Reviewerly/exHarmony huggingface dataset:
Reviewing the opportunities, challenges, and future directions for the digitalization of energy

Smart grids are the backbone of existing energy production and supply scenario in today’s society. Gradual increasing demand of energy has certainly emphasized the significance and provenance of reliability and predictive aspects over smart grids. Conventional smart grids under perform in terms of self-aware behavior, especially in delay tolerance, energy requirement, and dissemination of monitoring notions. Highly distributed geographical distribution sometimes cause problems for smart grids to provide necessary services to both the consumers and prosumers. Internet of Things (IoT) is thus integrated with smart grids to facilitate distributed monitoring services for smooth running of the smart grid. Although, IoT has remarkably supported smart grids to perform smarter than ever, it lacks in security, decentralization, transparency, and trust-less approaches. Thus, blockchain is envisaged to leverage minimizing such gaps and to pave new horizon in the blockchain-IoT enabled smart grid monitoring. In this paper, we investigate how blockchain and IoT together can improve existing smart grid ecosystem toward facilitation of better monitoring services. We do this via a Systematic Literature Review. Firstly, we present preliminaries behind the study, followed by in-depth review of different domains of IoT-based smart grid monitoring. Next, we discuss various attributes of blockchain-IoT derived smart grid management schemes. Then, we discuss possible opportunities and benefits of using blockchain with respect to blockchain-IoT based smart grid monitoring. Finally, we illustrate open research challenges and future directions in the aforementioned aspects. The article concludes that with certain changes in current blockchain technology, it can surely encompass the direction of enhanced monitoring of IoT based smart grid.
Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set

Biomedical applications of deep learning algorithms rely on large expert annotated data sets. The classification of bone marrow (BM) cell cytomorphology, an important cornerstone of hematological diagnosis, is still done manually thousands of times every day because of a lack of data sets and trained models. We applied convolutional neural networks (CNNs) to a large data set of 171 374 microscopic cytological images taken from BM smears from 945 patients diagnosed with a variety of hematological diseases. The data set is the largest expert-annotated pool of BM cytology images available in the literature. It allows us to train high-quality classifiers of leukocyte cytomorphology that identify a wide range of diagnostically relevant cell species with high precision and recall. Our CNNs outcompete previous feature-based approaches and provide a proof-of-concept for the classification problem of single BM cells. This study is a step toward automated evaluation of BM cell morphology using state-of-the-art image-classification algorithms. The underlying data set represents an educational resource, as well as a reference for future artificial intelligence-based approaches to BM cytomorphology.
Never trust, always verify: A multivocal literature review on current knowledge and research gaps of zero-trust

In response to weaknesses of current network security solutions, the zero-trust model follows the idea that no network – whether internal or external – is trustworthy. The concept of zero-trust is enjoying increasing attention in both research and practice due to its promise to fulfil complex new network security requirements. Despite zero-trust's advantages over traditional solutions, it has not yet succeeded in replacing existing approaches. Uncertainty remains regarding the concept's distinct benefits and drawbacks for organisations and individuals, which hinders a holistic understanding of zero-trust and wide-spread adoption. Research can make valuable contributions to the field by systematically providing new insights into zero-trust. To support researchers in this endeavour, we aim to consolidate the current state of the knowledge about zero-trust and to identify gaps in the literature. Thus, we conduct a multivocal literature review, analysing both academic and practice-oriented publications. We develop a research framework for zero-trust to structure the identified literature and to highlight future research avenues. Our results show that the academic literature has focused mainly on the architecture and performance improvements of zero-trust. In contrast, the practice-oriented literature has focused on organisational advantages of zero-trust and on potential migration strategies. However, economic analyses and user-related studies have been neglected by both academia and practice. Future research may rely on our findings to advance the field in meaningful ways.
Price competition and blockchain adoption in retailing markets

The blockchain is essentially a shared database, and the data stored is unforgeable, open and transparent, which may allow the retailers adopting the blockchain technology to attract more consumers by making product information more transparent, while it also leads to the risk of consumers privacy leakage due to storing the corresponding consumers’ personal information. In order to study the impact of blockchain technology on competitive retailers’ strategic pricing, we establish a retailer competition model including an initial retailer and an entrant retailer both of whom can decide whether to apply the blockchain technology. We obtain the optimal prices and corresponding profits of the two retailers when applying and not applying blockchain, respectively, and find that consumer privacy concerns decline both retailers’ prices and profits when adopting blockchain technology because consumer privacy concerns lower consumers’ valuations of products, thereby reducing demand. Furthermore, we analyze the two retailers’ equilibrium strategies of the application of blockchain technology, which suggests that it is not always beneficial for retailers to adopt blockchain technology in all situations. Both retailers will apply blockchain only when consumer privacy concerns are lower and information transparency promotion is higher because higher information transparency promotion increases consumers’ willingness to pay, which exceeds the cost of consumers privacy concerns. On the contrary, when consumer privacy concerns are higher and information transparency promotion is lower, neither retailer will apply blockchain technology. Interestingly, when both consumer privacy concerns and information transparency promotion are medium, the initial retailer will apply blockchain technology and the entrant retailer will not because consumers’ higher trust in initial retailer allows the initial retailer can bear higher cost of consumers privacy concerns which is hard to undertake for the new entrant retailer when information transparency promotion is medium.
Deep learning trained on hematoxylin and eosin tumor region of Interest predicts HER2 status and trastuzumab treatment response in HER2+ breast cancer

The current standard of care for many patients with HER2-positive breast cancer is neoadjuvant chemotherapy in combination with anti-HER2 agents, based on HER2 amplification as detected by in situ hybridization (ISH) or protein immunohistochemistry (IHC). However, hematoxylin & eosin (H&E) tumor stains are more commonly available, and accurate prediction of HER2 status and anti-HER2 treatment response from H&E would reduce costs and increase the speed of treatment selection. Computational algorithms for H&E have been effective in predicting a variety of cancer features and clinical outcomes, including moderate success in predicting HER2 status. In this work, we present a novel convolutional neural network (CNN) approach able to predict HER2 status with increased accuracy over prior methods. We trained a CNN classifier on 188 H&E whole slide images (WSIs) manually annotated for tumor Regions of interest (ROIs) by our pathology team. Our classifier achieved an area under the curve (AUC) of 0.90 in cross-validation of slide-level HER2 status and 0.81 on an independent TCGA test set. Within slides, we observed strong agreement between pathologist annotated ROIs and blinded computational predictions of tumor regions / HER2 status. Moreover, we trained our classifier on pre-treatment samples from 187 HER2+ patients that subsequently received trastuzumab therapy. Our classifier achieved an AUC of 0.80 in a five-fold cross validation. Our work provides an H&E-based algorithm that can predict HER2 status and trastuzumab response in breast cancer at an accuracy that may benefit clinical evaluations.
A Variational Framework for Underwater Image Dehazing and Deblurring

Underwater captured images are usually degraded by low contrast, hazy, and blurry due to absorbing and scattering, which limits their analyses and applications. To address these problems, a red channel prior guided variational framework is proposed based on the complete underwater image formation model (UIFM). Unlike most of the existing methods that only consider the direct transmission and backscattering components, we additionally include forward scattering component into the UIFM. In the proposed variational framework, we successfully incorporate the normalized total variation item and sparse prior knowledge of blur kernel together. In addition, we perform the estimation of blur kernel by varying image resolution in a coarse-to-fine manner to avoid local minima. Moreover, for solving the generated non-smooth optimization problem, we employ the alternating direction method of multipliers (ADMM) to accelerate the whole progress. Experimental results demonstrate that the proposed method has a good performance on dehazing and deblurring. Extensive qualitative and quantitative comparisons further validate its superiority against the other state-of-the-art algorithms. The code is available online at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Hou-Guojia/UNTV</uri>
Adaptive Adversarial Network for Source-free Domain Adaptation

Unsupervised Domain Adaptation solves knowledge transfer along with the coexistence of well-annotated source domain and unlabeled target instances. However, the source domain in many practical applications is not always accessible due to data privacy or the insufficient memory storage for small devices. This scenario defined as Source-free Domain Adaptation only allows accessing the well-trained source model for target learning. To address the challenge of source data unavailability, we develop an Adaptive Adversarial Network (A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net) including three components. Specifically, the first one named Adaptive Adversarial Inference seeks a target-specific classifier to advance the recognition of samples which the provided source-specific classifier difficultly identifies. Then, the Contrastive Category-wise Matching module exploits the positive relation of every two target images to enforce the compactness of subspace for each category. Thirdly, Self-Supervised Rotation facilitates the model to learn additional semantics from target images by themselves. Extensive experiments on the popular cross-domain benchmarks verify the effectiveness of our proposed model on solving adaptation task without any source data.
Light-DehazeNet: A Novel Lightweight CNN Architecture for Single Image Dehazing

Due to the rapid development of artificial intelligence technology, industrial sectors are revolutionizing in automation, reliability, and robustness, thereby significantly increasing quality and productivity. Most of the surveillance and industrial sectors are monitored by visual sensor networks capturing different surrounding environment images. However, during tempestuous weather conditions, the visual quality of the images is reduced due to contaminated suspended atmospheric particles that affect the overall surveillance systems. To tackle these challenges, this article presents a computationally efficient lightweight convolutional neural network referred to as Light-DehazeNet (LD-Net) for the reconstruction of hazy images. Unlike other learning-based approaches, which separately measure the transmission map and the atmospheric light, our proposed LD-Net jointly estimates both the transmission map and the atmospheric light using a transformed atmospheric scattering model. Furthermore, a color visibility restoration method is proposed to evade the color distortion in the dehaze image. Finally, we conduct extensive experiments using synthetic and natural hazy images. The quantitative and qualitative evaluation on different benchmark hazy datasets verify the superiority of the proposed method over other state-of-the-art image dehazing techniques. Moreover, additional experimentation validates the applicability of the proposed method in the object detection tasks. Considering the lightweight architecture with minimal computational cost, the proposed system is encouraged to be incorporated as an integral part of the vision-based monitoring systems to improve the overall performance.
Evaluating the Effectiveness of Deplatforming as a Moderation Strategy on Twitter

Deplatforming refers to the permanent ban of controversial public figures with large followings on social media sites. In recent years, platforms like Facebook, Twitter and YouTube have deplatformed many influencers to curb the spread of offensive speech. We present a case study of three high-profile influencers who were deplatformed on Twitter---Alex Jones, Milo Yiannopoulos, and Owen Benjamin. Working with over 49M tweets, we found that deplatforming significantly reduced the number of conversations about all three individuals on Twitter. Further, analyzing the Twitter-wide activity of these influencers' supporters, we show that the overall activity and toxicity levels of supporters declined after deplatforming. We contribute a methodological framework to systematically examine the effectiveness of moderation interventions and discuss broader implications of using deplatforming as a moderation strategy.
A chaotic strategy-based quadratic Opposition-Based Learning adaptive variable-speed whale optimization algorithm

In this paper, a chaotic strategy-based quadratic opposition-based learning adaptive variable-speed whale optimization algorithm is proposed. The improved algorithm is used to solve the problems that the whale optimization algorithm’s convergence accuracy and convergence speed are insufficient. Firstly, the proposed algorithm is initialized by a method based on chaotic maps and quadratic opposition-based learning strategy to obtain a population with better ergodicity. Secondly, by introducing an adaptive variable speed adjustment factor, each search link unites to form a negative feedback regulation network, thereby effectively balancing the algorithm’s exploration ability and exploitation ability. Finally, 20 benchmark test functions and 3 complex constrained engineering optimization problems were used to conduct extensive tests on the improved algorithm. The results show that the improved algorithm has better performance than others in terms of convergence speed and convergence accuracy in a majority of cases, and can effectively jump out of the local optimum.
New Clustering-Based Semantic Service Selection and User Preferential Model

Web service is the newest development in the contemporary system that transforms the web from a group of information into a disseminated computational device. This article proposes a new web service where the users are given the option to select the service that satisfies the quality of service (QoS) requirements from the set of discovered services. The ultimate aim of this article is to design a clustering-based semantic service selection model (SSSM) and user preferential model (UPM) to enhance the web services. The functional requirements of the service requested are mapped with the discovered services and the nonfunctional requirements are mapped to the QoS parameters of the services retrieved from the universal description, discovery, and integration. The quality attributes have been resolved by using the proposed two-tier user preference model. The Tier-I of the UPM deals with the qualification of the QoS parameters, where the user is presented with the available quality parameters for defining them in the model. The Tier-II of the UPM quantifies the qualified QoS parameters, where the user will set the preference values. Thus, the proposed clustering-based SSM and the UPM have improved the efficiency of the service selection operation, which has been shown using the three critical factors of the service retrieval discernment. The experimental results show the improved precision, recall, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> -measure values of the proposed method.
Self-restrained triplet loss for accurate masked face recognition

Using the face as a biometric identity trait is motivated by the contactless nature of the capture process and the high accuracy of the recognition algorithms. After the current COVID-19 pandemic, wearing a face mask has been imposed in public places to keep the pandemic under control. However, face occlusion due to wearing a mask presents an emerging challenge for face recognition systems. In this paper, we present a solution to improve masked face recognition performance. Specifically, we propose the Embedding Unmasking Model (EUM) operated on top of existing face recognition models. We also propose a novel loss function, the Self-restrained Triplet (SRT), which enabled the EUM to produce embeddings similar to these of unmasked faces of the same identities. The achieved evaluation results on three face recognition models, two real masked datasets, and two synthetically generated masked face datasets proved that our proposed approach significantly improves the performance in most experimental settings.
Statistical analysis of regional air temperature characteristics before and after dam construction

Dam construction brings both benefits and harms to the ecological environment. This paper focuses on the impact of the construction of mega-dams on regional climate. Based on the national precipitation and temperature data from 1990 to 2012 from more than 2400 national stations of the National Meteorological Information Center, this paper analyzes Xiaolangdi Dam's influence on the Yellow River's climate and the Three Gorges Dam of the Yangtze River with geostatistics knowledge. The following results are obtained. (1) According to the analysis of the climate time and space distribution in the Yellow River Basin and the Yangtze River Basin, the dam's construction caused a drop in temperature in the reservoir area, which decreased by about 0.12 °C. On the other hand, the dam's construction also caused the temperature in the downstream area to rise by about 0.6 °C. (2) According to the analysis of regional climate correlation, the rise of the dam's water level will cause the temperature of the reservoir area to drop to a certain extent. The correlation between the temperature in the Three Gorges and the water level changes in the Three Gorges reaches −0.578. (3) According to regional climate statistics analysis, the dam's construction has reduced the temperature difference in the reservoir area.
IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach

In recent years, colorectal cancer has become one of the most significant diseases that endanger human health. Deep learning methods are increasingly important for the classification of colorectal histopathology images. However, existing approaches focus more on end-to-end automatic classification using computers rather than human-computer interaction. In this paper, we propose an IL-MCAM framework. It is based on attention mechanisms and interactive learning. The proposed IL-MCAM framework includes two stages: automatic learning (AL) and interactivity learning (IL). In the AL stage, a multi-channel attention mechanism model containing three different attention mechanism channels and convolutional neural networks is used to extract multi-channel features for classification. In the IL stage, the proposed IL-MCAM framework continuously adds misclassified images to the training set in an interactive approach, which improves the classification ability of the MCAM model. We carried out a comparison experiment on our dataset and an extended experiment on the HE-NCT-CRC-100K dataset to verify the performance of the proposed IL-MCAM framework, achieving classification accuracies of 98.98% and 99.77%, respectively. In addition, we conducted an ablation experiment and an interchangeability experiment to verify the ability and interchangeability of the three channels. The experimental results show that the proposed IL-MCAM framework has excellent performance in the colorectal histopathological image classification tasks.
Image encryption scheme based on discrete cosine Stockwell transform and DNA-level modulus diffusion

A new image encryption scheme is presented by combining six-dimensional non-degenerate discrete hyper-chaotic system, two-dimensional discrete cosine Stockwell transform with DNA-level modulus diffusion. The significant advantages of this scheme are the large key space, strong anti-noise ability and resistance to common attacks. To resist the powerful chosen plaintext attack, the initial conditions of the chaotic systems are generated with the SHA-512 hash function value of the plaintext image and the external key. The transmission burden is reduced by compressing the original image with the two-dimensional discrete cosine Stockwell transform. Then random DNA encoding is performed on the compressed image to obtain the DNA image. To speed up the encryption, the DNA-level modulus diffusion algorithm is designed to scramble and diffuse pixels at the same time. Finally, the final encrypted image is obtained by re-encrypting the diffused DNA image with the bit-level permutation and the improved global dynamic diffusion. The two high-dimensional chaotic systems introduced in the image encryption scheme greatly increases the key space and then the image encryption scheme can resist the brute-force attack. The presented scheme is sensitive to both plaintext images and secret keys. Simulation results show that the proposed image encryption algorithm is feasible, secure and effective.
CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities

Large language models (LMs) offer unprecedented language generation capabilities and exciting opportunities for interaction design. However, their highly context-dependent capabilities are difficult to grasp and are often subjectively interpreted. In this paper, we argue that by curating and analyzing large interaction datasets, the HCI community can foster more incisive examinations of LMs’ generative capabilities. Exemplifying this approach, we present CoAuthor, a dataset designed for revealing GPT-3’s capabilities in assisting creative and argumentative writing. CoAuthor captures rich interactions between 63 writers and four instances of GPT-3 across 1445 writing sessions. We demonstrate that CoAuthor can address questions about GPT-3’s language, ideation, and collaboration capabilities, and reveal its contribution as a writing “collaborator” under various definitions of good collaboration. Finally, we discuss how this work may facilitate a more principled discussion around LMs’ promises and pitfalls in relation to interaction design. The dataset and an interface for replaying the writing sessions are publicly available at https://coauthor.stanford.edu.