The security of the Industrial Internet of Things (IIoT) has emerged as a prominent concern in cyber-security due to the potential impact of attacks against IIoT on physical infrastructure. Machine learning (ML)-based intrusion detection systems (IDSs) recently have been demonstrated to be an effective tool for protecting the systems in IIoT. However, the vulnerability of ML-based IDSs to adversarial attacks hinders their further application in IIoT. This article aims to further explore the adversarial attacks in IIoT to better evaluate the security of ML-based IDSs in this area. Our research primarily focuses on the generation of universal adversarial perturbations in IIoT, a topic that received limited attention in previous literature. Two novel attack methods based on a unified framework are proposed to utilize the original input-dependent adversarial perturbations of gradient-based or optimization-based adversarial attack methods to craft universal adversarial perturbations with better performance and transferability. The proposed attack methods conceal the underlying implementation details of the target attack methods, exploiting the original adversarial perturbations in a closed-box manner. This enhances their flexibility, making them applicable in a wider range of scenarios and enabling them to be combined with most gradient-based or optimization-based attack methods. Comprehensive experiments are conducted on three mainstream intrusion detection data sets, i.e., NSL-KDD, Gas Pipeline, and edge-IIoTset, to validate the effectiveness of the proposed methods. The preliminary experimental results demonstrate the feasibility of universal adversarial perturbations in IIoT and the superiority of the proposed methods to state-of-the-art attack methods.

Remaining useful life (RUL) prediction of bearings is essential for effective prognostics and health management (PHM). Although deep learning-based RUL prediction methods achieve high prediction accuracy, they often introduce significant parameter redundancy due to their inability to efficiently capture the intricate temporal dynamics in bearing degradation signals, leading to computationally expensive models with limited practical applicability. To address this challenge, we propose a novel RUL prediction framework that integrates the Wasserstein distance of cyclic spectrum (WDCS) with a Lightweight TimesNet (WDCS-LTN). Specifically, the WDCS serves as a health indicator, effectively extracting multiperiodic features from bearing degradation signals. Subsequently, the LTN transforms the 1-D WDCS sequence into multiple 2-D tensors with varying localities, enabling precise modeling of intraperiod and interperiod temporal dynamics. A shared lightweight inception block is constructed within the LTN to capture temporal variations in 2-D space while maintaining low model complexity. Experimental results on bearing degradation datasets show that WDCS-LTN achieves a prediction error (mean absolute error) of 0.091 with only 37k parameters, outperforming existing methods in terms of accuracy, parameter efficiency, and memory consumption. Through efficiently modeling the temporal dynamics, WDCS-LTN ensures practicality for industrial applications by addressing parameter redundancy while offering enhanced prediction capabilities.

In steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI), improving the recognition performance for new subjects without calibration data is the key challenge for practical application. Unsupervised transfer learning is an effective way to overcome it. However, existing studies focus solely on what to transfer, rather than how to effectively transfer, resulting in unsatisfactory transfer effectiveness or even negative transfer. In this study, an innovative unsupervised cross-subject transfer learning method for SSVEP-BCI was proposed, named SUTL. It involves that subject transferability estimation (STE) and a multi-domain alignment method were proposed to alleviate the potential interference of differences in SSVEP signal distribution among subjects. STE screens appropriate transferable subjects from the source subject pool, while domain alignment directly makes all subjects more similar. Then, SUTL sufficiently exploits the information of the selected source subjects, learning and transferring both generalization knowledge and subject-specific knowledge to boost the recognition performance for the new subject. The performance of SUTL was evaluated on two public SSVEP datasets (benchmark dataset and BETA dataset) with 40 classes, the extensive experimental results reveal that SUTL markedly boosts the effectiveness of SSVEP cross-subject transfer and dramatically outperforms the state-of-art methods. SUTL significantly enhances the recognition performance of SSVEP-BCI for new subjects and facilitates its practical application.

Robotic hand exoskeletons can provide assistance to people who suffer from hand functional disability or spinal cord injury (SCI). However, the current hand exoskeletons remain challenging with respect to having a user-friendly design that satisfies human motion with a lightweight structure. Here we propose a method of using topology optimization in the design of finger exoskeletons, which is a lightweight and integrate manufactured exoskeleton. The exoskeleton is designed by generating the topology configuration according to the bending state of the finger. The objective function in the optimization is set to be the maximization of output displacement of the design domain. After obtaining the topology configuration, a conjugate surface flexure hinge is developed as an elastic joint to replace the compliant part of the topologies. Finally, a rigid-compliant parallel exoskeleton is fabricated by a 3D printer and the performance of the mechanism is verified.

In the era of big data, convolutional neural network (CNN) has been widely used in the field of image classification and has achieved excellent performance. More and more researchers are beginning to combine deep neural networks with steganalysis to improve performance in recent years. However, most of the steganalysis algorithm based on the convolutional neural network has only run test against the WOW and S-UNIWARD algorithms; meanwhile, their versatility is insufficient due to long training time and the limit of image size. This paper proposes a new network architecture, called SFRNet, to solve these problems. The feature extraction and fusion layer can extract more features from the digital image. The RepVgg block is used to accelerate the inference and increase memory utilization. The SE block improves the detection accuracy rate because it can learn feature weights to make effective feature maps with significant weights and invalid or ineffective feature maps with small weights. Experimental results show that the SFRNet has achieved excellent performance in the detection accuracy rate against four state-of-the-art steganography algorithms in the spatial domain, e.g., HUGO, WOW, S-UNIWARD, and MiPOD, under different payloads. The SFRNet detection accuracy rate achieves 89.6% against S-UNIWARD algorithm with the payload of 0.4bpp and 72.5% at 0.2bpp. As the same time, the training time of our network is greatly reduced by 35% compared with Yedroudj-Net.

Convolutional neural networks as steganalysis have problems such as poor versatility, long training time, and limited image size. For these problems, we present a heterogeneous kernel residual learning framework called DRHNet—Dual Residual Heterogeneous Network—to save time on the networks during the training phase. Instead of using the image as an input of the network, we extract and merge the images into a feature matrix using the rich model and use the generated feature matrix as the real input of the network. The architecture we proposed has good versatility and can reduce the computation and the number of parameters while still getting higher accuracy. On BOSSbase 1.01, we evaluate the performance of DRHNet in the setting of the spatial domain and frequency domain. The preliminary experimental results show that DRHNet shows excellent steganalysis performance against the state-of-the-art steganographic algorithms.

The cross-correlation between the electroencephalogram (EEG) and surface electromyography (sEMG) has important research value both in human-robot integration and intelligent rehabilitation robots. The purpose of this study is to explore the relationship between EEG and sEMG when EEG has different trends. The asymmetric cross-correlation of EEG and sEMG in different states were analyzed by using the multifractal asymmetric detrended cross correlation analysis (MF-ADDCA) method. The synchronous EEG and sEMG in the process of tracking feedback force were analyzed. In order to investigate the dynamic process of asymmetric degree, the rolling window MF-ADDCA method was adopted. The analysis results showed that the asymmetries existed in the cross-correlation between EEG and sEMG and the asymmetric cross-correlation were multifractal. In a resting state, the cross-correlation between EEG and sEMG was symmetric. The asymmetries cross-correlation between EEG and sEMG were stronger in large fluctuations under myodynamia enhancement state, and were stronger in small fluctuations under myodynamia stability state.

The concern for privacy is real for any research that uses user data. Information Retrieval (IR) is not an exception. Many IR algorithms and applications require the use of users' personal information, contextual information and other sensitive and private information.

Preserving online privacy is becoming increasingly challenging due in large part to the continued growth of social media. Those who choose to share their information publicly may not realize what features of their profiles make their public data more identifiable and potentially vulnerable to cross-site record linkage. This paper proposes a risk reduction recommendation method that suggests removal or modification of a small number of attributes to make a profile less unique, thereby reducing the identifiability and vulnerability of the user. Empirical results on data collected from Google+, LinkedIn, and Foursquare show that users' vulnerability in terms of identifiability and data exposure level can be significantly reduced while public profile utility can be maintained using our proposed approach.

Query logs are valuable resources for Information Retrieval (IR) research. However, because they are also rich in private and personal information, the huge concern of leaking user privacy prevents query logs from being shared from the search companies to the broad research community. Bothered by the lack of good research data for years, the authors of this paper are motivated to explore ways to generate anonymized query logs that can still be effectively used to support the search task. We introduce a framework to anonymize query logs by differential privacy, the latest development in privacy research. The framework is empirically evaluated against multiple search algorithms on their retrieval utility, measured in standard IR evaluation metrics, using the anonymized logs. The experiments show that our framework is able to achieve a good balance between retrieval utility and privacy.

To help users better understand the potential risks associated with publishing data publicly, as well as the quantity and sensitivity of information that can be obtained by combining data from various online sources, we introduce a novel information exposure detection framework that generates and analyzes the web footprints users leave across the social web. Web footprints are the traces of one's online social activities represented by a set of attributes that are known or can be inferred with a high probability by an adversary who has basic information about a user from his/her public profiles. Our framework employs new probabilistic operators, novel pattern-based attribute extraction from text, and a population-based inference engine to generate web footprints. Using a web footprint, the framework then quantifies a user's level of information exposure relative to others with similar traits, as well as with regard to others in the population. Evaluation over public profiles from multiple sites (Google+, LinkeIn, FourSquare, and Twitter) shows that the proposed framework effectively detects and quantifies information exposure using a small amount of initial knowledge.

Modern information retrieval (IR) systems exhibit user dynamics through interactivity. These dynamic aspects of IR, including changes found in data, users, and systems, are increasingly being utilized in search engines. Session search is one such IR task—document retrieval within a session. During a session, a user constantly modifies queries to find documents that fulfill an information need. Existing IR techniques for assisting the user in this task are limited in their ability to optimize over changes, learn with a minimal computational footprint, and be responsive. This article proposes a novel query change retrieval model (QCM), which uses syntactic editing changes between consecutive queries, as well as the relationship between query changes and previously retrieved documents, to enhance session search. We propose modeling session search as a Markov decision process (MDP). We consider two agents in this MDP: the user agent and the search engine agent. The user agent’s actions are query changes that we observe, and the search engine agent’s actions are term weight adjustments as proposed in this work. We also investigate multiple query aggregation schemes and their effectiveness on session search. Experiments show that our approach is highly effective and outperforms top session search systems in TREC 2011 and TREC 2012.

Session search is a complex search task that involves multiple search iterations triggered by query reformulations. We observe a Markov chain in session search: user's judgment of retrieved documents in the previous search iteration affects user's actions in the next iteration. We thus propose to model session search as a dual-agent stochastic game: the user agent and the search engine agent work together to jointly maximize their long term rewards. The framework, which we term "win-win search", is based on Partially Observable Markov Decision Process. We mathematically model dynamics in session search, including decision states, query changes, clicks, and rewards, as a cooperative game between the user and the search engine. The experiments on TREC 2012 and 2013 Session datasets show a statistically significant improvement over the state-of-the-art interactive search and session search algorithms.

Log-based document re-ranking is a special form of session search. The task re-ranks documents from Search Engine Results Page (SERP) according to the search logs, in which both the search activities from other users and personalized query log for a user are available. The purpose of re-ranking is to provide the user with a new and better ordering of the initial retrieved documents. We test the system on the WSCD 2014 dataset, in which the actual content of the queries and documents are not available due to privacy concerns. The challenge is to perform effective re-ranking purely based on user behaviors, such as clicks and query reformulations rather than document content. In this paper, we propose to model log-based document re-ranking as a Partially Observable Markov Decision Process (POMDP). Experiments on the document re-ranking task show that our approach is effective and outperforms the baseline rankings provided by a commercial search engine.

Session search is the Information Retrieval (IR) task that performs document retrieval for a search session. During a session, a user constantly modifies queries in order to find relevant documents that fulfill the information need. This paper proposes a novel query change retrieval model (QCM), which utilizes syntactic editing changes between adjacent queries as well as the relationship between query change and previously retrieved documents to enhance session search. We propose to model session search as a Markov Decision Process (MDP). We consider two agents in this MDP: the user agent and the search engine agent. The user agent's actions are query changes that we observe and the search agent's actions are proposed in this paper. Experiments show that our approach is highly effective and outperforms top session search systems in TREC 2011 and 2012.

Session search is the Information Retrieval (IR) task that performs document retrieval for an entire session. During a session, users often change queries to explore and investigate the information needs. In this paper, we propose to use query change as a new form of relevance feedback for better session search. Evaluation conducted over TREC 2012 Session Track shows that query change is a highly effective form of feedback as compared with existing relevance feedback methods. The proposed method outperforms the state-of-the-art relevance feedback methods for the TREC 2012 Session Track by a significant improvement of >25%.

Soil erosion is a serious problem all over the world, especially in China. As the capital of China, soil erosion in Beijing has been concerned a lot by both government and scholars. Located in northwest Beijing, Miyun county is an important region because it includes the Miyun reservoir which is the water source for all of Beijing citizens. Based on the theory and model about soil erosion, the technique method and current situation of soil erosion in Miyun county is studied in this paper supported by GIS and RS. The conclusion is that the existing situation of soil erosion in Miyun county is well due to recent years planting, monitoring, and managing by both central and local government.