Reviewerly/exHarmony huggingface dataset:
Quantitative Analysis on Time-series Nodal Voltages in Linear-time Intervals

The power flow problem is fundamental to all aspects of modeling, analysis, operation, and control of transmission and distribution systems. In a nutshell, it amounts to solving for the nodal voltages in the nonlinear active- and reactive power balance equations that characterize the steady-states of AC electric networks. The traditional power flow algorithm is focused on the steady operational state under a single snapshot and calculates the corresponding voltage and power distributions for given nodal power injections and network topology. To better capture the temporal characteristics of power injections and system variables with high accuracy, a linear-time interval regarding nodal power injections is first defined in this paper, and the norms of nodal voltage derivatives are further analyzed, which is leveraged for simplifying the complexity of solving non-linear dynamic time-varying problems. The voltage monotonicity property has been guaranteed under the proposed linear-time interval. Simulation case studies on IEEE 5-bus and modified 118-bus systems have demonstrated the effectiveness and efficiency of the proposed algorithm.
Anonymous Expression in an Online Community for Women in China

Gender issues faced by women can range from workplace harassment to domestic violence. While publicly disclosing these issues on social media can be hard, some may incline to express themselves anonymously. We approached such an anonymous female community on Chinese social media where discussion on gender issues takes place with a qualitative content analysis. By observing anonymous experiences contributed by female users and made publicly available by an influencer, we identified 20 issues commonly discussed, with cheating-partner, controlling parents and age anxiety taking the lead. By describing the anonymously expressed social challenges faced by women in China, in the context of Chinese cultures and expectations about gender, we aim to motivate more policies and platform designs to accommodate the needs of the affected population.
Boosting Factual Consistency and High Coverage in Unsupervised Abstractive Summarization

ive summarization has gained attention because of the positive performance of large-scale, pretrained language models. However, models may generate a summary that contains information different from the original document. This phenomenon is particularly critical under the abstractive methods and is known as factual inconsistency. This study proposes an unsupervised abstractive method for improving factual consistency and coverage by adopting reinforcement learning. The proposed framework includes (1) a novel design to maintain factual consistency with an automatic question-answering process between the generated summary and original document, and (2) a novel method of ranking keywords based on word dependency, where keywords are used to examine the coverage of the key information preserved in the summary. The experimental results show that the proposed method outperforms the reinforcement learning baseline on both the evaluations for factual consistency and coverage.
Adapting Software Teams to the New Normal: An Early Case Study of Transitioning to Hybrid Work Under COVID-19

In the wake of the COVID-19 pandemic, many studies have begun to address what some refer to as the "new normal," comprising hybrid arrangements of employees working from home and working at the office with varying schedule arrangements. While many of the studies to date addressed how employees coped with work-from-home, we sought to investigate how employees dealt with a transition to the new normal of hybrid arrangements. To shed light on this topic, we conducted a survey-based case study at one office location of a large, multinational software corporation. The site sought to transition employees fully working from home to working two days remotely and three predefined days in their shared workspace. Our survey results indicated a substantial decline in work satisfaction since the beginning of this transition, which can be explained by diverse work preferences. Furthermore, some software developers felt frustrated during this transition time; they described challenges they underwent and proposed potential solutions. In this paper, we present our lessons learned in this case study and describe some actionable recommendations for practitioners facing such transitions.
Are we contributing? The who, when, where, and what of the Blockchain Research Landscape

The blockchain technology discourse is diverse, and diffusion is increasing. It is estimated that USD39 billion will be spent within the blockchain ecosystem by 2025. One can view this as an exciting time to be involved in technology. Or another can potentially view this as wasteful spending and exploitation of scarce resources. Additionally, projects and start-ups fail at an alarming rate, making it critical to provide tools to aid decision-makers. Current blockchain research has not yet answered what blockchain is nor what situations it is best suited to. This paper problematises the current discourse on blockchain technology through a systematic literature review using bibliometric techniques. We present blockchain research on who, when, where, and what. This research also extends the multi-discipline discourse by synthesising how blockchain technology is enacted. We present a benchmarking tool for assessing solutions. Further research topics are also presented.
The Impact of Politeness on Conversational Outcomes in Mobile Dating Apps

Mobile dating applications like Bumble and Tinder have grown in popularity, increasingly attracting scholarly attention. Our study focuses on the impact of politeness strategies and imposition on conversational outcomes on mobile dating apps. Using a 2 by 2 factorial design we examine the impact of degree of directness and imposition impact on perceptions of potential romantic partners and attitudes toward intensifying a relationship. We found that indirectness (a higher-order politeness strategy) and requests for face-to-face dates (high imposition) were positively associated with 1) attitudes toward intensifying a relationship, and 2) the perceived likeability of an interactional partner. Indirect politeness strategies more often resulted in request compliance and rejection strategies varied based on degrees of directness and the nature of the requests. Further exploration of how individuals evaluate imposition as it relates to requests for transitioning conversations from a mobile dating platform to face-to-face, is needed.
PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning

Due to the complexity of modern IT services, failures can be manifold, occur at any stage, and are hard to detect. For this reason, anomaly detection applied to monitoring data such as logs allows gaining relevant insights to improve IT services steadily and eradicate failures. However, existing anomaly detection methods that provide high accuracy often rely on labeled training data, which are time-consuming to obtain in practice. Therefore, we propose PULL, an iterative log analysis method for reactive anomaly detection based on estimated failure time windows provided by monitoring systems instead of labeled data. Our attention-based model uses a novel objective function for weak supervision deep learning that accounts for imbalanced data and applies an iterative learning strategy for positive and unknown samples (PU learning) to identify anomalous logs. Our evaluation shows that PULL consistently outperforms ten benchmark baselines across three different datasets and detects anomalous log messages with an F1-score of more than 0.99 even within imprecise failure time windows.
Engaging Business Students with “Low-Code” Model Driven Development: Self-Efficacy Beliefs in an Introductory MIS Course

“Low-code/no-code” (LC/NC) platforms are designed to empower non-IT business professionals to use model driven development to rapidly create sophisticated applications. Organizations are increasingly adding LC/NC platforms to their IT software portfolios. Thus, it is likely that current business students will encounter such tools and be expected to be able to use them. This research assesses the implementation of a low-code app development case within a business management information systems (MIS) course with the aim of identifying whether it promotes student self-efficacy of learning complex technology topics and how this relates to interest in further LC/NC skill development and interest in the MIS major.
A Comparison of Paper Sketch and Interactive Wireframe by Eye Movements Analysis, Survey, and Interview

Eye movement-based analyses have been extensively performed on graphical user interface designs, mainly on high-fidelity prototypes such as coded prototypes. However, practitioners usually initiate the development life cycle with low-fidelity prototypes, such as mock-ups or sketches. Since little or no eye movement analysis has been performed on the latter, would eye tracking transpose its benefits from high- to low-fidelity prototypes and produce different results? To bridge this gap, we performed an eye movement-based analysis that compares gaze point indexes, gaze event types and durations, fixation, and saccade indexes produced by $N{=}8$ participants between two treatments, a paper prototype vs. a wireframe. The paper also reports a qualitative analysis based on the answers provided by these participants in a semi-directed interview and on a perceived usability questionnaire with 14 items. Due to its interactivity, the wireframe seems to foster a more exploratory approach to design (e.g., testing and navigating more extensively) than the paper prototype.
Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time

Optimal decision making requires appropriate evaluation of advice. Recent literature reports that algorithm aversion reduces the effectiveness of predictive algorithms. However, it remains unclear how people recover from bad advice given by an otherwise good advisor. Previous work has focused on algorithm aversion at a single time point. We extend this work by examining successive decisions in a time series forecasting task using an online between-subjects experiment (N = 87). Our empirical results do not confirm algorithm aversion immediately after bad advice. The estimated effect suggests an increasing algorithm appreciation over time. Our work extends the current knowledge on algorithm aversion with insights into how weight on advice is adjusted over consecutive tasks. Since most forecasting tasks are not one-off decisions, this also has implications for practitioners.
Reorganization and Participation in Decentralized Platform Ecosystems: Evidence from Blockchain Forking

Like any organizational system, platform ecosystems reorganize to update its alignment with the internal and external environments. However, unlike reorganizations of centrally managed platforms performed by the owners, reorganizations of decentralized platforms ecosystems do not rely on formal authority. Instead, the network self-reorganizes to renew the structure, rules, and information to evolve. Little is known about how self-reorganizations influence the participation of various types of networks. In this study, we investigate nine reorganization events on Ethereum, a blockchain-based decentralized smart contract platform, to unpack how self-reorganization related to hard forking influence participation in the development, validation, transaction, and complementor networks. We find that, while participation increases across all networks show a small increase after hard forking events, more complex dynamics are at play within each network that builds on delicate trade-offs between participation structure, configuration, and incentives. Our findings have implications for blockchain research as well as for start-ups building decentralized applications on top of decentralized smart contract platforms.
Using Twitter Post Data to Ascertain the Sentiment of Alcohol-related Blackouts in the United States

Research shows variability in how alcohol-related blackouts (periods of memory loss during/after drinking) are subjectively evaluated. We accessed 3.5 million original Tweets written in the U.S. between July 2009 and February 2020 that referenced blackouts, and coded the sentiment (positive or negative) of those Tweets, using the machine learning function of a Twitter-sponsored commercial platform. The sentiment of Tweets was examined by day of week and compared to the sentiment of blackout Tweets on certain holidays to non-celebration matched days. Tweets were more likely to have a positive (73%) than negative sentiment, and positive Tweets were more common during weekends. Relative to typical non-celebratory weekends, a greater proportion of blackout Tweets were positive around Thanksgiving and New Year’s Eve, though differences were not observed relative to several other celebratory periods (e.g., Superbowl). Results have implications for online interventions, which can use social networking sites to target alcohol during high-risk periods.
Energy Efficiency of Training Neural Network Architectures: An Empirical Study

The evaluation of Deep Learning (DL) models has traditionally focused on criteria such as accuracy, F1 score, and related measures. The increasing availability of high computational power environments allows the creation of deeper and more complex models. However, the computations needed to train such models entail a large carbon footprint. In this work, we study the relations between DL model architectures and their environmental impact in terms of energy consumed and CO2 emissions produced during training by means of an empirical study using Deep Convolutional Neural Networks. Concretely, we study: (i) the impact of the architecture and the location where the computations are hosted on the energy consumption and emissions produced; (ii) the trade-off between accuracy and energy efficiency; and (iii) the difference on the method of measurement of the energy consumed using software-based and hardware-based tools.
The system of ordinary differential questions with rectangular matrices that is partially solved to the derivatives near the pole.

In modern theory of ordinary differential equations and systems of equations with an unknown complex-valued function of a complex variable, a prominent place is occupied by systems of equations that are either unsolved or partially solved with respect to derivatives. The system of ordinary differential equations, which is partially solved with respect to the derivatives, with rectangular matrices around the pole has been studied. The article presents conditions for transforming a system of ordinary differential equations, which is partially solvable with respect to derivatives, to a system of ordinary differential equations with a special form. The theorem with sufficient conditions of the existence at least one solution of the Cauchy problem is proven, some components of the solution are analytic functions in domains with the fixed singularity on the boundary, and the remaining components are functions chosen from a certain class of functions.
Kesulitan Pembelajaran Daring Menuju Luring di UPTD SD Negeri Banyuajuh 5

This study aims to determine the difficulties and changes in the online learning process during the Covid-19 period to offline for elementary school students. This study uses a qualitative method, namely by direct observation and interviews with educators and elementary school students, so that the authenticity of this article can be maintained. The purpose of this research article is to determine the adaptation of online to offline learning, online learning constraints, changes in learning systems, the impact of online learning, and solving student learning problems and offline learning processes. The results of this study are so that we as educators can find out what problems are being faced by students so that educators can provide solutions and problem solving for students so that they can run smoothly in the learning process, so that students will not experience learning difficulties where learning difficulties can occur. have an impact on the psychology or psychology of students, if the psychology of students is disturbed then students will experience psychological disorders that make the teaching and learning process hampered.
The implication of practising blended learning, and conventional learning towards student's achievement and attitude

This study aimed to use an appropriate learning method that can improve the achievement and attitudes of pupils from primary schools for Mathematics subjects. The type of this research was a quasi-experimental time-series design. The treatment group (n = 40) used blended learning (Moodle), while the control group (n = 40) used conventional teaching. The research instruments used were pre-Test, Test 1, Test 2, post-Test, and attitude questionnaire and interviews. The data analysis approach was evaluated using a one-way ANOVA to determine the differences between the experimental and control groups. The study found a statistically significant difference at p&lt;0.05 level between the two strategies. The findings of the study showed that blended learning has successfully improved pupils' achievement. The findings also showed improvement in pupils’ attitudes in the blended learning. The study findings through interviews also showed that pupils prefer blended learning to make mathematics easier to understand. In conclusion, pupils in the blended learning class outperformed those in conventional classes.
Information and Communication Technology Coordinators’ Competence in Public Elementary Schools

This study aimed to determine the level of competence of Information Communication Technology Coordinators in Elementary Schools of Ramon District, Isabela during the School Year 2019-2020. This study used descriptive method of research, employing the questionnaire checklist in gathering data. This method was adopted since the study aimed to find the level of competence of ICT coordinators. Mean was used to determine the level of competence of the ICT Coordinators. Results of the study shows that the respondents have an overall high level of competence on competencies related to technical operations and concepts, and productivity of various ICT tools. The respondents have an overall high level of competence on the standard 2. This implies that the ICT coordinators assumed they were highly skilled in using appropriate office and teaching productivity tools. Moreover, the respondents have an overall high level of competence related to social, ethical, legal and human issues, and community linkage, which means that the ICT coordinators acknowledge they were highly skilled in recognizing and practicing ethical use of technology in both personal and professional level. To effectively assess the level of competence of ICT coordinators, their roles, duties, and functions must be properly defined based on their job description in the Position Description form that every permanent government official obtained upon appointment for a permanent position. Therefore, the position ICT Coordinators should not remain as designation, but, should be included in the Plantilla of Personnel as a new regular item position. The findings of this study may be best translated through the conduct of trainings and seminars to update the ICT Coordinators with new trends and techniques in teaching. Likewise, the Department of Education may craft a policy to clearly define the roles and functions of ICT Coordinators in schools.
Embedding I-think Tools in an ITVC Chemistry Virtual Classroom: A Study Among High and Low Spatial Ability Students

The purpose of the study was to develop and investigate the effect of i-think embedded virtual chemistry classroom (ITVC) on students’ higher order thinking skills (HOTS). This study also differentiates the effect of ITVC on interest among students with high and low spatial ability. The development phase involved the use of ADDIE Model in designing ITVC and conventional virtual chemistry classroom (CVC). The effectiveness investigation phase used a quasi experimental design involving 66 forms four students in a secondary school in Kuantan. 33 students (control group) learnt via CVC while another 33 students (treatment group) learnt via ITVC. Both ITVC and CVC were developed using the Google Classroom application provided by Malaysia Ministry of Education. ITVC was enriched with the i-think thinking tools while the comparison table was used in CVC. Four research instruments used were electrochemistry HOTS achievement pre-test, electrochemistry HOTS achievement post-test, spatial ability test and interest questionnaires. Data collected were analysed using t-test to answer the research questions. Results show higher electrochemistry HOTS achievement for low spatial ability (LSA) and high spatial ability (HSA) students in post-test compared to pre-test in both groups. LSA in the treatment group shows higher electrochemistry HOTS achievement compared to the control group. ITVC also caused electrochemistry HOTS achievement for LSA in the treatment groups to be equal to HSA in the control group. The interest results indicated that LSA in both, control and treatment groups have high interest in electrochemistry. The findings revealed that embedding i-think learning tools in the virtual classroom could act effectively in enhancing HOTS and interest among chemistry students. ITVC also enabled teachers to apply 21st century learning among students towards the realization of 4.0 Industrial Revolution (IR4.0).
A FAIR protocol of the Best-RAD sequencing approach v1

The protocol is based on the Best-RAD sequencing approach developed in Ali et al. (2016) that allows two main improvements of the RAD-sequencing approach developed by Baird et al. (2008) : (1) a lower rate of PCR duplicates generated during the final enrichment and (2) an increase of the multiplexing capacity by a factor equal to the number of well barcode available (for a brief description, see Figure 1). We developed this protocol on individual wild samples of the Oriental fruit fly, Bactrocera dorsalis. We evaluated the library quality not only on a high amount of target DNA fragments but also on low amounts of PCR duplicates, chimeric fragments, and adaptor residues. To this aim, we tested and validated the following critical parameters : the quality and quantity of the DNA input, the ratio of the AMpure purifications and the number of PCR cycles of the final amplification. Consecutive recommendations and expectations (e.g., DNA concentration or fragment size) are indicated in notes throughout the protocol. Furthermore, special care has been taken to ensure the precision of each step (i.e. volumes, quantities, duration, materials with supplier references). Consequently, this protocol follows the FAIR principle and could be useful for an easy implementation of the Best-RAD sequencing approach in other laboratories and biological models.