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Despite current security implementations, Internet activity on DoD networks is susceptible to web trackers and commercial data collection, which have the potential to expose information about service members and unit operations. This report documents the outcomes of a study to characterize web tracking occurring on Army CONUS unclassified networks. We derived a dataset from the Cloud-Based Internet Isolation (CBII) platform, encompassing data measured over a two-month period in 2024. This dataset comprised the 1,000 most frequently accessed Internet resources, determined by the number of connection requests on CONUS DoDIN-A during the study period. We then compared all domains and subdomains in the dataset against Ghostery's WhoTracks.me, an open-source database of commercial tracking entities. We found that over 21% of the domains accessed during the study period were Internet trackers. The ACI recommends that the Army implement changes to its enterprise networks to limit commercial Internet-based tracking, as well as policy changes towards the same end. With relatively minor configuration changes, CBII can serve as a more effective mitigation against risks posed by commercially a

Unmanned Vehicles (UxVs) are increasingly used in modern military operations for reconnaissance, surveillance, and strike missions, enhancing situational awareness while reducing risk to personnel. Their affordability and rapid deployment have encouraged the adoption of commercial solutions. However, many rely on insecure protocols such as MAVLink, which lack authentication and encryption mechanisms. This paper designed, implemented, and evaluated a new secure command-and-control architecture that ensures confidentiality, integrity, and authentication (CIA) while supporting real-time control delegation between Ground Control Stations (GCSs). The proposed solution, named New Command and Control System (NC2S), enforces a zero-trust model integrating hierarchical credential-based privileges to regulate access and control among Tactical Commanders (TC), GCSs, and UxVs. It employs mutual Transport Layer Security (mTLS) with Elliptic Curve Digital Signature Algorithm (ECDSA) certificates and Elliptic Curve Diffie-Hellman (ECDH) key exchange, while message integrity is ensured through Hash-based Message Authentication Codes (HMAC). Multiple lightweight protocols were developed for credent

Vision Foundation Models (VFMs) have demonstrated outstanding performance on numerous downstream tasks. However, due to their inherent representation biases originating from different training paradigms, VFMs exhibit advantages and disadvantages across distinct vision tasks. Although amalgamating the strengths of multiple VFMs for downstream tasks is an intuitive strategy, effectively exploiting these biases remains a significant challenge. In this paper, we propose a novel and versatile "Swiss Army Knife" (SAK) solution, which adaptively distills knowledge from a committee of VFMs to enhance multi-task learning. Unlike existing methods that use a single backbone for knowledge transfer, our approach preserves the unique representation bias of each teacher by collaborating the lightweight Teacher-Specific Adapter Path modules with the Teacher-Agnostic Stem. Through dynamic selection and combination of representations with Mixture-of-Representations Routers, our SAK is capable of synergizing the complementary strengths of multiple VFMs. Extensive experiments show that our SAK remarkably outperforms prior state of the arts in multi-task learning by 10% on the NYUD-v2 benchmark, while

As one of the most crucial scenarios of the Internet of Things (IoT), wireless multimedia sensor networks (WMSNs) pay more attention to the information-intensive data (e.g., audio, video, image) for remote environments. The area coverage reflects the perception of WMSNs to the surrounding environment, where a good coverage effect can ensure effective data collection. Given the harsh and complex physical environment of WMSNs, which easily form the sensing overlapping regions and coverage holes by random deployment. The intention of our research is to deal with the optimization problem of maximizing the coverage rate in WMSNs. By proving the NP-hard of the coverage enhancement of WMSNs, inspired by the predation behavior of army ants, this article proposes a novel swarm intelligence (SI) technology army ant search optimizer (AASO) to solve the above problem, which is implemented by five operators: army ant and prey initialization, recruited by prey, attack prey, update prey, and build ant bridge. The simulation results demonstrate that the optimizer shows good performance in terms of exploration and exploitation on benchmark suites when compared to other representative SI algorithms.

We present the proof-of-concept for minimalist market design (Sönmez, 2023) as an effective methodology to enhance an institution based on the desiderata of stakeholders with minimal interference. Four objectives-respecting merit, increasing retention, aligning talent, and enhancing trust-guided reforms to US Army's centralized branching process of cadets to military specialties since 2006. USMA's mechanism for the Class of 2020 exacerbated challenges implementing these objectives. Formulating the Army's desiderata as rigorous axioms, we analyze their implications. Under our minimalist approach to institution redesign, the Army's objectives uniquely identify a branching mechanism. Our design is now adopted at USMA and ROTC.

We present OpenMU-Bench, a large-scale benchmark suite for addressing the data scarcity issue in training multimodal language models to understand music. To construct OpenMU-Bench, we leveraged existing datasets and bootstrapped new annotations. OpenMU-Bench also broadens the scope of music understanding by including lyrics understanding and music tool usage. Using OpenMU-Bench, we trained our music understanding model, OpenMU, with extensive ablations, demonstrating that OpenMU outperforms baseline models such as MU-Llama. Both OpenMU and OpenMU-Bench are open-sourced to facilitate future research in music understanding and to enhance creative music production efficiency.

Machine Learning (ML) models become vulnerable to Model Stealing Attacks (MSA) when they are deployed as a service. In such attacks, the deployed model is queried repeatedly to build a labelled dataset. This dataset allows the attacker to train a thief model that mimics the original model. To maximize query efficiency, the attacker has to select the most informative subset of data points from the pool of available data. Existing attack strategies utilize approaches like Active Learning and Semi-Supervised learning to minimize costs. However, in the black-box setting, these approaches may select sub-optimal samples as they train only one thief model. Depending on the thief model's capacity and the data it was pretrained on, the model might even select noisy samples that harm the learning process. In this work, we explore the usage of an ensemble of deep learning models as our thief model. We call our attack Army of Thieves(AOT) as we train multiple models with varying complexities to leverage the crowd's wisdom. Based on the ensemble's collective decision, uncertain samples are selected for querying, while the most confident samples are directly included in the training data. Our ap

Coverage of interest points is one of the most critical issues in directional sensor networks. However, considering the remote or inhospitable environment and the limitation of the perspective of directional sensors, it is easy to form perception blind after random deployment. The intension of our research is to deal with the bound-constrained optimization problem of maximizing the coverage of target points. A coverage enhancement strategy based on a discrete army ant search optimizer (DAASO) is proposed to solve the above problem, which is inspired by the biological habits of army ants. A set of experiments are conducted using different sensor parameters. Experimental results verify the effectiveness of the DAASO in coverage effect when compared to the existing methods.

Despite its long history, the classical game of peg solitaire continues to attract the attention of the scientific community. In this paper, we consider two problems with an algorithmic flavour which are related with this game, namely Solitaire-Reachability and Solitaire-Army. In the first one, we show that deciding whether there is a sequence of jumps which allows a given initial configuration of pegs to reach a target position is NP-complete. Regarding Solitaire-Army, the aim is to successfully deploy an army of pegs in a given region of the board in order to reach a target position. By solving an auxiliary problem with relaxed constraints, we are able to answer some open questions raised by Csákány and Juhász (Mathematics Magazine, 2000). To appreciate the combinatorial beauty of our solutions, we recommend to visit the gallery of animations provided at http://solitairearmy.isnphard.com.

This paper provides an overview of research programs in cyber security performed by the U.S Army Research Laboratory. Although ARL is the U.S. Army's corporate laboratory that focuses on fundamental and early applied research, the fundamental science endeavors are closely integrated with extensive operationally-oriented programs. One example is the Cyber Collaborative Research Alliance (CRA) that brings together ARL scientists with academic researchers from dozens of U.S. universities. ARL cyber scientists are largely driven by challenges unique to the ground operations of the Army; this paper outlines a few of these challenges and the ways in which they are addressed by ARL research efforts. The long-term campaign of cyber research is guided by the vision of the future Army battlefield. In the year 2040, it will be a highly converged virtual-physical space, where cyber operations will be an integral part of the battle.

Army ants perform the altruism that an ant sacrifices its own well-being for the benefit of another ants. Army ants build bridges using their own bodies along the path from a food to the nest. We developed the army ant inspired social evolutionary system which can perform the altruism. The system has 2 kinds of ant agents, `Major ant' and `Minor ant' and the ants communicate with each other via pheromones. One ants can recognize them as the signals from the other ants. The pheromones evaporate with the certain ratio and diffused into the space of neighbors stochastically. If the optimal bridge is found, the path through the bridge is the shortest route from foods to the nest. We define the probability for an ant to leave a bridge at a low occupancy condition of ants and propose the constructing method of the optimal route. In this paper, the behaviors of ant under the environment with two or more feeding spots are observed. Some experimental results show the behaviors of great interest with respect to altruism of ants. The description in some computer simulation is reported in this paper.

The solitaire army is a one-person peg jumping game where a player attempts to advance an "army" of pegs as far as possible into empty territory. The game was introduced by John Conway and is also known as "Conway's Soldiers". We consider various generalizations of this game in different 2D geometries, unify them under a common mathematical framework, and find the minimum size army capable of advancing a given number of steps.

Army cadets obtain occupations through a centralized process. Three objectives -- increasing retention, aligning talent, and enhancing trust -- have guided reforms to this process since 2006. West Point's mechanism for the Class of 2020 exacerbated challenges implementing Army policy aims. We formulate these desiderata as axioms and study their implications theoretically and with administrative data. We show that the Army's objectives not only determine an allocation mechanism, but also a specific priority policy, a uniqueness result that integrates mechanism and priority design. These results led to a re-design of the mechanism, now adopted at both West Point and ROTC.

In this working paper we explore the use of an NLP system to assist the work of Security Force Monitor (SFM). SFM creates data about the organizational structure, command personnel and operations of police, army and other security forces, which assists human rights researchers, journalists and litigators in their work to help identify and bring to account specific units and personnel alleged to have committed abuses of human rights and international criminal law. This working paper presents an NLP system that extracts from English language news reports the names of security force units and the biographical details of their personnel, and infers the formal relationship between them. Published alongside this working paper are the system's code and training dataset. We find that the experimental NLP system performs the task at a fair to good level. Its performance is sufficient to justify further development into a live workflow that will give insight into whether its performance translates into savings in time and resource that would make it an effective technical intervention.