Russia has established a weapons program in China to develop and produce long-range attack drones for use in the war against Ukraine, according to two sources from a European intelligence agency and documents reviewed by Reuters. IEMZ Kupol, a subsidiary of Russian state-owned arms company Almaz-Antey, has developed and flight-tested a new drone model called Garpiya-3 (G3) in China with the help of local specialists, according to one of the documents, a report that Kupol sent to the Russian defense ministry earlier this year outlining its work. Kupol told the defense ministry in a subsequent update that it was able to produce drones including the G3 at scale at a factory in China so the weapons could be deployed in the "special military operation" in Ukraine, the term Moscow uses for the war.

Large Language Models (LLMs) have transformed natural language processing (NLP) by enabling robust text generation and understanding. However, their deployment in sensitive domains like healthcare, finance, and legal services raises critical concerns about privacy and data security. This paper proposes a comprehensive framework for embedding trust mechanisms into LLMs to dynamically control the disclosure of sensitive information. The framework integrates three core components: User Trust Profiling, Information Sensitivity Detection, and Adaptive Output Control. By leveraging techniques such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), Named Entity Recognition (NER), contextual analysis, and privacy-preserving methods like differential privacy, the system ensures that sensitive information is disclosed appropriately based on the user's trust level. By focusing on balancing data utility and privacy, the proposed solution offers a novel approach to securely deploying LLMs in high-risk environments. Future work will focus on testing this framework across various domains to evaluate its effectiveness in managing sensitive data while maintaining system efficiency.

State of the art reinforcement learning methods sometimes encounter unsafe situations. Identifying when these situations occur is of interest both for post-hoc analysis and during deployment, where it might be advantageous to call out to a human overseer for help. Efforts to gauge the criticality of different points in time have been developed, but their accuracy is not well established due to a lack of ground truth, and they are not designed to be easily interpretable by end users. Therefore, we seek to define a criticality framework with both a quantifiable ground truth and a clear significance to users. We introduce true criticality as the expected drop in reward when an agent deviates from its policy for n consecutive random actions. We also introduce the concept of proxy criticality, a low-overhead metric that has a statistically monotonic relationship to true criticality. Safety margins make these interpretable, when defined as the number of random actions for which performance loss will not exceed some tolerance with high confidence. We demonstrate this approach in several environment-agent combinations; for an A3C agent in an Atari Beamrider environment, the lowest 5% of safety margins contain 47% of agent losses; i.e., supervising only 5% of decisions could potentially prevent roughly half of an agent's errors. This criticality framework measures the potential impacts of bad decisions, even before those decisions are made, allowing for more effective debugging and oversight of autonomous agents.

This paper explores the design and development of a language-based interface for dynamic mission programming of autonomous underwater vehicles (AUVs). The proposed 'Word2Wave' (W2W) framework enables interactive programming and parameter configuration of AUVs for remote subsea missions. The W2W framework includes: (i) a set of novel language rules and command structures for efficient language-to-mission mapping; (ii) a GPT-based prompt engineering module for training data generation; (iii) a small language model (SLM)-based sequence-to-sequence learning pipeline for mission command generation from human speech or text; and (iv) a novel user interface for 2D mission map visualization and human-machine interfacing. The proposed learning pipeline adapts an SLM named T5-Small that can learn language-to-mission mapping from processed language data effectively, providing robust and efficient performance. In addition to a benchmark evaluation with state-of-the-art, we conduct a user interaction study to demonstrate the effectiveness of W2W over commercial AUV programming interfaces. Across participants, W2W-based programming required less than 10% time for mission programming compared to traditional interfaces; it is deemed to be a simpler and more natural paradigm for subsea mission programming with a usability score of 76.25. W2W opens up promising future research opportunities on hands-free AUV mission programming for efficient subsea deployments.

Explanation is key to people having confidence in high-stakes AI systems. However, machine-learning-based systems -- which account for almost all current AI -- can't explain because they are usually black boxes. The explainable AI (XAI) movement hedges this problem by redefining "explanation". The human-centered explainable AI (HCXAI) movement identifies the explanation-oriented needs of users but can't fulfill them because of its commitment to machine learning. In order to achieve the kinds of explanations needed by real people operating in critical domains, we must rethink how to approach AI. We describe a hybrid approach to developing cognitive agents that uses a knowledge-based infrastructure supplemented by data obtained through machine learning when applicable. These agents will serve as assistants to humans who will bear ultimate responsibility for the decisions and actions of the human-robot team. We illustrate the explanatory potential of such agents using the under-the-hood panels of a demonstration system in which a team of simulated robots collaborate on a search task assigned by a human.

As AI takes on pivotal roles in essential applications, like self-driving vehicles, healthcare diagnosis, and financial services, it becomes a tempting target for malicious actors [16]. This study aims to comprehensively explore the realm of offensive AI, shedding light on its multifaceted dimensions, the techniques involved, its consequences, and potential future implications. Cyberattacks have surged in both complexity and frequency. This is evidenced by the escalating costs associated with data breaches. In 2022, businesses incurred an average loss of $4.35 million, an increase of $0.11 million from the previous year and a 12.7% rise from 2020 [22]. Moreover, the volume of data breaches has reached historic highs, with approximately 15 million records exposed during the third quarter of 2022. Furthermore, the third quarter of 2022 witnessed an alarming 57,116 distributed denial-of-service (DDoS) attacks [78]. Against this backdrop, understanding and mitigating security risks in machine learning (ML) has emerged as a pivotal aspect of cybersecurity.

As networks continue to expand and become more interconnected, the need for novel malware detection methods becomes more pronounced. Traditional security measures are increasingly inadequate against the sophistication of modern cyber attacks. Deep Packet Inspection (DPI) has been pivotal in enhancing network security, offering an in-depth analysis of network traffic that surpasses conventional monitoring techniques. DPI not only examines the metadata of network packets, but also dives into the actual content being carried within the packet payloads, providing a comprehensive view of the data flowing through networks. The integration of advanced deep learning techniques with DPI has introduced modern methodologies into malware detection. However, the challenge with the state-of-the-art supervised learning approaches is that they prevent the generalization to unseen attacks embedded in the payloads, prohibiting them from accurately detecting new attacks and transferring knowledge learned from previous attacks to the new attacks with small labeled sample sizes. This paper leverages the recent advancements in self-supervised learning and few-shot learning. Our proposed self-supervised approach trains a transformer to learn the embedding of the payloads from a vast amount of unlabeled datasets by masking portions of payloads, leading to a learnt representation that well generalizes to various downstream tasks. Once the representation is extracted from payloads, they are used to train a malware detection algorithm. The representation obtained from the transformer is then used to adapt the malware detector to novel types of attacks using few-shot learning approaches. Our experimental results across several datasets show the great success and generalization of the proposed approach to novel scenarios.

There is a mismatch between psychological and computational studies on emotions. Psychological research aims at explaining and documenting internal mechanisms of these phenomena, while computational work often simplifies them into labels. Many emotion fundamentals remain under-explored in natural language processing, particularly how emotions develop and how people cope with them. To help reduce this gap, we follow theories on coping, and treat emotions as strategies to cope with salient situations (i.e., how people deal with emotion-eliciting events). This approach allows us to investigate the link between emotions and behavior, which also emerges in language. We introduce the task of coping identification, together with a corpus to do so, constructed via role-playing. We find that coping strategies realize in text even though they are challenging to recognize, both for humans and automatic systems trained and prompted on the same task. We thus open up a promising research direction to enhance the capability of models to better capture emotion mechanisms from text.

Secure training, while protecting the confidentiality of both data and model weights, typically incurs significant training overhead. Traditional Fully Homomorphic Encryption (FHE)-based non-inter-active training models are heavily burdened by computationally demanding bootstrapping. To develop an efficient secure training system, we established a foundational framework, CryptoTrain-B, utilizing a hybrid cryptographic protocol that merges FHE with Oblivious Transfer (OT) for handling linear and non-linear operations, respectively. This integration eliminates the need for costly bootstrapping. Although CryptoTrain-B sets a new baseline in performance, reducing its training overhead remains essential. We found that ciphertext-ciphertext multiplication (CCMul) is a critical bottleneck in operations involving encrypted inputs and models. Our solution, the CCMul-Precompute technique, involves precomputing CCMul offline and resorting to the less resource-intensive ciphertext-plaintext multiplication (CPMul) during private training. Furthermore, conventional polynomial convolution in FHE systems tends to encode irrelevant and redundant values into polynomial slots, necessitating additional polynomials and ciphertexts for input representation and leading to extra multiplications. Addressing this, we introduce correlated polynomial convolution, which encodes only related input values into polynomials, thus drastically reducing the number of computations and overheads. By integrating CCMul-Precompute and correlated polynomial convolution into CryptoTrain-B, we facilitate a rapid and efficient secure training framework, CryptoTrain. Extensive experiments demonstrate that CryptoTrain achieves a ~5.3X training time reduction compared to prior methods.

While generalization over tasks from easy to hard is crucial to profile language models (LLMs), the datasets with fine-grained difficulty annotations for each problem across a broad range of complexity are still blank. Aiming to address this limitation, we present Easy2Hard-Bench, a consistently formatted collection of 6 benchmark datasets spanning various domains, such as mathematics and programming problems, chess puzzles, and reasoning questions. Each problem within these datasets is annotated with numerical difficulty scores. To systematically estimate problem difficulties, we collect abundant performance data on attempts to each problem by humans in the real world or LLMs on the prominent leaderboard. Leveraging the rich performance data, we apply well-established difficulty ranking systems, such as Item Response Theory (IRT) and Glicko-2 models, to uniformly assign numerical difficulty scores to problems. Moreover, datasets in Easy2Hard-Bench distinguish themselves from previous collections by a higher proportion of challenging problems. Through extensive experiments with six state-of-the-art LLMs, we provide a comprehensive analysis of their performance and generalization capabilities across varying levels of difficulty, with the aim of inspiring future research in LLM generalization. The datasets are available at https://huggingface.co/datasets/furonghuang-lab/Easy2Hard-Bench.