Sequential Keystroke Behavioral Biometrics for Mobile User Identification via Multi-view Deep Learning

With the rapid growth in smartphone usage, more organizations begin to focus on providing better services for mobile users. User identification can help these organizations to identify their customers and then cater services that have been customized for them. Currently, the use of cookies is the most common form to identify users. However, cookies are not easily transportable (e.g., when a user uses a different login account, cookies do not follow the user). This limitation motivates the need to use behavior biometric for user identification. In this paper, we propose DEEPSERVICE, a new technique that can identify mobile users based on user's keystroke information captured by a special keyboard or web browser. Our evaluation results indicate that DEEPSERVICE is highly accurate in identifying mobile users (over 93% accuracy). The technique is also efficient and only takes less than 1 ms to perform identification.Comment: 2017 Joint European Conference on Machine Learning and Knowledge Discovery in Database

Fast Hybrid Cascade for Voxel-based 3D Object Classification

Voxel-based 3D object classification has been frequently studied in recent years. The previous methods often directly convert the classic 2D convolution into a 3D form applied to an object with binary voxel representation. In this paper, we investigate the reason why binary voxel representation is not very suitable for 3D convolution and how to simultaneously improve the performance both in accuracy and speed. We show that by giving each voxel a signed distance value, the accuracy will gain about 30% promotion compared with binary voxel representation using a two-layer fully connected network. We then propose a fast fully connected and convolution hybrid cascade network for voxel-based 3D object classification. This threestage cascade network can divide 3D models into three categories: easy, moderate and hard. Consequently, the mean inference time (0.3ms) can speedup about 5x and 2x compared with the state-of-the-art point cloud and voxel based methods respectively, while achieving the highest accuracy in the latter category of methods (92%). Experiments with ModelNet andMNIST verify the performance of the proposed hybrid cascade network

Expediting the accuracy-improving process of SVMs for class imbalance learning

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly Detection

This technical report introduces the winning solution of the team Segment Any Anomaly for the CVPR2023 Visual Anomaly and Novelty Detection (VAND) challenge. Going beyond uni-modal prompt, e.g., language prompt, we present a novel framework, i.e., Segment Any Anomaly + (SAA$+$), for zero-shot anomaly segmentation with multi-modal prompts for the regularization of cascaded modern foundation models. Inspired by the great zero-shot generalization ability of foundation models like Segment Anything, we first explore their assembly (SAA) to leverage diverse multi-modal prior knowledge for anomaly localization. Subsequently, we further introduce multimodal prompts (SAA$+$) derived from domain expert knowledge and target image context to enable the non-parameter adaptation of foundation models to anomaly segmentation. The proposed SAA$+$ model achieves state-of-the-art performance on several anomaly segmentation benchmarks, including VisA and MVTec-AD, in the zero-shot setting. We will release the code of our winning solution for the CVPR2023 VAN.Comment: The first two author contribute equally. CVPR workshop challenge report. arXiv admin note: substantial text overlap with arXiv:2305.1072

Neighbourhood satisfaction in rural resettlement residential communities: the case of Suqian, China

Against the background of large-scale urbanisation and rural land expropriation, rural resettlement residential housing has been built to accommodate local rural residents in the peripheral areas of China. To explore the context-specific policy implications for improving neighbourhood satisfaction (NS) of residents in rural resettlement residential communities (RRRCs), this paper examines the determinants of NS, and their spatial effects, in rural resettlement residential neighbourhoods using Suqian, in Jiangsu Province, as a case study. This study contributes to the current literature in two ways: it constitutes the first attempt to examine NS among RRRCs; second, our spatial model helps to gain further understanding of horizontal and vertical spatial dependence effects. Our results indicate that income, gender, age, family structure, number of years living in a community, transport and architectural age all have significant effects on NS in RRRCs

Banning diesel vehicles in London: Is 2040 too Late?

Air pollution contributes to 9400 deaths annually in London and diesel vehicles are considered a major source of lethal air pollutants. Consequently, the UK government announced its intention to ban diesel vehicles by 2040 to achieve a sustainable zero-carbon road transport system. Since no empirical studies have used a bottom-up approach to seek Londoners’ views, it is therefore worth investigating the public opinion regarding this forthcoming ban. This paper aims to fill this research gap by taking London as a case study. A survey was designed, and fieldwork was conducted to distribute questionnaires to Londoners. Completed questionnaires were analysed using both quantitative and qualitative methods. The findings revealed that the majority of Londoners would be in favour of the ban if they were sufficiently exposed to the appropriate sources of information and were favourably disposed towards environmental protection measures. The results also showed that Londoners were more likely to switch to electric vehicles (EVs) if they were offered generous incentives and encouraged to use scrappage schemes. The present study makes a strong case for enforcing the ban well before 2040. The significance of this research is to provide clearer signals regarding the future of diesel vehicles, which in turn will strengthen the EV policy and uptake

Computational Screening of New Perovskite Materials Using Transfer Learning and Deep Learning

As one of the most studied materials, perovskites exhibit a wealth of superior properties that lead to diverse applications. Computational prediction of novel stable perovskite structures has big potential in the discovery of new materials for solar panels, superconductors, thermal electric, and catalytic materials, etc. By addressing one of the key obstacles of machine learning based materials discovery, the lack of sufficient training data, this paper proposes a transfer learning based approach that exploits the high accuracy of the machine learning model trained with physics-informed structural and elemental descriptors. This gradient boosting regressor model (the transfer learning model) allows us to predict the formation energy with sufficient precision of a large number of materials of which only the structural information is available. The enlarged training set is then used to train a convolutional neural network model (the screening model) with the generic Magpie elemental features with high prediction power. Extensive experiments demonstrate the superior performance of our transfer learning model and screening model compared to the baseline models. We then applied the screening model to filter out promising new perovskite materials out of 21,316 hypothetical perovskite structures with a large portion of them confirmed by existing literature

Correlation analysis of monocyte chemoattractant protein-1 and clinical characteristics and cognitive impairment in type 2 diabetes mellitus comorbid major depressive disorder

IntroductionType 2 diabetes mellitus (T2DM) and major depressive disorder (MDD) are both chronic diseases, and they are often co-morbid. Usually, T2DM and MDD are associated with cognitive impairment, and the comorbidity status of both may increase the risk of cognitive impairment, but the underlying pathogenesis is not clear. Studies have shown that inflammation, especially monocyte chemoattractant protein-1 (MCP-1), could be associated with the pathogenesis of type 2 diabetes mellitus comorbid major depressive disorder.AimsTo investigate the correlations of MCP-1 with clinical characteristics and cognitive impairment in type 2 diabetes mellitus patients combined with major depressive disorder.MethodsA total of 84 participants were recruited in this study, including 24 healthy controls (HC), 21 T2DM patients, 23 MDD patients, and 16 T2DM combined with MDD (TD) patients, to measure the serum MCP-1 levels using Enzyme-linked Immunosorbent Assay (ELISA). And the cognitive function, depression, and anxiety degree were assessed using Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), 17-item Hamilton Depression Scale (HAMD-17), and Hamilton Anxiety Scale (HAMA), respectively.Results(1) Serum MCP-1 expression levels in the TD group were higher than HC, T2DM, and MDD groups, respectively (p < 0.05). And compared with HC and MDD groups, serum MCP-1 levels in the T2DM group were higher (p < 0.05) statistically. Receiver Operating Characteristic (ROC) curve showed that MCP-1 could diagnose T2DM at cut-off values of 503.8 pg./mL (sensitivity 80.95%, specificity 79.17%, AUC = 0.7956) and of 718.1 pg./mL for TD (sensitivity 81.25%, specificity 91.67%, AUC = 0.9271). (2) Group differences in cognitive function were significant. Compared with the HC group, total RBANS scores, attention scores, and language scores in the TD group were lower, respectively (p < 0.05), and total RBANS scores, attention scores, and visuospatial/constructional scores in the MDD group were lower, respectively (p < 0.05). Compared with the T2DM group, immediate memory scores in HC, MDD, and TD groups were lower, respectively, and total RBANS scores in TD were lower (p < 0.05). (3) Correlation analysis showed that hip circumference was negatively correlated with MCP-1 levels in the T2DM group (R = −0.483, p = 0.027), but the correlation disappeared after adjusting age and gender (r = −0.372; p = 0.117), and there were no significant correlations between MCP-1 and other variables.ConclusionMCP-1 may be involved in the pathophysiology of type 2 diabetes mellitus patients combined with major depressive disorder. And MCP-1 may be significant for the early evaluation and diagnosis of TD in the future