Miracle-dz/UniFAR-FTUs huggingface dataset:
A smile – the key to everybody’s heart?

This paper aims to investigate how to improve the effectiveness of charitable advertising by matching emotional appeal (happy-faced vs sad-faced beneficiary) and message framing (recognition vs request) within advertising messages. ,Two experiments examining real donation allocations were conducted. Study 1 established the “match-up” effect between advertising image and message. Study 2 replicated the findings of Study 1 as well as testing the mediating role of hope and sympathy. ,The authors provide empirical evidence that consumers allocate a greater donation amount to a charity when they see an image of a sad-faced child combined with a request message (e.g. “please donate”), or an image of a happy-faced child combined with a recognition message (e.g. “thank you”). Notably, these effects are mediated by the emotions of hope and sympathy, respectively. ,This research highlights the importance of matching images of beneficiaries with the appropriate advertising copy. Depending on whether a charity seeks to position itself in a positive perspective to evoke hope, or alternatively, portray itself in relation to a sadder landscape that elicits sympathy, the respective choice of recognition or request messages can help boost donation outcomes. ,Charities and non-profit organizations can develop more effective charitable advertising by purposively matching specific emotional appeals and message framings when designing advertisements. ,The research illustrates a novel mechanism that shows when and how combining image and message can influence the effectiveness of charitable advertising.
Cyber Physical Systems: The Role of Machine Learning and Cyber Security in Present and Future

Today’s technology are changing very rapidly and making an important place in the heart of humans. For example, when internet connected things after connect with other devices, they make a big system which solve complex problems and make people life easier and longer to live. One of biggest/ popular revolution of technology is “Cyber Physical System”. Cyber Physical systems (CPSs) are the systems/ mechanism which are controlled and monitored remotely. CPSs system work with connecting other systems through Internet and its users (by computer-based algorithms). In near future in Industry 4.0 revolution, the uses of cyber physical systems will be on top position. Using such systems give invitation to attackers for performing attacks. But, an attacker can harm a physical and cyber space easily, i.e., affecting physical processes will affect computations (also efficiency of CPS systems). For example, nuclear facilities were affected in 2010 (in Iran) by poplar attack “Stuxnet”. Also, an attacker can control a medical device remotely which will be affected the condition of patients (also will leak private information of patient/ hospital employee). Such serious concerns are being highlighted in this article using machine learning and cyber security frequently (time to time) or an alternative schedule. Maintaining such system is really a difficult task, so if do we can save a lot of energies and unwanted attacks, etc., in overall. Hence, this article is being written with considering future serious concerns and challenges with CPSs, and listing out of such critical issues one by one (in detail).
Dispersive Quantization of the Linear Schrödinger Equation

The study of linear dispersive partial differential equations with piecewise constant periodic initial data leads to quantized structures at times which are rational multiples of π and fractal profiles at irrational multiples of π. We will present an overview of these results for the Schrodinger equation on the torus. §Introduction The purpose of this paper is to study the behavior of the solution to the Schrödinger wave equation with specific initial conditions. First, we go over the methods to analyze the linearly dispersive wave equation established in [6]. Next, we proceed to carry out the same analysis for the Schrödinger wave equation similar to [4] and write programs in MATLAB to compare two different methods for calculating solutions. We also present an overview of the Fast Fourier Algorithim(FFT) and plot the error involved in the solution to the Schrödinger wave equation. §1 Linearly dispersive wave equation The third order linearly dispersive wave equation is an initial-boundary value problem on the interval 0 ≤ x ≤ 2π given by ∂u ∂t = ∂3u ∂x3 (1) with initial condition, u(t, 0) = f(x), (2) and boundary conditions, u(t, 0) = u(t, 2π), (3)
Radka Picková Value-at-Risk estimation – non standard approaches

The topic of the presented work is Value-at-Risk (VaR) and its estimation. VaR is a financial risk measure and is defined as a quantile of the distribution of future returns, resp. losses. There exist various methods based on different assumptions how to estimate VaR. The most commonly used methods usually assume that the returns, resp. losses, are independently and identically distributed, especially that they are normally distributed. Since this assumption is not satisfied for most daily financial data, many alternative approaches have been suggested to estimate VaR. In the presented work two of them are discussed in detail, the CAViaR method and its asymptotic properties and the method of filtered historical simulation. One part of the work are numerical experiments with real data.
I-MOVE: Independent Moving Objects for Velocity Estimation

We introduce I-MOVE, the first publicly available RGBD/stereo dataset for estimating velocities of independently moving objects. Velocity estimation given RGB-D data is an unsolved problem. The I-MOVE dataset provides an opportunity for generalizable velocity estimation models to be created and have their performance be accurately and fairly measured. The dataset features various outdoor and indoor scenes of single and multiple moving objects. Compared to other datasets, I-MOVE is unique because the RGB-D data and speed for each object are supplied for a variety of different settings/environments, objects, and motions. The dataset includes training and test sequences captured from four different depth camera views and three 4Kstereo setups. The data are also time-synchronized with three Doppler radars to provide the magnitude of velocity ground truth. The I-MOVE dataset includes complex scenes from moving pedestrians via walking and biking to multiple rolling objects, all captured with the seven cameras, providing over 500 Depth/Stereo videos. To access the dataset please visit www.vast. uccs.edu/imove
Assessing carbon stocks and accumulation potential of mature forests and larger trees in U.S. federal lands

Mature and old-growth forests (collectively “mature”) and larger trees are important carbon sinks that are declining worldwide. Information on the carbon value of mature forests and larger trees in the United States has policy relevance for complying with President Joe Biden’s Executive Order 14072 directing federal agencies to define and conduct an inventory of them for conservation purposes. Specific metrics related to maturity can help land managers define and maintain present and future carbon stocks at the tree and forest stand level, while making an important contribution to the nation’s goal of net-zero greenhouse gas emissions by 2050. We present a systematic method to define and assess the status of mature forests and larger trees on federal lands in the United States that if protected from logging could maintain substantial carbon stocks and accumulation potential, along with myriad climate and ecological co-benefits. We based the onset of forest maturity on the age at which a forest stand achieves peak net primary productivity. We based our definition of larger trees on the median tree diameter associated with the tree age that defines the beginning of stand maturity to provide a practical way for managers to identify larger trees that could be protected in different forest ecosystems. The average age of peak net primary productivity ranged from 35 to 75 years, with some specific forest types extending this range. Typical diameter thresholds that separate smaller from larger trees ranged from 4 to 18 inches (10–46 cm) among individual forest types, with larger diameter thresholds found in the Western forests. In assessing these maturity metrics, we found that the unprotected carbon stock in larger trees in mature stands ranged from 36 to 68% of the total carbon in all trees in a representative selection of 11 National Forests. The unprotected annual carbon accumulation in live above-ground biomass of larger trees in mature stands ranged from 12 to 60% of the total accumulation in all trees. The potential impact of avoiding emissions from harvesting large trees in mature forests is thus significant and would require a policy shift to include protection of carbon stocks and future carbon accumulation as an additional land management objective on federal forest lands.
Impact of digital transformation on renewable energy companies’ performance: Evidence from China

The rapid growth of the renewable energy industry provides essential opportunities for China to achieve the goal of carbon peaking and carbon neutrality. A rising number of renewable energy companies are positively embracing digital transformation in the digital age. However, the relationship between digital transformation and the performance of renewable energy companies remains unclear. To fill this gap, leveraging the latest advances in textual analysis, we quantify the extent of a renewable energy enterprise’s digital transformation. Meanwhile, based on fixed effect model and mediating effect model, we investigate the influence of digital transformation on firm performance using a panel data of Chinese A-share listed renewable energy companies. The results indicate that digital transformation enhances a renewable energy enterprise’s performance. Further, the promotion effect of digital transformation is greater among state-owned enterprises and large firms and is only helpful for firms in the eastern area. Moreover, we document that when a renewable energy enterprise adopts digital transformation, it has higher operating efficiency, lower cost, and better innovation success resulting in better performance. This research elucidates the role of digital transformation in forwarding the development of renewable energy companies and bears significant policy implications.
Application of principal component and hierarchical cluster analyses in the classification of Serbian bottled waters and a comparison with waters from some other European countries

The contents of major ions in bottled waters were analyzed by principal component (PCA) and hierarchical cluster (HCA) analysis in order to investigate if these techniques could provide the information necessary for classifications of the water brands marketed in Serbia. Data on the contents of Ca2+, Mg2+, Na+, K+, Cl-, SO4, HCO3 and total dissolved solids (TDS) of 33 bottled waters was used as the input data set. The waters were separated into three main clusters according to their levels of TDS, Na+ and HCO3; sub-clustering revealed a group of soft waters with the lowest total hardness. Based on the determined chemical parameters, the Serbian waters were further compared with available literature data on bottled waters from some other European countries. To the best of our knowledge, this is the first report applying chemometric classification of bottled waters from different European countries, thereby representing a unique attempt in contrast to previous studies reporting the results primarily on a country-to-country scale. The diverse character of Serbian bottled waters was demonstrated as well as the usefulness of PCA and HCA in the fast classification of the water brands based on their main chemical parameters.
The role of computational and subjective features in emotional body expressions

Humans are experts at recognizing intent and emotion from other people’s body movements; however, the underlying mechanisms are poorly understood. Here, we computed quantitative features of body posture and kinematics and acquired behavioural ratings of these feature descriptors to investigate their role in affective whole-body movement perception. Representational similarity analyses and classification regression trees were used to investigate the relation of emotional categories to both the computed features and behavioural ratings. Overall, postural rather than kinematic features discriminated better between emotional movements for the computed as well as for the behavioural features. In particular, limb angles and symmetry appeared to be the most relevant ones. This was observed independently of whether or not the time-related information was preserved in the computed features. Interestingly, the behavioural ratings showed a clearer distinction between affective movements than the computed counterparts. Finally, the perceived directionality of the movement (i.e. towards or away from the observer) was found to be critical for the recognition of fear and anger.
Computational thinking and assignment resubmission predict persistence in a computer science MOOC

Massive open online course (MOOC) studies have shown that precourse skills (such as precomputational thinking) and course engagement measures (such as making multiple submission attempts with assignments when the initial submission is incorrect) predict students' grade performance, yet little is known about whether these factors predict students' course retention. In applying survival analysis to a sample of more than 20,000 participants from one popular computer science MOOC, we found that students' precomputational thinking skills and their perseverance in assignment submission strongly predict their persistence in the MOOC. Moreover, we discovered that precomputational thinking skills, programming experience, and gender, which were previously considered to be constant predictors of students' retention, have effects that attenuate over the course milestones. This finding suggests that MOOC educators should take a growth perspective towards students' persistence: As students overcome the initial hurdles, their resilience grows stronger.
Panic Disorder without Agoraphobia

P anic disorder is a chronic and recurrent illness associated with significant functional impairment. The estimated lifetime prevalence of panic attacks is 15%, with a 1-year prevalence of 7.3% (118); however, the prevalence of PD is somewhat lower, at 4.7% (lifetime) and 2.7% (1-year) (2,3). It is estimated that about one-third to one-half of patients with PD also have symptoms of agoraphobia (118). In a Canadian study conducted in 2002, 1.5% of adults had current PD, and 2.1% had a history of the disorder (119). PD and agoraphobia are more common in women than in men (118,120) and generally begin in late adolescence or early adulthood (51,119).
AOT-Effects of Group Support Size on Reflective E-Portfolio Development to Enhance Career Decision Self-Efficacy of Upper Secondary School Students in Thailand: Activity Theory Perspective

The term Social Group refers to groups made up of at least two people, who work together in an activity system. These people will also share the same objectives and common behaviors, which are under the rules defined by those groups. Activity theory as a social psychological perspective is used to state the supportive cycle in interpersonal relationships. These relationships occur as the group where the communication of its members is influenced by the structure of said group. A reflective e-portfolio can be used as a tool that looks to develop both oneself, and the achievements of the group. Revealed in this study, are the effects on self-efficacy skills in career decision making when using a reflective e-portfolio in a large and small social support groups. Students from grade 11, and from various parts of Thailand, were selected as the sample group. These students also attended the summer camp in Chulalongkorn University, Bangkok. The total size of the group was 80 students, and they were divided into a control (large group), and an experimental group (small group) equally through the method of purposive sampling. The data was then statistically analyzed by mean, standard deviation, and t-test. It was found in this study that the selfefficacy skills in career decision making in a small social support group was significantly higher than in a large group, at the significance level of . 05 (p=.02) M= . 02. However, the data have not found a significant difference at the level of . 05, in a large social support group.
INTEGRATING BUSINESS AND SOCIETY: THE LINK BETWEEN CSR AND COMPETITIVE ADVANTAGE

În această lucrare, propunem o nouă modalitate de a vedea relaţia dintre afaceri şi societate, succesul companiei şi prosperitatea socială nu este un joc cu sumă nulă. Integrarea afacerii cu nevoile sociale necesită mai mult decât bune intenţii şi o conducere puternică, ci adaptări organizaţionale. Companiile trebuie să-şi schimbe poziţia de la o postură fragmentată şi defensivă la o abordare afirmativă şi integrată. Datele empirice au fost colectate de la un eşantion compus din firme româneşti din industrie folosind un chestionar. Se ştie că firmele nu sunt responsabile pentru toate problemele cu care se confruntă lumea, nici nu au resursele necesare pentru a le putea rezolva pe toate.
The HJ-biplot visualization of the Singular Spectrum Analysis method

Time series data usually emerge in many scientific domains. The extraction of essential characteristics of this type of data is crucial to characterize the time series and produce, for example, forecasts. In this work, we take advantage of the trajectory matrix constructed in the Singular Spectrum Analysis, as well as of its decomposition through the Principal Component Analysis via Partial Least Squares, to implement a graphical display employing the Biplot method. In these graphs, one can visualize and identify patterns in time series from the simultaneous representation of both rows and columns of such decomposed matrices. The interpretation of various features of the proposed biplot is discussed from a real-world data set.
EFFECT OF CLOUDS ON SO2 CHEMISTRY: A COMBINATION OF SATELLITE MEASUREMENTS AND TRAJECTORY MODELING

The Kilauea volcano (Hawaii), currently one of the most active volcanos on Earth, has been continuously erupting since the beginning of 1983. A pronounced degassing phase in MarchNovember 2008 caused the formation of an extensive SO2 plume, which in turn led to the formation of sulfate aerosols. The steady trade winds and lack of interfering sources previously allowed us to determine the lifetime of SO2 from satellite-based measurements. Additionally, indications were found that SO2 is oxidized more rapidly in the presence of clouds. The current investigation was aimed at quantifying the effect of clouds on the SO2 oxidation rate. Trajectory calculations using the HYSPLIT model were combined with hourly GOES cloud data to reconstruct a “cloud history” of OMI pixels displaying enhanced levels of SO2. The SO2 data were subsequently sorted by the amount of clouds encountered since emission from the volcano. Thus obtained mean maps of SO2 data with no cloud encounters were compared to maps of SO2 data affected by clouds and significant differences were found which could, however, not be quantified.
Second Language Tutoring using Social Robots

—Many human-robot interaction systems involve a third component: a tablet, which can either be separate or integrated in the robot (as is the case in Softbank’s Pepper robot). Such a tablet can be used, for instance, to present information to the human user or to gain control over the robot’s complex surroundings, by introducing a virtual environment as a substitute for interactions that would normally happen in the physical world. While such a tablet can potentially have a big impact on the usability of the entire system and affect the interaction between human and robot, they are often not explicitly included when evaluating the user experience of human-robot interaction. This paper describes a case study where three evaluation methods were combined in order to get a comprehensive overview of the user experience of an Intelligent Tutoring System (ITS), consisting of a robot and a tablet. The results show several major usability issues with the virtual environment, which could have affected the experience of interacting with the robot. This underlines the importance of including not only the robot itself, but also any other interaction mediators in an iterative design process.
A Comprehensive Study of Climbing and Walking Robots (CLWAR) Paradigms

There are quite a number of naturally dangerous, expensive and hostile practices that are inimical and hazardous to the general health of human beings. Typical examples of such practices include emergence rescue mission, mineral exploration, planetary exploration, scaffolding, construction, painting of high rise buildings, surveillance as well as reconnaissance in urban environments. The use of human labor in these activities poses a high risk of accident which may result in fatalities and even death. One of the promising solutions to this problem is the use of climbing and walking robots (CLWAR). A CLWAR is typically defined as a mobile robot that possesses manipulative, perceptive, communicative and cognitive features which enable it to perform in diverse environments such as medicine, transportation, engineering as well as Information and Communication Technology (ICT). A CLWAR has two basic characteristics. These include locomotion and adhesion. With respect to locomotion, a CLWAR can be legged, winged, wheeled, tracked, crawling or hybrid. Adhesion refers to the ability of the robot to attach itself to surfaces such as walls, floors, glasses and ceilings. This is usually done by using adhesive mechanisms such as suction force, magnetic force, ropes, grippers and van der Waals forces. Nevertheless, the use of CLWAR is limited because their performances are usually unsatisfactory. This is because they are still bedeviled by locomotion and adhesion challenges. Hence, this paper comprehensively examines the typical examples of CLWAR, their applications in diverse domains as well as their challenges. This paper also considers the biologically inspired principles of locomotion and adhesion in CLWAR. The paper recommends that the environment, structure of the robot and the type of tasks to be performed by the robot are some of the factors to be considered during the design of a CLWAR.