Title
Topic
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‘Type Prediction With Program Decomposition and Fill-in-the-Type Training’
“TypeScript and Python are two programming languages that support optional type annotations, which are useful but tedious to introduce and maintain. This has motivated automated type prediction: given an untyped program, produce a well-typed output program. Large language models (LLMs) are promising for type prediction, but there are challenges. … We address these challenges [with] OpenTau, a search-based approach for type prediction that leverages large language models. We propose a new metric for type prediction quality, give a tree-based program decomposition that searches a space of generated types and present fill-in-the-type fine-tuning.” Find the paper and full list of authors…
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‘HVA_CPS Proposal: A Process for Hazardous Vulnerability Analysis in Distributed Cyber-Physical Systems’
“Society is increasingly dependent upon the use of distributed cyber-physical systems (CPSs), such as energy networks, chemical processing plants and transport systems. Such CPSs typically have multiple layers of protection to prevent harm to people or the CPS. However, if both the control and protection systems are vulnerable to cyber-attacks, an attack may cause CPS damage or breaches of safety. … This article identifies the attributes that a rigorous hazardous vulnerability analysis (HVA) process would require and compares them against related works. None fully meet the requirements for rigour.” Find the paper and full list of authors at PeerJ Computer Science.
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‘Companion: A Pilot Randomized Clinical Trial … for Detecting and Modifying Daily Inactivity Among Adults >60 Years — Design and Protocol’
“Supervised personal training is most effective in improving the health effects of exercise in older adults. … Strategies to extend the effect of trainer contact outside of supervision and that integrate meaningful and intelligent two-way communication to provide complex and interactive problem solving may motivate older adults to “move more and sit less” and sustain positive behaviors to further improve health. This paper describes … a technology-based behavior-aware text-based virtual “Companion” … to deliver behavior change strategies using socially engaging, contextually salient, and tailored text message conversations in near-real-time.” Find the paper and full list of authors at MDPI Sensors.
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‘SECDA-TFLite: A Toolkit for Efficient Development of FPGA-Based DNN Accelerators for Edge Inference’
“In this paper we propose SECDA-TFLite, a new open source toolkit for developing DNN hardware accelerators integrated within the TFLite framework. The toolkit leverages the principles of SECDA, a hardware/software co-design methodology, to reduce the design time of optimized DNN inference accelerators on edge devices with FPGAs. With SECDA-TFLite, we reduce the initial setup costs associated with integrating a new accelerator design within a target DNN framework, allowing developers to focus on the design.” Find the paper and full list of authors in the Journal of Parallel and Distributed Computing.
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‘A Study of Multi-Factor and Risk-Based Authentication Availability’
“Password-based authentication (PBA) remains the most popular form of user authentication on the web despite its long-understood insecurity. Given the deficiencies of PBA, many online services support multi-factor authentication (MFA) and/or risk-based authentication (RBA) to better secure user accounts. … In this paper, we present a study of 208 popular sites in the Tranco top 5K that support account creation to understand the availability of MFA and RBA on the web … and how logging into sites through more secure SSO providers changes the landscape of user authentication security.” Find the paper and full list of authors at USENIX Security…
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‘Wrapped in Story: The Affordances of Narrative for Citizen Science Games’
“Citizen science games enable public participation in scientific research, yet these games often struggle to engage wide audiences. As a potential solution, some game developers look to narrative as an experience-enhancing feature. … We investigated the effects of wrapping a story around the tutorial puzzles of the citizen science game Foldit. We found that the narrative increased the time players spent engaging with the game’s tutorial and its scientific puzzles but did not substantially affect their progress through the tutorial.” Find the paper and full list of authors in the 18th International Conference on the Foundations of Digital Games proceedings.
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‘Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution’
“Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. However, prevailing SR models suffer from prohibitive memory footprint and intensive computations, which limits further deployment on edge devices. This work investigates the potential of network pruning for super-resolution to take advantage of off-the-shelf network designs and reduce the underlying computational overhead. … We adopt unstructured pruning with sparse models directly trained from scratch.” Find the paper and full list of authors at ArXiv.
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‘UniControl: A Unified Diffusion Model for Controllable Visual Generation in the Wild’
“Achieving machine autonomy and human control often represent divergent objectives in the design of interactive AI systems. Visual generative foundation models such as Stable Diffusion show promise in navigating these goals, especially when prompted with arbitrary languages. However, they often fall short in generating images with spatial, structural or geometric controls. … In response, we introduce UniControl, a new generative foundation model that consolidates a wide array of controllable condition-to-image (C2I) tasks within a singular framework, while still allowing for arbitrary language prompts.” Find the paper and full list of authors at ArXiv.
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‘Linearity of Relation Decoding in Transformer Language Models’
“Much of the knowledge encoded in transformer language models (LMs) may be expressed in terms of relations: relations between words and their synonyms, entities and their attributes, etc. We show that, for a subset of relations, this computation is well-approximated by a single linear transformation on the subject representation. Linear relation representations may be obtained by constructing a first-order approximation to the LM from a single prompt, and they exist for a variety of factual, commonsense, and linguistic relations.” Find the paper and full list of authors at ArXiv.
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‘TikTok as Algorithmically Mediated Biographical Illumination: Autism, Self-Discovery and Platformed Diagnosis on #Autisktok’
“Scholarship in the sociology of medicine has tended to characterize diagnosis as disruptive to one’s self-concept. This categorization, though, requires reconsideration in light of public conversations about mental health and community building around neurocognitive conditions, particularly among youth online. … We explored the shifting nature of [‘biographical illumination’] through the case of TikTok. Combining quantitative and qualitative methods, we argue that TikTok serves as a space to discuss diagnosis and refine one’s sense of self as a result of diagnosis.” Find the paper and full list of authors at New Media & Society.
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‘Why Look at Dead Animals?’ asks Coughlin in provocative taxidermy study
“Lion Attacking a Dromedary was a sensational object for its first viewers at the Paris Universal Exposition in 1867. … As we now know, the Verreaux brothers embedded human remains in the figure of the rider that had formerly been assumed to be just a clothed mannequin. … This essay suggests that theoretical tools derived from Material Ecocriticism and Monster Theory that may help us to think about, or alongside, the affective power of this disturbing taxidermy assemblage, ever aware that this piece draws its power from the theatrical, colonial violence of extraction and extinction.”
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‘Deep Bayesian Active Learning for Accelerating Stochastic Simulation’
“Stochastic simulations such as large-scale, spatiotemporal, age-structured epidemic models are computationally expensive at fine-grained resolution. While deep surrogate models can speed up the simulations, doing so for stochastic simulations and with active learning approaches is an underexplored area. We propose Interactive Neural Process (INP), a deep Bayesian active learning framework for learning deep surrogate models to accelerate stochastic simulations. INP consists of two components, a spatiotemporal surrogate model built upon Neural Process (NP) family and an acquisition function for active learning.” Find the paper and full list of authors in the SIGKDD Conference on Knowledge Discovery and Data Mining proceedings.
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‘Disentangling Node Attributes From Graph Topology for Improved Generalizability in Link Prediction’
“Link prediction is a crucial task in graph machine learning with diverse applications. We explore the interplay between node attributes and graph topology and demonstrate that incorporating pre-trained node attributes improves the generalization power of link prediction models. Our proposed method, UPNA (Unsupervised Pre-training of Node Attributes), solves the inductive link prediction problem by learning a function that takes a pair of node attributes and predicts the probability of an edge, as opposed to Graph Neural Networks, … which can be prone to topological shortcuts in graphs with power-law degree distribution.” Find the paper and full list of authors at…
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‘Can Euclidean Symmetry Be Leveraged in Reinforcement Learning and Planning?’
“In robotic tasks, changes in reference frames typically do not influence the underlying physical properties of the system, which has been known as invariance of physical laws.These changes, which preserve distance, encompass isometric transformations such as translations, rotations, and reflections, collectively known as the Euclidean group. In this work, we delve into the design of improved learning algorithms for reinforcement learning and planning tasks that possess Euclidean group symmetry.” Find the paper and full list of authors at ArXiv.
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‘Leveraging Structure for Improved Classification of Grouped Biased Data’
“We consider semi-supervised binary classification for applications in which data points are naturally grouped … and the labeled data is biased. … The groups overlap in the feature space and consequently the input-output patterns are related across the groups. To model the inherent structure in such data, we assume the partition-projected class-conditional invariance across groups. … We demonstrate that under this assumption, the group carries additional information about the class, over the group-agnostic features, with provably improved area under the ROC curve.” Find the paper and full list of authors in the AAAI Conference on Artificial Intelligence proceedings.
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‘Accelerating Neural MCTS Algorithms Using Neural Sub-Net Structures’
“Neural MCTS algorithms are a combination of Deep Neural Networks and Monte Carlo Tree Search (MCTS) and have successfully trained Reinforcement Learning agents in a tabula-rasa way. … However, these algorithms … take a long time to converge, which requires high computational power and electrical energy. It also becomes difficult for researchers without cutting-edge hardware to pursue Neural MCTS research. We propose Step-MCTS, a novel algorithm that uses subnet structures, each of which simulates a tree that provides a lookahead for exploration.” Find the paper and full list of authors in the International Conference on Autonomous Agents and Multiagent Systems…
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‘Sustainable HCI Under Water: Opportunities for Research with Oceans, Coastal Communities and Marine Systems’
“Although the world’s oceans play a critical role in human well-being, they have not been a primary focus of the sustainable HCI (SHCI) community to date. In this paper, we present a scoping review to show how concerns with the oceans are threaded throughout the broader SHCI literature and to find new research opportunities. We identify several themes that could benefit from focused SHCI research, including marine food sources, culture and coastal communities, ocean conservation, and marine climate change impacts and adaptation strategies.” Find the paper and full list of authors at the Conference on Human Factors in Computing Systems.
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‘Rapid Convergence: The Outcomes of Making PPE During a Healthcare Crisis’
“The U.S. National Institute of Health (NIH) 3D Print Exchange is a public, open-source repository for 3D printable medical device designs with contributions from clinicians, expert-amateur makers, and people from industry and academia. In response to the COVID-19 pandemic, the NIH formed a collection to foster submissions of low-cost, locally manufacturable personal protective equipment (PPE). We evaluated the 623 submissions in this collection … [and] found an immediate design convergence to manufacturing-focused remixes of a few initial designs affiliated with NIH partners and major for-profit groups.” Find the paper and full list of authors at ACM Transactions on Computer-Human Interaction.
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‘OPTIMISM: Enabling Collaborative Implementation of Domain Specific Metaheuristic Optimization’
“For non-technical domain experts and designers it can be a substantial challenge to create designs that meet domain specific goals. This presents an opportunity to create specialized tools that produce optimized designs in the domain. However, implementing domain-specific optimization methods requires a rare combination of programming and domain expertise. … We present OPTIMISM, a toolkit which enables programmers and domain experts to collaboratively implement an optimization component of design tools.” Find the paper and full list of authors in the Conference on Human Factors in Computing Systems proceedings.
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Understanding human decision-making during supply chains shortages
“Research conducted by mechanical and industrial engineering associate professor Jacqueline Griffin, professor Ozlem Ergun, and professor Stacy Marsella [in the Khoury College of Computer science, titled] ‘Agent-Based Modeling of Human Decision-Makers Under Uncertain Information During Supply Chain Shortages’ was published in the proceedings from the 2023 International Conference on Autonomous Agents and Multiagent Systems.”