Tootfinder

Amid mass tech layoffs, some workers with years of experience or deep technical expertise are having trouble getting hired again while some seek non-tech jobs (Washington Post)
https://wapo.st/3Ssn0rC

The U.S. economy is booming. So why are tech companies laying off workers?
Google, Amazon, Microsoft and a raft of others fired thousands of workers in January, continuing a layoff wave that began in 2022.

Mit solchen Scheinargumenten stellt Bundesverkehrsminister #Wissing in einem unterwentwickelten Licht dar. Seine Abelhungsgründe sind entweder falsch (keine Akzeptanz) oder es sind Bedenken (Ausweichverkehre), die mit einfachen technischen Maßnahmen lösbar sind.
Obwohl das notwendige Wissen und die Fähigkeiten für die Problemlösungen in deutschen Kommunen, Planungsbüros und auch im BMDV selb…

Viel viel viel mehr als die Niederlage schmerzt das wir mit Eric den wohl wichtigsten und unersetzlichsten Spieler verloren haben. Und das evtl. für längere Zeit. Ohne #Smith kein Aufstieg. #FCSP

Viel viel viel mehr als die Niederlage schmerzt das wir mit Eric den wohl wichtigsten und unersetzlichsten Spieler verloren haben. Und das evtl. für längere Zeit. Ohne #Smith kein Aufstieg. #FCSP

This https://arxiv.org/abs/2403.16854 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

An Expert is Worth One Token: Synergizing Multiple Expert LLMs as Generalist via Expert Token Routing
We present Expert-Token-Routing, a unified generalist framework that facilitates seamless integration of multiple expert LLMs. Our framework represents expert LLMs as special expert tokens within the vocabulary of a meta LLM. The meta LLM can route to an expert LLM like generating new tokens. Expert-Token-Routing not only supports learning the implicit expertise of expert LLMs from existing instruction dataset but also allows for dynamic extension of new expert LLMs in a plug-and-play manner. I…

Obfuscated Malware Detection: Investigating Real-world Scenarios through Memory Analysis
S M Rakib Hasan, Aakar Dhakal
https://arxiv.org/abs/2404.02372 htt…

Obfuscated Malware Detection: Investigating Real-world Scenarios through Memory Analysis
In the era of the internet and smart devices, the detection of malware has become crucial for system security. Malware authors increasingly employ obfuscation techniques to evade advanced security solutions, making it challenging to detect and eliminate threats. Obfuscated malware, adept at hiding itself, poses a significant risk to various platforms, including computers, mobile devices, and IoT devices. Conventional methods like heuristic-based or signature-based systems struggle against this …

Mobile user experience from the lens of project-based learning
Maria Spichkova
https://arxiv.org/abs/2404.02470 https://arxiv.org/pdf…

Mobile user experience from the lens of project-based learning
This paper presents an overview of mobile application projects conducted at the RMIT University as a part of the Learning and Teaching activities within Bachelor and Master programs, in collaboration with industrial partners. We discuss the lessons learned over eight years of teaching the corresponding courses and compare the results of our student project to the trends summarised in the recently published approached from other universities and countries.

This https://arxiv.org/abs/2402.18162 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Out-of-Distribution Detection using Neural Activation Prior
Out-of-distribution detection is a crucial technique for deploying machine learning models in the real world to handle the unseen scenarios.In this paper, we propose a simple but effective Neural Activation Prior (NAP) for out-of-distribution detection (OOD). Our neural activation prior is based on a key observation that, for a channel before the global pooling layer of a fully trained neural network, the probability of a few of its neurons being activated with a larger response by an in-distri…

This https://arxiv.org/abs/2402.17944 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Large Language Models on Tabular Data -- A Survey
Recent breakthroughs in large language modeling have facilitated rigorous exploration of their application in diverse tasks related to tabular data modeling, such as prediction, tabular data synthesis, question answering, and table understanding. Each task presents unique challenges and opportunities. However, there is currently a lack of comprehensive review that summarizes and compares the key techniques, metrics, datasets, models, and optimization approaches in this research domain. This sur…

This https://arxiv.org/abs/2402.17944 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Large Language Models on Tabular Data -- A Survey
Recent breakthroughs in large language modeling have facilitated rigorous exploration of their application in diverse tasks related to tabular data modeling, such as prediction, tabular data synthesis, question answering, and table understanding. Each task presents unique challenges and opportunities. However, there is currently a lack of comprehensive review that summarizes and compares the key techniques, metrics, datasets, models, and optimization approaches in this research domain. This sur…