Tootfinder

Just read Plantin & Thomer's article, on the changes Figshare for Institutions introduces into the provision & organization of data-management labor in libraries. The authors worry that this commercial SaaS platform for institutional & data repositories, while fulfilling a number of library needs, will have detrimental effects. While it's a good piece of infrastructure-studies research, I question some of the analysis & conclusions.

Real-Time Agile Software Management for Edge and Fog Computing Based Smart City Infrastructure
Debasish Jana, Pinakpani Pal, Pawan Kumar
https://arxiv.org/abs/2506.12616

Real-Time Agile Software Management for Edge and Fog Computing Based Smart City Infrastructure
The evolution of smart cities demands scalable, secure, and energy-efficient architectures for real-time data processing. With the number of IoT devices expected to exceed 40 billion by 2030, traditional cloud-based systems are increasingly constrained by bandwidth, latency, and energy limitations. This paper leverages the ROOF (Real-time Onsite Operations Facilitation) framework with decentralized computing at intermediary fog and peripheral edge network layers to reduce latency by processing …

Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing
Chuanchao Gao, Niraj Kumar, Arvind Easwaran
https://arxiv.org/abs/2506.12686

Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing
Mobile-edge computing (MEC) has emerged as a promising paradigm for enabling Internet of Things (IoT) devices to handle computation-intensive jobs. Due to the imperfect parallelization of algorithms for job processing on servers and the impact of IoT device mobility on data communication quality in wireless networks, it is crucial to jointly consider server resource allocation and IoT device mobility during job scheduling to fully benefit from MEC, which is often overlooked in existing studies.…

InverTune: Removing Backdoors from Multimodal Contrastive Learning Models via Trigger Inversion and Activation Tuning
Mengyuan Sun, Yu Li, Yuchen Liu, Bo Du, Yunjie Ge
https://arxiv.org/abs/2506.12411

InverTune: Removing Backdoors from Multimodal Contrastive Learning Models via Trigger Inversion and Activation Tuning
Multimodal contrastive learning models like CLIP have demonstrated remarkable vision-language alignment capabilities, yet their vulnerability to backdoor attacks poses critical security risks. Attackers can implant latent triggers that persist through downstream tasks, enabling malicious control of model behavior upon trigger presentation. Despite great success in recent defense mechanisms, they remain impractical due to strong assumptions about attacker knowledge or excessive clean data requir…

Shelter Soul: Bridging Shelters and Adopters Through Technology
Yashodip Dharmendra Jagtap
https://arxiv.org/abs/2506.12739 https://a…

Shelter Soul: Bridging Shelters and Adopters Through Technology
Pet adoption processes often face inefficiencies, including limited accessibility, lack of real-time information, and mismatched expectations between shelters and adopters. To address these challenges, this study presents Shelter Soul, a technology-based solution designed to streamline pet adoption through an integrated, web-based platform. Developed using the MERN stack and GraphQL, Shelter Soul is a prototype system built to improve pet matching accuracy, shelter management efficiency, and se…

Fortified Proximal Causal Inference with Many Invalid Proxies
Myeonghun Yu, Xu Shi, Eric J. Tchetgen Tchetgen
https://arxiv.org/abs/2506.13152 https://

Fortified Proximal Causal Inference with Many Invalid Proxies
Causal inference from observational data often relies on the assumption of no unmeasured confounding, an assumption frequently violated in practice due to unobserved or poorly measured covariates. Proximal causal inference (PCI) offers a promising framework for addressing unmeasured confounding using a pair of outcome and treatment confounding proxies. However, existing PCI methods typically assume all specified proxies are valid, which may be unrealistic and is untestable without extra assumpt…

IDOL: Improved Different Optimization Levels Testing for Solidity Compilers
Lantian Li, Yejian Liang, Zhongxing Yu
https://arxiv.org/abs/2506.12760 https:/…

IDOL: Improved Different Optimization Levels Testing for Solidity Compilers
As blockchain technology continues to evolve and mature, smart contracts have become a key driving force behind the digitization and automation of transactions. Smart contracts greatly simplify and refine the traditional business transaction processes, and thus have had a profound impact on various industries such as finance and supply chain management. However, because smart contracts cannot be modified once deployed, any vulnerabilities or design flaws within the contract cannot be easily fix…

The party that was supposed to be good at managing the nation's finances cancelled the second leg of #HS2. Apparently, to save £36 billion, which "would instead be allocated to infrastructure projects to provide better transport links across the north of England." Yet we still do not really know the cancellation cost, or whether anything will really be spent extra on the north of Englan…

Introducing a vertex polynomial invariant for embedded graphs
Qi Yan, Qingying Deng, Metrose Metsidik
https://arxiv.org/abs/2506.07522 https://

Introducing a vertex polynomial invariant for embedded graphs
The ribbon group action extends geometric duality and Petrie duality by defining two embedded graphs as twisted duals precisely when they lie within the same orbit under this group action. Twisted duality yields numerous novel properties of fundamental graph polynomials. In this paper, we resolve a problem raised by Ellis-Monaghan and Moffatt [Trans. Amer. Math. Soc. 364 (2012), 1529--1569] for vertex counts by introducing the vertex polynomial: a generating function quantifying vertex distribu…

"The waste of having processes that create defects and then use inspection to catch them is certainly something to avoid. A significant part of the effort in code reviews should be geared toward capturing learning that can be applied to current processes to improve them so fewer bugs are created in the future.
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