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Temporal action proposal generation (TAPG) aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet plays an important role in many tasks of video analysis and understanding. Despite the great achievement in TAPG, most existing works ignore the human perception of interaction between agents and the surrounding environment by applying a deep learning model as a black-box to the untrimmed videos to extract video visual representation. Therefore, it is beneficial and potentially improve the performance of TAPG if we can capture these interactions between agents and the environment. In this paper, we propose a novel framework named Agent-Aware Boundary Network (ABN), which consists of two sub-networks (i) an Agent-Aware Representation Network to obtain both agent-agent and agents-environment relationships in the video representation, and (ii) a Boundary Generation Network to estimate the confidence score of temporal intervals. In the Agent-Aware Representation Network, the interactions between agents are expressed through local pathway, which operates at a local level to focus on the motions of agents whereas the overall perception of the surroundings are expressed through global pathway, which operates at a global level to perceive the effects of agents-environment. Comprehensive evaluations on 20-action THUMOS-14 and 200-action ActivityNet-1.3 datasets with different backbone networks (i.e C3D, SlowFast and Two-Stream) show that our proposed ABN robustly outperforms state-of-the-art methods regardless of the employed backbone network on TAPG. We further examine the proposal quality by leveraging proposals generated by our method onto temporal action detection (TAD) frameworks and evaluate their detection performances. The source code can be found in this URL https://github.com/vhvkhoa/TAPG-AgentEnvNetwork.git., Comment: Accepted in the journal of IEEE Access Vol. 9

Language prior plays an important role in the way humans detect and recognize text in the wild. Current scene text recognition methods do use lexicons to improve recognition performance, but their naive approach of casting the output into a dictionary word based purely on the edit distance has many limitations. In this paper, we present a novel approach to incorporate a dictionary in both the training and inference stage of a scene text recognition system. We use the dictionary to generate a list of possible outcomes and find the one that is most compatible with the visual appearance of the text. The proposed method leads to a robust scene text recognition model, which is better at handling ambiguous cases encountered in the wild, and improves the overall performance of state-of-the-art scene text spotting frameworks. Our work suggests that incorporating language prior is a potential approach to advance scene text detection and recognition methods. Besides, we contribute VinText, a challenging scene text dataset for Vietnamese, where some characters are equivocal in the visual form due to accent symbols. This dataset will serve as a challenging benchmark for measuring the applicability and robustness of scene text detection and recognition algorithms. Code and dataset are available at https://github.com/VinAIResearch/dict-guided.

It is necessary to develop an efficient and secure mechanism to verify customers digitally for various online transactions. Integrating biometric solutions into the online user registration and verification processes is a promising trend for electronic Know Your Customer (eKYC) systems. However, Deepfake or face manipulation techniques may become a threat for eKYC with face authentication. In this paper, we introduce this potential attack of Deepfake on eKYC by swapping and manipulating faces between source and target faces. We then propose to augment the security for current eKYC systems with Deepfake detection. We conduct the experiments on the 10K video clips in the private test of Deepfake Detection Challenge 2020, and our method, following the Capsule-forensics approach, achieves the Logloss score of 0.5189, among the top 6% best results among the 2114 teams worldwide. This result demonstrates that our deepfake detection algorithm can be a promising method to provide extra protection for eKYC solutions with face registration and authentication.

In everyday life, authentication is an indispensable process of human activities. Bio-metric authentication system is one of the effective solutions, because it uses human-based features, instead of other traditional features, such as pin, password, etc. However, to apply a face authentication system in practical applications, we need to ensure that the system must not try to recognize the face of an unknown person into known categories, meaning we need to reject faces of unknown people in our application. In this paper, we present the limitations of recent Deep Learning based methods in Face Recognition tasks. We then propose two methods helping Face Recognition system have the ability to reject faces from unknown people by using Open-Set concepts. We conduct the experiments on a subset of CASIA-WebFace dataset, with a train set that includes 7000 images of 100 known people and a test set that includes both known and unknown people. Without rejecting unknown faces, the regular face recognition, i.e. the baseline method, yields the accuracy of only 45.9%, as the method tries to classify all face photos into known classes. Our proposed methods, which are combined deep network of Facenet system with recent Open Set methods, are called Learning Placeholder on Facenet (P-Facenet) and Facenet with OpenMax (O-Facenet). They achieve the accuracy of 83.6% and 88.5% respectively. This is a potential approach for authentication with face recognition to decrease the error rate of the model when recognizing faces of unknown people in the wild.

In the context of modern Android application development, security issues and secure programming are considered unignorable aspects to ensure the safety of Android applications while still guarantee the development speed. The lack of attention to security factors in the software development process or the delay of traditional security assurance methods are the main causes of unsafe Android software. Those unsafe Android applications contain many vulnerabilities and a high risk of leaking user information, especially since Android applications are rapidly developed and published. Developers must adhere to a secure development process to counter Android application risks to avoid data leakage or access control flaws. Security has to be integrated throughout the application development process to secure the software development life cycle. This paper presents two main research contributions: summarizing common security issues in Android applications and developing a plug-in for Android Studio to support secure programming, 9Fix. The low-time-cost 9Fix plug-in can inspect your vulnerable code and instantly suggest an alternative secure code for developers in programming time that helps to improve the security and instruct the developers on how to write a secure code. Moreover, the developers can add their own security rules to 9Fix so 9Fix can adapt smoothly in a specific situation. We also demonstrate the effectiveness and the convenience based on the student feedback by experimenting with the 9Fix plugin.

Polyps detection plays an important role in colonoscopy, cancer diagnosis, and early treatment. Many efforts have been made to improve the encoder-decoder framework using the global feature with an attention mechanism to enhance local features, helping to effectively segment diversity polyps. However, using only global information derived from the last encoder block leads to the loss of regional information from intermediate layers. Furthermore, defining the boundaries of some polyps is challenging because there is visual interference between the benign region and the polyps at the border. To address these problems, we propose two novel modules: the Cascading Context module (CCM) and the Attention Balance module (BAM), aiming to build an effective polyp segmentation model. Specifically, CCM combines the extracted regional information of the current layer and the lower layer, then pours it into the upper layer - fusing regional and global information analogous to a waterfall pattern. The BAM uses the prediction output of the adjacent lower layer as a guide map to implement the attention mechanism for the three regions separately: the background, polyp, and boundary curve. BAM enhances local context information when deriving features from the encoder block. Our proposed approach is evaluated on three benchmark datasets with six evaluation metrics for segmentation quality and gives competitive results compared to other advanced methods, for both accuracy and efficiency. Code is available at https://github.com/ntcongvn/CCBANet.

Record companies invest billions of dollars in new talent around the globe each year. Gaining insight into what actually makes a hit song would provide tremendous benefits for the music industry. In this research, we tackle this question by focusing on predicting rank of hit songs in the next 6 months. Our dataset is used in ZALO AI CHALLENGE 2019 in Hit Song Prediction problem including not only songs but also its information such as composer, artist name, released date, etc. Because of that, while most previous work formulates hit song prediction as a regression or classification problem, we present in this paper how to apply Gradient Boosting technique to treat it as a ranking problem. The resulting best model has a good performance when predicting whether a song is a top 10 dance hit versus a lower listed position with 1.48815 Root Mean Square Error - our result dominates most of the solution in this competition (better than 3rd ranked solution of 87 in total). Moreover, it is possible to further improve by extracting chords, tones and more information from each song to obtain the highlights of songs and by using linguistics model to offer high-level features of metadata.

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.

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.

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.

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.

Searchable Symmetric Encryption (SSE) enables clients to securely store data on untrusted server while keeping the ability to search and update over the encrypted data efficiently. For practical purposes, we also need several additional properties to make our search function more versatile. In this paper, we introduce an extension type of SSE which we called Keyword-search Interval Dynamic SSE (KIDSSE). In KIDSSE we can search for encrypted files containing a keyword that occurs in a queried range. We also construct a solution for KIDSSE and show that our scheme maintains all important security properties such as forward privacy, backward privacy.

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.

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.

At DAVIS-2016 Challenge, many state-of-art video segmentation methods achieve potential results, but they still much depend on annotated frames to distinguish between background and foreground. It takes a lot of time and efforts to create these frames exactly. In this paper, we introduce a method to segment objects from video based on keywords given by user. First, we use a real-time object detection system - YOLOv2 to identify regions containing objects that have labels match with the given keywords in the first frame. Then, for each region identified from the previous step, we use Pyramid Scene Parsing Network to assign each pixel as foreground or background. These frames can be used as input frames for Object Flow algorithm to perform segmentation on entire video. We conduct experiments on a subset of DAVIS-2016 dataset in half the size of its original size, which shows that our method can handle many popular classes in PASCAL VOC 2012 dataset with acceptable accuracy, about 75.03%. We suggest widely testing by combining other methods to improve this result in the future.

Paraphrase identification is a core component for many significant tasks in natural language processing (e.g., text summarization, headline generation). A method suggested by Bach et al. for detecting Vietnamese paraphrase text using nine similarity metrics. The authors state that it is the first method for Vietnamese text. They evaluated the method on vnPara corpus with 3000 sentence pairs. However, this corpus is limited by collecting from few Vietnamese websites. Most other methods have focused on the English text. For instance, our previous method detected paraphrasing sentences by matching identical phrases and close words using Wordnet similarity. This method is unsuitable for Vietnamese due to the restriction of Wordnet corpora and morphological words in Vietnamese. Therefore, we extend the method to identify the paraphrase by proposing a SimVN metric which measures the similarity of two Vietnamese words. We evaluated the proposed method on the vnPara corpus. The result shows that the method achieves better accuracy (97.78%) comparing with the state-of-the-art method (accuracy = 89.10%). The proposed method then creates a high diversity paraphrase corpus with 3134 sentence pairs in eight main topics from the top fifteen popular Vietnamese news websites.

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.

Wireless environments such as GSM, 3G, and 4G are more and more popular. Consequently, communications in such networks need to be guarded. It is necessary to have a secure mutual authentication scheme to defend transactions between user and service provider against illegitimate adversaries. Especially, users are those vulnerable to attacks and there are many authentication schemes with smart-card proposed to protect them. Recently, Yung-Cheng Lee has suggested a dynamic identity based user authentication scheme to resist smart-card-theft attack. Nevertheless, he assumed that smart-card is tamperproof. In our opinion, this is not appropriate because Kocher and Messerges pointed that smart-card's confidential information could be extracted by physically monitoring its power consumption. Therefore, design of Yung-Cheng Lee cannot withstand this kind of attack. In addition, anyone who is a legal member can masquerade server or other legal users in his scheme. Moreover, legitimacy verification only starting from server side truly makes Lee's scheme be impractical. In this paper, we present an improvement to his scheme to isolate such problems.

With non-stop growth in network environments, communication security is necessary. A strong protocol guarantees that users and service providers are secure against many kinds of attacks, such as impersonation and replay attack. Sood et al. proposed an authentication scheme based on dynamic identity to prevent transactions from being intercepted by malicious users. Although they claimed that their scheme has advantages over previous schemes with the same approach, we prove that their scheme is vulnerable to impersonation attack and stolen verification attack, and can be affected by clock synchronization. Therefore we propose a novel authentication scheme to enhance security and overcome limitations existing in Sood's scheme. Our security analysis shows that our proposed method can efficiently resist known types of attacks. Experimental results also show that the method can be implemented and processed in real-time thus applicable for not only regular computers but also mobile devices.

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.

Video instance search or also well known as object retrieval is a fundamental task in computer vision field and has a lot of applications. Most state-of-the-art systems are based on the Bag-of-Words model (BOW) for representing video frames and target object. When searching on nearly planar and rich-textured objects such as buildings and book cover, BOW argue to be a suitable model with very high performance. However, when searching on harder but more popular objects such as a specific person, BOW model still keep a lower performance. In this paper, we consider a new type of query which covers most popular topics: searching a person in a specific location. Inspired by recent successes of deep learning techniques, we propose new framework which leverage the powerful of both BOW model and deep feature in instance search. In particular, we use a linear kernel classifier instead of using L2 distance to compute similarity between two deep features. For further improvement, scene tracking are employed to deal with the cases face of query person is not detected. To evaluate the proposed methods, we conduct experiments over a standard benchmark dataset (TRECVID Instance Search 2016) with more than 300 GB in storage and 464 hours in duration. The results show that, our proposed methods significant improve the baseline system.

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.

We propose and analyze in details the revised model of XPROB, an infinite family of pool-based anonymous communication systems that can be used in various applications including high performance computing environments. XPROB overcomes the limitations of APROB Channel that only resists a global delaying adversary (GDA). Each instance of XPROB uses a pool mix as its core component to provide resistance against a global active adversary (GAA), a stronger yet more practical opponent than a GDA. For XPROB, a GAA can drop messages from users but cannot break the anonymity of the senders of messages. Analysis and experimental evaluations show that each instance of XPROB provides greater anonymity than APROB Channel for the same traffic load and user behaviors (rate and number of messages sent). In XPROB, any message can be delivered with high probability within a few rounds after its arrival into the system; thus, an opponent cannot be certain when a message will be delivered. Furthermore, users can choose their own preference balance between anonymity and delay. Through the evaluation, we prove that XPROB can provide anonymity for users in high-performance computing environments.

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.

Bag-of-Word based model is one of the state-of-the-art approaches for object retrieval or also known as instance search problem. Although this model and its extensions are good for rich-textured objects, it is still unsolved for searching on textureless ones. In this paper, we propose to combine this model with Deformable Part Models object detector using late fusion technique to improve final result. To find the optimal weights for each type of query objects, we further propose to use a neural network to learn query features including object area, number of shared visual words to get optimal weights for each model. Experimental results on TRECVID Instance Search (INS) dataset with queries in INS2013 and INS2014 show that our proposed method significantly improves 18.48% and 14.63% in mAP respectively comparing to standard BOW model and outperform other state-of-the-art methods. This method opens a new way of adaptively combining DPM, an object detector, in a hybrid model for visual instance search.

Learn to read and understand a music sheet, then play it on a musical instrument are difficult tasks to most beginner music learners. This motivates the authors to propose Virtual Music Teacher, a system to assist beginner music learners in their learning process. By applying our proposed lightweight Optical Music Recognition algorithm to scan and recognize a music sheet, then combine with sound classifying technique, the proposed system can learn what note to be played next, then help a music learner to play it correctly. The experimental results on the dataset consisting of 15 musical scores for beginners show that the proposed system can classify with precision up to 99.9 % using multiple SVM classifiers approach, whereas the sound classifying technique using Fast Fourier Transform can classify note’s pitch recorded from a piano with precision up to 95.71 %. The system is implemented as an application on mobile devices and can be used to assist a music learner to play not only piano but other musical instruments as well.

Face verification is a promising method for user authentication. Besides existing methods with deep convolutional neural networks to handle millions of people using powerful computing systems, the authors aim to propose an alternative approach of a lightweight scheme of convolutional neural networks (CNN) for face verification in realtime. Our goal is to propose a simple yet efficient method for face verification that can be deployed on regular commodity computers for individuals or small-to-medium organizations without super-computing strength. The proposed scheme targets unconstrained face verification, a typical scenario in reality. Experimental results on original data of Labeled Faces in the Wild dataset show that our best CNN found through experiments with 10 hidden layers achieves the accuracy of \((82.58 \pm 1.30)\,\%\) while many other instances in the same scheme can also approximate this result. The current implementation of our method can run at 60 fps and 235 fps on a regular computer with CPU-only and GPU configurations respectively. This is suitable for deployment in various applications without special requirements of hardware devices.

As of today, millions of people share messages via online social networks, some of which probably contain sensitive information. An adversary can collect these freely available messages and specifically analyze them for privacy leaks, such as the users’ location. Unlike other approaches that try to detect these leaks using complete message streams, we put forward a rule-based approach that works on single and very short messages to detect location leaks. We evaluated our approach based on 2817 tweets from the Tweets2011 data set. It scores significantly better (accuracy = 84.95 %) on detecting whenever a message reveals the user’s location than a baseline using machine learning and three extensions using heuristic. Advantages of our approach are not only to apply for online social network messages but also to extend for other areas (such as email, military, health) and for other languages.

The authors propose a novel pre-processing phase that can be integrated into conventional methods to detect and recognize planar visual objects in printed materials with low computational cost and higher accuracy. A simple yet efficient visual saliency estimation technique based on regional contrast is developed to quickly filter out low informative regions in printed materials. By eliminating noisy or unimportant keypoint candidates, our proposed method not only reduces unnecessary computational cost of keypoint descriptors but also increases robustness and accuracy of visual object recognition. Our experimental results show that the whole visual object recognition process can be speeded up 46 times and the accuracy can increase up to 23%. These are desirable advantages for an augmented reality system, especially on mobile devices. Furthermore, this pre-processing stage is independent of the choice of features and matching model in a general process. Therefore it can be used to boost the performance of existing systems into real-time manner.

Graphical user interfaces are usually first sketched out manually as hand drawing pictures and then must be realized by software developers to become prototypes or usable user interfaces. This motivates our proposal of a smart CASE tool that can understand hand drawing sketches of graphical user interfaces, including forms and their navigations, then automatically transform such draft designs into real user interfaces of a prototype or an application. By using the ideas of modeling and model-transformation in model driven engineering, the authors also propose a mechanism to generate graphical user interfaces as forms targeting different platforms. Experimental results show that our sketch recognition to understand hand drawing graphical user interfaces can achieve the accuracy of 97.86% and 95% in recognizing 7 common UI controls and arrows for navigation respectively. Our model transformation engine can generate user interfaces as forms for applications on 3 different platforms of mobile devices, including Windows Phone, Android, and iOS. This approach follows the trend to develop a new generation of smart CASE tools that can understand and interpret conceptual software design models into concrete software elements and components to assist the software development process in a natural way.

Time-related information in text posted on-line is one type of personal information targeted by attackers, one reason that sharing information online can be risky. Therefore, time information should be anonymized before it is posted on social networking services. One approach to anonymizing information is to replace sensitive phrases with anonymous phrases, but attackers can usually spot such anonymization due to its unnaturalness. Another approach is to detect temporal passages in the text, but removal of these passages can make the meaning of the text unnatural. We have developed an algorithm that can be used to anonymize time-related personal information by removing the temporal passages when doing so will not change the natural meaning of the message. The temporal phrases are detected by using machine-learned patterns, which are represented by a subtree of the sentence parsing tree. The temporal phrases in the parsing tree are distinguished from other parts of the tree by using temporal taggers integrated into the algorithm. In an experiment with 4008 sentences posted on a social network, 84.53 % of them were anonymized without changing their intended meaning. This is significantly better than the 72.88 % rate of the best previous temporal phrase detection algorithm. Of the learned patterns, the top ten most common ones were used to detect 87.78 % the temporal phrases. This means that only some of the most common patterns can be used to the anonymize temporal phrases in most messages to be posted on an SNS. The algorithm works well not only for temporal phrases in text posted on social networks but also for other types of phrases (such as location and objective ones), other areas (religion, politics, military, etc.), and other languages.

Password is one of the simple and efficient methods to protect the transactions in insecure network environments. There are many authors researching in this area to suggest the protocols preventing illegitimate users from accessing the systems. In 2013, Y-H An proposed the scheme to isolate some problems which exist in Khan et al.’s scheme. In this paper, we demonstrate that Y-H An’s scheme is vulnerable to server forgery attack and cannot provide user’s anonymity. Furthermore, we also propose the modified scheme to overcome these limitations.

The application of fingerprinting techniques to relational data cannot protect personal information against a collusion attack, in which the attacker has access to a set of published data. The general fingerprinting techniques such as Li et at.’s, Guo et al.’s, and Schrittwieser et al.’s focus on detecting the traitor, who leaked the data. Among them, Schrittwieser et al.’s fingerprinting technique combines \(k\)-anonymity and full-domain generalization in order to not only detect traitors but also protect personal records. However, the technique has two main limitations. First, it does not allow the data provider to insert or delete records from the original data. Secondly, it does not create enough fingerprints for data recipients. To overcome these limitations, in this paper, we propose an (\(\alpha ,k\))-privacy protection model, an extension of \(m\)-invariance and (\(\alpha , k\))-anonymity, and a new top-down (\(\alpha , k\))-privacy fingerprinting algorithm based on that model. The model not only protects sensitive personal information against collusion attacks but also allows data providers to republish their updated original data without degrading the privacy protection. The algorithm embeds fingerprints in the generalized data and extracts them from leaked data to detect the traitors. We extensively evaluate the proposed algorithm on our own built software. The evaluation results show that our algorithm creates more fingerprints than Schrittwieser et al.’s algorithm (64000 vs 1536) while achieving the same generalized data quality. Moreover, our (\(\alpha , k\))-privacy algorithm creates generalized data even in the case of having small number of distinct sensitive values in the original data without adding faked records as in \(m\)-invariance.

Online transactions with mobile devices through internet environment have become popular worldwide. Therefore, many authentication schemes have been proposed to protect users from various potential attacks in e-transactions with online service providers from mobile devices. In 2013, Khan et. al. propose a key-hash based scheme on mobile devices to resist known kinds of attacks that previous schemes cannot resist. However, we prove that Khan et. al.’s scheme still cannot withstand impersonation, denial of service, and three-factor attacks. This motivates our proposal of an improved scheme to further overcome the found limitations in Khan’s scheme. The main idea of our proposed method is that the user ID and the secret key of the server are hashed together to prevent user impersonation. We also prove that our method can also resist against known attacks, such as server and user impersonation attack, replay attack, password guessing attack, malicious user attack, mobile device loss attack, attacks due to ID theft, attacks using login request.

Mobile devices (e.g., PDA, mobile phone, tablet, and notebook PC) become necessary for a convenient and modern life. So, we can use them to access services, for examples online shopping, internet banking. In such insecure environment, we see that communications are more and more essential because they defend users and providers against illegitimate adversaries. Recently, Shin et al have proposed scientific paper entitled ’A Remote User Authentication Scheme with Anonymity for Mobile Devices’ to enhance security for remote user authentication. They claimed that their scheme is truly more secure than previous ones and it can resist various attacks. However, it is not true because their scheme’s vulnerable to insider, impersonation and replay attacks. In this paper, we present an improvement to their scheme to isolate such problems.

Lateral connection is a fundamental element of human neural networks which enables sparse learning and topographical order in feature maps. Due to high complexity and computational cost, computer scientists tend to simplify it in practical implementations. To utilize the simplicity of traditional networks while preserving the effects of interconnections, the authors employ numerical filters in unsupervised learning networks. These filters suppress low activations and decorrelate high ones, which are similar to how inhibitory lateral connections behave. Inhibitory networks outperform conventional approach in both standard datasets CIFAR-10 and STL-10. Our method also yields competitive results in comparison with other single-layer unsupervised networks. Furthermore, it is promising to apply inhibitory networks into deep learning systems for complex recognition problem.

To boost up power of unsupervised feature learning and deep learning, there has been a great effort in optimizing network structure to learn more efficient high level features. It is crucial for a network to have a sufficient amount of learnable parameters yet still be able to capture in variances in data. In this paper, the authors propose spatial boosting networks, which employ convolutional feature learning networks as learning components. Each component in a network is assigned to a certain spatial region. This allows the network learn more adaptive features for each region. In order to make spatial boosting networks to capture relationship between regions of the visual field, we also propose convolutional pooling procedure. By expanding pooling scope into overlapping regions, we expect the features pooled in higher level to be more robust to noises and more invariant to transformation. Experiments show that using spatial boosting networks boosts up accuracy up to 3% from conventional approaches in standard datasets CIFAR and STL. Moreover, these results are competitive in comparison with other methods by using only a basic feature learning algorithm.

Because of non-stop applications' growth of network environment, making communications in such ambiance security is very important. Recently, Ashok Kumar Das has proposed a scheme to recover some pitfalls in Li et al.'s scheme. Das's protocol also utilizes biometrics and random value instead of time - stamp. Besides, storing temporary nonce to withstand replay attack is an inherited aspect of this scheme. However, in this paper, we show Das's scheme has many problems. To isolate them, we suggested a new design. Our scheme not only saves computational cost but also enhances security for transactions in insecure network channel.

Part 2: Asian Conference on Availability, Reliability and Security (AsiaARES); International audience; Communication is necessary operations in wireless environments. Therefore, we must have a secure remote authentication to defend transactions against illegitimate adversaries in such risky channel. Smart card is one of methods that many schemes used due to its convenience. Recently, Khurram Khan has proposed an enhancement scheme using smart card to recover some pitfalls in Wang et al.’s scheme. They claimed that their scheme remedy those security flaws. Nevertheless, we point out that Khan et al.’s scheme cannot protect user’s anonymity. Besides, it does not achieve secret key forward secrecy and cannot resist denial of service attack due to values stored in server’s database. Consequently, we present an improvement to their scheme to isolate such problems.

The authors propose a system that allows presenters to control presentations in a natural way by their body gestures and vocal commands. Thus a presentation no longer follows strictly a rigid sequential structure but can be delivered in various flexible and content adapted scenarios. Our proposed system fuses three interaction modules: gesture recognition with Kinect 3D skeletal data, key concepts detection by context analysis from natural speech, and small-scaled hand gesture recognition with haptic data from smart phone sensors. Each module can process in realtime with the accuracy of 95.0%, 91.2%, and 90.1% respectively. The system uses events generated from the three modules to trigger pre-defined scenarios in a presentation to enhance the exciting experience for audiences.

Recent researches in cognitive science and document recognition have been applied to deal with the problem of categorizing object. Bag-of-Features (BoF) and its extension Spatial Pyramid Matching (SPM) have made a breakthrough in resolving this kind of challenges. Many methods followed this guideline really enhance the recognition accuracy but still have drawbacks in developing a real-world application whose data size is many times bigger. In this paper we propose two kinds of strategy include five criteria to evaluate and select the most appropriate training samples using for building a high performance classifier. We also suggest a method called reinforcement codebook learning to make the codebook training process not only purpose-built to best fits with the most suitable criteria but also much more efficient by reducing significantly its complexity of computation. Experiments on benchmark object dataset demonstrate that our proposed framework outperforms remarkable results and is comparable with the state-of-the-art in spite of using just 20% of 9 · 106 descriptors for training the dictionary. These results give a promise of building a efficient and feasible object categorization system for practical application as so as suggest some ideas to improve the visual feature representation in future.

The wide variation in features and capabilities of mobile devices lead to difficulties in the development of the same application on different platforms. Therefore we propose an online model-driven integrated development environment to provide developers with a platform-independent GUI design for mobile applications. Our proposed system transforms an abstract platform-independent GUI into a platform-dependent GUI on a target platform. The generated project is entirely in the form with which an experienced developer on the platform is already familiar. Furthermore, as the generated application is not a web-based one, it can access naturally native features of a platform. The proposed flexible architecture enable the capability to handle and update different abstract UI and non-UI controls needed to design GUIs for mobile applications. Experimental results with volunteers show that our proposed solution can save up to 25--51% time to create GUIs of an application to three different platforms of Android, iOS and Windows Phone.

Dynamic ID based authentication scheme is more and more important in wireless environments such as GSM, CDPD, 3G and 4G. One of important properties of such authentication scheme is anonymity. It must be guaranteed to defend the privacy of mobile users against outside attacks, and the scheme of Cheng-Chi Lee, Tsung-Hung Lin and Rui-Xiang Chang satisfies that requirement. However, another important property that should be considered is impersonation. The scheme must have capability to resist this kind of attack to protect legal users from illegal adversaries. In this paper, we demonstrate that Lee et al.’s scheme is still vulnerable to masquerade attack and session key attack with stolen smart card. Then we present an improvement of their scheme in order to isolate such problems.

Sharing information is one of the most important parts of social activities. However, sharing information can leak users' information. Removing all direct identifiers is not enough. Sweeney proposed an approach that applying k-anonymity to protect users' identities from linking attack. Sweeney`s algorithm finds out the optimal anonymized dataset through minimal distortion metric. Other authors proposed other optimal algorithms but their proposals are still impractical due to their high computational cost. Another approach is to release the minimal anonymized dataset by applying some heuristics. Wang and Fung proposed Bottom-up Generalization and Top-down Specialization (TDS) to publish a minimal anonymized dataset with information loss metric, whose performance is more efficient. However, these algorithms still have some limitations. In this paper, we propose an algorithm to publish anonymized datasets through bottom-up generalization approach and information loss data metric. Our algorithm can save time by storing statistical information for later usage. The experimental results is performanced on Adult dataset, which is used in all former algorithms. Experimental results show that our algorithm can process 949,662 records dataset in 42.219s. Classification error on anonymized data, which is created by our algorithm, is lower than Wang's algorithm 3.8%.

Communications in the wireless environments are more and more popular. Consequently, it is necessary to have a secure authentication scheme to protect transactions between users and servers from illegal adversaries. Especially, users are people vulnerable to attacks and there are many authentication schemes with smart cards proposed to guarantee them. Recently, Li et al have proposed a biometrics-based scheme to enhance security for authentication. They claimed that their scheme truly is more secure than previous ones and it can resist various attacks. However, it is not true. Their scheme is vulnerable to replay attack, man-in-the-middle attack, stolen-verifier attack and doesn't have forward secrecy property. Furthermore, their scheme cannot protect user's anonymity. So, adversaries can steal identity to re-register to obtain secret key to impersonate user. In this paper, we present an improvement to their scheme in order to isolate such problems.

Texts, images, audio and video clips about products are important information for customers in shopping. However customers cannot have such information as soon as they see the products in physical stores. The authors propose a Fast Planar Object Detection in Multimedia Augmentation for Products system using mobile devices to provide useful information for customers when they go shopping. The experimental results show the strength of our proposed system in processing and displaying multimedia information on users' mobile devices in real-time. The system can be used as a smart assistant for customers to get extra useful information about products and help them decide the best choice for their demands.