Reviewerly/exHarmony huggingface dataset:
NetMARKS: Network Metrics-AwaRe Kubernetes Scheduler Powered by Service Mesh

Container technology has revolutionized the way software is being packaged and run. The telecommunications industry, now challenged with the 5G transformation, views containers as the best way to achieve agile infrastructure that can serve as a stable base for high throughput and low latency for 5G edge applications. These challenges make optimal scheduling of performance-sensitive containerized workflows a matter of emerging importance. Meanwhile, the wide adoption of Kubernetes across industries has placed it as a de-facto standard for container orchestration. Several attempts have been made to improve Kubernetes scheduling, but the existing solutions either do not respect current scheduling rules or only considered a static infrastructure viewpoint.To address this, we propose NetMARKS - a novel approach to Kubernetes pod scheduling that uses dynamic network metrics collected with Istio Service Mesh. This solution improves Kubernetes scheduling while being fully backward compatible. We validated our solution using different workloads and processing layouts. Based on our analysis, NetMARKS can reduce application response time up to 37 percent and save up to 50 percent of inter-node bandwidth in a fully automated manner. This significant improvement is crucial to Kubernetes adoption in 5G use cases, especially for multi-access edge computing and machine-to-machine communication.
SDTR: Soft Decision Tree Regressor for Tabular Data

Deep neural networks have been proved a success in multiple fields.However, researchers still favor traditional approaches to obtain more interpretable models, such as Bayesian methods and decision trees when processing heterogeneous tabular data.Such models are hard to differentiate, thus inconvenient to be integrated into end-to-end settings.On the other hand, traditional neural networks are differentiable but perform poorly on tabular data.We propose a hierarchical differentiable neural regression model, Soft Decision Tree Regressor (SDTR).SDTR imitates a binary decision tree by a differentiable neural network and is plausible for ensemble schemes like bagging and boosting.The SDTR method was evaluated on multiple tabular-based regression tasks (YearPredictionMSD, MSLR-Web10K, Yahoo LETOR, SARCOS and Wine quality).Its performance is comparable with non-differentiable models (gradient boosting decision trees) and better than uninterpretable models (regular FCNN).On top of that, it can produce fair results with a restricted number of parameters, only using a small forest or even a single tree.We also propose an ''average entropy'' metric to evaluate the level of interpretability of a trained, soft decision tree neural network.This metric also helps to select proper structure and hyperparameters for such networks.
HI-GAN: A hierarchical generative adversarial network for blind denoising of real photographs

Although deep convolutional neural networks (DCNNs) and generative adversarial networks (GANs) have achieved remarkable success in image denoising, they have been facing a severe problem of the trade-off between removing noise and artifacts on the one hand, and preserving details on the other. In comparison with conventional DCNNs, GANs might be better in balancing between erasing different types of noise and recovering texture details. However, they often generate fake details and unexpected artifacts in the image owing to the instability of their discriminator during training. In this study, we propose a hierarchical generative adversarial network (HI-GAN) that adopts useful solutions for handling these serious problems of image denoising. Unlike the conventional GAN, the proposed HI-GAN comprises three main generators. The first generator tackles the problem of losing high-frequency features such as edges and texture. This generator was trained together with the discriminator to improve its ability to preserve essential details. The second generator focuses on eliminating the effect of instabilities caused by the discriminator and restoring low-frequency features in the noisy image . Both generators use different criteria to evaluate the denoising performance, and none of them outperformed the other. Then, a third generator is employed to help them cooperate more effectively and boost reconstruction performance. Moreover, to improve the effectiveness of the generators, we also propose a novel boosted residual dense UNet, which is designed to maximize information flow to pass through all convolutional layers in the network. In addition, we propose the AdaRaGAN loss function that effectively prevents the instability of the discriminator of the HI-GAN and improves the denoising performance. The experimental results of the experiments involving challenging datasets of real-world noisy images show that our proposed method is superior to other state-of-the-art denoisers in terms of quantitative metrics and visual quality. Our source codes and datasets for HI-GAN are available at https://github.com/ZeroZero19/HI-GAN.git.
AutoDim: Field-aware Embedding Dimension Searchin Recommender Systems

Practical large-scale recommender systems usually contain thousands of feature fields from users, items, contextual information, and their interactions. Most of them empirically allocate a unified dimension to all feature fields, which is memory inefficient. Thus it is highly desired to assign various embedding dimensions to different feature fields according to their importance and predictability. Due to the large amounts of feature fields and the nuanced relationship between embedding dimensions with feature distributions and neural network architectures, manually allocating embedding dimensions in practical recommender systems can be challenging. To this end, we propose an AutoML-based framework (AutoDim) in this paper, which can automatically select dimensions for different feature fields in a data-driven fashion. Specifically, we first proposed an end-to-end differentiable framework that can calculate the weights over various dimensions in a soft and continuous manner for feature fields, and an AutoML-based optimization algorithm; then, we derive a hard and discrete embedding component architecture according to the maximal weights and retrain the whole recommender framework. We conduct extensive experiments on benchmark datasets to validate the effectiveness of AutoDim.
Video Super-Resolution and Caching—An Edge-Assisted Adaptive Video Streaming Solution

Edge computing provides the potential to improve users&#x2019; Quality of Experience (QoE) in ever-increasing video delivery. However, existing edge-based solutions cannot fully utilize the edge computing power and storage capacity. This paper proposes <b>VI</b>deo <b>S</b>uper-resolution and <b>CA</b>ching (VISCA), an edge-assisted adaptive video streaming solution, which integrates super-resolution and edge caching to improve users&#x2019; QoE. We design a novel edge-based ABR algorithm that makes bitrate and video chunk source decisions by considering network conditions, QoE objectives, and edge resource availability jointly. VISCA utilizes super-resolution to enhance the cached low-quality video at the edge. The super-resolution models used are trained for the most popular videos only in order to achieve quality improvements with a fraction of the computation. A novel cache strategy is also adopted to maximize caching efficiency. To assess the performance of VISCA, an implemented prototype of VISCA was deployed in synthetic and real network contexts. Compared with the existing video streaming solutions, VISCA improves video quality by 28.2&#x0025;-251.2&#x0025; and reduces rebuffering time by 16.1&#x0025;-95.6&#x0025; in all considered scenarios.
An amplitude-controllable 3-D hyperchaotic map with homogenous multistability

By introducing a sinusoidal function into a three-dimensional map, a hyperchaotic map with three positive Lyapunov exponents is derived. The map has two amplitude controllers, a total controller, and a partial controller. The hyperchaotic map shares a unique structure of two-leaf and three-leaf attractors under united Lyapunov exponents. Furthermore, homogenous multistability is found in the 3-D map, where the initial data determine the attractor structure combined with the distance between any two leaves. Experimental hardware based on STM32 is built to prove the numerical findings. The hyperchaotic map is introduced for color image encryption. The analysis of key space, histogram, information entropy, correlation, and antinoise infection shows its powerful performance in encryption and security.
Comprehensive Linguistic-Visual Composition Network for Image Retrieval

Composing text and image for image retrieval (CTI-IR) is a new yet challenging task, for which the input query is not the conventional image or text but a composition, i.e., a reference image and its corresponding modification text. The key of CTI-IR lies in how to properly compose the multi-modal query to retrieve the target image. In a sense, pioneer studies mainly focus on composing the text with either the local visual descriptor or global feature of the reference image. However, they overlook the fact that the text modifications are indeed diverse, ranging from the concrete attribute changes, like "change it to long sleeves", to the abstract visual property adjustments, e.g., "change the style to professional". Thus, simply emphasizing the local or global feature of the reference image for the query composition is insufficient. In light of the above analysis, we propose a Comprehensive Linguistic-Visual Composition Network (CLVC-Net) for image retrieval. The core of CLVC-Net is that it designs two composition modules: fine-grained local-wise composition module and fine-grained global-wise composition module, targeting comprehensive multi-modal compositions. Additionally, a mutual enhancement module is designed to promote local-wise and global-wise composition processes by forcing them to share knowledge with each other. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our CLVC-Net. We released the codes to benefit other researchers.
Semantic segmentation of breast ultrasound image with fuzzy deep learning network and breast anatomy constraints

Breast cancer is one of the most serious disease affecting women’s health. Due to low cost, portable, no radiation, and high efficiency, breast ultrasound (BUS) imaging is the most popular approach for diagnosing early breast cancer. However, ultrasound images are low resolution and poor quality. Thus, developing accurate detection system is a challenging task. In this paper, we propose a fully automatic segmentation algorithm consisting of two parts: fuzzy fully convolutional network and accurately fine-tuning post-processing based on breast anatomy constraints. In the first part, the image is pre-processed by contrast enhancement, and wavelet features are employed for image augmentation. A fuzzy membership function transforms the augmented BUS images into the fuzzy domain. The features from convolutional layers are processed using fuzzy logic as well. The conditional random fields (CRFs) post-process the segmentation result. The location relation among the breast anatomy layers is utilized to improve the performance. The proposed method is applied to the dataset with 325 BUS images, and achieves state-of-the-art performance compared with that of existing methods with true positive rate 90.33%, false positive rate 9.00%, and intersection over union (IoU) 81.29% on tumor category, and overall intersection over union (mIoU) 80.47% over five categories: fat layer, mammary layer, muscle layer, background, and tumor.
Bio-Inspired Algorithms and Its Applications for Optimization in Fuzzy Clustering

In recent years, new metaheuristic algorithms have been developed taking as reference the inspiration on biological and natural phenomena. This nature-inspired approach for algorithm development has been widely used by many researchers in solving optimization problems. These algorithms have been compared with the traditional ones and have demonstrated to be superior in many complex problems. This paper attempts to describe the algorithms based on nature, which are used in optimizing fuzzy clustering in real-world applications. We briefly describe the optimization methods, the most cited ones, nature-inspired algorithms that have been published in recent years, authors, networks and relationship of the works, etc. We believe the paper can serve as a basis for analysis of the new area of nature and bio-inspired optimization of fuzzy clustering.
Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.
On the Equivalence of Decoupled Graph Convolution Network and Label Propagation

The original design of Graph Convolution Network (GCN) couples feature transformation and neighborhood aggregation for node representation learning. Recently, some work shows that coupling is inferior to decoupling, which supports deep graph propagation better and has become the latest paradigm of GCN (e.g., APPNP [16] and SGCN [32]). Despite effectiveness, the working mechanisms of the decoupled GCN are not well understood.
Temporal Analysis of the Entire Ethereum Blockchain Network

With over 42 billion USD market capitalization (October 2020), Ethereum is the largest public blockchain that supports smart contracts. Recent works have modeled transactions, tokens, and other interactions in the Ethereum blockchain as static graphs to provide new observations and insights by conducting relevant graph analysis. Surprisingly, there is much less study on the evolution and temporal properties of these networks. In this paper, we investigate the evolutionary nature of Ethereum interaction networks from a temporal graphs perspective. We study the growth rate and model of four Ethereum blockchain networks, active lifespan and update rate of high-degree vertices. We detect anomalies based on temporal changes in global network properties, and forecast the survival of network communities in succeeding months leveraging on the relevant graph features and machine learning models.
Local classical MAX-CUT algorithm outperforms <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn>2</mml:mn></mml:math> QAOA on high-girth regular graphs

The $p$-stage Quantum Approximate Optimization Algorithm (QAOA$_p$) is a promising approach for combinatorial optimization on noisy intermediate-scale quantum (NISQ) devices, but its theoretical behavior is not well understood beyond $p=1$. We analyze QAOA$_2$ for the maximum cut problem (MAX-CUT), deriving a graph-size-independent expression for the expected cut fraction on any $D$-regular graph of girth $> 5$ (i.e. without triangles, squares, or pentagons). We show that for all degrees $D \ge 2$ and every $D$-regular graph $G$ of girth $> 5$, QAOA$_2$ has a larger expected cut fraction than QAOA$_1$ on $G$. However, we also show that there exists a $2$-local randomized classical algorithm $A$ such that $A$ has a larger expected cut fraction than QAOA$_2$ on all $G$. This supports our conjecture that for every constant $p$, there exists a local classical MAX-CUT algorithm that performs as well as QAOA$_p$ on all graphs.
Slot Self-Attentive Dialogue State Tracking

An indispensable component in task-oriented dialogue systems is the dialogue state tracker, which keeps track of users’ intentions in the course of conversation. The typical approach towards this goal is to fill in multiple pre-defined slots that are essential to complete the task. Although various dialogue state tracking methods have been proposed in recent years, most of them predict the value of each slot separately and fail to consider the correlations among slots. In this paper, we propose a slot self-attention mechanism that can learn the slot correlations automatically. Specifically, a slot-token attention is first utilized to obtain slot-specific features from the dialogue context. Then a stacked slot self-attention is applied on these features to learn the correlations among slots. We conduct comprehensive experiments on two multi-domain task-oriented dialogue datasets, including MultiWOZ 2.0 and MultiWOZ 2.1. The experimental results demonstrate that our approach achieves state-of-the-art performance on both datasets, verifying the necessity and effectiveness of taking slot correlations into consideration.
Entropy of a quantum channel

The von Neumann entropy of a quantum state is a central concept in physics and information theory, having a number of compelling physical interpretations. There is a certain perspective that the most fundamental notion in quantum mechanics is that of a quantum channel, as quantum states, unitary evolutions, measurements, and discarding of quantum systems can each be regarded as certain kinds of quantum channels. Thus, an important goal is to define a consistent and meaningful notion of the entropy of a quantum channel. Motivated by the fact that the entropy of a state $\rho$ can be formulated as the difference of the number of physical qubits and the "relative entropy distance" between $\rho$ and the maximally mixed state, here we define the entropy of a channel $\mathcal{N}$ as the difference of the number of physical qubits of the channel output with the "relative entropy distance" between $\mathcal{N}$ and the completely depolarizing channel. We prove that this definition satisfies all of the axioms, recently put forward in [Gour, IEEE Trans. Inf. Theory 65, 5880 (2019)], required for a channel entropy function. The task of quantum channel merging, in which the goal is for the receiver to merge his share of the channel with the environment's share, gives a compelling operational interpretation of the entropy of a channel. The entropy of a channel can be negative for certain channels, but this negativity has an operational interpretation in terms of the channel merging protocol. We define Renyi and min-entropies of a channel and prove that they satisfy the axioms required for a channel entropy function. Among other results, we also prove that a smoothed version of the min-entropy of a channel satisfies the asymptotic equipartition property.
Cross-Modality Compensation Convolutional Neural Networks for RGB-D Action Recognition

RGB-D-based human action recognition has attracted much attention recently because it can provide more complementary information than a single modality. However, it is difficult for two modalities to effectively learn spatial-temporal information from each other. To facilitate information interaction between different modalities, a cross-modality compensation convolutional neural network (ConvNet) is proposed for human action recognition, which enhances the discriminative ability by jointly learning compensation features from the RGB and depth modalities. Moreover, we design a cross-modality compensation block (CMCB) to extract compensation features from the RGB and depth modalities. Specifically, CMCB is incorporated into two typical network architectures, ResNet and VGG, to verify the ability to improve the performance of our model. The proposed architecture has been evaluated on three challenging datasets: NTU RGB+D 120, THU-READ and PKU-MMD. We experimentally verify that our proposed model with CMCB is effective for different input types, such as pairs of raw images and dynamic images constructed from the entire RGB-D sequence, and the experimental results show that the proposed framework achieves state-of-the-art performance on all three datasets.
A blockchain based patient centric electronic health record storage and integrity management for e-Health systems

Objectives In the digitization of health records, patients and doctors find difficulty in accessing their health records, especially when the health records are fragmented with different health service providers. In this paper, Patient Centric blockchain smart contracts are designed to provide a regulated solution to the requirements of patients, doctors and health service providers with integrity management. Methods The proposed system launches smart contracts namely i) Registration contract for immutable patient log creation. ii) Health Record Creation Contact to generate digital health records. iii) Health Record Storage Contract for secure storage and rapid access with a new Modified Merkle Tree data structure. iv) Update permission Contract that can provide access at emergency situations. v) Data sharing Permission Contract for exchange of health records between different stakeholders and vi) Viewership Permission Contract for viewing the health information by the patients for home care and future care. Results The work has been carried out on a number of trials to check the effectiveness of the proposed system. The qualitative and quantitative metrics of the proposed system have been measured to evaluate the performance of resources, transactions per second, and the latency of transaction. Conclusion The proposed system provides high security and integrity through cryptographic hash functions. The results are encouraging. The designed system is implemented and found to outperform the existing systems.
A data-driven approach to violin making

Of all the characteristics of a violin, those that concern its shape are probably the most important ones, as the violin maker has complete control over them. Contemporary violin making, however, is still based more on tradition than understanding, and a definitive scientific study of the specific relations that exist between shape and vibrational properties is yet to come and sorely missed. In this article, using standard statistical learning tools, we show that the modal frequencies of violin tops can, in fact, be predicted from geometric parameters, and that artificial intelligence can be successfully applied to traditional violin making. We also study how modal frequencies vary with the thicknesses of the plate (a process often referred to as plate tuning) and discuss the complexity of this dependency. Finally, we propose a predictive tool for plate tuning, which takes into account material and geometric parameters.