Landslide Detection in Real-Time Social Media Image Streams

Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. However, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence (AI) techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real time. To that end, we create a large landslide image dataset labeled by experts and conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response

Video scene change detection based on histogram analysis for hiding message

The rapid growth of internet technology has greatly increased the opportunity for secrecy communication. Most of communications implemented compression method in digital applications due to fast transferring data in the limited bandwidth. In addition, the information can be changed by a third party in communication. This paper proposed a new hiding technique by modifying DCT coefficients in the video frames. The scene changes of the video data are identified based on histogram analysis for hiding locations. Scene changes among video frames are detected by measuring the significant difference of histogram analysis. The proposed hiding technique in video is also evaluated against MPEG-4 compression. The experimental results show that the proposed scheme achieved high imperceptibility with minimum visual distortion on the video quality. The proposed hiding scheme also can recover the concealed data from the compressed video. The results show that the extracted hidden message is able to resistant against MPEG compression

Face recognition using faster R-CNN with inception-V2 architecture for CCTV camera

Detection and prevention of criminal incidents using CCTV are currently increasing trend, for example, car and motorcycle parking lot. However, not continuous people monitoring and careless of events produce useless CCTV function for the prevention of criminal incidents. In this paper, face recognition is used for the recognition of vehicle owners in parking lots that are CCTV installed. The Faster-RCNN method is used for face detection and also for face recognition. Inception V2 architecture is utilized due to has a high accuracy among Convolutional Neural Network architecture. The best learning rate and epoch parameters for the Faster R-CNN model are optimized to improve face recognition on CCTV. In this research, the dataset consists of 6 people images with 50 faces images for each people, which used as training data, testing data, and validation data

Creating smarter teaching and training environments: innovative set-up for collaborative hybrid learning

This paper brings together previous work from a number of research projects and teaching initiatives in an effort to introduce good practice in setting up supportive environments for collaborative learning. The paper discusses prior use of social media in learning support, the role of dashboards for learning analytics in Global Software Development training, the use of optical head-mounted displays for feedback and the use of NodeXl visualization in managing distributed teams. The scope of the paper is to provide a structured approach in organizing the creation of smarter teaching and training environments and explore ways to coordinate learning scenarios with the use of various techniques. The paper also discusses challenges from integrating multiple innovative features in educational contexts. Finally the paper attempts to investigate the use of smart laboratories in establishing additional learning support and gather primary data from blended and hybrid learning pilot studies

Lrp4 Regulates Initiation of Ureteric Budding and Is Crucial for Kidney Formation – A Mouse Model for Cenani-Lenz Syndrome

Background: Development of the kidney is initiated when the ureteric bud (UB) branches from the Wolffian duct and invades the overlying metanephric mesenchyme (MM) triggering the mesenchymal/epithelial interactions that are the basis of organ formation. Multiple signaling pathways must be integrated to ensure proper timing and location of the ureteric bud formation. Methods and Principal Findings: We have used gene targeting to create an Lrp4 null mouse line. The mutation results in early embryonic lethality with a subpenetrant phenotype of kidney agenesis. Ureteric budding is delayed with a failure to stimulate the metanephric mesenchyme in a timely manner, resulting in failure of cellular differentiation and resulting absence of kidney formation in the mouse as well as comparable malformations in humans with Cenani-Lenz syndrome. Conclusion: Lrp4 is a multi-functional receptor implicated in the regulation of several molecular pathways, including Wnt and Bmp signaling. Lrp4 2/2 mice show a delay in ureteric bud formation that results in unilateral or bilateral kidney agenesis. These data indicate that Lrp4 is a critical regulator of UB branching and lack of Lrp4 results in congenital kidne

Poly(3,4-ethylenedioxythiophene)/poly(bis(4-phenoxysulfonic acid)phosphazene) conductive composites: an alternative interfacial layer to PEDOT : PSS

Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT : PSS) is a popular solution-processable hole transporting layer used in organic semiconductor devices such as organic light-emitting diodes or organic photovoltaics. It has benefits such as suitability for orthogonal processing, tunable conductivity and smooth film formation, yet the PSS polyelectrolyte is prone to degradation, impacting device performance or lifetime. In this work we present the use of PEDOT blends with a poly(bis(4-phenoxysulfonic acid)phosphazene) (PSAP) polyelectrolyte and study the effect of the PEDOT : PSAP ratio on the composite material properties. A comparable doping level can be achieved in PEDOT : PSAP films with respect to PEDOT : PSS and, as a result, an appropriate electrical conductivity for use as a hole transport layer can be achieved. Finally, when applied in organic light-emitting diodes, the use of PEDOT : PSAP hole transport layers can boost the external quantum efficiency, highlighting the promising performance of PSAP polyelectrolyte in conductive blends

A near-real-time global landslide incident reporting tool demonstrator using social media and artificial intelligence

The development of a system that monitors social media continuously for general landslide-related content using a landslide classification model to identify and retain the most relevant information is described and validated. The system harvests photographs in real-time from these data and tags each image as landslide or not-landslide. A training model was developed with input from computer scientists, geologists (landslide specialists) and social media specialists to establish a large image dataset that has then been applied to the live Twitter data stream. The preliminary model was developed by training a convolutional neural network on the dataset. Quantitative verification of the system's performance during a real-world deployment shows that the system can detect landslide reports with Precision = 76%. The demonstrator model is currently running live https://landslide-aidr.qcri.org/service.php; the next stage of development will incorporate stakeholder and user feedback

A fuzzy feature fusion method for auto-segmentation of gliomas with multi-modality diffusion and perfusion magnetic resonance images in radiotherapy

The difusion and perfusion magnetic resonance (MR) images can provide functional information about tumour and enable more sensitive detection of the tumour extent. We aimed to develop a fuzzy feature fusion method for auto-segmentation of gliomas in radiotherapy planning using multi-parametric functional MR images including apparent difusion coefcient (ADC), fractional anisotropy (FA) and relative cerebral blood volume (rCBV). For each functional modality, one histogram-based fuzzy model was created to transform image volume into a fuzzy feature space. Based on the fuzzy fusion result of the three fuzzy feature spaces, regions with high possibility belonging to tumour were generated automatically. The auto-segmentations of tumour in structural MR images were added in fnal autosegmented gross tumour volume (GTV). For evaluation, one radiation oncologist delineated GTVs for nine patients with all modalities. Comparisons between manually delineated and auto-segmented GTVs showed that, the mean volume diference was 8.69% (±5.62%); the mean Dice’s similarity coefcient (DSC) was 0.88 (±0.02); the mean sensitivity and specifcity of auto-segmentation was 0.87 (±0.04) and 0.98 (±0.01) respectively. High accuracy and efciency can be achieved with the new method, which shows potential of utilizing functional multi-parametric MR images for target defnition in precision radiation treatment planning for patients with gliomas

Association of a de novo 16q copy number variant with a phenotype that overlaps with Lenz microphthalmia and Townes-Brocks syndromes

<p>Abstract</p> <p>Background</p> <p>Anophthalmia and microphthalmia are etiologically and clinically heterogeneous. Lenz microphthalmia is a syndromic form that is typically inherited in an X-linked pattern, though the causative gene mutation is unknown. Townes-Brocks syndrome manifests thumb anomalies, imperforate anus, and ear anomalies. We present a 13-year-old boy with a syndromic microphthalmia phenotype and a clinical diagnosis of Lenz microphthalmia syndrome.</p> <p>Case Presentation</p> <p>The patient was subjected to clinical and molecular evaluation, including array CGH analysis. The clinical features included left clinical anophthalmia, right microphthalmia, anteriorly placed anus with fistula, chordee, ventriculoseptal defect, patent ductus arteriosus, posteriorly rotated ears, hypotonia, growth retardation with delayed bone age, and mental retardation. The patient was found to have an approximately 5.6 Mb deletion of 16q11.2q12.1 by microarray based-comparative genomic hybridization, which includes the <it>SALL1 </it>gene, which causes Townes-Brocks syndrome.</p> <p>Conclusions</p> <p>Deletions of 16q11.2q12.2 have been reported in several individuals, although those prior reports did not note microphthalmia or anophthalmia. This region includes <it>SALL1</it>, which causes Townes-Brocks syndrome. In retrospect, this child has a number of features that can be explained by the <it>SALL1 </it>deletion, although it is not clear if the microphthalmia is a rare feature of Townes-Brocks syndrome or caused by other mechanisms. These data suggest that rare copy number changes may be a cause of syndromic microphthalmia allowing a personalized genomic medicine approach to the care of patients with these aberrations.</p