Reviewerly/exHarmony huggingface dataset:
Deep softmax collaborative representation for robust degraded face recognition

Deep convolutional neural networks (DCNN) have attracted much attention in the field of face recognition because they have achieved high performance than other approaches in the so-called in-the-wild datasets. However, in many real-world applications of face recognition, the performance of CNN-based algorithms is significantly decreased when images contain various kinds of degradations caused by random noise, motion blur, compression artifacts, uncontrolled illumination, and occlusion. Moreover, this is because the main weakness of existing DCNN models is the overfitting problem. To boost the recognition performance of state-of-the-art deep learning networks, we propose a deep softmax collaborative representation-based network, which can be used as a divide-and-conquer algorithm to help multiple DCCNs work together more effectively to solve multiple sub-problems of face reconstruction and classification. We demonstrated several experiments with challenging face recognition datasets. Our extensive experiments demonstrate that our proposed method is more robust and efficient in dealing with the challenging real-world problems in face recognition compared to related state-of-the-art methods.
A clustering method for graphical handwriting components and statistical writership analysis

Abstract Handwritten documents can be characterized by their content or by the shape of the written characters. We focus on the problem of comparing a person's handwriting to a document of unknown provenance using the shape of the writing, as is done in forensic applications. To do so, we first propose a method for processing scanned handwritten documents to decompose the writing into small graphical structures, often corresponding to letters. We then introduce a measure of distance between two such structures that is inspired by the graph edit distance, and a measure of center for a collection of the graphs. These measurements are the basis for an outlier tolerant K ‐means algorithm to cluster the graphs based on structural attributes, thus creating a template for sorting new documents. Finally, we present a Bayesian hierarchical model to capture the propensity of a writer for producing graphs that are assigned to certain clusters. We illustrate the methods using documents from the Computer Vision Lab dataset. We show results of the identification task under the cluster assignments and compare to the same modeling, but with a less flexible grouping method that is not tolerant of incidental strokes or outliers.
On properties of translation groups in the affine general linear group with applications to cryptography

The affine general linear group acting on a vector space over a prime field is a well-understood mathematical object. Its elementary abelian regular subgroups have recently drawn attention in applied mathematics thanks to their use in cryptography as a way to hide or detect weaknesses inside block ciphers. This paper is focused on building a convenient representation of their elements which suits better the purposes of the cryptanalyst. Several combinatorial counting formulas and a classification of their conjugacy classes are given as well.
RGB Line Pattern-Based Stereo Vision Matching for Single-Shot 3-D Measurement

Active stereo vision has been used for real-time measurement in both academic and industrial fields. Currently, it is still very challenging for active stereo vision to achieve the continuous and robust measurement of a dynamic object due to the lack of a robust pattern extraction and matching method. In this article, we introduce a novel red green blue (RGB) line pattern with coarse-to-fine features in which the intervals between the adjacent green lines are largest and the intervals between the adjacent red lines are smallest. The large interval represents the coarse feature of the pattern and the small interval represents the fine feature of the pattern. Accordingly, we propose a matched pixel difference modeling (MPDM) method to model the matching relationship between two camera views based on the designed pattern. The line pattern is extracted in the hue saturation value (HSV) color space by slope difference distribution (SDD)-based threshold selection. The coarse green lines in two camera views are matched according to the minimum distance principle after the centroid-based alignment. The matching relationship is then modeled based on the matched green lines by least squares (LS) method. With the matching model, the fine blue lines and the fine red lines are matched successively. After stereo matching, the 3-D point on the measured object is computed by the ray intersection method. Experimental results showed that the proposed method is robust in measuring the dynamic objects by single-shot.
PNPU: An Energy-Efficient Deep-Neural-Network Learning Processor With Stochastic Coarse–Fine Level Weight Pruning and Adaptive Input/Output/Weight Zero Skipping

Recently, deep-neural-network (DNN) learning processors for edge devices have been proposed, but they cannot reduce the complexity of over-parameterized network during training. Also, they cannot support energy-efficient zero-skipping because previous methods cannot be performed perfectly in backpropagation and weight gradient update. In this letter, energy-efficient DNN learning processor PNPU is proposed with three key features: 1) stochastic coarse-fine level pruning; 2) adaptive input, output, weight zero skipping; and 3) weight pruning unit with weight sparsity balancer. As a result, PNPU shows 3.14-278.39 TFLOPS/W energy efficiency, at 0.78 V and 50 MHz with FP8 and 0%-90% sparsity condition.
Teaching, virtually: a critical reflection

Purpose The purpose of this paper is to present critical educator reflections on the pivot from the traditional physical accounting classroom to the virtual learning environment amidst COVID-19 pandemic lockdowns. Design/methodology/approach This paper outlines the reflexive experiences of two accounting educators on their scholarly journey into virtual learning and their inhabiting of the virtual accounting classroom. We adopt a critical stance in exploring what has been lost and insights gained. Findings We heed caution in the ongoing reliance on digital technologies and virtual learning that strip accounting education of its richness and complexity. Although the virtual learning environment brings with it benefits of accessibility and flexibility, it fails to replace the complexity of human connection, authenticity and informal spontaneity found in face-to-face learning. We further contend that COVID-19 presents an opportunity to rethink accounting education. We encourage educators to embrace this opportunity as a force for educational transformation; to reimagine an accounting education that embraces change, ambiguity and humanistic qualities such as empathy, compassion and humility. Originality/value Our critical educator reflections explore the impact of COVID-19 on the humanistic qualities at the heart of education and on the future of accounting education. This paper contributes to the scholarship of teaching and learning during global pandemics and other crises.
Approximation Algorithms for Training One-Node ReLU Neural Networks

Training a one-node neural network with the ReLU activation function via optimization, which we refer to as the ON-ReLU problem, is a fundamental problem in machine learning. In this paper, we begin by proving the NP-hardness of the ON-ReLU problem. We then present an approximation algorithm to solve the ON-ReLU problem, whose running time is O(nk) where n is the number of samples, and k is a predefined integral constant as an algorithm parameter. We analyze the performance of this algorithm under two regimes and show that: (1) given any arbitrary set of training samples, the algorithm guarantees an (n/k)-approximation for the ON-ReLU problem - to the best of our knowledge, this is the first time that an algorithm guarantees an approximation ratio for arbitrary data scenario; thus, in the ideal case (i.e., when the training error is zero) the approximation algorithm achieves the globally optimal solution for the ON-ReLU problem; and (2) given training sample with Gaussian noise, the same approximation algorithm achieves a much better asymptotic approximation ratio which is independent of the number of samples n. Extensive numerical studies show that our approximation algorithm can perform better than the gradient descent algorithm. Our numerical results also show that the solution of the approximation algorithm can provide a good initialization for gradient descent, which can significantly improve the performance.performance.
Developing an AI IoT application with open software on a RISC-V SoC

RISC-V is an emergent architecture that is gaining strength in low-power IoT applications. The stabilization of the architectural extensions and the start of commercialization of RISC-V based SOCs, like the Kendryte K210, raises the question of whether this open standard will facilitate the development of applications in specific markets or not. In this paper we evaluate the development environments, the toolchain, the debugging processes related to the Sipeed MAIX Go development board, as well as the standalone SDK and the Micropython port for the Kendryte K210. The training pipeline for the built-in convolutional neural network accelerator, with support for Tiny YOLO v2, has also been studied. In order to evaluate all the above aspects in depth, two low-cost, low-power, IoT edge applications based on AI have been developed. The first one is capable of recognizing movement in a house and autonomously identify whether it was caused by a human or by a house pet, like for example a dog or a cat. In the context of the current COVID-19 pandemic, the second application is capable of labeling whether a pedestrian is wearing a face mask or not, doing real-time object recognition at a mean rate of 13 FPS. Throughout the process, we can conclude that, despite the potential of the hardware and its excellent performance/cost ratio, the documentation for developers is scarce, the development environments are in low maturity levels, and the debugging processes are sometimes nonexistent.
Video Shot Boundary Detection Based on Feature Fusion and Clustering Technique

For the problems of low accuracy and high complexity in detection of gradual shot boundary and long shot, a new video shot boundary detection algorithm based on feature fusion and clustering technique (FFCT) is proposed.In the algorithm, the interval frames of video sequence are selected, converted to gray images and scaled by sampling.With the frames, the speed-up robust features (SURF) and fingerprint features are extracted from non-compressed domain and compressed domain, and then the extracted features are fused.Next, K-means method is used to cluster the fused features, and linear discriminant analysis (LDA) is introduced to map the clusters to realize cohesion within classes and looseness among classes.Finally, the correlation of the feature classes between frames is calculated, and the features in each class are selected through density calculation and matched to realize the coarse detection and fine detection of video shot boundary.In the experiment, compared with the latest representative algorithms, it has the highest accuracy for the proposed algorithm.In particular, the detection of gradual shot boundary and long shot are also more accurate.Meanwhile, the average time consumption is also reduced.The experimental results show that the proposed algorithm has high accuracy and time efficiency, especially for gradual shot boundary and long shot detection.
GAN-Based Noise Model for Denoising Real Images

In the present paper, we propose a new approach for realistic image noise modeling based on a generative adversarial network (GAN). The model aims to boost performance of a deep network denoiser for real-world denoising. Although deep network denoisers, such as a denoising convolutional neural network, can achieve state-of-the-art denoised results on synthetic noise, they perform poorly on real-world noisy images. To address this, we propose a two-step model. First, the images are converted to raw image data before adding noise. We then trained a GAN to estimate the noise distribution over a large collection of images (1 million). The estimated noise was used to train a deep neural network denoiser. Extensive experiments demonstrated that our new noise model achieves state-of-the-art performance on real raw images from the Smartphone Image Denoising Dataset benchmark.
A Practical Key-Recovery Attack on 805-Round Trivium

The cube attack is one of the most important cryptanalytic techniques against Trivium. Many key-recovery attacks based on cube attacks have been established. However, few attacks can recover the 80-bit full key information practically. In particular, the previous best practical key-recovery attack was on 784-round Trivium proposed by Fouque and Vannet at FSE 2013. To mount practical key-recovery attacks, it requires a sufficient number of low-degree superpolies. It is difficult both for experimental cube attacks and division property based cube attacks with randomly selected cubes due to lack of efficiency. In this paper, we give a new algorithm to construct candidate cubes targeting linear superpolies. Our experiments show that the success probability is \( 100\% \) for finding linear superpolies using the constructed cubes. We obtain over 1000 linear superpolies for 805-round Trivium. With 42 independent linear superpolies, we mount a practical key-recovery attack on 805-round Trivium, which increases the number of attacked rounds by 21. The complexity of our attack is \( 2^{41.40} \), which could be carried out on a PC with a GTX-1080 GPU in several hours.
Adaptive Neuro-Fuzzy Inference System Predictor with an Incremental Tree Structure Based on a Context-Based Fuzzy Clustering Approach

We propose an adaptive neuro-fuzzy inference system (ANFIS) with an incremental tree structure based on a context-based fuzzy C-means (CFCM) clustering process. ANFIS is a combination of a neural network with the ability to learn, adapt and compute, and a fuzzy machine with the ability to think and to reason. It has the advantages of both models. General ANFIS rule generation methods include a method employing a grid division using a membership function and a clustering method. In this study, a rule is created using CFCM clustering that considers the pattern of the output space. In addition, multiple ANFISs were designed in an incremental tree structure without using a single ANFIS. To evaluate the performance of ANFIS in an incremental tree structure based on the CFCM clustering method, a computer performance prediction experiment was conducted using a building heating-and-cooling dataset. The prediction experiment verified that the proposed CFCM-clustering-based ANFIS shows better prediction efficiency than the current grid-based and clustering-based ANFISs in the form of an incremental tree.
Negative Pseudo Labeling Using Class Proportion for Semantic Segmentation in Pathology

In pathological diagnosis, since the proportion of the adenocarcinoma subtypes is related to the recurrence rate and the survival time after surgery, the proportion of cancer subtypes for pathological images has been recorded as diagnostic information in some hospitals. In this paper, we propose a subtype segmentation method that uses such proportional labels as weakly supervised labels. If the estimated class rate is higher than that of the annotated class rate, we generate negative pseudo labels, which indicate, "input image does not belong to this negative label," in addition to standard pseudo labels. It can force out the low confidence samples and mitigate the problem of positive pseudo label learning which cannot label low confident unlabeled samples. Our method outperformed the state-of-the-art semi-supervised learning (SSL) methods.
An efficient lossless compression technique for remote sensing images using segmentation based band reordering heuristics

The size of remote sensing (RS) image is indeed massive due to hundreds capturing wavelengths bands used for collecting information about the ground surface. The data in the RS images are often redundant spatially, spectrally and temporally which thus give a sufficient prospect for compressing the images in different domains. In this paper, we propose a band reordering scheme to be applied on the segmented subgroup datasets of an original RS image dataset towards presenting a hybrid lossless compression technique. The proposed dataset segmentation-based band reordering process employs two heuristics, i.e. consecutive continuity limit of previous bands and continuity breakdown thresholds to be used for efficient coding of reordered dataset towards attaining better on-board compression. The aim of the proposed compression strategy is thus to increase compression gain through reordering each highly correlated bands’ segment in the RS data-cube for efficient coding. The performance of the proposed band reordering method is measured using the Consultative Committee for Space Data Systems (CCSDS) recommended lossless CCSDS 123.0-B-1 and three-dimensional (3D) Context-based Adaptive Lossless Image Codec (CALIC) predictors. The experimental results illustrate substantially higher compression of the proposed method than that of the state-of-the-art standards using various real RS hyperspectral and hyperspectral sound datasets. Quantitatively, the compression performances are increased by 0.10%–0.66% using CCSDS and 0.65–2.52% using 3D-CALIC for hyperspectral Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) datasets, 0.26%–1.00% using CCSDS and 2.16–2.57% using 3D-CALIC for hyperspectral Hyperion datasets and 4.61%–7.36% using CCSDS and 4.05%–4.88% using 3D-CALIC for hyperspectral sounder datasets by the proposed compression technique. Besides, the proposed compression scheme outperforms state-of-the-art investigated compression methods.
Collaborative community-specific microblog sentiment analysis via multi-task learning

Microblog sentiment analysis has become a hot research area due to its wide applications. There are some methods utilizing social context, but they only built a global sentiment analysis model, failing to extract personalized expressions. Some personalized methods have been proposed to deal with this problem, but they suffer from data sparseness and inefficiency. Based on personalized sentiment analysis methods, we exploit social context information and capture users’ variable and distinctive expressions at a community level to handle these problems. In particular, we propose a collaborative microblog sentiment analysis approach. In our approach, two classifiers are constructed. One is the global microblog sentiment analysis model which can exploit the sentiment shared by all users. One is the community-specific microblog sentiment analysis model which can extract sentiment influenced by user personalities. In addition, we extract community similarity knowledge and employ it to improve the learning process of the community-specific sentiment model. Moreover, we incorporate social contexts into this model as regularization to encourage the sharing sentiment between connected microblogs. An accelerated algorithm is introduced to solve our model. Experiments on two real datasets show that our model can advance the performance of microblog sentiment classification effectively and outperform state-of-art methods significantly.
Improving Handwritten OCR with Augmented Text Line Images Synthesized from Online Handwriting Samples by Style-Conditioned GAN

By leveraging large amounts of training data and deep learning technologies, performances of modern handwritten optical character recognition (OCR) systems have been greatly improved. However, collecting and labeling massive handwriting images are both time-consuming and expensive. In this paper, we propose to augment handwritten OCR training with online handwriting samples. To achieve this goal, we propose a style-conditioned generative adversarial network (SC-GAN) with a novel training data pair generation strategy. Then this network is used to transfer the styles of real handwriting images to skeleton images extracted from online handwriting samples to generate photo-realistic text line images. Experimental results on a large scale handwritten OCR task show that the recognition accuracy of our handwritten OCR system is improved by using the augmented synthetic training data.
FRACTAL DIMENSION-BASED GENERALIZED BOX-COUNTING TECHNIQUE WITH APPLICATION TO GRAYSCALE IMAGES

Fractal Dimension (FD) estimation in digital image analysis has received much attention due to its dimensional significance and therefore has become an active area of research over the year. The earlier FD-based techniques often followed traditional box-counting and its different variation of differential box-counting (DBC) paradigms, in which the proper choice of box count has remained a major concern. However, most of the state-of-the-art DBC variants suffer from considerable limitations like over-counting (OC), under-counting (UC), and limited their application only to square-shaped images, and it is still a major research problem! In this backdrop, the current investigation proposes a generalized box-counting (graylevel invariant DBC); and compares it with other state-of-the-art techniques. The proposed model is evaluated on five benchmark texture datasets (which include real and generated synthetic images) and obtained better results than the existing methods and achieved all desired outcomes by eliminating both OC and UC problems. This algorithm works for any arbitrarily sized (both squared and rectangular) images. It gives a higher rate of accuracy in terms of less fitting error in detecting exact surface roughness from given datasets.
Single Sample Face Recognition Using Convolutional Neural Networks for Automated Attendance Systems

Convolutional Neural Networks (CNNs) have been developed as powerful models for image recognition problems requiring large-scale labeled training data. However, estimating millions parameters of deep CNNs requires a huge amount of labeled samples, restricting CNNs being applied to problems with limited training data. To address this problem, a two-phase method combining data augmentation and CNN transfer learning i.e., fine-tuning pre-trained CNN models are studied herein. In this paper, we focus on the case of a single sample face recognition problem, intending to develop a real-time visual-based presence application. In this context, five well-known pre-trained CNNs were evaluated. The experimental results prove that DenseNet121 is the best model for dealing with practice problems (up to 99% top-l accuracy) is the best and most robust model for dealing with the single sample per person problem, which are related to using deep CNNs on a small dataset and specifically to single sample per person face recognition task.
Human Movement Direction Classification using Virtual Reality and Eye Tracking

Collaborative robots are becoming increasingly more popular in industries, providing flexibility and increased productivity for complex tasks. However, the robots are not yet that interactive since they cannot yet interpret humans and adapt to their behaviour, mainly due to limited sensory input. Rapidly expanding research fields that could make collaborative robots smarter through an understanding of the operators intentions are; virtual reality, eye tracking, big data, and artificial intelligence. Prediction of human movement intentions could be one way to improve these robots. This can be broken down into the three stages, Stage One: Movement Direction Classification, Stage Two: Movement Phase Classification, and Stage Three: Movement Intention Prediction. This paper defines these stages and presents a solution to Stage One that shows that it is possible to collect gaze data and use that to classify a person's movement direction. The next step is naturally to develop the remaining two stages.