Reviewerly/exHarmony huggingface dataset:
A Fine-grained Semi-supervised Anomaly Detection Framework for Predictive Maintenance of Industrial Assets

Reliable operation of industrial assets is of high priority for businesses where productivity determines the ability to deliver safety-critical products of high quality in a timely manner. The aerospace industry leads the demand for predictive maintenance (PdM). In the manufacturing space, unscheduled down time causes production delay and increases operational costs while introducing potential risks in product quality and on-time delivery. In field application of these products, unexpected breakdown of critical components can result in safety-critical events. Failure events are, therefore, extremely rare in industrial settings. Diverse operating conditions in the manufacturing environment and field applications contribute to the heterogeneous nature of data collected from these assets. This work presents an anomaly detection framework for PdM of industrial assets to address the practical challenges of scarce failure data sources and heterogeneous data across assets. We introduce a fine-grained modeling approach that efficiently accounts for individual asset differences in a semi-supervised fashion which requires only normal operation data for model training. The framework is demonstrated with an industrial 4.0 use case. Vibration sensor data from pumps in one of our manufacturing facilities is ingested to enable PdM with 2 weeks lead time using the proposed framework. This transforms unexpected breakdown time to scheduled maintenance, thereby reducing cost of delays and operation interruptions. The systematic implementation of the framework in the case study covers the practical production aspects including data quality evaluation, model training, optimization and daily serving of predictions. Furthermore, implementation challenges and recommendations are discussed based on the end-to-end solution implementation experiences.
In the Shadow of Tomorrow: University in the Digital Age

The article considers the internal evolution of the university in recent years under the influence of social processes and information technology. Digitalization of higher education is developing in parallel with significant changes in the ideology and practice of teaching and learning. The first part of the article examines the theoretical foundations, key teaching methods, functionality, and competencies of the teacher related to the transformation processes taking place at the university. The second part analyzes the most significant effects of using e-learning, blended and hybrid learning, and their impact on teacher-student interaction. A present-day university is also a place where the teacher's role is being significantly transformed. Changes in the field of training, competencies, and functionality of a university teacher arise both as a result of current economic realities and from the global situation of increasing turbulence, doubtfulness, and unpredictability of the more and more complex world. Following the study of the processes of change in higher education, conclusions are made about the form and content of teaching, and the question is raised about the prospects for preserving the phenomenon of the university in the future.
Towards Low-Barrier Cybersecurity Research and Education for Industrial Control Systems

The protection of Industrial Control Systems (ICS) that are employed in public critical infrastructures is of utmost importance due to catastrophic physical damages cyberattacks may cause. The research community requires testbeds for validation and comparing various intrusion detection algorithms to protect ICS. However, there exist high barriers to entry for research and education in the ICS cybersecurity domain due to expensive hardware, software, and inherent dangers of manipulating real-world systems. To close the gap, built upon recently developed 3D high-fidelity simulators, we further showcase our integrated framework to automatically launch cyberattacks, collect data, train machine learning models, and evaluate for practical chemical and manufacturing processes. On our testbed, we validate our proposed intrusion detection model called Minimal Threshold and Window SVM (MinTWin SVM) that utilizes unsupervised machine learning via a one-class SVM in combination with a sliding window and classification threshold. Results show that MinTWin SVM minimizes false positives and is responsive to physical process anomalies. Furthermore, we incorporate our framework with ICS cybersecurity education by using our dataset in an undergraduate machine learning course where students gain hands-on experience in practicing machine learning theory with a practical ICS dataset. All of our implementations have been open-sourced.
PCEN: Potential Correlation-Enhanced Network for Multimodal Named Entity Recognition

Multimodal Named Entity Recognition (MNER) in social media posts plays an important role in both security and natural language processing domains. Existing approaches mainly include extracting useful visual features from images, and integrating them into text representation for NER via multimodal fusion. Nevertheless, there is potential correlation among samples in the dataset, but is ignored by most of the existing studies. In this paper, we propose a potential correlation-enhanced network (PCEN) for MNER. Specifically, we (1) consider the potential correlation as an important visual feature for MNER, and (2) utilize it to guide the final recognition of entities. To tackle the first issue, we employ unsupervised clustering to divide the images of training samples into clusters, and take the trainable embedding of each cluster label as a visual feature because samples with the same cluster label have higher potential correlation. To tackle the second issue, we argue that the samples in the same cluster are more likely to have similar distributions of entity types in their text. We design an inconsistency loss to encourage the consistency between the entity recognition result of each sample and the pre-trained entity type distribution of the corresponding cluster this sample belongs to. Experiments on two MNER benchmarks demonstrate the effectiveness of our proposed method.
Anomaly Detection and Fault Classification in Multivariate Time Series Using Multimodal Deep Models

In the realm of gear fault diagnosis, where various analytical methods often require extensive domain expertise, automation remains challenging due to diverse fault diagnosis tasks. To address these limitations, we propose a novel PHM algorithm integrating out-of-distribution detection and representation learning. Initial steps involve feature extraction using envelopes and fast Fourier transform (FFT). Representation Learning employs Transformers and Self-supervised learning for meaningful representations. The latent space values are then utilized for Out-of-Distribution Detection through kNN and classification, achieving a remarkable 99% accuracy. Our approach significantly enhances gear fault diagnosis automation, proving effective across diverse, unencountered problems.
Design and Implementation of a Model Selection Pipeline for Prognostics and Health Management in the Operational Environment

Model selection is a crucial aspect of Prognostics and Health Management (PHM). However, many PHM models are de- veloped for specific datasets and lack flexibility to adapt to different datasets with varying data quality considerations. To address this gap, we propose a generalizable model selection pipeline for PHM. Our approach involves creating a pipeline for testing models that users can tune in various ways. We designed a sequential pipeline of steps for model selection with a focus on implementation considerations which include recommendations for handling environmental variables, ca- pabilities for remote and local work environments, and stor- age considerations of the serialized pipeline. Performance metrics are designed to consider data quality characteristics such as ambiguous labeling. We illustrate the generalizability of our approach through a case study of our model selection pipeline applied to a field dataset with ambiguous labels. Our design accommodates data characteristics commonly found in field data, such as ambiguous labels and data wrangling. Our contribution fills a gap in real-world implementations of PHM by offering technology considerations and recommen- dations for effective deployment.
Style-Driven Multi-Perspective Relevance Mining Model for Hotspot Reprint Paragraph Prediction

Accurately predicting hotspot reprint paragraphs can timely provide valuable clues for topic selection, thereby improving the influence of the disseminated content. Most existing works in media reprint analysis focus on mining reprint relationships and reprint patterns. Meanwhile, few works predict the hotspot reprint paragraph from a fine-grained level. The writing style reflects the structure and semantic logic of the article to some extent. Thus, the challenge is to determine how to effectively incorporate writing style features into the semantic analysis while also reasoning deeply about the semantic relevance between sections of the article. This paper proposes a multi-perspective relevance collaborative modeling method called MPRCM-TS. It integrates writing styles of titles into the semantic representations and deeply mines the multi-perspective semantic relevance between the title and paragraphs on the basis of the attention mechanism. Simultaneously, multiple loss functions collaborate to enhance the parameter optimization ability. We evaluate the performance of the proposed model on a real-world dataset, and the experimental results demonstrate the efficacy.
Assessing the Vulnerabilities of the Open-Source Artificial Intelligence (AI) Landscape: A Large-Scale Analysis of the Hugging Face Platform

Artificial Intelligence (AI) has rapidly proliferated as a critical disruptive technology in the 21st century. Hugging Face hosts pre-trained models, facilitating the sharing and use of open-source code. Hugging Face has been used by 22,000+ organizations, including Intel and Microsoft, with 2.6+ billion model downloads. While Hugging Face democratizes access to AI models, these models may contain unknown security vulnerabilities. In this research, we automatically collect models from Hugging Face, link them to their underlying code bases on GitHub, and perform a large-scale vulnerability assessment of these repositories. Through our approaches, we collected about 110,000 models from Hugging Face and over 29,000 GitHub repositories. Our vulnerability assessment revealed a larger percentage (35.98 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) of high-severity vulnerabilities compared to low-severity vulnerabilities (6.79%). This trend in severity levels contradicts the results of severities detected in repositories forked from root repositories and searched repositories. Given that many of the vulnerabilities reside in fundamental AI repositories such as Transformers, the results of this vulnerability assessment have significant implications for supply chain software security and AI risk management more broadly.
A Multi-source Data Fusion Navigation Method of Spacecraft with Limited GNSS Signals

Global Navigation Satellite system (GNSS) is initially designed to provide services to objects on the earth surfaces, or in the earth-orbits with altitude below 3000 km. However, previous researches show that GNSS receivers mounted on GEO satellites can receive GNSS signals and generate positioning and navigation results as well. Considering that in some harsh situations, such as bad observation geometry, weak signal acquisition, etc., the on-board GNSS receivers can only transfer pseudo-range data to GEO satellites, this paper proposed an autonomous navigation method in which the information from GNSS receiver, optical attitude sensors, and inter-satellite links are fused together: at first, the extended state equation and measurement equation of a GEO satellite with optical sensor’s errors are deduced; then, an extended Kalman filter is designed to evaluate and compensate optical sensor’s errors via constructed earth-centered vector bias; later, a sequential correction is carried out using inter-satellite link measurements; finally, the performance of proposed method is analyzed through Monte-Carlo simulation. It shows that the average distance and velocity evaluation errors of proposed method are around 0.2 km and 0.05 m/s individually; the performance can meet the requirements of survivability of a class of high-orbit, long-life and high-value satellites.
Controllable News Comment Generation based on Attribute Level Contrastive Learning

News comments provide a convenient way for people to express opinions and exchange ideas. Positive comments en-courage a harmonious discussion atmosphere within news media communities. In contrast, offensive or insulting comments may result in cyberbullying and personal psychological trauma, which have particular practical impacts in security-related domains. The automatic generation of news comments with controllable attributes (e.g. sentiment) to assist users and news platform administrators is greatly needed. However, existing research for news comment generation has not addressed the controllable issue yet. On the other hand, existing methods for controllable text generation focus on token-level constraints, which are not applicable to controlling the sentence level attributes for news comment generation. To address this challenging issue, in this paper, we propose an attribute-level contrastive learning method for controllable news comment generation. To apply attribute level constraints on the generated text, our method considers the attributes of the generated comments and the pre-defined attributes as different views of the same attribute, and maximizes their similarity during the training process. We conduct experiments on two publicly available news comment datasets, and the experimental results show that our model achieves competitive performance in news comment generation and attribute controllability.
Named Entity Recognition for Epidemiological Investigation in COVID-19

The COVID-19 pandemic has had a global impact on communities, economies, and healthcare systems. To control the virus's spread, numerous epidemiological investigations have been made available online, leading to a growing demand for automated tools to extract valuable information from case reports and reduce the burden on news reporters. In response to this growing need, we have meticulously curated a comprehensive data set of COVID-19 epidemiological investigation corpora, specifically designed for named entity recognition (NER) applications. This data set enables researchers and analysts to efficiently identify and extract key information from the case reports, streamlining the process of understanding and communicating the findings. To further enhance the effectiveness of NER in the context of epidemiological investigations, we evaluated and compared the performance of three cutting-edge, pre-trained model-based methods: BERT-BiLSTM-CRF, ERNIE-BiLSTM- CRF and ALBERT-BiLSTM-CRF. All techniques demonstrated impressive performance in recognizing named entities within the case reports, showcasing their potential to revolutionize the way in which epidemiological data is analyzed and disseminated. By leveraging these advanced NER techniques, we aim to facilitate more accurate and timely reporting, ultimately contributing to better-informed decision-making processes and improved public health outcomes.
Deep Learning Based Behavior Anomaly Detection within the Context of Electronic Commerce

To address the threat of online misbehaviors by authenticated e-commerce (EC) consumers in exploiting business logic and regulation loopholes, this paper proposed Event-Based Behavior Analysis (EBBA), a reconfigurable framework with multiple deep-learning (DL) time-series forecasting models to detect and examine online behavioral anomaly. A prototype EBBA with two switchable DL models, Recurrent Neural Networks (RNN) and Long/Short Term Memory (LSTM), was constructed in Python and trained by real-world access logs from a tier-one EC company in Taiwan. Preliminary experimental results show that RNN and LSTM models achieved as high as 86.4% and 92.2% correctness ratios, with as low as zero and 0.0385% mis-granting ratios in prediction of online misbehavior occurrences, respectively.
Mapping Exploit Code on Paste Sites to the MITRE ATT&amp;CK Framework: A Multi-label Transformer Approach

Cyber-criminals often use information-sharing platforms such as paste sites (e.g., Pastebin) to share vast amounts of malicious text content, such as exploit source code. Careful analysis of malicious paste site content can provide Cyber Threat Intelligence (CTI) about potential threats. In this research, we propose a Convolutional BiLSTM Transformer multi-label classification method that automatically maps paste site exploit source code to the MITRE ATT&CK framework to identify adversarial techniques in support of proactive CTI. The Convolutional BiLSTM Transformer combines a convolutional neural network layer placed before a Transformer block, a concatenated pooling from a global max pooling and global average, and a BiLSTM pair-wise function within the Transformer to capture word and sequence orders. We conducted an multi-label classification experiment where our proposed Convolutional BiLSTM Transformer model achieved state-of-the-art results in terms of accuracy, recall, F1-score, and hamming loss. The results of a case study showed the tactics and tools that are used by malicious actors on paste sites.
Shoulder Surfing on Mobile Authentication: Perception vis-a-vis Performance from the Attacker's Perspective

Shoulder-surfing studies in the context of mobile user authentication have focused on evaluating the attackers' performance, yet have paid much less attention to their perception of the shoulder-surfing process. Whether and how the shoulder-surfing setting might affect the attackers' perception remains under-explored. This study aims to investigate the perception of shoulder surfers with two different password-based mobile user authentication methods and three different observation angles. Moreover, this work examines the relationship between the attackers' perception and performance in shoulder surfing and the possible moderating effect of the authentication method for the first time. Based on the data collected from an online experiment, our analysis results reveal the effects of authentication methods and observation angles on the attackers' perception in terms of cognitive workload, observation clarity, and repetitive learning advantage. In addition, the results also show that the relationship between the attackers' cognitive workload and performance in shoulder surfing varies with the mobile user authentication method. Our findings not only deepen the understanding of shoulder-surfing attacks from an attacker's perspective, but also facilitate developing countermeasures for shoulder-surfing attacks.
Neuro-Logic Learning for Relation Reasoning over Event Knowledge Graph

Security-related events are continuously emerging around the world, such as social unrest, armed conflicts and natural disasters. Although event knowledge graphs provide a concise and intuitive way to organize event information, they often suffer from the incompleteness problem. To solve this problem, the knowledge graph reasoning technique is usually adopted to infer new facts based on existing relations of entities. However, existing reasoning methods often concentrate on the entity relations, without considering the attributes of nodes for event knowledge graphs, such as event descriptions and event time, which contain rich information of events and are vital for event relation reasoning. Existing methods are also deficient in interpretability, which significantly benefits security-related decision-making scenarios. In this paper, we propose a Neuro-Logic Learning (NLL) method for relation reasoning over event knowledge graph. Our method first adopts different event attributes to learn the semantic representations of events, and ranks candidate event relation triples according to their similarity scores. The proposed method then develops the neural logic rule leaner to obtain interpretable reasoning rules. We construct a new large-scale event relation dataset EventKG-R based on EventKG to evaluate the effectiveness of our proposed method. Experimental results show that our proposed method achieves superior performances compared to the state-of-the-art baselines.
A Stage Model for Understanding Phishing Victimization Behavior in Embedded Training

Despite the widespread recognition that phishing attack is not a one-step process, existing studies on phishing victimization are dominated by binary decisions, which focus on understanding whether or not a user is victimized or susceptible to phishing attacks. There has been little empirical investigation into the stages of phishing victimization. Additionally, the literature presents mixed findings regarding influential factors in phishing victimization, which invite new explanations. Motivated by the potential of stage theorizing for effective anti-phishing recommendations, we propose a stage model for phishing victimization and examine the victimization stage's role in understanding the effects of privacy behavior, technical experience, and user demographics on victimization progression. We develop a series of hypotheses and test them by analyzing data collected from an embedded phishing awareness education program in a large organization. The findings provide strong evidence for the impacts of the stage model by improving our understanding of user characteristics and behaviors concerning different victimization stages and by offering fresh explanations for mixed findings about the effects of user characteristics in the literature. The findings lend themselves to a multitude of recommendations for improving the effectiveness of anti-phishing training.
Interpolate and Extrapolate Machine Learning Models using An Unsupervised Method

The 2023 PHM North America Data Challenge is intriguing because it requires one to predict outcomes and use data patterns that training models do not see. Modern machine learning models based on gradient boosting and neural networks are not designed to address such issues in usually circumstances. Our final approach to address the challenge consists of five steps. In our approach, we use an unsupervised method besides machine learning models to address the challenge.
Joint Compression-Encryption Technique Based On Arithmetic Coding for JPEG Images

An efficient joint compression-encryption technique for JPEG images using nonlinear properties of the arithmetic coder is proposed. The technique performs both entropy coding and encryption in a single step by manipulating the probability maps of the arithmetic coder while preserving the syntax structure of the image. The proposed scheme operates on a bit-by-bit basis while taking into account the previously processed data-bits keeping the advantages of both stream and block ciphers. The proposed technique is implemented using Libjpeg-turbo. The results demonstrate that the proposed scheme attains a security level comparable to that of AES while reducing both delay and system resource requirements while maintaining the compression efficiency of the standard JPEG encoder.