Search bias analysis is getting more attention in recent years since search results could affect In this work, we aim to establish an automated model for evaluating ideological bias in online news articles. The dataset is composed of news articles in search results as well as the newspaper articles. The current automated model results show that model capability is not sufficient to be exploited for annotating the documents automatically, thereby computing bias in search results.

The research described in this article was divided into three phases. The first phase involved gathering meteorological data Mosquito habitats and breeding ranges are expanding globally and larvae counts from various locations in the United States [1]. Habitat preferences are based on the interaction and using this data set to create a predictive model for mosquito of several factors, including temperature, humidity, rainfall, larvae abundance. The second phase involved extracting time elevation, and availability of hosts. Climate change has been series sequences of the said meteorological variables for identified as a key driving factor for the shifts in mosquito specific regions of interest, to allow for the forecasting of distribution over the past 70 years and is likely to continue to environmental conditions. The third phase involved feeding be the chief determinant of mosquito population spread [1].

This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (we refer to it as HyperGCL). We focus on the following question: How to construct contrastive views for hypergraphs via augmentations? We provide the solutions in two folds. First, guided by domain knowledge, we fabricate two schemes to augment hyperedges with higher-order relations encoded, and adopt three vertex augmentation strategies from graph-structured data. Second, in search of more effective views in a data-driven manner, we for the first time propose a hypergraph generative model to generate augmented views, and then an end-to-end differentiable pipeline to jointly learn hypergraph augmentations and model parameters. Our technical innovations are reflected in designing both fabricated and generative augmentations of hypergraphs. The experimental findings include: (i) Among fabricated augmentations in HyperGCL, augmenting hyperedges provides the most numerical gains, implying that higher-order information in structures is usually more downstream-relevant; (ii) Generative augmentations do better in preserving higher-order information to further benefit generalizability; (iii) HyperGCL also boosts robustness and fairness in hypergraph representation learning.

Instances are the increasing demand for food due to the growth of the world population [7], as well as the impact of practices which are detrimental for the ecosystem [10]. In these contexts, precision agriculture has recently attracted a lot of interest since playing a significant role in the development of advanced techniques that optimize the soil productivity in a sustainable way [39, 20]. The general goal is to preserve the stability of the ecosystem while fostering the reuse of the soil for future produce. The strong interest in this new way of conceiving agriculture justifies the spread of innovative start-ups, of services devoted to eco-friendly practices, and of software solutions which allow farmers to accurately estimate yields on a simple smartphone or tablet (see, for instance, [2, 3, 4, 5]). In particular, the availability of higher-quality measurements, offered by advanced in field-sensors as well as by satellite or drone data, supported the proposal of breakthrough software solutions using modern deep learning algorithms [15, 19, 9, 36, 30]. In this paper, we focus on the detection of possible diseases in crops. Anomaly detection in plants represents a pivotal procedure in agriculture since an early detection of the disease enables a timely intervention to prevent the anomaly from spreading to the rest of the plant. Additionally, a precise disease localization allows confining the use of pesticides and other treatments to small strategically selected areas of the plant.

Several prior works have shown that language models (LMs) can generate text containing harmful social biases and stereotypes. While decoding algorithms play a central role in determining properties of LM generated text, their impact on the fairness of the generations has not been studied. We present a systematic analysis of the impact of decoding algorithms on LM fairness, and analyze the trade-off between fairness, diversity and quality. Our experiments with top-$p$, top-$k$ and temperature decoding algorithms, in open-ended language generation, show that fairness across demographic groups changes significantly with change in decoding algorithm's hyper-parameters. Notably, decoding algorithms that output more diverse text also output more texts with negative sentiment and regard. We present several findings and provide recommendations on standardized reporting of decoding details in fairness evaluations and optimization of decoding algorithms for fairness alongside quality and diversity.

Predictive uncertainty estimation of pre-trained language models is an important measure of how likely people can trust their predictions. However, little is known about what makes a model prediction uncertain. Explaining predictive uncertainty is an important complement to explaining prediction labels in helping users understand model decision making and gaining their trust on model predictions, while has been largely ignored in prior works. In this work, we propose to explain the predictive uncertainty of pre-trained language models by extracting uncertain words from existing model explanations. We find the uncertain words are those identified as making negative contributions to prediction labels, while actually explaining the predictive uncertainty. Experiments show that uncertainty explanations are indispensable to explaining models and helping humans understand model prediction behavior.

Recording the dynamics of unscripted human interactions in the wild is challenging due to the delicate trade-offs between several factors: participant privacy, ecological validity, data fidelity, and logistical overheads. To address these, following a 'datasets for the community by the community' ethos, we propose the Conference Living Lab (ConfLab): a new concept for multimodal multisensor data collection of in-the-wild free-standing social conversations. For the first instantiation of ConfLab described here, we organized a real-life professional networking event at a major international conference. Involving 48 conference attendees, the dataset captures a diverse mix of status, acquaintance, and networking motivations. Our capture setup improves upon the data fidelity of prior in-the-wild datasets while retaining privacy sensitivity: 8 videos (1920x1080, 60 fps) from a non-invasive overhead view, and custom wearable sensors with onboard recording of body motion (full 9-axis IMU), privacy-preserving low-frequency audio (1250 Hz), and Bluetooth-based proximity. Additionally, we developed custom solutions for distributed hardware synchronization at acquisition and time-efficient continuous annotation of body keypoints and actions at high sampling rates. Our benchmarks showcase some of the open research tasks related to in-the-wild privacy-preserving social data analysis: keypoints detection from overhead camera views, skeleton-based no-audio speaker detection, and F-formation detection.

An MQ-28 Ghost Bat drone flies in tests for the Royal Australian Air Force. WASHINGTON -- The US Air Force is set to begin flight experiments with Boeing's MQ-28 Ghost Bat, a combat drone developed for the Australian air force that may help its American counterpart learn how to operate unmanned aircraft alongside fighter jets. Lt. Gen. Clint Hinote, who leads Air Force Futures, told Breaking Defense in a September 20 interview that the service is "getting ready to take delivery" of a drone prototype through the Pentagon's research and engineering office, also known as OSD (R&E). "It might look a lot like an Australian thing," he joked, referring to the Ghost Bat, which first flew in 2021 at Royal Australian Air Force Base Woomera. Pentagon spokesman Lt. Cdr. Tim Gorman confirmed that the research and engineering office is involved in development and experimentation efforts involving Ghost Bat, saying that "OSD (R&E) continually works with the services to validate technologies that are key to advancing and fielding next generation capabilities."

It might not be a surprise that some city governments are not only unnerved by it, they're regulating it. Some government officials are understandably worried about artificial intelligence programs taking away jobs -- but lately, some municipalities appear to be concerned that AI is being used to help people get jobs. For instance, New York City and the District of Columbia are among locales that are enacting or considering laws to restrict how employers utilize artificial intelligence programs in hiring and promoting decisions. If you're unaware of what is transpiring in the world of human resources, AI and city governments, here's what is at stake. Increasingly, recruiters and human resources departments have been using AI tools to help find job candidates by performing repetitive and time-consuming tasks like analyzing resumes, arranging interviews with job candidates, and scheduling job assessments.

The days of Spot being leveraged as a weapons platform and training alongside special forces operators are already coming to an end; Atlas as a back-flipping soldier of fortune will never come to pass. Their maker, Boston Dynamics, along with five other industry leaders announced on Thursday that they will not pursue, or allow, the weaponization of their robots, according to a non-binding, open letter they all signed. Agility Robotics, ANYbotics, Clearpath Robotics, Open Robotics and Unitree Robotics all joined Boston Dynamics in the agreement. "We believe that adding weapons to robots that are remotely or autonomously operated, widely available to the public, and capable of navigating to previously inaccessible locations where people live and work, raises new risks of harm and serious ethical issues," the group wrote. "Weaponized applications of these newly-capable robots will also harm public trust in the technology in ways that damage the tremendous benefits they will bring to society."