Reviewerly/exHarmony huggingface dataset:
A Novel Image Encryption Optimization Technique

Image file confidentiality is a difficult issue these days for a variety of reasons in a range of industries, including medical, defence, academia, and many more. A range of practical ways have been studied in recent years to safeguard image data, however owing to technological improvements, such existing solutions have significant limits in current systems. Several academics have investigated a number of methodologies and strategies for maintaining picture secrecy and shielding top-secret information from attackers while images are sent via communications infrastructure. In this paper, an unique picture encryption technique based on a chaotic map and DNA encoding is jointly used for image encryption in a more pragmatic approach, as necessary in today’s world to safeguard private data. This approach was tried and confirmed on a personal laptop with a 64-bit operating system, Windows 10, with 16 GB RAM and the MATLAB R2020a version loaded (Random Access Memory). The results of this suggested approach reflect the best NPCR (Number of Pixel Change Rate) and UACI (Unified Average Changed Intensity) scores for a variety of selected photos for testing. This method works with a variety of image formats and is in great demand in today’s world for safeguarding sensitive data or information while being transmitted across a communications infrastructure. More research is needed in the future to identify practical answers, as current communication networks are quickly changing throughout the world owing to technology breakthroughs.
Quality of experience of 360 video – subjective and eye-tracking assessment of encoding and freezing distortions

Abstract The research domain on the Quality of Experience (QoE) of 2D video streaming has been well established. However, a new video format is emerging and gaining popularity and availability: VR 360-degree video. The processing and transmission of 360-degree videos brings along new challenges such as large bandwidth requirements and the occurrence of different distortions. The viewing experience is also substantially different from 2D video, it offers more interactive freedom on the viewing angle but can also be more demanding and cause cybersickness. The first goal of this article is to complement earlier research by Tran , et al. (2017) [39] testing the effects of quality degradation, freezing, and content on the QoE of 360-videos. The second goal is to test the contribution of visual attention as an influence factor in the QoE assessment. Data was gathered through subjective tests where participants watched degraded versions of 360-videos through a Head-Mounted Display with integrated eye-tracking sensors. After each video they answered questions regarding their quality perception, experience, perceptual load, and cybersickness. Our results showed that the participants rated the overall QoE rather low, and the ratings decreased with added degradations and freezing events. Cyber sickness was found not to be an issue. The effects of the manipulations on visual attention were minimal. Attention was mainly directed by content, but also by surprising elements. The addition of eye-tracking metrics did not further explain individual differences in subjective ratings. Nevertheless, it was found that looking at moving objects increased the negative effect of freezing events and made participants less sensitive to quality distortions. More research is needed to conclude whether visual attention is an influence factor on the QoE in 360-video.
Refining OpenPose with a new sports dataset for robust 2D pose estimation

3D marker-less motion capture can be achieved by triangulating estimated multi-views 2D poses. However, when the 2D pose estimation fails, the 3D motion capture also fails. This is particularly challenging for sports performance of athletes, which have extreme poses. In extreme poses (like having the head down) state-of-the-art 2D pose estimator such as OpenPose do not work at all. In this paper, we propose a new method to improve the training of 2D pose estimators for extreme poses by leveraging a new sports dataset and our proposed data augmentation strategy. Our results show significant improvements over previous methods for 2D pose estimation of athletes performing acrobatic moves, while keeping state-of-the-art performance on standard datasets.
Empirical evaluation of performance degradation of machine learning-based predictive models – A case study in healthcare information systems

While there have been a very large number of academic studies of proposed machine learning-based health predictive models, it is widely recognized that machine learning-based models in all domains typically degrade in performance over time, post training. This known characteristic of machine learning-based models could present significant risks in the healthcare setting to patient quality of care or safety. Nevertheless, there has been little study of the performance degradation of such models on real-world data. In this article, we empirically measure performance degradation of predictive models that predict at time of admission, emergency patient mortality, drawing upon a large dataset of over 1.83 million patient discharge records. We demonstrate important empirical results including both relatively slow performance degradation over two and a half years, but also significant differences in the rate and extent of performance degradation between different machine learning model types and time period of the training set.
Combining complementary trackers for enhanced long-term visual object tracking

Several different algorithms have been studied to combine the capabilities of baseline trackers in the context of short-term visual object tracking. Despite such an extended interest, the long-term setting has not been taken into consideration by previous studies. In this paper, we explicitly consider long-term tracking scenarios and provide a framework to fuse the characteristics of complementary state-of-the-art trackers to achieve enhanced tracking performance. Our strategy perceives whether the two trackers are following the target object through an online learned deep verification model. Such a target recognition strategy enables the activation of a decision strategy which selects the best performing tracker as well as it corrects their performance when failing. The proposed solution is studied extensively and the comparison with several other approaches reveals that it beats the state-of-the-art on the long-term visual tracking benchmarks LTB-35, LTB-50, TLP, and LaSOT.
Word Discovery in Visually Grounded, Self-Supervised Speech Models

We present a method for visually-grounded spoken term discovery. After training either a HuBERT or wav2vec2.0 model to associate spoken captions with natural images, we show that powerful word segmentation and clustering capability emerges within the model's self-attention heads. Our experiments reveal that this ability is not present to nearly the same extent in the base HuBERT and wav2vec2.0 models, suggesting that the visual grounding task is a crucial component of the word discovery capability we observe. We also evaluate our method on the Buckeye word segmentation and ZeroSpeech spoken term discovery tasks, where we outperform all currently published methods on several metrics. Code and model weights are available at https://github.com/jasonppy/word-discovery.
An Improved Lightweight YOLOv5 Model Based on Attention Mechanism for Face Mask Detection

Coronavirus 2019 has brought severe challenges to social stability and public health worldwide. One effective way of curbing the epidemic is to require people to wear masks in public places and monitor their mask-wearing states by suitable automatic detectors. However, existing models struggle to simultaneously achieve the requirements of both high precision and real-time performance. To solve this problem, we propose an improved lightweight face mask detector based on YOLOv5, which can achieve an excellent balance of precision and speed. Firstly, a novel backbone ShuffleCANet that combines ShuffleNetV2 network with Coordinate Attention mechanism is proposed as the backbone. Afterward, an efficient path aggression network BiFPN is applied as the feature fusion neck. Furthermore, the localization loss is replaced with $$\alpha $$ -CIoU in model training phase to obtain higher-quality anchors. Some valuable strategies such as data augmentation, adaptive image scaling, and anchor cluster operation are also utilized. Experimental results on AIZOO face mask dataset show the superiority of the proposed model. Compared with the original YOLOv5, the proposed model increases the inference speed by 28.3% while still improving the precision by 0.58%. It achieves the best mean average precision of 95.2% compared with other seven existing models, which is 4.4% higher than the baseline.
In vivo microscopy as an adjunctive tool to guide detection, diagnosis, and treatment

There have been numerous academic and commercial efforts to develop high-resolution in vivo microscopes for a variety of clinical use cases, including early disease detection and surgical guidance. While many high-profile studies, commercialized products, and publications have resulted from these efforts, mainstream clinical adoption has been relatively slow other than for a few clinical applications (e.g., dermatology).Here, our goals are threefold: (1) to introduce and motivate the need for in vivo microscopy (IVM) as an adjunctive tool for clinical detection, diagnosis, and treatment, (2) to discuss the key translational challenges facing the field, and (3) to propose best practices and recommendations to facilitate clinical adoption.We will provide concrete examples from various clinical domains, such as dermatology, oral/gastrointestinal oncology, and neurosurgery, to reinforce our observations and recommendations.While the incremental improvement and optimization of IVM technologies should and will continue to occur, future translational efforts would benefit from the following: (1) integrating clinical and industry partners upfront to define and maintain a compelling value proposition, (2) identifying multimodal/multiscale imaging workflows, which are necessary for success in most clinical scenarios, and (3) developing effective artificial intelligence tools for clinical decision support, tempered by a realization that complete adoption of such tools will be slow.The convergence of imaging modalities, academic-industry-clinician partnerships, and new computational capabilities has the potential to catalyze rapid progress and adoption of IVM in the next few decades.
Embedding Graph Auto-Encoder for Graph Clustering

Graph clustering, aiming to partition nodes of a graph into various groups via an unsupervised approach, is an attractive topic in recent years. To improve the representative ability, several graph auto-encoder (GAE) models, which are based on semisupervised graph convolution networks (GCN), have been developed and they have achieved impressive results compared with traditional clustering methods. However, all existing methods either fail to utilize the orthogonal property of the representations generated by GAE or separate the clustering and the training of neural networks. We first prove that the relaxed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means will obtain an optimal partition in the inner-product distance used space. Driven by theoretical analysis about relaxed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means, we design a specific GAE-based model for graph clustering to be consistent with the theory, namely Embedding GAE (EGAE). The learned representations are well explainable so that the representations can be also used for other tasks. To induce the neural network to produce deep features that are appropriate for the specific clustering model, the relaxed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means and GAE are learned simultaneously. Meanwhile, the relaxed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means can be equivalently regarded as a decoder that attempts to learn representations that can be linearly constructed by some centroid vectors. Accordingly, EGAE consists of one encoder and dual decoders. Extensive experiments are conducted to prove the superiority of EGAE and the corresponding theoretical analyses.
Contrast-Reconstruction Representation Learning for Self-Supervised Skeleton-Based Action Recognition

Skeleton-based action recognition is widely used in varied areas, e.g., surveillance and human-machine interaction. Existing models are mainly learned in a supervised manner, thus heavily depending on large-scale labeled data, which could be infeasible when labels are prohibitively expensive. In this paper, we propose a novel Contrast-Reconstruction Representation Learning network (CRRL) that simultaneously captures postures and motion dynamics for unsupervised skeleton-based action recognition. It consists of three parts: Sequence Reconstructor (SER), Contrastive Motion Learner (CML), and Information Fuser (INF). SER learns representation from skeleton coordinate sequence via reconstruction. However the learned representation tends to focus on trivial postural coordinates and be hesitant in motion learning. To enhance the learning of motions, CML performs contrastive learning between the representation learned from coordinate sequences and additional velocity sequences, respectively. Finally, in the INF module, we explore varied strategies to combine SER and CML, and propose to couple postures and motions via a knowledge-distillation based fusion strategy which transfers the motion learning from CML to SER. Experimental results on several benchmarks, i.e., NTU RGB+D 60/120, PKU-MMD, CMU, and NW-UCLA, demonstrate the promise of the our method by outperforming state-of-the-art approaches.
Cell Localization and Counting Using Direction Field Map

Automatic cell counting in pathology images is challenging due to blurred boundaries, low-contrast, and overlapping between cells. In this paper, we train a convolutional neural network (CNN) to predict a two-dimensional direction field map and then use it to localize cell individuals for counting. Specifically, we define a direction field on each pixel in the cell regions (obtained by dilating the original annotation in terms of cell centers) as a two-dimensional unit vector pointing from the pixel to its corresponding cell center. Direction field for adjacent pixels in different cells have opposite directions departing from each other, while those in the same cell region have directions pointing to the same center. Such unique property is used to partition overlapped cells for localization and counting. To deal with those blurred boundaries or low contrast cells, we set the direction field of the background pixels to be zeros in the ground-truth generation. Thus, adjacent pixels belonging to cells and background will have an obvious difference in the predicted direction field. To further deal with cells of varying density and overlapping issues, we adopt geometry adaptive (varying) radius for cells of different densities in the generation of ground-truth direction field map, which guides the CNN model to separate cells of different densities and overlapping cells. Extensive experimental results on three widely used datasets (<i>i.e.</i>, VGG Cell, CRCHistoPhenotype2016, and MBM datasets) demonstrate the effectiveness of the proposed approach.
Efficient nano-photonic antennas based on dark states in quantum emitter rings

Nanoscopic arrays of quantum emitters can feature highly sub-radiant collective excitations with a lifetime exponentially growing with emitter number. Adding an absorptive impurity as an energy dump in the center of a ring shaped polygon allows to exploit this feature to create highly efficient single photon antennas. Here among regular polygons with an identical center absorbing emitter, a nonagon exhibits a distinct optimum of the absorption efficiency. This special enhancement originates from the unique emergence of a subradiant eigenstate with dominant center occupation. Only for nine emitters the sum of coupling strengths of each emitter to all others matches the center to the ring coupling. Analogous to a parabolic mirror the antenna ring then concentrates incoming radiation at its center without being significantly excited itself. Similar large efficiency enhancements, which even prevail for broadband excitation, can also be engineered for other antenna sizes by tailoring the frequency and magnitude of the central absorber. Interestingly, for very small structures a quantum treatment predicts an even stronger enhancement for the single photon absorption enhancement than a classical dipole model. As natural light harvesting structures are often based on ring shaped structures, the underlying principle might be exploited there as well.
Impact of Information Technology on Accounting Line of Works

The study was carried out on the impact of information technology on the accounting line of work. Purpose: The purpose was to determine the impact of information technology on the accounting line of works in the global system. Methodology: A survey methodology was adopted. In the course of gathering Information, questionnaires were constructed, validated, and dispensed to sampled elements of the targeted area, which had the relevant knowledge in the area of inquiry. Appropriate statistical tools were adopted to evaluate the raw facts obtained. Results/Conclusion: Results of the investigation had shown that information technology has a substantial influence on the accounting line of work and was therefore concluded that accounting line of work has changed from what it used to be before now to a line of works that developed in alliance with the trend in technical improvement and a globalized structure. Furthermore as inferred from the findings of the study, there is a great call for prompt and concerted efforts on several fronts in order to find ways of coping with the growing degree of window dressing account, the malady of accounting noise and fraud skyrocketing syndrome in the business and the non-business world due to non-adhering to tenets of information technology when carrying out an accounting line of works. Recommendation: It was consequently recommended that the accounting line of work will be greatly enhanced if information technology is allowed to penetrate and dominate accounting practices and operations.
Hiscox Cyber Readiness Report 2021

The general consensus about the Covid-19 pandemic, as it relates to information security, is that it has been a huge headache, opening up opportunities for cyber criminals. But the fifth edition of insurance firm Hiscox's annual report suggests that there may be a silver lining.
Application of constriction coefficient-based particle swarm optimisation and gravitational search algorithm for solving practical engineering design problems

The constriction coefficient-based particle swarm optimisation and gravitational search algorithm (CPSOGSA) is a heuristic optimisation technique. It uses the diversification capability of the GSA and the convergence power of CPSO. In this paper, the hybrid CPSOGSA is applied to three constrained engineering design problems, namely welded beam design (WBD), compression spring design (CSD), and pressure vessel design (PVD), to find the optimal value of the engineering objective function and design variables. The investigation of experimental outcomes has been performed through various performance metrics like statistical measures, run time analysis, convergence rate, and box plots. Moreover, statistical verification of experimental results is carried out using a signed Wilcoxon rank-sum test. Furthermore, seven heuristic algorithms were employed for comparative analysis. The simulation results clearly indicate that CPSOGSA provides better outcomes for all the three engineering benchmarks than classical PSO, standard GSA, and most of the competing algorithms.
Contrast Enhancementof MRI Images using AHE and CLAHE Techniques

Medical images require image enhancement, a category of image processing which provides better visualization that make diagnostic more accurate. The most commonly used method for improving the quality of medical image is Contrast enhancement.The main objective is to eliminate the use of contrast dye during the process of MRI scan and to find the parameters MSE, PSNR, AMBE and contrast and compare the result. The histogram equalization (HE) is the widely accepted method which is not productive when the contrast nature differs across the image. Adaptive Histogram Equalization (AHE) overcomes this limitation by considering and developing the mapping for each pixel from the histogram in a neighboring window. Another suitable technique is CLAHE. CLAHE is a refinement of AHE where the enhancement calculation is modified by imposing a user specified level to the height of local histogram. The enhancement is thereby reduced in very uniform areas of the image, which prevents over enhancement of noise and reduces the edge shadowing effect of unlimited AHE. After enhancing the image using AHE and CLAHE the comparison of their parameters is performed.
BLOCKCHAIN

Reports on the blockchain phenomena - what it is, how it is used, and prospects for blockchain use in the future.
Multi-Attribute Attention Network for Interpretable Diagnosis of Thyroid Nodules in Ultrasound Images

Ultrasound (US) is the primary imaging technique for the diagnosis of thyroid cancer. However, accurate identification of nodule malignancy is a challenging task that can elude less-experienced clinicians. Recently, many computer-aided diagnosis (CAD) systems have been proposed to assist this process. However, most of them do not provide the reasoning of their classification process, which may jeopardize their credibility in practical use. To overcome this, we propose a novel deep learning (DL) framework called multi-attribute attention network (MAA-Net) that is designed to mimic the clinical diagnosis process. The proposed model learns to predict nodular attributes and infer their malignancy based on these clinically-relevant features. A multi-attention scheme is adopted to generate customized attention to improve each task and malignancy diagnosis. Furthermore, MAA-Net utilizes nodule delineations as nodules spatial prior guidance for the training rather than cropping the nodules with additional models or human interventions to prevent losing the context information. Validation experiments were performed on a large and challenging dataset containing 4554 patients. Results show that the proposed method outperformed other state-of-the-art methods and provides interpretable predictions that may better suit clinical needs.