Miracle-dz/UniFAR-FTUs huggingface dataset:
Test Case Generation for Data Flow Testing using Cuckoo Search Algorithm

Software testing consumes the major portion of the total efforts required for software development. This activity is very time consuming and labor intensive. It is very hard to do testing in optimal manner. In this paper a new approach is proposed, which uses the nature inspired stochastic algorithm called Cuckoo Search Algorithm (CSA) for the automatic generation of test data for data flow testing. This approach considers all def-use as test adequacy criteria. For assistance to CSA in the state space a new fitness function is also proposed by using the concept of dominator tree and branch distance in a CFG. To validate the proposed approach experiments are carried out on 10 benchmarked programs and findings are contrasted with earlier work done in this domain. Further in order to prove that proposed approach performs better than the above mentioned approaches a statistical difference test (T-test) is also performed.
The Impact of the Tracer Size and the Temporal Gap between Images in the Extraction of Atmospheric Motion Vectors

The goal of this paper is to show the impact of the tracer size and the temporal gap between images in atmospheric motion vector (AMV) extraction schemes. A test has been performed using NWC SAF/High Resolutions Winds AMV software for different configurations with a tracer size varying between 8 3 8 and 403 40 pixels and a temporal gap between images varying between 5 and 90min. AMVs have been extracted for four different MSG/SEVIRI channels (HRVIS, VIS0.8, WV6.2, and IR10.8) over the European and Mediterranean area for a 6-month period (January–June 2010). The AMV performances have been tested against radiosonde winds and ECMWF model analysis winds. The results show a small impact of the tracer size on the number of valid AMVs, which is, however, more significant for clear air AMVs, and a significant impact of the temporal gap between images. The largest number of valid AMVs has been found in general for a temporal gap of 5min for the 1-km pixel scale and for a temporal gap of 10min for the 3-km pixel scale. Results also show a decrease of the mean AMV speed and the normalized BIAS (NBIAS) with larger tracer sizes, and a relatively small impact of the temporal gap on these parameters. Finally, the results show minimum values of the normalized root-mean-square vector difference (NRMSVD) for intermediate temporal gaps between 15 and 30min with a relatively small impact of the tracer size on this parameter.
Flocculatiom of fine-grained suspended solids in the river continuum

Knowledge of factors that control flocculation along a river gradient is necessary to develop more realistic models of fine-grained sediment and associated pollutant transport in the river continuum. A direct image analysis procedure is used to characterize the number and size distribution of both primary and flocculated particles suspended in the water column of the Nifh River. Sample locations in this southern Ontario river were determined according to the Strahler stream ordering system. Suspended solids concentration, water chemistry and river flow characteristics are examined to elucidate their effect on flocculation. In the Nith River, floes represent a small proportion of the total number of particles transported but most ( > 90%) of the total volume of suspended solids. An increase in the floe median diameter (D50) and floe number was observed with decreasing suspended solids concentration. Variables such as DO, pH, SS, DOC and TDS influence floe size (D50) and floe number (floc-primary particle ratio). While floe number was variable, floe size distributions were similar irrespective of stream order.
Molecular Regulation of Embryo Development in Norway Spruce

Ingouff M. 2001. Molecular regulation o f embryo development in Norway spruce. Doctor’s dissertation. ISBN 91-576-6312-2, ISSN 1401-6230 Plant embryogenesis is mainly concerned with establishing the apical-basal and radial tissue patterns of the future adult plant and accumulating food reserves required for seed germination. The present work describes the isolation of putative transcription factors expressed during somatic embryo development in the gymnosperm Norway spruce (Picea abies). Two Norway spruce homeobox (PaHB) genes belonging to the homeodomain-glabra2 (HD-GL2) family, were isolated. Both genes display a highly conserved intron pattern characteristic of their phylogenetically related angiosperm HD-GL2 genes. The two predicted gymnosperm proteins are also highly similar to the angiosperm HD-GL2 proteins. In proembryogenic masses, both genes are expressed in all embryogenic cells. In early maturing somatic embryos, PaHBl becomes restricted to the protoderm layer and PaHB2 is not expressed. At a later stage, PaHBl expression remains on the protoderm whereas PaHB2 transcripts are mainly detected in the underlying cortical layers. A stepwise peripheral to central radial patterning takes place during embryo development in Norway spruce. Ectopic expression of PaHBl led to an early block in somatic embryo development suggesting that the inner layers of the embryos must be devoid of PaHBl to proceed through embryogenesis. The conservation of protoderm-specific expression in HD-GL2 and lipid transfer protein {LTP) genes from divergent plants suggests putative common cis-regulatory elements in these genes. Sequence comparisons between the isolated PaHBl, Pal8 (encoding a predicted LTP) promoters and the angiosperm counterparts allowed us to identify candidate motifs for protoderm expression. The AtMLl promoter and PaHBl promoter, both fused to the reporter gene GUS, were transferred into Norway spruce and Arabidopsis respectively, enaling reporter gene analysis. The Norway spruce viviparousl (Pavpl) single-copy gene shows similar gene structure and protein domain organization as the angiosperm counterparts. The expression profile of Pavpl further suggests a similar role of vpl genes in maturation and desiccation processes in seed plants.
Use of granular material dynamics simulation for the study of cargo shift of ships

In the current paper a modelling approach of granul r material dynamics that can be interfaced with a ship motion model is described. Firstly, the exis ting simulation methods are critically assessed. Then, the adopted “discrete-particle” approach is d e cribed which is based on the so called “Molecular Dynamics” method. Some preliminary, yet characteristic, simulation results are produced, for a rectangular container vibrated in s way, in heave and in roll. The movement of cargo’s centre of mass is monitored. Critical param eter values (frequency, angle of tilt) for cargo shift and some preliminary comparison with the requ irements of the international grain transport regulations are included.
ABigDataAnalyticsApproach forDynamicFeedbackWarning for Complex Systems

School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China Beijing Laboratory of National Economic Security Early-Warning Engineering, Beijing Jiaotong University, Beijing 100044, China National Academy of Economic Security, Beijing Jiaotong University, Beijing 100044, China Beijing Center for Industrial Security and Development Research, Beijing Jiaotong University, Beijing 100044, China Postdoctoral Programme of China Centre for Industrial Security Research, Beijing Jiaotong University, Beijing 100044, China
Effect of solvent extraction system on the antioxidant activity and RP-HPLC based determination of phenolic and water soluble vitamins in an annual herb Mazus pumilus

The objective of the present study was to evaluate the content of water-soluble vitamins like ascorbic acid (C), thiamine (B1), riboflavin (B2), niacin (B3), pantothenic acid (B5), pyridoxine (B6) and folic acid (B9) in an ethno-medicinally edible plant Mazus pumilus Burm.f. by high performance liquid chromatography (HPLC) method. The in vitro antioxidant activity was carried out in different solvent system of varying polarity. The 70% Aqueous (Aq.) ethanol was appeared to be the most potent solvent for extraction of antioxidant components. The total phenolic and flavonoid content were found 65.640 ± 0.130mg/g dm (dry mass) and 18.877 ± 0.070mg/g dm respectively in the plant. The estimation of phenolic acids, polyphenolics in the 70 % aq. ethanol extract was also carried out by HPLC method. The water soluble B vitamins ranged between 0.072 to 5.632 mg/100g dm. The results confirm that this ethno medicinally important plant is a good source of natural antioxidants which could be used for the good of human being at the same time it can be used as a vitamin supplement to our diet. The present study also gives an account of traditional significance of the plants under investigation.
Learning Robot Policies for Untangling Dense Knots in Linear Deformable Structures

: Untangling linear deformable objects (LDOs) such as ropes, wires, and cables is a challenging robot task due to the vast set of possible arrangements, visual homogeneity, self-occlusions, and complex dynamics. Prior geometric methods have focused on loose knot conﬁgurations using full rope state estimation before planning, while prior data-driven approaches require substantial environment interaction. We consider dense (tight) knots that lack space between self-intersections and present an iterative algorithm that leverages geometric structure in conﬁgu-rations to untie knots. We then practically instantiate this algorithm with HULK: Hierarchical Untangling from Learned Keypoints, which combines learning-based perception with a bilateral geometric planner to perform untangling without performing full state estimation. To evaluate the learned policy, we develop a novel simulator to simulate rope with ﬁgure-eight and overhand knots and with varying appearances. We present experiments comparing two variants of HULK to three baselines and ﬁnd that HULK is able to untangle LDOs consisting of tight ﬁgure-eight and overhand knots. We ﬁnd that HULK achieves higher success rates and fewer empirical actions than analytical baselines while generalizing to varied textures and appearances. HULK is able to successfully untangle an LDO from a dense initial conﬁguration containing only up to two overhand and ﬁgure-eight knots in 97.9% of 378 simulated experiments with an average of 12.1 actions per trial, suggesting that the policy can learn the task of untangling effectively from an algorithmic supervisor.
Kinetic study of Escherichia coli BPPTCC-EgRK2 to produce recombinant cellulase for ethanol production from oil palm empty fruit bunch

Oil Palm Empty Fruit Bunch (OPEFB) is an abundant biomass resource in Indonesia, which contains 46,77% (w/w) of cellulose. The high cellulose content of OPEFB can be used as a substrate for bacteria cultivation to produce cellulase. By using OPEFB as an alternative substrate, the production cost of cellulase in industrial scale can be suppressed. However, currently there are no available research that simulate a cellulase production plant design. Prior to simulating the cellulase plant design, kinetic studies of bacteria used in cultivation are needed to create an accurate simulation. In this research, kinetic studies of E. coli BPPTCC-EgRK2 growth were examined with the Monod approach to get the Monod constant (Ks) and maximum specific growth rate (μmax). This study found that E. coli BPPTCC-EgRK2 have μmax and Ks of 1.581 and 0.0709 respectively. BPPTCC-EgRK2 produced intracellular cellulase, thus gave linear correlation between cell concentration and cellulase production.
Issues in Information Systems

An important component of knowledge management is the organization of documents for quick and easy access. One effective way of organizing these documents is to group them by a fixed set of specific knowledge categories. For large-scale technical teams, the number of categories can reach thousands or even tens of thousands, which makes this type of cataloging especially useful. Text classification is a sophisticated process that involves data preprocessing, transformation, dimensionality reduction, application of classification techniques, classifier evaluation, and classifier validation. This paper describes the preliminary results from phase one of a design-science research study for the development of a model that can be used for classification of financial software development documentation in knowledge discovery systems using machine learning. Specifically, testing with a small dataset of 64 documents from the Natural Language Technical Project Documentation dataset was conducted to assess the effectiveness of traditional text classification methods. Results indicate limitations to these traditional methods for classifying technical documents. The next steps include evaluating the performance on a bigger dataset of technical documents and testing new deep learning techniques.
European Journal of Physical Education and Sport Science-2501-1235

The minimal power that elicits VO2max and the time to exhaustion (tlimit) at this workload appear to determine cyclists’ endurance capabilities, analyze performance and help coaches to design training. Data in the literature are limited so as to elucidate this. The aim of this study was to investigate the tlimit at the power output, which corresponds to 90 (tlimit 90) and 100% VO2max (tlimit 100) in elite endurance cyclists. The contribution of tlimit in 3 km indoor individual time trial was also studied. Subjects were eleven elite male road cyclists (age 17.7  0.5 years, body mass 66.8  4.9 kg, body height 176.3  7.4 cm, VO2max 69.77  2.58 ml.kg-1.min-1). Power output at 90 and 100% VO2max was determined by continuous incremental testing. This protocol had steps of 2 min and increments of 30 W. The exhaustive trials tlimit 90 or tlimit 100 were performed in random order at least five days apart. Five days after the last exhaustive trial, cyclists performed an individual 3 km time trial on an indoor wooden track. Mean sd, tlimit 90 and tlimit 100 were 16:27.73  07:46.6 and 4:48.6  00:53.2 min:sec. Time to exhaustion at tlimit 90 and tlimit 100 ranged between 07:00-30:15 and 03:10-06:00 min:sec, respectively. Tlimit 100, tlimit 90 and VO2max (ml.min-1) did not correlate with 3 km cycling i Correspondence: email elzach@phed.uoa. gr, elzacharo@gmail.com Zacharogiannis E. , Pilianidis T. , Dallas G. , Mantzuranis N. , Argitaki P., Smirniotou A. , Paradisis G. TIME TO EXHAUSTION AT 90 AND 100% VO2MAX AND PHYSIOLOGICAL DETERMINANTS OF 3 KM PERFORMANCE IN ELITE CYCLISTS European Journal of Physical Education and Sport Science Volume 6 │ Issue 10 │ 2021 76 performance (r = 0.08, 0.16 and –0.59, p > 0.05). Tlimit 90 was inversely related (r = –0.49, p = 0.1) with VO2max (ml.min-1). Only power output which corresponded to ventilatory threshold and VO2max correlated significantly with 3 km performance (r = –0.83 and – 0.80, p < 0.01). The results of this study indicate that: a) if cyclists’ training intensity is based on %VO2max, individual determination of the tlimit at the %VO2max has to be considered due to a wide range of tlimit to exhaustion; b) 3 km performance directly depends on the power that corresponds with ventilatory threshold and VO2max.
RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor

Keypoint detector and descriptor are two main components of point cloud registration. Previous learning-based keypoint detectors rely on saliency estimation for each point or farthest point sample (FPS) for candidate points selection, which are inefficient and not applicable in large scale scenes. This paper proposes Random Sample-based Keypoint Detector and Descriptor Network (RSKDD-Net) for large scale point cloud registration. The key idea is using random sampling to efficiently select candidate points and using a learning-based method to jointly generate keypoints and descriptors. To tackle the information loss of random sampling, we exploit a novel random dilation cluster strategy to enlarge the receptive field of each sampled point and an attention mechanism to aggregate the positions and features of neighbor points. Furthermore, we propose a matching loss to train the descriptor in a weakly supervised manner. Extensive experiments on two large scale outdoor LiDAR datasets show that the proposed RSKDD-Net achieves state-of-the-art performance with more than 15 times faster than existing methods. Our code is available at this https URL.
GEOSTATISTICAL MODELING OF WATER INFILTRATION IN URBAN SOILS

The post-communist period (after 1989) caused important changes within the urban space of Iaşi municipality. The partial dismantling of the industrial zone, the urban sprawl against the periurban and rural areas, the new infrastructure works (water and sewage network) led to significant changes in soil morphology and physical properties, including in the infiltration rate (IR). The objectives of this study are: 1) to analyze the spatial variability of IR in urban soils of Iaşi municipality; 2) to identify correlations between IR and some auxiliary physical properties; 3) to choose the optimal geostatistical model, which will accurately estimate the IR in locations with no measurements, enabling the best version of cartographic representation. Technosols are characterized by high spatial variability of infiltration rate, when compared to other soils. Their physical properties differ from one location to another, even in the same landforms, fact that is also confirmed by a high coefficient of variation of IR (82.6%). There is no direct correlation between the IR and the auxiliary properties, and thus the co-kriging method will not useful here. Due to the high values of skewness, we have transformed the original data and used the lognormal ordinary kriging. A comparison between the predicted IR values for the unmodified grid with the measured values do not reveal significant differences, having obtained N/S=0.1798 and R=0.5184. The elimination of the maximum values of IR or their replacement with some interpolated values for similar conditions of soil and relief are not desirable tasks. Their consideration will lead to high values of the mean square error (MSE) and low goodness of prediction (G).
Using Selective Memoization to Defeat Regular Expression Denial of Service (ReDoS)

Regular expressions (regexes) are a denial of service vector in most mainstream programming languages. Recent empirical work has demonstrated that up to 10% of regexes have super-linear worst-case behavior in typical regex engines. It is therefore not surprising that many web services are reportedly vulnerable to regex denial of service (ReDoS). If the time complexity of a regex engine can be reduced transparently, ReDoS vulnerabilities can be eliminated at no cost to application developers. Unfortunately, existing ReDoS defenses — replacing the regex engine, optimizing it, or replacing regexes piecemeal — struggle with soundness and compatibility. Full memoization is sound and compatible, but its space costs are too high. No effective ReDoS defense has been adopted in practice. We present techniques to provably eliminate super-linear regex behavior with low space costs for typical regexes. We propose selective memoization schemes with varying space/time tradeoffs. We then describe an encoding scheme that leverages insights about regex engine semantics to further reduce the space cost of memoization. We also consider how to safely handle extended regex features. We implemented our proposals and evaluated them on a corpus of real-world regexes. We found that selective memoization lowers the space cost of memoization by an order of magnitude for the median regex, and that run-length encoding further lowers the space cost to constant for 90% of regexes. "Those who cannot remember the past are condemned to repeat it. " –George Santayana
A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data

Irregularly sampled time series (ISTS) data has irregular temporal intervals between observations and different sampling rates between sequences. ISTS commonly appears in healthcare, economics, and geoscience. Especially in the medical environment, the widely used Electronic Health Records (EHRs) have abundant typical irregularly sampled medical time series (ISMTS) data. Developing deep learning methods on EHRs data is critical for personalized treatment, precise diagnosis and medical management. However, it is challenging to directly use deep learning models for ISMTS data. On the one hand, ISMTS data has the intra-series and inter-series relations. Both the local and global structures should be considered. On the other hand, methods should consider the trade-off between task accuracy and model complexity and remain generality and interpretability. So far, many existing works have tried to solve the above problems and have achieved good results. In this paper, we review these deep learning methods from the perspectives of technology and task. Under the technology-driven perspective, we summarize them into two categories - missing data-based methods and raw data-based methods. Under the task-driven perspective, we also summarize them into two categories - data imputation-oriented and downstream task-oriented. For each of them, we point out their advantages and disadvantages. Moreover, we implement some representative methods and compare them on four medical datasets with two tasks. Finally, we discuss the challenges and opportunities in this area.
NLNDE at CANTEMIST: Neural Sequence Labeling and Parsing Approaches for Clinical Concept Extraction

The recognition and normalization of clinical information, such as tumor morphology mentions, is an important, but complex process consisting of multiple subtasks. In this paper, we describe our system for the CANTEMIST shared task, which is able to extract, normalize and rank ICD codes from Spanish electronic health records using neural sequence labeling and parsing approaches with context-aware embeddings. Our best system achieves 85.3 F1, 76.7 F1, and 77.0 MAP for the three tasks, respectively.
Polypyrimidine Tract Binding Protein blocks microRNA-124 biogenesis to enforce its neuronal specific expression

MicroRNA-124 is expressed in neurons, where it represses genes inhibitory for neuronal differentiation, including the RNA binding protein PTBP1. PTBP1 maintains non-neuronal splicing patterns of mRNAs that switch to neuronal isoforms upon neuronal differentiation. We find that pri-miR-124-1 is expressed in mouse embryonic stem cells (mESCs) where mature miR-124 is absent. PTBP1 binds to this precursor RNA upstream of the miRNA stem-loop to inhibit mature miR-124 expression in vivo, and DROSHA cleavage of pri-miR-124-1 in vitro. This new function for PTBP1 in repressing miR-124 biogenesis adds an additional regulatory loop to the already intricate interplay between these two molecules. Applying mathematical modeling to examine the dynamics of this regulation, we find that the pool of pri-miR-124 whose maturation is blocked by PTBP1 creates a robust and self-reinforcing transition in gene expression as PTBP1 is depleted during early neuronal differentiation. While interlocking regulatory loops are often modeled between miRNAs and transcriptional regulators, our results indicate that miRNA targeting of posttranscriptional regulators also reinforces developmental decisions. Notably, induction of neuronal differentiation observed upon PTBP1 knockdown likely results from direct de-repression of miR-124, in addition to indirect effects previously described.