Malware detection in IoT environments necessitates robust methodologies. This study introduces a CNN-LSTM hybrid model for IoT malware identification and evaluates its performance against established methods. Leveraging K-fold cross-validation, the proposed approach achieved 95.5% accuracy, surpassing existing methods. The CNN algorithm enabled superior learning model construction, and the LSTM classifier exhibited heightened accuracy in classification. Comparative analysis against prevalent techniques demonstrated the efficacy of the proposed model, highlighting its potential for enhancing IoT security. The study advocates for future exploration of SVMs as alternatives, emphasizes the need for distributed detection strategies, and underscores the importance of predictive analyses for a more powerful IOT security. This research serves as a platform for developing more resilient security measures in IoT ecosystems.

Language-conditioned robotic manipulation represents a cutting-edge area of research, enabling seamless communication and cooperation between humans and robotic agents. This field focuses on teaching robotic systems to comprehend and execute instructions conveyed in natural language. To achieve this, the development of robust language understanding models capable of extracting actionable insights from textual input is essential. In this comprehensive survey, we systematically explore recent advancements in language-conditioned approaches within the context of robotic manipulation. We analyze these approaches based on their learning paradigms, which encompass reinforcement learning, imitation learning, and the integration of foundational models, such as large language models and vision-language models. Furthermore, we conduct an in-depth comparative analysis, considering aspects like semantic information extraction, environment & evaluation, auxiliary tasks, and task representation. Finally, we outline potential future research directions in the realm of language-conditioned learning for robotic manipulation, with the topic of generalization capabilities and safety issues. The GitHub repository of this paper can be found at https://github.com/hk-zh/language-conditioned-robot-manipulation-models

Transformer-based models still face the structural limitation of fixed context length in processing long sequence input despite their effectiveness in various fields. While various external memory techniques were introduced, most previous techniques fail to avoid fateful forgetting, where even the most important memories are inevitably forgotten after a sufficient number of time steps. We designed Memoria, a memory system for artificial neural networks, drawing inspiration from humans and applying various neuroscientific and psychological theories related to memory. Experimentally, we demonstrated the effectiveness of Memoria in tasks such as sorting and language modeling, surpassing conventional techniques.

This paper aims to answer the question: Can deep learning models be cost-efficiently trained on a global market of spot VMs spanning different data centers and cloud providers? To provide guidance, we extensively evaluate the cost and throughput implications of training in different zones, continents, and clouds for representative CV, NLP, and ASR models. To expand the current training options further, we compare the scalability potential for hybrid-cloud scenarios by adding cloud resources to on-premise hardware to improve training throughput. Finally, we show how leveraging spot instance pricing enables a new cost-efficient way to train models with multiple cheap VMs, trumping both more centralized and powerful hardware and even on-demand cloud offerings at competitive prices.

Epilepsy affects more than 50 million people worldwide, making it one of the world's most prevalent neurological diseases. The main symptom of epilepsy is seizures, which occur abruptly and can cause serious injury or death. The ability to predict the occurrence of an epileptic seizure could alleviate many risks and stresses people with epilepsy face. We formulate the problem of detecting preictal (or pre-seizure) with reference to normal EEG as a precursor to incoming seizure. To this end, we developed several supervised deep learning approaches to identify preictal EEG from normal EEG. We further develop novel unsupervised deep learning approaches to train the models on only normal EEG, and detecting pre-seizure EEG as an anomalous event. These deep learning models were trained and evaluated on two large EEG seizure datasets in a person-specific manner. We found that both supervised and unsupervised approaches are feasible; however, their performance varies depending on the patient, approach and architecture. This new line of research has the potential to develop therapeutic interventions and save human lives.

EPFL scientists have developed METEOR – an application that can train algorithms to recognize new objects after being shown just a handful of images. Images taken by drones and satellites give scientists a wealth of information. These snapshots provide crucial insight into the changes taking place on the Earth's surface, such as in animal populations, vegetation, debris floating on the ocean surface and glacier coverage. In addition, experts can train neural networks to sort through the images at dizzying speed and spot and classify individual objects. "However, none of the AI programs currently available can immediately switch from recognizing one type of object to another – like from debris to a tree or building," says Professor Devis Tuia, the head of EPFL's Environmental Computational Science and Earth Observation Laboratory.

When Molly Crabapple touched down in Italy last year for the International Journalism Festival, she expected the usual. The annual conference bills itself as Europe's largest media event, and Crabapple had planned to give a talk about her career as an artist and writer reporting from the front lines of conflict zones. But as she took in some of the panels, she felt herself growing uneasy. Sprinkled among the journalists discussing topics such as the war in Ukraine and the state of podcasting, some of the speakers were promoting the use of generative AI. She overheard someone say that journalists write too much, that much of their work could be automated.

A prestigious portrait competition has defended allowing entrants to submit artwork generated by artificial intelligence, arguing art is not stagnant and should reflect societal change. The Brisbane Portrait Prize – with a top prize worth 50,0000 – has been described as Queensland's answer to the Archibalds with selected entries displayed at the Brisbane Powerhouse later in the year. In the terms and conditions of entry, the Brisbane Portrait Prize notes this year that it will accept entries "completed in whole or in part by generative artificial intelligence" so long as the artwork is original and "entirely completed and owned outright" by the entrant. A spokesperson for the prize told Guardian Australia that allowing AI entries acknowledged the definition of art was not stagnant and would always grow. "BPP prides itself on being a contemporary prize and we are always interested in what'contemporary' portraiture is while fostering both the ongoing evolution of art and engaging in the surrounding conversation," they said.

Abstract-- This paper introduces a novel numerical approach to achieving smooth lane-change trajectories in autonomous driving scenarios. The generation of smooth and dynamically feasible trajectories for the lane change maneuver is facilitated by combining polynomial curve fitting with particle propagation, which can account for vehicle dynamics. The proposed planning algorithm is capable of determining feasible trajectories with real-time computation capability. The simulation results validate the efficacy and effectiveness of our proposed approach. One example of this is Neural I. INTRODUCTION Network Model Predictive Control (NNMPC) [11,12], which We consider motion planning for autonomous vehicles in attempts to solve merging in dense traffic by combining highly dense traffic scenarios, as depicted in Figure 1.

Instruction Tuning (IT), the process of training large language models (LLMs) using instruction-response pairs, has emerged as the predominant method for transforming base pre-trained LLMs into open-domain conversational agents. While IT has achieved notable success and widespread adoption, its limitations and shortcomings remain underexplored. In this paper, through rigorous experiments and an in-depth analysis of the changes LLMs undergo through IT, we reveal various limitations of IT. In particular, we show that (1) IT fails to enhance knowledge or skills in LLMs. LoRA fine-tuning is limited to learning response initiation and style tokens, and full-parameter fine-tuning leads to knowledge degradation. (2) Copying response patterns from IT datasets derived from knowledgeable sources leads to a decline in response quality. (3) Full-parameter fine-tuning increases hallucination by inaccurately borrowing tokens from conceptually similar instances in the IT dataset for generating responses. (4) Popular methods to improve IT do not lead to performance improvements over a simple LoRA fine-tuned model. Our findings reveal that responses generated solely from pre-trained knowledge consistently outperform responses by models that learn any form of new knowledge from IT on open-source datasets. We hope the insights and challenges revealed inspire future work.