The proliferation of misinformation on social media has raised significant societal concerns, necessitating robust detection mechanisms. Large Language Models such as GPT-4 and LLaMA2 have been envisioned as possible tools for detecting misinformation based on their advanced natural language understanding and reasoning capabilities. This paper conducts a comparison of LLM-based approaches to detecting misinformation between text-based, multimodal, and agentic approaches. We evaluate the effectiveness of fine-tuned models, zero-shot learning, and systematic fact-checking mechanisms in detecting misinformation across different topic domains like public health, politics, and finance. We also discuss scalability, generalizability, and explainability of the models and recognize key challenges such as hallucination, adversarial attacks on misinformation, and computational resources. Our findings point towards the importance of hybrid approaches that pair structured verification protocols with adaptive learning techniques to enhance detection accuracy and explainability. The paper closes by suggesting potential avenues of future work, including real-time tracking of misinformation, federated learning, and cross-platform detection models.

Despite the current surge of interest in autonomous robotic systems, robot activity recognition within restricted indoor environments remains a formidable challenge. Conventional methods for detecting and recognizing robotic arms' activities often rely on vision-based or light detection and ranging (LiDAR) sensors, which require line-of-sight (LoS) access and may raise privacy concerns, for example, in nursing facilities. This research pioneers an innovative approach harnessing channel state information (CSI) measured from WiFi signals, subtly influenced by the activity of robotic arms. We developed an attention-based network to classify eight distinct activities performed by a Franka Emika robotic arm in different situations. Our proposed bidirectional vision transformer-concatenated (BiVTC) methodology aspires to predict robotic arm activities accurately, even when trained on activities with different velocities, all without dependency on external or internal sensors or visual aids. Considering the high dependency of CSI data to the environment, motivated us to study the problem of sniffer location selection, by systematically changing the sniffer's location and collecting different sets of data. Finally, this paper also marks the first publication of the CSI data of eight distinct robotic arm activities, collectively referred to as RoboFiSense. This initiative aims to provide a benchmark dataset and baselines to the research community, fostering advancements in the field of robotics sensing.

Widely applied large language models (LLMs) can generate human-like content, raising concerns about the abuse of LLMs. Therefore, it is important to build strong AI-generated text (AIGT) detectors. Current works only consider document-level AIGT detection, therefore, in this paper, we first introduce a sentence-level detection challenge by synthesizing a dataset that contains documents that are polished with LLMs, that is, the documents contain sentences written by humans and sentences modified by LLMs. Then we propose \textbf{Seq}uence \textbf{X} (Check) \textbf{GPT}, a novel method that utilizes log probability lists from white-box LLMs as features for sentence-level AIGT detection. These features are composed like \textit{waves} in speech processing and cannot be studied by LLMs. Therefore, we build SeqXGPT based on convolution and self-attention networks. We test it in both sentence and document-level detection challenges. Experimental results show that previous methods struggle in solving sentence-level AIGT detection, while our method not only significantly surpasses baseline methods in both sentence and document-level detection challenges but also exhibits strong generalization capabilities.

The extraordinary performance of large language models (LLMs) heightens the importance of detecting whether the context is generated by an AI system. More importantly, while more and more companies and institutions release their LLMs, the origin can be hard to trace. Since LLMs are heading towards the time of AGI, similar to the origin tracing in anthropology, it is of great importance to trace the origin of LLMs. In this paper, we first raise the concern of the origin tracing of LLMs and propose an effective method to trace and detect AI-generated contexts. We introduce a novel algorithm that leverages the contrastive features between LLMs and extracts model-wise features to trace the text origins. Our proposed method works under both white-box and black-box settings therefore can be widely generalized to detect various LLMs.(e.g. can be generalized to detect GPT-3 models without the GPT-3 models). Also, our proposed method requires only limited data compared with the supervised learning methods and can be extended to trace new-coming model origins. We construct extensive experiments to examine whether we can trace the origins of given texts. We provide valuable observations based on the experimental results, such as the difficulty level of AI origin tracing, and the AI origin similarities, and call for ethical concerns of LLM providers. We are releasing all codes and data as a toolkit and benchmark for future AI origin tracing and detecting studies. \footnote{We are releasing all available resource at \url{https://github.com/OpenLMLab/}.}

Internet of Things devices have seen a rapid growth and popularity in recent years with many more ordinary devices gaining network capability and becoming part of the ever-growing IoT network. With this exponential growth and the limitation of resources, it is becoming increasingly harder to protect against security threats such as malware due to its evolving faster than the defence mechanisms can handle with. The traditional security systems are not able to detect unknown malware as they use signature-based methods. In this paper, we aim to address this issue by introducing a novel IoT malware traffic analysis approach using neural network and binary visualisation. The prime motivation of the proposed approach is to faster detect and classify new malware (zero-day malware). The experiment results show that our method can satisfy the accuracy requirement of practical application.

As Hurricane Harvey ripped through Texas and neighboring gulf states in August 2017, leaving a record-breaking 30 million gallons of quickly-dirtied water in its wake, the Federal Emergency Management Agency, more commonly known as FEMA, moved into position. Among the personnel from federal agencies as varied as the Department of Health and Human Services and the Coast Guard were numerous Urban Search and Rescue teams--experts in finding people in the midst of a large-scale crisis, whether they're stranded on a roof, or trapped deep beneath the rubble. They're equipped with listening devices, heat detection equipment, and, most importantly, some loyal sniffers. "We use the dogs [as] locating tools," says Scott Mateyaschuk of the New York Police Department's K9 unit. "The dogs will locate live human scent under structural collapse."

General Electric's Research and Development Center (Schenectady, NY) has developed an expert system which is being used to increase the speed of design of new jet engines, electric motors, and other complex machines. The system, called Engineous, has been used to improve gas turbine designs, resulting in increased fuel efficiency for jet aircraft engines manufactured by GE. The Expert Sniffer, developed by Network General (Menlo Park, CA), is an expert system-based technology that automatically identifies network problems and recommends solutions to network managers. Software based on this technology will be a standard part of the Sniffer Network Analyzer, targeted initially for Ethernet and Token Ring environments. The objective of this project will be to define a standard execution environment for fuzzy systems.

Vehicle-to-vehicle communication is critical for the future of the autonomous car. But vehicle-to-infrastructure communication is what will tie everything together. "If we look at it in a very basic level, automated and connected vehicles, to make this happen … it requires an ecosystem to work together," said Tammy Meehan Russell, global portfolio manager for intelligent transportation at 3M. "Very basically that ecosystem is vehicle, human and infrastructure." While visiting the University of Michigan's Mobility Transformation Center in Ann Arbor, our group of reporters was given a tour of Mcity, a testing and training ground for autonomous, automated and self-driving vehicles. Mcity is a 32-acre artificial town built by MTC and opened in July 2015 for the specific purpose of testing autonomous cars in real world conditions. Just about everything an autonomous car might encounter is represented at Mcity: different kinds of roadways (concrete pavement, grooved pavement etc.), different kinds of road signs and lines, various types of traffic signals, highway conditions, urban conditions and so forth.

In the visual and aural realms, we very often interact with reproduced versions of an original -- a photograph of a scene, a recording of a concert. And as long as you know what the original looked and/or sounded like, it's easy to tell whether it's an accurate reproduction. For smells, the same does not hold true. Unlike audio or visual reproductions, it's hard to transmit a reproduction of a smell to someone. There have been a few attempts, of course, but while something visual can be mimicked using wavelength and luminance and sound is a matter of copying the tone, odors depend on the brain's perception of molecules.