The increasing prevalence of large language models (LLMs) is influencing global value systems. However, these models frequently exhibit a pronounced WEIRD (Western, Educated, Industrialized, Rich, Democratic) cultural bias due to lack of attention to minority values. This monocultural perspective may reinforce dominant values and marginalize diverse cultural viewpoints, posing challenges for the development of equitable and inclusive AI systems. In this work, we introduce a systematic framework designed to boost fair and robust cross-cultural consensus among LLMs. We model consensus as a Nash Equilibrium and employ a game-theoretic negotiation method based on Policy-Space Response Oracles (PSRO) to simulate an organized cross-cultural negotiation process. To evaluate this approach, we construct regional cultural agents using data transformed from the World Values Survey (WVS). Beyond the conventional model-level evaluation method, We further propose two quantitative metrics, Perplexity-based Acceptence and Values Self-Consistency, to assess consensus outcomes. Experimental results indicate that our approach generates consensus of higher quality while ensuring more balanced compromise compared to baselines. Overall, it mitigates WEIRD bias by guiding agents toward convergence through fair and gradual negotiation steps.

On-device computing, or edge computing, is becoming increasingly important for remote sensing, particularly in applications like deep network-based perception on on-orbit satellites and unmanned aerial vehicles (UAVs). In these scenarios, two brain-like capabilities are crucial for remote sensing models: (1) high energy efficiency, allowing the model to operate on edge devices with limited computing resources, and (2) online adaptation, enabling the model to quickly adapt to environmental variations, weather changes, and sensor drift. This work addresses these needs by proposing an online adaptation framework based on spiking neural networks (SNNs) for remote sensing. Starting with a pretrained SNN model, we design an efficient, unsupervised online adaptation algorithm, which adopts an approximation of the BPTT algorithm and only involves forward-in-time computation that significantly reduces the computational complexity of SNN adaptation learning. Besides, we propose an adaptive activation scaling scheme to boost online SNN adaptation performance, particularly in low time-steps. Furthermore, for the more challenging remote sensing detection task, we propose a confidence-based instance weighting scheme, which substantially improves adaptation performance in the detection task. To our knowledge, this work is the first to address the online adaptation of SNNs. Extensive experiments on seven benchmark datasets across classification, segmentation, and detection tasks demonstrate that our proposed method significantly outperforms existing domain adaptation and domain generalization approaches under varying weather conditions. The proposed method enables energy-efficient and fast online adaptation on edge devices, and has much potential in applications such as remote perception on on-orbit satellites and UAV.

The avalanche of AI deployment and its security-privacy concerns are two sides of the same coin. Article 17 of GDPR calls for the Right to Erasure; data has to be obliterated from a system to prevent its compromise. Extant research in this aspect focuses on effacing sensitive data attributes. However, several passive modes of data compromise are yet to be recognized and redressed. The embedding of footprints of training data in a prediction model is one such facet; the difference in performance quality in test and training data causes passive identification of data that have trained the model. This research focuses on addressing the vulnerability arising from the data footprints. The three main aspects are -- i] exploring the vulnerabilities of different classifiers (to segregate the vulnerable and the non-vulnerable ones), ii] reducing the vulnerability of vulnerable classifiers (through data obfuscation) to preserve model and data privacy, and iii] exploring the privacy-performance tradeoff to study the usability of the data obfuscation techniques. An empirical study is conducted on three datasets and eight classifiers to explore the above objectives. The results of the initial research identify the vulnerability in classifiers and segregate the vulnerable and non-vulnerable classifiers. The additional experiments on data obfuscation techniques reveal their utility to render data and model privacy and also their capability to chalk out a privacy-performance tradeoff in most scenarios. The results can aid the practitioners with their choice of classifiers in different scenarios and contexts.

Global conflicts and trouble spots have thrown the world into turmoil. Intelligence services have never been as necessary as they are today when it comes to providing political decision-makers with concrete, accurate, and up-to-date decision-making knowledge. This requires a common co-operation, a common working language and a common understanding of each other. The best way to create this "intelligence community" is through a harmonized intelligence education. In this paper, we show how joint intelligence education can succeed. We draw on the experience of Germany, where all intelligence services and the Bundeswehr are academically educated together in a single degree program that lays the foundations for a common working language. We also show how these experiences have been successfully transferred to a European level, namely to ICE, the Intelligence College in Europe. Our experience has shown that three aspects are particularly important: firstly, interdisciplinarity or better, transdisciplinarity, secondly, the integration of IT knowhow and thirdly, the development and learning of methodological skills. Using the example of the cyber intelligence module with a special focus on data-driven decision support, additionally with its many points of reference to numerous other academic modules, we show how the specific analytic methodology presented is embedded in our specific European teaching context.

Spinal cord injuries (SCIs) generally result in sensory and mobility impairments, with torso instability being particularly debilitating. Existing torso stabilisers are often rigid and restrictive. This paper presents an early investigation into a non-restrictive 1 degree-of-freedom (DoF) mechanical torso stabiliser inspired by devices such as centrifugal clutches and seat-belt mechanisms. Firstly, the paper presents a motion-capture (MoCap) and OpenSim-based kinematic analysis of the cable-based system to understand requisite device characteristics. The simulated evaluation resulted in the cable-based device to require 55-60cm of unrestricted travel, and to lock at a threshold cable velocity of 80-100cm/sec. Next, the developed 1-DoF device is introduced. The proposed mechanical device is transparent during activities of daily living, and transitions to compliant blocking when incipient fall is detected. Prototype behaviour was then validated using a MoCap-based kinematic analysis to verify non-restrictive movement, reliable transition to blocking, and compliance of the blocking.

Abstract-- In this paper, we present a solution for robot arm-controlled agricultural spraying, handling the spraying task as a constrained prioritized 3T2R task. The solution presented in this paper introduces a prioritization between the translational and rotational degrees of freedom of the 3T2R task, and we discuss the utility of this kind of approach for both velocity and positional inverse kinematics, which relate to continuous and selective agricultural spraying applications respectively. Figure 1: The scenario in this paper involves mounting the spray wand for manual vineyard spraying as the endeffector I. Introduction The nozzle used to apply the spraying agent is an axis-symmetric tool. Agricultural robotics is a rapidly advancing research field that focuses on developing and deploying robotic technology for various agricultural tasks. The goal is to enhance the efficiency and sustainability of different velocity of the spraying frame, depicted in Figure 1, and agricultural procedures and address labor shortages.

The ability to convert reciprocating, i.e., alternating, actuation into rotary motion using linkages is hindered fundamentally by their poor torque transmission capability around kinematic singularity configurations. Here, we harness the elastic potential energy of a linear spring attached to the coupler link of four-bar mechanisms to manipulate force transmission around the kinematic singularities. We developed a theoretical model to explore the parameter space for proper force transmission in slider-crank and rocker-crank four-bar kinematics. Finally, we verified the proposed model and methodology by building and testing a macro-scale prototype of a slider-crank mechanism. We expect this approach to enable the development of small-scale rotary engines and robotic devices with closed kinematic chains dealing with serial kinematic singularities, such as linkages and parallel manipulators.

With over 7.9 billion humans, Extensive research has been performed in the field of machine it is getting harder for the majority of the population learning for social science to discover new findings, to lead a healthy life. Around 9.9% of the population, which understand the causal effects, and make predictions. Scholars accounts for 811 million people, still go to bed on an empty have experimented with various traditional mathematical stomach. On the contrary, over 1.3 billion tonnes of food are models, machine learning models and deep learning wasted every year. The world's population is rapidly growing, methods for food demand forecasting. Some of the popular and it is estimated that there will be around 10 billion choices include ARIMA, Holt-Winters, supervised regression people on Earth by the year 2050. Environmentalists have models, and artificial neural networks like NARXNN been trying to find solutions to reduce the numbers in terms (non-linear auto regressive exogenous neural network). of hunger and food wastage. Sustainable food development The research (Lutoslawski et al. 2021) uses a nonlinear ensures that the current and future human population has autoregressive neural network for food demand prediction.

Inverse design of short single-stranded RNA and DNA sequences (aptamers) is the task of finding sequences that satisfy a set of desired criteria. Relevant criteria may be, for example, the presence of specific folding motifs, binding to molecular ligands, sensing properties, etc. Most practical approaches to aptamer design identify a small set of promising candidate sequences using high-throughput experiments (e.g. SELEX), and then optimize performance by introducing only minor modifications to the empirically found candidates. Sequences that possess the desired properties but differ drastically in chemical composition will add diversity to the search space and facilitate the discovery of useful nucleic acid aptamers. Systematic diversification protocols are needed. Here we propose to use an unsupervised machine learning model known as the Potts model to discover new, useful sequences with controllable sequence diversity. We start by training a Potts model using the maximum entropy principle on a small set of empirically identified sequences unified by a common feature. To generate new candidate sequences with a controllable degree of diversity, we take advantage of the model's spectral feature: an energy bandgap separating sequences that are similar to the training set from those that are distinct. By controlling the Potts energy range that is sampled, we generate sequences that are distinct from the training set yet still likely to have the encoded features. To demonstrate performance, we apply our approach to design diverse pools of sequences with specified secondary structure motifs in 30-mer RNA and DNA aptamers.

Recent advances in Internet of Things (IoT) technologies and the reduction in the cost of sensors have encouraged the development of smart environments, such as smart homes. Smart homes can offer home assistance services to improve the quality of life, autonomy and health of their residents, especially for the elderly and dependent. To provide such services, a smart home must be able to understand the daily activities of its residents. Techniques for recognizing human activity in smart homes are advancing daily. But new challenges are emerging every day. In this paper, we present recent algorithms, works, challenges and taxonomy of the field of human activity recognition in a smart home through ambient sensors. Moreover, since activity recognition in smart homes is a young field, we raise specific problems, missing and needed contributions. But also propose directions, research opportunities and solutions to accelerate advances in this field.