With the rapid growth of online social networks (OSNs), more and more people become concerned about their privacy exposure through the posted contents (e.g., photos and videos). Particularly, preserving the sensitive relationship contained in images has gained a lot of attention. Our recent work [1] pioneered the relationship preservation in images. However, it was limited to protecting sensitive relationships exposed in individual images. A critical and more challenging issue in practice is to preserve relationship privacy through the correlation between images. To address the problem, this work further identifies sensitive relationships among users presenting not only in each individual image but also across multiple images from the same event. A novel algorithm is developed to detect sensitive relationships among a group of images. Additionally, in order to protect the detected sensitive relationships, a linear programming algorithm and a greedy algorithm are designed to select faces to block, aimed at handling the trade-off between privacy preservation and utility loss (e.g., the aesthetic value of images). We conducted experiments on synthetic datasets to evaluate the performance of the proposed algorithms in terms of privacy protection and utility loss. Result comparison demonstrates that the enhanced algorithms provide better privacy protection given a group of images and more faces need to be blocked for preserving the detected sensitive relationships.
Traffic flow prediction (TFP) has become a crucial problem for billions of people who rely on traffic predictions to optimize their travels every day. In this paper, we present a decentralized federated meta-learning (DFML) framework for traffic flow prediction. Within this framework, traffic detectors spread across a road system can form federations with nearby peers in order to predict traffic collaboratively. A node that assembles a federation assumes the role of receiver while the remainder are assigned as broadcasters. The broadcasters learn model parameters from local data and then broadcast those models to receivers, thereby enriching the available information from which a receiver may learn. The framework combines the strength of decentralized federated learning and meta-learning frameworks to leverage distributed data sources without central aggregation, while also enabling models to generalize across diverse traffic conditions and environments efficiently. Evaluation of the DFML algorithm, using data from the Caltrans Performance Measurement System (PeMS), demonstrates initial viability for TFP and establishes a foundation for continued innovation.
Current assessments for depressive disorder are often influenced by cognitive function making them more susceptible to biases. Deep learning could provide more objective diagnoses with less access barriers for individuals who are unable to complete traditional assessments. In our study, we aim to explore the relations among speech, languages, and depression to demonstrate the feasibility of multi-lingual speech depression detection, and then build deep learning models using multi-lingual speech samples to support depression diagnosis. We first used a newly collected Chinese speech depression dataset to build a convolutional neural network (CNN) to conduct depression detection, and the accuracy of the test set reached 0.85. Besides, we tested the English depression speech dataset, DAIC-WOZ, using the same CNN model, and the accuracy of the test set was 0.73. While training the model using both Chinese and English speech samples and testing on mixture speeches, the accuracy achieved 0.74. We found that the CNN model can be applied across languages with a relatively stable performance of depression detection. This provides evidence that it is possible to develop a language-independent depression detection tool to support depression diagnostic and achieve worldwide long-term mental health monitoring.
Post-COVID the restaurant industry is experiencing a surge in demand, presenting a unique challenge of efficiently managing increased customer flow while ensuring seamless interactions. Chatbots have emerged as an innovative solution to meet the demand increase. The paper addresses the enhancement of AI chatbots through the integration of Retrieval-Augmented Generation (RAG) with the Large Language Model (LLM). This paper focuses on the development of a restaurant chatbot that not only engages in natural-language conversations but also addresses context optimization and LLM optimization for restaurant context learning. The approach uses a Neo4j Knowledge graph built using the restaurant data as an external source of knowledge. The graph is traversed to match the user question with appropriate answer tokens using Term Frequency - Inverse Document Frequency (TF-IDF) embeddings. The relevant tokens along with user questions are used to provide additional context to the T5 language model to provide nuanced responses to the users. This improvement is quantitatively evidenced by a Bilingual Evaluation Understudy (BLEU) score of 0.60, indicating a high level of precision in language understanding and generation. An extensive evaluation of the chatbot includes assessing AI testability on the level of words, sentences, and information. These evaluations include simulated dialogue assessments and performance analyses, with a focus on the chatbot's ability to retrieve and integrate information. Based on the AI testability evaluation, the models consistently produce more knowledgeable, diverse, and relevant answers as compared with state-of-the-art models with an average information score in the range of 0.6-0.8.
Investor sentiment significantly influences stock market dynamics, often displaying pronounced volatility. Con-currently, objective financial news remains a crucial determinant of market shifts. This study sourced an extensive collection of textual comments from the StockTwits trading platform. We then implemented a semi-supervised GAN-Bert methodology to perform sentiment analysis on these unlabeled remarks. Following this, the Inception V3 model facilitated sentiment evaluation of financial visuals from The Wall Street Journal (WSJ). Upon validation and integration of sentiment scores from these visuals, we amalgamated sentiments derived from both textual and visual data into a Bi-LSTM framework. This integrated model underwent training for predictive analytics, underpinning trading simulations based on anticipated returns. The experimental results demonstrated a notable return of 30.41% during our evaluation period, showcasing an improvement over single sentiment feature analysis.
