year’s break, the conference was back on track, and the next conference was successful in achieving its primary goal of lively scientific communication. The conference focuses on live interaction among participants. For better efficiency of communication among participants, most of the presentations are poster presentations. This format has proven to be highly effective. However, we have several oral sections, too. The history of the conference dates back to 2009 when 16 papers were presented. It began as a workshop and has evolved into a well-known conference. The idea of such a workshop originated at the Institute of Mathematics and Informatics, now the Institute of Data Science and Digital Technologies of Vilnius University. The Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea, which gained enthusiastic acceptance from both the Lithuanian and international scientific communities. This year’s conference features 84 presentations, with 137 registered participants from 11 countries. The conference serves as a gathering point for researchers from six Lithuanian universities, making it the main annual meeting for Lithuanian computer scientists. The primary aim of the conference is to showcase research conducted at Lithuanian and foreign universities in the fields of data science and software engineering. The annual organization of the conference facilitates the rapid exchange of new ideas within the scientific community. Seven IT companies supported the conference this year, indicating the relevance of the conference topics to the business sector. In addition, the conference is supported by the Lithuanian Research Council and the National Science and Technology Council (Taiwan, R. O. C.). The conference covers a wide range of topics, including Applied Mathematics, Artificial Intelligence, Big Data, Bioinformatics, Blockchain Technologies, Business Rules, Software Engineering, Cybersecurity, Data Science, Deep Learning, High-Performance Computing, Data Visualization, Machine Learning, Medical Informatics, Modelling Educational Data, Ontological Engineering, Optimization, Quantum Computing, Signal Processing. This book provides an overview of all presentations from the DAMSS-2023 conference

Using semantic drift on social media for event detection, differentiation and segmentation

With observable paradigm shift in computer science from predictive modeling to the generative one, it became important to maximise exploration of the pathways towards useful data production. With currently dominating statistical and compositional data augmentation strategies, opportunities also emerged for more application-driven routes. The main value of such approaches lies in their capacity to offer insights into context or event specific data productions, currently overlooked by more topologically neutral machine learning approaches. The purpose of this thesis is therefore to provide empirical evidence for useful data generation by dynamic event-specific lexical semantic resources. Various Web 2.0 applications due to their popularity have been accumulating large amounts of semantically rich metadata, which became readily available and easily exploitable. Tags, usually consisting of a single word, are one type of such data. Tag uses can vary largely across systems and platforms; Also known under the term folksonomy, tags are usually non-hierarchical and open-ended, thus re-flecting users' unique perspectives regarding various contexts, or resources. This platform-enabled liberty of expression, however, has led to situations of frequent semantic ambiguity due to spelling mistakes, morphological variations, polysemy, multilingualism or inaccurate tag-to-resource associations. As a consequence, tag spaces are often regarded as inconsistent, noisy and hardly reliable data sources. Recent surge of interest amongst distributional semanticists in long- and short-term fluctuations of word meanings on social media has suggested routes for successful temporal sense disambiguation, thus inviting discussions around useful real-world applications for such emerging data resources. One of such applications - event analytics from the crowd behaviour perspective - is gaining an increasing attention from researchers and practitioners, especially in the fields of operations and situational management. Pursuing pragmatic aims of event detection, differen- tiation and segmentation, this application domain is represented predominantly by repetitive catastrophic events (such as natural hazards), during which directly or indirectly exposed populations tend to share their situational experiences on social media. This thesis consists of three main parts, each corresponding to specific problem in event analytics: (i) detection, (ii) differentiation and (iii) segmentation. In the first part I used the concept of ontological semantic proximity on the words candidates for semantic drift in order to highlight the dynamics of their semantic oscillations within event-specific category (i.e., flooding). In my second experiment I followed on these initial findings and performed an analysis verifying whether semantically unstable lexical material can augment our knowledge about main sub-types of floods, such as `slow' (e.g., groundwater and pluvial floods) and `fast' (surface water and riverine floods) ones. In my third experiment I employed combined lexico-visual modalities of the crowdsourced material to reconstruct changing perceptions of flood events in order to understand how event severity can or cannot determine situationally resilient behaviours

Assessing the social impacts of extreme weather events using social media

The frequency and severity of extreme weather events such as flooding, hurricanes/storms and heatwaves are increasing as a result of climate change. There is a need for information to better understand when, where and how these events are impacting people. However, there are currently limited sources of impact information beyond traditional meteorological observations. Social sensing, which is the use of unsolicited social media data to better understand real world events, is one method that may provide such information. Social sensing has successfully been used to detect earthquakes, floods, hurricanes, wildfires, heatwaves and other weather hazards. Here social sensing methods are adapted to explore potential for collecting impact information for meteorologists and decision makers concerned with extreme weather events. After a review of the literature, three experimental studies are presented. Social sensing is shown to be effective for detection of impacts of named storms in the UK and Ireland. Topics of discussion and sentiment are explored in the period before, during and after a storm event. Social sensing is also shown able to detect high-impact rainfall events worldwide, validating results against a manually curated database. Additional events which were not known to this database were found by social sensing. Finally, social sensing was applied to heatwaves in three European cities. Building on previous work on heatwaves in the UK, USA and Australia, the methods were extended to include impact phrases alongside hazard-related phrases, in three different languages (English, Dutch and Greek). Overall, social sensing is found to be a good source of impact information for organisations that need to better understand the impacts of extreme weather. The research described in this project has been commercialised for operational use by meteorological agencies in the UK, including the Met Office, Environment Agency and Natural Resources Wales.Engineering and Physical Sciences Research Council (EPSRC