Reviewerly/exHarmony huggingface dataset:
Overirradiance conditions and their impact on the spectral distribution at low- and mid-latitude sites

Lately, the photovoltaics community has shown an increased interest in overirradiance conditions as there is the possibility that such conditions might lead to malfunctions in photovoltaic systems. Varying irradiance levels, recurrence, and duration of such conditions have been reported worldwide, but experimental studies on the spectral distribution of overirradiance conditions are still scarce. This work analyses measured spectral irradiance of overirradiance conditions along with spectra under clear and cloudy sky conditions in three different sites at low- (Lima-Peru) and mid-latitudes (Madrid-Spain and Berlin-Germany) collected for two years. The Average Photon Energy (APE) was used as a representative index of the spectral distribution. For each site, taking the APE under clear sky into account as a reference, it could be shown that the spectra under cloudy skies are blue-shifted, and the overirradiance spectra are red-shifted independently of the location. The red-shift is proportional to the irradiance enhancement intensity. In addition, all sites have different degrees of blue–shift for cloudy skies, with Lima, Madrid, and Berlin exhibiting a difference in APE compared to clear sky conditions of 17 meV, 38 meV, and 43 meV on average, respectively. This difference in APE for the overirradiance conditions compared to clear sky conditions is also independent of the location with a mean value of (8 ± 1) meV. These spectral shift observations experimentally confirm prior assumptions that overirradiance conditions predominantly cause an enhancement of the direct spectral irradiance.
Effects of Interaction Method, Size, and Distance to Object on Augmented Reality Interfaces

ABSTRACT Augmented reality (AR) technologies are becoming increasingly popular. However, studies regarding the usability of AR interfaces are scarce. This study aims to derive the optimal usability design of the AR interface by analyzing the performance of 12 interface conditions (2 button sizes × 3 distances × 2 interaction types). An experiment was conducted using Microsoft HoloLens, in which participants performed button selection tasks with an array of 3 × 3 virtual buttons. The task completion time and the number of errors were measured along with a subjective satisfaction score. The results showed that a clicker-based interaction was more effective in task completion time, the number of errors and user satisfaction than a hand gesture. Furthermore, large buttons required a significantly shorter task completion time than small buttons. There was no significant difference in the number of errors between the two sizes. A distance of at least 80 cm from the eye to the virtual object is most favorable for a good performance. There was a significant difference in the task completion times between 40 cm and 80 cm and between 40 cm and 120 cm. These results can aid the interface designs for AR applications and devices.
Analysis of the confirmation time in proof-of-work blockchains

In blockchain networks driven by Proof of Work, clients spend a certain amount of cryptocurrency (called fees) to control the speed of confirmation of the transactions that they generate. In fact, transactions are confirmed according to a strong priority policy that favours those offering the highest fees. The problem of determining the optimal fee to offer to satisfy certain delay requirements is still widely open and, at the state of the art, mainly reactive methods based on historical data are available. In this work, we propose a queueing model based on the exact transient analysis of a M/MB/1 system to address this problem. The model takes into account (i) the state of the Mempool (the backlog of pending work) when the transaction is generated, (ii) the current transaction arrival intensity and (iii) the distribution of the fees offered by other transactions to the miners. We apply the model to study the performance of the Bitcoin blockchain. Its parameterisation is based on an extensive statistical analysis of the transaction characteristics. To this aim, we collected data from over 1.5 million of pending transactions observed in the Mempool of our Bitcoin node. The outcome of our analysis allows us to provide an algorithm to quickly compute the expected transaction confirmation time given the blockchain state, and to highlight new insights on the relations between the transaction fees and confirmation time in BTC blockchain.
ASCFL: Accurate and Speedy Semi-Supervised Clustering Federated Learning

The influence of non-Independent Identically Distribution (non-IID) data on Federated Learning (FL) has been a serious concern. Clustered Federated Learning (CFL) is an emerging approach for reducing the impact of non-IID data, which employs the client similarity calculated by relevant metrics for clustering. Unfortunately, the existing CFL methods only pursue a single accuracy improvement, but ignore the convergence rate. Additionlly, the designed client selection strategy will affect the clustering results. Finally, traditional semi-supervised learning changes the distribution of data on clients, resulting in higher local costs and undesirable performance. In this paper, we propose a novel CFL method named ASCFL, which selects clients to participate in training and can dynamically adjust the balance between accuracy and convergence speed with datasets consisting of labeled and unlabeled data. To deal with unlabeled data, the prediction labels strategy predicts labels by encoders. The client selection strategy is to improve accuracy and reduce overhead by selecting clients with higher losses participating in the current round. What is more, the similarity-based clustering strategy uses a new indicator to measure the similarity between clients. Experimental results show that ASCFL has certain advantages in model accuracy and convergence speed over the three state-of-the-art methods with two popular datasets.
Machine Learning-Based Label Quality Assurance for Object Detection Projects in Requirements Engineering

In recent years, the field of artificial intelligence has experienced significant growth, which has been primarily attributed to advancements in hardware and the efficient training of deep neural networks on graphics processing units. The development of high-quality artificial intelligence solutions necessitates a strong emphasis on data-centric approaches that involve the collection, labeling and quality-assurance of data and labels. These processes, however, are labor-intensive and often demand extensive human effort. Simultaneously, there exists an abundance of untapped data that could potentially be utilized to train models capable of addressing complex problems. These raw data, nevertheless, require refinement to become suitable for machine learning training. This study concentrates on the computer vision subdomain within artificial intelligence and explores data requirements within the context of requirements engineering. Among the various data requirement activities, label quality assurance is crucial. To address this problem, we propose a machine learning-based method for automatic label quality assurance, especially in the context of object detection use cases. Our approach aims to support both annotators and computer vision project stakeholders while reducing the time and resources needed to conduct label quality assurance activities. In our experiments, we trained a neural network on a small set of labeled data and achieved an accuracy of 82% in differentiating good and bad labels on a large set of labeled data. This demonstrates the potential of our approach in automating label quality assurance.
Topic modeling methods for short texts: A survey

In the present day, online users are incentivized to engage in short text-based communication. These short texts harbor a significant amount of implicit information, including opinions, topics, and emotions, which are of notable value for both exploration and analysis. By alleviating the sparsity in short texts, topic models can be used to discover topics from large collections of short texts. While there is a large body of surveys focused on topic modeling, but only a few of them have focused on the short texts. This paper presents a comprehensive overview of topic modeling methods for short texts from a novel perspective. Firstly, it discusses short text probabilistic topic models and outlines the directions in which they can be improved. Secondly, it explores short text neural topic models, which can be categorized into three groups based on their underlying structures. In addition, this paper provides a detailed investigation of embedding methods in topic modeling. Moreover, various applications and corresponding works are surveyed, with a focus on short texts. The commonly used public corpora and evaluation indicators for topic modeling are also summarized. Finally, the advantages and disadvantages of short text topic modeling are discussed in detail, and future research directions are proposed.
Do Information and Communications Technology (ICT) and financial development contribute to economic diversification? Evidence from sub-Saharan Africa

Abstract This study based on a panel of 37 sub-Saharan Africa countries over the period of 2000–2019 explores the effect of a number of Information and Communications Technology variables namely fixed broad band, fixed line telephone, Information and Communications Technology good imports, internet, mobile, and secure internet servers, and financial development measured by private sector domestic credit to GDP on economic diversification as measured by a computed Herfindahl–Hirschman Index of economic diversification. Model estimation was performed using pooled ordinary least squares regression, panel data fixed effects regression, and generalized method of moments regression. The results from findings indicated that the Information and Communications Technology variables: fixed-line telephone, and ICT imports significantly reduced economic diversification, while internet and mobile were, respectively, insignificant for boosting economic diversification, and fixed broadband and secure internet servers were insignificant in adversely affecting economic diversification. As regards financial development, it was insignificant in boosting economic diversification of sub-Saharan Africa countries. The study recommended amongst others that individuals in sub-Saharan Africa countries should have improved access to Information and Communications Technology infrastructure and governments should ensure adequate provision of quality Information and Communications Technology infrastructure.
Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks

With the proliferation of video surveillance system deployment and related applications, real-time video analysis is very critical to achieving intelligent monitoring, autonomous driving, etc. Analyzing video stream with high accuracy and low latency through the traditional cloud computing represents a non-trivial problem. In this paper, we propose a non-orthogonal multiple access (NOMA)-based edge real-time video analysis framework with one edge server (ES) and multiple user equipments (UEs). A cost minimization problem composed of delay, energy and accuracy is formulated to improve the quality of experience (QoE) of the UEs. In order to efficiently solve this problem, we propose the joint video frame resolution scaling, task offloading, and resource allocation algorithm based on the Deep Q-Learning Network (JVFRS-TO-RA-DQN), which effectively overcomes the sparsity of the single-layer reward function and accelerates the training convergence speed. JVFRS-TO-RA-DQN consists of two DQN networks to reduce the curse of dimensionality, which, respectively, select the offloading and resource allocation action, as well as the resolution scaling action. The experimental results show that JVFRS-TO-RA-DQN can effectively reduce the cost of edge computing and has better performance in terms of convergence compared to other baseline schemes.
User Perceptions of AI-Based Comment Filtering Technology

As artificial intelligence (AI)-powered technology enables the efficient processing of large volumes of comments, more companies, including news publications, are experimenting with and adopting AI moderation to manage their commenting platforms. However, the resulting user experiences have been largely underexplored in relation to these technical advances in comments section management. This study used an experiment to examine the impact of AI filters on individuals’ perceptions about comments sections (i.e., bias, credibility, positive and negative affect, and use intention). The findings indicate that AI moderation had statistically significant impacts on perceived credibility and use intention. Beyond the main effects, priming with a deceptive comment issue moderated the impacts of comment filtering on user perceptions as well.
Empirical analysis of security-related code reviews in npm packages

Security issues are a major concern in software packages and their impact can be detrimental if exploited. Modern code review is a widely-used practice that project maintainers adopt to improve the quality of contributed code. Prior work has shown that code review has an important role in improving software quality, however, in-depth analyses on code review in relation to security issues are limited. Therefore, in this paper, we aim to explore the role of code review in finding and mitigating security issues. In particular, we investigate active and popular npm packages to understand what types of security issues are raised during code review, and what kind of mitigation strategies are employed by package maintainers to address them. With pull requests (PRs) being the medium of code review under study, we analyze 171 PRs with raised security issues. We find that such issues are discussed at length by package maintainers. Moreover, we find that code review is effective at identifying certain types of security concerns, e.g., Race Condition, Access Control, and ReDOS, as dealing with such issues requires in-depth knowledge of the project domain and implementation specifics. Interestingly, we also observe that some projects have automated tools integrated in the project development cycle, which enhances the identification of frequent cases of certain security issues. When analyzing how maintainers respond to the raised security issues, we find that most of the issues (55%) are frequently addressed and mitigated. In other cases, security concerns ended up not being fixed or are ignored by project maintainers. Leveraging our findings, we offer several implications for project maintainers to support the role of reviewing code in finding and fixing security concerns.
KPT++: Refined knowledgeable prompt tuning for few-shot text classification

Recently, the new paradigm “pre-train, prompt, and predict” has achieved remarkable few-shot learning achievements compared with the “pre-train, fine-tune” paradigm. Prompt-tuning inserts the prompt text into the input and converts the classification task into a masked language modeling task. One of the key steps is to build a projection between the labels and the label words, i.e., the verbalizer. Knowledgeable prompt-tuning (KPT), which integrates external knowledge into the verbalizer to improve and stabilize prompt-tuning. KPT uses word embeddings and various knowledge graphs to expand the label words space to hundreds of words per class. However, some unreasonable label words in the verbalizer may damage the accuracy. In this paper, a new method called KPT++ is proposed to improve the few-shot text classification. KPT++ is refined knowledgeable prompt-tuning, which can also be regarded as an upgraded version of KPT. Specifically, KPT++ uses two newly proposed prompt grammar refinement (PGR) and probability distribution refinement (PDR) to refine the knowledgeable verbalizer. Extensive experiments on few-shot text classification tasks demonstrate that our KPT++ outperforms state-of-the-art method KPT and other baseline methods. Furthermore, ablation experiments and case studies demonstrate the effectiveness of both PGR and PDR refining methods.
Adaptive orthogonal gradient descent algorithm for fully complex-valued neural networks

For optimization algorithms of fully complex-valued neural networks, complex-valued stepsize is helpful to make the training escape from saddle points. In this paper, an adaptive orthogonal gradient descent algorithm with complex-valued stepsize is proposed for the efficient training of fully complex-valued neural networks. The basic idea is that, at each iteration, the search direction is constructed as a combination of two orthogonal gradient directions by using the algebraic representation of complex-valued stepsize. It is then shown that the determination of suitable complex-valued stepsize is facilitated by a decoupling method such that the computational complexity involved in the training process is greatly reduced. The experiments are finally conducted on pattern classification, nonlinear channel equalization and signal prediction to confirm the advantages of the proposed algorithm.
RoseMatcher: Identifying the impact of user reviews on app updates

The release planning of mobile apps has become an area of active research, with most studies centering on app analysis through release notes in the Apple App Store and tracking user reviews via issue trackers. However, the correlation between these release notes and user reviews in App Store remains understudied. In this paper, we introduce RoseMatcher, a novel automatic approach to match relevant user reviews with app release notes, and identify matched pairs with high confidence. We collected 944 release notes and 1,046,862 user reviews from 5 mobile apps in the Apple App Store as research data to evaluate the effectiveness and accuracy of RoseMatcher, and conducted deep content analysis on matched pairs. Our evaluation shows that RoseMatcher can reach a hit ratio of 0.718 for identifying relevant matched pairs, and with the manual labeling and content analysis of 984 relevant pairs, we identify 8 roles that user reviews play in app updates according to the relationship between release notes and user reviews in the relevant matched pairs. Our findings indicate that both app development teams and users pay close attention to release notes and user reviews, with release notes typically addressing feature requests, bug reports, and complaints, and user reviews offering positive, negative, and constructive feedback. Overall, the study highlights the importance of the communication between app development teams and users in the release planning of mobile apps, with relevant reviews tending to be posed within a short period before and after the release of release notes, with the average time interval between the post time of release notes and user reviews being approximately one year.
MENet: Map-enhanced 3D object detection in bird’s-eye view for LiDAR point clouds

Three-dimensional (3D) object detection utilizes numerous onboard sensors to determine the position, size, and motion information of surrounding objects. Recently, some researchers have utilized HD maps in 3D object detection for LiDAR point clouds. However, existing LiDAR–map fusion detection methods simply take the HD map as an additional input and more prior information in the map is underexplored. To address this limitation, we employ the HD map in data augmentation for LiDAR 3D detection to eliminate the bias of distributions between the augmented training and test sets. Furthermore, we propose a map-enhanced 3D object detection network, called MENet, for LiDAR point clouds. Compared with the standard LiDAR-only detector in bird’s eye view (BEV), MENet extends LiDAR-based baselines by using a map encoder to encode the input rasterized HD map into a BEV feature. In addition, it uses a multimodal fuser to aggregate the map BEV feature with the LiDAR BEV feature extracted in the LiDAR stream. MENet achieved a mean average precision (mAP) of 56.90% and nuScenes detection score (NDS) of 63.43%, improving the mAP and NDS of its LiDAR-only backbone by 4.18% and 2.22%, respectively. Although the HD map is necessary at training time, the map-enhanced 3D detector can work robustly and perform better than the original baseline when there was no map input at test time. Experimental results further indicate that the proposed map plugins can improve the performance of both anchor-based and anchor-free models on different datasets. The code will be open-source at https://github.com/WHU-USI3DV/MENet.
Risk translation: how cryptocurrency impacts company risk, beta and returns

Purpose As cryptocurrencies continue to gain viability as an asset class, institutional investors and publicly traded firms have started taking investment positions in digital currencies. What firms may not be considering, however, is the effect these assets may have on their risk profiles. This study aims to (1) measure the effect of cryptocurrencies on the risk and return characteristics of publicly traded companies; (2) decipher the motives behind holding cryptocurrencies as an asset class; and (3) determine whether one reason for holding is more effective than another. To conduct this research, the four largest publicly traded holders of cryptocurrency as well as four of the most prominent cryptocurrencies are explored. Design/methodology/approach The cross-sectional analysis approach has been used to analyze the daily returns, volatility, betas and Sharpe Ratios of firms during periods without cryptocurrency strategies and during periods with cryptocurrency strategies. Findings The impact of the cryptocurrency asset class on common stock performance and corporate disclosures are documented. The importance of risk disclosures on cryptocurrency holdings is emphasized: Firms must better inform their stakeholders through comprehensive disclosures in financial statements. Firms utilize cryptocurrencies for various reasons such as treasury management tools or as direct sources of income. Consequently, the impact on returns and risks varies substantially. Originality/value To the best of the authors’ knowledge, this is one of the first studies on cryptocurrency investments in the treasury departments of publicly traded companies. The study contributes to the literature by extracting relevant information regarding company risk reporting and cryptocurrency risk at firms. The conclusions also promote firm transparency with detailed reporting of cryptocurrency holding risks.
Digital Competence in University Lecturers: A Meta-Analysis of Teaching Challenges

This meta-analysis (random effects) studies the self-perceived digital competence of university lecturers in university teaching, using 7470 lecturers from Europe and Latin America collected in K = 31 samples, with teaching experience of between 6 and 15 years. The effect size obtained from a moderate random effects model of r = −0.21 with a 99% confidence interval is significant, negative, and moderate, confirming the low competence level. The meta-regression results show that the area of knowledge plays an important role. The systematic review of the literature shows that the perception of ICTs is positive, while the level of competence is low, and there are institutional and training challenges to be solved.