Miracle-dz/UniFAR-FTUs huggingface dataset:
TOWARDS FINE-GRAINED ROAD MAPS EXTRACTION USING SENTINEL-2 IMAGERY

Nowadays, it is highly important to keep road maps up-to-date since a great deal of services rely on them. However, to date, these labours have demanded a great deal of human attention due to their complexity. In the last decade, promising attempts have been carried out to fully-automatize the extraction of road networks from remote sensing imagery. Nevertheless, the vast majority of methods rely on aerial imagery (< 1 m), whose costs are not yet affordable for maintaining up-to-date maps. This work proves that it is also possible to accurately detect roads using high resolution satellite imagery (10 m). Accordingly, we have relied on Sentinel-2 imagery considering its freely availability and the higher revisit times compared to aerial imagery. It must be taken into account that the lack of spatial resolution of this sensor drastically increases the difficulty of the road detection task, since the feasibility to detect a road depends on its width, which can reach sub-pixel size in Sentinel-2 imagery. For that purpose, a new deep learning architecture which combines semantic segmentation and super-resolution techniques is proposed. As a result, fine-grained road maps at 2.5 m are generated from Sentinel-2 imagery.
Virtual Autonomous Driving with Reinforcement Learning

Autonomous driving is becoming the trend for future transportation. One of its most signiﬁcant challenges is to recognize trafﬁc signs and obey trafﬁc rules speciﬁed by the signs. In this paper, the particular topic of optimal vehicle speed control whenever a vehicle reaches a speed limit sign is studied. This research is conducted in a longitudinal environment, which only has one dimension, the straight trafﬁc lane, along which a vehicle will drive through. There will be multiple trafﬁc signs in the trafﬁc lane, which the vehicle can recognize 150 meters in advance. Three factors are taken into account during control optimization, that is, energy consumption of the vehicle, jerk (change of acceleration), and most importantly, speed of the vehicle. Methods of Q learning with the deep neural network are implemented in the research for speed limit control. More concisely, three deep learning methods are implemented, which are DDPG, TD3 and SAC. The last of the three is the default method for this environment and thus will serve as the baseline. Experiment results show that TD3 and SAC algorithms both achieved comparably high performance within ﬁve-hour training span. Speciﬁcally, TD3 gained larger policy improvement per epoch of training while SAC achieved faster training efﬁciency. On the other hand, DDPG achieved worse performance than the other two algorithms due to its instability during training and slow training efﬁciency. The presentation link is "zoom video".
EVALUATING AN AUTOMATED OBJECT-ORIENTED METHOD TO DELINEATE DRUMLINS FROM BOTH TERRESTRIAL AND SUBMARINE DIGITAL ELEVATION MODELS

Abstract. In the field of geomorphological mapping, the demand for automated delineation of bedforms is growing due to the increasing availability of Digital Elevation Models (DEMs) in small to medium resolutions. This automated technique is not commonly applied in submarine DEMs, where bedform morphology is often subdued due to erosion and part-burial. Here we analyse drumlins in both terrestrial and submarine environments to compare and contrast the set of rules needed for their automated delineation from 3D topographic data. An existing set of rules for automated extraction to delineate the perimeter of terrestrial drumlins was developed in 2011 using object-oriented classification tools, available through eCognition Developer (V.8.7.2). This partly supervised method is evaluated here and subsequently adjusted to be applied to extract drumlins from a submarine DEM with a higher resolution. Several adjustments were needed due to the morphologic differences between the terrestrial and the submarine drumlins. For submarine drumlins, a focus on variation in elevation in the tool is needed, as part-burial and overprinting by other bedforms is common in submarine settings. A Canny Edge Detector filter was used instead of the Sobel Edge detection filter, whilst slope gradient and direction played a larger role in the set of rules. Visual and quantitative comparison with manually delineated drumlin perimeters confirms the success of this revised automated extraction method in both terrestrial and submarine environments. The flexibility and precision of this method thus allow for the future development of object-oriented classification tools to delineate a wide range of bedforms from large-scale DEMs collected from all environments.
Anxiety and panic buying of population of India during COVID-19: the mediating role of adjustment

Later christened as COVID-19, coronavirus disease was first detected in Wuhan, China, towards the last quarter of 2019. It then spread quickly across the globe in January 2020, and by 11 March 2020, it was officially declared a pandemic by the WHO. It has affected 213 countries and territories around the world. This disease is caused by severe acute respiratory syndrome coronavirus-2. The first case of COVID-19 in India was reported on 30 Jan 2020. In UP, the first positive case of COVID-19 was confirmed on 4 March 2020, in Ghaziabad.
SCIENTIFIC BASES FOR STOCK MARKET FIASCO FORECASTING TECHNOLOGY WITH USE OF INFORMATION SPACE ENTROPY

The article contains a theoretical study and description of general algorithm for predicting a stock market fiasco caused by non-financial and other factors. Market fiasco is considered as non-periodical, sudden and random event which can arise due to the many latent reasons. Methods of technical and fundamental analysis are useless to solve this problem, therefore, the use of systems analysis methods is proposed. The author’s idea is the numerical calculation of search queries entropy as a part of global information space. Decrease in the Renyi’s entropy, associated with rapid grow search queries, containing key terms from the subject area, indicates the possible stock market fiasco in the near future. This article presents an algorithm for the dynamic calculation of Renyi’s entropy, allowing predict rare events which are not reflected in statistical data (or frequency of their realizations is too small). The method and algorithm can be realized in trade information systems and decision-making systems in economic sphere.
Analysis of productivity in the Health Care Centers of the Community of Madrid (Spain) 2015-2017

The main objective of this work has focused on extending the existing literature on the analysis of total factor productivity of the Health Care Centers (HCC) of the Community of Madrid (Spain) in the period 2015-2017, with two important contributions; the application and comparison of the Malmquist indices with two robust methodologies such as the Hicks-Moorsteen and Färe Primont indices which, except for error, have not been previously used in this sector and the regional scope, which has been scarcely analyzed. The main results reveal decreases in HC productivity due to the three proposed methodologies. These decreases range between 4.90% and 0.25% for the Färe-Primont and HicksMoorsteen indices respectively between 2015/17 in average terms. Convergence analyses confirm that this takes place in the case of the Malmquist and HicksMoorsteen proposals.
Adherence and Constancy in LIME-RS Explanations for Recommendation (Long paper)

Explainable Recommendation has attracted a lot of attention due to a renewed interest in explainable artificial intelligence. In particular, post-hoc approaches have proved to be the most easily applicable ones to increasingly complex recommendation models, which are then treated as black-boxes. The most recent literature has shown that for post-hoc explanations based on local surrogate models, there are problems related to the robustness of the approach itself. This consideration becomes even more relevant in human-related tasks like recommendation. The explanation also has the arduous task of enhancing increasingly relevant aspects of user experience such as transparency or trustworthiness. This paper aims to show how the characteristics of a classical post-hoc model based on surrogates is strongly model-dependent and does not prove to be accountable for the explanations generated.
Agon: A Scalable Competitive Scheduler for Large Heterogeneous Systems

This work proposes a competitive scheduling approach, designed to scale to large heterogeneous multicore systems. This scheduler overcomes the challenges of (1) the high computation overhead of near-optimal schedulers, and (2) the error introduced by inaccurate performance predictions. This paper presents Agon, a neural network-based classifier that selects from a range of schedulers, from simple to very accurate, and learns which scheduler provides the right balance of accuracy and overhead for each scheduling interval. Agon also employs a de-noising frontend allowing the individual schedulers to be tolerant towards noise in performance predictions, producing better overall schedules. By avoiding expensive scheduling overheads, Agon improves average system performance by 6\% on average, approaching the performance of an oracular scheduler (99.1% of oracle performance).
Studies on the Effect of Starvation on Prolonged Callus Cultures and Alterations in SERK Gene Expression during Somatic Embryogenesis in Momordica charantia Linn.

Momordica charantia Linn. commonly known as bitter gourd is an annual climbing herb of Cucurbitaceae family. It is widely cultivated in Asia and South East Asia throughout India, Bangladesh and Thailand (Behera et al. 2011). It has been studied extensively for its medicinal properties in treating a number of diseases and traditionally used as antidiabetic and blood purifying agent (Joseph and Jini 2013). In somatic embryogenesis, embryo-like structures are derived from somatic cells other than gametes by evading the normal fertilization process, hence the offspring produced are genetically identical to their parent tissue (Feher et al. 2003). The switching of a somatic cell to an embryogenic state involves few changes in the composition of the cell wall. Periodic renewal of the culture medium is required, for maintaining the nutritional balance in the embryogenic cells (Raemakers et al. 1996). In somatic embryogenesis regulation, there is involvement of various genes and proteins at the molecular level and acquisition of embryogenic competence in somatic cells. Amongst all the genes, a novel gene named Somatic Embryogenesis Receptor Kinase (SERK) was first isolated by Schmidt et al. (1997) from carrot embryogenic cells, known as DcSERK. SERK gene acts as a molecular marker for somatic embryogenesis and its involvement in signal transduction and response against biotic and abiotic stress (Santos and Aragão 2009). In this study, seeds of Momordica charantia Linn. variety Charantia of Murshidabad district, West Bengal were used to perform all experiments. Germination experiment was repeated for five times with 10 seeds each. Seeds of M. charantia were decoated and imbibed in sterile distilled water for overnight imbibition. Surface sterilization was done following the protocol standardized earlier in our lab by Paul et al. (2009).
Generalized design principles for hydrodynamic electron transport in anisotropic metals

Interactions of charge carriers with lattice vibrations, or phonons, play a critical role in unconventional electronic transport of metals and semimetals. Recent observations of phonon-mediated collective electron ﬂow in bulk semimetals, termed electron hydrodynamics, present new opportunities in the search for strong electron-electron interactions in high carrier density materials. Here we present the general transport signatures of such a second-order scattering mechanism, along with analytical limits at the Eliashberg level of theory. We study electronic transport, using ab initio calculations, in ﬁnite-size channels of semimetallic ZrSiS and TaAs 2 with and without topological band crossings, respectively. The order of magnitude separation between momentum-relaxing and momentum-conserving scattering length-scales across a wide temperature range make both of them promising candidates for further experimental observation of electron hydrodynamics. More generally, our calculations show that the hydrodynamic transport regime can be realized in a much broader class of anisotropic metals and does not, to ﬁrst order, rely on the topological nature of the bands. Finally, we discuss general design principles guiding future search for hydrodynamic candidates, based on the analytical formulation and our ab initio predictions. We ﬁnd that systems with strong electron-phonon interactions, reduced electronic phase space, and suppressed phonon-phonon scattering at temperatures of interest are likely to feature hydrodynamic electron transport. We predict that layered and/or anisotropic semimetals composed of half-ﬁlled 𝑑 -shells and light group V/VI elements with lower crystal symmetry are promising candidates to observe hydrodynamic phenomena in future.
Brief View and Analysis to Latest Android Security Issues and Approaches

Due to the continuous improvement of performance and functions, Android remains the most popular operating system on mobile phone today. However, various malicious applications bring great threats to the system. Over the past few years, significant changes occured in both malwares and counter measures. Specifically, malwares are continuously evolving, and advanced approaches are adopted for more accurate detection. To keep up with the latest situation, in this paper, we conduct a wide range of analysis, including latest malwares, Android security features, and approaches. We also provide some finding when we are gathering information and carrying on experiments, which we think is useful for further researches and has not been mentioned in previous works.
Influence of Plyometric Training on the Level of Speed Ability with Changes of Direction in Ice Hockey

AIM: Speed skills are among the decisive factors in sports performance in ice hockey. Therefore, it is necessary to examine them in all age categories. Our goal was to determine the effect of plyometric exercises on the level of running and skating speed at 40 m with changes of direction in young hockey players aged 14–15 years. METHODS: The research group consisted of 33 ice hockey players (Male; age: 14.7 ± 0.7; height: 166.5cm ± 7.3; weight: 53.4kg ± 6.9) divided into two sets. Experimental group (n = 18), and control group (n = 15). During 8 weeks, the players completed training units of general preparation and training units on ice, in the experimental set was added an experimental factor of plyometric exercises carried out according to the training protocol. Speed diagnostics consisted of the following two tests: running at 40 m with changes of direction and skating forward at 40 m with changes of direction. RESULTS: Between the results of the tests in running speed and skating speed, significant differences were found both in the pretest and in the posttest between the experimental (EXP) and the control (KO) set in favor of the EXP set. Thus, the EXP set did not show the effect of plyometric exercises on improving the level of running speed and only a small effect on improving the level of skating speed. CONCLUSION: The results of the quasi-experiment showed that the influence of plyometric exercises on the improvement of the level of running and skating speed of the players of the experimental group was not proven. In further research, it will be necessary to modify the training protocol and verify its application to comparable files.
A New Approach to the Bi-objective Green Vehicle Routing Problem: Optimization in Newspaper Distribution

The purpose of this work is to present a methodology to provide a solution to a Bi-objective Green Vehicle Routing Problem (BGVRP). The methodology, illustrated using a case study (newspaper distribution problem) and instances from the literature, was divided into three stages: Stage 1, data treatment; Stage 2, metaheuristic approaches (hybrid or non-hybrid), used comparatively, and, Stage 3, analysis of the results, with a comparison of the algorithms. An optimization of 19.9% was achieved for Objective Function 1 (OF1; minimization of CO2 emissions) and consequently the same percentage for the minimization of total distance, and 87.5% for Objective Function 2 (OF2; minimization of the difference in demand). Metaheuristic approaches hybrid achieved superior results for case study and instances.
Effect of electron beam irradiation on polydopamine and its application in polymer solar cells

Polydopamine (PDA) films were irradiated by an electron beam linear accelerator at different irradiation doses ranging from 10 to 150 kGy. The irradiated samples were characterized by Fourier transform infrared spectrometry, UV‐Vis, scanning electron microscopy, atomic force microscope, X‐ray diffraction, cyclic voltammetry, electrochemical impedance spectroscopy, and thermogravimetric analysis. Changes in surface morphology, chemical structure, contact angle, frontier orbitals, and bandgap were analyzed. PDA films modified by electron beam irradiation were used in the interface design and control of polymer solar cells. Devices with the structures of ITO/ZnO/PTB7:PC71BM/MoO3/Al and ITO/PDA‐ZnO/PTB7:PC71BM/MoO3/Al were fabricated. The solar cells with a 100‐kGy electron beam‐irradiated PDA film‐modified ZnO electron transport layer had a significantly improved short circuit current density, and its efficiency reached a maximum value. The short circuit current density and power conversion efficiency reached 13.70 mA/cm2 and 3.82%, respectively. Electron beam irradiation is an effective method to modify PDA, which can be used as an interface modifier in polymer solar cells.
How Suitable Are Subword Segmentation Strategies for Translating Non-Concatenative Morphology?

Data-driven subword segmentation has become the default strategy for open-vocabulary machine translation and other NLP tasks, but may not be sufficiently generic for optimal learning of non-concatenative morphology. We design a test suite to evaluate segmentation strategies on different types of morphological phenomena in a controlled, semi-synthetic setting. In our experiments, we compare how well machine translation models trained on subword- and character-level can translate these morphological phenomena. We find that learning to analyse and generate morphologically complex surface representations is still challenging, especially for non-concatenative morphological phenomena like reduplication or vowel harmony and for rare word stems. Based on our results, we recommend that novel text representation strategies be tested on a range of typologically diverse languages to minimise the risk of adopting a strategy that inadvertently disadvantages certain languages.
JPPE 24권3호_5차편집(0629).hwp

Background and objective: Particulate matter (PM) has a fatal effect on health. There have been many studies on the use of plants such as trees and shrubs as eco-friendly and sustainable biofilter for the removal of PM. In forming more green space, ground cover plants play an important role in multi-layered planting. This study was conducted to investigate the ability of plants to reduce PM, targeting Korean native ground cover plants with high availability in urban green spaces. Methods: For 4 species of Asteraceae, 4 species of Liliaceae, and 3 species of Rosaceae, one species of plants at a time were placed in an acrylic chamber (800 × 800 × 1000 mm, L × W × H) modeling an indoor space. After the injection of PM, the amount of PM remaining in the chamber over time was investigated. Results: For all three types of PM (PM10, PM2.5, PM1), significant difference occurred in the amount of PM remaining between plant species after 1 hour in the Liliaceae chamber, 3 hours in the Asteraceae chamber, and 5 hours in the Rosaceae chamber. With Liliaceae, the leaf area and the amount of PM remaining in the chamber showed a negative (-) correlation. With the Asteraceae and Rosaceae, there was a weak negative correlation between the leaf area and the amount of PM remaining in the chamber. Conclusion: When using ground cover plants as a biofilter to remove PM, it is considered effective to select a species with a large total leaf area, especially for Liliaceae.
ConQX: Semantic Expansion of Spoken Queries for Intent Detection based on Conditioned Text Generation

Intent detection of spoken queries is a challenging task due to their noisy structure and short length. To provide additional information regarding the query and enhance the performance of intent detection, we propose a method for semantic expansion of spoken queries, called ConQX, which utilizes the text generation ability of an auto-regressive language model, GPT-2. To avoid off-topic text generation, we condition the input query to a structured context with prompt mining. We then apply zero-shot, one-shot, and few-shot learning. We lastly use the expanded queries to fine-tune BERT and RoBERTa for intent detection. The experimental results show that the performance of intent detection can be improved by our semantic expansion method.
2 S ep 2 02 1 Precise option pricing by the COS method-How to choose the truncation interval

The Fourier cosine expansion (COS) method is used for pricing European options numerically very fast. To apply the COS method, a truncation interval for the density of the log-returns need to be provided. Using Markov’s inequality, we derive a new formula to obtain the truncation interval and prove that the interval is large enough to ensure convergence of the COS method within a predefined error tolerance. We also show by several examples that the classical approach to determine the truncation interval by cumulants may lead to serious mispricing. Usually, the computational time of the COS method is of similar magnitude in both cases.
A Survey on Open Set Recognition

Open Set Recognition (OSR) is about dealing with unknown situations that were not learned by the models during training. In this paper, we provide a survey of existing works about OSR and distinguish their respective advantages and disadvantages to help out new researchers interested in the subject. The categorization of OSR models is provided along with an extensive summary of recent progress. Additionally, the relationships between OSR and its related tasks including multi-class classification and novelty detection are analyzed. It is concluded that OSR can appropriately deal with unknown instances in the real-world where capturing all possible classes in the training data is not practical. Lastly, some new directions for future research topics are suggested.