Reviewerly/exHarmony huggingface dataset:
An Approach to Imbalanced Data Classification Based on Instance Selection and Over-Sampling

The paper referees to a problem of learning from class-imbalanced data. The class imbalance problem arises when the number of instances from different classes differs substantially. Instance selection aims at deciding which instances from the training set should be retained and used during the learning process. Over-sampling is an approach dedicated to duplicate minority class instances. In the paper, a hybrid approach for the imbalanced data learning using the over-sampling and instance selection techniques is proposed. Instances are selected to reduce the number of instances belonging to the majority class, while the number of instances belonging to the minority class is expanded. The process of instance selection is based on clustering, where the authors’ approach to clustering and instance selection using an agent-based population learning algorithm is applied. As a result a more balanced distribution of instances belonging to different classes is obtained and a dataset size is reduced. The proposed approach is validated experimentally using several benchmark datasets.
An Online Personalized Reputation Estimation Model for Service-Oriented Systems

In service-oriented computing environments, many Web services are provided for users to build service-oriented systems. Since the performance of the same Web service is different from different users' perspectives, users have to personally select the optimal Web services according to quality-of-service(QoS) data observed by other similar users. However, users with low reputations will provide unreliable data, which will have a negative impact on service selection. Moreover, the QoS data vary over time due to changes in user reputation. Therefore, how to estimate a personalized reputation for each user at runtime remains a significant problem. To address this critical challenge, this paper proposes an online reputation estimation method, called OPRE, to efficiently provide a personalized reputation for each user. Based on the users' observed QoS data, OPRE employs matrix factorization and online learning techniques to estimate personalized reputations. The experimental results show that OPRE has high effectiveness compared to other approaches.
Age Estimation Using Trainable Gabor Wavelet Layers In A Convolutional Neural Network

In this paper, we propose a trainable Gabor wavelet (TGW) layer and cascade it with a convolutional neural network (CNN) for the age estimation. Unlike an existing method that uses fixed (hand-tuned) Gabor filters at the head of a CNN, we use Gabor wavelets that can be adapted for the given input as well as for the targeting task. This is enabled by (a) estimating hyperparameters of Gabor wavelets from the input and (b) using a 1 × 1 convolution layer for the selection of orientation parameter. The proposed TGW layers are trained with the standard gradient-descent method and can be easily incorporated with conventional CNNs in an end-to-end training manner. We conduct experiments on the Adience dataset and show that the proposed network outperforms the baseline CNN without TGW layers and efficiently used trainable parameters than ordinary CNN based methods.
Generalized Hamming weights of projective Reed–Muller-type codes over graphs

Let G be a connected graph and let X be the set of projective points defined by the column vectors of the incidence matrix of G over a field K of any characteristic. We determine the generalized Hamming weights of the Reed–Muller-type code over the set X in terms of graph theoretic invariants. As an application to coding theory we show that if G is non-bipartite and K is a finite field of char(K)≠2, then the rth generalized Hamming weight of the linear code generated by the rows of the incidence matrix of G is the rth weak edge biparticity of G. If char(K)=2 or G is bipartite, we prove that the rth generalized Hamming weight of that code is the rth edge connectivity of G.
Learning Visually Consistent Label Embeddings for Zero-Shot Learning

In this work, we propose a zero-shot learning method to effectively model knowledge transfer between classes via jointly learning visually consistent word vectors and label embedding model in an end-to-end manner. The main idea is to project the vector space word vectors of attributes and classes into the visual space such that word representations of semantically related classes become more closer, and use the projected vectors in the proposed embedding model to identify unseen classes. We evaluate the proposed approach on two benchmark datasets and the experimental results show that our method yields significant improvements in recognition accuracy.
Competitive Segmentation Performance on Near-Lossless and Lossy Compressed Remote Sensing Images

Image segmentation lies at the heart of multiple image processing chains, and achieving accurate segmentation is of utmost importance as it affects later processing. Image segmentation has recently gained interest in the field of remote sensing, mostly due to the widespread availability of remote sensing data. This increased availability poses the problem of transmitting and storing large volumes of data. Compression is a common strategy to alleviate this problem. However, lossy or near-lossless compression prevents a perfect reconstruction of the recovered data. This letter investigates the image segmentation performance in data reconstructed after a near-lossless or a lossy compression. Two image segmentation algorithms and two compression standards are evaluated on data from several instruments. Experimental results reveal that segmentation performance over previously near-lossless and lossy compressed images is not markedly reduced at low and moderate compression ratios (CRs). In some scenarios, accurate segmentation performance can be achieved even for high CRs.
Radar data simulation using deep generative networks

Due to the high cost of real experiments, radar data simulation plays an important role in radar applications. However, the accuracy and the calculation speed of existing simulation methods is limited by the model error and the heavy calculation of electromagnetic simulation. Here, a radar data simulation method based on deep generative network (DGN) is proposed. DGN is generative model involving deep network as the representation tool, in which the model is trained with labelled data. When the training phase is finished, the generative model can generate data samples which are similar to the real samples. The performance of the DGN is evaluated on the ground-based radar dataset, and the results show that the distribution of the generated radar data is similar to the training radar data.
Practical Applicability of Advanced Implementations of Priority Queues in Finding Shortest Paths

A priority queue belongs to most useful abstract data types. It plays a key role in graph algorithms, such as Dijkstra's or Prim's algorithm, in data mining algorithms, such as OPTICS, in discrete-event simulation, or in CPU scheduling. Various implementations of priority queues have been proposed during years. Most powerful ones are based on heap data structure that allows performing inserts and removals in O(log n) time. Using specialized heaps, such as pairing or Fibonacci, inserts can be done in O(1) time. Furthermore, Fibonacci heap or more complex heaps, such as Brodal queue or strict Fibonacci heap, support execution of another operation that is increment of priority of any element stored in the priority queue in O(1) time. This fact can be used to decrease time complexity of various algorithms that use priority queues. On the other hand, the constant factors hidden behind O notation of most of the operations can be really big what can significantly decrease practical applicability of these advanced implementations of priority queues. In this paper, we focus on three implementations of priority queues that are Fibonacci heap, Brodal queue, and strict Fibonacci heap, and we try to investigate their applicability in Dijkstra's algorithm implemented in C++ programing language.
High-Resolution Face Recognition Via Deep Pore-Feature Matching

Because of the advancement of capturing devices, both image resolution and image quality have been significantly improved. Efficiently utilizing facial information is beneficial in enhancing the performance of face recognition methods. For high-resolution face images, pore-scale facial features can be observed. The positions and local patterns of pore features are biologically discriminative, so they can be explored for face identification. In this paper, we extend the previous work on pore-scale features, by proposing a new learning-based descriptor, namely PoreNet. Experiment results show that our proposed descriptor achieves an excellent performance on two high-resolution face datasets, namely Bosphorus and MultiPIE. More importantly, our proposed method significantly outperforms the state-of-the-art Convolutional Neural Network (CNN)-based face recognition method, when query faces are highly occluded. The code of our proposed method is available at: https://github.com/johnnysclai/PoreNet.
Equivalence of 2-rotation symmetric quartic Boolean functions

A Boolean function in n variables is 2-rotation symmetric if it is invariant under even powers of ρ(x1,……,xn)=(x2,…,xn,x1), but not under the first power (ordinary rotation symmetry); we call such a function a 2-function. A 2-function is called monomial rotation symmetric (MRS) if it is generated by applying powers of ρ2 to a single monomial. If the quartic MRS 2-function in 2n variables has a monomial x1xqxrxs, then we use the notation 2-(1, q, r, s)2n for the function. This paper gives a detailed theory of equivalence of quartic MRS 2-functions in 2n variables. Such a theory was provided for the cubic MRS 2-functions in two 2015 papers of Cusick and Johns. As in the earlier papers, the two main topics in the theory are describing the affine equivalence classes of the functions under certain groups of permutations; and giving details of the linear recursions that the Hamming weights of any sequence of functions 2-(1, q, r, s)2n (with q < r < s, say), n=s,s+1,… can be shown to satisfy. The discussion for both of these topics uses new ideas because the quartic theory naturally divides into two cases.
Imprecise Data Handling with MTE

Measurements, indications and forecasts are randomly and very often systematically corrupted. The distribution of stochastic distortions is empirically evaluated; an estimate is usually given by histogram. Empirical distributions available from various sources differ. Discrepancies are due to testing methodology, conditions of experiments etc. Whatever the reason they introduce some kind of doubtfulness. In many modern applications propagation of uncertainty is an important issue. The ability to model and process uncertainty through traditional approaches is rather limited. To propose new solutions, one should start with an alternative approach towards modelling and processing uncertainty. Mathematical Theory of Evidence might be useful provided supporting measures on representing the true value by any location in the neighborhood of observation are available. Theoretical Gaussian density distribution as well as histograms are exploited. In recent paper by the author transformation from probability density to probability distribution with fuzzy sets was presented. In order to obtain most probable forecast various height histogram bins are converted to fuzzy limited ones. Necessary membership functions are proposed. Paper concludes with example calculations results.
Strategic Prospective: Definitions and Key Concepts

The prospective approach introduced the possibility of recognizing several possible states in the next time interval, because it does not reduce the dynamics of change to a simple rational choice, as is the case when reflections on the future focus only on the identification or extrapolation of trends. Thus, the prospective provides the basis for long-term reflection on the system being studied that give a multi-dimensional and multi-scale vision of it. There are a variety of available methods and techniques that have been developed to deal with long-term strategic reflections, which ability to decision-makers to make estimations of futures, identify future-bearing facts and to make inferences about the future. Therefore, this chapter hopes to help the reader identify the key definitions and concepts around the prospective approach, in addition to recognizing similarities and divergences with other approaches in the field of Future Studies.
Image Smoothing Via Gradient Sparsity and Surface Area Minimization

Image smoothing is a very important topic in image processing. Among these image smoothing methods, the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> gradient minimization method is one of the most popular ones. However, the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> gradient minimization method suffers from the staircasing effect and over-sharpening issue, which highly degrade the quality of the smoothed image. To overcome these issues, we use not only the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> gradient term for finding edges, but also a surface area based term for the purpose of smoothing the inside of each region. An alternating minimization algorithm is suggested to efficiently solve the proposed model, where each subproblem has a closed-form solution. Leveraging the introduced surface area term, the proposed method can effectively alleviate the staircasing effect and the over-sharpening issue. The superiority of our method over the state-of-the-art methods is demonstrated by a series of experiments.
An application niche for finite mixture models in forest resource surveys

We propose design-based inference with finite mixture models (FMM) in settings where heterogeneity cannot be addressed by more conventional modelling. In FMM, a model is estimated for each of K latent model subgroups in a population under study. We evaluated the FMM approach with a difference estimator with K = 2 in 600 replications of simulated equal probability sampling from 12 artificial populations. An example with a forest population in southern Norway demonstrated a practical implementation. The artificial populations were composed of one, two, three, or four actual model subgroups generated from models that were either of the same form as the estimation model or different. We compare bias and variance in estimates of a population mean with standard results for K = 1. All estimates with K = 2 were nearly unbiased. Bias was largest when actual subgroups were clustered on y. Variances in sample means with K = 1 were 60% larger than with K = 2. An important reduction in variance with K = 2 was confirmed in the case study. A reliable estimate of variance requires a medium to large sample size.
Describing and displaying numerical and categorical data

<br>The set of observations recorded during research work is termed data. Data can be described as numerical or categorical. While numerical data are further divided into discrete or continuous, categorical data are further divided into nominal or ordinal data. These data may be represented in a textual manner or with the help of illustrations (tables or graphs). The selection of a proper mode of representation of data helps in the optimal understanding of results. The level of importance of each parameter determines the mode of representation. The present article attempts to introduce the various methods of data presentation and throw some light on the benefits and limitations of each mode of data presentation.<br>
A 3-Step Cartesian Genetic Programming for Designing Combinational Logic Circuits with Multiplexers

The design of digital circuits has been widely investigated in the literature but the evolution of complex combinational logic circuits is not an easy task for Cartesian Genetic Programming (CGP). We propose here a new approach in order to increase the capacity of CGP in finding feasible circuits (those with the same response of the truth table). The proposed procedure uses a 3-step evolution by coupling a 2-input multiplexer in each circuit’s output. These multiplexers divide the truth table and the similarity between its inputs is maximized. Thus, don’t-care situations are generated for the controls of the multiplexers, making the evolution of CGP easier. Also, a variant of the standard evolutionary strategy commonly adopted in CGP is proposed, where the following procedures are considered: (i) the Single Active Mutation (SAM), (ii) the Guided Active Mutation (GAM), and (iii) a crossover. The proposed methods are applied to combinational logic circuits with multiple outputs and the results obtained are compared to those found by a standard CGP with SAM. Benchmark problems with inputs from 9 to 12 are used in the computational experiments, and the objective is to find circuits that match the truth tables. The results show that (i) the combination of crossover, SAM, and GAM increases the performance of CGP, and (ii) the proposed 3-step method is the only technique tested here able to obtain feasible solutions in all independent runs.
A Content-Based Approach for Saliency Estimation in 360 Images

In this paper we present a technique for saliency estimation in 360 images. Existing approaches exploit high/low-level image features, head movement, and eye-gazes of observers. However, we believe that the saliency of an image is influenced also by its content. The proposed approach consists of three modules. In the first module, a segmentation of the image is performed and its local and global features are extracted. The second module combines the extracted features for summarizing the content that the image portrays. Finally, this content knowledge is utilised for image saliency estimation. Experimental results show effectiveness of the proposed system with respect to ground truth saliency maps.
Fashion Style Recognition Using Component-Dependent Convolutional Neural Networks

The fashion style recognition is important in online marketing applications. Several algorithms have been proposed, but their accuracy is still unsatisfactory. In this paper, we share our proposed method for creating an improved fashion style recognition algorithm, component-dependent convolutional neural networks (CD-CNNs). Given that a lot of fashion styles largely depend on the features of specific body parts or human body postures, first, we obtain images of the body parts and postures by using semantic segmentation and pose estimation algorithms; then, we pre-train CD-CNNs. We perform the classification by the concatenated outputs of CD-CNNs and a support vector machine (SVM). Experimental results using the HipsterWars and FashionStyle14 datasets prove that our method is effective and can improve classification accuracy, namely 85.3% for HipsterWars and 77.7% for FashionStyle14, while those of existing methods were 80.9% for HipsterWars and 72.0% for FashionStyle14.
Edge-Computing Convolutional Neural Network with Homography-Augmented Data for Facial Emotion Recognition

We design a convolutional neural network with homography-augmented data to deal with facial emotion recognition applications. Different to other convolutional neural networks, our AsicNet is well-designed for embedded CPU and even aiming for ASIC, such as Intel Movidius VPU. We adjust the architecture of our AsicNet and train our AsicNet on the GPU server, meanwhile we consider the computation costs of embedded systems and ASICs. Moreover, we reconsider the deep learning flow and train the homography-augmented data so as to reach higher accuracy. Experimental results on both FER2013 anf JAFFE face datasets show that our AsicNet can not only have high accuracy (72.42% on FER2013; 99.82% on JAFFE) as compared to the state-of-arts but also reach 41.22 millisecond (24.26 FPS) on the embedded CPU and 15.25 millisecond (65.57 FPS) on Intel Movidius VPU to tell the facial emotion from a face image.
Semi-Supervised Robust One-Class Classification in RKHS for Abnormality Detection in Medical Images

Abnormality detection in medical images is a one-class classification problem for which typical methods use variants of kernel principal component analysis or one-class support vector machines. However, in practical deployment scenarios, many such methods are sensitive to the outliers present in the imperfectly-curated training sets. Current robust methods use heuristics for model fitting or lack formulations to leverage even a small amount of high-quality expert feedback. In contrast, we propose a novel method combining (i) robust statistical modeling, extending the multivariate generalized-Gaussian to a reproducing kernel Hilbert space, with (ii) semi-supervised learning to leverage a small expert-labeled outlier set. Results on simulated and real-world data, including endoscopy data, show that our method outperforms the state of the art in accurately detecting abnormalities.
Scene Retrieval for Video Summarization Based on Text-to-Image gan

We present a new scene retrieval method based on text-to-image Generative Adversarial Network (GAN) and its application to query-based video summarization. Text-to-image GAN is a deep learning method that can generate images from their corresponding sentences. In this paper, we reveal a characteristic that deep learning-based visual features extracted from images generated by text-to-image GAN include semantic information sufficiently. By utilizing the generated images as queries, the proposed method achieves higher scene retrieval performance than those of the state-of-the-art methods. In addition, we introduce a novel architecture that can consider order relationship of the input sentences to our method for realizing a target video summarization. Specifically, the proposed method generates multiple images thorough text-to-image GAN from multiple sentences summarizing target videos. Their summarized video can be obtained by performing the retrieval of corresponding scenes from the target videos according to the generated images with considering the order relationship. Experimental results show the effectiveness of the proposed method in the retrieval and summarization performance.
Format-Compliant Perceptual Encryption Method for JPEG XT

High dynamic range (HDR) image allows more details to be seen in a digital image, where objects in the darker (e.g., shadow) and brighter (e.g., sky) regions are made more visible. Therefore, there is an increasing number of devices that include the HDR imaging capturing capability. In this work, a method is put forward to encrypt high dynamic range image encoded in the JPEG XT standard. To achieve diffusion, DC coefficients in the base layer are shuffled in groups of variable sizes. Similarly, AC coefficients in the base and residual layers are shuffled in the form of (zero-run-size, value) pair. Our encryption method is able to achieve: (a) suppression of bit stream size expansion, (b) sufficiently mask the visual appearance of an HDR image, and (c) format-compliant. Experiments are carried out to verify the basic performance of the proposed method. In the best case scenario, an HDR image can be completely distorted to mask its perceptual semantics with slightly reduced file size.