Tootfinder

I have a meeting on MS Teams. If I try to join with various browsers on linux, the connection is blocked. iPhone works. Tried Windows virtual machine and, surprise, it also works.
Has someone else with a linux Desktop experienced blocking by Microsoft? It looks very fishy.

QRscript: Embedding a Programming Language in QR codes to support Decision and Management
Stefano Scanzio, Gianluca Cena, Adriano Valenzano
https://arxiv.org/abs/2404.05073

QRscript: Embedding a Programming Language in QR codes to support Decision and Management
Embedding a programming language in a QR code is a new and extremely promising opportunity, as it makes devices and objects smarter without necessarily requiring an Internet connection. In this paper, all the steps needed to translate a program written in a high-level programming language to its binary representation encoded in a QR code, and the opposite process that, starting from the QR code, executes it by means of a virtual machine, have been carefully detailed. The proposed programming la…

Set up a local Pi-Hole virtual machine to protect me from Ads when outside of my home network. Advertising really got out of hand for most popular sites. #pihole

This https://arxiv.org/abs/2311.18377 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Transfer Learning across Different Chemical Domains: Virtual Screening of Organic Materials with Deep Learning Models Pretrained on Small Molecule and Chemical Reaction Data
Machine learning is becoming a preferred method for the virtual screening of organic materials due to its cost-effectiveness over traditional computationally demanding techniques. However, the scarcity of labeled data for organic materials poses a significant challenge for training advanced machine learning models. This study showcases the potential of utilizing databases of drug-like small molecules and chemical reactions to pretrain the BERT model, enhancing its performance in the virtual scr…

In preparation for teaching a 3-day "Computing within limits" workshop @ University of Applied Sciences in Augsburg, almost exactly a year ago (next week) I created a little fantasy console (heavily inspirered by TIC80) to introduce students to:
- the idea of virtual machines / computing environments
- the freedom to design & control any aspect of that environment (and how this relates to the overall idea of personal computing these days)
- designing & build…

Broadly scoped ecosystem & mono-repository of 190 TypeScript projects for general purpose, functional, data driven development

Heckler: Breaking Confidential VMs with Malicious Interrupts
Benedict Schl\"uter, Supraja Sridhara, Mark Kuhne, Andrin Bertschi, Shweta Shinde
https://arxiv.org/abs/2404.03387

Heckler: Breaking Confidential VMs with Malicious Interrupts
Hardware-based Trusted execution environments (TEEs) offer an isolation granularity of virtual machine abstraction. They provide confidential VMs (CVMs) that host security-sensitive code and data. AMD SEV-SNP and Intel TDX enable CVMs and are now available on popular cloud platforms. The untrusted hypervisor in these settings is in control of several resource management and configuration tasks, including interrupts. We present Heckler, a new attack wherein the hypervisor injects malicious non-t…

This https://arxiv.org/abs/2402.18826 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

The Machine Can't Replace the Human Heart
What is the true heart of mental healthcare -- innovation or humanity? Can virtual therapy ever replicate the profound human bonds where healing arises? As artificial intelligence and immersive technologies promise expanded access, safeguards must ensure technologies remain supplementary tools guided by providers' wisdom. Implementation requires nuance balancing efficiency and empathy. If conscious of ethical risks, perhaps AI could restore humanity by automating tasks, giving providers more ti…

Dynamic Resource Allocation for Virtual Machine Migration Optimization using Machine Learning
Yulu Gong, Jiaxin Huang, Bo Liu, Jingyu Xu, Binbin Wu, Yifan Zhang
https://arxiv.org/abs/2403.13619

Dynamic Resource Allocation for Virtual Machine Migration Optimization using Machine Learning
The paragraph is grammatically correct and logically coherent. It discusses the importance of mobile terminal cloud computing migration technology in meeting the demands of evolving computer and cloud computing technologies. It emphasizes the need for efficient data access and storage, as well as the utilization of cloud computing migration technology to prevent additional time delays. The paragraph also highlights the contributions of cloud computing migration technology to expanding cloud com…

Odd things Ubuntu does
Having just booted a new VM up using the official #Canonical #Ubuntu image on #Azure, I noticed that, during the first boot cycle, it makes a call out to "entropy.ubuntu.com"
This is done by the "polliniate" package which describes itself:
pollinate is an entropy as a service client. It will fetch a random seed from one or more remote servers, and feed those into a (pseudo) random number generator. This is often useful in cloud and virtual machine environments with limited sources of entropy.
https://manpages.ubuntu.com/manpages/jammy/man1/pollinate.1.html
There's something oddly jarring about this. Not least because this call-out for "supplementary" entropy is done after generating the host's SSH keys.
I hope, hope very much, that someone with Prof in their title from a very well regarded Computer Science department has written a paper suggesting this is ok.

Morph, an Ethereum Layer 2 network that uses a combination of optimistic and zero-knowledge rollup technologies, raised a $19M seed led by DragonFly Capital (Niamh Rowe/Fortune)
https://fortune.com/crypto/2024/03/20/morph-ethereum-laye…

Morph, an Ethereum layer-2 for app building, announces $19 million seed round
Morph, a layer-2 Ethereum Virtual Machine, just raised a $19 million seed round.

Diffraction and Scattering Aware Radio Map and Environment Reconstruction using Geometry Model-Assisted Deep Learning
Wangqian Chen, Junting Chen
https://arxiv.org/abs/2403.00229 …

Diffraction and Scattering Aware Radio Map and Environment Reconstruction using Geometry Model-Assisted Deep Learning
Machine learning (ML) facilitates rapid channel modeling for 5G and beyond wireless communication systems. Many existing ML techniques utilize a city map to construct the radio map; however, an updated city map may not always be available. This paper proposes to employ the received signal strength (RSS) data to jointly construct the radio map and the virtual environment by exploiting the geometry structure of the environment. In contrast to many existing ML approaches that lack of an environmen…

https://bernsteinbear.com/blog/ddcg/ - A quick look at register allocation with destination-driven code generation.

OMG!!
Crossover 24.0.0 fixed the problem with Powerwash Simulator!!
I can play it again, which is something I can't really do in Windows 11, so I can stop using that garbage!
YAY!!
https://www.codeweavers.com/crossover

This https://arxiv.org/abs/2211.00090 has been replaced.
link: https://scholar.google.com/scholar?q=a

Machine learning-based sampling of virtual experiments within the full stress state
This paper presents a new machine learning-based approach to investigate anisotropic yield surfaces of sheet metals by means of virtual experiments. The new sampling approach is based on the machine learning technique known as active learning, which has been adapted to efficiently sample virtual experiments with respect to the full stress state in order to identify parameters of anisotropic yield functions. The approach was employed to sample virtual experiments based on the crystal plasticity …

#Clojure folk, are you (or is anyone you know) making serious use of Clojure CLR (the port of Clojure to Microsoft's dot-net virtual machine)?
Is the project stable? Is there a good community, good tooling? Is anyone using it in production?

Toward Improving Binary Program Comprehension via Embodied Immersion: A Survey
Dennis Brown, Emily Mulder, Samuel Mulder
https://arxiv.org/abs/2404.17051 h…

Toward Improving Binary Program Comprehension via Embodied Immersion: A Survey
Binary program comprehension is critical for many use cases but is difficult, suffering from compounded uncertainty and lack of full automation. We seek methods to improve the effectiveness of the human-machine joint cognitive system performing binary PC. We survey three research areas to perform an indirect cognitive task analysis: cognitive models of the PC process, related elements of cognitive theory, and applicable affordances of virtual reality. Based on common elements in these areas, we…

Dynamic Resource Allocation for Virtual Machine Migration Optimization using Machine Learning
Yulu Gong, Jiaxin Huang, Bo Liu, Jingyu Xu, Binbin Wu, Yifan Zhang
https://arxiv.org/abs/2403.13619

Dynamic Resource Allocation for Virtual Machine Migration Optimization using Machine Learning
The paragraph is grammatically correct and logically coherent. It discusses the importance of mobile terminal cloud computing migration technology in meeting the demands of evolving computer and cloud computing technologies. It emphasizes the need for efficient data access and storage, as well as the utilization of cloud computing migration technology to prevent additional time delays. The paragraph also highlights the contributions of cloud computing migration technology to expanding cloud com…

Active Learning with Task Adaptation Pre-training for Speech Emotion Recognition
Dongyuan Li, Ying Zhang, Yusong Wang, Funakoshi Kataro, Manabu Okumura
https://arxiv.org/abs/2405.00307

Active Learning with Task Adaptation Pre-training for Speech Emotion Recognition
Speech emotion recognition (SER) has garnered increasing attention due to its wide range of applications in various fields, including human-machine interaction, virtual assistants, and mental health assistance. However, existing SER methods often overlook the information gap between the pre-training speech recognition task and the downstream SER task, resulting in sub-optimal performance. Moreover, current methods require much time for fine-tuning on each specific speech dataset, such as IEMOCA…

This https://arxiv.org/abs/2304.09914 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning
Online media has revolutionized the way political information is disseminated and consumed on a global scale, and this shift has compelled political figures to adopt new strategies of capturing and retaining voter attention. These strategies often rely on emotional persuasion and appeal, and as visual content becomes increasingly prevalent in virtual space, much of political communication too has come to be marked by evocative video content and imagery. The present paper offers a novel approach…

Trustworthy confidential virtual machines for the masses
Anna Galanou, Khushboo Bindlish, Luca Preibsch, Yvonne-Anne Pignolet, Christof Fetzer, R\"udiger Kapitza
https://arxiv.org/abs/2402.15277

Trustworthy confidential virtual machines for the masses
Confidential computing alleviates the concerns of distrustful customers by removing the cloud provider from their trusted computing base and resolves their disincentive to migrate their workloads to the cloud. This is facilitated by new hardware extensions, like AMD's SEV Secure Nested Paging (SEV-SNP), which can run a whole virtual machine with confidentiality and integrity protection against a potentially malicious hypervisor owned by an untrusted cloud provider. However, the assurance of suc…

Reduced and All-at-Once Approaches for Model Calibration and Discovery in Computational Solid Mechanics
Ulrich R\"omer, Stefan Hartmann, Jendrik-Alexander Tr\"oger, David Anton, Henning Wessels, Moritz Flaschel, Laura De Lorenzis
https://arxiv.org/abs/2404.16980

Reduced and All-at-Once Approaches for Model Calibration and Discovery in Computational Solid Mechanics
In the framework of solid mechanics, the task of deriving material parameters from experimental data has recently re-emerged with the progress in full-field measurement capabilities and the renewed advances of machine learning. In this context, new methods such as the virtual fields method and physics-informed neural networks have been developed as alternatives to the already established least-squares and finite element-based approaches. Moreover, model discovery problems are starting to emerge…

Scalable Virtual Valuations Combinatorial Auction Design by Combining Zeroth-Order and First-Order Optimization Method
Zhijian Duan, Haoran Sun, Yichong Xia, Siqiang Wang, Zhilin Zhang, Chuan Yu, Jian Xu, Bo Zheng, Xiaotie Deng
https://arxiv.org/abs/2402.11904

Scalable Virtual Valuations Combinatorial Auction Design by Combining Zeroth-Order and First-Order Optimization Method
Automated auction design seeks to discover empirically high-revenue and incentive-compatible mechanisms using machine learning. Ensuring dominant strategy incentive compatibility (DSIC) is crucial, and the most effective approach is to confine the mechanism to Affine Maximizer Auctions (AMAs). Nevertheless, existing AMA-based approaches encounter challenges such as scalability issues (arising from combinatorial candidate allocations) and the non-differentiability of revenue. In this paper, to a…

Oscillations between Grid-Forming Converters in Weakly Connected Offshore WPPs
Sulav Ghimire, Kanakesh V. Kkuni, Gabriel M. G. Guerreiro, Emerson D. Guest, Kim H. Jensen, Guangya Yang
https://arxiv.org/abs/2402.14317

Oscillations between Grid-Forming Converters in Weakly Connected Offshore WPPs
This paper studies control interactions between grid-forming (GFM) converters exhibited by power and frequency oscillations in a weakly connected offshore wind power plant (WPP). Two GFM controls are considered, namely virtual synchronous machine (VSM) and virtual admittance (VAdm) based GFM. The GFM control methods are implemented in wind turbine generators (WTGs) of a verified aggregated model of a WPP and the control interaction between these GFM WTGs is studied for several cases: cases with…

Exploratory Landscape Analysis for Mixed-Variable Problems
Raphael Patrick Prager, Heike Trautmann
https://arxiv.org/abs/2402.16467 https://arxiv.org/pdf/2402.16467
arXiv:2402.16467v1 Announce Type: new
Abstract: Exploratory landscape analysis and fitness landscape analysis in general have been pivotal in facilitating problem understanding, algorithm design and endeavors such as automated algorithm selection and configuration. These techniques have largely been limited to search spaces of a single domain. In this work, we provide the means to compute exploratory landscape features for mixed-variable problems where the decision space is a mixture of continuous, binary, integer, and categorical variables. This is achieved by utilizing existing encoding techniques originating from machine learning. We provide a comprehensive juxtaposition of the results based on these different techniques. To further highlight their merit for practical applications, we design and conduct an automated algorithm selection study based on a hyperparameter optimization benchmark suite. We derive a meaningful compartmentalization of these benchmark problems by clustering based on the used landscape features. The identified clusters mimic the behavior the used algorithms exhibit. Meaning, the different clusters have different best performing algorithms. Finally, our trained algorithm selector is able to close the gap between the single best and the virtual best solver by 57.5% over all benchmark problems.

I clicked continue. It did not work.
#windows #hyperv #virtualmachine #vm

An Improved Metric and Benchmark for Assessing the Performance of Virtual Screening Models
Michael Brocidiacono, Konstantin I. Popov, Alexander Tropsha
https://arxiv.org/abs/2403.10478

An Improved Metric and Benchmark for Assessing the Performance of Virtual Screening Models
Structure-based virtual screening (SBVS) is a key workflow in computational drug discovery. SBVS models are assessed by measuring the enrichment of known active molecules over decoys in retrospective screens. However, the standard formula for enrichment cannot estimate model performance on very large libraries. Additionally, current screening benchmarks cannot easily be used with machine learning (ML) models due to data leakage. We propose an improved formula for calculating VS enrichment and i…

Sources: Monad Labs, which is building an Ethereum virtual machine-compatible Layer 1 blockchain, is in talks to raise $200M led by Paradigm at a $3B valuation (Fortune)
https://fortune.com/crypto/2024/03/11/monad-paradigm-venture-capital…

Monad Labs in talks to raise more than $200 million round led by Paradigm, valuing the Jump Crypto-linked startup at $3 billion
As the prices of digital assets skyrocket, the Monad round would be the largest in crypto so far in 2024.

DIO: Dataset of 3D Mesh Models of Indoor Objects for Robotics and Computer Vision Applications
Nillan Nimal, Wenbin Li, Ronald Clark, Sajad Saeedi
https://arxiv.org/abs/2402.11836

DIO: Dataset of 3D Mesh Models of Indoor Objects for Robotics and Computer Vision Applications
The creation of accurate virtual models of real-world objects is imperative to robotic simulations and applications such as computer vision, artificial intelligence, and machine learning. This paper documents the different methods employed for generating a database of mesh models of real-world objects. These methods address the tedious and time-intensive process of manually generating the models using CAD software. Essentially, DSLR/phone cameras were employed to acquire images of target object…

Non-Linear Paging
Ilan Doron-Arad (Seffi), Joseph (Seffi), Naor
https://arxiv.org/abs/2404.13334 https://arxiv.org/pdf/2404.13334…

Non-Linear Paging
We formulate and study non-linear paging - a broad model of online paging where the size of subsets of pages is determined by a monotone non-linear set function of the pages. This model captures the well-studied classic weighted paging and generalized paging problems, and also submodular and supermodular paging, studied here for the first time, that have a range of applications from virtual memory to machine learning. Unlike classic paging, the cache threshold parameter $k$ does not yield goo…

Workload Intelligence: Punching Holes Through the Cloud Abstraction
Lexiang Huang, Anjaly Parayil, Jue Zhang, Xiaoting Qin, Chetan Bansal, Jovan Stojkovic, Pantea Zardoshti, Pulkit Misra, Eli Cortez, Raphael Ghelman, \'I\~nigo Goiri, Saravan Rajmohan, Jim Kleewein, Rodrigo Fonseca, Timothy Zhu, Ricardo Bianchini
https://arxi…

Workload Intelligence: Punching Holes Through the Cloud Abstraction
Today, cloud workloads are essentially opaque to the cloud platform. Typically, the only information the platform receives is the virtual machine (VM) type and possibly a decoration to the type (e.g., the VM is evictable). Similarly, workloads receive little to no information from the platform; generally, workloads might receive telemetry from their VMs or exceptional signals (e.g., shortly before a VM is evicted). The narrow interface between workloads and platforms has several drawbacks: (1) …

Active Learning with Task Adaptation Pre-training for Speech Emotion Recognition
Dongyuan Li, Ying Zhang, Yusong Wang, Funakoshi Kataro, Manabu Okumura
https://arxiv.org/abs/2405.00307

Active Learning with Task Adaptation Pre-training for Speech Emotion Recognition
Speech emotion recognition (SER) has garnered increasing attention due to its wide range of applications in various fields, including human-machine interaction, virtual assistants, and mental health assistance. However, existing SER methods often overlook the information gap between the pre-training speech recognition task and the downstream SER task, resulting in sub-optimal performance. Moreover, current methods require much time for fine-tuning on each specific speech dataset, such as IEMOCA…

This https://arxiv.org/abs/2304.00083 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Generative Framework for Low-Cost Result Validation of Machine Learning-as-a-Service Inference
The growing popularity of Machine Learning (ML) has led to its deployment in various sensitive domains, which has resulted in significant research focused on ML security and privacy. However, in some applications, such as Augmented/Virtual Reality, integrity verification of the outsourced ML tasks is more critical--a facet that has not received much attention. Existing solutions, such as multi-party computation and proof-based systems, impose significant computation overhead, which makes them u…

Reduced and All-at-Once Approaches for Model Calibration and Discovery in Computational Solid Mechanics
Ulrich R\"omer, Stefan Hartmann, Jendrik-Alexander Tr\"oger, David Anton, Henning Wessels, Moritz Flaschel, Laura De Lorenzis
https://arxiv.org/abs/2404.16980

Reduced and All-at-Once Approaches for Model Calibration and Discovery in Computational Solid Mechanics
In the framework of solid mechanics, the task of deriving material parameters from experimental data has recently re-emerged with the progress in full-field measurement capabilities and the renewed advances of machine learning. In this context, new methods such as the virtual fields method and physics-informed neural networks have been developed as alternatives to the already established least-squares and finite element-based approaches. Moreover, model discovery problems are starting to emerge…

Eclipse Labs, which is developing an Ethereum Layer-2 blockchain using the Solana Virtual Machine, raised a $50M Series A co-led by Placeholder and Hack VC (Vishal Chawla/The Block)
https://www.theblock.co/post/281566/eclips

This https://arxiv.org/abs/2309.03780 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepe…

Reduced Simulations for High-Energy Physics, a Middle Ground for Data-Driven Physics Research
Subatomic particle track reconstruction (tracking) is a vital task in High-Energy Physics experiments. Tracking is exceptionally computationally challenging and fielded solutions, relying on traditional algorithms, do not scale linearly. Machine Learning (ML) assisted solutions are a promising answer. We argue that a complexity-reduced problem description and the data representing it, will facilitate the solution exploration workflow. We provide the REDuced VIrtual Detector (REDVID) as a comple…

This https://arxiv.org/abs/2308.10913 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Automated mapping of virtual environments with visual predictive coding
Humans construct internal cognitive maps of their environment directly from sensory inputs without access to a system of explicit coordinates or distance measurements. While machine learning algorithms like SLAM utilize specialized visual inference procedures to identify visual features and construct spatial maps from visual and odometry data, the general nature of cognitive maps in the brain suggests a unified mapping algorithmic strategy that can generalize to auditory, tactile, and linguisti…

LFOC : A Fair OS-level Cache-Clustering Policy for Commodity Multicore Systems
Juan Carlos Saez, Fernando Castro, Graziano Fanizzi, Manuel Prieto-Matias
https://arxiv.org/abs/2402.07693

LFOC+: A Fair OS-level Cache-Clustering Policy for Commodity Multicore Systems
Commodity multicore systems are increasingly adopting hardware support that enables the system software to partition the last-level cache (LLC). This support makes it possible for the operating system (OS) or the Virtual Machine Monitor (VMM) to mitigate shared-resource contention effects on multicores by assigning different co-running applications to various cache partitions. Recently cache-clustering (or partition-sharing) strategies have emerged as a way to improve system throughput and fair…

The Full-scale Assembly Simulation Testbed (FAST) Dataset
Alec G. Moore, Tiffany D. Do, Nayan N. Chawla, Antonia Jimenez Iriarte, Ryan P. McMahan
https://arxiv.org/abs/2403.08969 …

The Full-scale Assembly Simulation Testbed (FAST) Dataset
In recent years, numerous researchers have begun investigating how virtual reality (VR) tracking and interaction data can be used for a variety of machine learning purposes, including user identification, predicting cybersickness, and estimating learning gains. One constraint for this research area is the dearth of open datasets. In this paper, we present a new open dataset captured with our VR-based Full-scale Assembly Simulation Testbed (FAST). This dataset consists of data collected from 108…