Miracle-dz/UniFAR-FTUs huggingface dataset:
Q-learning strategies for action selection in the TESTAR automated testing tool

TESTAR is an open source tool for automated software testing that generates test sequences on the fly based only on information derived from the Graphical User Interface (GUI). At the core of TESTAR is the way to automatically select which actions to test; finding the right algorithm to carry out this task can make significant differences to the testing outcome. In this work we evaluate Q-learning as a metaheuristic for action selection and carry out experiments with a range of paramenters, using random selection as a baseline for the comparison. Two applications are used as Software Undder Test (SUT) in the experiments, namely MS Powerpoint (a proprietary desktop application) and the Odoo enterprise management system (an open source web-based application). We introduce metrics to evaluate the performance of the testing with TESTAR, which are valid even under the assumption that access to the source code is not available and testing is only possible via the GUI. These metrics are used to perform statistical analysis, showing that the superiority of action selection by Q-learning can only be achieved through an adequate choice of parameters. Mots-Clefs. Automated GUI Testing,Testing Metrics, Testing Web Applications, Q-learning
Local and remote mean and extreme temperature response to regional aerosol emissions reductions

Abstract. The climatic implications of regional aerosol and precursor emissions reductions implemented to protect human health are poorly understood. We investigate the mean and extreme temperature response to regional changes in aerosol emissions using three coupled chemistry–climate models: NOAA GFDL CM3, NCAR CESM1, and NASA GISS-E2. Our approach contrasts a long present-day control simulation from each model (up to 400 years with perpetual year 2000 or 2005 emissions) with 14 individual aerosol emissions perturbation simulations (160–240 years each). We perturb emissions of sulfur dioxide (SO2) and/or carbonaceous aerosol within six world regions and assess the statistical significance of mean and extreme temperature responses relative to internal variability determined by the control simulation and across the models. In all models, the global mean surface temperature response (perturbation minus control) to SO2 and/or carbonaceous aerosol is mostly positive (warming) and statistically significant and ranges from +0.17 K (Europe SO2) to −0.06 K (US BC). The warming response to SO2 reductions is strongest in the US and Europe perturbation simulations, both globally and regionally, with Arctic warming up to 1 K due to a removal of European anthropogenic SO2 emissions alone; however, even emissions from regions remote to the Arctic, such as SO2 from India, significantly warm the Arctic by up to 0.5 K. Arctic warming is the most robust response across each model and several aerosol emissions perturbations. The temperature response in the Northern Hemisphere midlatitudes is most sensitive to emissions perturbations within that region. In the tropics, however, the temperature response to emissions perturbations is roughly the same in magnitude as emissions perturbations either within or outside of the tropics. We find that climate sensitivity to regional aerosol perturbations ranges from 0.5 to 1.0 K (W m−2)−1 depending on the region and aerosol composition and is larger than the climate sensitivity to a doubling of CO2 in two of three models. We update previous estimates of regional temperature potential (RTP), a metric for estimating the regional temperature responses to a regional emissions perturbation that can facilitate assessment of climate impacts with integrated assessment models without requiring computationally demanding coupled climate model simulations. These calculations indicate a robust regional response to aerosol forcing within the Northern Hemisphere midlatitudes, regardless of where the aerosol forcing is located longitudinally. We show that regional aerosol perturbations can significantly increase extreme temperatures on the regional scale. Except in the Arctic in the summer, extreme temperature responses largely mirror mean temperature responses to regional aerosol perturbations through a shift of the temperature distributions and are mostly dominated by local rather than remote aerosol forcing.
A PROSPECTIVE STUDY ON THE BIDIRECTIONAL ASSOCIATION BETWEEN PARENTING STYLES AND SELF- REGULATION AMONG UNIVERSITY STUDENTS

The objective of this study is to identify the bidirectional relationship between parenting style and selfregulation among Filipino university studens. The relationship was assessed using the Scale of Parenting Style and Self-Regulation Questionnaire. Statistical results showed that many of the freshmen (48%) and senior (44%) university students have a negligent style for self regulation. It also showed that there is negligible correlation between the parenting style and the self-regulation of senior and freshmen university students (p < .05). It was revealed that freshmen students have poorer self-control, and this is not just due to having uninvolved parents; other factors may play a role. Senior students of the said university today are more controlled with their emotions and behaviours despite having negligent parents, most probably due to other factors such as self-determination. Selfregulation level may be high or low regardless of what type of parenting style the children’s parents may have, because factors, such as self-determination, exist. Recommendations for further studies could be conducted with regard to the other factors for influencing self-regulation, such as assessment of the coping mechanism used by the children who have negligent parents. Other instruments could be used to measure students’ selfregulation to measure the consistency of the results of the present study.
Classifiers Fusion for Arabic Named Entity Recognition

: This paper presents a new approach to Arabic Name Entity Recognition (ANER). The introduced approach uses different sets of features that are both language independent and language specific in a discriminative and generative machine learning frameworks namely, conditional random fields (CRF), support vector machines (SVM), Naive Bayes(NB), Decision Tree (DT), SVM for sequence tagging using Hidden Markov Models (SVM hmm ), K-nearest neighbors(K-NN), Logistic classifier and the other SVM Weka model called (SMO). Also all these classifiers have been fused together and the fusion configuration provided more accurate ANER than any one of the classifiers when used individually. The proposed approach has been evaluated using two data sets, the first dataset is a recently published corpus called ALTEC Named Entity Corpus for Modern Standard Arabic proposed by the Arabic Language Technology Center (ALTEC), and the second dataset is a standard dataset in Arabic NER called ANERcrop proposed by Benajiba. The proposed approach proved that it outperforms state of art Arabic NER systems for both of the two data sets using the 6-fold evaluation criterion.
CSE200: Complexity theory Randomized algorithms

So far we considered several computational resources: time, space, nondeterminism and nonuniformity. Now we turn to study randomness. Randomness is a useful mechanism that often simplifies algorithms for many problems. First, we formally define the model of a randomized Turing machine. A randomized Turing machine is a Turing machine which allows to “toss coins” during the computation. There are two equivalent ways to formalize this notion. The first is an implicit definition, where we assume that the TM has two transition functions δ1, δ2 instead of just one, and at each step it uses one of them randomly and uniformly. The other definition, which is sometimes easier to argue about, is an explicit definition. We assume that the randomness is given on a separate “random tape”, which is filled with random bits. The random tape is read-only, and the head on the random tape can only move to the right. This means that each random bit can only by read once. Let M be a randomized TM, which for simplicity we assume outputs one bit, 1 (accept) or 0 (reject). Given an input x, we denote by M(x) ∈ {0, 1} the output of the TM, which is a random variable. We say that M decides a language L ⊂ {0, 1}∗ if the following holds for all inputs x ∈ {0, 1}∗:
The Impact of Stock Market Development on Economic Growth: The Case of Botswana

This study examines the impacts of the stock market development on economic growth using Botswana as a case study. The study uses times series data covering a decade from 2006 to 2016. The method of analysis used is the Auto regressive distributed lag (ARDL) bounds model. The stock market capitalization ratio (MCR) was used as a proxy for market size while value of shares traded ratio (ST) and Turnover ratio (TR) were used as a proxy for liquidity, collectively representing stock market development. Real gross domestic product (GDP) growth rate was used to represent economic growth . The results show that market capitalization and turnover ratio have a negative correlation with economic growth, while the value of shares traded has a strong positive correlation with economic growth. This result implies that liquidity has propensity to stimulate economic growth in Botswana. The results of this study also found that there exists no causality relationship between stock market development and economic growth. The government should make policies that boost the interest of domestic investors in Botswana as this might spur investors’ interest and boost stock market activity which will improve liquidity and therefore stimulate economic growth.
e-Parking: Multi-agent Smart Parking Platform for Dynamic Pricing and Reservation Sharing Service

Parking is a key element of a sustainable urban mobility policy. It plays a fundamental role in travel planning and transport management, as the foremost vector of modal choice, but also as a potential means of freeing up public spaces. In this article we define the smart parking concept, as an application of smart mobility, present a historical analysis of the evolution of smart parking framework and show a statistical analysis of the published patent applications in this field around the world using the ORBIT database. Then, we propose a new smart parking architecture based on multi-agent features. Finally, we introduce the e-Parking system, platform to improve the driver experience of crowded cities. It provides real-time parking prices and offers a reservation and guidance services. In addition, the system assigns an optimal parking for a driver based on the user’s requirements that combine proximity to destination, parking cost and dwell time, while ensuring a fair sharing of public space among users and improves traffic conditions. Our approach is based on dynamic pricing policy. Our scheme is suitable for mixed-usage areas, as it considers the presence of reserved and not reserved driver in the same parking area.
MULTI-TEMPORAL MODIS FOR DETECTION AND PUBLISHED LITERATURES FOR VALIDATION OF ALGAL BLOOMS IN SABAH AND SARAWAK, MALAYSIA

aInstitute of Oceanography and Environment (INOS), University Malaysia Terengganu (UMT), 21030 Kuala Nerus, Terengganu, Malaysia bGeoscience and Digital Earth Centre (INSTeG), Research Institute for Sustainable Environment (RISE), 81310 UTM Johor Bahru, Malaysia cFaculty of Built Environment & Surveying, Universiti Teknologi Malaysia (UTM), 81310 UTM Johor Bahru, Malaysia Article history Received 8 July 2018 Received in revised form 1 March 2019 Accepted 5 March 2019 Published online 18 April 2019
Learning Multiple Mappings: an Evaluation of Interference, Transfer, and Retention with Chorded Shortcut Buttons

Touch interactions with current mobile devices have limited expressiveness. Augmenting devices with additional degrees of freedom can add power to the interaction, and several augmentations have been proposed and tested. However, there is still little known about the effects of learning multiple sets of augmented interactions that are mapped to different applications. To better understand whether multiple command mappings can interfere with one another, or affect transfer and retention, we developed a prototype with three pushbuttons on a smartphone case that can be used to provide augmented input to the system. The buttons can be chorded to provide seven possible shortcuts or transient mode switches. We mapped these buttons to three different sets of actions, and carried out a study to see if multiple mappings affect learning and performance, transfer, and retention. Our results show that all of the mappings were quickly learned and there was no reduction in performance with multiple mappings. Transfer to a more realistic task was successful, although with a slight reduction in accuracy. Retention after one week was initially poor, but expert performance was quickly restored. Our work provides new information about the design and use of chorded buttons for augmenting input in mobile interactions.
A Survey of SLAM Research based on LiDAR Sensors

With the wide application of Lidar sensors in many fields, the lidar-based SLAM technology has also developed rapidly. This paper first gives a brief overview of the application and practical significance of lidar-based SLAM technology in the fields of robotics, mapping and so on. Then, we introduce the commonly used experimental platform and data for lidar-based SLAM. According to the sensor and theoretical methods used in the classic SLAM technology scheme, the development history of SLAM technology and related algorithms are introduced. Finally, the discussion and prospects of lidar-based SLAM technology are discussed.
Reinforcement Learning in Aviation, Either Unmanned or Manned, with an Injection of AI

We propose a novel theme of aviation with the injection of AI in the form of a reinforcement learning (RL) agent that learns flying skills by observing the pilot’s psychological reaction and flight path in a simulator. The pilot and the RL agent learn flying skills simultaneously, forming a symbiotic relationship. The episodes for training the reinforcement learning agent can be simulated by a pilot flying in a simulator, or unmanned using a game on a computer. In a typical episode, the reinforcement learning agent provides a sequence of actions for the pilot to follow. These instructions produce one of the two types of results, either success or failure. The agent observes the psychological reaction of the pilot as well as the flight environment and receives a positive or negative reward. The trained RL agent represents a novel form of AI that assists the pilot for various phases of flight.
Psychometric Design of a Questionnaire for the Prevention of Induced Demand for Medicine Prescription

Unnecessary demand for healthcare services for patients is a major concern in health economics research [1-3], which may include various medical interventions ranging from medicine prescription to operational interventions [4,5]. According to the literature, some of the main contributing factors to unnecessary medicine prescription include physician-related factors, patient-related factors, and political and institutional factors [6,7]. According to the World Health Organization (WHO), more than 50% of medicines are not prescribed or sold appropriately, and approximately 50% of patients use medicines improperly [8]. Furthermore, a study in this regard indicated that the main consequences of induced demand for medicine prescription were health consequences and social and economic burdens [9]. Irrational medicine prescription imposes an additional burden on the healthcare system and wastes health resources, thereby leading to health and economic costs for patients [8,10]. In a research in this regard, Celik et al. (2013) evaluated the effects of the irrational use of medicine, reporting that irrational medicine use could cause medicine resistance, lack of patient recovery, disease prolongation, treatment ineffectiveness, and waste of economic resources in the healthcare system and patients [11]. Furthermore, Ahmed Journal of Human, Environment, and Health Promotion
University of Southern Denmark Duration of therapy – Does it matter? A systematic review and meta-regression of the duration of psychosocial treatments for alcohol use disorder

• You may download this work for personal use only. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying this open access version If you believe that this document breaches copyright please contact us providing details and we will investigate your claim. Please direct all enquiries to puresupport@bib.sdu. dk
ADVANCED STROKE LABELLING TECHNIQUE BASED ON DIRECTIONS FEATURES FOR ARABIC CHARACTER SEGMENTATION TARIK ABDEL-KAREEM ABU-AIN SITI NORUL HUDA SHEIKH ABDULLAH KHAIRUDDIN OMAR SITI ZAHARAH ABD RAHMAN

Offline Character segmentation of text images is an important step in many document image analysis and recognition (DIAR) applications. However, the character segmentation of both writing styles (printed and handwritten) remains an open problem. Moreover, the manual segmentation is time-consuming and impractical for large numbers of documents. Based on the unconstraint-cursive handwritten perspective, the auto character segmentation is more challenging and complex. The Arabic script writing style suffers from many common problems, such as sub-words overlapping, characters overlapping, and missed characters. These challenging issues have attracted the attention of researchers in the field of DIAR for Arabic character segmentation. The proposed method combines a new advanced Stroke Labelling based on Direction Features (SLDF2) technique and a modified vertical projection histogram (MVPH) technique. This technique extracts the relationship between each text stroke pixel and its 8 neighboring foreground pixels and labels it with the proper value before identify the possible segmentation points. The text preparation for the segmentation process was achieved using multiple preprocessing steps and developing an advanced stroke labelling technique based on direction features. Several Arabic language structural-rules were made to detect the candidate segmentation points (CSP), detect many character overlapping cases, solve the missed characters problem that appears as a result of using the text skeleton in VPH, and validate the CSP. All techniques and methods are tested on the ACDAR benchmark database. The validation method used to measure segmentation accuracy was a quantitative analysis that includes Recall, Precision, and F-measurement tests. The average accuracy of the proposed segmentation method was 92.44%, which outperforms the state-of-the-art method.
Effect of black seeds (Nigella sativa) volatile oil on the cervical cancer: In vitro study, on Hela cell lines

Cervical cancer is the second most common type of gynecological tumors in North America. Most chemotherapeutic agents used by oncologists were found to kill cancer cells as well as healthy ones. Unfortunately, the side effects may overwhelm its beneficial effect. Seeds of Nigella sativa, often referred to as black seeds or black cumin, have been used by traditional medicinal practitioners for centuries to treat inflammation, infections, and cancers. In fact, previous studies have shown the herb to have anti-cancer effects on colon and prostate cancers. In the present study, volatile oil was prepared from 2 patches of N. sativa seeds (Alqassim and Ethiopia). HeLa cell lines (derived from cervical cancer cells) were used. N. sativa volatile oil was incubated with the cells at different concentrations (10, 100, 1000) and compared with cisplatin. Hela cells were used to assess the cytotoxic effect of N. sativa. Study showed decrease in Hela cells survival rate at 100 concentration. A decrease the adhesion protein of Hela cells in a dose dependant manner with extract from Alqassim patch was reported. Anti-cancer effect was confirmed by morphological study where shrinkage and apoptotic changes were observed in cultured cells. In conclusion, N. sativa volatile oil has a similar anti-cancer effect to cisplatin and could be used as adjuvant therapy.
KINDEX – Automatic Subject Indexing with Knowledge Graph Services

Automatic subject indexing has been a longstanding goal of digital curators to facilitate effective retrieval access to large collections of both online and offline information resources. Controlled vocabularies are often used for this purpose, as they standardise annotation practices and help users to navigate online resources through following interlinked topical concepts. However, to this date, the assignment of suitable text annotations from a controlled vocabulary is still largely done manually, or at most (semi-)automatically, even though effective machine learning tools are already in place. This is because existing procedures require a sufficient amount of training data and they have to be adapted to each vocabulary, language and application domain anew. Against the background of tight budgets and missing IT personnel in cultural heritage as well as research infrastructure institutions, adoption of automatic subject annotation tools is hindered, while manual assignment of index terms is an even greater burden on the available financial resources. In this paper, we argue that there is a third solution to subject indexing which harnesses cross-domain knowledge graphs (i.e., DBpedia and Wikidata) to facilitate cost-effective automatic descriptor assignments that can be done without any algorithm tuning and training corpora. Our KINDEX approach fuses distributed knowledge graph information from different sources. Experimental evaluation shows that the approach achieves good accuracy scores by exploiting correspondence links of publicly available knowledge graphs.
Archimedes’ Principle and the Concept of Gravitation

Simple experimental evidence shows that the current physical interpretation of the phenomenon of a solid material body floating on the surface of a liquid body - known as ‘Archimedes’ Principle’ - is not correct. As this interpretation is a consequence of the assumption that a volume of material, when immersed in a fluid, does not ‘lose’ its ‘weight’, what we believe of gravitational mechanical action is also not correct. A material volume immersed in a fluid is currently believed to be subjected to two mechanical actions, ‘gravitational mechanical action’ or ‘weight of the material volume’ and ‘Archimedes’ force’. This is not in fact correct: the material volume is subjected to only one mechanical action, proportional to volume and to the difference in density between the matter of the material volume and that of the fluid. We propose to call this mechanical action the ‘weight of the material volume in that fluid’, ceteris paribus. At the Earth’s surface, floating of a solid material volume B on the surface of a liquid volume is the result of the concurrent action of two ‘weights’, the ‘weight in air’ of the part of volume B which is immersed in air, directed downwards, and the ‘weight in the liquid’ of the part of volume B which is immersed in the liquid, directed upwards.