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

EEG Classification based on Image Configuration in Social Anxiety Disorder

The problem of detecting the presence of Social Anxiety Disorder (SAD) using Electroencephalography (EEG) for classification has seen limited study and is addressed with a new approach that seeks to exploit the knowledge of EEG sensor spatial configuration. Two classification models, one which ignores the configuration (model 1) and one that exploits it with different interpolation methods (model 2), are studied. Performance of these two models is examined for analyzing 34 EEG data channels each consisting of five frequency bands and further decomposed with a filter bank. The data are collected from 64 subjects consisting of healthy controls and patients with SAD. Validity of our hypothesis that model 2 will significantly outperform model 1 is borne out in the results, with accuracy $6$--$7\%$ higher for model 2 for each machine learning algorithm we investigated. Convolutional Neural Networks (CNN) were found to provide much better performance than SVM and kNNs

Future Frontiers in Organic Synthesis

The role of organic synthesis to the mankind is of paramount importance since the early nineteen century [1]. In 1828, Friedrich Wöhler discovered the synthesis of urea from ammonium cyanate, marking the starting point of modern organic synthesis. Wöhler concluded to his mentor Jöns Jacob Berzelius, "I cannot, so to say, hold my chemical water and must tell you that I can make urea without thereby needing to have kidneys, or anyhow, an animal, be it human or dog". Since then, organic synthesis has become an indispensable tool in industries such as petrochemicals, pharmaceutical, flavors, fragrances, agrochemical, and others. This is evident by the number of Nobel prizes awarded to organic chemists. The Nobel Prize in Chemistry 2001 was awarded to William S. Knowles, Ryoji Noyori, and K. Barry Sharpless for their work in asymmetric synthesis. This was followed by the award of the Nobel Prize in Chemistry 2005 to Yves Chauvin, Robert H. Grubbs and Richard R. Schrock "for the development of the metathesis method in organic synthesis". And just recently, Richard F. Heck, Ei-ichi Negishi and Akira Suzuki won the Nobel Prize in Chemistry 2010 for “palladium-catalyzed cross couplings in organic synthesis”

DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection

The increasing use of electronic forms of communication presents new opportunities in the study of mental health, including the ability to investigate the manifestations of psychiatric diseases unobtrusively and in the setting of patients' daily lives. A pilot study to explore the possible connections between bipolar affective disorder and mobile phone usage was conducted. In this study, participants were provided a mobile phone to use as their primary phone. This phone was loaded with a custom keyboard that collected metadata consisting of keypress entry time and accelerometer movement. Individual character data with the exceptions of the backspace key and space bar were not collected due to privacy concerns. We propose an end-to-end deep architecture based on late fusion, named DeepMood, to model the multi-view metadata for the prediction of mood scores. Experimental results show that 90.31% prediction accuracy on the depression score can be achieved based on session-level mobile phone typing dynamics which is typically less than one minute. It demonstrates the feasibility of using mobile phone metadata to infer mood disturbance and severity.Comment: KDD 201

Patient-Specific Prosthetic Fingers by Remote Collaboration - A Case Study

The concealment of amputation through prosthesis usage can shield an amputee from social stigma and help improve the emotional healing process especially at the early stages of hand or finger loss. However, the traditional techniques in prosthesis fabrication defy this as the patients need numerous visits to the clinics for measurements, fitting and follow-ups. This paper presents a method for constructing a prosthetic finger through online collaboration with the designer. The main input from the amputee comes from the Computer Tomography (CT) data in the region of the affected and the non-affected fingers. These data are sent over the internet and the prosthesis is constructed using visualization, computer-aided design and manufacturing tools. The finished product is then shipped to the patient. A case study with a single patient having an amputated ring finger at the proximal interphalangeal joint shows that the proposed method has a potential to address the patient's psychosocial concerns and minimize the exposure of the finger loss to the public.Comment: Open Access articl

Recurrence and progression of periodontitis and methods of management in long-term care: A systematic review and meta-analysis

Aim: To systematically review the literature to evaluate the recurrence of disease of people in long-term supportive periodontal care (SPC), previously treated for periodontitis, and determine the effect of different methods of managing recurrence. The review focused on stage IV periodontitis. Materials and methods: An electronic search was conducted (until May 2020) for prospective clinical trials. Tooth loss was the primary outcome. Results: Twenty-four publications were retrieved to address recurrence of disease in long-term SPC. Eight studies were included in the meta-analyses for tooth loss, and three studies for disease progression/recurrence (clinical attachment level [CAL] loss ≥2 mm). For patients in SPC of 5–20 years, prevalence of losing more than one tooth was 9.6% (95% confidence interval [CI] 5%–14%), while experiencing more than one site of CAL loss ≥2 mm was 24.8% (95% CI 11%–38%). Six studies informed on the effect of different methods of managing recurrence, with no clear evidence of superiority between methods. No data was found specifically for stage IV periodontitis. Conclusions: A small proportion of patients with stage III/IV periodontitis will experience tooth loss in long-term SPC (tendency for greater prevalence with time). Regular SPC appears to be important for reduction of tooth loss. No superior method to manage disease recurrence was found

Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis

Network analysis of human brain connectivity is critically important for understanding brain function and disease states. Embedding a brain network as a whole graph instance into a meaningful low-dimensional representation can be used to investigate disease mechanisms and inform therapeutic interventions. Moreover, by exploiting information from multiple neuroimaging modalities or views, we are able to obtain an embedding that is more useful than the embedding learned from an individual view. Therefore, multi-view multi-graph embedding becomes a crucial task. Currently, only a few studies have been devoted to this topic, and most of them focus on the vector-based strategy which will cause structural information contained in the original graphs lost. As a novel attempt to tackle this problem, we propose Multi-view Multi-graph Embedding (M2E) by stacking multi-graphs into multiple partially-symmetric tensors and using tensor techniques to simultaneously leverage the dependencies and correlations among multi-view and multi-graph brain networks. Extensive experiments on real HIV and bipolar disorder brain network datasets demonstrate the superior performance of M2E on clustering brain networks by leveraging the multi-view multi-graph interactions