Reviewerly/exHarmony huggingface dataset:
A Challenging Benchmark of Anime Style Recognition

Given two images of different anime roles, anime style recognition (ASR) aims to learn abstract painting style to determine whether the two images are from the same work, which is an interesting but challenging problem. Unlike biometric recognition, such as face recognition, iris recognition, and person re-identification, ASR suffers from a much larger semantic gap but receives less attention. In this paper, we propose a challenging ASR benchmark. Firstly, we collect a large-scale ASR dataset (LSASRD), which contains 20,937 images of 190 anime works and each work at least has ten different roles. In addition to the large-scale, LSASRD contains a list of challenging factors, such as complex illuminations, various poses, theatrical colors and exaggerated compositions. Secondly, we design a cross-role protocol to evaluate ASR performance, in which query and gallery images must come from different roles to validate an ASR model is to learn abstract painting style rather than learn discriminative features of roles. Finally, we apply two powerful person re-identification methods, namely, AGW and TransReID, to construct the baseline performance on LSASRD. Surprisingly, the recent transformer model (i.e., TransReID) only acquires a 42.24% mAP on LSASRD. Therefore, we believe that the ASR task of a huge semantic gap deserves deep and long-term research. We will open our dataset and code at https://github.com/nkjcqvcpi/ASR.
Target-Aware Auto-Augmentation for Unsupervised Domain Adaptive Object Detection

Recent researches show that data auto-augmentation strategies can enhance the performance of object detection models. However, the existing works mainly focus on in-domain generalization. There is still a blank in out-of-domain generalization. In this paper, for the first time, we propose an auto-augmentation problem under unsupervised domain adaptation scenarios. To solve this problem, we propose a simple yet effective target-aware auto-augmentation technique to search for an optimal data augmentation strategy on labeled source data, so as to boost the detection ability on the given unlabeled target data. Our method can be easily plugged into the existing domain adaptation methods. Extensive experiments have been carried out to verify the effectiveness.
Determination of Insulation Parameters with Optimization Algorithms

Insulation is one of the essential energy efficiency and sustainability topics. While insulation is primarily the subject of buildings, insulation can also be made in pipes and heat exchangers in the factory environment. In the first example of this study, the insulation material that should be used for the specified heat loss in the case of a basic circular heat source being covered with insulation material was determined, while in the second case, the targeted insulation thickness with the accepted maximum heat loss and insulation material coefficient was tried to be determined. The results obtained show that errors were zero in particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms in the first case. In the second case, PSO, ABC, and firefly (FA) optimization algorithms have the lowest average error with 3x10<sup>-5</sup>%, 1x10<sup>-5</sup>%, 3x10<sup>-5</sup>%, respectively.
Comparative Analysis of Deep Learning Models for Aspect Level Amharic News Sentiment Analysis

Nowadays, in the era of social media, customer input and feedback have significant impact on firm&#x2019;s services and products. Companies face a significant challenge in extracting meaningful information from this unstructured, unorganized, massive, and fragmented data. Some research works has been done on Amharic sentiment analysis (AMSA), however none of them have looked at the aspect level by utilizing a deep learning approach. This work focuses on sentiment analysis of Amharic text utilizing aspect level with a hybrid deep learning approach. The dataset was acquired from Amhara Media Corporation's official Facebook page in Microsoft Excel format. Comment exporter software was used to create a dataset of 10,000 in excel format. Different machine learning techniques such as Convolutional neural network (CNN), Long short-term Memory (LSTM), CNN-LSTM and CNN-GRU were used to train and test the dataset. The result of the study shows that LSTM model performed better than other models with training accuracy of 99.10% having a very little difference from CNN-GRU model with 99.08% training accuracy.
Improving Factored Hybrid HMM Acoustic Modeling without State Tying

In this work, we show that a factored hybrid hidden Markov model (FH-HMM) which is defined without any phonetic state-tying outperforms a state-of-the-art hybrid HMM. The factored hybrid HMM provides a link to transducer models in the way it models phonetic (label) context while preserving the strict separation of acoustic and language model of the hybrid HMM approach. Furthermore, we show that the factored hybrid model can be trained from scratch without using phonetic state-tying in any of the training steps. Our modeling approach enables triphone context while avoiding phonetic state-tying by a decomposition into locally normalized factored posteriors for monophones/HMM states in phoneme context. Experimental results are provided for Switchboard 300h and LibriSpeech. On the former task we also show that by avoiding the phonetic state-tying step, the factored hybrid can take better advantage of regularization techniques during training, compared to the standard hybrid HMM with phonetic state-tying based on classification and regression trees (CART).
Deep Rank Cross-Modal Hashing with Semantic Consistent for Image-Text Retrieval

Cross-modal hashing retrieval approaches maps heterogeneous multi-modal data into a common hamming space to achieve efficient and flexible retrieval performance. However, existing cross-modal methods mainly exploit feature-level similarity between multi-modal data, the label-level similarity and relative ranking relationship between adjacent instances have been ignored. To address these problems, we propose a novel Deep Rank Cross-modal Hashing(DRCH) method that fully explores the intra-modal semantic similarity relationship. Firstly, DRCH preserves semantic similarity by combining both label-level and feature-level information. Secondly, the inherent gap between modalities are narrowed by proposing a ranking alignment loss function. Finally, the compact and efficient hash codes are optimized from the common semantic space. Extensive experiments on two real-world image-text retrieval datasets demonstrate the superiority of DRCH compared with several state-of-the-art(SOTA) methods.
Failed disruption propagation in integer genetic programming

We inject a random value into the evaluation of highly evolved deep integer GP trees 9743720 times and find 99.7percent Suggesting crossover and mutation's impact are dissipated and seldom propagate outside the program. Indeed only errors near the root node have impact and disruption falls exponentially with depth at between exp(-depth/3) and exp(-depth/5) for recursive Fibonacci GP trees, allowing five to seven levels of nesting between the runtime perturbation and an optimal test oracle for it to detect most errors. Information theory explains this locally flat fitness landscape is due to FDP. Overflow is not important and instead, integer GP, like deep symbolic regression floating point GP and software in general, is not fragile, is robust, is not chaotic and suffers little from Lorenz' butterfly. Keywords: genetic algorithms, genetic programming, SBSE, information loss, information funnels, entropy, evolvability, mutational robustness, optimal test oracle placement, neutral networks, software robustness, correctness attraction, diversity, software testing, theory of bloat, introns
Initial Results on the Effectiveness of Distributed Edge Detection for Large Image Data Sets

Image data is expanding rapidly, along with technology development, so efficient solutions must be considered to achieve high, real-time performance in the case of processing large image datasets. Parallel processing is increasingly used as an attractive alternative to improve the performance, when using existing distributed architectures but also for sequential commodity computers. It can provide speedup, efficiency, reliability, incremental growth, and flexibility. We present such an alternative and stress the effectiveness of the methods to accelerate computations on a small cluster of PCs compared to a single CPU. Our paper is focused on applying edge detection on large image data sets, as a fundamental and challenging task in image processing and computer vision. Five different techniques, mainly Sobel, Prewitt, LoG, Canny, and Roberts, are compared in a simple experimental setup that includes the OpenCV library functions for image pixels manipulation. Gaussian blur is used to reduce high-frequency components to manage the noise that edge detection is impacted by. Overall, this work is part of a more extensive investigation of image segmentation methods on large image datasets, but the results presented are relevant and show the effectiveness of our approach.
PC‐SNet for automated detection of prostate cancer in multiparametric‐magnetic resonance imaging

Abstract Prostate cancer (PCa) is responsible for the maximum deaths of men across the world after lung cancer; hence, it should be diagnosed in the initial stages. Magnetic resonance imaging (MRI) commonly diagnoses PCa due to better visibility of desired organ and cancerous region over other modalities. Therefore, development of MRI‐based computer‐aided diagnosis (CAD) systems for PCa has become a recent area of research. Conventional methodologies used by researchers and radiologists were time consuming and prone to subjective errors due to manual interpretation. Thus, the CAD system helps in the early detection of PCa by reducing the computational complexity and increasing the detection accuracy with less chances of subjective errors. This article proposes a deep learning–based methodology named prostate cancer segmentation network (PC‐SNet) for the segmentation of the region of interest (ROI) from the MRI sub‐modalities T2‐weighted (T2W) and dynamic contrast enhanced (DCE). Further, the performance is analyzed using parameters such as accuracy, Mathews correlation coefficient (MCC), dice similarity coefficient (DSC), Jaccard Index (JI) or intersection over union (IOU), F‐score, and Hausdorff distance (HD). Finally, the performance of PC‐SNet is found to outperform fully convolutional network (FCN), semantic pixel wise segmentation (SegNet), residual network (ResNet), UNet, and ENet architectures.
Self-Attention for Incomplete Utterance Rewriting

Incomplete utterance rewriting (IUR) has recently become an essential task in NLP, aiming to complement the incomplete utterance with sufficient context information for comprehension. In this paper, we propose a novel method by directly extracting the coreference and omission relationship from the self-attention weight matrix of the transformer in-stead of word embeddings and edit the original text accordingly to generate the complete utterance. Benefiting from the rich information in the self-attention weight matrix, our method achieved competitive results on public IUR datasets.
High-Quality Self-Supervised Snapshot Hyperspectral Imaging

Hyperspectral image (HSI) reconstruction is about recovering a 3D HSI from its 2D snapshot measurements, to which deep models have become a promising approach. However, most existing studies train deep models on large amounts of organized data, the collection of which can be difficult in many applications. This paper leverages the image priors encoded in untrained neural networks (NNs) to have a self-supervised learning method which is free from training datasets while adaptive to the statistics of a test sample. To induce better image priors and prevent the NN overfitting undesired solutions, we construct an unrolling-based NN equipped with fractional max pooling (FMP). Furthermore, the FMP is used with randomness to enable self-ensemble for reconstruction accuracy improvement. In the experiments, our self-supervised learning approach enjoys high-quality reconstruction and outperforms recent methods including the supervised ones.
Agcyclegan: Attention-Guided Cyclegan for Single Underwater Image Restoration

Underwater image restoration is a fundamental problem in image processing and computer vision. It has broad application prospects for underwater operations, especially underwater robot operations. The challenging work is how to keep the color authenticity of the captured underwater image. In this paper, we propose a novel network architecture based on CycleGAN. Specifically, in the generator part, we adopt the U-Net structure because the long skip connection of U-Net will obtain more detailed information. Besides, we append the pixel-level attention block to provide greater flexibility for detail structure modeling. It assigns different weights to each channel to pay more attention to the critical feature. We also verify its generalization performance on several benchmark datasets. The extensive experiments with comparisons to state-of-the-art approaches demonstrate the superiority of the proposed model.
Trajectory Control of Quadcopter in Matlab Simulation Environment

An unmanned aerial vehicle (UAV) is an autonomous aircraft without a pilot and passenger. UAVs are also called "drone". However, while drone is used to refer to any type of UAV in the common language, it mainly refers to UAV mostly used in military context. UAVs and today's competent usage areas are mentioned in this study. A detailed examination of quadcopters, which is a four-engine unmanned aerial vehicle, is given and it is emphasized why simulation is necessary for UAVs. The implementation of the study is carried out in Matlab-Simulink and the quadcopter is simulated in the Matlab environment. Two different simulation processes are considered in the study and with the Simulink model used for the first simulation, the trajectory tracking problem is tried to be overcome by adding a direct Eulerrate script instead of the PD controller output. A second simulation is needed to fly the UAV in a circular trajectory. When the results obtained for both simulations are examined, it has been determined that the second simulation provides a more periodic trajectory tracking than the first simulation.
IoT based Real-time Face Detection and Recognition System

In the modern era, security is a matter of utmost importance and it is something that can never be perfect. With more and more advancements in the field of security, there have been various systems that have been developed to enhance security and to prevent security breaches. But there is a need for an efficient and cost-effective system that provides security in the best way possible. This project aims on using &#x201C;face&#x201D; which is one of the important things which cannot be hidden and thus making facial recognition an integral part of the security process. The project explores various aspects of facial recognition in Python with OpenCV. Haar Cascade algorithm is used to detect faces and to capture facial features. The complete project is implemented with Raspberry Pi. It plans to replace the age-old convention of cards and registers with something more secure and durable, which in this case is facial recognition. The plan is to explore various facial recognition techniques and aim at finding the most efficient one. The result shows the successful detection and recognition of the face in real-time mode.
Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model Using Subspace Perturbation

Privacy has become a major concern in machine learning. In fact, the federated learning is motivated by the privacy concern as it does not allow to transmit the private data but only intermediate updates. However, federated learning does not always guarantee privacy-preservation as the intermediate updates may also reveal sensitive information. In this paper, we give an explicit information-theoretical analysis of a federated expectation maximization algorithm for Gaussian mixture model and prove that the intermediate updates can cause severe privacy leakage. To address the privacy issue, we propose a fully decentralized privacy-preserving solution, which is able to securely compute the updates in each maximization step. Additionally, we consider two different types of security attacks: the honest-but-curious and eavesdropping adversary models. Numerical validation shows that the proposed approach has superior performance compared to the existing approach in terms of both the accuracy and privacy level.
Rethinking Two-B-Real Net for Real-Time Salient Object Detection

Exploring a fast and accurate salient object detection (SOD) model is a promising research area. TBRS [1] has been proposed a two-branch network for real-time SOD. However, its principle of adding an extra path to encode spatial information is time-consuming. And its backbone is borrowed from image classification tasks, may be inefficient for SOD due to the deficiency of task-specific design. To handle these problems, we propose a novel and efficient structure named short-range concatenate module (SRCM) by removing structure redundancy. Specifically, we gradually reduce the dimension of feature maps and use the aggregation of them for image representation, which forms the basic module of SRCM network. Moreover, we propose an efficient detail guidance branch (DBG) to further encode detail structural information in low-level stages instead of the time-consuming perceptual branch used in TBRS. Finally, low-level features and high-level features are fused by the feature projection module (FPM). Extensive evaluations and analysis demonstrate that our proposed algorithm achieves the leading accuracy performance with real-time speed (216fps). We hope that our series of works can motivate future research for real-time SOD task.
An approach to enhance performance of age invariant face recognition

Age invariant face recognition (AIFR) is currently a study topic with several potential uses. It offers a variety of real-world applications, including passport renewal, driver’s license renewal, locating missing children, locating criminals, providing security to VIPs etc. In the field of AIFR, scientific efforts have increased. Matching faces of big age differences is thus a challenge, owing to the significant variation in appearance between young and elderly age. The appearance and form of the face deteriorate with age, making facial recognition the most difficult task. AIFR has become a highly common and difficult chore in recent years. In this discipline, the set of feature extraction and classification algorithms is crucial. This paper addresses the issues raised above by proposing an enhanced ASM approach for extracting features from 2D search regions using handcrafted and deep image features in conjunction with a 7-layer CNN architecture and a smaller image size of 32x32 pixels to reduce delay time and space complexity. Using the standard dataset LAG, the study approach entails running many tests to evaluate the proposed system’s performance. The results show that the suggested method beats state-of-the-art algorithms in face recognition and achieves good accuracy throughout the age spectrum. The presented methodology achieves a maximum accuracy of 91.76 percent for the LAG database, outperforming all existing state-of-the-art methodologies.
Hybrid Feature Selection Method with Multi-objective Grey Wolf Optimizer for High Dimension Data

Feature selection is an indispensable activity in machine learning, which aims at identifying the relevant predictors from a very high dimensional feature space to improve the performance and reduce the learning time of the model. However, a stupendous surge in the feature dimension space possesses a significant challenge to feature selection methods. This study is performed with an aim to cater to this challenge, and we propose a hybrid feature selection method composed of a metaheuristic optimization method and a filter-based feature selection. The proposed strategy cherry picks the advantages of both strategies while minimizing the overall drawbacks. We have examined and evaluated the proposed model over three benchmark datasets provided by University of California, Irvine Open-Source Repository. We benchmarked the performance and the accuracy of the proposed work against the standard feature selection methods and the existing hybrid models proposed in preceding studies. Adaptive boosting is used to assess the classification accuracy of the model. The set of standard classification metrics, such as, Accuracy, F1-Score, Recall and Precision are evaluated across all the three datasets for different feature selection strategies. The study result unanimously concludes that the proposed model outclasses the present standard and other state-of-art feature selection methods. The proposed model delivers accuracy of 96.60&#x0025; over 95.68&#x0025; for Ionosphere dataset. For the Sonar dataset, our model gives accuracy of 85.71&#x0025; over 81.11&#x0025;. For the Clean dataset, the proposed model&#x0027;s precision and recall are 96.31&#x0025; and 95.10&#x0025; over 95.01&#x0025; and 94.93&#x0025; respectively.
Texture Information Boosts Video Quality Assessment

Automatically evaluating the quality of in-the-wild videos is challenging since both the distortion types and reference videos are unknown. In general, humans can make a fast and accurate judgment for video quality. Fortunately, deep neural networks have been developed to effectively model the human visual system (HVS). In this paper, we deeply investigate three elements of HVS, including texture masking, content-dependency, and temporal-memory effects from an experimental perspective. Based on the investigation, we propose to make full use of texture information to boost the performance of video quality assessment (VQA), termed TiVQA in this paper. To be specific, TiVQA first uses the local binary pattern (LBP) operator to detect texture information of each video frame. Then a two-stream ResNet is employed to extract the texture masking and content-dependency embeddings, respectively. Finally, TiVQA integrates both the gated recurrent unit and subjectively-inspired temporal pooling layer to model the temporal-memory effects. Extensive experiments on benchmark datasets including KoNViD-1k, CVD2014, LIVE-Qualcomm, and LSVQ show that the proposed TiVQA obtains state-of-the-art performance in terms of SRCC and PLCC.
Indian Currency Recognition System Using Image Processing Techniques

In today&#x0027;s modern world, an intelligent system is the main requirement for current automation systems like money exchange machines, electronic banking, etc. Such automated currency recognition systems helps in reducing the human efforts to recognize the monetary value of the currency. The currency notes are sometimes blurry or damaged that makes the recognition task difficult. The correct algorithms and features extraction and selection methods are very important to select in the solution. The practical implementation of these systems should be less complex, simple with good efficiency and speed. In this paper, an automated currency system is proposed using image processing techniques. Image processing techniques are used to improve the quality of currency image so that correct information can be extracted. Basically currency is scanned for obtaining image, pre-processing operations are performed which include blurring, noise removal etc. Then the boundary detection and cropping is completed so the feature extraction is the last process after which the result is displayed. By these phases the currency recognition is processed and helps us to recognize currency easily.