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Camouflaged objects attempt to conceal their texture into the background and discriminating them from the background is hard even for human beings. The main objective of this paper is to explore the camouflaged object segmentation problem, namely, segmenting the camouflaged object(s) for a given image. This problem has not been well studied in spite of a wide range of potential applications including the preservation of wild animals and the discovery of new species, surveillance systems, search-and-rescue missions in the event of natural disasters such as earthquakes, floods or hurricanes. This paper addresses a new challenging problem of camouflaged object segmentation. To address this problem, we provide a new image dataset of camouflaged objects for benchmarking purposes. In addition, we propose a general end-to-end network, called the Anabranch Network, that leverages both classification and segmentation tasks. Different from existing networks for segmentation, our proposed network possesses the second branch for classification to predict the probability of containing camouflaged object(s) in an image, which is then fused into the main branch for segmentation to boost up the segmentation accuracy. Extensive experiments conducted on the newly built dataset demonstrate the effectiveness of our network using various fully convolutional networks. \url{https://sites.google.com/view/ltnghia/research/camo}, Comment: Published in CVIU 2019. Project page: https://sites.google.com/view/ltnghia/research/camo

Document vectorization with an appropriate encoding scheme is an essential component in various document processing tasks, including text document classification, retrieval, or generation. Training a dedicated document in a specific domain may require large enough data and sufficient resource. This motivates us to propose a novel document representation scheme with two main components. First, we train TD2V, a generic pre-trained document embedding for English documents from more than one million tweets in Twitter. Second, we propose a domain adaptation process with adversarial training to adapt TD2V to different domains. To classify a document, we use the rank list of its similar documents using query expansion techniques, either Average Query Expansion or Discriminative Query Expansion. Experiments on datasets from different online sources show that by using TD2V only, our method can classify documents with better accuracy than existing methods. By applying adversarial adaptation process, we can further boost and achieve the accuracy on BBC, BBCSport, Amazon4, 20NewsGroup datasets. We also evaluate our method on a specific domain of sensitivity classification and achieve the accuracy of higher than $$95\%$$ even with a short text fragment having 1024 characters on 5 datasets: Snowden, Mormon, Dyncorp, TM, and Enron.

Most popular systems for object instance search are based on the bag-of-visual-word model. The inherent weaknesses of this standard model such as quantization error, unstructured representation, burstiness phenomenon are to some extent solved. However, it has a serious problem of searching small objects on a database with cluttered background. In many situations, even the irrelevant objects which share the same texture or shape with a query object get higher score than relevant ones. To overcome this problem, we propose a novel fusion method to significantly boost the accuracy of instance search systems. Firstly, we use the state-of-the-art object detector with denser feature for finding object bounding box and similarity score. Secondly, to exploit the spatial relationship of each visual word with an object proposal, a detected area that might contain a query object, we define three categories of visual word pairs, i.e., discriminative, weak relevant, and context inferred ones. Finally, we propose a new re-ranking scheme with three weighting functions corresponding to the three categories of visual word pairs to compute the final similarity score between a query topic and a video shot. To illustrate the efficiency of the proposed method, we conduct experiments on datasets which have a wide variety of types of query objects. Experimental results on TRECVID Instance Search datasets (INS2013 and INS2014) show the superiority of our proposed method over the state-of-the-art approaches.

Flow-based generative models have recently become one of the most efficient approaches to model data generation. Indeed, they are constructed with a sequence of invertible and tractable transformations. Glow first introduced a simple type of generative flow using an invertible 1×1 convolution. However, the 1×1 convolution suffers from limited flexibility compared to the standard convolutions. In this paper, we propose a novel invertible n×n convolution approach that overcomes the limitations of the invertible 1×1 convolution. In addition, our proposed network is not only tractable and invertible but also uses fewer parameters than standard convolutions. The experiments on CIFAR-10, ImageNet and Celeb-HQ datasets, have shown that our invertible n×n convolution helps to improve the performance of generative models significantly.

3D scene shape retrieval is a brand new but important research direction in content-based 3D shape retrieval. To promote this research area, two Shape Retrieval Contest (SHREC) tracks on 2D scene sketch-based and image-based 3D scene model retrieval have been organized by us in 2018 and 2019, respectively. In 2018, we built the first benchmark for each track which contains 2D and 3D scene data for ten (10) categories, while they share the same 3D scene target dataset. Four and five distinct 3D scene shape retrieval methods have competed with each other in these two contests, respectively. In 2019, to measure and compare the scalability performance of the participating and other promising Query-by-Sketch or Query-by-Image 3D scene shape retrieval methods, we built a much larger extended benchmark for each type of retrieval which has thirty (30) classes and organized two extended tracks. Again, two and three different 3D scene shape retrieval methods have contended in these two tracks, separately. To solicit state-of-the-art approaches, we perform a comprehensive comparison of all the above methods and an additional new retrieval methods by evaluating them on the two benchmarks. The benchmarks, evaluation results and tools are publicly available at our track websites (Yuan et al., 2019 [1]; Abdul-Rashid et al., 2019 [2]; Yuan et al., 2019 [3]; Abdul-Rashid et al., 2019 [4]), while code for the evaluated methods are also available: http://github.com/3DSceneRetrieval .

It is essential to develop efficient methods to detect abnormal events, such as car-crashes or stalled vehicles, from surveillance cameras to provide in-time help. This motivates us to propose a novel method to detect traffic accidents in traffic videos. To tackle the problem where anomalies only occupy a small amount of data, we propose a semi-supervised method using Generative Adversarial Network trained on regular sequences to predict future frames. Our key idea is to model the ordinary world with a generative model, then compare a predicted frame with the real next frame to determine if an abnormal event occurs. We also propose a new idea of encoding motion descriptors and scaled intensity loss function to optimize GAN for fast-moving objects. Experiments on the Traffic Anomaly Detection dataset of AI City Challenge 2019 show that our method achieves the top 3 results with F1 score 0.9412 and RMSE 4.8088, and S3 score 0.9261. Our method can be applied to different related applications of anomaly and outlier detection in videos.

Recognition across domains has recently become an active topic in the research community. However, it has been largely overlooked in the problem of recognition in new unseen domains. Under this condition, the delivered deep network models are unable to be updated, adapted, or fine-tuned. Therefore, recent deep learning techniques, such as domain adaptation, feature transferring, and fine-tuning, cannot be applied. This paper presents a novel approach to the problem of domain generalization in the context of deep learning. The proposed method1 is evaluated on different datasets in various problems, i.e. (i) digit recognition on MNIST, SVHN, and MNIST-M, (ii) face recognition on Extended Yale-B, CMU-PIE and CMU-MPIE, and (iii) pedestrian recognition on RGB and Thermal image datasets. The experimental results show that our proposed method consistently improves performance accuracy. It can also be easily incorporated with any other CNN frameworks within an end-to-end deep network design for object detection and recognition problems to improve their performance.

The Lifelog Search Challenge (LSC) is an international content retrieval competition that evaluates search for personal lifelog data. At the LSC, content-based search is performed over a multi-modal dataset, continuously recorded by a lifelogger over 27 days, consisting of multimedia content, biometric data, human activity data, and information activities data. In this work, we report on the first LSC that took place in Yokohama, Japan in 2018 as a special workshop at ACM International Conference on Multimedia Retrieval 2018 (ICMR 2018). We describe the general idea of this challenge, summarise the participating search systems as well as the evaluation procedure, and analyse the search performance of the teams in various aspects. We try to identify reasons why some systems performed better than others and provide an outlook as well as open issues for upcoming iterations of the challenge.

This paper presents an overview of the ImageCLEF 2020 lab that was organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2020. ImageCLEF is an ongoing evaluation initiative (first run in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2020, the 18th edition of ImageCLEF runs four main tasks: (i) a medical task that groups three previous tasks, i.e., caption analysis, tuberculosis prediction, and medical visual question answering and question generation, (ii) a lifelog task (videos, images and other sources) about daily activity understanding, retrieval and summarization, (iii) a coral task about segmenting and labeling collections of coral reef images, and (iv) a new Internet task addressing the problems of identifying hand-drawn user interface components. Despite the current pandemic situation, the benchmark campaign received a strong participation with over 40 groups submitting more than 295 runs.

This paper presents the methods that have participated in the SHREC’20 contest on retrieval of surface patches with similar geometric reliefs and the analysis of their performance over the benchmark created for this challenge. The goal of the context is to verify the possibility of retrieving 3D models only based on the reliefs that are present on their surface and to compare methods that are suitable for this task. This problem is related to many real world applications, such as the classification of cultural heritage goods or the analysis of different materials. To address this challenge, it is necessary to characterize the local ”geometric pattern” information, possibly forgetting model size and bending. Seven groups participated in this contest and twenty runs were submitted for evaluation. The performances of the methods reveal that good results are achieved with a number of techniques that use different approaches.

The previous version of our retrieval system has shown some significant results in some retrieval tasks such as Lifelog’s moment retrieval tasks. In this paper, we adapt our platform to the Video Browser Showdown’s KIS and AVS tasks and present how our system performs in video search tasks. In addition to the smart features in our retrieval system that take advantage of the provided analysis data, we enhance the data with object color detection by employing Mask R-CNN and clustering. In this version of our search system, we try to extract the location information of the entities appearing in the videos and aim to exploit the spatial relationship between these entities. We also focus on designing efficient user interaction and a high-performance way to transfer data in the system to minimize the retrieval time.

Inspired by recent advances in computer vision and deep learning, we propose new enhancements to tackle problems appearing in endoscopic image analysis, especially abnormality finding and anatomical landmark detection. In details, a combination of Residual Neural Network and Faster R-CNN are jointly applied in order to take all of their advantages and improve the overall performance. Nevertheless, novel data augmentation is designed and adapted to corresponding domains. Our approaches prove their competitive results in term of not only the accuracy but also the inference time in Medico: The 2018 Multimedia for Medicine Task and The Biomedia ACM MM Grand Challenge 2019. These results show the great potential of the collaborating between deep learning models and data augmentation in medical image analysis applications. Especially, more than 4900 bounding boxes localizing the symptom of some classes from KVASIR dataset that we annotated and used in this project are shared online for future research.

SharkTank is a television show where start-ups pitch their idea to a panel of five investors (sharks) in hopes of striking a deal in the form of equity or royalties for money and other business perks. Since its inception, SharkTank has been a center of discussion and analysis for fans, statisticians, and business people alike in hopes of cracking the code to the start-up world and figuring out the formula for the next big ‘thing’. Most of these discussions and analyses have come in the form of blogs, articles, and academic research. However, there has been a lack of complete datasets and application of computational models for further analysis. In this paper, we investigate factors that play into the SharkTank deal. To this end, we first collect a new dataset, SharkTank Deal Dataset (STDD), by combining data from multiple public sources. The dataset includes descriptive features of each start-up such as product category, team composition, valuation, equity offering, specific sharks that appear on that episode, and state origin. For the computational model, we propose a new computational model to predict whether a start-up strikes a deal with a shark. We conduct experiments to demonstrate the superiority of our model over the baselines.

Despite recent advances in deep learning, the crucial task of nuclear segmentation for computational pathology remains challenging. Recently, deep learning, and specifically U-Nets, have shown significant improvements for this task, but there is still room for improvement by further enhancing the design and training of U-Nets for nuclear segmentation. Specifically, we consider enforcing rotation equivariance in the network, the placement of residual blocks, and applying novel data augmentation designed specifically for histopathology images, and show the relative improvement and merit of each. Incorporating all of these enhancements in the design and training of a U-Net yields significantly improved segmentation results while still maintaining a speed of inference that is sufficient for real-world applications, in particular, analyzing whole-slide images (WSIs). Code for our enhanced U-Net is available at https://github.com/thatvinhton/G-U-Net.

With non-stop development of e-commerce and different internet-based applications’ demands, service providers include many physical servers scattering all over the world. If some users would like to use various services, they have to repeatedly register. Therefore, they must remember all these information such as, user-names or passwords. To solve this problem, many authentication schemes in multi-server environment are proposed. Furthermore, to prevent the adversary from keeping track of another user when he or she logins, many schemes apply dynamic identity, but they have some limitations with popular kinds of attacks such as, replay attack, impersonation attack, or man-in-the-middle attack... In 2014, Yeh (Department of Information Management, National Dong Hwa University) proposed multi-server scheme using Rivest–Shamir–Adleman (RSA). However, we discover this scheme cannot achieve two basic properties, mutual authentication and session-key agreement. In this paper, we concretely demonstrate that discovery and propose a different provable version using elliptic curve cryptosystem in multi-server environment which overcomes the limitation of Yeh’s scheme and satisfies security and efficiency.

Part 5: Virtual and Augmented Reality II; International audience; There are many techniques for locomotion and navigation that can support the exploration of large virtual environments in a limited physical area. Previous studies focused on measuring curvature gains and bending gains applied to the walking direction in the real world. However, the effects of different moving techniques and their relationship with shapes and patterns of virtually moving paths have not been studied extensively before. In this study, we present our experimental results on how users perceive two different traveling-in-place techniques with different bending gains of moving paths using a hybrid electric bike simulator. Moreover, the impact of different factors including road textures, road widths, and road curve directions and their relationships with the techniques are investigated. Generally, users could travel along a curve without noticing with a point of subjective equality (PSE) at bending angle $$\beta $$ = 1.42$$^\circ $$, and a just-noticeable difference (JND) of 0.75$$^\circ $$ for a movement at around 20 km/h during 5 s. In addition, movement technique, curve direction, and future travel path significantly affected how they perceived the curvature of their travel path.

City anomalies such as residential fires or urban floods can cause a lot of damage each year from loss of human lives to negative effects on the productivity of the city. One possible way to deal with these anomalies in a quick and effective manner is to use the information provided by the network of citizens that is likely present at the scene of any serious anomaly. However, this source of information may be of varying quality and there may be too much data for human operators to manually inspect in a speedy manner. This motivates us to propose an architecture to effectively make use of the network of citizens to deal with city anomalies. At the center of our architecture is a neural network that automatically classify incoming image data and use this information to assist anomaly handling efforts. In order for our classifier to work well, we have also collected a dataset of city anomalies specific to Vietnam, consisting of 9963 images. We experiment with several neural network models on our collected dataset. In overall, they all have decent accuracies with the best being MobileNet with 92.3% accuracy.

This paper presents an overview of the foreseen ImageCLEF 2019 lab that will be organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2019. ImageCLEF is an ongoing evaluation initiative (started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2019, the 17th edition of ImageCLEF will run four main tasks: (i) a Lifelog task (videos, images and other sources) about daily activities understanding, retrieval and summarization, (ii) a Medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with newer data, (iii) a new Coral task about segmenting and labeling collections of coral images for 3D modeling, and (iv) a new Security task addressing the problems of automatically identifying forged content and retrieve hidden information. The strong participation, with over 100 research groups registering and 31 submitting results for the tasks in 2018 shows an important interest in this benchmarking campaign and we expect the new tasks to attract at least as many researchers for 2019. acceptedVersion

With nonstop development of communication technologies, all aspects of social life continuously change and so do network systems. When establishing connection is easy, the convenience of online-service receives many users’ attentions, for example, the patients directly access medical system to be advised by doctors at any time. Therefore, user authentication scheme is necessary when we want to provide privacy and security for working sessions. Storing a password list for verification is an old method and not secure. This list can be easily leaked, and adversary can launch an offline password-guessing attack. In addition, information exchanged between user and server needs being prevented from attacker’s decryption. It can be said that current authentication schemes are unsuitable for new security standard. We need a strong user authentication scheme using new approach to overcome existing limitations and guarantee time efficiency. In this paper, we make a design with Chebyshev polynomial to achieve our goals and resist some kinds of attacks.

Many categories of the illusion of self-motion have been widely studied with the potential support of virtual reality. However, the effects of directly and indirectly powering the movement on the possibility of perceiving changes in moving speed and their relationship with sensory feedback on users' speed change perception have not been investigated before. In this paper, we present the results of our user study on the difference in perceiving changes in moving speed between two different movement techniques: "pedaling" and "throttling". We also explore the effects of different velocity gains, accelerations and speeds of airflow, and their interactions with the movement techniques on users' perception of speed changes in addition to user performance and perception. We built a bike simulator that supports both of the movement techniques and provides sensory feedback. In general, "pedaling" gave users more possibility to perceive changes in moving velocity than "throttling".

Lifelogging data provides useful insight understanding about our lives during daily activities. Thus, it is essential to develop a system to assist users to retrieve events or memories from lifelogging data from ad-hoc text queries. In this paper, we first propose a method to process lifelogging data by grouping images into visual shots and clusters, then extract semantic concepts on scene category and attributes, entities, and actions. We then develop a query system that supports 4 main types of query conditions: temporal, spatial, entity and action, and extra data criteria. Our system is expected to efficiently assist users to search for past moments in daily logs.

Writing diary is not only a hobby but also provides a personal lifelog for better analysis and understanding of a user's daily activities and events. However, in a busy society, people may not have enough time to write in diary all their social interaction. This motivates our proposal to develop a ubiquitous system to automatically generate daily text diary using our novel method for image captioning from photos taken periodically from wearable cameras. We propose to incorporate common visual concepts extracted from a photo to enhance the details of the image description. We also propose a wide trail beam search strategy to enhance the naturalness of text caption. Our captioning method improves the results on MSCOCO dataset on four metrics: BLEU, METEOR, ROUGE-L, CIDEr. As compared to the method proposed by Xu et.al and Neuraltalk of Karpathy, our model has better performance on all four metrics. We also develop smart glasses and a prototype smart workplace in which people can have their personal diary generated from photos taken by smart glasses. Furthermore, we also apply a transformer machine translation model in order to translate captions into Vietnamese language. The results are promising and can be used for Vietnamese people.

News classification is among essential needs for people to organize, better understand, and utilize information from the Internet. This motivates the authors to propose a novel method to classify news from social media. First, we propose to vectorize an article with TD2V, our pre-trained Twitter-based universal document representation following Doc2Vec approach. We then define Modified Distance to better measure the semantic distance between two document vectors. Finally, we apply retrieval and automatic query expansion to get the most relevant labeled documents in a corpus to determine the category for a new article. As our TD2V is created from 297 million words in 420,351 news articles from more than one million tweets in Twitters from 2010 to 2017, it can be used as one of the efficient pre-trained models for English document representation in various applications. Experiments on datasets from different online sources show that our method achieves the classification accuracy better than existing methods, specifically 98.4±0.3% (BBC dataset), 98.9±0.7% (BBC Sport dataset), 94.1±0.2% (Amazon4 dataset), and 78.6% (20NewsGroup dataset). Furthermore, in the classification training process, we just encode all articles in the training set with TD2V, not to train a dedicated classification model for each of these datasets.

Document sensitivity classification is essential to prevent potential sensitive data leakage for individuals and organizations. As most of existing methods use regular expressions or data fingerprinting to classify sensitive documents, they may not fully exploit the semantic and content of a document, especially with informal messages and files. This motivates the authors to propose a novel method to classify document sensitivity in realtime with better semantic and content analysis. Taking advantages of deep learning in natural language processing, we use our pre-trained Twitter-based document embedding TD2V to encode a document or a text fragment into a fixed length vector of 300 dimensions. Then we use retrieval and automatic query expansion to retrieve a re-ranked list of semantically similar known documents, and determine the sensitivity score for a new document from those of the retrieved documents in this list. Experimental results show that our method can achieve classification accuracy of more than 99.9% for 4 datasets (snowden, Mormon, Dyncorp, TM) and 98.34% for Enron dataset. Furthermore, our method can early predict a sensitive document from a short text fragment with the accuracy higher than 98.84%.

Gait recognition has been considered as a new promising approach for biometric-based authentication. Gait signals are commonly obtained by collecting motion data from inertial sensors (accelerometers, gyroscopes) integrated in mobile and wearable devices. Motion data is subsequently transformed to a feature space for recognition procedure. One fashionable, effective way to extract features automatically is using conventional Convolutional Neural Networks (CNN) as feature extractors. In this paper, we propose DeepSense-Inception (DSI), a new method inspired from DeepSense, to recognize users from their gait features using Inception-like modules for better feature extraction than conventional CNN. Experiments for user identification on UCI Human Activity Recognition dataset demonstrate that our method not only achieves an accuracy of 99.9%, higher than that of DeepSense (99.7%), but also uses only 149K parameters, less than one third of the parameters in DeepSense (529K parameters). Thus, our method can be implemented more efficiently in limited resource systems.

During a lifetime, a person can have many wonderful and memorable moments that he/she wants to keep. With the development of technology, people now can store a massive amount of lifelog information via images, videos or texts. Inspired by this, we develop a system to automatically generate caption from lifelog pictures taken from wearable cameras. Following up on our previous method introduced at the SoICT 2018 conference, we propose two improvements in our captioning method. We trained and tested the model on the baseline MSCOCO datasets and evaluated on different metrics. The results show better performance compared to our previous model and to some other image captioning methods. Our system also shows effectiveness in retrieving relevant data from captions and achieve high rank in ImageCLEF 2018 retrieval challenge.

Human Activity Recognition is one of the attractive topics to develop smart interactive environment in which computing systems can understand human activities in natural context. Besides traditional approaches with visual data, inertial sensors in wearable devices provide a promising approach for human activity recognition. In this paper, we propose novel methods to recognize human activities from raw data captured from inertial sensors using convolutional neural networks with either 2D or 3D filters. We also take advantage of hand-crafted features to combine with learned features from Convolution-Pooling blocks to further improve accuracy for activity recognition. Experiments on UCI Human Activity Recognition dataset with six different activities demonstrate that our method can achieve 96.95%, higher than existing methods.

Face clustering is a task to partition facial images into disjoint clusters. In this paper, we investigate a specific problem of face clustering in videos. Unlike traditional face clustering problem with a given collection of images from multiple sources, our task deals with set of face tracks with information about frame ID. Thus, we can exploit two kinds of prior knowledge about the temporal and spatial information from face tracks: sequence of faces in the same track and contemporary faces in the same frame. We utilize this forehand lore and characteristic of low rank representation to introduce a new light weight but effective method entitled Very Fast Sparse Clustering (VFSC). Since the superior speed of VFSC, the method can be adapted into large scale real-time applications. Experimental results with two public datasets (BF0502 and Notting-Hill), on which our proposed method significantly breaks the limits of not only speed but also accuracy clustering of state-of-the-art algorithms (up to 250 times faster and 10% higher in accuracy), reveal the imminent power of our approach.

In this paper, we propose two approaches to deal with the problems of video searching: ad-hoc video search and known item search. First, we propose to combine multiple semantic concepts extracted from multiple networks trained on many data domains. Second, to help user find exactly video shot that has been shown before, we propose a sketch based search system which detects and indexes many objects proposed by an object proposal algorithm. By this way, we not only leverage the concepts but also the spatial relations between them.

People frequently post their photos on social network sites (e.g. Facebook, Instagram) as a way to share memorable moments, emotions, or locations visited. While sharing photos with the same subjects as past photos could lead to user reminiscence and potentially create valuable benefits, existing products still cannot support this adequately. Based on a survey on user habits in sharing and revisiting photos on social network sites, we propose NowAndThen, a photo recommendation concept and tool that assists reminiscence when sharing photos on social network sites. By combining visual features of the photos and associated tags, a prototype we developed can recommend old photos that have common subjects with the user's current photos of interest. Our study with the prototype shows that this approach can help users positively revive past memories and connections with their friends. In addition, our results include various design insights and implications for future reminiscence-support systems.

In the internet banking environment, using credit cards to pay the services is one of the most popular and simplest methods. However, this is dangerous if users information or smart-card is stolen. With a non-stop development of mobile and wearable devices, user authentication scheme which is security and low-resource efficient is a subject of interest. Modern devices help securing login phase by exploit biometrics collecting from them. Recently, Saru Kumari et al. proposed a remote scheme with key agreement to overcome popular kinds of attacks. However, in this paper, we realize their scheme is still vulnerable to two-factor attack and does not achieve user anonymity. In addition to security analysis, we presented an improved scheme to have a better solution with elliptic curve cryptosystem.

Recently, the telecare medicine information system (TMIS) is one of the most convenient health-care deliveries. It helps the patient and doctor keep frequent connection, so the quality of medical treatment is enhanced. Two main problems needed to be considered are the security and privacy of patient. Many schemes proposed to satisfy such requirements are not suitable for public medical environment because of their some limitations. For example, the patients identity and password are not protected, time consuming computations in such schemes take a lot of time in authentication phase. In this paper, we survey some typical previous results in this area to inherit some advantages. Afterward, we propose a provable lightweight dedicated scheme appropriate for TMIS in insecure channel.

Advances in modern computing technology have enabled a wide variety of applications in many areas. Realizing that mobile devices such as smartphones and tablets are great learning platforms for educating young children, the authors create an interactive system that helps young kids learn new concepts via videos. By allowing kids to choose objects in the video that they are watching, the system provides relevant information about those objects. It has the effect of enhancing young kids’ understanding about new concepts in the video. The system employs Cross-based Local Multipoint Filtering (CLMF) for object selection and Support Vector Machine (SVM) for recognition. Experimental results show that our system has achieved 95 % recognition accuracy.

The rapid growth of virtual reality in recent years has brought this technology to a wide variety of common users. Realizing the potential of this technology in building and planning cities; the authors introduce a system in which architectures are brought together into a 3-dimensional virtual environment in order to collaborate in building cities. This system uses the Oculus Rift as the VR device, combining with Leap Motion to detect user’s hand-gestures. The authors walkthrough all the details in building such systems including object modeling, communication protocols and gesture recognition technique.

Emotional and memorable moments are usually kept and shared on different online services such as Facebook, Flickr, Instagram, and Google Photos. As a result, one of users’ practical needs is to have their photos annotated automatically, especially with personalized tags. This motivates the authors to propose a system that can suggest personalized annotations for a photo uploaded to online services. Our system provides 2 major features. First, the system automatically recommends personalized annotations for newly uploaded photos based on visually similar photos uploaded in the past. Second, our system propagates manual annotations of users to other similar photos existed in their albums. To evaluate the performance of our system, we use the Oxford 5K Building Dataset and our own dataset consisting of personal photos collected from Facebook. Our systems achieves the mean Average Precision of 0.844 and 0.749 respectively on these two datasets. This demonstrates that our proposed solution can be potentially integrated as a useful utility or extension for online photo sharing services.

Deformable Part Models and Convolutional Neural Network are state-of-the-art approaches in object detection. While Deformable Part Models makes use of the general structure between parts and root models, Convolutional Neural Network uses all information of input to create meaningful features. These two types of characteristics are necessary for face detection. Inspired by this observation, first, we propose an extension of DPM by adaptively integrating CNN for face detection called DeepFace DPM and propose a new combined model for face representation. Second, a new way of calculating non-maximum suppression is also introduced to boost up detection accuracy. We use Face Detection Data Set and Benchmark to evaluate the merit of our method. Experimental results show that our method surpasses the highest result of existing methods for face detection on the standard dataset with 87.06i??% in true positive rate at 1000 number false positive images. Our method sheds a light in face detection which is commonly regarded as a saturated area.