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Billions of secure messaging users have adopted end-to-end encryption (E2EE). Nevertheless, challenges remain. Most communication applications do not provide E2EE, and application silos prevent interoperability. Our qualitative analysis of privacy-conscious users' discussions of E2EE on Reddit reveals concerns about trusting client applications with plaintext, lack of clear indicators about how encryption works, high cost to switch apps, and concerns that most apps are not open source. We propose InfoGuard, a system enabling E2EE for user-to-user communication in any application. InfoGuard allows users to trigger encryption on any textbox, even if the application does not support E2EE. InfoGuard encrypts text before it reaches the application, eliminating the client app's access to plaintext. InfoGuard also incorporates visible encryption to make it easier for users to understand that their data is being encrypted and give them greater confidence in the system's security. The design enables fine-grained encryption, allowing specific sensitive data items to be encrypted while the rest remains visible to the server. Participants in our user study found InfoGuard usable and trustworthy, expressing a willingness to adopt it.

As deep learning models become increasingly large, they pose significant challenges in heterogeneous devices environments. The size of deep learning models makes it difficult to deploy them on low-power or resource-constrained devices, leading to long inference times and high energy consumption. To address these challenges, we propose FlexTrain, a framework that accommodates the diverse storage and computational resources available on different devices during the training phase. FlexTrain enables efficient deployment of deep learning models, while respecting device constraints, minimizing communication costs, and ensuring seamless integration with diverse devices. We demonstrate the effectiveness of FlexTrain on the CIFAR-100 dataset, where a single global model trained with FlexTrain can be easily deployed on heterogeneous devices, saving training time and energy consumption. We also extend FlexTrain to the federated learning setting, showing that our approach outperforms standard federated learning benchmarks on both CIFAR-10 and CIFAR-100 datasets.

The rapidly expanding landscape of bioimage analysis tools presents a navigational challenge for both experts and newcomers. Traditional search methods often fall short in assisting users in this complex environment. To address this, we introduce the BioImage$.$IO Chatbot, an AI-driven conversational assistant tailored for the bioimage community. Built upon large language models, this chatbot provides personalized, context-aware answers by aggregating and interpreting information from diverse databases, tool-specific documentation, and structured data sources. Enhanced by a community-contributed knowledge base and fine-tuned retrieval methods, the BioImage$.$IO Chatbot offers not just a personalized interaction but also a knowledge-enriched, context-aware experience. It fundamentally transforms the way biologists, bioimage analysts, and developers navigate and utilize advanced bioimage analysis tools, setting a new standard for community-driven, accessible scientific research.

Large Language Models (LLMs) have emerged as promising agents for web navigation tasks, interpreting objectives and interacting with web pages. However, the efficiency of spliced prompts for such tasks remains underexplored. We introduces AllTogether, a standardized prompt template that enhances task context representation, thereby improving LLMs' performance in HTML-based web navigation. We evaluate the efficacy of this approach through prompt learning and instruction finetuning based on open-source Llama-2 and API-accessible GPT models. Our results reveal that models like GPT-4 outperform smaller models in web navigation tasks. Additionally, we find that the length of HTML snippet and history trajectory significantly influence performance, and prior step-by-step instructions prove less effective than real-time environmental feedback. Overall, we believe our work provides valuable insights for future research in LLM-driven web agents.

Deep policy networks enable robots to learn behaviors to solve various real-world complex tasks in an end-to-end fashion. However, they lack transparency to provide the reasons of actions. Thus, such a black-box model often results in low reliability and disruptive actions during the deployment of the robot in practice. To enhance its transparency, it is important to explain robot behaviors by considering the extent to which each input feature contributes to determining a given action. In this paper, we present an explicit analysis of deep policy models through input attribution methods to explain how and to what extent each input feature affects the decisions of the robot policy models. To this end, we present two methods for applying input attribution methods to robot policy networks: (1) we measure the importance factor of each joint torque to reflect the influence of the motor torque on the end-effector movement, and (2) we modify a relevance propagation method to handle negative inputs and outputs in deep policy networks properly. To the best of our knowledge, this is the first report to identify the dynamic changes of input attributions of multi-modal sensor inputs in deep policy networks online for robotic manipulation.

Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment. Maintaining their up-to-date status is a pressing concern in the current era. This paper provides a comprehensive review of recent advances in aligning LLMs with the ever-changing world knowledge without re-training from scratch. We categorize research works systemically and provide in-depth comparisons and discussion. We also discuss existing challenges and highlight future directions to facilitate research in this field. We release the paper list at //github.com/hyintell/awesome-refreshing-llms

Solving complicated AI tasks with different domains and modalities is a key step toward artificial general intelligence. While there are abundant AI models available for different domains and modalities, they cannot handle complicated AI tasks. Considering large language models (LLMs) have exhibited exceptional ability in language understanding, generation, interaction, and reasoning, we advocate that LLMs could act as a controller to manage existing AI models to solve complicated AI tasks and language could be a generic interface to empower this. Based on this philosophy, we present HuggingGPT, a framework that leverages LLMs (e.g., ChatGPT) to connect various AI models in machine learning communities (e.g., Hugging Face) to solve AI tasks. Specifically, we use ChatGPT to conduct task planning when receiving a user request, select models according to their function descriptions available in Hugging Face, execute each subtask with the selected AI model, and summarize the response according to the execution results. By leveraging the strong language capability of ChatGPT and abundant AI models in Hugging Face, HuggingGPT is able to cover numerous sophisticated AI tasks in different modalities and domains and achieve impressive results in language, vision, speech, and other challenging tasks, which paves a new way towards artificial general intelligence.

Knowledge plays a critical role in artificial intelligence. Recently, the extensive success of pre-trained language models (PLMs) has raised significant attention about how knowledge can be acquired, maintained, updated and used by language models. Despite the enormous amount of related studies, there still lacks a unified view of how knowledge circulates within language models throughout the learning, tuning, and application processes, which may prevent us from further understanding the connections between current progress or realizing existing limitations. In this survey, we revisit PLMs as knowledge-based systems by dividing the life circle of knowledge in PLMs into five critical periods, and investigating how knowledge circulates when it is built, maintained and used. To this end, we systematically review existing studies of each period of the knowledge life cycle, summarize the main challenges and current limitations, and discuss future directions.

We present VeriX, a first step towards verified explainability of machine learning models in safety-critical applications. Specifically, our sound and optimal explanations can guarantee prediction invariance against bounded perturbations. We utilise constraint solving techniques together with feature sensitivity ranking to efficiently compute these explanations. We evaluate our approach on image recognition benchmarks and a real-world scenario of autonomous aircraft taxiing.