The Contrastive Language-Image Pretraining (CLIP) model has significantly advanced vision-language modeling by aligning image-text pairs from large-scale web data through self-supervised contrastive learning. Yet, its reliance on uncurated Internet-sourced data exposes it to data poisoning and backdoor risks. While existing studies primarily investigate image-based attacks, the text modality, which is equally central to CLIP's training, remains underexplored. In this work, we introduce ToxicTextCLIP, a framework for generating high-quality adversarial texts that target CLIP during the pre-training phase. The framework addresses two key challenges: semantic misalignment caused by background inconsistency with the target class, and the scarcity of background-consistent texts. To this end, ToxicTextCLIP iteratively applies: 1) a background-aware selector that prioritizes texts with background content aligned to the target class, and 2) a background-driven augmenter that generates semantically coherent and diverse poisoned samples. Extensive experiments on classification and retrieval tasks show that ToxicTextCLIP achieves up to 95.83% poisoning success and 98.68% backdoor Hit@1, while bypassing RoCLIP, CleanCLIP and SafeCLIP defenses. The source code can be accessed via https://github.com/xinyaocse/ToxicTextCLIP/.

José Pires serves as the Global Excellence & Innovation (E&I) Leader for Andeavor Corporation, where he oversees the global identification, prioritization and execution of mission critical business improvements and innovations that add value to the company, business partners and external clients in multiple markets. Prior to his current role, Pires held Excellence and Innovation leadership positions in large, global companies in the electronics (Sony), semiconductor (Cymer-ASML), food (Nestlé) and infrastructure (Black & Veatch) industries. Throughout his career, Pires developed and refined E&I as an award winning program for innovation, leadership development, strategy execution and value creation globally. Pires is an advisory board leader and keynote speaker for several global conferences on innovation, operational excellence, leadership development, strategy execution, business transformation, customer engagement and growth acceleration. He holds a Bachelor in Engineering Physics from the University of Kansas and a Master in Business Administration focused in Investment Banking and Entrepreneurship from the University of San Diego.

This article is a survey on deep learning methods for single and multiple sound source localization. We are particularly interested in sound source localization in indoor/domestic environment, where reverberation and diffuse noise are present. We provide an exhaustive topography of the neural-based localization literature in this context, organized according to several aspects: the neural network architecture, the type of input features, the output strategy (classification or regression), the types of data used for model training and evaluation, and the model training strategy. This way, an interested reader can easily comprehend the vast panorama of the deep learning-based sound source localization methods. Tables summarizing the literature survey are provided at the end of the paper for a quick search of methods with a given set of target characteristics.

Carlos M. Meléndez is the COO and Co-Founder of Wovenware, an artificial intelligence and software development company. "These days, the reality is a bummer. Everyone is looking for a way to escape," said Wade Watts, the protagonist of the Ernest Cline novel Ready Player One, which was turned into a movie by Steven Spielberg in 2018. In the novel and film, which takes place in 2045, the world is on the verge of collapse, but a virtual reality universe, OASIS, has given people something to find hope in. While Ready Player One is clearly a science fiction story about a contest taking place in a virtual community, it draws many parallels to the rise of the metaverse.

The TECHNGI research project at Loughborough University invite you to our Artificial Intelligence and Next Generation Insurance Services Conference 2021/22, supported by the Willis Research Network. The TECHNGI project at Loughborough University TECHNGI – Technology driven Next Generation Insurance has been conducting research on artificial intelligence (AI) in insurance for the past two and half years. Our project has been examining insurance industry experience with employing AI, building a core base of knowledge on the broader challenges of adoption, including the implication for business models and the organisational and public policy challenges. This online conference will be delivered across four separate themed sessions. We will draw on the insights of the project, bringing together a range of perspectives on the business application of Artificial Intelligence (AI) and its role in the ongoing digital transformation of the insurance industry.

On November 29, 2021, World Artificial Intelligence & Data Congress organized a conference to discuss'Next generation advancement with Artificial Intelligence & Data Science'. Speakers from reputed companies and organizations made their viewpoints on the current and future implications of technology. The World Data Congress event started with an introductory note. The first speaker, Ashley Casovan, Executive Director of Responsible AI Institute and Director of Data and Digital, Government of Canada, gave a curtain-raiser keynote talk on the influence of artificial intelligence. Following this, Dr. Kevin M. Coleman, Empowerment Coach, Trainer, and Speaker, CEO KMCEmpowerment, USA conducted a workshop on "A Seat at the Table: Candid Conversation on Diversity, Equity, and Inclusion… Are you ready to talk?"

On November 29, 2021, World Artificial Intelligence & Data Congress organized a conference to discuss on'Next generation advancement with Artificial Intelligence & Data Science'. Speakers from reputed companies and organizations made their viewpoints on the current and future implications of technology. The World Data Congress event started with an introductory note. The first speaker, Ashley Casovan, Executive Director of Responsible AI Institute and Director of Data and Digital, Government of Canada, gave a curtain-raiser keynote talk on the influence of artificial intelligence. Following this, Dr. Kevin M. Coleman, Empowerment Coach, Trainer, and Speaker, CEO KMCEmpowerment, USA conducted a workshop on "A Seat at the Table: Candid Conversation on Diversity, Equity, and Inclusion… Are you ready to talk?"

We still have discount rooms at the AT&T. If you are coming from out of town, this is where all the action is. Sometime in the middle of May 2021, we started receiving a flood of emails wanting to know: Are we gonna do Data Day 2022? So we sent out a survey to a few hundred past attendees asking if they felt comfortable getting together in January 2022. The response was overwhelmingly yes.

Abstract: Conversational agents are a recent trend in human-computer interaction, deployed in multidisciplinary applications to assist the users. In this paper, we introduce "Atreya", an interactive bot for chemistry enthusiasts, researchers, and students to study the ChEMBL database. Atreya is hosted by Telegram, a popular cloud-based instant messaging application. This user-friendly bot queries the ChEMBL database, retrieves the drug details for a particular disease, targets associated with that drug, etc. This paper explores the potential of using a conversational agent to assist chemistry students and chemical scientist in complex information seeking process.

IRCAI is the result of a long tradition of Slovenian research in AI and a number of large-scale partnerships across several decades over a number of European networks of excellence and almost a decade of activities with UNESCO. It exists within an ecosystem of AI players, across different research departments at the Jozef Stefan Institute, non-profits such as the Knowledge 4 All Foundation and a number of start-ups between London and Ljubljana. Its mission is to cover the vertical from research, to implementation and policy input showcasing unbiased, rigorous, and comprehensive dialogue for policymakers, researchers, journalists, executives, and the general public to develop a deeper understanding of the complex field of AI.