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Bears have been getting contacted by 'angry' NFL coaches who seem jealous about this one Chicago draft pick

https://www.cbssports.com/nf…

Bears have been getting contacted by 'angry' NFL coaches who seem jealous about this one Chicago draft pick
The Bears are sure happy they didn't 'punt' on this pick

LLMs' Reading Comprehension Is Affected by Parametric Knowledge and Struggles with Hypothetical Statements
Victoria Basmov, Yoav Goldberg, Reut Tsarfaty
https://arxiv.org/abs/2404.06283

LLMs' Reading Comprehension Is Affected by Parametric Knowledge and Struggles with Hypothetical Statements
The task of reading comprehension (RC), often implemented as context-based question answering (QA), provides a primary means to assess language models' natural language understanding (NLU) capabilities. Yet, when applied to large language models (LLMs) with extensive built-in world knowledge, this method can be deceptive. If the context aligns with the LLMs' internal knowledge, it is hard to discern whether the models' answers stem from context comprehension or from LLMs' internal information. …

"Some members of the state party reacted with anger, expressing worry about other private conversations Ms. Lake might have recorded. Ms. Lake was greeted with boos at a meeting to elect a new chair." According to the report, key Republicans in the state now wonder if she has been secretly recording them also."
Kari Lake scrambling to deal with fallout over secret recording: report - Raw Story

Kari Lake scrambling to deal with fallout over secret recording: report
Kari Lake's second bid for political office is facing multiple hurdles related to her conduct after losing a 2022 run for governor of Arizona including a belief by some key Republicans in the state that she can't be trusted.The former TV personality, who appears slated to be the Republican nominee f...

Bears have been getting contacted by 'angry' NFL coaches who seem jealous about this one Chicago draft pick

https://www.cbssports.com/nf…

Bears have been getting contacted by 'angry' NFL coaches who seem jealous about this one Chicago draft pick
The Bears are sure happy they didn't 'punt' on this pick

Overcoming Anchoring Bias: The Potential of AI and XAI-based Decision Support
Felix Haag, Carlo Stingl, Katrin Zerfass, Konstantin Hopf, Thorsten Staake
https://arxiv.org/abs/2405.04972

Overcoming Anchoring Bias: The Potential of AI and XAI-based Decision Support
Information systems (IS) are frequently designed to leverage the negative effect of anchoring bias to influence individuals' decision-making (e.g., by manipulating purchase decisions). Recent advances in Artificial Intelligence (AI) and the explanations of its decisions through explainable AI (XAI) have opened new opportunities for mitigating biased decisions. So far, the potential of these technological advances to overcome anchoring bias remains widely unclear. To this end, we conducted two o…

What Impacts the Quality of the User Answers when Asked about the Current Context?
Ivano Bison, Haonan Zhao, Fausto Giunchiglia
https://arxiv.org/abs/2405.04054

What Impacts the Quality of the User Answers when Asked about the Current Context?
Sensor data provide an objective view of reality but fail to capture the subjective motivations behind an individual's behavior. This latter information is crucial for learning about the various dimensions of the personal context, thus increasing predictability. The main limitation is the human input, which is often not of the quality that is needed. The work so far has focused on the usually high number of missing answers. The focus of this paper is on \textit{the number of mistakes} made when…

The Fact Selection Problem in LLM-Based Program Repair
Nikhil Parasaram, Huijie Yan, Boyu Yang, Zineb Flahy, Abriele Qudsi, Damian Ziaber, Earl Barr, Sergey Mechtaev
https://arxiv.org/abs/2404.05520

The Fact Selection Problem in LLM-Based Program Repair
Recent research has shown that incorporating bug-related facts, such as stack traces and GitHub issues, into prompts enhances the bug-fixing capabilities of large language models (LLMs). Considering the ever-increasing context window of these models, a critical question arises: what and how many facts should be included in prompts to maximise the chance of correctly fixing bugs? To answer this question, we conducted a large-scale study, employing over 19K prompts featuring various combinations …

Japan’s answer to seasonal allergies: A subsidized tropical escape
Spring is a miserable time for millions of Japanese.
One of them is Naoki Shigihara, whose hay fever symptoms make it difficult to focus at work.
Luckily for him, his employer, an IT company called Aisaac, offers a “tropical escape” program, allowing employees to work remotely from other parts of the country with low pollen counts.
It even offers to subsidize the temporary relocation to the tune of…

Japan’s answer to seasonal allergies: A subsidized tropical escape
So many people call in sick with hay fever during the peak pollen season that Japanese companies are taking novel steps to allow them to continue to work.

ViboPneumo: A Vibratory-Pneumatic Finger-Worn Haptic Device for Altering Perceived Texture Roughness in Mixed Reality
Shaoyu Cai, Zhenlin Chen, Haichen Gao, Ya Huang, Qi Zhang, Xinge Yu, Kening Zhu
https://arxiv.org/abs/2403.05182

ViboPneumo: A Vibratory-Pneumatic Finger-Worn Haptic Device for Altering Perceived Texture Roughness in Mixed Reality
Extensive research has been done in haptic feedback for texture simulation in virtual reality (VR). However, it is challenging to modify the perceived tactile texture of existing physical objects which usually serve as anchors for virtual objects in mixed reality (MR). In this paper, we present ViboPneumo, a finger-worn haptic device that uses vibratory-pneumatic feedback to modulate (i.e., increase and decrease) the perceived roughness of the material surface contacted by the user's fingerpad …

This https://arxiv.org/abs/2307.08669 has been replaced.
link: https://scholar.google.com/scholar?q=a

Leveraging Recommender Systems to Reduce Content Gaps on Peer Production Platforms
Peer production platforms like Wikipedia commonly suffer from content gaps. Prior research suggests recommender systems can help solve this problem, by guiding editors towards underrepresented topics. However, it remains unclear whether this approach would result in less relevant recommendations, leading to reduced overall engagement with recommended items. To answer this question, we first conducted offline analyses (Study 1) on SuggestBot, a task-routing recommender system for Wikipedia, then…