Visual Emotion Recognition (VER) is an important research topic due to its wide range of applications, including opinion mining and advertisement design. Extending this capability to recognize emotions at the individual level further broadens its potential applications. Recently, Multimodal Large Language Models (MLLMs) have attracted increasing attention and demonstrated performance comparable to that of conventional VER methods. However, MLLMs are trained on large and diverse datasets containing general opinions, which causes them to favor majority viewpoints and familiar patterns. This tendency limits their performance in a personalized VER, which is crucial for practical and real-world applications, and indicates a key area for improvement. To address this limitation, the proposed method employs discrete prompt tuning inspired by the process of humans' prompt engineering to adapt the VER task to each individual. Our method selects the best natural language representation from the generated prompts and uses it to update the prompt for the realization of accurate personalized VER.

Tactile sensors play a crucial role in enabling robots to interact effectively and safely with objects in everyday tasks. In particular, visuotactile sensors have seen increasing usage in two and three-fingered grippers due to their high-quality feedback. However, a significant gap remains in the development of sensors suitable for humanoid robots, especially five-fingered dexterous hands. One reason is because of the challenges in designing and manufacturing sensors that are compact in size. In this paper, we propose HumanFT, a multimodal visuotactile sensor that replicates the shape and functionality of a human fingertip. To bridge the gap between human and robotic tactile sensing, our sensor features real-time force measurements, high-frequency vibration detection, and overtemperature alerts. To achieve this, we developed a suite of fabrication techniques for a new type of elastomer optimized for force propagation and temperature sensing. Besides, our sensor integrates circuits capable of sensing pressure and vibration. These capabilities have been validated through experiments. The proposed design is simple and cost-effective to fabricate. We believe HumanFT can enhance humanoid robots' perception by capturing and interpreting multimodal tactile information.

With the rapid advancement of technology, the recognition of underwater acoustic signals in complex environments has become increasingly crucial. Currently, mainstream underwater acoustic signal recognition relies primarily on time-frequency analysis to extract spectral features, finding widespread applications in the field. However, existing recognition methods heavily depend on expert systems, facing limitations such as restricted knowledge bases and challenges in handling complex relationships. These limitations stem from the complexity and maintenance difficulties associated with rules or inference engines. Recognizing the potential advantages of deep learning in handling intricate relationships, this paper proposes a method utilizing neural networks for underwater acoustic signal recognition. The proposed approach involves continual learning of features extracted from spectra for the classification of underwater acoustic signals. Deep learning models can automatically learn abstract features from data and continually adjust weights during training to enhance classification performance.

This study introduces and empirically tests a novel predictive model for digital information engagement (IE) - the READ model, an acronym for the four pivotal attributes of engaging information: Representativeness, Ease-of-use, Affect, and Distribution. Conceptualized within the theoretical framework of Cumulative Prospect Theory, the model integrates key cognitive biases with computational linguistics and natural language processing to develop a multidimensional perspective on information engagement. A rigorous testing protocol was implemented, involving 50 randomly selected pairs of synonymous words (100 words in total) from the WordNet database. These words' engagement levels were evaluated through a large-scale online survey (n = 80,500) to derive empirical IE metrics. The READ attributes for each word were then computed and their predictive efficacy examined. The findings affirm the READ model's robustness, accurately predicting a word's IE level and distinguishing the more engaging word from a pair of synonyms with an 84% accuracy rate. The READ model's potential extends across various domains, including business, education, government, and healthcare, where it could enhance content engagement and inform AI language model development and generative text work. Future research should address the model's scalability and adaptability across different domains and languages, thereby broadening its applicability and efficacy.

The field of intelligent connected in modern vehicles continues to expand, and the functions of vehicles become more and more complex with the development of the times. This has also led to an increasing number of vehicle vulnerabilities and many safety issues. Therefore, it is particularly important to identify high-risk vehicle intelligent connected systems, because it can inform security personnel which systems are most vulnerable to attacks, allowing them to conduct more thorough inspections and tests. In this paper, we develop a new model for vehicle risk assessment by combining I-FAHP with FCA clustering: VSRQ model. We extract important indicators related to vehicle safety, use fuzzy cluster analys (FCA) combined with fuzzy analytic hierarchy process (FAHP) to mine the vulnerable components of the vehicle intelligent connected system, and conduct priority testing on vulnerable components to reduce risks and ensure vehicle safety. We evaluate the model on OpenPilot and experimentally demonstrate the effectiveness of the VSRQ model in identifying the safety of vehicle intelligent connected systems. The experiment fully complies with ISO 26262 and ISO/SAE 21434 standards, and our model has a higher accuracy rate than other models. These results provide a promising new research direction for predicting the security risks of vehicle intelligent connected systems and provide typical application tasks for VSRQ. The experimental results show that the accuracy rate is 94.36%, and the recall rate is 73.43%, which is at least 14.63% higher than all other known indicators.

Students may want to think twice before using a chatbot to complete their next assignment. Language models that generate text in response to user prompts plagiarize content in more ways than one, according to a Penn State-led research team that conducted the first study to directly examine the phenomenon. "Plagiarism comes in different flavors," said Dongwon Lee, professor of information sciences and technology at Penn State. "We wanted to see if language models not only copy and paste but resort to more sophisticated forms of plagiarism without realizing it." The researchers focused on identifying three forms of plagiarism: verbatim, or directly copying and pasting content; paraphrase, or rewording and restructuring content without citing the original source; and idea, or using the main idea from a text without proper attribution.

Research articles are being shared in increasing numbers on multiple online platforms. Although the scholarly impact of these articles has been widely studied, the online interest determined by how long the research articles are shared online remains unclear. Being cognizant of how long a research article is mentioned online could be valuable information to the researchers. In this paper, we analyzed multiple social media platforms on which users share and/or discuss scholarly articles. We built three clusters for papers, based on the number of yearly online mentions having publication dates ranging from the year 1920 to 2016. Using the online social media metrics for each of these three clusters, we built machine learning models to predict the long-term online interest in research articles. We addressed the prediction task with two different approaches: regression and classification. For the regression approach, the Multi-Layer Perceptron model performed best, and for the classification approach, the tree-based models performed better than other models. We found that old articles are most evident in the contexts of economics and industry (i.e., patents). In contrast, recently published articles are most evident in research platforms (i.e., Mendeley) followed by social media platforms (i.e., Twitter).

Mobile devices use facial recognition technology to help users quickly and securely unlock their phones, make a financial transaction or access medical records. But facial recognition technologies that employ a specific user-detection method are highly vulnerable to deepfake-based attacks that could lead to significant security concerns for users and applications, according to new research involving the Penn State College of Information Sciences and Technology. The researchers found that most application programming interfaces that use facial liveness verification--a feature of facial recognition technology that uses computer vision to confirm the presence of a live user--don't always detect digitally altered photos or videos of individuals made to look like a live version of someone else, also known as deepfakes. Applications that do use these detection measures are also significantly less effective at identifying deepfakes than what the app provider has claimed. "In recent years we have observed significant development of facial authentication and verification technologies, which have been deployed in many security-critical applications," said Ting Wang, associate professor of information sciences and technology and one principal investigator on the project.

Prof. Ahmed Banafa has extensive experience in research, operations and management, with a focus on IoT, Blockchain, Cybersecurity and AI. His researches cited in studies by international organizations like NATO, WTO, and APEC. He is a reviewer and a technical contributor for the publication of several technical books. He served as an instructor at well-known universities and colleges, including Stanford University, University of California, Berkeley; California State University-East Bay; San Jose State University; and University of Massachusetts. He is the recipient of several awards, including Distinguished Tenured Staff Award, Instructor of the year for 4 years in a row, and Certificate of Honor from the City and County of San Francisco.

Clickbait might not lure readers as before – and using artificial intelligence to detect fake news might be much more complex than previously thought, a team of researchers suggest. Clickbait headlines might not be as enticing to readers as once thought, according to a team of researchers. They added that artificial intelligence – AI – may also come up short when it comes to correctly determining whether a headline is clickbait. In a series of studies, the researchers found that clickbait – headlines that often rely on linguistic gimmicks to tempt readers to read further – often did not perform any better and, in some cases, performed worse than traditional headlines. Because fake news is a concern on social media, researchers have explored using AI to systematically identify and block clickbait.