Reviewerly/exHarmony huggingface dataset:
Role of Blockchain-Enabled Smart Contracts in Automating Construction Progress Payments

The construction industry has long been plagued with late and nonpayments. Despite the desired benefits of payment automation and today’s increased access to digitized progress data, too often payment applications still rely on inefficient workflows and documents that are time-consuming to prepare, review, approve, and execute. This paper argues that current payment applications, even if computerized, cannot support reliable automation of progress payments due to their reliance on centralized control mechanisms and lack of guaranteed execution. The paper examines why blockchain-based and decentralized smart contracts can address these limitations. It explores the conceptual underpinning for the design of an automated payment system and investigates the role of smart contracts in enabling reliable and autonomous conditioning of cash flow on product flow status. To illustrate these points, a use case is presented for smart contract-based progress payments in the context of unmanned aerial vehicle-based progress monitoring. The paper concludes with a discussion of challenges facing blockchain-based payment automation and the broader implications for dispute resolution and construction law informatics, namely zero-trust computing and the creation of an auditable single source of truth for projects.
DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning

Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require time-consuming, error-prone calculations using static variable thresholds. These methods do not capitalize on the emerging availability of streaming electronic health record data or capture time-sensitive individual physiological patterns, a critical task in the intensive care unit. We propose a novel acuity score framework (DeepSOFA) that leverages temporal measurements and interpretable deep learning models to assess illness severity at any point during an ICU stay. We compare DeepSOFA with SOFA (Sequential Organ Failure Assessment) baseline models using the same model inputs and find that at any point during an ICU admission, DeepSOFA yields significantly more accurate predictions of in-hospital mortality. A DeepSOFA model developed in a public database and validated in a single institutional cohort had a mean AUC for the entire ICU stay of 0.90 (95% CI 0.90-0.91) compared with baseline SOFA models with mean AUC 0.79 (95% CI 0.79-0.80) and 0.85 (95% CI 0.85-0.86). Deep models are well-suited to identify ICU patients in need of life-saving interventions prior to the occurrence of an unexpected adverse event and inform shared decision-making processes among patients, providers, and families regarding goals of care and optimal resource utilization.
An integrated iterative annotation technique for easing neural network training in medical image analysis

Neural networks promise to bring robust, quantitative analysis to medical fields, but adoption is limited by the technicalities of training these networks. To address this translation gap between medical researchers and neural networks in the field of pathology, we have created an intuitive interface which utilizes the commonly used whole slide image (WSI) viewer, Aperio ImageScope (Leica Biosystems Imaging, Inc.), for the annotation and display of neural network predictions on WSIs. Leveraging this, we propose the use of a human-in-the-loop strategy to reduce the burden of WSI annotation. We track network performance improvements as a function of iteration and quantify the use of this pipeline for the segmentation of renal histologic findings on WSIs. More specifically, we present network performance when applied to segmentation of renal micro compartments, and demonstrate multi-class segmentation in human and mouse renal tissue slides. Finally, to show the adaptability of this technique to other medical imaging fields, we demonstrate its ability to iteratively segment human prostate glands from radiology imaging data.
Code smells and refactoring: A tertiary systematic review of challenges and observations

In this paper, we present a tertiary systematic literature review of previous surveys, secondary systematic literature reviews, and systematic mappings. We identify the main observations (what we know) and challenges (what we do not know) on code smells and refactoring. We show that code smells and refactoring have a strong relationship with quality attributes, i.e., with understandability, maintainability, testability, complexity, functionality, and reusability. We argue that code smells and refactoring could be considered as the two faces of a same coin. Besides, we identify how refactoring affects quality attributes, more than code smells. We also discuss the implications of this work for practitioners, researchers, and instructors. We identify 13 open issues that could guide future research work. Thus, we want to highlight the gap between code smells and refactoring in the current state of software-engineering research. We wish that this work could help the software-engineering research community in collaborating on future work on code smells and refactoring.
Deep Learning for Sequential Recommendation

In the field of sequential recommendation, deep learning--(DL) based methods have received a lot of attention in the past few years and surpassed traditional models such as Markov chain-based and factorization-based ones. However, there is little systematic study on DL-based methods, especially regarding how to design an effective DL model for sequential recommendation. In this view, this survey focuses on DL-based sequential recommender systems by taking the aforementioned issues into consideration. Specifically, we illustrate the concept of sequential recommendation, propose a categorization of existing algorithms in terms of three types of behavioral sequences, summarize the key factors affecting the performance of DL-based models, and conduct corresponding evaluations to showcase and demonstrate the effects of these factors. We conclude this survey by systematically outlining future directions and challenges in this field.
Feature-Metric Loss for Self-supervised Learning of Depth and Egomotion

Photometric loss is widely used for self-supervised depth and egomotion estimation. However, the loss landscapes induced by photometric differences are often problematic for optimization, caused by plateau landscapes for pixels in textureless regions or multiple local minima for less discriminative pixels. In this work, feature-metric loss is proposed and defined on feature representation, where the feature representation is also learned in a self-supervised manner and regularized by both first-order and second-order derivatives to constrain the loss landscapes to form proper convergence basins. Comprehensive experiments and detailed analysis via visualization demonstrate the effectiveness of the proposed feature-metric loss. In particular, our method improves state-of-the-art methods on KITTI from 0.885 to 0.925 measured by $$\delta _1$$ for depth estimation, and significantly outperforms previous method for visual odometry.
Few-Shot Semantic Segmentation with Democratic Attention Networks

Few-shot segmentation has recently generated great popularity, addressing the challenging yet important problem of segmenting objects from unseen categories with scarce annotated support images. The crux of few-shot segmentation is to extract object information from the support image and then propagate it to guide the segmentation of query images. In this paper, we propose the Democratic Attention Network (DAN) for few-shot semantic segmentation. We introduce the democratized graph attention mechanism, which can activate more pixels on the object to establish a robust correspondence between support and query images. Thus, the network is able to propagate more guiding information of foreground objects from support to query images, enhancing its robustness and generalizability to new objects. Furthermore, we propose multi-scale guidance by designing a refinement fusion unit to fuse features from intermediate layers for the segmentation of the query image. This offers an efficient way of leveraging multi-level semantic information to achieve more accurate segmentation. Extensive experiments on three benchmarks demonstrate that the proposed DAN achieves the new state-of-the-art performance, surpassing the previous methods by large margins. The thorough ablation studies further reveal its great effectiveness for few-shot semantic segmentation.
Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection

In this paper, we aim to develop an efficient and compact deep network for RGB-D salient object detection, where the depth image provides complementary information to boost performance in complex scenarios. Starting from a coarse initial prediction by a multi-scale residual block, we propose a progressively guided alternate refinement network to refine it. Instead of using ImageNet pre-trained backbone network, we first construct a lightweight depth stream by learning from scratch, which can extract complementary features more efficiently with less redundancy. Then, different from the existing fusion based methods, RGB and depth features are fed into proposed guided residual (GR) blocks alternately to reduce their mutual degradation. By assigning progressive guidance in the stacked GR blocks within each side-output, the false detection and missing parts can be well remedied. Extensive experiments on seven benchmark datasets demonstrate that our model outperforms existing state-of-the-art approaches by a large margin, and also shows superiority in efficiency (71 FPS) and model size (64.9 MB).
A (new) role for business – Promoting the United Nations’ Sustainable Development Goals through the internet-of-things and blockchain technology

We outline the business opportunity for the provision of measurement technology, linked to the internet, i.e. the internet-of-things (IoT), which feeds information into blockchains, providing reliable and trusted data and an incentive for others to contribute towards progress on the United Nations’ Sustainable Development Goals (SDGs). Both existing businesses and start-ups could exploit these new opportunities, which could inspire the participation of employees, volunteers, donors, and other participants. We provide a conceptual framework for the different ways business can play a role in facilitating measurement of SDGs, and trust in these measurements, by harnessing technology.
A framework of human action recognition using length control features fusion and weighted entropy-variances based feature selection

In this article, we implement an action recognition technique based on features fusion and best feature selection. In the proposed method, HSI color transformation is performed in the first step to improve the contrast of video frames and then extract their motion features by optical flow algorithm. The frames fusion approach extracts the moving regions that find out by optical flow. After that, extract shape and texture features fused by a new parallel approach name length control features. A new Weighted Entropy-Variances approach is applied to a combined vector and selects the best of them for classification. Finally, features are passed in M-SVM for final features classification into relevant human actions. The experimental process is conducted in four famous action datasets- Weizmann, KTH, UCF Sports, and UCF YouTube, with recognition rate 97.9%, 100%, 99.3%, and 94.5%, respectively. Experimental results show that the proposed scheme performed significantly sound output concerning listed methods.
A systematic literature review of blockchain and smart contract development: Techniques, tools, and open challenges

Blockchain platforms and languages for writing smart contracts are becoming increasingly popular. However, smart contracts and blockchain applications are developed through non-standard software life-cycles, in which, for instance, delivered applications can hardly be updated or bugs resolved by releasing a new version of the software. Therefore, this systematic literature review oriented to software engineering aims at highlighting current problems and possible solutions concerning smart contracts and blockchain applications development. In this paper, we analyze 96 articles (written from 2016 to 2020) presenting solutions to tackle software engineering-specific challenges related to the development, test, and security assessment of blockchain-oriented software. In particular, we review papers (that appeared in international journals and conferences) relating to six specific topics: smart contract testing, smart contract code analysis, smart contract metrics, smart contract security, Dapp performance, and blockchain applications. Beyond the systematic review of the techniques, tools, and approaches that have been proposed in the literature to address the issues posed by the development of blockchain-based software, for each of the six aforementioned topics, we identify open challenges that require further research.
A Privacy-Preserving Optimization of Neighborhood-Based Recommendation for Medical-Aided Diagnosis and Treatment

Nowadays, patients' physiological data and medical records are outsourced to the medical cloud to help the patients and doctors to obtain valuable and reliable services, i.e., diagnosis decision and treatment recommendation, which provides subtle and precise guidance for medical cases. Neighborhood-based recommendation (NBR) provides an effective and vivid approach to finding like-minded nodes to generate recommendations, automating what is usually known as word of mouth. Unfortunately, both diverse medical data and outsource technology raise stringent privacy concerns since the patient data is generally associated with attribute sets which contain somewhat sensitive information, such as age, gender, epidemic, physiological data and clinic result etc. The great privacy-revealing risk will occur if this sensitive medical information is maliciously exploited by network eavesdroppers, untrusted cloud servers or doctors. Considering the above privacy-revealing issues in medical data, this article proposes a privacy-preserving optimization of neighborhood-based recommendation scheme (namely, PPO-NBR), which achieves a secure and privacy-preserving recommendation for medical-aided diagnosis and treatment without revealing the patients' sensitive information. We propose a privacy-preserving medical graph construction protocol and a medical treatment recommendation protocol to implement the secure recommendation on the encrypted medical data, in which it employs BGN Cryptosystem as the building modular to encrypt data and then creatively applies graph theory to expand neighbors to search the best similar features, and uses BLS signature to ensure the authentication, and meanwhile, it deploys a ( t,n) oblivious transfer protocol to preserve the confidentiality of recommendation. A couple of experiments are implemented to evaluate the performance in terms of computational costs and communication overheads and indicate that our scheme is efficient and practical for medical-aided diagnosis.
Anthropomorphism brings us closer: The mediating role of psychological distance in User–AI assistant interactions

In the current era, interacting with Artificial Intelligence (AI) has become an everyday activity. Understanding the interaction between humans and AI is of potential value because, in future, such interactions are expected to become more pervasive. Two studies—one survey and one experiment—were conducted to demonstrate positive effects of anthropomorphism on interactions with smart-speaker-based AI assistants and to examine the mediating role of psychological distance in this relationship. The results of Study 1, an online survey, showed that participants with a higher tendency to anthropomorphize their AI assistant/s evaluated it/them more positively, and this effect was mediated by psychological distance. In Study 2, the hypotheses were tested in a more sophisticated experiment. Again, the results indicated that, in the high-anthropomorphism (vs. low-anthropomorphism) condition, participants had more positive attitudes toward the AI assistant, and the effect was mediated by psychological distance. Though several studies have demonstrated the effect of anthropomorphism, few have probed the underlying mechanism of anthropomorphism thoroughly. The current research not only contributes to the anthropomorphism literature, but also provides direction to research on facilitating human–AI interaction.
IDE: Image Dehazing and Exposure Using an Enhanced Atmospheric Scattering Model

Atmospheric scattering model (ASM) is one of the most widely used model to describe the imaging processing of hazy images. However, we found that ASM has an intrinsic limitation which leads to a dim effect in the recovered results. In this paper, by introducing a new parameter, i.e., light absorption coefficient, into ASM, an enhanced ASM (EASM) is attained, which can address the dim effect and better model outdoor hazy scenes. Relying on this EASM, a simple yet effective gray-world-assumption-based technique called IDE is then developed to enhance the visibility of hazy images. Experimental results show that IDE eliminates the dim effect and exhibits excellent dehazing performance. It is worth mentioning that IDE does not require any training process or extra information related to scene depth, which makes it very fast and robust. Moreover, the global stretch strategy used in IDE can effectively avoid some undesirable effects in recovery results, e.g., over-enhancement, over-saturation, and mist residue, etc. Comparison between the proposed IDE and other state-of-the-art techniques reveals the superiority of IDE in terms of both dehazing quality and efficiency over all the comparable techniques.
Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge

We study federated learning (FL) at the wireless edge, where power-limited devices with local datasets collaboratively train a joint model with the help of a remote parameter server (PS). We assume that the devices are connected to the PS through a bandwidth-limited shared wireless channel. At each iteration of FL, a subset of the devices are scheduled to transmit their local model updates to the PS over orthogonal channel resources, while each participating device must compress its model update to accommodate to its link capacity. We design novel scheduling and resource allocation policies that decide on the subset of the devices to transmit at each round, and how the resources should be allocated among the participating devices, not only based on their channel conditions, but also on the significance of their local model updates. We then establish convergence of a wireless FL algorithm with device scheduling, where devices have limited capacity to convey their messages. The results of numerical experiments show that the proposed scheduling policy, based on both the channel conditions and the significance of the local model updates, provides a better long-term performance than scheduling policies based only on either of the two metrics individually. Furthermore, we observe that when the data is independent and identically distributed (i.i.d.) across devices, selecting a single device at each round provides the best performance, while when the data distribution is non-i.i.d., scheduling multiple devices at each round improves the performance. This observation is verified by the convergence result, which shows that the number of scheduled devices should increase for a less diverse and more biased data distribution.
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT

The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT equipments generating massive amounts of user data every moment. According to the different requirement of end users, these data usually have high heterogeneity and privacy, while most of users are reluctant to expose them to the public view. How to manage these time series data in an efficient and safe way in the field of IIoT is still an open issue, such that it has attracted extensive attention from academia and industry. As a new machine learning paradigm, federated learning (FL) has great advantages in training heterogeneous and private data. This article studies the FL technology applications to manage IIoT equipment data in wireless network environments. In order to increase the model aggregation rate and reduce communication costs, we apply deep reinforcement learning (DRL) to IIoT equipment selection process, specifically to select those IIoT equipment nodes with accurate models. Therefore, we propose a FL algorithm assisted by DRL, which can take into account the privacy and efficiency of data training of IIoT equipment. By analyzing the data characteristics of IIoT equipments, we use MNIST, fashion MNIST, and CIFAR-10 datasets to represent the data generated by IIoT. During the experiment, we employ the deep neural network model to train the data, and experimental results show that the accuracy can reach more than 97%, which corroborates the effectiveness of the proposed algorithm.
Quantum-Inspired Blockchain-Based Cybersecurity: Securing Smart Edge Utilities in IoT-Based Smart Cities

Blockchain plays a vital task in cybersecurity. With the exerted efforts for realising large-scale quantum computers, most current cryptographic mechanisms may be hacked. Accordingly, we need a quantum tool utilised for designing blockchain frameworks to have the ability to be executed in the level of digital computers and resist the probable attacks from both digital and quantum computers. Quantum walks may be utilised as a quantum-inspired model for designing new cryptographic algorithms. In this paper, we present a new authentication and encryption protocol based on quantum-inspired quantum walks (QIQW). The proposed protocol is utilized to build a blockchain framework for secure data transmission among IoT devices. Instead of using classical cryptographic hash functions, quantum hash functions based on QIQW are employed for linking blocks of the chain. The main advantages of the presented framework are helping IoT nodes to effectively share their data with other nodes and full control of their records. Security analysis demonstrates that our proposed protocol can defend against message attack and impersonation attacks, thus ensuring secure transmission of data among IoT devices.