We consider the problem of an autonomous agent equipped with multiple sensors, each with different sensing precision and energy costs. The agent's goal is to explore the environment and gather information subject to its resource constraints in unknown, partially observable environments. The challenge lies in reasoning about the effects of sensing and movement while respecting the agent's resource and dynamic constraints. We formulate the problem as a trajectory optimization problem and solve it using a projection-based trajectory optimization approach where the objective is to reduce the variance of the Gaussian process world belief. Our approach outperforms previous approaches in long horizon trajectories by achieving an overall variance reduction of up to 85% and reducing the root-mean square error in the environment belief by 50%. This approach was developed in support of rover path planning for the NASA VIPER Mission.

This paper explores in-context learning for image copy detection (ICD), i.e., prompting an ICD model to identify replicated images with new tampering patterns without the need for additional training. The prompts (or the contexts) are from a small set of image-replica pairs that reflect the new patterns and are used at inference time. Such in-context ICD has good realistic value, because it requires no fine-tuning and thus facilitates fast reaction against the emergence of unseen patterns. To accommodate the "seen $\rightarrow$ unseen" generalization scenario, we construct the first large-scale pattern dataset named AnyPattern, which has the largest number of tamper patterns ($90$ for training and $10$ for testing) among all the existing ones. We benchmark AnyPattern with popular ICD methods and reveal that existing methods barely generalize to novel patterns. We further propose a simple in-context ICD method named ImageStacker. ImageStacker learns to select the most representative image-replica pairs and employs them as the pattern prompts in a stacking manner (rather than the popular concatenation manner). Experimental results show (1) training with our large-scale dataset substantially benefits pattern generalization ($+26.66 \%$ $\mu AP$), (2) the proposed ImageStacker facilitates effective in-context ICD (another round of $+16.75 \%$ $\mu AP$), and (3) AnyPattern enables in-context ICD, i.e., without such a large-scale dataset, in-context learning does not emerge even with our ImageStacker. Beyond the ICD task, we also demonstrate how AnyPattern can benefit artists, i.e., the pattern retrieval method trained on AnyPattern can be generalized to identify style mimicry by text-to-image models. The project is publicly available at https://anypattern.github.io.

Active intelligent reflecting surface (IRS) is a revolutionary technique for the future 6G networks. The conventional far-field single-IRS-aided directional modulation(DM) networks have only one (no direct path) or two (existing direct path) degrees of freedom (DoFs). This means that there are only one or two streams transmitted simultaneously from base station to user and will seriously limit its rate gain achieved by IRS. How to create multiple DoFs more than two for DM? In this paper, single large-scale IRS is divided to multiple small IRSs and a novel multi-IRS-aided multi-stream DM network is proposed to achieve a point-to-point multi-stream transmission by creating $K$ ($\geq3$) DoFs, where multiple small IRSs are placed distributively via multiple unmanned aerial vehicles (UAVs). The null-space projection, zero-forcing (ZF) and phase alignment are adopted to design the transmit beamforming vector, receive beamforming vector and phase shift matrix (PSM), respectively, called NSP-ZF-PA. Here, $K$ PSMs and their corresponding beamforming vectors are independently optimized. The weighted minimum mean-square error (WMMSE) algorithm is involved in alternating iteration for the optimization variables by introducing the power constraint on IRS, named WMMSE-PC, where the majorization-minimization (MM) algorithm is used to solve the total PSM. To achieve a lower computational complexity, a maximum trace method, called Max-TR-SVD, is proposed by optimize the PSM of all IRSs. Numerical simulation results has shown that the proposed NSP-ZF-PA performs much better than Max-TR-SVD in terms of rate. In particular, the rate of NSP-ZF-PA with sixteen small IRSs is about five times that of NSP-ZF-PA with combining all small IRSs as a single large IRS. Thus, a dramatic rate enhancement may be achieved by multiple distributed IRSs.

With the ever-increasing reliance on digital networks for various aspects of modern life, ensuring their security has become a critical challenge. Intrusion Detection Systems play a crucial role in ensuring network security, actively identifying and mitigating malicious behaviours. However, the relentless advancement of cyber-threats has rendered traditional/classical approaches insufficient in addressing the sophistication and complexity of attacks. This paper proposes a novel 3-stage intrusion detection system inspired by a simplified version of the Lockheed Martin cyber kill chain to detect advanced multi-step attacks. The proposed approach consists of three models, each responsible for detecting a group of attacks with common characteristics. The detection outcome of the first two stages is used to conduct a feasibility study on the possibility of predicting attacks in the third stage. Using the ToN IoT dataset, we achieved an average of 94% F1-Score among different stages, outperforming the benchmark approaches based on Random-forest model. Finally, we comment on the feasibility of this approach to be integrated in a real-world system and propose various possible future work.

The burgeoning influence of Large Language Models (LLMs) in shaping public discourse and decision-making underscores the imperative to address inherent biases within these AI systems. In the wake of AI's expansive integration across sectors, addressing racial bias in LLMs has never been more critical. This paper introduces a novel framework called Comprehensive Bias Neutralization Framework (CBNF) which embodies an innovative approach to quantifying and mitigating biases within LLMs. Our framework builds on the Large Language Model Bias Index (LLMBI) [Oketunji, A., Anas, M., Saina, D., (2023)] and Bias removaL with No Demographics (BLIND) [Orgad, H., Belinkov, Y. (2023)] methodologies to create a new metric called Bias Intelligence Quotient (BiQ) which detects, measures, and mitigates racial bias in LLMs without reliance on demographic annotations. By introducing a new metric called BiQ that enhances LLMBI with additional fairness metrics, CBNF offers a multi-dimensional metric for bias assessment, underscoring the necessity of a nuanced approach to fairness in AI [Mehrabi et al., 2021]. This paper presents a detailed analysis of Latimer AI (a language model incrementally trained on black history and culture) in comparison to ChatGPT 3.5, illustrating Latimer AI's efficacy in detecting racial, cultural, and gender biases through targeted training and refined bias mitigation strategies [Latimer & Bender, 2023]. Through empirical studies, our approach not only demonstrates the feasibility of detecting and measuring racial bias but also offers a scalable solution adaptable to various AI applications, underscoring our commitment to fostering more equitable and reliable AI technologies. Our method focuses on providing a comprehensive framework for the detection, quantification, and mitigation of racial biases in monolingual LLMs, with a special emphasis on Retrieval Augmented Generation (RAG) based models [Lewis, Perez et.al.

Controversial gunshot-detection company ShotSpotter has deployed more than 25,000 microphones across 170 cities worldwide. This week, WIRED and South Side Weekly revealed the company may continue to provide gunshot data to police in cities even after contracts have ended. Internal emails seen by the publications suggest ShotSpotter sensors may have stayed online despite law enforcement deals having expired, raising questions about what will happen to 2,500 microphones in Chicago when its contract runs out at the end of the year. Elsewhere, Change Healthcare finally admitted to paying a ransom to the AlphV hackers, also known as BlackCat, that extorted the medical company. Weeks ago, WIRED revealed the attackers were paid 22 million, one of the largest ransomware payments ever. However, in a statement this week the company admitted for the first time that it paid the ransom as part of its effort "to do all it could to protect patient data from disclosure."

It's not every day that you are told to get inside quickly because four drones are watching the compound you are in – probably weaponised United States drones. There is a split-second pause and then you gather your gear and move inside. As we drive into the nondescript building, past the lawn, we are asked to park in the shade, presumably to provide cover from the prying killer robots watching us. It is April 18, one day before Israel launched several drones at Iran after Iran itself launched a barrage of drones and missiles on Israel on April 13. That, in turn, was a response to an Israeli attack on Iran's consulate compound in Damascus which killed 16 people, including two senior generals.

The United States says it will supply Patriot air defence missile systems to Ukraine as part of a 6bn additional aid package, Defense Secretary Lloyd Austin announced on Friday, calling it the largest security assistance package to Kyiv since Russia's invasion in 2022. The package is the second this week after President Joe Biden signed a much-delayed bill to provide a total of 61bn of new funding for Ukraine. The package also includes more munitions for the National Advanced Surface-to-Air Missile Systems, or NASAMS, and additional gear to integrate Western air defence launchers, missiles and radars into Ukraine's existing weaponry, much of which still dates back to the Soviet era. Ukrainian President Volodymyr Zelenskyy discussed the need for Patriots early on Friday with the Ukraine Defense Contact Group, a coalition of about 50 countries gathering virtually in a Pentagon-led meeting. The meeting fell on the second anniversary of the group, which Austin said has "moved heaven and Earth" since April 2022 to source millions of rounds of ammunition, rocket systems, armoured vehicles and even jets to help Ukraine rebuff Russia.

Fox News Digital was on the ground in El Paso Sector as Border Patrol agents caught illegal immigrants entering the U.S., including one group that cut into a border fence. SUNLAND PARK, N.M. -- As Border Patrol agents work to combat the movement of illegal immigrants across the southern border in the El Paso Sector, they say a multi-layered enforcement system that has been expanded in recent years and combines the use of barriers with technology and other forms of enforcement has helped thwart cartel smuggling operations and nab illegal immigrants moving into the U.S. Overshadowing the border in Sunland Park, New Mexico, is miles of border wall. Some of it is border fence built during the Obama administration, while other parts consist of Trump-era bollard wall. Fox News Digital was on the ground when agents nabbed illegal immigrants just feet from the fence they had cut a hole through. Even though they got through, it gave agents time to apprehend them.

"How many tennis balls can fit in a passenger plane?" Neeraj, a young economics graduate from the premier Indian Institute of Technology (IIT), was given 15 minutes to solve this question during his interview rounds at Nation With Namo (NwN), one of the in-house political consultancies of India's governing Bharatiya Janata Party (BJP). He got the calculation right and joined a small team of graduates from India's top universities who were dispatched to the eastern state of Tripura to conduct surveys, collect and analyse voter data for elections that were due in February last year. Their job was to identify who was not voting for the BJP, separate them into demographic cohorts – age, gender, caste, tribe, religion – find a common concern, issue or fear and strategise how to exploit that in the BJP's favour. And they were to do all this while staying under the radar.