Miracle-dz/UniFAR-FTUs huggingface dataset:
UI Screens Identification and Extraction from Mobile Programming Screencasts

Mobile applications demand is on the rise, leading to more programmers learning to develop or having to maintain this kind of programs. Developers often refer to online resources to find inspiration or answers to questions they have about mobile programming topics and screencasts are a popular resource. However, given the multitude of screencasts available, it can be difficult to quickly comprehend which of the many videos is relevant to one's needs. We propose a novel approach, called UIScreens, which detects, extracts, and presents the most representative user interface (UI) screens embedded in mobile development screencasts. This could help developers quickly comprehend what an app displayed in a video is about, therefore saving time searching for useful videos. UIScreens has been evaluated in two empirical studies on iOS and Android programming screencasts. The first study investigates the accuracy of our UI extraction and shows that our approach is able to detect and extract UI screens with an accuracy of 94%. The second is a user study with mobile app developers, who evaluated both the accuracy and the usefulness of UI Screens. They agreed that UIScreens is accurate and extracts representative UI screens from videos. They considered that the extracted UI screens are useful for understanding what a video is about and if it is relevant to a search. Our approach has been implemented as a free online tool.
Computational tools for understanding air pollution

Ambient fine particulate (PM2.5) is the most significant risk factor for premature death, shortening life expectancy at birth by 1.5 to 1.9 years [2]. 91% of the world's population lives in areas where air pollution exceeds safety limits1. 99% of the people in countries like India, Pakistan, Nepal, and Bangladesh experience ambient exposures of PM2.5 exceeding 75 μg/m3 to 100 μg/m3 [3]. My Ph.D. thesis will be on understanding the perception of air pollution among people using social media data. I also intend to develop a wearable air pollution exposure monitor and design an air pollution visualisation tool to reduce the entry barrier for air pollution research.
BugSum: Deep Context Understanding for Bug Report Summarization

During collaborative software development, bug reports are dynamically maintained and evolved as a part of a software project. For a historical bug report with complicated discussions, an accurate and concise summary can enable stakeholders to reduce the time effort perusing the entire content. Existing studies on bug report summarization, based on whether supervised or unsupervised techniques, are limited due to their lack of consideration of the redundant information and disapproved standpoints among developers. comments. Accordingly, in this paper, we propose a novel un-supervised approach based on deep learning network, called Bug-Sum. Our approach integrates an auto-encoder network for feature extraction with a novel metric (believability) to measure the degree to which a sentence is approved or disapproved within discussions. In addition, a dynamic selection strategy is employed to optimize the comprehensiveness of the auto-generated summary represented by limited words. Extensive experiments show that our approach outperforms 8 comparative approaches over two public datasets. In particular, the probability of adding controversial sentences that are clearly disapproved by other developers during the discussion, into the summary is reduced by up to 69.6%.
Formal Model-Based Assurance Cases in Isabelle/SACM : An Autonomous Underwater Vehicle Case Study

Isabelle/SACM is a tool for automated construction of model-based assurance cases with integrated formal methods, based on the Isabelle proof assistant. Assurance cases show how a system is safe to operate, through a human comprehensible argument demonstrating that the requirements are satisfied, using evidence of various provenances. They are usually required for certification of critical systems, often with evidence that originates from formal methods. Automating assurance cases increases rigour, and helps with maintenance and evolution. In this paper we apply Isabelle/SACM to a fragment of the assurance case for an autonomous underwater vehicle demonstrator. We encode the metric unit system (SI) in Isabelle, to allow modelling requirements and state spaces using physical units. We develop a behavioural model in the graphical RoboChart state machine language, embed the artifacts into Isabelle/SACM, and use it to demonstrate satisfaction of the requirements.
Facial expression based satisfaction index for empathic buildings

In this work, we examine the suitability of automatic facial expression recognition to be used for satisfaction analysis in an Empathic Building environment. We use machine learning based facial expression recognition on the working stations to integrate an online satisfaction index into Empathic Building platform. To analyze the suitability of facial expression recognition to reflect longer-term satisfaction, we examine the changes and trends in the happiness curves of our test users. We also correlate the happiness curve with temperature, humidity, and light intensity of the test users' local city (Tampere Finland). The results indicate that the proposed analysis indeed shows some trends that may be used for long-term satisfaction analysis in different kinds of intelligent buildings.
ActivityGAN: generative adversarial networks for data augmentation in sensor-based human activity recognition

Label Scarcity and Data Augmentation have long been challenging problems in the research of Human-oriented Artificial Intelligence. Following the trends of Deep Learning, Human Activity Recognition (HAR) tasks have been significantly optimized in the recent decade with handful of industrial applications on health evaluation and security monitoring. Nevertheless, data acquisition on human activities has been increasingly problematic considering the limited sensor resources and high cost of investment on labor of human volunteers. In this paper, we propose a pioneering unified architecture of convolutional generative adversarial networks, namely ActivityGAN, to effectively generate sensor-based data simulating human physical activities. This architecture comprises of a generation model which is a stack of one-dimensional convolution (1D-convolution) and transposed convolution (1D-transposed convolution) layers, and a discrimination model which employs two-dimensional convolution networks (2D-convolution) with reshaped input of time series. We train the proposed architecture on a collection of activity data and evaluate the generator's output, namely synthetic data, with three approaches of visualization. We then assess the usability of synthetic data by evaluating the test accuracy of models trained with mixed real and synthetic data or with synthetic data that substitutes real data. The study's results show that our proposed architecture is able to generate sufficient synthetic data which are distinguishable by visualization techniques and trainable for HAR machine learning models.
Generic POI recommendation

For avoiding excessive congestion of tourists that causes overtourism, we propose a Generic Point of Interest (POI), which is an alternative sightseeing spot potentially attractive enough for tourists to replace a well-known sightseeing spot. We also propose a method to discover generic POIs and evaluate it. While the rapid spread of social networking services (SNSs) and social media makes tourism more familiar to people, it is further aggravating overtoursim around the world due to the nature of SNSs and social media, where users simultaneously find the same posts or articles recommending specific tourist spots and are attracted to the same destinations at the same time. As overtourism has severe influences on both visitors and local residents, it is essential to solve this problem. Although there are many studies providing ways of recommending less crowded tourist spots or mining less-known spots in a famous sightseeing area, we cannot apply those methods as a fundamental solution for overtourism for two reasons: 1) in many cases, the number of tourists already exceeds the touring area's total capacity; and 2) many approaches relying on a number of user-generated data points cannot discover unbusy sightseeing spots since users hardly post reviews nor images. To address these challenges, we propose a novel concept of generic POIs, alternative sightseeing spots to famous spots, and we propose a method to discover generic POIs, whose images are similar to those of existing famous sightseeing spots. We also evaluate our method with collected examples of generic POIs. We hope that the proposed method will help alleviate the overtourism problem in the real world as a first step.
Mobile vocabulometer: a context-based learning mobile application to enhance English vocabulary acquisition

Vocabulary acquisition is the basis of learning a language, and using flashcards applications is a popular method for learners to memorize the meaning of unknown words. Unfortunately, this method alone is not effective for learners to remember the meaning of words when they appear in sentences. To solve this, we developed the Mobile Vocabulometer which allows users to acquire new vocabulary with context-based learning. Based on the correlation between comprehension and interests, we use the learning materials that adapt to users' interests and language skills. This system harnesses the power of the original Vocabulometer, and modifies it to be effective for mobile learning. An experiment on Japanese university students showed that, overall, learners achieved better results compared to using a simple flashcard application. This result indicates that this system provides a significant advantage over context-free learning systems.
Qualitative Research in Entrepreneurship Studies: A State-of-Science

Qualitative research is becoming increasingly popular in organisational studies. There is growing consensus that qualitative research contributes positively to the diversity of academic inquiry in entrepreneurship, advancing rich and novel insights about entrepreneurial phenomena. We conduct the first large-scale synthesis of the qualitative entrepreneurship research literature. Based on a sample of 460 articles published across 45 quality journals, we offer an in-depth analysis of the state of qualitative research in entrepreneurship and unearth specific patterns in common practices of published qualitative investigations. Our analysis reveals several potentially fruitful avenues for the continued application of qualitative approaches in entrepreneurship research (e.g. entrepreneur-opportunity nexus). Suggestions on why and how to use qualitative research are provided, and recommendations are offered to guide future qualitative studies to advance better understanding of entrepreneurial phenomena.
Exploring chatbot user interfaces for mood measurement: a study of validity and user experience

With the growth of interactive text or voice-enabled systems, such as intelligent personal assistants and chatbots, it is now possible to easily measure a user's mood using a conversation-based interaction instead of traditional questionnaires. However, it is still unclear if such mood measurements would be valid, akin to traditional measures, and user-engaging. Using smartphones, we compare in this paper two of the most popular traditional measures of mood: International PANAS-Short Form (I-PANAS-SF) and Affect Grid. For each of these measures, we then investigate the validity of mood measurement with a modified, chatbot-based user interface design. Our preliminary results suggest that some mood measures may not be resilient to modifications and that their alteration could lead to invalid, if not meaningless results. This exploratory paper then presents and discusses four voice-based mood tracker designs and summarizes user perception of and satisfaction with these tools.
A multi-view architecture for the SHL challenge

To recognize locomotion and transportation modes in a user-independent manner with an unknown target phone position, we (team Eagles) propose an approach based on two main steps: reduction of the impact of regular effects that stem from each phone position, followed by the recognition of the appropriate activity. The general architecture is composed of three groups of neural networks organized in the following order. The first group allows the recognition of the source, the second group allows the normalization of data to neutralize the impact of the source on the activity learning process, and the last group allows the recognition of the activity itself. We perform extensive experiments and the preliminary results encourage us to follow this direction, including the source learning to reduce the phone position's biases and activity separately.
Electron flow shift in Clostridium acetobutylicum fermentation by lactate

Clostridium acetobutylicum produced more butanol in the medium containing corn steep liquor (CSL) than in a complex medium without CSL Addition of CSL to CAB medium increased sugar consumption by the bacterium. Similar results were obtained in the fermentation using CAB medium containing lactate. The ratio for the butanol produced to acetone of the control culture was 1.8, whilst that of the culture containing 44 mM lactate was 5.2. From these results it is hypothesized that lactate functions as an electron flow modulator in the fermentation. This finding has been utilized for the successful butanol fermentation of a non-corn based agricultural byproduct, palm oil waste.
The exp-6 potential fluid at very high pressures: computer simulations and theory

The exp-6 potential fluids for four values of the softness parameter α, ranging from 11·5 to 14·5, have been investigated both by computer simulations and theory at supercritical temperatures and very high densities (pressures). Tables of the simulation data for pressure include altogether 141 thermodynamic state points generated at six temperatures for each α. In addition to computation of the thermodynamic properties, great attention has been paid to the structure, and an approximate location of the onset of freezing has been determined. To estimate the properties of the fluid from theory, a modified version of the Weeks–Chandler–Anderson (mWCA) theory and the optimized reference hypernetted chain (RHNC) theory were used. It is shown that for the thermodynamic properties mWCA theory performs very well under all stable fluid conditions considered, whereas RHNC theory, comparable with mWCA at the lower densities considered, gradually loses its accuracy in the very high density range. However, for structur.
Poly(ketomalonate) by catalytic oxidation of glycerol(4)anionic polymerization

The anionic polymerization of ketomalonic acid disodium salt via a ketone carbonyl group, initiated by the in situ generation of the carbanion of tartronic acid disodium salt, was observed in the preparation of tartronic acid disodium salt by catalytic oxidation of glycerol in a basic aqueous solution using a fixed-bed reactor packed with palladium based catalyst. Model reactions using authentic reagents of tartronic acid and ketomalonic acid demonstrated the anionic polymerization of ketomalonic acid disodium salt. © 1998 John Wiley & Sons, Inc. J Polym Sci A: Polym Chem 36: 195–205, 1998
Ecogeographical variation of 12 morphological traits within Pinus tabulaeformis: the effects of environmental factors and demographic histories

Aims More data are needed about how genetic variation (GV) and environmental factors influence phenotypic variation within the natural populations of long-lived species with broad geographic distributions. To fill this gap, we examined the correlations among environmental factors and phenotypic variation within and among 13 natural populations of Pinus tabulaeformis consisting of four demographically distinct groups within the entire distributional range. Methods Using the Akaike's Information Criterion (AIC) model, we measured 12 morphological traits and constructed alternative candidate models for the relationships between each morphological trait and key climatic variables and genetic groups. We then compared the AIC weight for each candidate model to identify the best approximating model for ecogeographical variation of P. tabulaeformis. The partitioning of variance was assessed subsequently by evaluating the independent variables of the selected best models using partial redundancy analysis. Important Findings Significant phenotypic variation of the morphological traits was observed both within individual populations and among populations. Variation partition analyses showed that most of the phenotypic variation was co-determined by both GV and climatic factors. GV accounted for the largest proportion of reproductive trait variation, whereas local key climatic factors (i.e. actual evapotranspiration, AET) accounted for the largest proportion of phenotypic variation in the remaining investigated traits. Our results indicate that both genetic divergence and key environmental factors affect the phenotypic variation observed among populations of this species, and that reproductive and vegetative traits adaptively respond differently with respect to local environmental conditions. This partitioning of factors can inform those making predictions about phenotypic variation in response to future changes in climatic conditions (particularly those affecting AET).
The Impact of surface perturbations on snow-slope stability

Measurements and observations by others indicate that a potential slab avalanche consists of a relatively cohesive slab of snow overlying a thin weak layer that coniains flaws where locally the shear stress from the overburden is not fully supported. Under favorable conditions, snow will shear strain-soften, which provides the basis for applying a slip-weakening model to examine the size of flaw needed to initiate sub-critical crack propagation along the weak layer. Using typical values for snow properties, the model predicts sub-critical crack growth can initiate from a relatively small flaw well before the shear stress from the overburden approaches the peak shear strength at tin-bed. The occurrence of small flaws or imperfections in the basal layer would explain field measurements which usually indicate that avalanching occurs before the applied shear stress exceeds the shear strength at the basal layer. Widespread slab-avalanche activity often increases significantly soon after the onset of rain on new snow. Measurements of temperature and mechanical properties show that only the upper 0.15 m or less of the slab has been altered at the time of avalanching; alterations at the sliding layer have not yet been detected. Results from the slip-weakening model indicate that the rain-induced alterations would reduce the size of flaw needed to initiate sub-critical crack growth by 10–20%. The observations and model results show clearly the importance of the slab properties; it is evident that both the slab and the weak layer act together to control slope stability. A further implication is that the stability of freshly deposited snow is often close to critical, because a relatively small surface perturbation is often sufficient to cause avalanching. This is not surprising, because it is well known from field observations that new snow on slopes should be treated with caution.
Prospects of genetic modified maize crop in Africa

Genetic modified maize crop increases annually as a result of food insecurity and limited land caused by rapid population increase of over seven billion in the world. Scientists have been playing their role to address this food insecurity problem. The use of genetically modified (GM) maize crop to feed people is one of the proposed ways, because it yields more compared to the conventional varieties. However, there are several contradictions which hinder the adoption of this new technology. Some studies have shown that GM maize is risky to human health, animals and not friendly to environmental conservation, which may lead to the death of other bio-diversities. Generally, other studies have supported the consumption of GM maize. However, after being approved by the scientist in the countries concerned, the GM maize varieties which seem to be hazardous to human health must be prohibited in research centres so as to avoid transportation to other countries. Regarding the new technology of GM maize, the conventional method of breeding is still important to keep maize seeds available in the gene bank. Therefore, researchers should consider this for further research issues on maize improvement. Key words: Bacillus thuringensis-maize, Bacillus thuringensis protein, conventional breeding, environment, food crisis, genetic modified maize, genetic modified organism, health risk, landraces.
Psychoacoustic consequences of compression in the peripheral auditory system

Input-output functions on the basilar membrane of the cochlea show a strong compressive nonlinearity at midrange levels for frequencies close to the characteristic frequency of a given place. This article shows how many different phenomena can be explained as consequences of this nonlinearity, including the "excess" masking produced when 2 nonsimultaneous maskers are combined, the nonlinear growth of forward masking with masker level, the influence of component phase on the effectiveness of complex forward maskers, changes in the ability to detect increments and decrements with level, temporal integration, and the influence of component phase and level on the perception of vowellike sounds. Cochlear hearing loss causes basilar-membrane responses to become more linear. This can account for loudness recruitment, linear additivity of nonsimultaneous masking, linear growth of forward masking, reduced temporal resolution for sounds with fluctuating envelopes, and reduced temporal integration.
Democratic Transition? The 1997 Mexican Elections

Mexico's 1997 midterm elections promise to democratize Mexico's autocratic and centralized presidentialist regime and thereby change Mexican politics profoundly. The electoral blow dealt the Institutional Revolutionary Party (Partido Revolucionario Institucional, or PRI) by voters on July 6 brings an elected mayor to the world's largest city, places more non-PRI governors into Mexican state houses assuring that demands for decentralization will intensify, and takes from President Ernesto Zedillo the congressional basis for the six-year dictatorship that the Mexican presidency has been since the 1930s. Surely the struggle for democracy in Mexico is not over, but a critical battle has been won by democrats of all ideological persuasions. Zedillo himself claimed, in an address to the nation made shortly after the preliminary electoral figures appeared on election night, to be pleased by the progress made by his nation, saying that the day had been a “great democratic fiesta” (Zedillo 1997, 7). Unlike most elections, the critical issues facing Mexican voters in 1997 had little to do with public policy. Although Mexico is a dozen years into a neoliberal economic restructuring program that has had great consequences for the distribution of income, for the number of Mexicans living in poverty, and for the penetration of foreign capital, goods, and services into the Mexican economy (see Otero 1996), neoliberalism was only peripherally an issue in this election. Both major parties of opposition campaigned on the issue of tax relief, specifically promising to lower Mexico's value added tax from 15% to 10% (Mayolo and Luna 1997, A1; Chacón Albarrán 1997).