Reviewerly/exHarmony huggingface dataset:
Du2Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels

Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges. We present a novel approach based on neural networks for depth estimation that combines stereo from dual cameras with stereo from a dual-pixel sensor, which is increasingly common on consumer cameras. Our network uses a novel architecture to fuse these two sources of information and can overcome the above-mentioned limitations of pure binocular stereo matching. Our method provides a dense depth map with sharp edges, which is crucial for computational photography applications like synthetic shallow-depth-of-field or 3D Photos. Additionally, we avoid the inherent ambiguity due to the aperture problem in stereo cameras by designing the stereo baseline to be orthogonal to the dual-pixel baseline. We present experiments and comparisons with state-of-the-art approaches to show that our method offers a substantial improvement over previous works.
Segmentations-Leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation

Today’s success of state of the art methods for semantic segmentation is driven by large datasets. Data is considered an important asset that needs to be protected, as the collection and annotation of such datasets comes at significant efforts and associated costs. In addition, visual data might contain private or sensitive information, that makes it equally unsuited for public release. Unfortunately, recent work on membership inference in the broader area of adversarial machine learning and inference attacks on machine learning models has shown that even black box classifiers leak information on the dataset that they were trained on. We show that such membership inference attacks can be successfully carried out on complex, state of the art models for semantic segmentation. In order to mitigate the associated risks, we also study a series of defenses against such membership inference attacks and find effective counter measures against the existing risks with little effect on the utility of the segmentation method. Finally, we extensively evaluate our attacks and defenses on a range of relevant real-world datasets: Cityscapes, BDD100K, and Mapillary Vistas. Our source code and demos are available at https://github.com/SSAW14/segmentation_membership_inference .
HMQ: Hardware Friendly Mixed Precision Quantization Block for CNNs

Recent work in network quantization produced state-of-the-art results using mixed precision quantization. An imperative requirement for many efficient edge device hardware implementations is that their quantizers are uniform and with power-of-two thresholds. In this work, we introduce the Hardware Friendly Mixed Precision Quantization Block (HMQ) in order to meet this requirement. The HMQ is a mixed precision quantization block that repurposes the Gumbel-Softmax estimator into a smooth estimator of a pair of quantization parameters, namely, bit-width and threshold. HMQs use this to search over a finite space of quantization schemes. Empirically, we apply HMQs to quantize classification models trained on CIFAR10 and ImageNet. For ImageNet, we quantize four different architectures and show that, in spite of the added restrictions to our quantization scheme, we achieve competitive and, in some cases, state-of-the-art results.
Security Issues and Software Updates Management in the Industrial Internet of Things (IIoT) Era

New generation Industrial Automation and Control Systems (IACS) are providing advanced connectivity features, enabling new automation applications, services and business models in the Industrial Internet of Things (IIoT) era. Nevertheless, due to the extended attack surface and increasing number of cyber-attacks against industrial equipment, security concerns arise. Hence, these systems should provide enough protection and resiliency against cyber-attacks throughout their entire lifespan, which, in the case of industrial systems, may last several decades. A sound and complete management of security issues and software updates is fundamental to achieve such goal, since leading-edge security countermeasures implemented in the development phase may eventually become out-of-date. In this article, a review of the IEC 62443 industrial security standard concerning the security maintenance of IIoT systems and components is given, along with guidelines for the implementation of such processes. As concluded, the security issues and software updates management shall jointly be addressed by the asset owner, service providers and product suppliers. These security processes should also be compatible with the safety procedures established by safety standards.
Challenges for developing health-care knowledge in the digital age

Purpose Health-care knowledge is dispersed among different departments in a health care organization, which makes it difficult at times to provide quality care services to patients. Therefore, this study aims to identify the main challenges in adopting health information technology (HIT). Design/methodology/approach This study surveyed 148 stakeholders in 4 key categories [patients, health-care providers, United Arab Emirates (UAE) citizens and foresight experts] to identify the challenges they face in adopting health care technologies. Responses were analyzed using exploratory (EFA) and confirmatory factor analysis (CFA). Findings EFA revealed four key latent factors predicting resistance to HIT adoption, namely, organizational strategy (ORGS); technical barriers; readiness for big data and the internet of things (IoT); and orientation (ORI). ORGS accounted for the greatest amount of variance. CFA indicated that readiness for big data and the IoT was only moderately correlated with HIT adoption, but the other three factors were strongly correlated. Specific items relating to cost, the effectiveness and usability of the technology and the organization were strongly correlated with HIT adoption. These results indicate that, in addition to financial considerations, effective HIT adoption requires ensuring that technologies will be easy to implement to ensure their long-term use. Research limitations/implications The results indicate that readiness for big data and the IoT-related infrastructure poses a challenge to HIT adoption in the UAE context. Respondents believed that the infrastructure of big data can be helpful in more efficiently storing and sharing health-care information. On the technological side, respondents felt that they may experience a steep learning curve. Regarding ORI, stakeholders expected many more such initiatives from health-care providers to make it more knowledge-specific and proactive. Practical implications This study has implications for knowledge management in the health -care sector for information technologies. The HIT can help firms in creating a knowledge eco-system, which is not possible in a dispersed knowledge environment. The utilization of the knowledge base that emerged from the practices and data can help the health care sector to set new standards of information flow and other clinical services such as monitoring the self-health condition. The HIT can further influence the actions of the pharmaceutical and medical device industry. Originality/value This paper highlights the challenges in HIT adoption and the most prominent factors. The conceptual model was empirically tested after the collection of primary data from the UAE using stakeholder theory.
DEBOHID: A differential evolution based oversampling approach for highly imbalanced datasets

Class distribution of the samples in the dataset is one of the critical factors affecting the classification success. Classifiers trained with imbalanced datasets classify majority class samples more successfully than minority class samples. Oversampling, which is based on increasing the minority class samples, is a frequently used method to overcome the class imbalance. More than two decades, many oversampling methods are presented for the class imbalance problem. Differential Evolution is a metaheuristic algorithm that achieves successful results in a lot of domains. One of the main reasons for this success is that DE has an effective candidate individual generation mechanism. In this work, we propose a novel oversampling method based on a differential evolution algorithm for highly imbalanced datasets, and it is named as DEBOHID (A differential evolution based oversampling approach for highly imbalanced datasets). In order to show the success of DEBOHID, 44 highly imbalanced ratio datasets are used in experiments. The obtained results are compared with nine different state-of-art oversampling methods. In order to show the independence of the experimental results to classifier, Support Vector Machines (SVM), k-Nearest Neighbor (kNN), and Decision Tree (DT) are used as a classifier in the experiments. AUC and G-Mean metrics are used for the performance measurements. The experimental results and statistical analyses have shown the triumph of the DEBOHID.
Fast Unmediated Hashing for Cross-Modal Retrieval

Cross-modal hashing is for the purpose of compressing heterogeneous multi-modal data into compact binary codes for the cross-modal retrieval, where accuracy and efficiency are two primary issues. To achieve high accuracy and efficiency, we put forward a novel method named Fast Unmediated Hashing (FUH) for cross-modal retrieval. For this method, motivated by the fact that label vector is a natural binary representation of samples for retrieval, we directly learn the cross-modal hash codes from semantic labels without any intermediate representation. This will capture more relations among different modalities, and reduce the number of variables. However, directly learning hash codes from labels would weaken the discrimination of hash codes. To address this issue, double supervision involving label information and pairwise similarity is proposed to enhance the discrimination. In addition, to decrease the training time, we present a strategy to bypass the similarity matrix-related operation in each iteration of optimization, thus some other related terms can also be computed offline to lower training complexity. Compared to several state-of-the-art techniques on three public datasets, the experimental results have manifested the superiority of FUH concerning efficiency and accuracy.
MDCC-Net: Multiscale double-channel convolution U-Net framework for colorectal tumor segmentation

Multiscale feature fusion is a feasible method to improve tumor segmentation accuracy. However, current multiscale networks have two common problems: 1. Some networks only allow feature fusion between encoders and decoders of the same scale. It is obvious that such feature fusion is not sufficient. 2. Some networks have too many dense skip connections and too much nesting between the coding layer and the decoding layer, which causes some features to be lost and means that not enough information will be learned from multiple scales. To overcome these two problems, we propose a multiscale double-channel convolution U-Net (MDCC-Net) framework for colorectal tumor segmentation. In the coding layer, we designed a dual-channel separation and convolution module and then added residual connections to perform multiscale feature fusion on the input image and the feature map after dual-channel separation and convolution. By fusing features at different scales in the same coding layer, the network can fully extract the detailed information of the original image and learn more tumor boundary information. The segmentation results show that our proposed method has a high accuracy, with a Dice similarity coefficient (DSC) of 83.57%, which is an improvement of 9.59%, 6.42%, and 1.57% compared with nnU-Net, U-Net, and U-Net++, respectively. The experimental results show that our proposed method has good performance in the segmentation of colorectal tumors and is close to the expert level. The proposed method has potential clinical applicability.
Entanglement preparation and nonreciprocal excitation evolution in giant atoms by controllable dissipation and coupling

We investigate the dynamics of a giant atom (or atoms) in a waveguide QED scenario, where the atom couples to the coupled resonator waveguide via two sites. For a single-giant-atom setup, we find that the atomic dissipation rate can be adjusted by tuning its size. For the two-giant-atom system, the waveguide will induce the controllable individual and collective dissipation as well as effective interatom coupling. As a result, we can realize theoretically the robust entangled state preparation and nonreciprocal excitation evolution. We hope our study can be applied in quantum information processing based on a photonic and acoustic waveguide setup.
The digital competence framework for primary and secondary schools in Europe

Abstract There has been a lot of interest to competence‐based education in research and scholarship. How competence‐based education has been developed and implemented varies across education systems in Europe. The European Commission defines digital competence as one of the eight key competence standards for lifelong learning. The European digital competence framework DigComp provides a comprehensive and exhaustive framework for citizens; it has the potential to be adapted to the needs of specific target groups. Nevertheless, no current research has focused on the need for developing a common framework for primary and secondary education. To address this gap we present a digital competence framework for European primary and secondary students. DigComp and a set of European frameworks are analysed in this paper. Combining categorical analysis with the collaboration of teachers and experts in the field, we define performance criteria and components that contribute to competence by five categories, providing a framework to promote the acquisition and evaluation of digital competence. This framework can be implemented in any EU country, it incorporates the most recent theoretical advances in research on information and communication technologies.
Robustness Analysis of Neural Networks via Efficient Partitioning With Applications in Control Systems

Neural networks (NNs) are now routinely implemented on systems that must operate in uncertain environments, but the tools for formally analyzing how this uncertainty propagates to NN outputs are not yet commonplace. Computing tight bounds on NN output sets (given an input set) provides a measure of confidence associated with the NN decisions and is essential to deploy NNs on safety-critical systems. Recent works approximate the propagation of sets through nonlinear activations or partition the uncertainty set to provide a guaranteed outer bound on the set of possible NN outputs. However, the bound looseness causes excessive conservatism and/or the computation is too slow for online analysis. This letter unifies propagation and partition approaches to provide a family of robustness analysis algorithms that give tighter bounds than existing works for the same amount of computation time (or reduced computational effort for a desired accuracy level). Moreover, we provide new partitioning techniques that are aware of their current bound estimates and desired boundary shape (e.g., lower bounds, weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{\infty }$ </tex-math></inline-formula> -ball, convex hull), leading to further improvements in the computation-tightness tradeoff. The letter demonstrates the tighter bounds and reduced conservatism of the proposed robustness analysis framework with examples from model-free RL and forward kinematics learning.
Two potential fields fused adaptive path planning system for autonomous vehicle under different velocities

Path planning is a basic function for autonomous vehicle (AV). However, it is difficult to adapt to different velocities and different types of obstacles including dynamic obstacle and static obstacle (such as road boundary) for AV. To solve the problem of path planning under different velocities and different types of obstacles, a two potential fields fused adaptive path planning system (TPFF-APPS) which includes two parts, a potential field fusion controller and an adaptive weight assignment unit, is presented in this work. In the potential field fusion controller, a novel potential velocity field is built by velocity information and fused with a traditional artificial potential field for adapting various velocities. The adaptive weight assignment unit is designed to distribute adaptively the weights of two potential fields for adapting different types of obstacles at the same time, including road boundary and dynamic obstacles. The simulation is carried on the Carsim-Matlab co-simulation platform, and the simulation results indicate that the proposed TPFF-APPS has excellent performance for path planning adapting to different velocities and different types of obstacles.
Do higher-order realized moments matter for cryptocurrency returns?

This study utilizes intraday price data of Bitcoin, Ethereum, and Ripple to investigate how sensitive cryptocurrency returns are to higher-order realized moments (i.e., variance, skewness, kurtosis, hyper-skewness, hyper-kurtosis), and whether such sensitivity, if any, varies across bear and bull market conditions. We also evaluate the forecasting power of higher-order moments for future cryptocurrency returns. The empirical analysis draws on a quantile regression approach, after orthogonalizing raw returns with respect to a diverse set of global influences and risk factors. The results reveal that all moments up to the fifth order are generally relevant to explaining cryptocurrency returns, but with different degrees, depending on both the type and state of the cryptomarket. Moreover, both skewness and hyper-skewness show statistically significant predictive capabilities, whether in-sample or out-of-sample, for subsequent returns. Our evidence provides practical implications for asset pricing and risk management decisions.
Anomaly detection of power consumption in yarn spinning using transfer learning

Anomaly detection of spinning power consumption is crucial for energy saving in yarn manufacturing. Data based methods are widely adopted for anomaly detection in industry due to historical power consumption data can be easily obtained today. However, data cannot be collected sufficiently and representatively in a short time when we study a newly-built yarn spinning workshop. Transfer learning has become an effective approach because the data and knowledge of old spinning workshops with rich power consumption records can be utilized. However, the abnormal patterns in a new yarn spinning workshop may be not exactly the same as in an old one, in most cases, there are less anomaly patterns in a new one. This pattern mismatch results in underutilization of knowledge of the data-rich workshop, and makes a transfer learning model less effective. In this paper, we propose a Cluster-based Deep Adaptation Network (CDAN) model to improve the efficiency of transfer learning for spinning power consumption anomaly detection. A cluster-based adaptation layer is inserted between the feature layers of source and target networks. It is designed specially to reduce the mismatch of transfer learning. The proposed CDAN model was applied in a real case study: a yarn spinning workshop in Xinjiang, China. With effective consideration of the mismatch in transfer learning, experimental results show that the proposed method can detect the anomaly of spinning power consumption compared with higher accuracy than state-of-the-art methods.
A Permissioned Blockchain System to Reduce Peak Demand in Residential Communities via Energy Trading: A Real-World Case Study

Residential energy trading systems (RETS) enable homeowners with distributed energy resources (DERs) to participate in virtualized energy markets that have the potential to reduce the peak demand of residential communities.Blockchains are key enablers of RETS, by virtue of providing a decentralized, self-governed network that mitigates concerns regarding privacy and transparency.However, more real-world case studies are needed to evaluate the techno-economic viability of blockchain-based RETS to improve their positive uptake.Thus, this article develops a permissioned blockchain-based RETS, which enables homeowners to select bidding strategies that consider the individual preferences of their DERs, and further evaluates the impact of the bidding strategies on reducing the peak demand of the community.The proposed system is implemented on the permissioned Hyperledger Fabric platform, where a decentralized ledger is used to store all energy bids, and a smart contract is used to execute a double auction mechanism and dispatch the homeowner DERs.The proposed system is validated by conducting simulations on a 8-home community using real-world data, and also by deploying the system to a Canadian microgrid, where the smart contract execution time is benchmarked.Simulation results demonstrate the efficacy of the proposed system by achieving a peak demand reduction of up to 48 kW (62%), which leads to an average savings of $1.02 M for the distribution system operator by avoiding transformer upgrades.Also, the simulation results show that the execution time of the proposed smart contract is 17.12 seconds across 12 nodes, which is sufficient for RETS.
Exploring User Expectations of Proactive AI Systems

Recent advances in artificial intelligence (AI) enabled digital assistants to evolve towards proactive user support. However, expectations as to when and to what extent assistants should take the initiative are still unclear; discrepancies to the actual system behavior might negatively affect user acceptance. In this paper, we present an in-the-wild study for exploring user expectations of such user-supporting AI systems in terms of different proactivity levels and use cases. We collected 3,168 in-situ responses from 272 participants through a mixed method of automated user tracking and context-triggered surveying. Using a data-driven approach, we gain insights into initial expectations and how they depend on different human factors and contexts. Our insights can help to design AI systems with varying degree of proactivity and preset to meet individual expectations.
Experimental Setup for Investigating the Efficient Load Balancing Algorithms on Virtual Cloud

Cloud computing has emerged as the primary choice for developers in developing applications that require high-performance computing. Virtualization technology has helped in the distribution of resources to multiple users. Increased use of cloud infrastructure has led to the challenge of developing a load balancing mechanism to provide optimized use of resources and better performance. Round robin and least connections load balancing algorithms have been developed to allocate user requests across a cluster of servers in the cloud in a time-bound manner. In this paper, we have applied the round robin and least connections approach of load balancing to HAProxy, virtual machine clusters and web servers. The experimental results are visualized and summarized using Apache Jmeter and a further comparative study of round robin and least connections is also depicted. Experimental setup and results show that the round robin algorithm performs better as compared to the least connections algorithm in all measuring parameters of load balancer in this paper.