Pattern Anomaly Detection based on Sequence-to-Sequence Regularity Learning

Anomaly detection in traffic surveillance videos is a challenging task due to the ambiguity of anomaly definition and the complexity of scenes. In this paper, we propose to detect anomalous trajectories for vehicle behavior analysis via learning regularities in data. First, we train a sequence-to-sequence model under the autoencoder architecture and propose a new reconstruction error function for model optimization and anomaly evaluation. As such, the model is forced to learn the regular trajectory patterns in an unsupervised manner. Then, at the inference stage, we use the learned model to encode the test trajectory sample into a compact representation and generate a new trajectory sequence in the learned regular pattern. An anomaly score is computed based on the deviation of the generated trajectory from the test sample. Finally, we can find out the anomalous trajectories with an adaptive threshold. We evaluate the proposed method on two real-world traffic datasets and the experiments show favorable results against state-of-the-art algorithms. This paper\u27s research on sequence-to-sequence regularity learning can provide theoretical and practical support for pattern anomaly detection

The Mirror of Contemporary Chinese Women: Images of Women in Contemporary Chinese Romantic Fictions

早稲田大学博士(国際コミュニケーション学)早大学位記番号:新9406doctoral thesi

Implementation of Support Programs for Life Long Educational Inclusion of Students with Special Needs

With the implementation of inclusive education, students with special needs, such as learning disabilities, emotional & behavioral disorders, speech & language disabilities, autism, and gifted children, are in need of professional support. In the future, inclusive education will focus on compulsory education and continuously extend to early intervention and eldly service until lifelong education. Based on the overall education goals of the 2030 Agenda for Sustainable Development issued by the United Nations Development Summit, Shanghai Changning District has carried out a practical exploration from mainstreaming to lifelong inclusive education, with the concept of “universal, inclusive and lifelong”, has developed and implemented support programs, providing “group”, “categories” and “individual” support for school-age, preschool and post-school-age students with special needs, developed and implemented 37 transition service programs including five stages: from home to kinder garden, from kindergarten to primary school, from primary school to junior middle school, from junior middle school to special vocational school; after special vocational school

Precessing motion in stratified radial swirl flow

AbstractVortex/flame interaction is an important mechanism for unsteady combustion in a swirl combustion system. Technology of low emission stirred swirl (TeLESS), which is characterized with stratified swirl flow, has been developed in Beihang University to reduce NOX emission. However, large-scale flow structure would be induced in strong swirl flow. Experiments and computational fluid dynamics (CFD) simulation were carried out to investigate the unsteady flow feature and its mechanism in TeLESS combustor. Hotwire was firstly applied to testing the unsteady flow feature and a distinct mode with 2244Hz oscillation frequency occurred at the pilot swirl outlet. The flow mode amplitude decayed convectively. Large eddy simulation (LES) was then applied to predicting this flow mode and know about its mechanism. The deviation of mode prediction compared with hotwire test was 0.8%. The spiral isobaric structure in pilot flow passage indicates that precessing vortex core (PVC) existed. The velocity spectrum and phase lag analysis suggest that the periodic movement at the pilot outlet was dominated by precessing movement. Negative tangential momentum gradient reflects that the swirl flow was unstable. Another phenomenon was found out that the PVC movement was intermittently rotated along the symmetric axis

AdvFoolGen: Creating Persistent Troubles for Deep Classifiers

Researches have shown that deep neural networks are vulnerable to malicious attacks, where adversarial images are created to trick a network into misclassification even if the images may give rise to totally different labels by human eyes. To make deep networks more robust to such attacks, many defense mechanisms have been proposed in the literature, some of which are quite effective for guarding against typical attacks. In this paper, we present a new black-box attack termed AdvFoolGen, which can generate attacking images from the same feature space as that of the natural images, so as to keep baffling the network even though state-of-the-art defense mechanisms have been applied. We systematically evaluate our model by comparing with well-established attack algorithms. Through experiments, we demonstrate the effectiveness and robustness of our attack in the face of state-of-the-art defense techniques and unveil the potential reasons for its effectiveness through principled analysis. As such, AdvFoolGen contributes to understanding the vulnerability of deep networks from a new perspective and may, in turn, help in developing and evaluating new defense mechanisms.Comment: 11 pages, 5 figure

A Review on Properties, Opportunities, and Challenges of Transformer Oil-Based Nanofluids

The mineral oil or synthetic oil in conjunction with paper is mainly being applied as dielectric medium in many of the high voltage apparatus. However, the advent of high voltage levels such high voltage alternating current (HVAC) and high voltage direct current (HVDC) has prompted researchers to direct their focus onto an insulation system which can bear the rising high voltage levels. The modern insulating liquid material development is guided by various factors such as high electrical insulation requirements and other safety and economic considerations. Therefore transformer manufacturer companies have to design transformers with these new specific requirements. The transformer oil-based nanofluids with improved dielectric and thermal properties have the potential to replace mineral oil base products in the market place. They are favorable because they function more superior than mineral oil and they contribute definite insulating and thermal gains. This paper reviews recent status of nanofluids use as transformer oils. The nanofluids used as transformer oils are presented and their advantages are described in comparison with mineral oil. The multiple experimental works carried out by different researchers are described, providing an overview of the current research conducted on nanofluids. In addition scope and challenges being confronted in this area of research are clearly presented

Flexible Structural Neighborhood—a database of protein structural similarities and alignments

Protein structures are flexible, changing their shapes not only upon substrate binding, but also during evolution as a collective effect of mutations, deletions and insertions. A new generation of protein structure comparison algorithms allows for such flexibility; they go beyond identifying the largest common part between two proteins and find hinge regions and patterns of flexibility in protein families. Here we present a Flexible Structural Neighborhood (FSN), a database of structural neighbors of proteins deposited in PDB as seen by a flexible protein structure alignment program FATCAT, developed previously in our group. The database, searchable by a protein PDB code, provides lists of proteins with statistically significant structural similarity and on lower menu levels provides detailed alignments, interactive superposition of structures and positions of hinges that were identified in the comparison. While superficially similar to other structural protein alignment resources, FSN provides a unique resource to study not only protein structural similarity, but also how protein structures change. FSN is available from a server and by direct links from the PDB database