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House Oversight chair James Comer claims Bill Clinton and Hillary Clinton agreed to depositions in the Epstein investigation after facing bipartisan contempt votes that surprised them.

Abstract--Understanding the nuances of speech emotion dataset curation and labeling is essential for assessing speech emotion recognition (SER) model potential in real-world applications. Most training and evaluation datasets contain acted or pseudo-acted speech (e.g., podcast speech) in which emotion expressions may be exaggerated or otherwise intentionally modified. Furthermore, datasets labeled based on crowd perception often lack transparency regarding the guidelines given to annotators. These factors make it difficult to understand model performance and pinpoint necessary areas for improvement. T o address this gap, we identified the Switchboard corpus as a promising source of naturalistic conversational speech, and we trained a crowd to label the dataset for categorical emotions (anger, contempt, disgust, fear, sadness, surprise, happiness, tenderness, calmness, and neutral) and dimensional attributes (activation, valence, and dominance). We refer to this label set as Switchboard-Affect (SWB-Affect). In this work, we present our approach in detail, including the definitions provided to annotators and an analysis of the lexical and paralinguistic cues that may have played a role in their perception. In addition, we evaluate state-of-the-art SER models, and we find variable performance across the emotion categories with especially poor generalization for anger . These findings underscore the importance of evaluation with datasets that capture natural affective variations in speech. We release the labels for SWB-Affect to enable further analysis in this domain. Speech emotion recognition (SER) has the potential to enhance human-computer interaction, improve our ability to monitor mental health and well-being [1], [2], and better understand customer service, entertainment, and education experiences [3], [4].

There is a mismatch between psychological and computational studies on emotions. Psychological research aims at explaining and documenting internal mechanisms of these phenomena, while computational work often simplifies them into labels. Many emotion fundamentals remain under-explored in natural language processing, particularly how emotions develop and how people cope with them. To help reduce this gap, we follow theories on coping, and treat emotions as strategies to cope with salient situations (i.e., how people deal with emotion-eliciting events). This approach allows us to investigate the link between emotions and behavior, which also emerges in language. We introduce the task of coping identification, together with a corpus to do so, constructed via role-playing. We find that coping strategies realize in text even though they are challenging to recognize, both for humans and automatic systems trained and prompted on the same task. We thus open up a promising research direction to enhance the capability of models to better capture emotion mechanisms from text.

Vice President Kamala Harris recently warned donors in San Diego that Donald Trump has "threatened to weaponize the Department of Justice against his political enemies" if elected. Does our clueless vice president not get that half the country believes the Biden-Harris White House has been doing exactly that for over three years? While Joe Biden prattles on about threats to democracy, his Department of Justice has created the ultimate threat to democracy -- ruthlessly waging war on MAGA Republicans, Catholics, pro-life advocates, parents' groups -- anyone and everyone who does not buy into their progressive agenda. It is not just the outrageous legal persecution of the former president – the four dubious cases brought against Trump, each less credible than the last. It is not just Trump's conviction on flimsy charges brought by a politically-motivated district attorney and overseen by a clearly conflicted judge. DOJ CLAIMS IT CAN'T RELEASE BIDEN-HUR INTERVIEW DUE TO THREAT OF AI DEEPFAKES It is also the pursuit and prosecution of Trump allies including Peter Navarro, Roger Stone, Paul Manafort, Rick Gates, George Papadopoulos, Allen Weisselberg and Steve Bannon, all of whom have been sentenced to time in prison.

Nigerians have a notable online presence and actively discuss political and topical matters. This was particularly evident throughout the 2023 general election, where Twitter was used for campaigning, fact-checking and verification, and even positive and negative discourse. However, little or none has been done in the detection of abusive language and hate speech in Nigeria. In this paper, we curated code-switched Twitter data directed at three musketeers of the governorship election on the most populous and economically vibrant state in Nigeria; Lagos state, with the view to detect offensive speech in political discussions. We developed EkoHate -- an abusive language and hate speech dataset for political discussions between the three candidates and their followers using a binary (normal vs offensive) and fine-grained four-label annotation scheme. We analysed our dataset and provided an empirical evaluation of state-of-the-art methods across both supervised and cross-lingual transfer learning settings. In the supervised setting, our evaluation results in both binary and four-label annotation schemes show that we can achieve 95.1 and 70.3 F1 points respectively. Furthermore, we show that our dataset adequately transfers very well to three publicly available offensive datasets (OLID, HateUS2020, and FountaHate), generalizing to political discussions in other regions like the US.

Understanding the facial expressions of our interlocutor is important to enrich the communication and to give it a depth that goes beyond the explicitly expressed. In fact, studying one's facial expression gives insight into their hidden emotion state. However, even as humans, and despite our empathy and familiarity with the human emotional experience, we are only able to guess what the other might be feeling. In the fields of artificial intelligence and computer vision, Facial Emotion Recognition (FER) is a topic that is still in full growth mostly with the advancement of deep learning approaches and the improvement of data collection. The main purpose of this paper is to compare the performance of three state-of-the-art networks, each having their own approach to improve on FER tasks, on three FER datasets. The first and second sections respectively describe the three datasets and the three studied network architectures designed for an FER task. The experimental protocol, the results and their interpretation are outlined in the remaining sections.

A surge of Haitians is being deported, or let free. As President Joe Biden vacationed at Rehoboth Beach, the disaster at the U.S. southern border continued to metastasize. Anyone who has seen pictures of thousands of people crossing the Rio Grande en masse knows the administration has achieved complete failure. Anyone who has seen the overhead drone footage of more than 12,000 people gathered under one bridge in South Texas knows that massive, historic incompetence is being allowed to flourish. At the same time, it's clear that – despite all the Big Government Socialists in Biden's party who complain about America – we remain the one country people desperately try to get into.

This study reviews research on social emotions in robotics. In robotics, emotions are pursued for a long duration, such as recognition, expression, and computational modeling of the basic mechanism behind them. Research has been promoted according to well-known psychological findings, such as category and dimension theories. Many studies have been based on these basic theories, addressing only basic emotions. However, social emotions, also called higher-level emotions, have been studied in psychology. We believe that these higher-level emotions are worth pursuing in robotics for next-generation social-aware robots. In this review paper, while summarizing the findings of social emotions in psychology and neuroscience, studies on social emotions in robotics at present are surveyed. Thereafter, research directions towards implementation of social emotions in robots are discussed.

Slate is now asking those who read the most to support our journalism more directly by subscribing to Slate Plus. Dear Prudence is online weekly to chat live with readers. Here's an edited transcript of this week's chat. I know it's a petty, marriage-killing thing to dwell on … but I'm smarter than my husband. Because he insisted we both get IQ tests. It turns out I qualify for MENSA and he just does not. Except now he's telling our friends his fairly impressive IQ and when they ask about me, he says: "Oh well, it doesn't really matter. What's important is how you use what God gave you."