511 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

    A Friend of Tax Collectors and Sinners : An Intertextual Reading of Luke\u27s Jesus According to Divine Identity and YHWH Shepherd Language

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    Luke’s Gospel has been heralded as the Gospel for the outcast. This study suggests a biblical-theological approach to Luke’s depiction of Jesus that may guide an interpretation of Jesus’ person and activity. This study assumes an intertextual reading of Luke and identifies qualities and activities that he possesses and assumes according to Old Testament texts. Old Testament prophetic texts and Second Temple Jewish texts detailing YHWH’s intentions to return as Shepherd to his scattered and exiled people are examined. Luke’s birth narrative and accounts of dynamic moments in Jesus’ ministry (Lk 15:1-7; 19:1-10) are read in light of this intertextual relationship, and divine identity concepts and creedal rhythms are suggested as components of a framework that contributes to an understanding of Luke’s Jesus in light of the larger movement of Israel’s Scripture. Ultimately, it is suggested that this reading of Luke presents Jesus as possessing the identity of YHWH Shepherd

    A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection

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    The truth is significantly hampered by massive rumors that spread along with breaking news or popular topics. Since there is sufficient corpus gathered from the same domain for model training, existing rumor detection algorithms show promising performance on yesterday's news. However, due to a lack of training data and prior expert knowledge, they are poor at spotting rumors concerning unforeseen events, especially those propagated in different languages (i.e., low-resource regimes). In this paper, we propose a unified contrastive transfer framework to detect rumors by adapting the features learned from well-resourced rumor data to that of the low-resourced. More specifically, we first represent rumor circulated on social media as an undirected topology, and then train a Multi-scale Graph Convolutional Network via a unified contrastive paradigm. Our model explicitly breaks the barriers of the domain and/or language issues, via language alignment and a novel domain-adaptive contrastive learning mechanism. To enhance the representation learning from a small set of target events, we reveal that rumor-indicative signal is closely correlated with the uniformity of the distribution of these events. We design a target-wise contrastive training mechanism with three data augmentation strategies, capable of unifying the representations by distinguishing target events. Extensive experiments conducted on four low-resource datasets collected from real-world microblog platforms demonstrate that our framework achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.Comment: A significant extension of the first contrastive approach for low-resource rumor detection (arXiv:2204.08143

    Unveiling the frontiers of deep learning: innovations shaping diverse domains

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    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

    AI: Limits and Prospects of Artificial Intelligence

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    The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence

    Teachers’ Perceptions of Social Emotional Learning Instruction and High Residential Mobility Students’ Reading Literacy

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    Based on recommendations from research, leaders in a north Texas public school district implemented social emotional learning (SEL) to address academic gaps among high residential mobility (HRM) students. Incorporating SEL instruction improves academic achievement 11 percentile points on average; however, in Adoniram School District (ASD), 2021 passing rates fell 8% from 2019 despite adopting SEL curriculum. The purpose of this qualitative case study was to explore how ASD elementary teachers instructed SEL, and their perceptions of how SEL techniques influenced HRM elementary students’ reading literacy development. The study on social and emotional skills (SSES) conceptual framework guided this study because SEL instruction may improve student academic growth, including reading literacy. Data were collected by interviewing 15 ASD teachers and reviewing their lesson plans and analyzed thematically through open and axial coding strategies. According to results, teachers perceived HRM students’ academic growth was positively impacted when SEL was taught each morning and reinforced during core content instruction. Participants indicated the level to which SEL influenced students depended primarily on leadership attitudes toward SEL, and collaboration in professional learning communities. The findings led to a white paper offering ASD leaders research-based recommendations to support teachers in SEL implementation during reading literacy instruction. This study may contribute to positive social change when teachers integrate SEL principles in core content lessons to provide HRM students with resilience strategies that facilitate acquiring new skills, including reading literacy, resulting in satisfactory performance on state assessments

    An “other” experience of videogames: analyzing the connections between videogames and the lived experience of chronic pain

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    In this dissertation I argue for the connections between the lived experience of chronic pain and videogames, exploring what interacts with and influences them. To answer this, I draw on cripistemology as I engage in autoethnography, close-reading and close-gameplay, restorying, mixed methods design, formal interviews, surveys, and inductive coding. I further argue for pushing back against the unhelpful binaries that define the “human” and a false idea of “universal” experience or ability, instead pointing to the intersectionality that better reflects the biopolitics of disability, including both debility and capacity. I engage with these methods in three specific projects that consider additional sub-questions to further tease out why videogames disability, chronic pain, game design, lived experience, human centered design, embodiment in video games have impacted me so deeply and how this ties to my identity as a disabled woman. I further offer this dissertation to highlight the growing research of lived experience and disability in the field of game studies, providing empirical data that offers a foundational look of how I as a member of the chronic pain community think and feel about videogames, as well as how a small portion of the chronic pain community discusses videogames and the range of experiences this encompasses. In doing so, I unpack and argue on the relationship that exists between chronic pain and videogames, and further articulate why this matters. In Chapter 1 I provide necessary history and information regarding my research to better articulate the findings as presented in the following chapters. In Chapter 2, I analyze my connection to Animal Crossing: New Leaf (AC:NL) (Nintendo EAD, 2012) and explore opportunities about genre and mechanics as reflections of my own daily lived experience with chronic pain, especially including my experience in a 2014 pain rehabilitation program. Through this process, I define the “slice of life” genre and argue that AC:NL is exemplary of its markers. In Chapter 3 I provide a deep reading and analysis of Nintendo’s GameCube release Chibi-Robo! (Skip Ltd. et al., 2005) to “restory” the titular main character to have chronic pain like my own. Through the lens of debility and capacitation machines, I map these ideas onto the biopsychosocial model to organize a thorough analysis of his restoried identity. In modding the game’s narrative to reflect a lived experience of chronic pain like my own, I interweave fanfiction with deep reading and deep gameplay to unpack what representation I am looking for in videogames both narratively and mechanically. In this I further argue how this practice can be used to inform future game design. Finally, in Chapter 4, I interview members of the chronic pain community to understand their perspective on the connections between their lived experience with chronic pain and videogames, as well as how additional factors of their identity impact those experiences. For this I engage in a mixed methods design to conduct a survey and formal interviews to offer foundational work on how the chronic pain community interacts with videogames. I offer this project to intersect current research in chronic pain and videogames (and its related technology) that focuses on games as tools for “curing” pain, and argue the importance of considering what embodiment people with chronic pain already have in videogames instead. Ultimately, I argue for the necessity to complicate current design practices in human centered design (HCD) and game design. To do so, I highlight the lived experience of Othered identities to combat misguided notions of “universal” intent. In this, I analyze the inherent connections between videogames and disability, in this case chronic pain, through embodiment and lived experience. I center in on how my experience of chronic pain has impacted the way in which I engage and think about with videogames, and further, how my experiences align with that of the chronic pain community

    A web-based platform promoting family communication and cascade genetic testing for families with hereditary breast and ovarian cancer (DIALOGUE study)

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    The overall aim of this dissertation is to develop an eHealth intervention to promote family communication and cascade genetic testing among families concerned with Hereditary Breast and Ovarian Cancer (HBOC) syndrome. Within this context an international, multi-centre scientific project entitled "DIALOGUE" was designed that aims to develop (Phase A), and test the feasibility (Phase B) of an intervention within various genetic clinics across Switzerland and South Korea. This dissertation describes only the Phase A, the adaptation of an intervention, a web-based platform designed for families with HBOC to share genetic test results, including usability testing in a sample from Switzerland. Chapter 1 provides a general introduction to the current field of hereditary cancer and cascade genetic testing, including the current state of eHealth technologies in science. The chapter also includes a short introduction to the prototype developed in the U.S.—as well as a description of the DIALOGUE study. In addition, the chapter summarises the main conceptual models, i.e. the Ottawa Decision Support Framework (ODSF) and the Medical Research Council (MRC) framework. These models are commonly implemented in the development and evaluation of complex interventions. The rational of this dissertation is guided by all of these elements. Chapter 2 provides a detailed description of the dissertation’s specific aims, including the three studies conducted. The articles presented in Chapter 3 describe the methodology and findings of the dissertation. Study I comprises a systematic literature review of previous studies, with a particular focus on HBOC and Lynch syndromes. The literature review identified and synthesised evidence from psychoeducational interventions designed to facilitate family communication of genetic testing results and/or cancer predisposition and to promote cascade genetic testing. A meta-analysis was also conducted to assess intervention efficacy in relation to these two research aims. Our findings highlight the need to develop new interventions and approaches to family communication and cascade testing for cancer susceptibility. Study II describes the state-of-the-art text mining techniques used to detect and classify valuable information from interviews with study participants concerning determinants of open intrafamilial communication regarding genetic cancer risk. This study had two major aims: 1) to quantify openness of communication about HBOC cancer risk, and 2) to examine the role of sentiment in predicting openness of communication. Our findings showed that the overall expressed sentiment was associated with the communication of genetic risk among HBOC families. This analysis identified additional factors that affect openness to communicate genetic risk. These were defined as “high-risk” factors and integrated into the design and development of the intervention. Study III describes the development of the intervention, a web-based platform designed for families with HBOC to share genetic test results. The platform was developed in line with the quality criteria set by the MRC framework. Being web-based, the platform could be accessed via a laptop, smartphone or tablet. Usability testing was applied to evaluate the prototype intervention which received high ratings on a satisfaction scale. Chapter 4 synthesises and discusses the key findings of all the studies presented in the previous chapter, and addresses study limitations and implications for future research

    Advanced analytical methods for fraud detection: a systematic literature review

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    The developments of the digital era demand new ways of producing goods and rendering services. This fast-paced evolution in the companies implies a new approach from the auditors, who must keep up with the constant transformation. With the dynamic dimensions of data, it is important to seize the opportunity to add value to the companies. The need to apply more robust methods to detect fraud is evident. In this thesis the use of advanced analytical methods for fraud detection will be investigated, through the analysis of the existent literature on this topic. Both a systematic review of the literature and a bibliometric approach will be applied to the most appropriate database to measure the scientific production and current trends. This study intends to contribute to the academic research that have been conducted, in order to centralize the existing information on this topic
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