Designing resilient Internet of Things (IoT) systems requires i) identification of IoT Critical Objects (ICOs) such as services, devices, and resources, ii) threat analysis, and iii) mitigation strategy selection. However, the traditional process for designing resilient IoT systems is still manual, leading to inefficiencies and increased risks. In addition, while tools such as ChatGPT could support this manual and highly error-prone process, their use raises concerns over data privacy, inconsistent outputs, and internet dependence. Therefore, we propose RITA, an automated, open-source framework that uses a fine-tuned RoBERTa-based Named Entity Recognition (NER) model to identify ICOs from IoT requirement documents, correlate threats, and recommend countermeasures. RITA operates entirely offline and can be deployed on-site, safeguarding sensitive information and delivering consistent outputs that enhance standardization. In our empirical evaluation, RITA outperformed ChatGPT in four of seven ICO categories, particularly in actuator, sensor, network resource, and service identification, using both human-annotated and ChatGPT-generated test data. These findings indicate that RITA can improve resilient IoT design by effectively supporting key security operations, offering a practical solution for developing robust IoT architectures.

RITA presents a high-quality real-time interactive framework While SadTalker and similar models excel in built upon generative models, designed with practical their domain, they are primarily tethered to offline processing, applications in mind. Our framework enables the transformation owing to the intricate computations required to ensure of user-uploaded photos into digital avatars that can synchronicity between audio cues and facial movements, engage in real-time dialogue interactions. By leveraging including lip motion, head pose, and eye blinks. These the latest advancements in generative modeling, we have models, despite their efficacy, fall short in applications demanding developed a versatile platform that not only enhances the real-time interaction, thus limiting their utility in user experience through dynamic conversational avatars dynamic, user-centric scenarios.

High-quality traffic flow generation is the core module in building simulators for autonomous driving. However, the majority of available simulators are incapable of replicating traffic patterns that accurately reflect the various features of real-world data while also simulating human-like reactive responses to the tested autopilot driving strategies. Taking one step forward to addressing such a problem, we propose Realistic Interactive TrAffic flow (RITA) as an integrated component of existing driving simulators to provide high-quality traffic flow for the evaluation and optimization of the tested driving strategies. RITA is developed with consideration of three key features, i.e., fidelity, diversity, and controllability, and consists of two core modules called RITABackend and RITAKit. RITABackend is built to support vehicle-wise control and provide traffic generation models from real-world datasets, while RITAKit is developed with easy-to-use interfaces for controllable traffic generation via RITABackend. We demonstrate RITA's capacity to create diversified and high-fidelity traffic simulations in several highly interactive highway scenarios. The experimental findings demonstrate that our produced RITA traffic flows exhibit all three key features, hence enhancing the completeness of driving strategy evaluation. Moreover, we showcase the possibility for further improvement of baseline strategies through online fine-tuning with RITA traffic flows.

In what has the distinctively zoned-out vibe of another lockdown-born project, Natalie Kennedy's sci-fi psychological thriller sees Clare Rivers (Rachel Shelley), an author with writer's block, sign up for a deluxe writing retreat operated entirely by AI. Sealed hermetically into her unit by a virus that corrupts the system, she can't leave until she has produced a book, making Blank play out like Misery and Ex Machina spliced. Taking place in a near future where writing is all holographic word processors and genial AI assistants rather than tattered notebooks and half-eaten Twixes, the profession seems to have moved on. Or perhaps not: Clare's blockage is aggravated by being locked in with only a malfunctioning amnesiac android called Rita (Heida Reed) for company. Reset every day and refusing to open the external doors until Clare has delivered the goods, in the face of the writer's exasperation Rita can only passive-aggressively reel off: "You seem distressed. Maybe you should have a lie down."

Timeseries analytics is of great importance in many real-world applications. Recently, the Transformer model, popular in natural language processing, has been leveraged to learn high quality feature embeddings from timeseries, core to the performance of various timeseries analytics tasks. However, the quadratic time and space complexities limit Transformers' scalability, especially for long timeseries. To address these issues, we develop a timeseries analytics tool, RITA, which uses a novel attention mechanism, named group attention, to address this scalability issue. Group attention dynamically clusters the objects based on their similarity into a small number of groups and approximately computes the attention at the coarse group granularity. It thus significantly reduces the time and space complexity, yet provides a theoretical guarantee on the quality of the computed attention. The dynamic scheduler of RITA continuously adapts the number of groups and the batch size in the training process, ensuring group attention always uses the fewest groups needed to meet the approximation quality requirement. Extensive experiments on various timeseries datasets and analytics tasks demonstrate that RITA outperforms the state-of-the-art in accuracy and is significantly faster -- with speedups of up to 63X.

Companies have been trying out automatons to serve food in restaurants, make home deliveries or do chores in stores, partly in hopes of easing the worker shortage. Among the disenchanted, FedEx Corp. said last month it was powering down Roxo, its last-mile delivery robot, to prioritize several "nearer-term opportunities," a spokeswoman said. Inc. said it was ending field tests of Scout, its home-delivery robot, after learning that some aspects of its "unique delivery experience" weren't "meeting customers' needs," a company spokeswoman said. And over the summer, DoorDash Inc. said it was shutting down its Chowbotics business -- best known for Sally, the salad-making robot -- roughly 18 months after buying it. "While we gained valuable insights into how to better serve this market, we concluded our current approach was not meeting our very high thresholds for continued investment," a DoorDash spokesman said.

Among the disenchanted, FedEx Corp. said last month it was powering down Roxo, its last-mile delivery robot, to prioritize several "nearer-term opportunities," a spokeswoman said. Inc. said it was ending field tests of Scout, its home-delivery robot, after learning that some aspects of its "unique delivery experience" weren't "meeting customers' needs," a company spokeswoman said. And over the summer, DoorDash Inc. said it was shutting down its Chowbotics business--best known for Sally, the salad-making robot--roughly 18 months after buying it. "While we gained valuable insights into how to better serve this market, we concluded our current approach was not meeting our very high thresholds for continued investment," a DoorDash spokesman said. Companies have entertained hopes that the growing variety of robots could help them not only weather the worker shortage, but speed up labor-intensive tasks, improve customer service by reducing the number of things the human workers have to do, and as an added bonus, position their brands as innovative and forward-leaning.

Downstream open-source experimentation is important to discover surprising and unpredictable capabilities In this work we introduce RITA: a suite of autoregressive that are hard to discern without large-scale experimentation generative models for protein sequences, (Ganguli et al., 2022). This was recently exemplified with up to 1.2 billion parameters, trained on over when independent researchers discovered that AlphaFold 2 280 million protein sequences belonging to the (Jumper et al., 2021) could successfully predict multimer UniRef-100 database. Such generative models interactions, even though it had only been trained to predict hold the promise of greatly accelerating protein the structure of single protein chains (Yoshitaka, 2021; Baek, design. We conduct the first systematic study of 2021). In addition, there exists no systematic study about how capabilities evolve with model size for autoregressive the evolution of capabilities with respect to model size in transformers in the protein domain: the protein domain: Rao et al. (2020) and Rives et al. (2021) we evaluate RITA models in next amino acid prediction, provided such a study for bidirectional transformers, and zero-shot fitness, and enzyme function Madani et al. (2020) simply noted that their largest model prediction, showing benefits from increased scale.

Healthcare Artificial Intelligence (AI) is a challenging field to enter, with a lack of widespread commercial success. This is not the case with other industries such as Financial Services which has seen ventures into AI go from strength to strength. Why, given the relative transferability of the technologies, have we not seen widespread successful implementations in Healthcare and the NHS? More importantly, how do we get it right going forward? The regulatory environment for Healthcare is complex and ever-evolving.

While her peers reveled in an unprecedented virtual school year, the self-described "technology enthusiast," Harita Suresh, 13, was bored. She decided on an online course and settled on IBM Skills Network's "AI chatbots without programming." She lacked experience with artificial intelligence, but was eager to learn through the self-paced course. Harita is more than a little familiar with tech, "I have been interested in technology since I was 5," she said. "My first coding challenge was the Lightbot Hour of Code. I was fascinated that the code I wrote could control the actions of the characters on screen. Since then, I pursued coding on multiple platforms like code.org, The more I learned about tech, the more I wanted to know. In fifth grade, I took a Python programming course offered by Georgia Tech."