Reviewerly/exHarmony huggingface dataset:
Facial Expression Recognition Using Fusion Features Based on Center-Symmetric Local Octonary Pattern

A local feature descriptor has gained a lot of interest in many applications, such as image retrieval, texture classification, and face recognition. This paper proposes a novel local feature descriptor: center-symmetric local octonary pattern (CS-LOP) for facial expression recognition. A CS-LOP operator not only considers the difference of the gray value between central pixels and neighboring pixels in all eight directions but also compares the gray value of four pairs of center-symmetric pixels. Besides, this paper used the CS-LOP to extract diverse features from the preprocessed facial image, the feature map of gradient magnitude, and the feature map of Gabor, and also to make extracted features more abundant and detailed. To evaluate the performance of the proposed method, experiments on JAFFE and CK facial expression datasets demonstrate that the proposed method outperforms the method using the individual descriptor. Compared with other state-of-the-art methods, our approach improves the overall recognition accuracy.
Multilevel Thresholding for Image Segmentation Based on Metaheuristic Algorithms

Multilevel image thresholding is effectually technique used to segment many types of images. It usually applied in image preprocessing phase. In this chapter, a review of gray level image segmentation using multilevel thresholding based on metaheuristic algorithms is introduced. Nine algorithms and their studies in multilevel thresholding segmentation are presented namely cuckoo search, bat algorithm, artificial bee colony, particle swarm optimization, firefly algorithm, social spider optimization algorithm, whale optimization algorithm, moth-flame optimization algorithm, and gray wolf optimization algorithm. The objective function, performance measures, and the number of images and thresholds that applied on the studies are mentioned. The review concludes that the multilevel thresholding segmentation is a challenge and many studies till now work to solve it.
Disentangling Geometry and Appearance with Regularised Geometry-Aware Generative Adversarial Networks

Deep generative models have significantly advanced image generation, enabling generation of visually pleasing images with realistic texture. Apart from the texture, it is the shape geometry of objects that strongly dictates their appearance. However, currently available generative models do not incorporate geometric information into the image generation process. This often yields visual objects of degenerated quality. In this work, we propose a regularized Geometry-Aware Generative Adversarial Network (GAGAN) which disentangles appearance and shape in the latent space. This regularized GAGAN enables the generation of images with both realistic texture and shape. Specifically, we condition the generator on a statistical shape prior. The prior is enforced through mapping the generated images onto a canonical coordinate frame using a differentiable geometric transformation. In addition to incorporating geometric information, this constrains the search space and increases the model’s robustness. We show that our approach is versatile, able to generalise across domains (faces, sketches, hands and cats) and sample sizes (from as little as $$\sim \, 200{-}30{,}000$$ to more than 200, 000). We demonstrate superior performance through extensive quantitative and qualitative experiments in a variety of tasks and settings. Finally, we leverage our model to automatically and accurately detect errors or drifting in facial landmarks detection and tracking in-the-wild.
A Novel and Computationally Efficient Joint Unscented Kalman Filtering Scheme for Parameter Estimation of a Class of Nonlinear Systems

Unscented Kalman filter (UKF) is one type of the sigma point Kalman filters and it is based on unscented transformation.UKF is used for parameter estimation of various dynamic systems and for such purpose either joint UKF (JUKF) or dual UKF (DUKF) schemes are considered.JUKF is based on estimating states and parameters together by using only one filter.For DUKF, states and parameters are decoupled and two separate filters are considered.In this paper, a modification to standard JUKF is proposed for parameter estimation which is based on decoupling parameter vector and updating parameter estimates by considering the error transformation between measurements and transformed sigma points during measurement update into the parameter errors.A linear transformation is proposed for such a purpose.Thus, the computational complexity of the standard JUKF is reduced significantly since parameters are decoupled from the state vector while the convergence of parameter estimate(s) is guaranteed.The new modified JUKF scheme is promising to be used for the parameter estimation of dynamic systems for which a linear transformation between measurement and parameter errors can be obtained.The effectiveness of this new scheme is proven by applying it to two nonlinear dynamic systems.
Comparison of Projective Augmented Reality Concepts to Support Medical Needle Insertion

Augmented reality (AR) is a promising tool to improve instrument navigation in needle-based interventions. Limited research has been conducted regarding suitable navigation visualizations. In this work, three navigation concepts based on existing approaches were compared in a user study using a projective AR setup. Each concept was implemented with three different scales for accuracy-to-color mapping and two methods of navigation indicator scaling. Participants were asked to perform simulated needle insertion tasks with each of the resulting 18 prototypes. Insertion angle and insertion depth accuracies were measured and analyzed, as well as task completion time and participants' subjectively perceived task difficulty. Results show a clear ranking of visualization concepts across variables. Less consistent results were obtained for the color and indicator scaling factors. Results suggest that logarithmic indicator scaling achieved better accuracy, but participants perceived it to be more difficult than linear scaling. With specific results for angle and depth accuracy, our study contributes to the future composition of improved navigation support and systems for precise needle insertion or similar applications.
Learning content-specific codebooks for blind quality assessment of screen content images

This paper presents a novel blind quality assessment method for screen content images (SCIs) by learning content-specific codebooks. Instead of manually extracting quality-aware features for quality evaluation, the proposed method automatically generates effective features based on a simple feature encoding technique over content-specific codebooks. Considering the mixed content type in SCIs, content-specific codebooks including textual codebook and pictorial codebook are first learned in an offline manner. Given an input SCI, a textual/pictorial segmentation method is first applied to divide the SCI into textual and pictorial regions. Then, patches in different regions are respectively encoded using the learned textual and pictorial codebooks to produce the corresponding feature codes. Finally, the feature codes of each patch are aggregated, by using a percentage-based local pooling scheme, to yield the global feature codes of different regions. The final quality-predictive features used for quality regression are the combined global feature codes of different regions. Despite its simplicity, our method delivers low computational complexity, making it well suitable for real-time applications. Extensive experiments are conducted on three public SCI databases to validate the performance of our method, the results well confirm its superiority over the existing relevant full reference and no reference SCI quality assessment methods.
AMANDA: Semi-supervised density-based adaptive model for non-stationary data with extreme verification latency

Concept drift refers to an alteration in the relations between input and output data in the distribution over time. Thus, a gradual concept drift alludes to a smooth and gradual change in these relations. It generates a model obsolescence and quality decrease in predictions. Besides, there is a challenging task: the extreme verification latency to certify the labels. For batch scenarios, state-of-the-art methods do not properly tackle the problems aforementioned due to their high computational time, lack of representing samples of the drift or even for having several hyperparameters for tuning. Therefore, we propose AMANDA, a semi-supervised density-based adaptive model for non-stationary data. It has two variations: AMANDA-FCP, which selects a fixed number of samples; and AMANDA-DCP, which, in turn, dynamically selects samples from data. Our results indicate that these two variations outperform the state-of-the-art methods for almost all synthetic and real datasets, with an improvement up to 27.98% regarding the average error. AMANDA-FCP improved the results for a gradual concept drift, even with a small size of initial labeled data. Moreover, our results indicate that semi-supervised classifiers are improved when they work along with our density-based methods. Therefore, we emphasize the importance of research directions based on this approach.
Latency-Based Analytic Approach to Forecast Cloud Workload Trend for Sustainable Datacenters

Cloud datacenters are turning out to be massive energy consumers and environment polluters, which necessitate the need for promoting sustainable computing approaches for achieving environment-friendly datacentre execution. Direct causes of excess energy consumption of the datacentre include running servers at low level of workloads and over-provisioning of server resources to the arriving workloads during execution. To this end, predicting the future workload demands and their respective behaviors at the datacenters are being the focus of recent researches in the context of sustainable datacenters. But prediction analytics of cloud workloads suffer various limitations imposed by the dynamic and unclear characteristics of Cloud workloads. This paper proposes a novel forecasting model named K-means based Rand Variable Learning Rate Backpropagation Neural Network (K-RVLBPNN) for predicting the future workload arrival trend, by exploiting the latency sensitivity characteristics of Cloud workloads, based on a combination of improved K-means clustering algorithm and Backpropagation Neural Network (BPNN) algorithm. Experiments conducted on real-world Cloud datasets shows that the proposed model shows better prediction accuracy, outperforming the traditional Hidden Markov Model, Naïve Bayes Classifier, and our earlier RVLBPNN model, respectively.
A view of programming scalable data analysis: from clouds to exascale

Scalability is a key feature for big data analysis and machine learning frameworks and for applications that need to analyze very large and real-time data available from data repositories, social media, sensor networks, smartphones, and the Web. Scalable big data analysis today can be achieved by parallel implementations that are able to exploit the computing and storage facilities of high performance computing (HPC) systems and clouds, whereas in the near future Exascale systems will be used to implement extreme-scale data analysis. Here is discussed how clouds currently support the development of scalable data mining solutions and are outlined and examined the main challenges to be addressed and solved for implementing innovative data analysis applications on Exascale systems.
Neighborhood-enhanced transfer learning for one-class collaborative filtering

Recommender systems have become more prevalent in recent years for providing users with personalized services such as movie recommendation and news recommendation. In real-world scenarios, they are naturally thought of as one-class collaborative filtering (OCCF) problems because most behavioral data are users’ interaction records, e.g., browses or clicks, which are referred to as one-class feedback or implicit feedback. In these problems, the sparsity of observed feedback and the ambiguity of unobserved feedback make it difficult to capture users’ true preferences. In order to alleviate that, two well-known approaches have been proposed, including factorization-based methods aiming to learn the relationships between users and items via latent factors, and neighborhood-based methods focusing on similarities between users or items. However, these two types of approaches are rarely studied in one single framework or solution for OCCF. In this paper, we propose a novel transfer learning solution, i.e., transfer by neighborhood-enhanced factorization (TNF), which combines these two approaches in a unified framework. Specifically, we extract the local knowledge of the neighborhood information among users, and then transfer it to a global preference learning task in an enhanced factorization-based framework. Our TNF is expected to exploit the local knowledge in a global learning manner well. Extensive empirical studies on five real-world datasets show that our proposed solution can perform significantly more accurate than the state-of-the-art methods.
Keep Your Eye on the Puck: Automatic Hockey Videography

While hockey involves a large playing surface, instantaneous play is typically localized to a smaller region of the ice. Live spectators thus attentively shift their gaze to follow play, and professional sports videographers pan and tilt their cameras to mimic this process. Unfortunately, manual videography is economically prohibitive below the elite level. Here we propose a system for automatically tracking play, allowing a high-definition video feed to be dynamically cropped and retargeted to a spectator's display device. We employ the puck as an objective surrogate for the location of play, and develop a novel method for ground-truthing puck location from high-definition video. This allows us to train a deep network regressor that uses the video imagery, optic flow, estimated player positions and team affiliation to predict the location of play. We show that our algorithm outperforms a simple 'follow the herd' strategy and results in a practical system for delivering high-quality curated video of amateur-level hockey games to remote spectators.
Calculus Module for Derivative Application Materials with an Islamic Contextual Teaching and Learning Approach

Available teaching materials have not been able to meet the needs of students, so innovation is needed. This is the background of this research. The purpose of this study was to determine the development and feasibility of the modules produced, namely the basic calculus module of derivative applications with the approach of contextual teaching and learning with the nuances of Islamic religious values. The method used by Research and Development (R & D) with a 4D model by Thiagarajan with four stages, namely Define (Defining), Design (Design), Development (Development), and Dissemination (Spread). Expert judgment and product trials are conducted to determine the level of product worthiness and attractiveness. Based on the expert's assessment, the following results were obtained: 1) material experts obtained a final average value of 3.90 with the criteria of "very feasible", 2) Islamic scholars obtained the final average score of 3.88 with the criteria "very feasible", and 3) media experts obtain the final average score of 3.96 with the criteria "very feasible". Meanwhile, a limited trial to find out the level of product attractiveness obtained a final average value of 3.35 with the criteria of "very interesting". After the product is said to be feasible and attractive, the product is distributed through the website at www.pspm.tarbiyah.radenintan.ac.id.
SRIFA: Stochastic Ranking with Improved-Firefly-Algorithm for Constrained Optimization Engineering Design Problems

Firefly-Algorithm (FA) is an eminent nature-inspired swarm-based technique for solving numerous real world global optimization problems. This paper presents an overview of the constraint handling techniques. It also includes a hybrid algorithm, namely the Stochastic Ranking with Improved Firefly Algorithm (SRIFA) for solving constrained real-world engineering optimization problems. The stochastic ranking approach is broadly used to maintain balance between penalty and fitness functions. FA is extensively used due to its faster convergence than other metaheuristic algorithms. The basic FA is modified by incorporating opposite-based learning and random-scale factor to improve the diversity and performance. Furthermore, SRIFA uses feasibility based rules to maintain balance between penalty and objective functions. SRIFA is experimented to optimize 24 CEC 2006 standard functions and five well-known engineering constrained-optimization design problems from the literature to evaluate and analyze the effectiveness of SRIFA. It can be seen that the overall computational results of SRIFA are better than those of the basic FA. Statistical outcomes of the SRIFA are significantly superior compared to the other evolutionary algorithms and engineering design problems in its performance, quality and efficiency.
An evaluation framework for network security visualizations

Visualization helps to comprehend and analyze large amounts of data, a fundamental necessity for network security due to the large volume of audits traces produced each day. In this paper, we dissect the majority of recent work conducted in network security visualization and offer a taxonomy that provides a basis for classifying recently published works using nine criteria. Moreover, a comprehensive evaluation framework for comparing and ranking network security visualization systems and techniques is developed and presented. Finally, we present a taxonomy of network attacks, which covers most of the existing network attacks and provides a framework for the categorization of recent network security visualization systems.
Task Relation Networks

Multi-task learning is popular in machine learning and computer vision. In multitask learning, properly modeling task relations is important for boosting the performance of jointly learned tasks. Task covariance modeling has been successfully used to model the relations of tasks but is limited to homogeneous multi-task learning. In this paper, we propose a feature based task relation modeling approach, suitable for both homogeneous and heterogeneous multi-task learning. First, we propose a new metric to quantify the relations between tasks. Based on the quantitative metric, we then develop the task relation layer, which can be combined with any deep learning architecture to form task relation networks to fully exploit the relations of different tasks in an online fashion. Benefiting from the task relation layer, the task relation networks can better leverage the mutual information from the data. We demonstrate our proposed task relation networks are effective in improving the performance in both homogeneous and heterogeneous multi-task learning settings through extensive experiments on computer vision tasks.
New set of generalized legendre moment invariants for pattern recognition

In this paper, we present a new set of rotation, scale and translation invariants, named Generalized Legendre Moment Invariants (GLMI). This new set of invariants is defined on the Cartesian coordinate system, where we can derive the GLMI based on the algebraic relation between the fractional-order Legendre polynomials and the geometric basis. Consequently, several experiments are carried out to evaluate the performance of the proposed GLMI, with regard to their invariability property, object recognition capability and computation efficiency, in comparison with the most representative families of moment invariants. In addition, we have presented a systematic parameter selection method for finding the optimal fractional parameter values with respect to pattern recognition applications. Just as important, we have introduced an adaptive scheme to set the fractional parameters according to the characteristics of the image. The obtained results clearly show that the proposed invariants provide higher features accuracy and discrimination power even in the presence of noisy effects.
Analyzing the entanglement properties of graph states with generalized concurrence

We propose a new classification for the entanglement in graph states based on generalized concurrence. The numerical results indicate that the eight different three-qubit graph states in three categories, 64 four-qubit graph states in five categories and 1024 five-qubit graph states in 10 classes. We also compare this classification with equivalence classes of these graph states under local complementation (LC) operator, and the obtained result suggests that classification by generalized concurrence is not in contradiction with the LC-rule. Furthermore, our results suggest that the generalized concurrence is a suitable measure of entanglement for multi-qubit graph states.
Fused Convolutional Neural Network for White Blood Cell Image Classification

Blood cell image classification is an important part for medical diagnosis system. In this paper, we propose a fused convolutional neural network (CNN) model to classify the images of white blood cell (WBC). We use five convolutional layer, three max-pooling layer and a fully connected network with single hidden layer. We fuse the feature maps of two convolutional layers by using the operation of max-pooling to give input to the fully connected neural network layer. We compare the result of our model accuracy and computational time with CNN-recurrent neural network (RNN) combined model. We also show that our model trains faster than CNN-RNN model.
Constructing odd-variable RSBFs with optimal algebraic immunity, good nonlinearity and good behavior against fast algebraic attacks

Rotation symmetric Boolean functions have raised widespread attention because of the good properties in cryptosystem. This paper presents a new construction of odd-variable rotation symmetric Boolean functions with optimal algebraic immunity. The nonlinearity of the new functions is higher than some existing theoretical constructions of rotation symmetric Boolean functions with optimal algebraic immunity. Further, it is also checked that such functions have almost optimal immunity against fast algebraic attacks for small variables. Besides, the algebraic degree of the constructed functions is also high enough.
The Understanding Capacity and Information Dynamics in the Human Brain

This article proposes a theory of neuronal processes underlying cognition, focusing on the mechanisms of understanding in the human brain. Understanding is a product of mental modeling. The paper argues that mental modeling is a form of information production inside the neuronal system extending the reach of human cognition “beyond the information given” (Bruner, J.S., Beyond the Information Given, 1973). Mental modeling enables forms of learning and prediction (learning with understanding and prediction via explanation) that are unique to humans, allowing robust performance under unfamiliar conditions having no precedents in the past history. The proposed theory centers on the notions of self-organization and emergent properties of collective behavior in the neuronal substrate. The theory motivates new approaches in the design of intelligent artifacts (machine understanding) that are complementary to those underlying the technology of machine learning.