Reviewerly/exHarmony huggingface dataset:
Facial Expression Recognition by Inter-Class Relational Learning

Certain correlation exists between various expressions of the human face, yet few works have explored it. In this paper, we propose to recognize facial expressions by extracting independent expressive representation through a novel learning procedure, called Inter-class Relational Learning (IcRL). First, an elaborately designed CNN architecture is utilized for simultaneously extracting features from two different expression images. Then, two extracted features are integrated with a random ratio so that a hybrid feature can be attained. An attention module is proposed to assign a weight for each pixel of the hybrid feature. At last, we feed the weighted hybrid feature to the subsequent classification module. The goal of the whole network training is to output the correct ratio of each expression. Different from previous work, our novel method is capable of learning the mutual relations between different classes of expression and enlarging Fisher's criterion between any two classes, i.e., the ratio of inter-class distance to intra-class distance, by accurately discriminating the hybrid feature. In this way, we will be able to enhance the discriminative power of learned features of each expression and, therefore, can improve the classification performance. The IcRL method has been evaluated on five public expression databases: CK+, JAFFE, TFEID, BAUM-2i, and FER2013. The experimental results demonstrate the superiority of the proposed method over many state-of-the-art approaches.
The Sponge Structure Modulation Application to Overcome the Security Breaches for the MD5 and SHA-1 Hash Functions

This paper presents a Sponge structure modulation of the MD5 and SHA-1 hash functions. The work employs the Keccak permutation function to build the proposed scheme. The work discusses the main two security breaches that threaten the cryptography hash standards which are collision and length extension attacks. Through analyzing several examples of collided messages of both algorithms (SHA-1 and MD5), we describe the potentials to overcome the collision and length extension attacks. Moreover, a proper replacement technique to avoid such attacks is discussed in this paper.
Real-Time Detection of Cyberbullying in Arabic Twitter Streams

Cyberbullying is a form of psychological abuse, which is very dangerous as the victims of cyberbullying, especially children and teenagers, suffer from many mental issues that could lead to suicide thoughts. Cyberbullying is also becoming a significant issue in the Middle East. Existing contributions for cyberbullying detection focus mainly on English language. This paper presents an approach to detect cyberbullying in Arabic Twitter streams in real-time. In addition, it classifies the bullying messages based on their strength. In case a cyberbullying message is detected, the system notifies the user and proposes a set of actions to take based on the strength of the bullying message. We demonstrate the relevance of the proposed approach by showing how it could be used by a parent to monitor his kids' activities and get notified in case a suspicious activity is detected. The experiments show that the proposed system was able to effectively identify the cyberbullying messages in near real-time.
A survey on freehand sketch recognition and retrieval

With the development of digital devices and pressure sensing equipment, research into freehand sketches from touch-screen interfaces has increased significantly in recent years. As such, we provide the first comprehensive survey of recognition tasks based on sketch generation, freehand sketch classification, sketch-based image retrieval (SBIR), fine-grained sketch-based image retrieval (FG-SBIR), and sketch-based 3D shape image retrieval. Specifically, SBIR and FG-SBIR were the main focus of the survey. Primary technologies and benchmark datasets related to all sketch-based recognition topics are also discussed, along with future trends for this promising technology.
Design of a negative group delay filter via reservoir computing approach: Real-time prediction of chaotic signals

An optimal arbitrary order filter with negative group delay is proposed for a real-time prediction of complex band-limited signals. The filter consists of a set of second-order linear dissipative oscillators and is determined by a rational transfer function with fixed poles and optimized zeros. The filter design and optimization algorithm is based on a reservoir computing approach. The predictive properties of the filter are demonstrated numerically for two chaotic model systems — the finite-dimensional Rössler system and the infinite-dimensional Mackey-Glass system, as well as for the real biological signal of the fingertip photoplethysmogram.
Generate pairwise constraints from unlabeled data for semi-supervised clustering

Pairwise constraint selection methods often rely on the label information of data to generate pairwise constraints. This paper proposes a new method of selecting pairwise constraints from unlabeled data for semi-supervised clustering to improve clustering accuracy. Given a dataset without any label information, it is first clustered by using the I-nice method into a set of initial clusters. From each initial cluster, a dense group of objects is obtained by removing the faraway objects. Then, the most informative object and the informative objects are identified with the local density estimation method in each dense group of objects. The identified objects are used to form a set of pairwise constraints, which are incorporated in the semi-supervised clustering algorithm to guide the clustering process toward a better solution. The advantage of this method is that no label information of data is required for selection pairwise constraints. Experimental results demonstrate that the new method improved the clustering accuracy and outperformed four state-of-the-art pairwise constraint selection methods, namely, random, FFQS, min–max, and NPU, on both synthetic and real-world datasets.
How the peak glottal area affects linear predictive coding-based formant estimates of vowels

The estimation of formant frequencies from acoustic speech signals is mostly based on Linear Predictive Coding (LPC) algorithms. Since LPC is based on the source-filter model of speech production, the formant frequencies obtained are often implicitly regarded as those for an infinite glottal impedance, i.e., a closed glottis. However, previous studies have indicated that LPC-based formant estimates of vowels generated with a realistically varying glottal area may substantially differ from the resonances of the vocal tract with a closed glottis. In the present study, the deviation between closed-glottis resonances and LPC-estimated formants during phonation with different peak glottal areas has been systematically examined both using physical vocal tract models excited with a self-oscillating rubber model of the vocal folds, and by computer simulations of interacting source and filter models. Ten vocal tract resonators representing different vowels have been analyzed. The results showed that F1 increased with the peak area of the time-varying glottis, while F2 and F3 were not systematically affected. The effect of the peak glottal area on F1 was strongest for close-mid to close vowels, and more moderate for mid to open vowels.
Evolutionary data augmentation in deep face detection

We present an evolutionary approach for Data Augmentation (DA) in deep Face Detection (FD). The approach is fully automatic and creates new face instances by recombining facial parts from different faces. We explore the selection of the facial parts that construct each new face instance using two strategies: random and evolutionary. The evolutionary strategy employs a Genetic Algorithm (GA) with automatic fitness assignment based on a pre-trained face detector. The evolutionary approach is able to find new face instances that exploit the vulnerabilities of the detector. Then we add these new instances to the training dataset, retrain the detector, and analyse the improvement of the performance of the detector. The presented approach is tested using deep object detectors, trained with instances from the CelebFaces Attributes (CelebA) dataset. The experimental results show that the presented approach improves face detection performance when comparing to base models trained using standard DA techniques. Also, the approach generates new realistic faces with interesting and unexpected features.
Survey on Applications of Machine Learning in the Field of Computer Vision

Machine learning and computer vision have taken all over today’s world which include medical diagnostics, statistical algorithm, logistic regression trees, etc. Such implementations are already been done in the arena such as smartphone applications, computer applications and online websites. We are getting a lot of features and advanced accessibility from machine learning and computer vision like Google Maps, Uber and Snapchat. We have put forth an introduction of some of the apps which use machine learning. We have also focussed on the algorithms used for machine learning implementation and their future uses. Some of the famous machine learning algorithms are random forest distribution algorithm, Naïve Bayes algorithm, decision tree. In today’s world of advanced technologies, cybersecurity has become paramount. Online threat analysis uses machine learning from the very grass-roots level. We have also discussed advancement done in medical science field through machine learning in computer vision.
Deep-learning-based macro-pixel synthesis and lossless coding of light field images

Abstract This paper proposes a novel approach for lossless coding of light field (LF) images based on a macro-pixel (MP) synthesis technique which synthesizes the entire LF image in one step. The reference views used in the synthesis process are selected based on four different view configurations and define the reference LF image. This image is stored as an array of reference MPs which collect one pixel from each reference view, being losslessly encoded as a base layer. A first contribution focuses on a novel network design for view synthesis which synthesizes the entire LF image as an array of synthesized MPs. A second contribution proposes a network model for coding which computes the MP prediction used for lossless encoding of the remaining views as an enhancement layer. Synthesis results show an average distortion of 29.82 dB based on four reference views and up to 36.19 dB based on 25 reference views. Compression results show an average improvement of 29.9% over the traditional lossless image codecs and 9.1% over the state-of-the-art.
A Flexible Poisoning Attack Against Machine Learning

Recent years have witnessed tremendous academic efforts and industry growth in machine learning. The security of machine learning has become increasingly prominent. Poisoning attack is one of the most relevant security threats to machine learning which focuses on polluting the training data that machine learning needs during the training process. Specifically, the attacker blends crafted poisoning samples into training data in order to make the learned model beneficial to him. To the best of our knowledge, existing researches about poisoning attack focused on either integrity attack or availability attack, which did not unify these two attacks together. Aside from that, from the attacker's perspective, attacker's strategy is not flexible enough. Finally, existing proposals only concentrated on increasing the test error of the learned model but ignored the importance of the concealment of attack. To overcome these issues, we firstly present a thorough adversarial model for poisoning attack in which attacker's strategy is defined from two aspects, i.e., the effect of attack and the concealment of attack. Then we unify integrity attack and availability attack together in similar formulations. Furthermore, in order to enhance flexibility, a tradeoff parameter is inserted into attacker's objective function which means the attacker can balance the attraction of effect against the requirement of concealment. Finally, as examples, extensive experiments are conducted on linear regression and logistic regression to demonstrate the effectiveness of attack.
A Customized Convolutional Neural Network with Low Model Complexity for JPEG Steganalysis

Nowadays, convolutional neural network (CNN) is appied to different types of image classification tasks and outperforms almost all traditional methods. However, one may find it difficult to apply CNN to JPEG steganalysis because of the extremely low SNR (embedding messages to image contents) in the task. In this paper, a selection-channel-aware CNN for JPEG steganalysis is proposed by incorporating domain knowledge. Specifically, instead of random strategy, kernels of the first convolutional layer are initialized with hand-crafted filters to suppress the image content. Then, truncated linear unit (TLU), a heuristically-designed activation function, is adopted in the first layer as the activation function to better adapt to the distribution of feature maps. Finally, we use a generalized residual learning block to incorporate the knowledge of selection channel in the proposed CNN to further boost its performance. J-UNIWARD, a state-of-the-art JPEG steganographic scheme, is used to evaluate the performance of the proposed CNN and other competing JPEG steganalysis methods. Experiment results show that the proposed CNN steganalyzer outperforms other feature-based methods and rivals the state-of-the-art CNN-based methods with much reduced model complexity, at different payloads.
A Formal Approach to Explainability

We regard explanations as a blending of the input sample and the model's output and offer a few definitions that capture various desired properties of the function that generates these explanations. We study the links between these properties and between explanation-generating functions and intermediate representations of learned models and are able to show, for example, that if the activations of a given layer are consistent with an explanation, then so do all other subsequent layers. In addition, we study the intersection and union of explanations as a way to construct new explanations.
Automatic Fuzzy Clustering Using Non-Dominated Sorting Particle Swarm Optimization Algorithm for Categorical Data

Categorical data clustering has been attracted a lot of attention recently due to its necessary in the real-world applications. Many clustering methods have been proposed for categorical data. However, most of the existing algorithms require the predefined number of clusters which is usually unavailable in real-world problems. Only a few works focused on automatic clustering, but mainly handled for numerical data. This study develops a novel automatic fuzzy clustering using non-dominated sorting particle swarm optimization (AFC-NSPSO) algorithm for categorical data. The proposed AFC-NSPSO algorithm can automatically identify the optimal number of clusters and exploit the clustering result with the corresponding selected number of clusters. In addition, a new technique is investigated to identify the maximum number of clusters in a dataset based on the local density. To select a final solution in the first Pareto front, some internal validation indices are used. The performance of the proposed AFC-NSPSO on the real-world datasets collected from the UCI machine learning repository exhibits effectiveness compared with some other existing automatic categorical clustering algorithms. Besides, this study also applies the proposed algorithm to analyze a real-world case study with an unknown number of clusters.
Excitable interplay between lasing quantum dot states

The optically injected semiconductor laser system has proven to be an excellent source of experimental nonlinear dynamics, particularly regarding the generation of excitable pulses. Typically for low-injection strengths, these pulses are the result of a small above-threshold perturbation of a stable steady state, the underlying physics is well described by the Adler phase equation, and each laser intensity pulse is accompanied by a 2π phase rotation. In this article, we show how, with a dual-state quantum dot laser, a variation of type I excitability is possible that cannot be described by the Adler model. The laser is operated so that emission is from the excited state only. The ground state can be activated and phase locked to the master laser via optical injection while the excited state is completely suppressed. Close to the phase-locking boundary, a region of ground-state emission dropouts correlated to excited-state pulses can be observed. We show that the phase of the ground state undergoes bounded rotations due to interactions with the excited state. We analyze the system both experimentally and numerically and find excellent agreement. Particular attention is devoted to the bifurcation conditions needed for an excitable pulse as well as its time evolution.
CNN-based Rescaling Factor Estimation

We demonstrate the estimation of image resampling parameters in a deep learning framework by regressing the rescaling factor of 64x64-sized patches on features learned directly from grayscale intensities by a convolutional neural network (CNN). Our end-to-end network design comprises a simple concatenation of 25 convolutional layers with small 3x3 receptive fields and largely abstains from the use of pooling. We report experimental results on a large set of rescaled patches, for which the proposed CNN outperforms state-of-the-art frequency-domain estimators particularly in the case of downscaling. A critical discussion of sensitivities to mismatch between training and testing data points to failure cases.
Quantitative Mitigation of Timing Side Channels

Timing side channels pose a significant threat to the security and privacy of software applications. We propose an approach for mitigating this problem by decreasing the strength of the side channels as measured by entropy-based objectives, such as min-guess entropy. Our goal is to minimize the information leaks while guaranteeing a user-specified maximal acceptable performance overhead. We dub the decision version of this problem Shannon mitigation, and consider two variants, deterministic and stochastic. First, we show that the deterministic variant is NP-hard. However, we give a polynomial algorithm that finds an optimal solution from a restricted set. Second, for the stochastic variant, we develop an approach that uses optimization techniques specific to the entropy-based objective used. For instance, for min-guess entropy, we used mixed integer-linear programming. We apply the algorithm to a threat model where the attacker gets to make functional observations, that is, where she observes the running time of the program for the same secret value combined with different public input values. Existing mitigation approaches do not give confidentiality or performance guarantees for this threat model. We evaluate our tool Schmit on a number of micro-benchmarks and real-world applications with different entropy-based objectives. In contrast to the existing mitigation approaches, we show that in the functional-observation threat model, Schmit is scalable and able to maximize confidentiality under the performance overhead bound.
Optimum web viewer application for DICOM whole slide image visualization in anatomical pathology

Digital scanners are being increasingly adopt-ed in anatomical pathology, but there is still a lack of a standardized whole slide image (WSI) format. This translates into the need for interoperability and knowledge representation for shareable and computable clinical information. This work describes a robust solution, called Visilab Viewer, able to interact and work with any WSI based on the DICOM standard.Visilab Viewer is a web platform developed and integrated alongside a proposed web architecture following the DICOM definition. To prepare the information of the pyramid structure proposed in DICOM, a specific module was defined. The same structure is used by a second module that aggregates on the cache browser the adjacent tiles or frames of the current user's viewport with the aim of achieving fast and fluid navigation over the tissue slide. This solution was tested and compared with three different web viewers, publicly available, with 10 WSIs.A quantitative assessment was performed based on the average load time per frame together with the number of fully loaded frames. Kruskal-Wallis and Dunn tests were used to compare each web viewer latency results and finally to rank them. Additionally, a qualitative evaluation was done by 6 pathologists based on speed and quality for zooming, panning and usability. The proposed viewer obtained the best performance in both assessments. The entire architecture proposed was tested in the 2nd worldwide DICOM Connectathon, obtaining successful results with all participant scanner vendors.The online tool allows users to navigate and obtain a correct visualization of the samples avoiding any restriction of format and localization. The two strategical modules allow to reduce time in displaying the slide and therefore, offer high fluidity and usability. The web platform manages not only the visualization with the developed web viewer but also includes the insertion, manipulation and generation of new DICOM elements. Visilab Viewer can successfully exchange DICOM data. Connectathons are the ultimate interoperability tests and are therefore required to guarantee that solutions as Visilab Viewer and its architecture can successfully exchange data following the DICOM standard. Accompanying demo video. (Link to Youtube video.).
Blockchain-enabled Data Provenance in Cloud Datacenter Reengineering

Cloud datacenter reengineering has become an emerging technical term along with the rapid growth of cloud computing. There are a few reasons for implementing cloud reengineering. One of the key driven forces of adopting cloud reengineering is that securing data provenance is a major restriction for most contemporary applications. Many cloud-based applications require multiple cloud vendors' collaborations, such that data usage can be rarely controlled or governed during the data life cycle. This work focuses on the issue of data provenance in cloud computing and proposes an approach that uses blockchain techniques to achieve data tracing for a full data life cycle. The proposed method em- phasizes tracing data transfers between cloud datacenter. The data regeneration also is concerned by the proposed approach. Our experiment evaluation has assessed the efficiency performance of our approach.