Reviewerly/exHarmony huggingface dataset:
Real-aware motion deblurring using multi-attention CycleGAN with contrastive guidance

Most existing motion deblurring methods need a large amount of paired sharp and blurred images for network training. However, this restricts network representation ability and fails to maintain satisfactory deblurring results under real-world circumstances. To overcome these limitations, we propose an unsupervised real-aware motion deblurring method using multi-attention CycleGAN with contrastive guidance. The network architecture has two streams, including a forward sharp translation stream and a backward blurred translation stream, to handle unpaired sharp and blurred images based on a cycle-consistent mechanism. First, we develop a multi-attention GAN for each translation stream to embrace real-aware information from unpaired real sharp and blurred statistics. The multi-attention includes a long-short attention block, a multi-kernel attention block, and an adversarial attention block. Second, we propose a gradient contrastive loss function in the generator and an adversarial contrastive loss function in the discriminator. They exploit inherent sharp information and increase network representation in practical applications. Third, we design hybrid loss functions for sharp and blurred translations to train the network. Extensive experiments on four benchmark datasets demonstrate that the proposed method achieves better-restored performance than current state-of-the-art methods for unsupervised motion deblurring.
Density estimation for spherical data using nonparametric mixtures

Nonparametric density estimation is studied for spherical data that may arise in many scientific and practical fields. In particular, nonparametric mixture models based on likelihood maximization are used. A nonparametric mixture has component distributions mixed together with a mixing distribution that is completely unspecified and needs to be determined from data. For mixture components, a two-parameter distribution family can be used, with one parameter as the mixing variable and the other to control the smoothness of the density estimator. For example, the popular von Mises-Fisher distributions can be readily used for this purpose. Numerical studies with various spherical data sets show that the resultant mixture-based density estimators are strong competitors with the best of the other density estimators.
CSAIL2019 Crypto-Puzzle Solver Architecture

The CSAIL2019 time-lock puzzle is an unsolved cryptographic challenge introduced by Ron Rivest in 2019, replacing the solved LCS35 puzzle. Solving these types of puzzles requires large amounts of intrinsically sequential computations (i.e. computations which cannot be parallelized), with each iteration performing a very large (3072-bit in the case of CSAIL2019) modular multiplication operation. The complexity of each iteration is several times greater than known FPGA implementations, and the number of iterations has been increased by about 1000x compared to LCS35. Because of the high complexity of this new puzzle, a number of intermediate, or milestone versions of the puzzle have been specified.
Error Analysis and Graphical Evidence of Randomness in Two Methods of Color Visual Cryptography

Analysis of errors in two methods of color visual cryptography with random shares introduced by us in previous publications was performed. In both methods the shares are random, and consequently, errors occur in the decoded image. In one of the methods, where the coding is done by unhiding the pixels, there are two types of errors: missing color errors and hiding failure errors. In the other method, where the coding is done by unhiding, only the missing color errors occur. Probabilities of the missing color errors were modelled mathematically and frequencies of two types of errors were tested experimentally for both methods. Tests demonstrate that the model is correct. The results show in which cases the considered methods exhibit more accurate decoding results. Also, the extended results of randomness tests conducted with the NIST statistical test suite on the results of coding for a set of typical benchmark images are presented in the form of histograms of p-values. These graphical results indicate that the shares are indeed truly random.
Online Complaint Management System at Government Degree College Wari

Student Complaint is critical information that reflects students' voices and is a significant indicator of student unhappiness. As the complexity and number of college student complaints increase, a well-structured web app with a good database will be required to assist lecturers and school officials in combating this issue trend; a computerized complaint system will provide a solution to this problem. This online application was created to respond to student concerns addressed by the proper body. The online application is divided into three sections: Student Login, Student Register, and administration. The object-oriented design and analysis methodology (OOADM) was used in this study, which is a technical technique for evaluating and developing an application or system using objects throughout the software development process. HTML, CSS, JAVASCRIPT, PHP, SQL, and JQUERY are the programming languages used. Web programming languages were selected since it is a web-based application that is platform neutral. The Complaint record contains information about any complaint made by the user (the student) and is viewable by the administrator, who refers complaints to the proper authorities. The Student Login needs the student to log into the program before filing a complaint; the Student Register is a form that collects the student's information before they can use the system; and the administrator is in control of the system and has access to all filed Complaints. The goal of developing this program is to create a computerized complaint system that allows students to file complaints or report any wrongdoing in college, which will be handled by an experienced administrator who will forward the complaints to the relevant body.
Theoretical analysis of norm selection for robustness verification of neural networks

In the robustness verification of neural network, Lp-norm is generally used to measure the distance between two different data. However, different kinds of Lp-norm examine the specific differences between each dimension of the data. Some Lp-norm computational methods magnify specific differences between datasets. To verify the robustness of the network, the usual method is to calculate the different robustness radii of the L∞-norm. But which Lp norm performs best under a given network is rarely considered. To better reflect the nature of the network, this paper proposes a theoretical analysis of the robustness of the network that can be verified by choosing any norm type, and the relationship between the size of Lp-ball corresponding to different robust radii is proved. We conducted experiments on different classification networks and obtained a general norm selection method. As a result, a specific norm can be considered first for network robustness without computing all the different Lp-norm.
Scalable cognitive developmental network: A strategy for integrating new perception online using relation evolution SOINN

Cognitive development can make robots more intelligent. However, current cognitive robots are still limited to learning fixed types of information. To this end, we propose a scalable cognitive developmental network (SCDN) based on self-organizing incremental neural network (SOINN), which allows new types of perception information to be added into the existing cognitive network online. SCDN has three hierarchical layers for developing concrete and abstract knowledge. Firstly, the sample layer learns the sample features of four modal information, including shape, color, name and taste. Secondly, the symbol layer generates respective symbol representations. Finally, the associative layer realizes the fusion of new perception information and native networks. Specifically, the Relation Evolution SOINN (RE-SOINN) is proposed in the associative layer to add new information into existing relationships, which contributes to the evolution of associative relationships. We evaluate the performance of SCDN on a fruits and vegetables dataset. Experimental results show that SCDN can effectively learn unknown sensory information online and form the correct link with the known information without learning from scratch.
Dataset and Performance Metrics towards Semantic Segmentation

Interest and ideas on semantic segmentation move on the increasing trend in the area of autonomous driving. This meets the rise in the deep learning approach. The first step in the training of a segmentation model is the dataset preparation. For this, RGB images and its corresponding segmentation images are required such that, the size of these remain the same. Each class in the image is assigned with a unique ID. The pixel value in the segmentation image denotes the class ID of the corresponding pixel. Moreover, as jpg format of the image is lossy, bmp or png formats are usually preferred. The success of the model is measured using metrics, which helps in grading the model. This paper deals with the examination of the widely used datasets in the field of semantic segmentation. The mIoU metric of the datasets on various models have been comparative studied at the end of the analysis.
A sharper lower bound on Rankin's constant

Rankin's constant is an important lattice constant which has applications in many fields including cryptography and communications. In spite of its importance, few of exact values of Rankin's constant are known. In this paper, we develop a lower bound on Rankin's constant γ2k,k which corresponds to the half volume problem. Compared with the previous best lower bound developed by Wen et al., ours is more than k2 times better. This improved lower bound on Rankin's constant directly leads to a sharper lower bound on Schnorr's constant and helps to better understand the intrinsic limitations of the 2k-block-Rankin reduction.
The final nail in the coffin of statistically-secure obfuscator

We present an elementary proof of a theorem of Goldwasser and Rothblum [10], [11] that the existence of a perfect (or even "slightly" imperfect) statistically-secure obfuscator implies a collapse of the polynomial hierarchy. In fact, our proof gives rise to a more general, quantified version of the theorem exhibiting an (impossible) tradeoff between the correctness and the security of the obfuscator. We also extend the result to a relaxed notion of security.
A High-Speed Architecture for Lung Cancer Diagnosis

Here the authors propose a simplified technique and its architecture for blind segmentation of histopathological images of lung cancer, combining the K-Means and Histogram analysis. An improved version of Otsu's algorithm is introduced for performing histogram analysis to determine the number of clusters for executing the automatic segmentation of histopathological images. The architecture is input with Biopsy images of cancer patients suffering from different stages of Lung cancer, procured from standard hospital databases to evaluate the performance. The results obtained are compared with the existing works from the literature showing considerable improvement in the overall efficiency of the image segmentation process. Segmentation output in terms of quantitative parameters like PSNR, SSIM, time of execution, etc., as well as qualitative analysis, clearly reveals the usefulness of this technique in high-speed cytological evaluation. The proposed architecture gives promising results in terms of its performance with a time of execution of 192.25ms.
Covid Live Multi-Threaded Live COVID 19 Data Scraper

COVID-19 began in 2019, and by the advent of 2020, it had become widespread and adversely affected the world. In this work—Covid Live, COVID-19 data is scraped from an online website, which gives an overview of the status of the pandemic in the desired format. The authors built an application interface using a Python micro web Flask framework. The data scraping uses a multi-threading concept to reduce the program's runtime error, resulting in receiving the data quickly, and faster than existing web crawlers and scrapers. This paper focuses on dealing with storing scraped data in the desired format. It also provides options to hear the audio of the scraped data and to download the scraped data. The authors present visualizations of current trends with scraping period details and demonstrate an efficient application that does the data scraping quickly and efficiently.
Ontology Development Methodology for Virtual Reality-Based Training (VRT) in Ophthalmology Domain

In healthcare, the concept of virtual reality (VR) has already been around for decades. Virtual reality training (VRT) in ophthalmology, for example, has the potential to offer signiðcant levels of skills transfer to novice ophthalmic surgeons, improved patient safety, and surgical competency. However, VRT in this domain still lacks the ability of capturing training scenarios and learning contents (e.g., surgical contents) in an explicit manner. In this context, ontology arriving from artificial intelligence has become a backbone of VRT. Nowadays, there is an increasing concern towards approaches that apply ontologies as domain-driven conceptual models that help to explicitly represent the training scenario taking place in virtual environments (VE). Unfortunately, recent studies have reported that all designed ontologies for VRT in general areas apply deficient components of ontological engineering, specifically methodology. Therefore, it is a good pioneer initiative to establish a unified methodology and apply the best ontology design approaches.
Explaining deep convolutional models by measuring the influence of interpretable features in image classification

Abstract The accuracy and flexibility of Deep Convolutional Neural Networks (DCNNs) have been highly validated over the past years. However, their intrinsic opaqueness is still affecting their reliability and limiting their application in critical production systems, where the black-box behavior is difficult to be accepted. This work proposes EBAnO , an innovative explanation framework able to analyze the decision-making process of DCNNs in image classification by providing prediction-local and class-based model-wise explanations through the unsupervised mining of knowledge contained in multiple convolutional layers. EBAnO provides detailed visual and numerical explanations thanks to two specific indexes that measure the features’ influence and their influence precision in the decision-making process. The framework has been experimentally evaluated, both quantitatively and qualitatively, by (i) analyzing its explanations with four state-of-the-art DCNN architectures, (ii) comparing its results with three state-of-the-art explanation strategies and (iii) assessing its effectiveness and easiness of understanding through human judgment, by means of an online survey. EBAnO has been released as open-source code and it is freely available online.
An advanced image registration method based on weighted nuclear norm minimization and SIFT

Synthetic aperture radar (SAR) image registration is significant for mapping, measurement, navigation, and so on. However, it is problematic for the SAR image alignment because the multiplicative speckle noise in the image and the coherent imagery mechanism of the SAR. Therefore, an advanced feature-based image registration method for the SAR image is proposed in this paper. Firstly, the speckle noise is filtered in natural SAR image based on the weighted nuclear norm minimization, which makes the amount of the false feature point reduce. Secondly, with the defined gradient for the SAR image, the improved SIFT method is employed to extract the feature point and generate the descriptor. The experimental results show that, compared to other methods, the proposed method improves both the accuracy of alignment and utilization of feature point significantly.
Modeling and expression of parts' relationship of multi-granularity spatio-temporal object based on mereology logic

The parts of multi-granularity spatio-temporal objects is to describe the "whole-part" relationship between different objects in the pan-spatial information system. It is the key to describe the internal parts, dynamic changes and classification system of spatio-temporal objects, and it is also an important part of octet description objects in the pan-spatial information system. The traditional relational description models of spatio-temporal objects have some shortcomings in complex relationship, semantic relevance and scene dynamic expansion process. This paper intends to elaborate the basic concept, classification method and description framework of the "whole-part" relationship of multigranularity spatio-temporal objects based on mereology logic. The application scenarios, life cycle and the "whole-part" relationship of multi granularity spatio-temporal objects are tested on Cesium platform. The experimental results show that this model can effectively make up for the deficiency of the current "whole-part" relationship expression model, which provides the corresponding theoretical basis and data organization mode for multi-granularity spatio-temporal modeling of pan-spatial information application scenarios.
Non-Linear CNN-Based Read Channel for Hard Disk Drive With 30% Error Rate Reduction and Sequential 200-Mbits/s Throughput in 28-nm CMOS

In this work, we present the world first ASIC implementation of a machine learning (ML)-based data detection channel for hard disk drives (HDDs) using a customized convolutional neural network (CNN). The chip demonstrates a 30.3% error rate reduction over the state-of-the-art HDD detection channel with two-dimensional magnetic recording (TDMR) setting. The work incorporates a full-scale co-optimization flow between the ML algorithms and the customized hardware design for achieving: 1) superior detection accuracy; 2) high-throughput sequential detections; and 3) improved power efficiency at the same time. This work also demonstrates for the first time the non-linear detection capability being embedded into the HDD reading channels. The key features of our chip include: 1) a fully unrolled CNN with dedicated silicon and hardware implementation for each convolutional layer to produce fast sequential time-series data detection at 200-Mbits/s; 2) in total six depthwise-separable convolutional layers implemented with two types of systolic arrays and 100% PE utilization for continuous data flow and high pipelining; 3) integer-only convolutions with reduced-precision weights (8 bit) and internal data (6 bit) for detection at an improved detection power efficiency at 0.86 nJ/bit and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim $ </tex-math></inline-formula> 4TOPS/W; and 4) customized quantized ReLU (QReLU) units designed to maintain low-precision feature maps and recovery accuracy loss from the model quantization.
Container Scheduling in Co-Located Environments Using QoE Awareness

Existing Cloud deployments usually perform automated scheduling and rescheduling based on Quality of Service (QoS) objectives. Services are migrating towards Quality of Experience (QoE), which maps the user experience more effectively than QoS. This work proposes extensions to the Kubernetes scheduler in order to employ QoE objectives into the algorithm. For that, we created deep learning models (using LSTM) to estimate user’s QoE that the cloud can offer. The evaluation was performed on a testbed, and considered two QoE-aware applications (live classroom and video on demand). Experimental results in a testbed show that our scheduler improves the average QoE by at least 61.5% compared to other schedulers, while our proposed resource rescheduling improved the QoE by up to 119%, keeping the average QoE closer to the maximum.
Multi-Task Edge Detection for Building Vectorization From Aerial Images

The extraction of building outline vectors is an essential task in supporting various applications. Although the recent development of deep-learning-based techniques has made advancements in the automation of this task, the accuracy and precision are insufficient due to errors caused by abundant noise and obstruction around buildings in aerial images. To better address this issue, this letter presents a new approach called multi-task edge detection (MTED) for building vectorization with the following characteristics. First, instead of detecting building corner points that are very sensitive to noise effects, a deep-learning-based rotated bounding box (RBB) detector is introduced for building edge detection to increase robustness to interference. Second, a multi-task learning strategy is designed to integrate building segmentation inside the METD framework to closely guide edge detection using spatial context. Third, a simple yet effective geometry-guided postprocessing method is designed to reconstruct vectorized building outlines based on the detected edges and learned building shape prior knowledge. The comparative experiments conducted on benchmark very-high-resolution optical aerial images indicate that the proposed approach can significantly outperform the state-of-the-art in terms of vertex-based building outline accuracy metrics. With a test time of 58 ms per building, this method enables efficient building polygon labeling in interactive mapping applications for building surveying and mapping. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yifanthomaswu/MTED_framework</uri> .
Semantic-Guided Selective Representation for Image Captioning

Grid-based features have been proven to be as effective as region-based features in multi-modal tasks such as visual question answering. However, its application to image captioning encounters two main issues, namely, noisy features and fragmented semantics. In this paper, we propose a novel feature selection scheme, with a Relation-Aware Selection (RAS) and a Fine-grained Semantic Guidance (FSG) learning strategy. Based on the grid-wise interactions, RAS can enhance the salient visual regions and channels, and suppress the less important ones. In addition, this selection process is guided by FSG, which uses fine-grained semantic knowledge to supervise the selection process. Experimental results on the MS COCO show the proposed RAS-FSG scheme achieves state-of-the-art performance on both the off-line and on-line testing, i.e., 134.3 CIDEr for the off-line testing and 135.4 for the on-line testing of MSCOCO. Extensive ablation studies and visualizations also validate the effectiveness of our scheme.