Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel Steklov-Poincar\'e type metric for efficient PDE constrained optimization in shape spaces

Recent progress in PDE constrained optimization on shape manifolds is based on the Hadamard form of shape derivatives, i.e., in the form of integrals at the boundary of the shape under investigation, as well as on intrinsic shape metrics. From a numerical point of view, domain integral forms of shape derivatives seem promising, which rather require an outer metric on the domain surrounding the shape boundary. This paper tries to harmonize both points of view by employing a novel Steklov-Poincar\'e type intrinsic metric, which is derived from an outer metric. Based on this novel metric, efficient shape optimization algorithms are proposed, which also reduce the analytical labor, so far involved in the derivation of shape derivatives.
Corporate Charitable Donation Under the Theory of Organizational Legitimacy from the Perspective of Listed Companies' Violations in a Regional Institutional Environment

This paper studies the legitimacy of listed companies from the perspective of violation. We use the violation data of all A-share non-financial listed companies in Shanghai and Shenzhen from 2006 to 2019 and look at the relationship between corporate violation and charitable donation, and the possible influence of the regional institutional environment of enterprises and the degree of association between enterprises and government on the relationship between corporate violations and charitable donations. Compared with violations at the organizational level, enterprises with violations at the executive level are more active regarding charitable donations. The moderating effect indicates that the institutional environment level of the geographical region where the enterprise is located strengthens the negative relationship between the offending enterprise and charitable donation. After dividing into congenital and acquired government enterprise association, the congenital Government Enterprise Association has a strengthening effect on the charitable donation behavior of illegal enterprises.
The Roman Glass from Aldborough (Isurium Brigantum), North Yorkshire

Abstract This paper presents a comprehensive catalogue and review of the Roman glass from the Roma town of Isurium Brigantum (Aldborough, North Yorkshire). It includes the material found in work on the site in the 19th century as well as that from a variety of excavations down to the 1960s. This material now forms part of the English Heritage collection. The study provides new information about the supply of glass to the site. First century material is scarce, and the bulk of the vessels date to the 2nd-3rd centuries, with limited 4th century finds. Window glass, glass tesserae, beads and bangles are also present.
Evaluating Feature Robustness for Windows Malware Family Classification

Machine learning approaches to classify malware by family save analysts valuable time during incident response. A key challenge for these approaches is selecting features that are robust against concept drift, which describes the change in malware over time. In this paper, we evaluate a dynamic feature set based on Windows handles (e.g., files, registry keys) for malware family classification. Specifically, we examine the features' vulnerabilities and evaluate their robustness against concept drift. We curated a novel dataset that simulates the manipulations that attackers may invoke on malware samples. We demonstrate improved robustness to concept drift over traditional API call-based features by training machine learning classifiers on malware collected in the wild, and testing the classifiers against samples that underwent manipulations. Further, we investigate time decay due to concept drift using temporally consistent evaluations that do not assume access to newer information. The evaluation shows that our features are robust against malware obfuscation. Furthermore, we empirically demonstrate how malware labeling conventions (malware type or family) can affect results, and make recommendations for dataset construction.
Teachers’ Perceptions of Comprehensible Input on English Vocabulary Acquisition

This study aims at exploring the teacher's perceptions about comprehensible input on English vocabulary acquisition, their knowledge about it, and the ways they provide it in the classroom. The participants are 10 teachers of English who are teaching General English at a university in the Mekong Delta. A questionnaire was used to collect data from the teachers. The results indicate that the teacher's knowledge of Krashen's hypothesis is quite good. Even though some teachers are unfamiliar with the Input Hypothesis, they still place much value on comprehensible input based on their own teaching experience. However, to raise the quality of teaching English, all the teachers should expand their knowledge and improve their professional development. Krashen’s Hypothesis about comprehensible input should be spread more widely in the education field.
Molecular engineering‐device efficiency relation: Performance boosting of triboelectric nanogenerator through doping of small molecules

Triboelectric nanogenerators (TENGs) are promising new generation systems with their basic motion‐based working principle using both triboelectric and electrostatic effects. Today, the energy densities of TENGs are insufficient for many electronic devices and new strategies are needed to increase their power conversion efficiency. In this study, two different Perylene‐based organic structures were added to the triboelectric layers as well as the electrochemical properties of these structures, and the device parameters related to these properties were investigated. A large variety of instrumental analyses, including cyclic voltammetry, contact angle, scanning electron microscopy, atomic force microscopy, and so on, have been used to identify the relationship between doped molecules, their doping ratios, and obtained fiber structures. Depending on molecular structure and even any small variations in side groups of molecules, different doping rates brought about various device outputs. Compared with undoped layers, doping of small molecules led to a ~3.3 times increase in the maximum power of the best‐performed devices, and a very high voltage value of 500 V was obtained. The analysis of doping with small molecules undertaken here has extended our knowledge of how material design improves the electrical output and contributes to the device performance in TENGs.
Nudge: Improving Decisions about Health, Wealth, and Happiness

NUDGE: IMPROVING DECISIONS ABOUT HEALTH, WEALTH, AND HAPPINESS by Richard H. Thaler and Cass R. Sunstein Penguin Books, 2009, 312 pp, ISBN 978-0-14-311526-7This book is best described formally as a general explanation of and advocacy for libertarian paternalism, a term coined by the authors in earlier publications. Informally, it is about how leaders, systems, organizations, and governments can nudge people to do the things the nudgers want and need done for the betterment of the nudgees, or of society. It is paternalism in the sense that "it is legitimate for choice architects to try to influence people's behavior in order to make their lives longer, healthier, and better", (p. 5) It is libertarian in that "people should be free to do what they like - and to opt out of undesirable arrangements if they want to do so", (p. 5) The built-in possibility of opting out or making a different choice preserves freedom of choice even though people's behavior has been influenced by the nature of the presentation of the information or by the structure of the decisionmaking system. I had never heard of libertarian paternalism before reading this book, and I now find it fascinating.Written for a general audience, this book contains mostly social and behavioral science theory and models, but there is considerable discussion of structure and process that has roots in mathematical and quantitative modeling. One of the main applications of this social system is economic choice in investing, selecting and purchasing products and services, systems of taxes, banking (mortgages, borrowing, savings), and retirement systems. Other quantitative social choice systems discussed include environmental effects, health care plans, gambling, and organ donations. Softer issues that are also subject to a nudge-based approach are marriage, education, eating, drinking, smoking, influence, spread of information, and politics. There is something in this book for everyone.The basis for this libertarian paternalism concept is in the social theory called "science of choice", the study of the design and implementation of influence systems on various kinds of people. The terms Econs and Humans, are used to refer to people with either considerable or little rational decision-making talent, respectively. The various libertarian paternalism concepts and systems presented are tested and compared in light of these two types of people. Two foundational issues that this book has in common with another book, Network of Echoes: Imitation, Innovation and Invisible Leaders, that was also reviewed for this issue of the Journal are that 1 ) there are two modes of thinking (or components of the brain) - an automatic (intuitive) process and a reflective (rational) process and 2) the need for conformity and the desire for imitation are powerful forces in human behavior. …
An Anthropomorphic Robotic Finger With Innate Human-Finger-Like Biomechanical Advantages Part I: Design, Ligamentous Joint, and Extensor Mechanism

Exploring human hand fundamental biomechanical features and exploiting them to robotic hands have been proven to be an effective approach to enhancing artificial hands' performance, especially when interacting with various objects in dynamic unstructured environments. In this article, a bioinspired anthropomorphic robotic finger is first proposed, which embeds human finger musculoskeletal features in the design. Based on this design, three human-finger-like biomechanical advantages are systematically investigated and embodied in the bioinspired robotic finger. This article for the first time derives, presents, and experimentally verifies the mathematical models for the variable stiffness of finger ligamentous joints and self-adaptive morphing mechanism of finger flexible tendon sheaths, and validates and compares the influence of the reticular and linear extensor morphologies on fingertip feasible forces in three-dimensional (3-D) space. In this Part I of the article, two of the biomechanical properties, i.e., joint stiffness generated by the ligamentous joint of the finger, and fingertip feasible force space influenced by the reticular extensor mechanism are systematically investigated through theoretical modeling and experimental verification. Correspondingly, two biomechanical advantages were found, i.e., the ligamentous joint of the finger could provide anisotropic variable joint stiffness, enhancing the adaptivity, dexterity, and stability of fingers; and a reticular extensor mechanism could enlarge the fingertip feasible force space in 3-D space by 30.9% theoretically and 146.4% experimentally on average compared with the linear extensor, contributing to enrich force conditions during interactions. The third biomechanical advantage, i.e., fingertip force–velocity workspace can be augmented through the flexible tendon sheath, and grasping tests for a robotic hand designed with the aforementioned advantages are presented in Part II of this article.
The influence of the distribution of cosmic star formation at different metallicities on the properties of merging double compact objects

Binaries that merge within the local Universe originate from progenitor systems that formed at different times and in various environments. The efficiency of formation of double compact objects is highly sensitive to metallicity of the star formation. Therefore, to confront the theoretical predictions with observational limits resulting from gravitational waves observations one has to account for the formation and evolution of progenitor stars in the chemically evolving Universe. In particular, this requires knowledge of the distribution of cosmic star formation rate at different metallicities and times, probed by redshift (SFR(Z,z)). We investigate the effect of the assumed SFR(Z,z) on the properties of merging double compact objects, in particular on their merger rate densities. Using a set of binary evolution models from Chruslinska et al. (2018) we demonstrate that the reported tension between the merger rates of different types of double compact objects and current observational limits in some cases can be resolved if a SFR(Z,z) closer to that expected based on observations of local star-forming galaxies is used, without the need for changing the assumptions about the evolution of progenitor stars of different masses. This highlights the importance of finding tighter constraints on SFR(Z,z) and understanding the associated uncertainties.
A New Hate Speech Detection System based on Textual and Psychological Features

—Hate speech often spreads on social media and harms individuals and the community. Machine learning models have been proposed to detect hate speech in social media; however, several issues presently limit the performance of current approaches. One challenge is the issue of having diverse comprehensions of hate speech constructs which will lead to many speech categories and different interpretations. In addition, certain language-specific features, and short text issues, such as Twitter, exacerbate the problem. Moreover, current machine learning approaches lack universality due to small datasets and the adoption of a few features of hateful speech. This paper develops and builds new feature sets based on frequencies of textual tokens and psychological characteristics. Then, the study evaluates several machine learning methods over a large dataset. Results showed that the Random Forest and BERT methods are the most valuable for detecting hate speech content. Furthermore, the most dominant features that are helpful for hate speech detection methods combine psychological features and Term-Frequency Inverse Document-Frequency (TFIDF) features. Therefore, the proposed approach could identify hate speech on social media platforms like Twitter.
Social Physics: How Good Ideas Spread - the Lessons from a New Science

SOCIAL PHYSICS: HOW GOOD IDEAS SPREAD - THE LESSONS FROM A NEW SCIENCE Alex Pentland New York, Penguin Press, 2014,300 pp. ISBN: 978-1-59420-565-1This book is about "mathematizing" the social sciences. Here information and network science are used in the forms of analysis and big data models to develop the means of improving an understanding of society in order to create and operate better social and human systems. From a slightly different perspective, this book provides a new, more quantitative modeling framework for studying, understanding, and shaping society in order to make it better. The most cogent line in the book is the first line, which establishes the author's perspective: "I live in the future. " (p. vii)Based on that futuristic perspective, maintained by his position at Massachusetts Institute of Technology's Media Lab, Alex Pentland has laid out a viable, inspiring road map for society to follow into its future. One of Pentland's goals is to "get the language of social physics into general use, where it can provide much needed nuance to the traditional language of market competition and regulation", (p. x) He states that "the social physics that is emerging brings together branches of economics, sociology, and psychology, along with network, complexity, decision, and ecology sciences and fuses them together using big data", (p. 17) His other goal for this work is to help "build a society that is better at avoiding market crashes, ethnic and religious violence, political stalemates, widespread corruption, and dangerous concentration of power", (p. 17)Pentland is systematic and precise in his writing. He is careful to define his important terms in a chart. These terms include information, idea, idea flow, social influence, social learning, social norms, social pressure, society, strategy, trust, and value.This book is divided into four main parts: Social Physics, Idea Machines, Data-Driven Cities, and Data-Driven Society. Since I am interested in organization structure and process, I perused and enjoyed "Idea Machines" with its chapters on Collective Intelligence, Shaping Organizations, and Organizational Change. Pentland begins by introducing the ideas of collective intelligence and social engagement: "The collective intelligence of a community comes from idea flow; we learn from the ideas that surround us, and others learn from us. When the flow of ideas incorporates a constant stream of outside ideas as well, then the individuals in the community make better decisions than they could on their own. So even though today's society tends to glorify the individual, the vast majority of our decisions are shaped by common sense, the habits and beliefs we have in common with our peers. …
EFFECT OF VALUE ADDED TAX (VAT), INCOME, PRICE AND PAYMENT METHODS AGAINST CONSUMER DEMAND ON SMARTPHONES

This study aims to estimate the influence of Value Added Tax (VAT), Income, Price and Payment Method on Demand. The data used is primary data using questionnaire techniques with data obtained through google forms. Sampling was carried out using purposive sampling techniques. In this study, a sample of 213 respondents was used. The analysis method used is SEM analysis using the AMOS 23 application. The results showed that Value Added Tax (VAT) has a negative influence on demand, Income has no influence on demand, Price has a negative influence on demand and Payment Methods have a positive effect on demand.
Understanding the Processes of how Small and Medium Enterprises derive Value from Business Intelligence and Analytics

This paper provides an in-depth study of how small and medium enterprises (SMEs) use business intelligence and analytics (BI&A) to derive business value and why so many SMEs fail to do so. A qualitative research approach based on semi-structured interviews with five SMEs in Australia was applied with the goal is to understand the process in which SMEs adopt BI&A to derive business value. This involved exploring how owners and managers lead their employees in using data and analytical processes to derive insights to make business decisions. The findings suggest that SMEs which adopt BI&A use a short and simple six-step iterative BI&A process to derive insights for business process application. In addition to the short process, a longer three phase process has been identified which progresses SMEs from solving operational issues to strategic challenges. The resulting short and long BI&A implementation process framework provides a progressive pathway for SME owners and managers to initiate and lead BI&A transformation in their SMEs to derive greater business value. The process model considers dimensions of data, analysis, business process change, social influence, level of information use and financial returns.
The effect of base flow uncertainty on transitional channel flows

We study the effect of white-in-time additive stochastic base flow perturbations on the mean-square properties of the linearized Navier-Stokes equations. Such perturbations enter the linearized dynamics as multiplicative sources of uncertainty. We adopt an input-output approach to analyze the mean-square stability and frequency response of the flow subject to additive and multiplicative uncertainty. For transitional channel flows, we uncover the Reynolds number scaling of critical base flow variances and identify length scales that are most affected by base flow uncertainty. For small-amplitude perturbations, we adopt a perturbation analysis to efficiently compute the variance amplification of velocity fluctuations around the uncertain base state. Our results demonstrate the robust amplification of streamwise elongated flow structures in the presence of base flow uncertainty and that the wall-normal shape of base flow modulations can influence the amplification of various length scales.
Statistical, machine learning and deep learning forecasting methods: Comparisons and ways forward

Abstract The purpose of this paper is to test empirically the value currently added by Deep Learning (DL) approaches in time series forecasting by comparing the accuracy of some state-of-the-art DL methods with that of popular Machine Learning (ML) and statistical ones. The paper consists of three main parts. The first part summarizes the results of a past study that compared statistical with ML methods using a subset of the M3 data, extending however its results to include DL models, developed using the GluonTS toolkit. The second part widens the study by considering all M3 series and comparing the results obtained with that of other studies that have used the same data for evaluating new forecasting methods. We find that combinations of DL models perform better than most standard models, both statistical and ML, especially for the case of monthly series and long-term forecasts. However, these improvements come at the cost of significantly increased computational time. Finally, the third part describes the advantages and drawbacks of DL methods, discussing the implications of our findings to the practice of forecasting. We conclude the paper by discussing how the field of forecasting has evolved over time and proposing some directions for future research.
Stunting incidents in children in the working area of Puskesmas Moncongloe, Maros Regency

Monitoring of Nutritional Status reveals that the incidence of stunting (short) in children is a major nutritional problem faced by Indonesia. The prevalence of stunting children has increased from 27.5% in 2016 to 29.6% in 2017. The purpose of this study was to describe the incidence of stunting in children in the working area of the PUSKESMAS Moncongloe, Maros Regency. The type of this research was descriptive observational with a population of 147 stunting children. The sampling method used accidental sampling with the number of sample was 108 respondents. The results of this study showed that from 108 children (100.0%) there were 16 children (14.8%) who had low birth weight and 92 children (85.2%) had normal birth weight, there were 62 children (57.4%) who were not exclusively breastfed and 46 children (42.6%) are exclusively breastfed, there were 51 children (47.2%) with incomplete immunization status and 57 children (52.8 %) with complete immunization status, there were 44 children (40.7%) with stunting in the very short category and 63 children (59.3%) in the short category. The conclusion of this study is the incidence of stunting in children is related to low birth weight, exclusive breastfeeding and immunization status.
Do zoom meetings really help? A comparative analysis of synchronous and asynchronous online learning during Covid‐19 pandemic

Abstract Background Online learning and teaching were globally popularized due to the impact of Covid‐19. The pandemic has made both synchronous and asynchronous online learning inevitable in regions privileged with the technological affordance. Aims This study was designed to examine and compare the effectiveness of both learning modes through the Community of Inquiry framework. Materials & Methods Comparative analyses on a sample of N = 170 undergraduate students who took both synchronous and asynchronous online courses in Spring 2021. Results The paired‐sample T‐tests results indicated a significant difference in social presence, cognitive presence and self‐evaluated performance. Discussion & Conclusion Teaching presence significantly influenced social presence and cognitive presence in both learning modes. However, under synchronous learning mode, social presence significantly impacted self‐evaluation, grades and school identification. While social presence only influenced school identification under asynchronous learning mode. Theoretical and practical implications were also included.