Reviewerly/exHarmony huggingface dataset:
Students’ Communication Self-efficacy and Its Impact on the Enhancement of Communication Skills in Software Engineering Project Courses

Developing the communication skills of software engineering graduates to meet industry requirements is a challenge for educators. This study presents a project-based learning framework that promotes students’ communication skills in a software engineering project course. The questionnaire on self-efficacy for software development (CSESD) was designed for students’ self-assessment of their confidence in communication skills. Findings indicate that students’ CSESD increased significantly after the course ended. Educators can apply the designed framework to software development-related project courses. The CSESD questionnaire can be used to assess students' confidence in their communication skills and assist educators in preparing students’ readiness before graduation.
Investigating the attributes that drives the quality of decision-making process in emerging economies

This study examines the attributive factors of decision-making process among selected institutions in emerging economies. The researchers used a survey research design to study accounting information quality and its relationship with decision-making approach. The current work was quantitative in nature where purposive and random sampling methods were employed. The sample consists of thirty-seven (37) respondents with a mix percentage of male and female. Results from the study indicated that the quality of accounting information such as accuracy, timeliness, conciseness, relevancy, and reliability significantly aid and determine the decision-making process. From the findings of this study, it can be concluded that the quality of accounting information had a positive and significant impact on decision-making process in public-sector organizations. It was noted that decision-making and traits of financial information are cyclical in nature. They are described as input and output relationship. The study suggests that new initiatives be implemented to improve the predictive value aimed at improving decision-making using accounting information.
The Role of Sustainable Finance and Technology at Bank BJB in Supporting the "Sustainable Development Goals"

On September 25, 2015, UN member states raised a series of the 2030 Sustainable Development agenda which includes 17 Sustainable Development Goals (SDGs). The SDGs consist of: No poverty, No hunger, Healthy and prosperous life, Quality education, Gender equality, Clean water and proper sanitation, Clean and affordable energy, Decent work and economic growth, Industry-innovation and infrastructure, Less gap, Sustainable cities and settlements, Responsible consumption and production, Handling climate change, Marine Ecosystem, Land ecosystem, Peace-justice and strong institutions, Partnership to achieve goals. Fintech activities in small and medium enterprises in the community have been proven to be accelerated quickly with the digitization of MSMEs. This is supported by the availability of quality and sustainable infrastructure, as well as access to Information and Communication Technology (ICT). The Government's commitment to implementing sustainable development is specifically regulated in the National Medium-Term Development Plan (RPJMN) for 2020-2024. Sustainable development has been defined as one of the important aspects that aim to provide access to equitable and inclusive development, as well as protect the environment. The role of sustainable finance in World Wildlife Funding ( WWF) and the Financial Services Authority (OJK) jointly encourage the Banking Industry to play a role in Sustainable Finance Development while supporting the process of community economic development. The activities above also make environmentalists, companies and the government mutually encourage all these positive activities to be carried out nationally and globally so as to contribute to sustainable economic development and support equality in the economic, social and cultural sectors.
Building and Deploying a Classification Schema using Open Standards and Technology

This tutorial offers a full introduction to Building and Deploying a Classification schema using Open Standards and Technology. Beyond accessing the command line, no prior knowledge is assumed as all the steps are described in detail. However, the tutorial will take you through some more complex technical steps. In order to profit best from the example workflows of the last chapter, it is good if you have some idea of how to work with OpenRefine and/or to have a TEI Publisher server set up to play around with. With this tutorial, you ‑ the reader ‑ should gain an understanding of: ‑ what different classification schemes can contribute to your project, ‑ what to pay attention to when building a classification scheme, ‑ how to code the classification scheme (in SKOS), ‑ how to publish the classification scheme (on GitHub pages), ‑ what some possible scenarios of using classifications schemes in actual project workflows are.
Blockchain-Enabled-Trust for Construction Project Governance: A Conceptual Framework

Construction project governance (CPG) acts as a ‘steering wheel’ that keeps a construction project steady in a challenging external environment. CPG frameworks can be divided into two types: control-based hierarchical governance and trust-based relational governance. To increase cooperation and networking between the participants in a project, effective CPG depends on well-combined control and trust mechanisms. However, the current CPG framework, which is control-based, is biased toward hierarchical governance and can lead to a lack of trust in the construction project. These issues have resulted in several studies that have focused on blockchain technology (BT) as a potential enabler for addressing the low levels of trust in CPG. However, existing research in this area has failed to address the nature of the relationship between trust as a key CPG challenge and BT. Hence, this paper aims to identify trust as a major CPG challenge and potential BT capabilities to reveal the relationship, through a state-of-the-art review. The findings show that the effective use of three decentralized BT capabilities (decentralized data storage, validity, and access) can positively influence the three relational norms (mutuality, flexibility, and solidarity), which are the functional tools of relational governance. Thus, a more flexible trust-based CPG framework is proposed by establishing relational governance with blockchain technology. Ultimately, this study is expected to enhance the trust between the key stakeholders in construction projects and enable more efficient collaboration and networking between them.
Analytical study on the perception and usage of e-journals in Maharshi Dayanand University, Rohtak, Haryana

E-Journals, short for electronic journals, have revolutionized the way we access and disseminate scholarly information in the digital age. As the digital counterpart to traditional print journals, e-journals provide researchers, academics, and students with an efficient and convenient means of accessing peer- reviewed articles, research papers, and other scholarly content through online platforms. These journals cover a wide range of disciplines and offer numerous advantages over their print counterparts, such as immediate availability, enhanced searchability, interactive features, and global accessibility. In this era of rapid information exchange and technological advancements, e-journals have emerged as a fundamental resource for scholarly communication, fostering collaboration, knowledge sharing, and innovation across various fields of study.E-Journals occupy a prominent place in academic and research environment. The new technologies have enhanced access to and usage of e-journals. The study has adopted simple random sampling with sample replacement method to collect information from the students/ researcher at University of Madras. The study found that majority of the users prefer e-journals rather than conventional type of printed journals. Further, the users prefer internet search engines such as Google, Yahoo and MSN as their initial search engine point for accessing e-journals. UGC-INFONET web portal is also used by majority of the users.
How to apply deep reinforcement learning to multi-dimensional intelligent reasoning

With the rapid development of artificial intelligence, the research on perceptual intelligence is becoming more and more mature. For the next stage of cognitive intelligence research, knowledge graph (KG) is one of the key directions. Knowledge reasoning is an important part of knowledge graph and has a wide range of application requirements. To solve the problems of current large-scale KG, including poor interpretability, low accuracy and efficiency of knowledge reasoning, this paper proposes a method RLPTransE which combines knowledge representation, relational path with deep reinforcement learning. This method generates a path vector by semantically combining the vectors of all relations on the path. Then, a reinforcement learning environment is established in the vector space, and through training a multi-step inference policy network based on diverse reward functions, the reinforcement learning agent can efficiently complete inference in the process of interacting with the environment. This paper conducted link prediction and fact prediction experiments with the RLPTransE method on NELL-995 and FB15K-237 datasets, and compared with knowledge representation-based, relational path-based and fusion-based methods, the experimental results show that RLPTransE method achieves better performance on large-scale dataset inference tasks.
Settling a Tomoe-sen: Wrestling with Chip Firing

Click to increase image sizeClick to decrease image size Disclosure statementNo potential conflict of interest was reported by the author.Additional informationNotes on contributorsBevin MaultsbyBevin Maultsby is an associate teaching professor at North Carolina State University. Prior to that, she taught for two years in the University of Minnesota Talented Youth Mathematics Program (UMTYMP).
Towards Thousands to One Reference: Can We Trust the Reference Image for Quality Assessment?

Traditional full-reference image quality assessment (FR-IQA) methods predict the perceptual quality of a distorted image with a given pristine-quality image as the reference. However, the near-threshold visual perception suggests that there could be numerous pristine-quality representations that are indistinguishable in a scene, and the so-called pristine image used in FR-IQA for reference is just one of them. With numerous approaches proposed for FR-IQA by evaluating the perceptual similarity, much less work has been dedicated to locating the best reference for the deterministic perceptual This paper aims to answer the question that whether enabling the freedom in reference image selection could lead to better performance by designing a new FR-IQA paradigm FLexible REference (FLRE). The FLRE paradigm is developed in the feature space by attempting to obtain the feature-level reference of the distorted image via the selection of its corresponding best explanation within an equal-quality space. To this end, we devise the Perceptually Near-Threshold Estimation (PNTE) and the Pseudo-Reference Search (PRS) strategies. In particular, the PNTE module predicts the equal-quality map of a given pristine-quality feature, forming an equal-quality space. Subsequently, the PRS strategy is employed to locate the reference of the distorted feature within the equal-quality space in an element-wise minimum distance search manner. Due to the lack of the ground-truth reference (i.e., best explanation) of each distorted image, we optimize the pseudo-reference feature learning under three constraints, i.e., the quality regression loss, the disturbance maximization loss, and the content loss. We implement the FLRE as a plug-in module before the deterministic FR-IQA process, and experimental results have demonstrated that combining FLRE with the existing deep feature-based FR-IQA models can significantly improve the quality prediction performance, largely surpassing the state-of-the-art methods. The implementation of our method is publicly available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ytian73/FLRE</uri> .
Training on digital footprint management for teenagers in Simo District, Boyolali

This training is based on the condition that teenagers in Simo District are productive internet users. Concerns are raised on how their digital footprint will impact their future. Teenagers in Simo District are mindful of their digital footprint, but unlikely have awareness of the positive potential of digital footprint. Digital footprint management is essential skill for teenagers to become good online citizen. This community service aims to provides training on how to manage digital footprints to members of PIK Pemuda Kampung KB. The results obtained from this training are that members of PIK Pemuda Kampung KB understand the importance of managing digital footprints as important effort in managing data security as well as to build a positive footprint that will benefit their future.
All Around The World: Maps and Flags in R

Our lab is international. People born all over the world have come to work in my group. I’m proud of this fact, especially in the current political climate. I’ve previously used the GoogleMaps API to display a heat map on our lab webpage. It shows where in the world people in the lab come from. This was OK, but I wanted to get an R based solution to make this graphic to make it easier to automate updates.
Tips From The Blog XII: Improving your Twitter experience

This is a quick set of tips to improve your Twitter experience. YMMV on these tips. Plus I can see Twitter changing things so that they no longer work, but this advice is correct as of today.
Speech and Translation Technologies

Cross-language communication in healthcare is urgently needed. However, while development of the relevant linguistic technologies and related infrastructure has been accelerating, widespread adoption of translation- and speech-enabled systems remains slow. This chapter examines obstacles to adoption and directions for overcoming them, with emphasis on reliability and customization issues; discusses two major types of speech translation systems and their respective approaches to the same obstacles; surveys some healthcare-oriented communication systems, past and future; and concludes with an optimistic forecast for speech and translation applications in healthcare, tempered by due cautions.
Hedged Mutual Fund Blockchain Protocol: High Water Marks During Low Market Prices

We describe several innovations designed to bring the best practices of traditional investment funds to the blockchain landscape. We highlight two main issues with the operation of mutual funds and hedge funds and suggest how blockchain technology can alleviate those concerns. Our innovations combine the superior mechanisms of mutual funds and hedge funds and provide detailed steps to implement them as blockchain smart contracts. We discuss how our designs overcome several blockchain bottlenecks and how we can make smart contracts smarter. Specifically, we illustrate how fund prices can be updated regularly like mutual funds and performance fees can be charged like hedge funds. In addition we show how mutually hedged blockchain investment funds can operate with investor protection schemes such as high water marks and measures to offset trading related slippage costs when redemptions happen.
Network Traffic Prediction Based on Time Series Modeling

Predicting the network traffic of web pages is one of the areas that has increased focus in recent years. Modeling traffic helps find strategies for distributing network loads, identifying user behaviors and malicious traffic, and predicting future trends. Many statistical and intelligent methods have been studied to predict web traffic using time series of network traffic. In this paper, the use of machine learning algorithms to model Wikipedia traffic using Google's time series dataset is studied. Two data sets were used for time series, data generalization, building a set of machine learning models (XGboost, Logistic Regression, Linear Regression, and Random Forest), and comparing the performance of the models using (SMAPE) and (MAPE). The results showed the possibility of modeling the network traffic time series and that the performance of the linear regression model is the best compared to the rest of the models for both series.
The Weeding Handbook: A Shelf-by-shelf Guide, Second Edition. (2022)

"The Weeding Handbook: A Shelf-by-shelf Guide, Second Edition. (2022)." Journal of Electronic Resources Librarianship, 35(3), pp. 247–248
Resource Discovery for the Twenty-First Century: Case Studies and Perspectives on the Role of IT in User Engagement and Empowerment.

"Resource Discovery for the Twenty-First Century: Case Studies and Perspectives on the Role of IT in User Engagement and Empowerment.." Journal of Electronic Resources Librarianship, 35(3), pp. 246–247
Lung Cancer Classification and Prediction of Disease Severity Score Using Deep Learning

The World Health organization (WHO) recent statistics show that Cancer is a life-threatening disease that causes 10 million deaths every year around the globe. Lung Cancer is a leading cause of death worldwide, accounting for nearly 2.21 million deaths in 2020. Lung cancer is increasing day by day so early detection is much needed to initiate proper treatment to save the life of cancer patients. Lung cancer detection at an early stage has become very important and easy with image processing and deep learning techniques. The proposed work uses histopathological images (microscopic examination of a biopsy) to classify different cancer categories. This paper presents the use of Adaptive fine-tuned EfficientNetB7 architecture to classify three categories (2-cancer types Adenocarcinoma, Squamous cell carcinoma, and 1-normal i.e benign). The classification results enable the doctors to detect benign or malignant categories to initiate proper treatment. In this work measured performance ma such as Recall, Fl-Score, Precision, and classification accuracy. The proposed work enhanced the classification accuracy from 97.5% to 99.5% compared to the existing work. Later predicted the disease severity score in four levels based on the number of diseased cells present in the image.
MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement Learning

In cooperative multi-agent tasks, a team of agents jointly interact with an environment by taking actions, receiving a team reward and observing the next state. During the interactions, the uncertainty of environment and reward will inevitably induce stochasticity in the long-term returns and the randomness can be exacerbated with the increasing number of agents. However, such randomness is ignored by most of the existing value-based multi-agent reinforcement learning (MARL) methods, which only model the expectation of Q-value for both individual agents and the team. Compared to using the expectations of the long-term returns, it is preferable to directly model the stochasticity by estimating the returns through distributions. With this motivation, this work proposes a novel value-based MARL framework from a distributional perspective, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , parameterizing value function via <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> ixture of <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> ategorical distributions for MARL. Specifically, we model both individual Q-values and global Q-value with categorical distribution. To integrate categorical distributions, we define five basic operations on the distribution, which allow the generalization of expected value function factorization methods ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , VDN and QMIX) to their MCMARL variants. We further prove that our MCMARL framework satisfies <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Distributional-Individual-Global-Max</i> (DIGM) principle with respect to the expectation of distribution, which guarantees the consistency between joint and individual greedy action selections in the global Q-value and individual Q-values. Empirically, we evaluate MCMARL on both a stochastic matrix game and a challenging set of StarCraft II micromanagement tasks, showing the efficacy of our framework.
Child’s Play: pi-hole set up for a safer internet

I have been running a pi-hole to block ads on my home network for a while. It’s great! Not only are ads blocked, but it speeds up internet browsing because… the ads do not load. I wondered if it would be possible to use a pi-hole to make a child-safe internet experience to protect the little people in the house. Sure, there are ways to do this in most routers but they are not ideal.
Policy and Practice

In this chapter, we offer a number of recommendations for those who are in a position to do something technically, structurally, and legally or otherwise to minimize the risk of psychological harm that comes with the public’s use of social media and other online sites, especially their engagement with graphic or other upsetting digital material. We outline the policy implications of what we’ve learned from more than three years of desk research and original interviews that we have conducted with dozens of people, ranging from technologists to psychologists to content moderators to human rights investigators and beyond. We first spotlight the competing interests that underscore social media companies and governments’ policy deliberations with regard to content moderation. Next, we lay out our suggestions for companies, governments, and individuals with regard to how to improve the experiences of both content moderators and everyday social media users. We close with suggestions for creating a more “pro-social” online environment, one that not only better mitigates the risks of psychological harm but potentially encourages greater connection, resulting in wellness and even flourishing.
The Impact of an Intelligent Tutoring System (ITS) on the Academic Progress and Success of EFL Jordanian Students: A Quasi-Experimental Study

This study’s objective was to investigate the impact that using an Intelligent Tutoring System (ITS) had on the English language abilities of English as a Foreign Language (EFL) students from Jordan who were enrolled in university-level programs. Sixty students from Jadara University in Jordan were chosen at random and assigned to one of two groups: an experimental group that got instruction based on ITS or a control group that received education in a more traditional classroom setting. Both groups were shown content for a total of 12 hours spread out over four weeks. Participants in the study who were given an education based on ITS showed significant gains in both their learning and their performance after receiving this kind of instruction. The post-test results of the experimental group in grammar, vocabulary, and reading comprehension were significantly better than the findings of the control group. Additionally, individuals in the experimental group reported higher levels of happiness with the training when compared to those in the control group. The findings of this study suggest that integrating an ITS into EFL instruction may prove to be an effective method for boosting the learning and performance of students studying EFL at institutions located in Jordan. This has enormous implications for the future of the development and implementations of instructional technology in EFL classrooms.