The Browser Company, makers of the Arc Browser, revealed a few weeks ago that it was going back to the drawing board to create a new browser. Turns out, the browser -- named Dia -- will be very AI-centric. According to this early peek video, the company believes "the future of AI is not a button. It will be an entirely new computing environment, built at the browser layer." The company is calling Dia a "smart web browser" that can do things such as perform actions on the user's behalf.

Google may be close to unveiling an AI agent that can operate a web browser to help users automate everyday tasks. The Information reports that the company is working on a "computer-using agent" under the codename Project Jarvis, and it may be ready to be previewed as soon as December. According to sources that spoke to The Information, Jarvis "responds to a person's commands by capturing frequent screenshots of what's on their computer screen, and interpreting the shots before taking actions like clicking on a button or typing into a text field." Jarvis is reportedly made to work only with web browsers -- particularly Chrome -- to assist with common tasks like research, shopping and booking flights. It comes as Google continues to expand the capabilities of its Gemini AI, the next-gen model of which is expected to be revealed in December, as reported by The Verge.

The capacity to reason logically is a hallmark of human cognition. Humans excel at integrating multimodal information for logical reasoning, as exemplified by the Visual Question Answering (VQA) task, which is a challenging multimodal task. VQA tasks and large vision-and-language models aim to tackle reasoning problems, but the accuracy, consistency and integrity of the generated answers is hard to evaluate in the absence of a VQA dataset that can offer formal, comprehensive and systematic complex logical reasoning questions. To address this gap, we present LoRA, a novel Logical Reasoning Augmented VQA dataset that requires formal and complex description logic reasoning based on a food-and-kitchen knowledge base. Our main objective in creating LoRA is to enhance the complex and formal logical reasoning capabilities of VQA models, which are not adequately measured by existing VQA datasets. We devise strong and flexible programs to automatically generate 200,000 diverse description logic reasoning questions based on the SROIQ Description Logic, along with realistic kitchen scenes and ground truth answers. We fine-tune the latest transformer VQA models and evaluate the zero-shot performance of the state-of-the-art large vision-and-language models on LoRA. The results reveal that LoRA presents a unique challenge in logical reasoning, setting a systematic and comprehensive evaluation standard.

Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents.

The Dark Web represents a hotbed for illicit activity, where users communicate on different market forums in order to exchange goods and services. Law enforcement agencies benefit from forensic tools that perform authorship analysis, in order to identify and profile users based on their textual content. However, authorship analysis has been traditionally studied using corpora featuring literary texts such as fragments from novels or fan fiction, which may not be suitable in a cybercrime context. Moreover, the few works that employ authorship analysis tools for cybercrime prevention usually employ ad-hoc experimental setups and datasets. To address these issues, we release VeriDark: a benchmark comprised of three large scale authorship verification datasets and one authorship identification dataset obtained from user activity from either Dark Web related Reddit communities or popular illicit Dark Web market forums. We evaluate competitive NLP baselines on the three datasets and perform an analysis of the predictions to better understand the limitations of such approaches. We make the datasets and baselines publicly available at https://github.com/bit-ml/VeriDark.

Thank you for your feedback. Reviewer 1: Regarding Web and data mining conferences, we agree that this work is relevant to them as well. Reviewer 2: To answer your question about domain-level modeling of change rates: absolutely! In the same vein, it is common to do it at the site level. This won't affect our RL algorithm's theoretical guarantees, but will certainly improve its empirical convergence rate.

I've lost count of how many web browsers I've tested and used over the years. From text-based to the weird and wonderful, I've tried them all. Knowing how many web browsers are available, it never ceases to amaze me that some languish in the shadows of obscurity, even those that are superior to the ones most people use. I believe that many of those alternative browsers aren't more widely used because most people simply don't know about them. Below are five web browsers worth your time to test and compare to your current default.

Whether it's Google Chrome or Microsoft Edge, the browser is the program we use on a day-to-day basis. That said, most of us continue to use the same old browser even when there are better contenders out there. New York-based The Browser Company hopes to change that with the launch of Arc. Further reading: Arc's new browser for Windows is too twee for me So far, challengers such as Brave, Firefox, and Tor Browser have focused primarily on privacy issues, as Google Chrome and Microsoft Edge are notorious for their surveillance. Arc also promises to respect privacy, but the focus is on ease of use.

With over 781 billion website visits globally each month [51], their popularity highlights the growing need for developers to maintain high standards of quality and functionality. Traditional manual web testing approaches, however, can be time-consuming and challenging [8], leading to the increased adoption of automated testing methodologies to streamline the quality assurance process [5, 12, 13, 19, 24, 27, 30, 44, 48, 53, 56, 64]. Despite these advances, conventional testing tools may exhibit challenges and shortcomings regarding testing coverage, potentially overlooking critical bugs and usability issues [18, 19]. The discrepancy between tests generated by conventional methods and real user interactions further compounds these challenges [63], resulting in suboptimal testing outcomes. Web applications typically encompass a spectrum of actions, including form submissions, button clicks, and navigation through various pages. Automated testing tools for web applications encounter challenges stemming from the intricate and dynamic nature of modern web interfaces, which can feature diverse layouts, interactions, and non-deterministic states [3]. To address these challenges and mitigate the limitations of the traditional test generation methods, there has been a growing interest in leveraging deep learning (DL) [12, 13] and reinforcement learning (RL) [22, 23, 26, 27, 30, 31, 48, 64] techniques for automated testing in web applications. By assimilating insights from human testers' behavior, such automated testing approaches aim to emulate human-like interactions with web interfaces, thereby improving the comprehensiveness and effectiveness of testing. However, these DL and RL-based methodologies are not without their constraints.

Oceans are vital for sustaining require continuous monitoring of various indicators to detect life on Earth and they contribute substantially to global food or alert us to changes. Current observational deployments sources, oxygen production, and carbon dioxide absorption are often restricted to the ocean surface and a few measurable (Riebesell et al., 2009). Marine environments suffer from variables and there are limited tools to process the data numerous sources of stress, mostly from human activities in and extract useful knowledge. This underscores the need coastal areas, urban, agricultural, and industrial discharges, for advanced modeling techniques to bridge gaps in our habitat destruction, introduction of invasive species, and oil comprehension and to allow intelligent action-taking. But, spills, which interact synergistically with the consequences more importantly, the mere detection of problems may not of climate change. In addition to classic pollutants, such be sufficient since, on the one hand, the homeorhetic mechanisms as heavy metals or pesticides, with a long tradition in human of biological systems may mask such indicators until activities such as mining, industry, or agriculture, new it is too late and, on the other hand, the speed of ecosystem emerging pollutants are continually appearing, derived from deterioration is often greater than the human capacity to take drugs or cosmetics, whose effects on health are not always corrective and management measures.