Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cultural contexts, respect local sensitivities, and support low-resource languages, all while effectively integrating corresponding visual cues. In pursuit of culturally diverse global multimodal models, our proposed All Languages Matter Benchmark (ALM-bench) represents the largest and most comprehensive effort to date for evaluating LMMs across 100 languages. ALM-bench challenges existing models by testing their ability to understand and reason about culturally diverse images paired with text in various languages, including many low-resource languages traditionally underrepresented in LMM research. The benchmark offers a robust and nuanced evaluation framework featuring various question formats, including true/false, multiple choice, and open-ended questions, which are further divided into short and long-answer categories. ALM-bench design ensures a comprehensive assessment of a model’s ability to handle varied levels of difficulty in visual and linguistic reasoning. To capture the rich tapestry of global cultures, ALM-bench carefully curates content from 13 distinct cultural aspects, ranging from traditions and rituals to famous personalities and celebrations. Through this, ALM-bench not only provides a rigorous testing ground for state-of-the-art open and closed-source LMMs but also highlights the importance of cultural and linguistic inclusivity, encouraging the development of models that can serve diverse global populations effectively. Our benchmark is publicly available at https://mbzuai-oryx.github.io/ALM-Bench/.

Internet of Things (IoT) is expanding rapidly as its use cases and technology advances. However, along with these benefits, IoT also faces many attacks. One of the most common and difficult-to-detect attacks is the DDoS attack and its variations (MrDDoS). Implementing security solutions in the IoT ecosystem is challenging due to resource-constrained devices. The proposed security solution, namely, MAD-HOT utilized the Hoeffding tree and placement strategies for MrDDoS attack detection with reasonable accuracy. The placement problem is formulated with a hypergraph and a game theory algorithm. Security module based on Hoeffding trees is the main classifier, along with many other classifiers for model training. Finally, for prediction, we have used logistic regression as the meta-classifier. Extensive experimentation on the Contiki cooja simulator shows that the MAD-HOT can best execute the balance between MrDDoS detection and energy overhead.

Large Language Models (LLMs) have demonstrated remarkable planning abilities across various domains, including robotic manipulation and navigation. While recent work in robotics deploys LLMs for high-level and low-level planning, existing methods often face challenges with failure recovery and suffer from hallucinations in long-horizon tasks. To address these limitations, we propose a novel multi-agent LLM framework, Multi-Agent Large Language Model for Manipulation (MALMM). Notably, MALMM distributes planning across three specialized LLM agents, namely high-level planning agent, low-level control agent, and a supervisor agent. Moreover, by incorporating environment observations after each step, our framework effectively handles intermediate failures and enables adaptive re-planning. Unlike existing methods, MALMM does not rely on pre-trained skill policies or in-context learning examples and generalizes to unseen tasks. In our experiments, MALMM demonstrates excellent performance in solving previously unseen long-horizon manipulation tasks, and outperforms existing zero-shot LLM-based methods in RLBench by a large margin. Experiments with the Franka robot arm further validate our approach in real-world settings.

In this article, we introduce a novel problem of audio-visual autism behavior recognition, which includes social behavior recognition, an essential aspect previously omitted in AI-assisted autism screening research. We define the task at hand as one that is audio-visual autism behavior recognition, which uses audio and visual cues, including any speech present in the audio, to recognize autism-related behaviors. To facilitate this new research direction, we collected an audio-visual autism spectrum dataset (AV-ASD), currently the largest video dataset for autism screening using a behavioral approach. It covers an extensive range of autism-associated behaviors, including those related to social communication and interaction. To pave the way for further research on this new problem, we intensively explored leveraging foundation models and multimodal large language models across different modalities. Our experiments on the AV-ASD dataset demonstrate that integrating audio (mainly ambient sound), visual, and speech (predominately spoken language) modalities significantly enhances the performance in autism behavior recognition. Additionally, we explored the use of apost-hoc to ad-hocpipeline in a multimodal large language model to investigate its potential to augment the model's explanatory capability during autism behavior recognition.

Support vector machines (SVMs) are established as highly successful classifiers in a broad range of applications, including numerous medical ones. Nevertheless, their current employment is restricted by a limitation in the manner in which they are trained, most often the training-validation-test or kfold cross-validation approaches, which are wasteful both in terms of the use of the available data as well as computational resources. This is a particularly important consideration in many medical problems, in which data availability is low (be it because of the inherent difficulty in obtaining sufficient data, or because of practical reasons, e.g. pertaining to privacy and data sharing). In this paper we propose a novel approach to training SVMs which does not suffer from the aforementioned limitation, which is at the same time much more rigorous in nature, being built upon solid information theoretic grounds. Specifically, we show how the training process, that is the process of hyperparameter inference, can be formulated as a search for the optimal model under the minimum description length (MDL) criterion, allowing for theory rather than empiricism driven selection and removing the need for validation data. The effectiveness and superiority of our approach are demonstrated on the Wisconsin Diagnostic Breast Cancer Data Set.

This paper presents a novel application of various machine learning (ML)-based approaches towards prediction of path loss (PL) parameter for a smart campus environment. Measured data from [1] are used to train and evaluate the performance of popular ML techniques such as artificial neural network (ANN) and random forest (RF). Simulation results are presented to verify the PL prediction accuracy of the ML-based schemes. Further, a detailed comparison with the widely used empirical COST-231 Hata model demonstrates the superiority over conventional techniques thereby validating the suitability of employing ML for path loss prediction in challenging 5G wireless scenarios.

We present an efficient multi-query event stream platform to support query processing over high-frequency event streams. Our platform is built over reconfigurable hardware -- FPGAs -- to achieve line-rate multi-query processing by exploiting unprecedented degrees of parallelism and potential for pipelining, only available through custom-built, application-specific and low-level logic design. Moreover, a multi-query event stream processing engine is at the core of a wide range of applications including real-time data analytics, algorithmic trading, targeted advertisement, and (complex) event processing.

We present fpga-ToPSS (Toronto Publish/Subscribe System), an efficient event processing platform to support high-frequency and low-latency event matching. fpga-ToPSS is built over reconfigurable hardware---FPGAs---to achieve line-rate processing by exploring various degrees of parallelism. Furthermore, each of our proposed FPGA-based designs is geared towards a unique application requirement, such as flexibility, adaptability, scalability, or pure performance, such that each solution is specifically optimized to attain a high level of parallelism. Therefore, each solution is formulated as a design trade-off between the degree of parallelism versus the desired application requirement. Moreover, our event processing engine supports Boolean expression matching with an expressive predicate language applicable to a wide range of applications including real-time data analysis, algorithmic trading, targeted advertisement, and (complex) event processing.

In this demo, we present fpga-ToPSS (Toronto Publish/Subscribe System Family), an efficient event processing platform for high-frequency and low-latency algorithmic trading. Our event processing platform is built over reconfigurable hardware---FPGAs---to achieve line-rate processing. Furthermore, our event processing engine supports Boolean expression matching with an expressive predicate language that models complex financial strategies to autonomously buy and sell stocks based on real-time financial data.