Miracle-dz/UniFAR-FTUs huggingface dataset:
4. What Determinants Matter When Users Engage in Particular Collaborative Storytelling Websites? Exploring Attractive Gamification Features and Design Guidelines

: Gamification refers to the incorporation of game mechanics into non-game settings, which aims to increase user’s engagement with the product or service and facilitate certain behaviors. Gamification receives considerable attention for various social networking services and product design. Specifically, gamification is considered as the predictor of the user’s engagement with collaborative storytelling websites. Although previous studies have mentioned a broad range of factors that may influence gamification, they neither depicted the actual design features nor relative attractiveness among them. In line with this, the present aims to identify attractive gamification features for collaborative storytelling websites. We first constructed a hierarchical system structure of gamification design for collaborative storytelling websites and conducted a focus group interview with eighteen frequent users to identify 35 gamification features. Following this, the relative attractiveness of these gamification features was determined by administrating an online survey to 6333 collaborative storytelling websites users. The results indicated that the top 10 most attractive gamification features could account for more than 50% of attractiveness among these 35 gamification features. The feature of unpredictable time pressure is important to website users, yet was not revealed in previous studies. Based on these findings a number of design guidelines were created for gamification in collaborative storytelling websites.
An experimental study on vehicle lateral and yaw motion control

A vehicle lateral velocity and yaw rate control algorithm based on a front and rear independent steering system has been proposed. The authors describe a steering actuator control algorithm and practical aspects involved in implementation of the vehicle lateral velocity and yaw rate controller. An experimental vehicle prepared for the evaluation of the algorithm is introduced. An LQ regulator-based synchronized controller is adopted to control independently actuated right and left steering at the same angle. The vehicle lateral and yaw motion control performance, including the steering actuator control subsystem, is evaluated based on experiments with the vehicle. <<ETX>>
xD-track: leveraging multi-dimensional information for passive wi-fi tracking

We describe the design and implementation of xD-Track, the first practical Wi-Fi based device-free localization system that employs a simultaneous and joint estimation of time-of-flight, angle-of-arrival, angle-of-departure, and Doppler shift to fully characterize the wireless channel between a sender and receiver. Using this full characterization, xD-Track introduces novel methods to measure and isolate the signal path that reflects off a person of interest, allowing it to localize a human with just a single pair of access points, or a single client-access point pair. Searching the multiple dimensions to accomplish the above is highly computationally burdensome, so xD-Track introduces novel methods to prune computational requirements, making our approach suitable for real-time person tracking. We implement xD-Track on the WARP software-defined radio platform and evaluate in a cluttered office environment. Experiments tracking people moving indoors demonstrate a 230% angle-of-arrival accuracy improvement and a 98% end-to-end tracking accuracy improvement over the state of the art localization scheme SpotFi, adapted for device-free localization. The general platform we propose can be easily extended for other applications including gesture recognition and Wi-Fi imaging to significantly improve performance.
Gaming to Safety: The Design and Evaluation of Feedback Gamification for Mitigating Driver Distraction

This thesis examined the gamification of feedback systems for potential benefits in mitigating driver distraction and enhancing driving performance. Distraction is detrimental to traffic safety. Integrating driver feedback with elements of game design could effectively reduce distraction and motivate lasting behavioral changes. A simulator study was conducted with younger drivers (n = 42) comparing no feedback, a real-time feedback system, a post-drive feedback system (real-time feedback + post-drive feedback), and a gamification feedback system (real-time feedback + post-drive feedback + game design elements). No differences were observed between no feedback and the real-time feedback system. Comparing the gamification and post-drive feedback systems, the gamification system provided more benefits in the rate of manual interactions with an in-vehicle display and the post-drive system provided more benefits in the 95 percentile glance duration to the in-vehicle display. Despite these differences, both gamification and post-drive feedback systems were effective, short term countermeasures of
TeMoto: A Software Framework for Adaptive and Dependable Robotic Autonomy with Dynamic Resource Management

For widespread deployment of robots in challenging environments (fire fighting, search and rescue, planetary exploration, etc), the software of the robot must allow for reliability and adaptability. For many existing systems, an unexpected change in mission specification, component failures, or energy conservation requires downtime for adaption (redesign of mission logic, switching sensor data processing pipeline, etc.). This is because software and hardware components for robotic applications are commonly chosen or designed based on task requirements and integrated either directly in the source code or via system configuration scripts, such as ROS launch files, leading to a fixed monolithic design. As the necessity and extent of adaptive behaviors is not always known prior to deployment, the structure of a robot’s software needs to support it by design. In this paper, we propose TeMoto, a novel architecture for adaptive autonomous robots, and a ROS-based framework of openly available software tools that implement the TeMoto architecture. TeMoto is a developer tool which combines dynamic (run-time) task and resource management, encourages modular and scalable system design and is task and platform agnostic - TeMoto provides the foundation for an adaptive robotic system. The feasibility of the TeMoto framework is qualitatively assessed via experiments on single and multi-robot setups spanning common scenarios (teleoperation, autonomous surveillance, cargo delivery, etc.). TeMoto-based systems exhibit increased fault tolerance and dynamic reconfigurability of software and hardware resources, as well as up to a 47% reduction in power consumption compared to the non TeMoto-enabled reference setup.
Dinamički model naftnog bušnog sustava

Opisani su osnovni elementi naftnog busnog sustava, napravljen je sažet pregled znacajnijih matematickih modela trenja te matematicki modeliran mehanicki sustav za upravljanje brzinom prodiranja dlijeta. Pri busenju tla u potrazi za energentima kao i kod svakog drugog busenja, potrebno je istovremeno djelovati normalnom silom i torzijskim momentom na dlijeto da bi se ostvarilo njegovo prodiranje u tlo i rezanje. Normalna sila na dlijeto se ostvaruje djelovanjem vlastite težine sustava cijevi i dlijeta koji su ovjeseni na kuki koloturnog prijenosnika. Popustanjem užeta koloturnog prijenosnika može se utjecati na iznos normalne sile na dlijeto. Uže se popustanjem odmotava s bubnja, a zakretanjem bubnja se upravlja preko pojasne kocnice. Djelovanje pojasne kocnice stvara silu trenja za cije opisivanje mogu poslužiti razliciti matematicki modeli. O primijenjenom matematickom modelu trenja ovisi koliko detaljno ce biti simulirani iznos i posljedice djelovanja trenja. U radu je prikazan nacin dobivanja jednadžbi gibanja elemenata sustava koje su, kasnije, modeliranjem u racunalnom paketu Simulink primijenjene za simulacije gibanja elemenata sustava.
Gender and Age Differences in Preferences on Game Elements and Platforms

This paper analyzes different player type models and game elements in the literature, particularly focusing on the case of online games. Research based on an exploratory study is presented; it aims to explore the different types of interaction with gameful digital applications. The study is based on a survey and provides findings from the literature review and empirical insights about users’ differences and preferences regarding game elements. The results reveal demographics regarding player profiles and the relationships between gender, age, culture, and the influence of different game design elements and platforms. The main contribution of this study fulfills the need for knowledge about the relationship between game element design, platforms/devices, and players (types and preferences).
WEBSITE STRUCTURE IMPROVEMENT BY USING TAILORING METHOD

The www grows rapidly and increases the complexity of web applications and navigation of websites. This paper presents a widespread overview of web mining techniques and methods used for the estimation of reconciling systems to get better website navigation efficiency to enhance the efficiency of web site. The goal of this project is to make adaptive Web sites by developing the site structure to help user access. This technique consists of three steps: preprocessing, classification of page, and site reorganization. In particular, a mathematical programming model is used to enhance the navigation of user on a web while decreasing alterations to its present structure. Especially, two evaluation metrics are defined and used them to judge the performance of enhanced website using real data set. Specifically, the modules that include a Web personalization system is introduced, which gives focus on the Web usage mining. The proposed techniques are achieved superior web navigation very effectively and maintain efficiency and it is more efficient from existing system.
Evaluation on the Physical Condition of Football Extracurricular Participants before and during the COVID-19 Pandemic

Background: One of the most popular extracurricular activities at school is football. However, extracurricular activities have been temporarily suspended due to the COVID-19 outbreak, which on the other hand will affect students' physical abilities. The purpose of this study was to evaluate the physical condition of students which included speed, endurance, agility, before and during the COVID-19 pandemic. The participants were 15 male students aged 14-15 years with a weight of 51-71± kg and a height of 157-173± cm. Material and method: This study used a quantitative descriptive method with a survey approach. The instruments used include a multistage fitness test, a 30-meter sprint, and a shuttle run. Data were analyzed based on percentage data analysis and the Wilcoxon test. Result: The Wilcoxon test results on the speed test showed sig (2 tailed) 0.826> 0.05, endurance test 0.001 < 0.05, and agility test 0.033 > 0.05. If categorized, the overall percentage results on the physical condition of students have not been included in the "good" category. Conclusion: The findings of this study revealed that physical condition tests before and during the COVID-19 pandemic, both fall into the "poor" and "very poor" categories. Furthermore, tests carried out during the COVID-19 pandemic experienced a higher percentage decline compared to tests before the COVID-19 pandemic. The research results obtained can thus be used as material for evaluation and improvement for teachers, coaches, and extracurricular football students, especially training related to improving physical condition so that better results can be obtained in the future. © 2022 by authors, all rights reserved.
LiDAR- and Radar-Based Robust Vehicle Localization with Confidence Estimation of Matching Results

Localization is an important technology for autonomous driving. Map-matching using road surface pattern features gives accurate position estimation and has been used in autonomous driving tests on public roads. To provide highly safe autonomous driving, localization technology that is not affected by the environment is required. In particular, in snowy environments, the features of the road surface pattern may not be used for matching because the road surface is hidden. In such cases, it is necessary to construct a robust system by rejecting the matching results or making up for them with other sensors. On the other hand, millimeter-wave radar-based localization methods are not as accurate as LiDAR-based methods due to their ranging accuracy, but it has successfully achieved autonomous driving in snowy environments. Therefore, this paper proposes a localization method that combines LiDAR and millimeter-wave radar. We constructed a system that emphasizes LiDAR-based matching results during normal conditions when the road surface pattern is visible and emphasizes radar matching results when the road surface is not visible due to snow cover or other factors. This method achieves an accuracy that allows autonomous driving to continue regardless of normal or snowy conditions and more robust position estimation.
Analyzing Emotional Stability on Social Media Post using Machine Learning Approach

Depression or emotion is the most important concern of health organizations today. We consider the capability of influencing social media postings as a new type of mirror in understanding depression and also the type of personality in populations. Social network analysis is the study of a group of people and the relationships that exist between them. It has become so important in our lives that if I want to know anything about a stranger, I can find out with the help of social media websites. The arrival of various social media networking sites has helped everyone to easily express and share their opinions and feelings about anything with millions of people around the world. Social media is a valuable resource for identifying an individual's personality traits based on their posts, comments, or activities on social media. The proposed methodology, we have developed the application of a web extension to connect with social media networks to extract the post by the individual person. Extracted post has been used to identify the emotional stability of a person. NLP and Machine Learning algorithms are used to classify individual emotional stability as stable, depressed, or tending towards depression. According to our study, significant feature selections and their combinations were considered. Hence it improves the performance and accuracy of classification.
Some concrete steps for journal editorial boards: A commentary on Kidd and Garcia (2022)

As members of the editorial team at Language Acquisition, we read Kidd and Garcia’s target article with enthusiasm. In our commentary, we outline some specific ideas for how journals can help to alleviate the issues raised by Kidd and Garcia, some of which are in progress or in place at Language Acquisition, and some of which we hope to undertake alongside other journals in the field. We focus on concrete steps, acknowledging that systemic changes, too, will be required to ensure that our science reflects the diversity of languages learned.
Enhanced Photocatalysis of Black TiO2/Graphene Composites Synthesized by a Facile Sol–Gel Method Combined with Hydrogenation Process

In this study, in situ TiO2 was grown on the surface of graphene by a facile sol–gel method to form black TiO2/graphene composites with highly improved photocatalytic activity. The combination of graphene and TiO2 was beneficial to eliminate the recombination of photogenerated electron holes. The self-doping Ti3+ was introduced, accompanied by the crystallization of amorphous TiO2, during the hydrogenation process. Consequently, the narrowed bandgap caused by self-doping Ti3+ enhanced the visible light absorption and thus made the composites appear black. Both of them improved the photocatalytic performance of the synthesized black TiO2/graphene composites. The band structure of the composite was analyzed by valence band XPS, revealing the reason for the high visible light catalytic performance of the composite. The results proved that the black TiO2/graphene composites synthesized show attractive potential for applications in environmental and energy issues.
On Generalist and Domain-Specific Music Classification Models and Their Impacts on Brazilian Music Genre Recognition

Deep learning has become the standard procedure to deal with Music Information Retrieval problems. This category of machine learning algorithms has achieved state-of-the-art results in several tasks, such as classification and auto-tagging. However, obtaining a good-performing model requires a significant amount of data. At the same time, most of the music datasets available lack cultural diversity. Therefore, the performance of the currently most used pre-trained models on underrepresented music genres is unknown. If music models follow the same direction that language models in Natural Language Processing, they should have poorer performance on music styles that are not present in the data used to train them. To verify this assumption, we use a well-known music model designed for auto-tagging in the task of genre recognition. We trained this model from scratch using a large general-domain dataset and two subsets specifying different domains. We empirically show that models trained on specific-domain data perform better than generalist models to classify music in the same domain, even trained with a smaller dataset. This outcome is distinctly observed in the subset that mainly contains Brazilian music, including several usually underrepresented genres.
Continuous Bioleaching of Arsenic-Containing Copper-Zinc Concentrate and Shift of Microbial Population under Various Conditions

The goal of this work was to study the bioleaching of arsenic-containing polymetallic concentrate that contained 6.2% Cu, 7.3% Zn and 1.7% As, depending on different temperatures and in the presence of CO2 and molasses in the medium, as well as the difference in the composition of microbial population formed under various conditions. A mixed population of moderately thermophilic and thermotolerant acidophilic microorganisms formed during the continuous bioleaching of copper concentrate was used as an inoculum. The experiments were carried out in a continuous mode in laboratory scale reactors, with a temperature range of 40 °C to 60 °C. To assess the effect of CO2 and molasses on metal leaching and microbial population composition, the experiments were carried out in three reactors: CO2 (~0.01 L/min) was supplied into the first reactor; 0.02% molasses were added to the pulp of the second reactor; and no additional carbon sources were supplied into the control reactor. The highest copper recovery (27%) was achieved at 50°C in the experiment with molasses, while the highest zinc recovery (82.1%) was reached at 45°C in the control experiment. Additional carbon sources affected the extraction of non-ferrous metals only at 60 °C and increased the extraction of copper and zinc by 12.6% and 24.2%, respectively. Both the temperature and carbon source used affected the microbial population composition. The main microbial genera revealed in the populations by next generation sequencing (NGS) were bacteria of the genera Sulfobacillus and Acidithiobacillus, as well as archaea of the genera Ferroplasma, Acidiplasma, and Cuniculiplasma. At low temperatures (40 and 45 °C), Acidithiobacillus, Sulfobacillus, and Ferroplasma predominated, while at temperatures 50–55 °C, the decrease in relative abundance of these genera occurred, and the predominance of Acidiplasma archaea was observed. The usage of both CO2 and molasses led to the increase in Sulfobacillus and Acidiplasma in relative abundance.
Dynamic Performance Comparison for MPPT-PV Systems using Hybrid Pspice/Matlab Simulation

Abstract The power generated by solar photovoltaic (PV) module depends on the surrounding irradiance and temperature. This paper presents a hybrid Matlab™/Pspice™ simulation model of PV system, combined with Cadence software SLPS. The hybridization is performed in order to gain the advantages of both simulation tools such as accuracy and efficiency in both Pspice electronic circuit and Matlab™ mathematical modelling respectively. For this purpose, the PV panel and the boost converter are developed using Pspice™ and hybridized with the mathematical Matlab™ model of maximum power point method controller (MPPT) through SLPS. The main objective is verify the significance of using the proposed hybrid simulation techniques in comparing the different MPPT algorithms such as the perturbation and observation (P&O), incremental of conductance (Inc-Cond) and counter reaction voltage using pilot cell (Pilot-Cell). Various simulations are performed under different atmospheric conditions in order to evaluate the dynamic behaviour for the system under study in terms of stability, efficiency and rapidity.
Geostatistical Evaluation of Fracture Frequency and Crushing

This work details how to estimate the Fracture Frequency (FF), ratio of a number of fractures divided by a sample length. The difficulty is that often, a part of the sample cannot be analyzed by the geologist because it is crushed, a characteristics of the rock strength that must also be considered for the Rock Mass Rating. After analyzing the usual practices, the paper describes the (geo)statistical link between fracturing and crushing and the resulting method to obtain a unbiased estimate of FF at a block or point support scale. Some concepts are introduced: "True" FF, "Crushed" FF, crushing probability and crushing proportion. The study is based on a real data set containing more than 13,000 samples. An appendix gives a very general formal demonstration on how to obtain an unbiased ratio estimation.
Impact on Poly(Vinyl chloride) of trimethoprim schiff bases as stabilizers

Poly(vinyl chloride) PVC materials are used in many industrial and household outdoor applications which exposed to sunlight ultraviolet. This exposure motivates photo-degradation of the PVC and hence, reduces its life time and leads to undesirable physical properties. Herein, three new Schiff bases containing a trimethoprim moiety were investigated as photo-stabilizers for poly(vinyl chloride) against photo-decomposition. Merely 0.5% by weight of these Schiff bases was mixed with the polymer to form blend films with thickness of (40 µm). The films were irradiated for a period of time from 0 to 300 h and the irradiation impact on the PVC stabilization was monitored every 50 h. The efficiency of these additives as photo-stabilizers was investigated as a function of different parameters during the course of irradiation, such as weight loss, growing carbonyl and alkene groups as well as the surface morphology of the prepared films. The changes during irradiation were obvious for the blank films in comparison to the films containing the new additives. Therefore, these Schiff bases were found to act as photo-degradation inhibitors through reducing the elimination of hydrogen chloride molecules and as free radical scavengers or peroxide decomposers.
A quantitative survivability test method in the large scale network based on SD

Survivability is a necessary property of network system in disturbed environment, and recovery ability plays an important role in survivability. This paper presents a novel survivability metric-Network Recovery Time. A concept of Source-Destination Pair, i.e. SD Pair, is created to measure this metric in quantity, and a test method based on SD Pair for calculating the Network Recovery Time is provided. In the method how to select SD Pairs to satisfy the test requirement in the large scale network is also discussed in detail. Analysis and experiment shows that the method can measure the Network Recovery Time by calculating the SD Recovery Time, and can be used to test and evaluate quantitative survivability of different strategies in the large scale network.