9,008 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Graduate Catalog of Studies, 2023-2024

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    Towards Sybil Resilience in Decentralized Learning

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    Federated learning is a privacy-enforcing machine learning technology but suffers from limited scalability. This limitation mostly originates from the internet connection and memory capacity of the central parameter server, and the complexity of the model aggregation function. Decentralized learning has recently been emerging as a promising alternative to federated learning. This novel technology eliminates the need for a central parameter server by decentralizing the model aggregation across all participating nodes. Numerous studies have been conducted on improving the resilience of federated learning against poisoning and Sybil attacks, whereas the resilience of decentralized learning remains largely unstudied. This research gap serves as the main motivator for this study, in which our objective is to improve the Sybil poisoning resilience of decentralized learning. We present SybilWall, an innovative algorithm focused on increasing the resilience of decentralized learning against targeted Sybil poisoning attacks. By combining a Sybil-resistant aggregation function based on similarity between Sybils with a novel probabilistic gossiping mechanism, we establish a new benchmark for scalable, Sybil-resilient decentralized learning. A comprehensive empirical evaluation demonstrated that SybilWall outperforms existing state-of-the-art solutions designed for federated learning scenarios and is the only algorithm to obtain consistent accuracy over a range of adversarial attack scenarios. We also found SybilWall to diminish the utility of creating many Sybils, as our evaluations demonstrate a higher success rate among adversaries employing fewer Sybils. Finally, we suggest a number of possible improvements to SybilWall and highlight promising future research directions

    GenAssist: Making Image Generation Accessible

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    Blind and low vision (BLV) creators use images to communicate with sighted audiences. However, creating or retrieving images is challenging for BLV creators as it is difficult to use authoring tools or assess image search results. Thus, creators limit the types of images they create or recruit sighted collaborators. While text-to-image generation models let creators generate high-fidelity images based on a text description (i.e. prompt), it is difficult to assess the content and quality of generated images. We present GenAssist, a system to make text-to-image generation accessible. Using our interface, creators can verify whether generated image candidates followed the prompt, access additional details in the image not specified in the prompt, and skim a summary of similarities and differences between image candidates. To power the interface, GenAssist uses a large language model to generate visual questions, vision-language models to extract answers, and a large language model to summarize the results. Our study with 12 BLV creators demonstrated that GenAssist enables and simplifies the process of image selection and generation, making visual authoring more accessible to all.Comment: For accessibility tagged pdf, please refer to the ancillary fil

    Financial and Economic Review 22.

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    Semantics-based privacy by design for Internet of Things applications

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    As Internet of Things (IoT) technologies become more widespread in everyday life, privacy issues are becoming more prominent. The aim of this research is to develop a personal assistant that can answer software engineers’ questions about Privacy by Design (PbD) practices during the design phase of IoT system development. Semantic web technologies are used to model the knowledge underlying PbD measurements, their intersections with privacy patterns, IoT system requirements and the privacy patterns that should be applied across IoT systems. This is achieved through the development of the PARROT ontology, developed through a set of representative IoT use cases relevant for software developers. This was supported by gathering Competency Questions (CQs) through a series of workshops, resulting in 81 curated CQs. These CQs were then recorded as SPARQL queries, and the developed ontology was evaluated using the Common Pitfalls model with the help of the Protégé HermiT Reasoner and the Ontology Pitfall Scanner (OOPS!), as well as evaluation by external experts. The ontology was assessed within a user study that identified that the PARROT ontology can answer up to 58% of privacy-related questions from software engineers

    Chatbot recommender systems in tourism : a systematic review and a benefit-cost analysis

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    This research is focused on the utilization of artificially intelligent (AI), customer service chatbots in travel, tourism and hospitality. Rigorous criteria were used to search, screen, extract and synthesize articles on conversational, automated systems. The results shed light on the most-cited articles on the use of “chatbots” and “tourism” or “hospitality”. The researchers scrutinize the extracted articles, synthesize the findings and outline the pros and cons of using these interactive technologies. This contribution implies that there is scope for tourism businesses to continue improving their online customer services in terms of their efficiency and responsiveness to consumers and prospects. For the time being, AI chatbots are still not in a position to replace human agents in all service interactions as they cannot resolve complex queries and complaints. However, works are in progress to improve their verbal, vocal and anthropomorphic capabilities to deliver a better consumer experience.peer-reviewe

    Interdisciplinarity in the Scholarly Life Cycle

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    This open access book illustrates how interdisciplinary research develops over the lifetime of a scholar: not in a single project, but as an attitude that trickles down, or spirals up, into research. This book presents how interdisciplinary work has inspired shifts in how the contributors read, value concepts, critically combine methods, cope with knowledge hierarchies, write in style, and collaborate. Drawing on extensive examples from the humanities and social sciences, the editors and chapter authors show how they started, tried to open up, dealt with inconsistencies, had to adapt, and ultimately learned and grew as researchers. The book offers valuable insights into the conditions and complexities present for interdisciplinary research to be successful in an academic setting. This is an open access book
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