Reviewerly/exHarmony huggingface dataset:
Comparative analysis between different edge detection techniques on mammogram images using PSNR and MSE

Edge detection is an important technique of image processing and computer vision detecting points where the image properties change at a steady rate. Being operated by detection of brightness discontinuities, it further detects object boundaries within images also. It is used for image segmentation and data extraction in fields like image processing, computer vision, and machine vision. This paper is aimed at description and evaluation of comparative observation of different operators of edge detection in image processing. Pertinently, MATLAB is a development tool being used to find performance of various operators. Besides, the efficiency of the Canny edge detection operator has also been proved to be outperforming as compared to Sobel, Roberts, Prewitt, Zero crossing, and LoG (Laplacian of Gaussian) in the current analysis.
Enhancing Short-Term Sales Prediction with Microblogs: A Case Study of the Movie Box Office

Microblogs are one of the major social networks in people’s daily life. The increasing amount of timely microblog data brings new opportunities for enterprises to predict short-term product sales based on microblogs because the daily microblogs posted by various users can express people’s sentiments on specific products, such as movies and books. Additionally, the social influence of microblogging platforms enables the rapid spread of product information, implemented by users’ forwarding and commenting behavior. To verify the usefulness of microblogs in enhancing the prediction of short-term product sales, in this paper, we first present a new framework that adopts the sentiment and influence features of microblogs. Then, we describe the detailed feature computation methods for sentiment polarity detection and influence measurement. We also implement the Linear Regression (LR) model and the Support Vector Regression (SVR) model, selected as the representatives of linear and nonlinear regression models, to predict short-term product sales. Finally, we take movie box office predictions as an example and conduct experiments to evaluate the performance of the proposed features and models. The results show that the proposed sentiment feature and influence feature of microblogs play a positive role in improving the prediction precision. In addition, both the LR model and the SVR model can lower the MAPE metric of the prediction effectively.
An exploratory study of organizational cyber resilience, its precursors and outcomes

Evidence shows that it is paramount for stakeholders to understand the cybersecurity of relevant organizations. However, the secrecy surrounding cyber attacks and how organizations manage their cyber resilience make it impossible for stakeholders to develop this understanding. This paper analyzes organizational cyber resilience, its contextual factors and its impact on the outcomes of cyber attacks based on publicly available data. Using the PRISMA methodology, we collated and analyzed a dataset of 1,145 publicly known cyber attacks. We conceptualize and operationalize cyber resilience from a governance perspective. Our findings indicate that organizations that suffered cyber attacks had the following cyber resilience characteristics: a relatively low level of cyber resilience reflected in the low frequency of cybersecurity roles, low reliance on cybersecurity frameworks, and relatively low strength of prevention, detection, and recovery controls. Cyber resilience is found to be associated with the sector, size, and digital intensity. Linear regression indicates that, expectedly, stronger prevention, detection, and recovery processes are related to lower breach severity and occurrence of investigations or penalties, but contrary to expectations, cybersecurity roles and frameworks are not. Furthermore, better organizational responses are associated with higher breach severity but they are not found to have an impact on the level of investigations, fines, and penalties imposed. We discuss our findings and their implications for cyber resilience regulation, future research, and sector cooperation.
Effective spin models of Kerr-nonlinear parametric oscillators for quantum annealing

A method of quantum annealing (QA) using Kerr-nonlinear parametric oscillators (KPOs) was proposed. This method is described by bosonic operators and has different characteristics from QA based on the transverse-field Ising model. As the first step to describe this method in the conventional framework of QA, we propose effective spin models of KPOs. The spin models are obtained via a variant of the Holstein-Primakoff transformation and are described by spin-$s$ operators. The terms for detuning, coherent driving, parametric driving, and the Kerr effect are mapped to the transverse field, longitudinal field, nonlinear terms for the z- and x-components of spins, respectively. By analyzing the spin models corresponding to KPOs in several settings, we demonstrate that the present spin models for a rather large $s$ and tuned parameters qualitatively reproduce behavior of KPOs.
Label Attention Network for Structured Prediction

Sequence labeling assigns a label to each token in a sequence, which is a fundamental problem in natural language processing (NLP). Many NLP tasks, including part-of-speech tagging and named entity recognition, can be solved in a form of sequence labeling problem. Other tasks such as constituency parsing and non-autoregressive machine translation can also be transformed into sequence labeling tasks. Neural models have been shown powerful for sequence labeling by employing a multi-layer sequence encoding network. Conditional random field (CRF) is proposed to enrich information over label sequences, yet it suffers large computational complexity and over-reliance on Marko assumption. To this end, we propose label attention network (LAN) to hierarchically refine representation of marginal label distributions bottom-up, enabling higher layers to learn more informed label sequence distribution based on information from lower layers. We demonstrate the effectiveness of LAN through extensive experiments on various NLP tasks including POS tagging, NER, CCG supertagging, constituency parsing and non-autoregressive machine translation. Empirical results show that LAN not only improves the overall tagging accuracy with similar number of parameters, but also significantly speeds up the training and testing compared to CRF.
Meta-Learning Paradigm and CosAttn for Streamer Action Recognition in Live Video

As an emerging field of network content production, live video has been in the vacuum zone of cyberspace governance for a long time. Streamer action recognition is conducive to the supervision of live video content. In view of the diversity and imbalance of streamer actions, it is attractive to introduce few-shot learning to realize streamer action recognition. Therefore, a meta-learning paradigm and CosAttn for streamer action recognition method in live video is proposed, including: (1) the training set samples similar to the streamer action to be recognized are pretrained to improve the backbone network; (2) video-level features are extracted by R(2+1)D-18 backbone and global average pooling in the meta-learning paradigm; (3) the streamer action is recognized by calculating cosine similarity after sending the video-level features to CosAttn to generate a streamer action category prototype. Experimental results on several real-world action recognition datasets demonstrate the effectiveness of our method.
Lite-Stereo: A Resource-Efficient Hardware Accelerator for Real-Time High-Quality Stereo Estimation Using Binary Neural Network

Stereo estimation plays a key role in many autonomous systems, such as robotics and self-driving cars. Recent work on StereoEngine, an FPGA-based accelerator for deep neural network (DNN)-based stereo estimation, has been demonstrated as a promising solution to achieve both real-time and high accuracy performance for depth sensing. However, this solution still suffers from over-utilizing the hardware resource of FPGAs. In this article, we present Lite-Stereo, a resource-efficient DNN-based stereo vision accelerator to improve the hardware efficiency for StereoEngine running on a resource-constrained FPGA. To achieve this, we design a set of optimized hardware architectures for resource-demanding bottleneck modules. In order to balance the gap between the processing speed and resource efficiency, the process elements in binary neural network modules are shared within and across modules. In addition, we provide reusing strategies on path aggregation and neighbor calculation to improve the resource efficiency of the semi-global matching module. Evaluation results demonstrate that Lite-Stereo reduces the hardware cost of ALUTs and RAM bits by 60% and 29%, respectively, without compromising the accuracy and energy efficiency compared with StereoEngine.
Fault Diagnosis Based on Subsystem Structures of Data Center Network BCube

Data center networks (DCNs) always strive to ensure high reliability and fault tolerance when Big Data processing and cloud computing are carried out. One effective method is to conduct the fault diagnosis based on subsystem structures (establish the subsystem-based reliability), which can be transformed into solving the problem of the probability that there exists at least one fault-free subnetwork in one network. In this article, we establish the subsystem-based reliability of BCube, which is the first time that fault diagnosis is carried out in the subsystem structures of the DCN. More specifically, we compute the upper and lower bounds on the subsystem-based reliability of BCube and determine the approximation on the subsystem-based reliability of BCube. Furthermore, we conduct some numerical simulations to validate the established analytical formulation. Our results show that the precise value of subsystem-based reliability of BCube can be basically represented by the approximation value of subsystem-based reliability of BCube, which means that it is much easier to evaluate the reliability of the network even when the network scale is sufficient large. Although the analysis is done for a particular network (BCube), the outcome can serve as a useful reference, and can shed light on the effectiveness of the fault diagnosis for other DCNs.
How Domain Experts Work with Data: Situating Data Science in the Practices and Settings of Craftwork

Domain experts play an essential role in data science by helping data scientists situate their technical work beyond the statistical analysis of large datasets. How domain experts themselves may engage with data science tools as a type of end-user remains largely invisible. Understanding data science as domain expert-driven depends on understanding how domain experts use data. Drawing on an ethnographic study of a craft brewery in Korea, we show how craft brewers worked with data by situating otherwise abstract data within their brewing practices and settings. We contribute theoretical insight into how domain experts use data distinctly from technical data scientists in terms of their view of data (situated vs. abstract), purposes for engaging with data (guiding processes over predicting outcomes), and overall goals of using data (flexible control vs. precision). We propose four ways in which working with data can be supported through the design of data science tools, and discuss how craftwork can be a useful lens for integrating domain expert-driven understandings of data science into CSCW and HCI research.
Human, Hybrid, or Machine? Exploring the Trustworthiness of Voice-Based Assistants

This study investigates how people assess the trustworthiness of perceptually hybrid communicative technologies such as voice-based assistants (VBAs). VBAs are often perceived as hybrids between human and machine, which challenges previously distinct definitions of human and machine trustworthiness. Thus, this study explores how the two trustworthiness models can be combined in a hybrid trustworthiness model, which model (human, hybrid, or machine) is most applicable to examine VBA trustworthiness, and whether this differs between respondents with different levels of prior experience with VBAs. Results from two surveys revealed that, overall, the human model exhibited the best model fit; however, the hybrid model also showed acceptable model fit as prior experience increased. Findings are discussed considering the ongoing discourse to establish adequate measures for HMC research.
Federation based joint client and server side Machine Learning for 5G and beyond Wireless Channel Estimation

In this paper, we propose a Machine Learning (ML) based approach to address Wireless Channel Estimation (WCE) problem for 5G and beyond wireless networks. Accurate wireless channel estimation plays a crucial role for provision of high quality of service to billions of wireless devices in current and future wireless networks. Our proposed approach uses contemporary ML technique, Federated Learning (FL), in conjunction with the Stochastic Gradient Descent (SGD) algorithm to optimise the WCE problem. Our proposed approach leverages the local user wireless channel information to locally optimise the objective at users and then uses this locally optimised information at the server to optimise the global objective function. The proposed approach is referred to as joint Federated Server Learning and Federated Client Learning (j-FSL-FCL) in the paper. We formulate the WCE problem with a novel loss function to be used for the optimisation problem. To evaluate the performance of our proposed j-FSL-FCL approach (with and without SGD), we consider a Down Link (DL) wireless channel model with Multiple-Input Multiple-Output (MIMO) setting that mimic closely to the wireless channel for 5G and beyond wireless channel models. The performance measuring parameters for j-FSL-FCL are to minimise the difference between actual and estimated wireless channel parameters (channel strength and direction). We compare the results of our proposed approach with the other techniques in the literature based on Least Squares (LS), Linear Regression (LR) and Mean Square Error (MSE). It is shown that the proposed algorithm converges to the optimal solution quickly when used with SGD compared to other existing techniques. It is also shown that the efficiency of the proposed approach for WCE problem is much higher compared to other LS and MSE based techniques. Finally, we present some interesting futuristic applications of FL in the context of 5G and beyond wireless networks.
Probabilistic Temporal Modeling for Unintentional Action Localization

Humans have the inherent advantage of understanding action intention, while it is an enormous challenge to train the machine to localize unintentional action in videos due to the lack of reliable annotations for stable training. The annotations of unintentional action are unreliable since different annotators are affected by their subjective appraisals and intrinsic ambiguity, which brings heavy difficulties for the training. To address this issue, we propose a probabilistic framework for unintentional action localization by modeling the uncertainty of annotations. Our framework consists of two main components, including Temporal Label Aggregation (TLA) and Dense Probabilistic Localization (DPL). We first formulate each annotated failure moment as a temporal label distribution. Then we propose a TLA component to aggregate temporal label distributions of different failure moments in an online manner and generate dense probabilistic supervision. Based on TLA, We further develop a DPL component to jointly train three heads (i.e., probabilistic dense classification, probabilistic temporal detection, and probabilistic regression) with different supervision granularities and make them highly collaborative. We evaluate our approach on the largest unintentional action dataset OOPS and demonstrate that our approach can achieve significant improvement over the baseline and state-of-the-art methods.
Cloud-Assisted Secure and Cost-Effective Authenticated Solution for Remote Wearable Health Monitoring System

Security and privacy are the leading solicitude in cloud computing since users have restricted privilege on the data maintained by distinct service providers at remote locations. The situation becomes more strenuous when a huge amount of data is sensed and produced by wearable devices as it is usually considered more sensitive and heterogeneous. The majority of existing authenticated solutions for such a challenging environment either suffer due to high computation and communication costs or due to the fact that they are susceptible to known cyber threats making them impractical for utilization. Therefore we introduced a cost-effective and secure authenticated scheme for the cloud-assisted remote wearable health monitoring system. Informal security proof is discussed to showcase the security strength of the proposed scheme against known security threats. After that a detailed performance analysis of the proposed scheme with related protocols reveals the fact that it can reduce the computation and communication costs by more than 37% and 40%, respectively. Moreover, our scheme offers some added security features like physical and desynchronization attacks prevention.
Adaptive Relative Reflection Harris Hawks Optimization for Global Optimization

The Harris Hawks optimization (HHO) is a population-based metaheuristic algorithm; however, it has low diversity and premature convergence in certain problems. This paper proposes an adaptive relative reflection HHO (ARHHO), which increases the diversity of standard HHO, alleviates the problem of stagnation of local optimal solutions, and improves the search accuracy of the algorithm. The main features of the algorithm define nonlinear escape energy and adaptive weights and combine adaptive relative reflection with the HHO algorithm. Furthermore, we prove the computational complexity of the ARHHO algorithm. Finally, the performance of our algorithm is evaluated by comparison with other well-known metaheuristic algorithms on 23 benchmark problems. Experimental results show that our algorithms performs better than the compared algorithms on most of the benchmark functions.
Object Removal for Testing Object Detection in Autonomous Vehicle Systems

An object detection system is a critical part of autonomous vehicle systems. To ensure the safety and efficiency of autonomous vehicles, object detection is required to satisfy high sensitivity and accuracy. However, the state-of-the-art object detection systems fully rely on the construction of Deep Neural Networks (DNNs), which are complex and difficult to understand. It is difficult to employ white-box testing on DNNs since the output of a single neuron is inexplicable to developers. In this paper, we propose a black-box testing method based on metamorphic testing to test object detection systems. This method can reveal errors in object detection and generate high-quality test input data, i.e., a large amount of mutated images. To this end, we set up a metamorphic relation for evaluation on the testing results of prediction and design a novel strategy via object removal to generate mutated images. Instead of existing methods of adding noises to images, our method constructs mutated images by removing an object from the image background. This work can generate new images for testing from input images and detect errors in object detection in autonomous vehicle systems.
Teaching Quality Evaluation method for College English Translation Based on Three-dimensional Teaching

The traditional teaching quality evaluation methods of English translation have some problems, such as poor evaluation effect of teaching quality and low completion rate of evaluation training. Therefore, this paper proposes a Teaching Quality Evaluation method for College English Translation Based on Three-dimensional Teaching. This paper defines the objectives of teaching evaluation of College English translation course, defines the contents of College English teaching quality evaluation, and constructs a diversified evaluation structure; Under the background of three-dimensional teaching, a double Bert comprehensive teaching quality evaluation model is established, and the two-way three-dimensional feature evaluation method is used to evaluate the quality of College English translation teaching.The experimental results show that compared with the traditional two-way evaluation method, the evaluation method designed in this paper has a higher completion rate of evaluation training, and its evaluation effect is better. It is more comprehensive and comprehensive in the evaluation of English translation teaching quality, and has potential application significance.
Hybrid Pipeline Infinity Laplacian Plus Convolutional Stage Applied to Depth Completion

Lazcano, Vanel Calderero, FelipeInterpolation of the sparse depth map is a fundamental task for applications such as video games or autonomous vehicles. Frequently, depth maps present a lack of information due to sensor misinterpretations or occlusion. Traditional image processing methods have been used to tackle this interpolation problem. Nowadays, convolutional neural networks have been used to solve this problem with diverse approaches. On one hand, traditional methods are simple to implement, but the model is constructed by the designer. On the other hand, convolutional networks need a large database in order to train a model and are far to be implemented in a real platform such as FPGA. The question is if it is possible to state a hybrid model that considers the advantages of both points of view. Our proposal considers the infinity Laplacian (or AMLE) which is the most straightforward operator that satisfies a set of simple axioms. Embedding a reference color image with an anisotropic metric, we propose a three-stage pipeline to interpolate depth maps: color features selection, AMLE solution, and post-filtering. Inspired by convolutional networks, we constructed two convolution max pooling stages for color features selection. Taking those features, we used them as the color reference image, which defines the metric $$g_{ij}$$ , and then we solved the AMLE. Finally, a second convolutional stage was applied to the interpolated map. The obtained results show that the proposed convolutional stage can be replaced by a median filter. This fact increases the performance of the complete pipeline, and this pipeline outperforms contemporary methods in the KITTI dataset showing clearly the contribution of a convolutional preprocessing stage. The convolution stage enforces strong edges and smooth textures in the reference image to let the anisotropic diffusion process fill in the empty regions bounded by strong edges.