93 research outputs found
Implementing BERT and fine-tuned RobertA to detect AI generated news by ChatGPT
The abundance of information on social media has increased the necessity of
accurate real-time rumour detection. Manual techniques of identifying and
verifying fake news generated by AI tools are impracticable and time-consuming
given the enormous volume of information generated every day. This has sparked
an increase in interest in creating automated systems to find fake news on the
Internet. The studies in this research demonstrate that the BERT and RobertA
models with fine-tuning had the best success in detecting AI generated news.
With a score of 98%, tweaked RobertA in particular showed excellent precision.
In conclusion, this study has shown that neural networks can be used to
identify bogus news AI generation news created by ChatGPT. The RobertA and BERT
models' excellent performance indicates that these models can play a critical
role in the fight against misinformation
Recommended from our members
Photoinduced StrainâAssisted Synthesis of a StiffâStilbene Polymer by RingâOpening Metathesis Polymerization
Developing a novel strategy to synthesize photoresponsive polymers is of significance owing to their potential applications. We report a photoinduced strainâassisted synthesis of mainâchain stiffâstilbene polymers by using ringâopening metathesis polymerization (ROMP), activating a macrocyclic Ïâbond connected to a stiffâstilbene photoswitch through a linker. Since the linker acts as an external constraint, the photoisomerization to the Eâform leads to the stiffâstilbene being strained and thus reactive to ROMP. The photoisomerization of Zâform to Eâform was investigated using timeâdependent NMR studies and UV/Vis spectroscopy. The DFT calculation showed that the Eâform was less stable due to a lack of planarity. By the internal strain developed due to the linker constraint through photoisomerization, the Eâform underwent ROMP by a second generation Grubbs catalyst. In contrast, Zâform did not undergo polymerization under similar conditions. The MALDIâTOF spectrum of Eâform after polymerization showed the presence of oligomers of >5.2â
kDa
ChatLaw: Open-Source Legal Large Language Model with Integrated External Knowledge Bases
Large Language Models (LLMs) have shown the potential to revolutionize
natural language processing tasks in various domains, sparking great interest
in vertical-specific large models. However, unlike proprietary models such as
BloombergGPT and FinGPT, which have leveraged their unique data accumulations
to make strides in the finance domain, there hasn't not many similar large
language models in the Chinese legal domain to facilitate its digital
transformation.
In this paper, we propose an open-source legal large language model named
ChatLaw. Due to the importance of data quality, we carefully designed a legal
domain fine-tuning dataset. Additionally, to overcome the problem of model
hallucinations in legal data screening during reference data retrieval, we
introduce a method that combines vector database retrieval with keyword
retrieval to effectively reduce the inaccuracy of relying solely on vector
database retrieval. Furthermore, we propose a self-attention method to enhance
the ability of large models to overcome errors present in reference data,
further optimizing the issue of model hallucinations at the model level and
improving the problem-solving capabilities of large models. We also
open-sourced our model and part of the data at
https://github.com/PKU-YuanGroup/ChatLaw
Recommended from our members
SelfâHealable and Recyclable Tactile Force Sensors with PostâTunable Sensitivity
It is challenging to postâtune the sensitivity of a tactile force sensor. Herein, a facile method is reported to tailor the sensing properties of conductive polymer composites by utilizing the liquidâlike property of dynamic polymer matrix at low strain rates. The idea is demonstrated using dynamic polymer composites (CB/dPDMS) made via evaporationâinduced gelation of the suspending toluene solution of carbon black (CB) and acidâcatalyzed dynamic polydimethylsiloxane (dPDMS). The dPDMS matrices allow CB to redistribute to change the sensitivity of materials at the liquidâlike state, but exhibit typical solidâlike behavior and thus can be used as strain sensors at normal strain rates. It is shown that the gauge factor of the polymer composites can be easily postâtuned from 1.4 to 51.5. In addition, the dynamic polymer matrices also endow the composites with interesting selfâhealing ability and recyclability. Therefore, it is envisioned that this method can be useful in the design of various novel tactile sensing materials for many applications
ChatFace: Chat-Guided Real Face Editing via Diffusion Latent Space Manipulation
Editing real facial images is a crucial task in computer vision with
significant demand in various real-world applications. While GAN-based methods
have showed potential in manipulating images especially when combined with
CLIP, these methods are limited in their ability to reconstruct real images due
to challenging GAN inversion capability. Despite the successful image
reconstruction achieved by diffusion-based methods, there are still challenges
in effectively manipulating fine-gained facial attributes with textual
instructions.To address these issues and facilitate convenient manipulation of
real facial images, we propose a novel approach that conduct text-driven image
editing in the semantic latent space of diffusion model. By aligning the
temporal feature of the diffusion model with the semantic condition at
generative process, we introduce a stable manipulation strategy, which perform
precise zero-shot manipulation effectively. Furthermore, we develop an
interactive system named ChatFace, which combines the zero-shot reasoning
ability of large language models to perform efficient manipulations in
diffusion semantic latent space. This system enables users to perform complex
multi-attribute manipulations through dialogue, opening up new possibilities
for interactive image editing. Extensive experiments confirmed that our
approach outperforms previous methods and enables precise editing of real
facial images, making it a promising candidate for real-world applications.
Project page: https://dongxuyue.github.io/chatface
Printing surface charge as a new paradigm to program droplet transport
Directed, long-range and self-propelled transport of droplets on solid
surfaces, especially on water repellent surfaces, is crucial for many
applications from water harvesting to bio-analytical devices. One appealing
strategy to achieve the preferential transport is to passively control the
surface wetting gradients, topological or chemical, to break the asymmetric
contact line and overcome the resistance force. Despite extensive progress, the
directional droplet transport is limited to small transport velocity and short
transport distance due to the fundamental trade-off: rapid transport of droplet
demands a large wetting gradient, whereas long-range transport necessitates a
relatively small wetting gradient. Here, we report a radically new strategy
that resolves the bottleneck through the creation of an unexplored gradient in
surface charge density (SCD). By leveraging on a facile droplet printing on
superamphiphobic surfaces as well as the fundamental understanding of the
mechanisms underpinning the creation of the preferential SCD, we demonstrate
the self-propulsion of droplets with a record-high velocity over an ultra-long
distance without the need for additional energy input. Such a Leidenfrost-like
droplet transport, manifested at ambient condition, is also genetic, which can
occur on a variety of substrates such as flexible and vertically placed
surfaces. Moreover, distinct from conventional physical and chemical gradients,
the new dimension of gradient in SCD can be programmed in a rewritable fashion.
We envision that our work enriches and extends our capability in the
manipulation of droplet transport and would find numerous potential
applications otherwise impossible.Comment: 11 pages, 4 figure
DeepC2: AI-powered Covert Botnet Command and Control on OSNs
Botnets are one of the major threats to computer security. In previous botnet
command and control (C&C) scenarios using online social networks (OSNs),
methods for addressing (e.g., IDs, links, or DGAs) are hardcoded into bots.
Once a bot is reverse engineered, the botmaster and C&C infrastructure will be
exposed. Additionally, abnormal content from explicit commands may expose
botmasters and raise anomalies on OSNs. To overcome these deficiencies, we
proposed DeepC2, an AI-powered covert C&C method on OSNs. By leveraging neural
networks, bots can find botmasters by avatars, which are converted into feature
vectors and embedded into bots. Adversaries cannot infer botmasters' accounts
from the vectors. Commands are embedded into normal contents (e.g., tweets and
comments) using text data augmentation and hash collision. Experiments on
Twitter show that command-embedded contents can be generated efficiently, and
bots can find botmasters and obtain commands accurately. Security analysis on
different scenarios show that DeepC2 is robust and hard to be shut down. By
demonstrating how AI may help promote covert communication on OSNs, this work
provides a new perspective on botnet detection and confrontation.Comment: 13 pages, 15 figures, 7 tables. Discussion on possible
countermeasures update
Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models
Video-based large language models (Video-LLMs) have been recently introduced,
targeting both fundamental improvements in perception and comprehension, and a
diverse range of user inquiries. In pursuit of the ultimate goal of achieving
artificial general intelligence, a truly intelligent Video-LLM model should not
only see and understand the surroundings, but also possess human-level
commonsense, and make well-informed decisions for the users. To guide the
development of such a model, the establishment of a robust and comprehensive
evaluation system becomes crucial. To this end, this paper proposes
\textit{Video-Bench}, a new comprehensive benchmark along with a toolkit
specifically designed for evaluating Video-LLMs. The benchmark comprises 10
meticulously crafted tasks, evaluating the capabilities of Video-LLMs across
three distinct levels: Video-exclusive Understanding, Prior Knowledge-based
Question-Answering, and Comprehension and Decision-making. In addition, we
introduce an automatic toolkit tailored to process model outputs for various
tasks, facilitating the calculation of metrics and generating convenient final
scores. We evaluate 8 representative Video-LLMs using \textit{Video-Bench}. The
findings reveal that current Video-LLMs still fall considerably short of
achieving human-like comprehension and analysis of real-world videos, offering
valuable insights for future research directions. The benchmark and toolkit are
available at: \url{https://github.com/PKU-YuanGroup/Video-Bench}.Comment: Benchmark is available at
https://github.com/PKU-YuanGroup/Video-Benc
Recommended from our members
Dynamic polymer systems with self-regulated secretion for the control of surface properties and material healing
Approaches for regulated fluid secretion, which typically rely on fluid encapsulation and release from a shelled compartment, do not usually allow a fine continuous modulation of secretion, and can be difficult to adapt for monitoring or function-integration purposes. Here, we report self-regulated, self-reporting secretion systems consisting of liquid-storage compartments in a supramolecular polymer-gel matrix with a thin liquid layer on top, and demonstrate that dynamic liquid exchange between the compartments, matrix and surface layer allows repeated, responsive self-lubrication of the surface and cooperative healing of the matrix. Depletion of the surface liquid or local material damage induces secretion of the stored liquid via a dynamic feedback between polymer crosslinking, droplet shrinkage and liquid transport that can be read out through changes in the systemâs optical transparency. We foresee diverse applications in fluid delivery, wetting and adhesion control, and material self-repair.Chemistry and Chemical BiologyEngineering and Applied Science
Bioinspired Underwater Bonding and Debonding on Demand
Mussel glue: Bioinspired underwater chemical bonding with the possibility of phototriggered debonding is reported. A four-arm star-poly(ethyleneglycol) end-functionalized by nitrodopamine was synthesized. The nitrodopamine offered the reactivity of catechol and the chemistry of the photocleavable o-nitrophenyl ethyl group (see picture)
- âŠ