Reviewerly/exHarmony huggingface dataset:
Extended interval type-II and kernel based sparse representation method for face recognition

In the world of ubiquitous computing, fuzzy logic has been emerged as an important research area in the field of face recognition (FR) applications. In this paper, a new efficient and advanced method; inspired from interval type-II fuzzy membership concept of fuzzy logic is proposed. The motivation behind our method is to exploit the benefit of an extended interval type-II membership function: a new concept to fuzzy logics; in collaboration with kernel based sparse representation for FR. To integrate all the pluton, we propose a method called: extended interval type-II and kernel based sparse representation method (ExIntTy2KBSRM). In our proposed method, first we figure out the measure of participation of individual pixels in identifying face images using a variant of Interval type-II fuzzy logic i.e. extended interval type-II membership function in place of type 1 fuzzy logic. Next, we calculate K nearest neighbor to training specimens using simple Euclidean distance metric for sparse representation of each test specimen as a combinatorial of calculated K nearest training specimens. After this, we do classification based on contribution made by calculated train specimens in representation results. The efficacy and effectiveness of our proposed method is shown based on experiments performed on various standard databases. The experimental results show that our method deal with challenges of face recognition more efficiently as compared to other state of art methods, as it integrates the pluton of two different membership function based extended interval type-II fuzzy logics in collaboration with kernel sparse representation. Also, our experimental analysis tells that our proposed method improves the classification accuracy to 2–10% greater than the other existing relevant methods. Impact and significance of the proposed method: The main impact of the proposed method in FR based expert and intelligent systems is that, it considers unseen information available in pixel values of a face image present due to non-linear variations and overlapping of pixels. It also contains the advantage of spatial similar structure information present in face images. This makes the system more effective and efficient in processing face images for FR. Our method also works where Gaussian membership function does not work and discretizes linear and non-linear functions appropriately. Thus, the proposed method is universally applicable to solve more challenges of FR viz. illumination, occlusion, expression etc. and also has application in other areas viz. medical image processing, decision making problems, hand written words recognition, speech processing, watermarking etc. Also, our method makes FR systems computationally more efficient and cost effective by using sparse concept to matrices, which makes system to consume less memory and process data faster.
A calibration method for stereovision system based on solid circle target

All kinds of calibration methods have many optimal variables and complicated computation. How to calibrate stereovision system rapidly and precisely is always a pressing problem in the field of visual measurement. Therefore, a calibration method for stereovision system based on solid circle target is proposed in the present paper. A solid circle calibration pattern is adopted to calibrate the stereovision system. The centers of solid circles on calibration pattern are extracted by Canny-Zernike combination algorithm and sorted based on a triangle mark on calibration pattern. According to the pinhole camera model, the intrinsic and extrinsic parameters of left and right cameras are firstly obtained. Then, the distortion model including four parameters is adopted, Levenberg-Marquardt algorithm is used to separately optimize the image coordinates of projection of 3D point on the imaging plane of left and right cameras, the intrinsic parameters including distortion coefficients and extrinsic parameters of stereovision system are acquired in this step. In the last step, the distance between two centers on diagonal direction of calibration pattern is regarded as the constraint to further optimize the extrinsic parameters of stereovision system. Furthermore, a calibration software for stereovision system based on solid circle target is compiled with MATLAB, and the comparison test of two calibration methods and two calibration patterns are performed in lab. Results show that the proposed method has a higher precision, can automatically proceed calibration and can be widely used in engineering.
Analysing of exchange rate and gross domestic product (GDP) by adaptive neuro fuzzy inference system (ANFIS)

In this paper was investigated the effect of exchange rate pass-through (ERPT) into aggregate import prices. The results shown in the long run pass-through was not observed. The results of our research confirm the results of previous investigations according to which pass-through is somewhat higher in transition countries than in developed ones and the pass-through degree is declining. Afterwards exchange rate effect on gross domestic product (GDP) was investigated by adaptive neuro fuzzy inference system (ANFIS). Dataset was collected from EUROSTAT database for 37 countries. The dataset was divided into two sets, for training and for testing of the ANFIS model. The main goal was to predict GDP in relation to exchange rate. ANFIS model was created by three Bell shaped membership functions since it was the best model according to the obtained results. Linear membership functions was used for output. Results shown that the ANFIS model can be used for GDP prediction based on exchange rate values.
Anticipating Where People will Look Using Adversarial Networks

We introduce a new problem of gaze anticipation on future frames which extends the conventional gaze prediction problem to go beyond current frames. To solve this problem, we propose a new generative adversarial network based model, Deep Future Gaze (DFG), encompassing two pathways: DFG-P is to anticipate gaze prior maps conditioned on the input frame which provides task influences; DFG-G is to learn to model both semantic and motion information in future frame generation. DFG-P and DFG-G are then fused to anticipate future gazes. DFG-G consists of two networks: a generator and a discriminator. The generator uses a two-stream spatial-temporal convolution architecture (3D-CNN) for explicitly untangling the foreground and background to generate future frames. It then attaches another 3D-CNN for gaze anticipation based on these synthetic frames. The discriminator plays against the generator by distinguishing the synthetic frames of the generator from the real frames. Experimental results on the publicly available egocentric and third person video datasets show that DFG significantly outperforms all competitive baselines. We also demonstrate that DFG achieves better performance of gaze prediction on current frames in egocentric and third person videos than state-of-the-art methods.
A coupled encoder–decoder network for joint face detection and landmark localization

Face detection and landmark localization have been extensively investigated and are the prerequisite for many face related applications, such as face recognition and 3D face reconstruction. Most existing methods address only one of the two problems. In this paper, we propose a coupled encoder–decoder network to jointly detect faces and localize facial key points. The encoder and decoder generate response maps for facial landmark localization. Moreover, we observe that the intermediate feature maps from the encoder and decoder represent facial regions, which motivates us to build a unified framework for multi-scale cascaded face detection by coupling the feature maps. Experiments on face detection using two public benchmarks show improved results compared to the existing methods. They also demonstrate that face detection as a pre-processing step leads to increased robustness in face recognition. Finally, our experiments show that the landmark localization accuracy is consistently better than the state-of-the-art on three face-in-the-wild databases.
Deep learning for printed document source identification

Due to the rapid development of the information technology and wide use of the Internet, Information is easily to be obtained in the form of digital format. Digital content can be freely printed into documents since the convenience and accessibility of the printers. On the other hand, printed documents can be illegally manipulated by some criminal issues such as: forged documents, counterfeit currency, copyright infringement, and so on. Therefore, how to develop an efficient and appropriate safety testing tool to identify the source of printed documents is an important task in the meantime. Currently, the forensic system using the statistical methods and support vector machine technology has been able to identify the source printer for the text and the image documents. Such an approach belongs to the category of shallow machine learning with human interaction during the stages of feature extraction, feature selection and data pre-processing. In this paper, a deep learning system to solve the complex image classification problem is developed by Convolutional Neural Networks (CNNs) of deep learning which can learn the features automatically. Systematic experiments have been performed for both systems. For microscopic documents, feature based SVM system outperforms the deep learning system with limited gap. For scanned documents, both system can achieve equally well with high accuracy. Both systems should be constantly evaluated and compared for the best interest in universal utilization.
Practical Applications of Improved Gaussian Sampling for Trapdoor Lattices

Lattice trapdoors are an important primitive used in a wide range of cryptographic protocols, such as identity-based encryption (IBE), attribute-based encryption, functional encryption, and program obfuscation. In this paper, we present software implementations of the Gentry-Peikert-Vaikuntanathan (GPV) digital signature, IBE and ciphertext-policy attribute-based encryption (CP-ABE) schemes based on an efficient Gaussian sampling algorithm for trapdoor lattices, and demonstrate that these three important cryptographic protocols are practical. One important aspect of our implementation is that it supports prime moduli, which are required in many cryptographic schemes. Also, our implementation uses bases larger than two for the gadget matrix whereas most previous implementations use the binary base. We show that the use of higher bases significantly decreases execution times and storage requirements. We adapt IBE and CP-ABE schemes originally based on learning with errors (LWE) hardness assumptions to a more efficient Ring LWE (RLWE) construction. To the best of our knowledge, ours are the first implementations employing the Gaussian sampling for non-binary bases of the gadget matrix. The experimental results demonstrate that our lattice-based signature, IBE and CP-ABE implementations, which are based on standard assumptions with post-quantum security, provide a performance comparable to the recent state-of-the-art implementation works based on stronger/non-post-quantum assumptions.
DN-ResNet: Efficient Deep Residual Network for Image Denoising

A deep learning approach to blind denoising of images without complete knowledge of the noise statistics is considered. We propose DN-ResNet, which is a deep convolutional neural network (CNN) consisting of several residual blocks (ResBlocks). With cascade training, DN-ResNet is more accurate and more computationally efficient than the state of art denoising networks. An edge-aware loss function is further utilized in training DN-ResNet, so that the denoising results have better perceptive quality compared to conventional loss function. Next, we introduce the depthwise separable DN-ResNet (DS-DN-ResNet) utilizing the proposed Depthwise Seperable ResBlock (DS-ResBlock) instead of standard ResBlock, which has much less computational cost. We propose cascade evolution of DS-DN-ResNet from DN-ResNet by incrementally transforming the ResBlocks to DS-ResBlocks, while building on the previous training. As a result, high accuracy and good computational efficiency are achieved concurrently. Whereas previous state of art deep learning methods focused on denoising either Gaussian or Poisson corrupted images, we consider denoising images having the more practical Poisson with additive Gaussian noise as well. The results show that DN-ResNets are more efficient, robust, and perform better denoising than current state of art deep learning methods, as well as the popular variants of the BM3D algorithm, in cases of blind and non-blind denoising of images corrupted with Poisson, Gaussian or Poisson-Gaussian noise. Our network also works well for other image enhancement task such as compressed image restoration.
ECDSA-Based Message Authentication Scheme for BeiDou-II Navigation Satellite System

This paper proposes a BeiDou-II navigation message authentication scheme to defend against the generating spoofing attacks. In this scheme, a digital signature is generated by the elliptic curve digital signature algorithm and is inserted into the reserved bits of the BeiDou-II navigation message. This signature will verify the integrity and authenticity of the navigation data to avoid entity masquerade and data tampering. Furthermore, the key exchange process is also designed through the short message service or digital certificate. The effectiveness and security of the scheme is evaluated in the simulation. The results indicate that the authentication of BeiDou-II navigation message can be realized in the harsh environment, and the resistance ability of civil BeiDou-II against a spoofing attack could be enhanced by this scheme.
Iterative spatial fuzzy clustering for 3D brain magnetic resonance image supervoxel segmentation

Although supervoxel segmentation methods have been employed for brain Magnetic Resonance Image (MRI) processing and analysis, due to the specific features of the brain, including complex-shaped internal structures and partial volume effect, their performance remains unsatisfactory.To address these issues, this paper presents a novel iterative spatial fuzzy clustering (ISFC) algorithm to generate 3D supervoxels for brain MRI volume based on prior knowledge. This work makes use of the common topology among the human brains to obtain a set of seed templates from a population-based brain template MRI image. After selecting the number of supervoxels, the corresponding seed template is projected onto the considered individual brain for generating reliable seeds. Then, to deal with the influence of partial volume effect, an efficient iterative spatial fuzzy clustering algorithm is proposed to allocate voxels to the seeds and to generate the supervoxels for the overall brain MRI volume.The performance of the proposed algorithm is evaluated on two widely used public brain MRI datasets and compared with three other up-to-date methods.The proposed algorithm can be utilized for several brain MRI processing and analysis, including tissue segmentation, tumor detection and segmentation, functional parcellation and registration.
VMSAGE: A virtual machine scheduling algorithm based on the gravitational effect for green Cloud computing

The area of sustainable green smart computing highlights key challenges towards reducing cost and carbon dioxide emissions due to the high-energy consumption of Cloud data centres. Here, we focus on the Cloud virtual machine (VM) scheduling that is usually based on simple algorithms, e.g. VM placement on nodes with low memory usage. This approach fails to consider the actual configuration of nodes inside the server rack resulting in local overheating of Cloud data centres. To solve this, we propose a VM scheduling algorithm based on the gravitational effect, called VMSAGE, to optimize energy efficiency of Cloud computing systems. Inspired by the physical gravitation model, we define the thermal repulsion and logical gravitation factors between physical nodes and VMs. To achieve optimized VM scheduling, we propose a gravitation function that refers to the calculation of the logical quality of each VM, host and rack through the algorithm, so as to draw the attractiveness between them. Based on the concept of dimension reduction, VMSAGE conducts the two-dimensional plane target selection twice to reduce the computational cost. Additionally, VMSAGE evaluates attributes of the computer room to carry out the VM deployment. To demonstrate the effectiveness of our solution, we compare it with the Best Fit Heuristic (BFH) and the dynamic voltage and frequency scaling (DVFS) algorithms. The results indicate that our algorithm achieves 10% and 20% optimized energy consumption respectively. The experimental results highlight our contribution, in where VMSAGE can significantly reduce energy consumption rates and VM migration times.
Is mass classification in mammograms a solved problem? - A critical review over the last 20 years

Breast cancer is one of the most common and deadliest cancers that affect mainly women worldwide, and mammography examination is one of the main tools to help early detection. Several papers have been published in the last decades reporting on techniques to automatically recognize breast cancer by analyzing mammograms. These techniques were used to create computer systems to help physicians and radiologists obtain a more precise diagnosis. The objective of this paper is to present an overview regarding the use of machine learning and pattern recognition techniques to discriminate masses in digitized mammograms. The main differences we found in the literature between the present paper and the other reviews are: 1) we used a systematic review method to create this survey; 2) we focused on mass classification problems; 3) the broad scope and spectrum used to investigate this theme, as 129 papers were analyzed to find out whether mass classification in mammograms is a problem solved. In order to achieve this objective, we performed a systematic review process to analyze papers found in the most important digital libraries in the area. We noticed that the three most common techniques used to classify mammographic masses are artificial neural network, support vector machine and k-nearest neighbors. Furthermore, we noticed that mass shape and texture are the most used features in classification, although some papers presented the usage of features provided by specialists, such as BI-RADS descriptors. Moreover, several feature selection techniques were used to reduce the complexity of the classifiers or to increase their accuracies. Additionally, the survey conducted points out some still unexplored research opportunities in this area, for example, we identified that some techniques such as random forest and logistic regression are little explored, while others, such as grammars or syntactic approaches, are not being used to perform this task.
Ensemble SW image steganalysis: A low dimension method for LSBR detection

Blind steganalysis examines digital media for the likely existence of hidden messages, without prior knowledge of the steganographic algorithm that may have been used to hide such messages. This paper puts forward a novel learning-based blind image steganalysis method for tackling spatial domain least significant bit (LSB) flipping. Its key steganalytic feature is the correlation between message length and the regression of the quantity of intensity-identical pixels and channels. A specially designed support vector machine (SVM) kernel is trained to analyze each pixel as an individual analysis unit, with the combined results of the analysis determining the ultimate steganalysis decision. This method makes a number of innovative contributions to the field of blind steganalysis. First, it offers a novel steganalytic feature for measuring similarity between the weight of pixels and channels. Second, it involves pixels in the steganalytic process according to the degree of their detected membership, thus avoiding neutral pixels influencing the process. Third, it extracts reference statistical behavior from cover and stego pixels, thereby enhancing the sensitivity of the steganalyzer. Fourth, its SVM kernel enhances sensitivity using statistical functions combined with trapezoidal fuzzy membership. Finally, with all these innovations it is capable of achieving a sensitivity of 99.626% for 0.25 bpp stego images through only two analysis dimensions.
Can government intervention be both a curse and a blessing? Evidence from China's finance sector

Earlier work views government intervention as a “grabbing hand,” whereas more recent studies report that it acts as a “helping hand.” Can government intervention be both a curse and a blessing? This paper investigates this issue by investigating the impact of government intervention on firm financing and financial corruption in China, using the 2005 World Bank Investment Climate survey data. To do so, we first use instrumental variable estimations to confirm that government intervention promotes financial access and encourage corruption. Next, we adopt a mediator model to document that government intervention promotes firms' access to finance through informal payment. The mediation effect is significant for split samples that capture different type of political and economic climate. The policy implications of the findings are discussed.
Structural bias in differential evolution: A preliminary study

This paper extends the study of structural bias in popular metaheuristic global optimisation methods. Previously, it has been shown that both Genetic Algorithms and Particle Swarm Optimisation suffer from such bias. This means that difficulties already posed for a structurally biased algorithm by the fitness landscape itself are further unnecessarily exacerbated by the unexpected oversampling of some regions of the search space and avoidance of the others, to potential great detriment of the overall optimisation performance. Such bias is inherent in the core design of the algorithm. After careful examination, the authors conclude that some variants of Differential Evolution are not free of the structural bias. However, investigation suggests that the mechanisms of the formation of structural bias in Differential Evolution is different and can be balanced through a more careful design.
Novel dynamic multiple classification system for network traffic

Traffic classification has been widely used in networking and security issues. Previous works have involved many different techniques for mapping traffic to the application. However, little attention has been paid to traffic classification for dynamic network stream. In this paper, we propose a Dynamic Multiple Traffic Classification System (DMTCS). We first introduce the time-based distribution of the traffic protocol information to the traffic classification problem, as the traffic data is a data stream with time continuity. The packets are treated as documents and protocols are seen as topics. Thus, we can apply topic models to cluster packets. In our system, after initialization, packets arrived at a time point are classified as of some protocols. Then, these packets are assembled to clusters according to the protocol distribution at the last time point. Finally, we use these clusters to classify packets arrived at the next time point. Our method has several advantages: 1) does not require the prior knowledge of target applications; 2) tolerant with both TCP and UDP protocols; 3) support multiple classification; 4) preserve high accuracy for the traffic stream with dynamic and imbalanced traffic distribution. Evaluations on DMTCS are carried on two different datasets, and the experimental results demonstrate that DMTCS has an impressive performance in classification on the real-world network stream and the dynamic simulation stream. Whats more, DMTCS outperforms other state-of-the-art models in our experiment.