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For patients with disorders of consciousness (DoC), accurate assessment of residual consciousness levels and cognitive abilities is critical for developing appropriate rehabilitation interventions. In this study, we investigated the potential of electrooculography (EOG) in assessing language processing abilities and consciousness levels. Patients’ EOG data and related electrophysiological data were analysed before and after explicit language learning. The results showed distinct differences in vocabulary learning patterns among patients with varying levels of consciousness. While minimally conscious patients showed significant neural tracking of artificial words and notable learning effects similar to those observed in healthy controls, whereas patients with unresponsive wakefulness syndrome did not show such effects. Correlation analysis further indicated that EOG detected vocabulary learning effects with comparable validity to electroencephalography, reinforcing the credibility of EOG indicator as a diagnostic tool. Critically, EOG also revealed significant correlations between individual patients’ linguistic learning performance and their Oromotor/verbal function as assessed through behavioural scales. In conclusion, this study explored the differences in language processing abilities among patients with varying consciousness levels. By demonstrating the utility of EOG in evaluating consciousness and detecting vocabulary learning effects, as well as its potential to guide personalised rehabilitation, our findings indicate that EOG indicators show promise as a rapid, accurate and effective additional tool for diagnosing and managing patients with DoC.

Pre-evaluating scientific paper quality aids in alleviating peer review pressure and fostering scientific advancement. Although prior studies have identified numerous quality-related features, their effectiveness and representativeness of paper content remain to be comprehensively investigated. Addressing this issue, we propose a content-based interpretable method for pre-evaluating the quality of scientific papers. Firstly, we define quality attributes of computer science (CS) papers as integrity, clarity, novelty, and significance, based on peer review criteria from 11 top-tier CS conferences. We formulate the problem as two classification tasks: Accepted/Disputed/Rejected (ADR) and Accepted/Rejected (AR). Subsequently, we construct fine-grained features from metadata and knowledge entity networks, including text structure, readability, references, citations, semantic novelty, and network structure. We empirically evaluate our method using the ICLR paper dataset, achieving optimal performance with the Random Forest model, yielding F1 scores of 0.715 and 0.762 for the two tasks, respectively. Through feature analysis and case studies employing SHAP interpretable methods, we demonstrate that the proposed features enhance the performance of machine learning models in scientific paper quality evaluation, offering interpretable evidence for model decisions.

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A dedicated test bench is employed to record acoustic emission signals from dry gas seals under various operating conditions. Time-domain and frequency-domain analysis methods are utilized to process and analyze the acoustic emission signals during start/stop, stable operation, and two common fault states (end-face defects and compensation spring failure). Furthermore, feature recognition research is conducted. A method for identifying the operational states of seals (low-speed friction, gradual detachment, stable operation) based on the root mean square (RMS) was established, with transition points at speeds of 100 and 1000 RPM, respectively. Additionally, spectral analysis is conducted using Fourier transform to determine the frequency band of acoustic emission signals (240–320 kHz) generated during contact wear of dry gas seals. Investigation into two typical faults of dry gas seals reveals that the RMS value of the acoustic emission signal gradually increases with the rotational speed during the operation of dry gas seal end-face defects. This is attributed to the insufficient dynamic pressure effect on the end face, resulting in long-term wear and tear. When the dry gas seal compensates for spring failure, the RMS value of the acoustic emission signal initially increases, then decreases, and finally increases again as the speed increases. It reaches the stable operating inflection point when the end-face speed is 800 r/min.

Online product reviews play a critical role for consumers to make a decision of product purchasing, and become an important data source for vendors to build recommendation systems. Some consumers won’t even buy a product without reading online reviews first, and some vendors invite reviewers to write product reviews before a product is released to the market. However, the review quality could greatly impact the use of the reviews for supporting the purchase decision and recommendation. In order to produce enough high-quality reviews, it is a common practice that vendors provide incentives for writing product reviews. Current research is divided. Some research showed that incentive reviews could be more biased compared to organic reviews, but other scholars showed incentive reviews contain more useful information. Furthermore, other academic publications showed very different results regarding the differences between the incentivized and organic reviews. One of the reasons explaining the observation differences could be due to the quality of the reviewers and reviews. Therefore, it is necessary to control the quality of the reviewers and their reviews for a more objective comparison. In this research, we first discuss an approach for ensuring the quality of the data collection and processing to ensure the quality of the comparison study. Then, we explore the differences between incentivized reviews and organic reviews collected from a website that provides reviews for enterprise software systems. Several parameters of the reviews, such as the overall score, sentiment, and subjectivity of the reviews, were analyzed and compared. Our results could provide a reference for appropriately using reviews and managing the reviewing process.

Intrusion detection is an essential task for protecting the cyber environment from attacks. Many studies have proposed sophisticated models to detect intrusions from a large amount of data, yet they ignored the fact that poor data quality has a direct impact on the performance of intrusion detection systems. Examples of poor data quality include mislabeled, inaccurate, incomprehensive, irrelevant, inconsistent, duplicated, and overlapped data. In order to investigate how data quality may affect machine learning performance, we conducted a series of experiments on 11 host-based intrusion datasets using eight machine learning (ML) models and two pre-trained language models BERT and GPT-2. The experimental results showed: 1. BERT and GPT-2 outperformed the other models on every dataset. 2. Data duplications and overlaps in a dataset had different performance impacts on the pre-trained models and the classic ML models. The pre-trained models were less susceptible to duplicate and overlapped data than the classic ML models. 3. Removing overlaps and duplicates from training data with a normal range of sequence similarities could improve the pre-trained models’ performances on most datasets. However, it may have adverse effects on model performance in datasets with highly similar sequences. 4. The reliability of model evaluation could be affected when testing data contains duplicates. 5. The overlapped rate between the normal class and the intrusion class seemed to have an inverse relationship to the performance of the pre-trained models in intrusion detection. Given the results, we proposed a framework for model selection and data quality assurance for building a high-quality machine learning-based intrusion detection system.

Semantic dementia patients present with a core semantic impairment and variations of language, behavioural and face recognition abilities. Here, the authors build a unified multidimensional model to capture all these graded symptoms and map them to the variations in the patients’ frontotemporal atrophy.

Prior evidence suggests domain-specific working memory (WM) buffers for maintaining phonological (i.e., speech sound) and semantic (i.e., meaning) information. The phonological WM buffer’s proposed location is in the left supramarginal gyrus (SMG), whereas semantic WM has been related to the left inferior frontal gyrus (IFG), the middle frontal gyrus (MFG), and the angular gyrus (AG). However, less is known about the white matter correlates of phonological and semantic WM. We tested 45 individuals with left hemisphere brain damage on single word processing, phonological WM, and semantic WM tasks and obtained T1 and diffusion weighted neuroimaging. Virtual dissections were performed for each participants’ arcuate fasciculus (AF), inferior fronto-occipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF), middle longitudinal fasciculus (MLF), and uncinate fasciculus (UF), which connect the proposed domain-specific WM buffers with perceptual or processing regions. The results showed that the left IFOF and the posterior segment of the AF were related to semantic WM performance. Phonological WM was related to both the left ILF and the whole AF. This work informs our understanding of the white matter correlates of WM, especially semantic WM, which has not previously been investigated. In addition, this work helps to adjudicate between theories of verbal WM, providing some evidence for separate pathways supporting phonological and semantic WM.

Introduction: Previous literature has revealed that the anterior temporal lobe (ATL) is the semantic hub of left-sided or mixed semantic dementia (SD), whilst the semantic hub of right-sided SD has not been examined. Methods: Seventeen patients with right-sided SD, 18 patients with left-sided SD and 20 normal controls (NC) underwent neuropsychological assessments and magnetic resonance imaging scans. We investigated the relationship between the degree of cerebral atrophy in the whole brain and the severity of semantic deficits in left and right-sided SD samples, respectively. Results: We found the semantic deficits of right-sided SD patients were related to bilateral fusiform gyri and left temporal pole, whilst the left fusiform gyrus correlated with the semantic performance of left-sided SD patients. Moreover, all the findings couldn't be accounted for by total gray matter volume (GMV) or general cognitive degradation of patients. Discussion: These results provide novel evidence for the current semantic theory, that the important regions for semantic processing include both anterior and posterior temporal lobes. Keywords: Semantic dementia, Lesion-behavior mapping, Laterality of brain atrophy, Semantic deficits

Individuals with semantic dementia (SD) typically suffer from selective semantic deficits due to degenerative brain atrophy. Although some brain regions have been found to be correlated with the semantic impairments of SD patients, it is unclear if the damage is actually responsible for SD patients’ semantic disorders because these findings were primarily obtained by examining the roles of local individual regions themselves without considering the influence of other regions that are functionally or structurally connected to the local individual regions. To resolve this problem, we investigated, from the brain network perspective, the relationship between the brain-network measures of regions and connections with semantic performance in 17 SD patients. We found that the severity of semantic deficits of SD patients was significantly correlated with the degree centrality values of the left anterior hippocampus (aHIP). Moreover, the semantic performance of the patients was also significantly correlated with the strength of gray matter functional connectivity of this region and two other regions: the left temporal pole/insula (TP/INS) and the left middle temporal gyrus. We further observed that the strength of the white matter structural connectivity of the left aHIP-left TP/INS tract could effectively predict the semantic performance of SD patients. When we controlled for a wide range of potential confounding factors (e.g., total gray matter volume), the above effects still held well. These findings revealed the critical brain network with the left aHIP as the center that could be contributing to the semantic impairments of SD.