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Informationization is a prevailing trend in today's world. The increasing demand for information in decision-making processes poses significant challenges for investigation activities, particularly in terms of effectively allocating limited resources to plan investigation programs. This paper addresses the investigation path planning problem by formulating it as a multi-traveling salesman problem (MTSP). Our objective is to minimize costs, and to achieve this, we propose a chaotic artificial fish swarm algorithm based on multiple population differential evolution (DE-CAFSA). To overcome the limitations of the artificial fish swarm algorithm, such as low optimization accuracy and the inability to consider global and local information, we incorporate adaptive field of view and step size adjustments, replace random behavior with the 2-opt operation, and introduce chaos theory and sub-optimal solutions to enhance optimization accuracy and search performance. Additionally, we integrate the differential evolution algorithm to create a hybrid algorithm that leverages the complementary advantages of both approaches. Experimental results demonstrate that DE-CAFSA outperforms other algorithms on various public datasets of different sizes, as well as showcasing excellent performance on the examples proposed in this study.

Stochastic gradient descent (SGD) and its variants are the main workhorses for solving large-scale optimization problems with nonconvex objective functions. Although the convergence of SGDs in the (strongly) convex case is well-understood, their convergence for nonconvex functions stands on weak mathematical foundations. Most existing studies on the nonconvex convergence of SGD show the complexity results based on either the minimum of the expected gradient norm or the functional sub-optimality gap (for functions with extra structural property) by searching the entire range of iterates. Hence the last iterations of SGDs do not necessarily maintain the same complexity guarantee. This paper shows that an $\epsilon$-stationary point exists in the final iterates of SGDs, given a large enough total iteration budget, $T$, not just anywhere in the entire range of iterates -- a much stronger result than the existing one. Additionally, our analyses allow us to measure the density of the $\epsilon$-stationary points in the final iterates of SGD, and we recover the classical $O(\frac{1}{\sqrt{T}})$ asymptotic rate under various existing assumptions on the objective function and the bounds on the stochastic gradient. As a result of our analyses, we addressed certain myths and legends related to the nonconvex convergence of SGD and posed some thought-provoking questions that could set new directions for research.

Recognizing vulnerability is crucial for understanding and implementing targeted support to empower individuals in need. This is especially important at the European Court of Human Rights (ECtHR), where the court adapts Convention standards to meet actual individual needs and thus ensures effective human rights protection. However, the concept of vulnerability remains elusive at the ECtHR and no prior NLP research has dealt with it. To enable future research in this area, we present VECHR, a novel expert-annotated multi-label dataset comprising of vulnerability type classification and explanation rationale. We benchmark the performance of state-of-the-art models on VECHR from both prediction and explainability perspectives. Our results demonstrate the challenging nature of the task with lower prediction performance and limited agreement between models and experts. Further, we analyze the robustness of these models in dealing with out-of-domain (OOD) data and observe overall limited performance. Our dataset poses unique challenges offering significant room for improvement regarding performance, explainability, and robustness.

Telling lies and faking emotions is quite common in human-human interactions: though there are risks, in many situations such behaviours provide social benefits. In recent years, there have been many social robots and chatbots that fake emotions or behave deceptively with their users. In this paper, I present a few examples of such robots and chatbots, and analyze their ethical aspects. Three scenarios are presented where some kind of lying or deceptive behaviour might be justified. Then five approaches to deceptive behaviours - no deception, blatant deception, tactful deception, nudging, and self deception - are discussed and their implications are analyzed. I conclude by arguing that we need to develop localized and culture-specific solutions to incorporating deception in social robots and chatbots.

Expressing attack-defence trees in a multi-agent setting allows for studying a new aspect of security scenarios, namely how the number of agents and their task assignment impact the performance, e.g. attack time, of strategies executed by opposing coalitions. Optimal scheduling of agents' actions, a non-trivial problem, is thus vital. We discuss associated caveats and propose an algorithm that synthesises such an assignment, targeting minimal attack time and using the minimal number of agents for a given attack-defence tree. We also investigate an alternative approach for the same problem using Rewriting Logic, starting with a simple and elegant declarative model, whose correctness (in terms of schedule's optimality) is self-evident. We then refine this specification, inspired by the design of our specialised algorithm, to obtain an efficient system that can be used as a playground to explore various aspects of attack-defence trees. We compare the two approaches on different benchmarks.

Model checking of multi-agent systems (MAS) is known to be hard, both theoretically and in practice. A smart abstraction of the state space may significantly reduce the model, and facilitate the verification. In this paper, we propose and study an intuitive agent-based abstraction scheme, based on the removal of variables in the representation of a MAS. This allows to do the reduction without generating the global model of the system. Moreover, the process is easy to understand and control even for domain experts with little knowledge of computer science. We formally prove the correctness of the approach, and evaluate the gains experimentally on models of a postal voting procedure.

Neural networks have become a powerful tool as surrogate models to provide numerical solutions for scientific problems with increased computational efficiency. This efficiency can be advantageous for numerically challenging problems where time to solution is important or when evaluation of many similar analysis scenarios is required. One particular area of scientific interest is the setting of inverse problems, where one knows the forward dynamics of a system are described by a partial differential equation and the task is to infer properties of the system given (potentially noisy) observations of these dynamics. We consider the inverse problem of inferring the location of a wave source on a square domain, given a noisy solution to the 2-D acoustic wave equation. Under the assumption of Gaussian noise, a likelihood function for source location can be formulated, which requires one forward simulation of the system per evaluation. Using a standard neural network as a surrogate model makes it computationally feasible to evaluate this likelihood several times, and so Markov Chain Monte Carlo methods can be used to evaluate the posterior distribution of the source location. We demonstrate that this method can accurately infer source-locations from noisy data.

Every major technical invention resurfaces the dual-use dilemma -- the new technology has the potential to be used for good as well as for harm. Generative AI (GenAI) techniques, such as large language models (LLMs) and diffusion models, have shown remarkable capabilities (e.g., in-context learning, code-completion, and text-to-image generation and editing). However, GenAI can be used just as well by attackers to generate new attacks and increase the velocity and efficacy of existing attacks. This paper reports the findings of a workshop held at Google (co-organized by Stanford University and the University of Wisconsin-Madison) on the dual-use dilemma posed by GenAI. This paper is not meant to be comprehensive, but is rather an attempt to synthesize some of the interesting findings from the workshop. We discuss short-term and long-term goals for the community on this topic. We hope this paper provides both a launching point for a discussion on this important topic as well as interesting problems that the research community can work to address.

Today, the security of many domains rely on the use of Machine Learning to detect threats, identify vulnerabilities, and safeguard systems from attacks. Recently, transformer architectures have improved the state-of-the-art performance on a wide range of tasks such as malware detection and network intrusion detection. But, before abandoning current approaches to transformers, it is crucial to understand their properties and implications on cybersecurity applications. In this paper, we evaluate the robustness of transformers to adversarial samples for system defenders (i.e., resiliency to adversarial perturbations generated on different types of architectures) and their adversarial strength for system attackers (i.e., transferability of adversarial samples generated by transformers to other target models). To that effect, we first fine-tune a set of pre-trained transformer, Convolutional Neural Network (CNN), and hybrid (an ensemble of transformer and CNN) models to solve different downstream image-based tasks. Then, we use an attack algorithm to craft 19,367 adversarial examples on each model for each task. The transferability of these adversarial examples is measured by evaluating each set on other models to determine which models offer more adversarial strength, and consequently, more robustness against these attacks. We find that the adversarial examples crafted on transformers offer the highest transferability rate (i.e., 25.7% higher than the average) onto other models. Similarly, adversarial examples crafted on other models have the lowest rate of transferability (i.e., 56.7% lower than the average) onto transformers. Our work emphasizes the importance of studying transformer architectures for attacking and defending models in security domains, and suggests using them as the primary architecture in transfer attack settings.

Depression has emerged as a significant mental health concern due to a variety of factors, reflecting broader societal and individual challenges. Within the digital era, social media has become an important platform for individuals navigating through depression, enabling them to express their emotional and mental states through various mediums, notably music. Specifically, their music preferences, manifested through sharing practices, inadvertently offer a glimpse into their psychological and emotional landscapes. This work seeks to study the differences in music preferences between individuals diagnosed with depression and non-diagnosed individuals, exploring numerous facets of music, including musical features, lyrics, and musical networks. The music preferences of individuals with depression through music sharing on social media, reveal notable differences in musical features and topics and language use of lyrics compared to non-depressed individuals. We find the network information enhances understanding of the link between music listening patterns. The result highlights a potential echo-chamber effect, where depression individual's musical choices may inadvertently perpetuate depressive moods and emotions. In sum, this study underscores the significance of examining music's various aspects to grasp its relationship with mental health, offering insights for personalized music interventions and recommendation algorithms that could benefit individuals with depression.