Biblio
Every so often Humans utilize non-verbal gestures (e.g. facial expressions) to express certain information or emotions. Moreover, countless face gestures are expressed throughout the day because of the capabilities possessed by humans. However, the channels of these expression/emotions can be through activities, postures, behaviors & facial expressions. Extensive research unveiled that there exists a strong relationship between the channels and emotions which has to be further investigated. An Automatic Facial Expression Recognition (AFER) framework has been proposed in this work that can predict or anticipate seven universal expressions. In order to evaluate the proposed approach, Frontal face Image Database also named as Japanese Female Facial Expression (JAFFE) is opted as input. This database is further processed with a frequency domain technique known as Discrete Cosine transform (DCT) and then classified using Artificial Neural Networks (ANN). So as to check the robustness of this novel strategy, the random trial of K-fold cross validation, leave one out and person independent methods is repeated many times to provide an overview of recognition rates. The experimental results demonstrate a promising performance of this application.
Deep learning based facial expression recognition (FER) has received a lot of attention in the past few years. Most of the existing deep learning based FER methods do not consider domain knowledge well, which thereby fail to extract representative features. In this work, we propose a novel FER framework, named Facial Motion Prior Networks (FMPN). Particularly, we introduce an addition branch to generate a facial mask so as to focus on facial muscle moving regions. To guide the facial mask learning, we propose to incorporate prior domain knowledge by using the average differences between neutral faces and the corresponding expressive faces as the training guidance. Extensive experiments on three facial expression benchmark datasets demonstrate the effectiveness of the proposed method, compared with the state-of-the-art approaches.
An experiment and numerical simulations analyze low-speed OSC derived XPM-induced phase noise penalty in 100-Gbps WDM systems. WDM transmission performance exhibits signal bit-pattern dependence on OSC, which is due to deterioration in SD-FEC performance.
Quantum low probability of intercept transmits ciphertext in a way that prevents an eavesdropper possessing the decryption key from recovering the plaintext. It is capable of Gbps communication rates on optical fiber over metropolitan-area distances.
The paper introduces a method of efficient partial firmware update with several advantages compared to common methods. The amount of data to transfer for an update is reduced, the energetic efficiency is increased and as the method is designed for over the air update, the radio spectrum occupancy is decreased. Herein described approach uses Lua scripting interface to introduce updatable fragments of invokable native code. This requires a dedicated memory layout, which is herein introduced. This method allows not only to distribute patches for deployed systems, but also on demand add-ons. At the end, the security aspects of proposed firmware update system is discussed and its limitations are presented.
Quick UDP Internet Connections (QUIC) is an experimental transport protocol designed to primarily reduce connection establishment and transport latency, as well as to improve security standards with default end-to-end encryption in HTTPbased applications. QUIC is a multiplexed and secure transport protocol fostered by Google and its design emerged from the urgent need of innovation in the transport layer, mainly due to difficulties extending TCP and deploying new protocols. While still under standardisation, a non-negligble fraction of the Internet's traffic, more than 7% of a European Tier1-ISP, is already running over QUIC and it constitutes more than 30% of Google's egress traffic [1].
Cyber-physical systems (CPS) are state-of-the-art communication environments that offer various applications with distinct requirements. However, security in CPS is a nonnegotiable concept, since without a proper security mechanism the applications of CPS may risk human lives, the privacy of individuals, and system operations. In this paper, we focus on PHY-layer security approaches in CPS to prevent passive eavesdropping attacks, and we propose an integration of physical layer operations to enhance security. Thanks to the McEliece cryptosystem, error injection is firstly applied to information bits, which are encoded with the forward error correction (FEC) schemes. Golay and Hamming codes are selected as FEC schemes to satisfy power and computational efficiency. Then obtained codewords are transmitted across reliable intermediate relays to the legitimate receiver. As a performance metric, the decoding frame error rate of the eavesdropper is analytically obtained for the fragmentary existence of significant noise between relays and Eve. The simulation results validate the analytical calculations, and the obtained results show that the number of low-quality channels and the selected FEC scheme affects the performance of the proposed model.
To solve the high-resolution three-dimensional (3D) microwave imaging is a challenging topic due to its inherent unmanageable computation. Recently, deep learning techniques that can fully explore the prior of meaningful pattern embodied in data have begun to show its intriguing merits in various areas of inverse problem. Motivated by this observation, we here present a deep-learning-inspired approach to the high-resolution 3D microwave imaging in the context of Generative Adversarial Network (GAN), termed as GANMI in this work. Simulation and experimental results have been provided to demonstrate that the proposed GANMI can remarkably outperform conventional methods in terms of both the image quality and computational time.
Person re-identification(Person Re-ID) means that images of a pedestrian from cameras in a surveillance camera network can be automatically retrieved based on one of this pedestrian's image from another camera. The appearance change of pedestrians under different cameras poses a huge challenge to person re-identification. Person re-identification systems based on deep learning can effectively extract the appearance features of pedestrians. In this paper, the feature enhancement experiment is conducted, and the result showed that the current person reidentification datasets are relatively small and cannot fully meet the need of deep training. Therefore, this paper studied the method of using generative adversarial network to extend the person re-identification datasets and proposed a label smoothing regularization for outliers with weight (LSROW) algorithm to make full use of the generated data, effectively improved the accuracy of person re-identification.
In this paper, we present a semi-supervised remote sensing change detection method based on graph model with Generative Adversarial Networks (GANs). Firstly, the multi-temporal remote sensing change detection problem is converted as a problem of semi-supervised learning on graph where a majority of unlabeled nodes and a few labeled nodes are contained. Then, GANs are adopted to generate samples in a competitive manner and help improve the classification accuracy. Finally, a binary change map is produced by classifying the unlabeled nodes to a certain class with the help of both the labeled nodes and the unlabeled nodes on graph. Experimental results carried on several very high resolution remote sensing image data sets demonstrate the effectiveness of our method.
Efficient monitoring of high speed computer networks operating with a 100 Gigabit per second (Gbps) data throughput requires a suitable hardware acceleration of its key components. We present a platform capable of automated designing of hash functions suitable for network flow hashing. The platform employs a multi-objective linear genetic programming developed for the hash function design. We evolved high-quality hash functions and implemented them in a field programmable gate array (FPGA). Several evolved hash functions were combined together in order to form a new reconfigurable hash function. The proposed reconfigurable design significantly reduces the area on a chip while the maximum operation frequency remains very close to the fastest hash functions. Properties of evolved hash functions were compared with the state-of-the-art hash functions in terms of the quality of hashing, area and operation frequency in the FPGA.
In order to study the application of improved image hashing algorithm in image tampering detection, based on compressed sensing and ring segmentation, a new image hashing technique is studied. The image hash algorithm based on compressed sensing and ring segmentation is proposed. First, the algorithm preprocesses the input image. Then, the ring segment is used to extract the set of pixels in each ring region. These aggregate data are separately performed compressed sensing measurements. Finally, the hash value is constructed by calculating the inner product of the measurement vector and the random vector. The results show that the algorithm has good perceived robustness, uniqueness and security. Finally, the ROC curve is used to analyze the classification performance. The comparison of ROC curves shows that the performance of the proposed algorithm is better than FM-CS, GF-LVQ and RT-DCT.