Towards Deep Learning Interpretability: A Topic Modeling Approach

The recent development of deep learning has achieved the state-of-the-art performance in various machine learning tasks. The IS research community has started to leveraged deep learning-based text mining for analyzing textual documents. The lack of interpretability is endemic among the state-of-the-art deep learning models, constraining model improvement, limiting additional insights, and prohibiting adoption. In this study, we propose a novel text mining research framework, Neural Topic Embedding, capable of extracting useful and interpretable representations of texts through deep neural networks. Specifically, we leverage topic modeling to enrich deep learning data representations with meaning. To demonstrate the effectiveness of our proposed framework, we conducted a preliminary evaluation experiment on a testbed of fake review detection and our interpretable representations improves the state-of-the-art by almost 8 percent as measured by F1 score. Our study contributes to the IS community by opening the gate for future adoption of the state-of-the-art deep learning methods

Understanding Health Video Engagement: An Interpretable Deep Learning Approach

Health misinformation on social media devastates physical and mental health, invalidates health gains, and potentially costs lives. Understanding how health misinformation is transmitted is an urgent goal for researchers, social media platforms, health sectors, and policymakers to mitigate those ramifications. Deep learning methods have been deployed to predict the spread of misinformation. While achieving the state-of-the-art predictive performance, deep learning methods lack the interpretability due to their blackbox nature. To remedy this gap, this study proposes a novel interpretable deep learning approach, Generative Adversarial Network based Piecewise Wide and Attention Deep Learning (GAN-PiWAD), to predict health misinformation transmission in social media. Improving upon state-of-the-art interpretable methods, GAN-PiWAD captures the interactions among multi-modal data, offers unbiased estimation of the total effect of each feature, and models the dynamic total effect of each feature when its value varies. We select features according to social exchange theory and evaluate GAN-PiWAD on 4,445 misinformation videos. The proposed approach outperformed strong benchmarks. Interpretation of GAN-PiWAD indicates video description, negative video content, and channel credibility are key features that drive viral transmission of misinformation. This study contributes to IS with a novel interpretable deep learning method that is generalizable to understand other human decision factors. Our findings provide direct implications for social media platforms and policymakers to design proactive interventions to identify misinformation, control transmissions, and manage infodemics.Comment: WITS 2021 Best Paper Awar

An Interpretable Deep Learning Approach to Understand Health Misinformation Transmission on YouTube

Health misinformation on social media devastates physical and mental health, invalidates health gains, and potentially costs lives. Deep learning methods have been deployed to predict the spread of misinformation, but they lack the interpretability due to their blackbox nature. To remedy this gap, this study proposes a novel interpretable deep learning, Generative Adversarial Network based Piecewise Wide and Attention Deep Learning (GAN-PiWAD), to predict health misinformation transmission in social media. GAN-PiWAD captures the interactions among multi-modal data, offers unbiased estimation of the total effect of each feature, and models the dynamic total effect of each feature. Interpretation of GAN-PiWAD indicates video description, negative video content, and channel credibility are key features that drive viral transmission of misinformation. This study contributes to IS with a novel interpretable deep learning that is generalizable to understand human decisions. We provide direct implications to design interventions to identify misinformation, control transmissions, and manage infodemics

Few-Shot Learning for Chronic Disease Management: Leveraging Large Language Models and Multi-Prompt Engineering with Medical Knowledge Injection

This study harnesses state-of-the-art AI technology for chronic disease management, specifically in detecting various mental disorders through user-generated textual content. Existing studies typically rely on fully supervised machine learning, which presents challenges such as the labor-intensive manual process of annotating extensive training data for each disease and the need to design specialized deep learning architectures for each problem. To address such challenges, we propose a novel framework that leverages advanced AI techniques, including large language models and multi-prompt engineering. Specifically, we address two key technical challenges in data-driven chronic disease management: (1) developing personalized prompts to represent each user's uniqueness and (2) incorporating medical knowledge into prompts to provide context for chronic disease detection, instruct learning objectives, and operationalize prediction goals. We evaluate our method using four mental disorders, which are prevalent chronic diseases worldwide, as research cases. On the depression detection task, our method (F1 = 0.975~0.978) significantly outperforms traditional supervised learning paradigms, including feature engineering (F1 = 0.760) and architecture engineering (F1 = 0.756). Meanwhile, our approach demonstrates success in few-shot learning, i.e., requiring only a minimal number of training examples to detect chronic diseases based on user-generated textual content (i.e., only 2, 10, or 100 subjects). Moreover, our method can be generalized to other mental disorder detection tasks, including anorexia, pathological gambling, and self-harm (F1 = 0.919~0.978)

Design and Control of a Novel Bionic Mantis Shrimp Robot

This article presents the development of a novel bionic robot, which is inspired by agile and fast mantis shrimp in the ocean. The developed bionic mantis shrimp robot has ten rigid-flexible swimming feet (pleopods) for swimming propulsion and a rope-driven spine for its body bending. By studying the motion trajectory of biological mantis shrimp, the kinematic gait planning of the bionic pleopod is completed and the central pattern generator controller of the bionic mantis shrimp robot applicable to the coupled motion of multiple pleopods is proposed. The controller is experimentally verified to effectively simulate the swimming motion of mantis shrimp, which enables the robot to reach a maximum swimming velocity of 0.28 m/s (0.46 body length per second) and a minimum turning radius of 0.36 m.The influence of control parameters on the robot's swimming performance is then investigated. Experiments are conducted to show that the oscillation frequency of the bionic pleopod plays a major positive role in the robot's swimming speed. This article has demonstrated the effectiveness of the proposed mechanism design and motion control method for a bionic mantis shrimp robot and laid the foundation for the further exploration of bionic mantis shrimp robots in rugged seabed environments

Design and control of a novel bionic mantis shrimp robot

This article presents the development of a novel bionic robot, which is inspired by agile and fast mantis shrimp in the ocean. The developed bionic mantis shrimp robot has ten rigid-flexible swimming feet (pleopods) for swimming propulsion and a rope-driven spine for its body bending. By studying the motion trajectory of biological mantis shrimp, the kinematic gait planning of the bionic pleopod is completed and the central pattern generator controller of the bionic mantis shrimp robot applicable to the coupled motion of multiple pleopods is proposed. The controller is experimentally verified to effectively simulate the swimming motion of mantis shrimp, which enables the robot to reach a maximum swimming velocity of 0.28 m/s (0.46 body length per second) and a minimum turning radius of 0.36 m.The influence of control parameters on the robot's swimming performance is then investigated. Experiments are conducted to show that the oscillation frequency of the bionic pleopod plays a major positive role in the robot's swimming speed. This article has demonstrated the effectiveness of the proposed mechanism design and motion control method for a bionic mantis shrimp robot and laid the foundation for the further exploration of bionic mantis shrimp robots in rugged seabed environments

Extracting Visual Words from Images for Effective Medical Image Analysis

Extracting visual words to represent images is useful in many applications. Existing approaches usually use traditional methods such as SIFT to extract features and clustering algorithms such as k-means to extract visual words. However, this kind of approaches has some drawbacks such as focusing on the local information without considering high-level semantics of images and being hard to determine the number of clusters. Things can get even worse in medical images because medical images are usually very similar to each other. In this paper, we propose a new approach based on deep learning model to extract visual words, which are then used to train classification model and topic model from images. To show the effectiveness of our proposed methods, we conducted experiments on real retinal images. Experimental results show that our proposed methods can achieve better accuracy in glaucoma diagnosis and can find meaningful topics in topic modeling

Linking Exploits from the Dark Web to Known Vulnerabilities for Proactive Cyber Threat Intelligence: An Attention-Based Deep Structured Semantic Model

Black hat hackers use malicious exploits to circumvent security controls and take advantage of system vulnerabilities worldwide, costing the global economy over $450 billion annually. While many organizations are increasingly turning to cyber threat intelligence (CTI) to help prioritize their vulnerabilities, extant CTI processes are often criticized as being reactive to known exploits. One promising data source that can help develop proactive CTI is the vast and ever-evolving Dark Web. In this study, we adopted the computational design science paradigm to design a novel deep learning (DL)- based exploit-vulnerability attention deep structured semantic model (EVA-DSSM) that includes bidirectional processing and attention mechanisms to automatically link exploits from the Dark Web to vulnerabilities. We also devised a novel device vulnerability severity metric (DVSM) that incorporates the exploit post date and vulnerability severity to help cybersecurity professionals with their device prioritization and risk management efforts. We rigorously evaluated the EVA-DSSM against state-of-theart non-DL and DL-based methods for short text matching on 52,590 exploit-vulnerability linkages across four testbeds: web application, remote, local, and denial of service. Results of these evaluations indicate that the proposed EVA-DSSM achieves precision at 1 scores 20% - 41% higher than non-DL approaches and 4% - 10% higher than DL-based approaches. We demonstrated the EVA-DSSM’s and DVSM’s practical utility with two CTI case studies: openly accessible systems in the top eight U.S. hospitals and over 20,000 Supervisory Control and Data Acquisition (SCADA) systems worldwide. A complementary user evaluation of the case study results indicated that 45 cybersecurity professionals found the EVADSSM and DVSM results more useful for exploit-vulnerability linking and risk prioritization activities than those produced by prevailing approaches. Given the rising cost of cyberattacks, the EVA-DSSM and DVSM have important implications for analysts in security operations centers, incident response teams, and cybersecurity vendors

Cross-Lingual Cybersecurity Analytics in the International Dark Web with Adversarial Deep Representation Learning

International dark web platforms operating within multiple geopolitical regions and languages host a myriad of hacker assets such as malware, hacking tools, hacking tutorials, and malicious source code. Cybersecurity analytics organizations employ machine learning models trained on human-labeled data to automatically detect these assets and bolster their situational awareness. However, the lack of human-labeled training data is prohibitive when analyzing foreign-language dark web content. In this research note, we adopt the computational design science paradigm to develop a novel IT artifact for cross-lingual hacker asset detection (CLHAD). CLHAD automatically leverages the knowledge learned from English content to detect hacker assets in non-English dark web platforms. CLHAD encompasses a novel Adversarial deep representation learning (ADREL) method, which generates multilingual text representations using generative adversarial networks (GANs). Drawing upon the state of the art in cross-lingual knowledge transfer, ADREL is a novel approach to automatically extract transferable text representations and facilitate the analysis of multilingual content. We evaluate CLHAD on Russian, French, and Italian dark web platforms and demonstrate its practical utility in hacker asset profiling, and conduct a proof-of-concept case study. Our analysis suggests that cybersecurity managers may benefit more from focusing on Russian to identify sophisticated hacking assets. In contrast, financial hacker assets are scattered among several dominant dark web languages. Managerial insights for security managers are discussed at operational and strategic levels

Identification of Key Features for VR Applications with VREVIEW: A Topic Model Approach

These are a series of online platforms that allow users to rate and comment on VR virtual reality applications. In this paper, we develop a topic model, namely the general and sparse topic model, that automatically identifies a set of features of VR applications from user reviews. In our context, we overcome two severe challenges (i.e., internal noise and limited features mentioned in each review) to successfully learn the features of VR applications. Specifically, we introduce a general topic and a “spike and slab” prior. In addition, we design a collapsed Gibbs sampling algorithm for model inference. We apply this topic model to a dataset from Oculus (namely VREVIEW), and show that our model can identify some distinct, economically meaningful features for VR applications, e.g., “entertainment and fun,” “challenge,” “immersive,” and “sickness.” Our research provides implications for VR consumer behavior analysis, optimizing user experience in virtual environments, and VR application recommendation.This work is supported by the National Natural Science Foundation of China (72101072, 91846201, 72171071, 71722010), the Postdoctoral Research Foundation of China (2021M690852), the Fundamental Research Funds for the Central Universities (JZ2021HGQB0272) and the National Engineering Laboratory for Big Data Distribution and Exchange Technologies