Reviewerly/exHarmony huggingface dataset:
Data analysis in visual power line inspection: An in-depth review of deep learning for component detection and fault diagnosis

The widespread popularity of unmanned aerial vehicles enables an immense amount of power line inspection data to be collected. It is an urgent issue to employ massive data especially the visible images to maintain the reliability, safety, and sustainability of power transmission. To date, substantial works have been conducted on the data analysis for power line inspection. With the aim of providing a comprehensive overview for researchers interested in developing a deep-learning-based analysis system for power line inspection data, this paper conducts a thorough review of the current literature and identifies the challenges for future study. Following the typical procedure of data analysis in power line inspection, current works in this area are categorized into component detection and fault diagnosis. For each aspect, the techniques and methodologies adopted in the literature are summarized. Valuable information is also included such as data description and method performance. In particular, an in-depth discussion of existing deep-learning-based analysis methods of power line inspection data is proposed. To conclude the paper, several study trends for the future in this area are presented including data quality problems, small object detection, embedded application, and evaluation baseline.
Enhancing Our Understanding of the Relationship Between Leadership, Team Characteristics, Emotional Intelligence and Their Effect on Team Performance: A Critical Review

Teams are the core base of every business and institute, targeted toward achieving particular common projects and strategies through effective collaboration. Many scholars studied the various variables that affect the team performance level. Recently, they shed light on the psychological factors, including “Emotional Intelligence (EI)”. This paper aim toward elevating our understanding of the relationship between leadership, team characteristics, emotional intelligence and their effect on team performance through the implication of systematic review approach to review and synthesize EI studies related to team performance aiming to provide a comprehensive analysis of 19 research articles from 2010 to 2020. The main findings include the discovery of 14 external factors that have a relationship with EI and team performance in two or more of the relevant studies. Most importantly, out of the 14 factors, 6 factors were selected in which all of them demonstrated a positive relationship with EI and team performance. Moreover, a quantitative approach was the primarily relied research method for data collection. Additionally, most of the analyzed studies were undertaken in the US; this is followed by Australia, in addition to other countries. Besides, most of the analyzed studies were frequently conducted in Academic context, followed by construction, in addition to other contexts.
Privacy protection for fog computing and the internet of things data based on blockchain

Abstract With the development of the Internet of Things (IoT) field, more and more data are generated by IoT devices and transferred over the network. However, a large amount of IoT data is sensitive, and the leakage of such data is a privacy breach. The security of sensitive IoT data is a big issue, as the data is shared over an insecure network channel. Current solutions include symmetric encryption and access controls to secure the data transfer, but they have some drawbacks such as a single point of failure. Blockchain is a promising distributed ledger technology that can prevent the malicious tampering of data, offering reliable data storage. This paper proposes a distributed access control system based on blockchain technology to secure IoT data. The proposed mechanism is based on fog computing and the concept of the alliance chain. This method uses mixed linear and nonlinear spatiotemporal chaotic systems (MLNCML) and the least significant bit (LSB) to encrypt the IoT data on an edge node and then upload the encrypted data to the cloud. The proposed mechanism can solve the problem of a single point of failure of access control by providing the dynamic and fine-grained access control for IoT data. The experimental results of this method demonstrated that it can protect the privacy of IoT data efficiently.
Franck-Condon factors by counting perfect matchings of graphs with loops

We show that the Franck-Condon Factor (FCF) associated to a transition between initial and final vibrational states in two different potential energy surfaces, having $N$ and $M$ vibrational quanta, respectively, is equivalent to calculating the number of perfect matchings of a weighted graph with loops that has $P = N+M$ vertices. This last quantity is the loop hafnian of the (symmetric) adjacency matrix of the graph which can be calculated in $O(P^3 2^{P/2})$ steps. In the limit of small numbers of vibrational quanta per normal mode our loop hafnian formula significantly improves the speed at which FCFs can be calculated. Our results more generally apply to the calculation of the matrix elements of a bosonic Gaussian unitary between two multimode Fock states having $N$ and $M$ photons in total and provide a useful link between certain calculations of quantum chemistry, quantum optics and graph theory.
On-chip transverse-mode entangled photon pair source

Abstract Integrated entangled photon pair source is an essential resource for both fundamental investigations and practical applications of quantum information science. Currently there have been several types of entanglement, among which the transverse-mode entanglement is becoming attractive because of its unique advantages. Here, we report an on-chip transverse-mode entangled photon pair source via the spontaneous four-wave mixing processes in a multimode silicon waveguide. Transverse-mode photon pairs are verified over multiple frequency channels within a bandwidth of ~2 THz, and a maximally entangled Bell state is also produced with a net fidelity of 0.96 ± 0.01. Our entangled photon pair source is the key element for quantum photonics based on transverse-mode, and also has the possibility to extend to higher-dimensional Hilbert space. Furthermore, the transverse-mode entanglement can be converted coherently to path and polarization entanglement, which paves the way to realizing highly complex quantum photonic circuits with multiple degrees of freedom.
Integrated model‐driven engineering of blockchain applications for business processes and asset management

Abstract Blockchain has attracted broad interests to build decentralised applications. A typical class of applications uses blockchain for the management of cross‐organisational business processes as well as assets. However, developing such applications without introducing vulnerabilities is hard for developers, not the least because the deployed code is immutable and can be called by anyone with access to the network. Model‐driven engineering (MDE) helps to reduce those risks, by combining proven code snippets as per the model specification, which is easier to understand than source code. Therefore, in this article, we present an approach for integrated MDE across business processes and asset management (e.g., for settlement). Our approach includes methods for fungible/non‐fungible asset registration, escrow for conditional payment, and asset swap. The proposed MDE approach is implemented in a smart contract generation tool called Lorikeet, and evaluated in terms of feasibility, functional correctness, and cost effectiveness.
Pre-Training Transformers as Energy-Based Cloze Models

We introduce Electric, an energy-based cloze model for representation learning over text.Like BERT, it is a conditional generative model of tokens given their contexts.However, Electric does not use masking or output a full distribution over tokens that could occur in a context.Instead, it assigns a scalar energy score to each input token indicating how likely it is given its context.We train Electric using an algorithm based on noise-contrastive estimation and elucidate how this learning objective is closely related to the recently proposed ELECTRA pre-training method.Electric performs well when transferred to downstream tasks and is particularly effective at producing likelihood scores for text: it reranks speech recognition n-best lists better than language models and much faster than masked language models.Furthermore, it offers a clearer and more principled view of what ELECTRA learns during pre-training.
Single-particle digitization strategy for quantum computation of a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>ϕ</mml:mi><mml:mn>4</mml:mn></mml:msup></mml:math> scalar field theory

Motivated by the parton picture of high energy quantum chromodynamics, we develop a single-particle digitization strategy for the efficient quantum simulation of relativistic scattering processes in a $d+1$ dimensional scalar $\phi^4$ field theory. We work out quantum algorithms for initial state preparation, time evolution and final state measurements. We outline a non-perturbative renormalization strategy in this single-particle framework.
On the Security and Privacy of Hyperledger Fabric: Challenges and Open Issues

In the last few years, a countless number of permissioned blockchain solutions have been proposed, with each one to claim that it revolutionizes the way of the transaction processing along with the security and privacy preserving mechanisms that it provides. Hyperledger Fabric is one of the most popular permissioned blockchain architectures that has made a significant impact on the market. However, there are only few papers of finding architectural risks regarding the security and the privacy preserving mechanisms of Hyperledger Fabric. This paper separates the attack surface of the blockchain platform into four components, namely, consensus, chaincode, network and privacy preserving mechanisms, in all of which an attacker (from inside or outside the network) can exploit the platform's design and gain access to or misuse the network. In addition, we highlight the appropriate counter-measures that can be taken in each component to address the corresponding risks and provide a significantly secure and enhanced privacy preserving Fabric network. We hope that by bringing this paper into light, we can aid developers to avoid security flaws and implementations that can be exploited by attackers but also to motivate further research to harden the platform's security and the client's privacy.
Boosted Whale Optimization Algorithm With Natural Selection Operators for Software Fault Prediction

Software fault prediction (SFP) is a challenging process that any successful software should go through it to make sure that all software components are free of faults. In general, soft computing and machine learning methods are useful in tackling this problem. The size of fault data is usually huge since it is obtained from mining software historical repositories. This data consists of a large number of features (metrics). Determining the most valuable features (i.e., Feature Selection (FS) is an excellent solution to reduce data dimensionality. In this paper, we proposed an enhanced version of the Whale Optimization Algorithm (WOA) by combining it with a single point crossover method. The proposed enhancement helps the WOA to escape from local optima by enhancing the exploration process. Five different selection methods are employed: Tournament, Roulette wheel, Linear rank, Stochastic universal sampling, and random-based. To evaluate the performance of the proposed enhancement, 17 available SFP datasets are adopted from the PROMISE repository. The deep analysis shows that the proposed approach outperformed the original WOA and the other six state-of-the-art methods, as well as enhanced the overall performance of the machine learning classifier.
Spatio-temporal fusion for Macro- and Micro-expression Spotting in Long Video Sequences

In this paper, we aim to construct a spotting framework automatically. It is still a great challenge to spot micro-expression(ME) intervals accurately due to short duration, low intensity, and shaking. Under the uncontrolled condition, the transformation is caused by head shaking. In order to remove the global movement caused by head shaking, we propose a simple yet effective method to disentangle local movement vector from the global optical flow field by the estimation of mean optical flow in the nose region. After preprocessing, we extract the completed specific pattern(SP) of ME in each region of interest(ROI). The pattern consists of two sub-patterns: magnitude and angle. However, influenced by frame rate and different intensities of micro- and macro- expressions, we propose to use a multi-scale filter to improve the ability to spot both micro- and macro-expressions. The spotting result performed on $CAS(ME)^{2}$ and SAMM shows that our proposed method outperforms the basline method.
Calibrate Before Use: Improving Few-Shot Performance of Language Models

GPT-3 can perform numerous tasks when provided a natural language prompt that contains a few training examples. We show that this type of few-shot learning can be unstable: the choice of prompt format, training examples, and even the order of the training examples can cause accuracy to vary from near chance to near state-of-the-art. We demonstrate that this instability arises from the bias of language models towards predicting certain answers, e.g., those that are placed near the end of the prompt or are common in the pre-training data. To mitigate this, we first estimate the model's bias towards each answer by asking for its prediction when given the training prompt and a content-free test input such as N/A. We then fit calibration parameters that cause the prediction for this input to be uniform across answers. On a diverse set of tasks, this contextual calibration procedure substantially improves GPT-3 and GPT-2's average accuracy (up to 30.0% absolute) and reduces variance across different choices of the prompt.
Blockchain research in healthcare: a bibliometric review and current research trends

Abstract The literature on blockchain-enabled use cases has grown exponentially over recent years. Yet, studies are missing that apply bibliometrics and visualization techniques to unravel the dynamics and current discussions pertaining to the nexus of blockchain technology (BCT) and the healthcare field. To close this knowledge gap, we examine the knowledge base and research hotspots of BCT research in the field of healthcare. We carry out a series of bibliometric analyses on the extant literature, including the scholarly production, developmental pattern of the annual total number of authors, and identification of productive academic institutions, countries, and leading authors. Additionally, we conduct a keyword co-occurrence analysis and identify the major research hotspots and trends for the future. The findings of this research are valuable for scholars and practitioners who seek to better understand the development status, dynamics, and trends pertaining to BCT in healthcare.
The human side of digital transformation in sales: review &amp; future paths

The digital transformation of organizations is a pervasive force which fundamentally changes companies and, in fact, society as a whole. For many companies, the sales force is at the center of this transformation seeing the essential role of salespeople at the customer-company interface and the exceptional quantifiability of salespeople’s work outputs and inputs. New, cutting-edge sales technologies such as predictive sales analytics, virtual or augmented reality, or AI bots hold the promise of significant productivity gains, yet, simultaneously may entail insidious side effects for sales employees and companies. While past research made significant progress in understanding the benefits of these new technologies, the intricate ‘human side’ of the digital transformation remains relatively ‘uncharted territory’. With this special issue, we aim to contribute to a better understanding of the human side in digital transformation of sales—to augment academic knowledge, provide immediate guidance to practitioners, and improve academic sales education. This editorial reviews the works in the special issue, the state of digital sales transformation in practice, and on this basis derives ideas for future research.
Robot vs human: expectations, performances and gaps in off-premise restaurant service modes

Purpose Off-premise restaurant service has a new addition – food delivery robots. This new technology and off-premise service, in general, has received little research attention, despite continued year-over-year sales growth for both carry-out and delivery. Therefore, this study aims to analyze off-premise service modes, including food delivery robots, for service quality gaps between consumer expectations and actual performance and among the various modes. Design/methodology/approach Performance of three off-premise restaurant service modes (robot-delivery, human-delivery and carry-out) were evaluated using a mixed-methods approach. Consumer expectations were ascertained about food- and service-related performance factors using a survey, and a field observation study was conducted to obtain actual performance data for these factors. Findings from both approaches were compared to identify gaps and differences. Findings For food-related performance, consumers reported lower expectations for food safety and food quality from robot- and human-delivered food; however, no differences were observed among the three modes in the field study. Consumers also expected lower service-related performance from robot-delivery for service efficiency and ease of use (than human-delivery and carry-out) and monetary value (than carry-out). Consumers deemed robots the most sustainable and human-delivery the most convenient compared to other modes – however, not all service-related expectations aligned with actual performances. Originality/value This study was the first to comparatively examine off-premise restaurant service. Identification of a missing link in service gap analysis was among the theoretical contributions of this study. Managerially, this study provides previously unavailable insights into opportunities for improvement for off-premise service and use of delivery robots.
Detecting sentiment dynamics and clusters of Twitter users for trending topics in COVID-19 pandemic

COVID-19 caused a significant public health crisis worldwide and triggered some other issues such as economic crisis, job cuts, mental anxiety, etc. This pandemic plies across the world and involves many people not only through the infection but also agitation, stress, fret, fear, repugnance, and poignancy. During this time, social media involvement and interaction increase dynamically and share one’s viewpoint and aspects under those mentioned health crises. From user-generated content on social media, we can analyze the public’s thoughts and sentiments on health status, concerns, panic, and awareness related to COVID-19, which can ultimately assist in developing health intervention strategies and design effective campaigns based on public perceptions. In this work, we scrutinize the users’ sentiment in different time intervals to assist in trending topics in Twitter on the COVID-19 tweets dataset. We also find out the sentimental clusters from the sentiment categories. With the help of comprehensive sentiment dynamics, we investigate different experimental results that exhibit different multifariousness in social media engagement and communication in the pandemic period.
Multi-hop Attention Graph Neural Networks

Self-attention mechanism in graph neural networks (GNNs) led to state-of-the-art performance on many graph representation learning tasks. Currently, at every layer, attention is computed between connected pairs of nodes and depends solely on the representation of the two nodes. However, such attention mechanism does not account for nodes that are not directly connected but provide important network context. Here we propose Multi-hop Attention Graph Neural Network (MAGNA), a principled way to incorporate multi-hop context information into every layer of attention computation. MAGNA diffuses the attention scores across the network, which increases the receptive field for every layer of the GNN. Unlike previous approaches, MAGNA uses a diffusion prior on attention values, to efficiently account for all paths between the pair of disconnected nodes. We demonstrate in theory and experiments that MAGNA captures large-scale structural information in every layer, and has a low-pass effect that eliminates noisy high-frequency information from graph data. Experimental results on node classification as well as the knowledge graph completion benchmarks show that MAGNA achieves state-of-the-art results: MAGNA achieves up to 5.7% relative error reduction over the previous state-of-the-art on Cora, Citeseer, and Pubmed. MAGNA also obtains the best performance on a large-scale Open Graph Benchmark dataset. On knowledge graph completion MAGNA advances state-of-the-art on WN18RR and FB15k-237 across four different performance metrics.
A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images

Breast cancer screening is one of the most common radiological tasks with over 39 million exams performed each year. While breast cancer screening has been one of the most studied medical imaging applications of artificial intelligence, the development and evaluation of the algorithms are hindered due to the lack of well-annotated large-scale publicly available datasets. This is particularly an issue for digital breast tomosynthesis (DBT) which is a relatively new breast cancer screening modality. We have curated and made publicly available a large-scale dataset of digital breast tomosynthesis images. It contains 22,032 reconstructed DBT volumes belonging to 5,610 studies from 5,060 patients. This included four groups: (1) 5,129 normal studies, (2) 280 studies where additional imaging was needed but no biopsy was performed, (3) 112 benign biopsied studies, and (4) 89 studies with cancer. Our dataset included masses and architectural distortions which were annotated by two experienced radiologists. Additionally, we developed a single-phase deep learning detection model and tested it using our dataset to serve as a baseline for future research. Our model reached a sensitivity of 65% at 2 false positives per breast. Our large, diverse, and highly-curated dataset will facilitate development and evaluation of AI algorithms for breast cancer screening through providing data for training as well as common set of cases for model validation. The performance of the model developed in our study shows that the task remains challenging and will serve as a baseline for future model development.