Miracle-dz/UniFAR-FTUs huggingface dataset:
RTailor: Parameterizing Soft Error Resilience for Mixed-Criticality Real-Time Systems

Equipping real-time systems with soft error resilience can be challenging due to the tradeoff of the timing and failure requirements for mixed-criticality tasks. Violation of these requirements yields failed task scheduling in one way or another. However, not every task requires the same degree of soft error resilience. For example, low-criticality tasks can run with low or even no soft error resilience, whereas mid- or highcriticality tasks may require relatively high resilience depending on their inherent failure requirement. Unfortunately, existing soft error resilience schemes do not have the ability to control the degree of their resilience in a fine-grained way, i.e., they can only be turned on or off as a whole during task execution. To this end, this paper presents RTailor (Resilience Tailor), a compiler-directed parameterized soft error resilience scheme that achieves the desired level of soft error protection according to the demand of each task. The key idea is that for a given protection ratio, compilers can transform a hot loop such that the number of its iterations protected over the total iterations matches the ratio. Compared to full resilience protecting every iteration, RTailor's parameterized soft error resilience significantly reduces the performance overhead of tasks, thereby improving their real-time schedulability. The experimental results highlight that for four representative fault rates, RTailor achieves 15%~average schedulability improvements over the state-of-the-art work that lacks parameterized soft error resilience.
Toward Embedding Robotics in Learning Environments With Support to Teachers: The IDEE Experience

Nowadays, there is an increasing interest in using different technologies, such as educational robotics in classrooms. However, in many cases, teachers have neither the necessary background to efficiently use these kits nor the information about how students are using robotics in classroom. To support teachers, learning environments with robotics tools should monitor the students' interaction data while they are interacting with the different resources provided. With the analysis of this data, teachers can obtain valuable information about students' learning progress. In previous work, we presented integrated didactic educational environment (IDEE), an integrated learning environment that uses robotics to support physics laboratories in secondary education. Students' interactions with IDEE are stored and analyzed using the additive factor model to show the teachers the most significant skills in the learning process and those students who have difficulties with these skills. Now, our goal is to enhance the information given to the teachers to allow them to focus on the specific needs of each student on every different skill involved in the activities and not only the significant skills. To this end, we use a conjunctive knowledge tracing model based on a hidden Markov model. In this article: first, we describe how the CKT model has been adapted to the pedagogical model of IDEE, second, we show that this model can identify the skills that each student masters, and thus, support teachers in identifying learning criticalities in students.
Attributions for ML-based ICS Anomaly Detection: From Theory to Practice

—Industrial Control Systems (ICS) govern critical infrastructure like power plants and water treatment plants. ICS can be attacked through manipulations of its sensor or actuator values, causing physical harm. A promising technique for detecting such attacks is machine-learning-based anomaly detection, but it does not identify which sensor or actuator was manipulated and makes it difficult for ICS operators to diagnose the anomaly’s root cause. Prior work has proposed using attribution methods to identify what features caused an ICS anomaly-detection model to raise an alarm, but it is unclear how well these attribution methods work in practice. In this paper, we compare state-of-the-art attribution methods for the ICS domain with real attacks from multiple datasets. We find that attribution methods for ICS anomaly detection do not perform as well as suggested in prior work and identify two main reasons. First, anomaly detectors often detect attacks either immediately or significantly after the attack start; we find that attributions computed at these detection points are inaccurate. Second, attribution accuracy varies greatly across attack properties, and attribution methods struggle with attacks on categorical-valued actuators. Despite these challenges, we find that ensembles of attributions can compensate for weaknesses in individual attribution methods.
Traces of the active Capitignano and San Giovanni faults (Abruzzi Apennines, Italy)

ABSTRACT We present a 1:20,000 scale map of the traces of the active Capitignano and San Giovanni faults in the area of the Montereale basin (central Apennines, Italy) covering an area of about 80 km2. Detailed fault mapping is based on high-resolution topography from airborne LiDAR imagery validated by extensive ground truthing and geophysical prospecting. Our analysis allowed the recognition of several features related to fault activity, even in scarcely accessible areas characterized by dense vegetation cover and rugged terrain. The identified fault traces run at the base of the NW-SE striking Montereale basin-bounding mountain front and along the base of the southwestern slope of the Monte Mozzano ridge, and have a length of about 12 and 8 km, respectively. Improving the knowledge of fault geometry is a critical issue not only for the recognition of seismogenic sources but also for surface fault hazard assessment and for local urban planning. The knowledge of the exact location of the fault traces is also crucial for the seismogenic characterization of the active faults by means of paleoseismological trenching.
UAV4TREE: DEEP LEARNING-BASED SYSTEM FOR AUTOMATIC CLASSIFICATION OF TREE SPECIES USING RGB OPTICAL IMAGES OBTAINED BY AN UNMANNED AERIAL VEHICLE

Abstract. Automated tree classification from unmanned aerial vehicle (UAV) images is a challenging task with several applications in forest management and conservation. In this study, we propose UAV4Tree a Deep Learning based system that automatically classifies RGB optical images obtained by the UAV. In particular, we explore the use of augmented datasets and various deep learning models, including ResNet, DenseNet, InceptionV3, and Vision Transformer, for the classification of tree images obtained from UAVs. Our experiments show that the use of an augmented dataset can significantly improve the accuracy of the classification by approximately 10 points compared to the use of a non-augmented dataset. We also found that fine-tuning and the introduction of dropout were essential for improving the generalization ability of the models on the augmented dataset. Furthermore, the use of Super Resolution Generative Adversarial Network (SR-GAN) in the original dataset allowed us to increase the performance of some models. Our findings provide valuable insights into the use of deep learning models for automated tree classification from UAV imagery, which has significant implications for sustainable forest management and conservation.
L'Observatoire de Recherche en Environnement, ORE HYBAM sur les grands fleuves amazoniens

L'ORE HYBAM permet d'acquerir, a un pas de temps journalier, decadaire ou mensuel, des donnees hydrologiques, sedimentaires, geochimiques et physico-chimiques a une quinzaine de stations, reparties sur les grands fleuves amazoniens, afin de controler les zones de piedmont, les affluents de plaine, ceux originaires des Andes et ceux drainant les boucliers bresilien et guyanais. Maintenu par des institutions de recherche et des organismes gestionnaires de reseaux hydrometriques nationaux, l'ORE HYBAM s'interesse aux transferts de matieres a l'interieur du bassin et vers l'Atlantique, a leur sensibilite a la variabilite climatique actuelle et aux activites anthropiques, au role des zones humides dans la transformation des elements transportes. Importantes egalement pour la connaissance des bilans globaux, ces informations obtenues avec des methodes de collecte homogenes, sont disponibles librement sur Internet (http://www.ore-hybam.org/). sous forme de graphiques et peuvent etre extraites sous forme de fichiers.
A COMPARISON OF DEEP LEARNING-BASED SUPER-RESOLUTION FRAMEWORKS FOR SENTINEL-2 IMAGERY IN URBAN AREAS

Abstract. The high-resolution images are in demand for many applications in the monitoring of urban areas. The advent of remote sensing satellites such as Sentinel-2 has made data more accessible as it provides free multispectral imagery. However, the spatial resolution of these images is not sufficient for many of the tasks. With the advent of deep learning techniques, significant progress has been made in the field of super-resolution, which has shown promising results in the improvement of the spatial resolution of satellite images. In this study, we compare four the most common deep learning-based models for the super-resolution of Sentinel-2 imagery in dense urban areas using aerial images. These methods are including enhanced deep super-resolution network (EDSR), super-resolution generative adversarial networks (ESRGAN), residual feature distillation network (RFDN), and Super-Resolution Convolutional Neural Network (SRCNN). To determine the effectiveness of the models in improving image resolution, they were evaluated using visual quality and quantitative metrics. The super-resolution results show that deep learning-based models have high potential for the generation of the high-resolution dataset from Sentinel-2 imagery in urban areas. The RFDN outperformed other deep learning-based models that achieved the peak signal-to-noise ratio (PSNR) more than 17.8.
Does Thinning Intensity Affect Wood Quality? An Analysis of Calabrian Pine in Southern Italy Using a Non-Destructive Acoustic Method

In the middle of XIX century, Calabrian pine was planted in southern Italy to increase the forest cover in mountainous areas. Many of these forest stands were never managed, since they were considered non-profitable for wood production. Therefore, in order to promote timber value, it is fundamental to study, more deeply, the characteristics and management options for this species. The acoustic technologies applied to predict the mechanical and physical properties of timber are well-established practices in forest research. In this study, we hypothesized that the tree stand density could influence the dynamic modulus of elasticity (MOEd) and, therefore, the future wood quality. We specifically aimed to verify if different management options, when applied, could influence the timber quality of Calabrian pine growing in similar environmental conditions. The study was conducted in the Aspromonte National Park (Calabria, Southern Italy). We derived the MOEd values from data obtained by the acoustic velocity measured through the TreeSonic™ timer. Calabrian pine trees were selected in stands where different intensities of thinning were applied eleven years before this study began (no thinning, thinning 25%, thinning 50%, and thinning 75%). The percentage refers to the number of trees cut with respect to the total number of occurring trees. The analyses were conducted on a total of 804 trees (201 trees for each intensity of thinning). A strong positive correlation was observed between the acoustic velocity, the thinning treatments and diameter at breast height (DBH). The thinning realized at 25% induced better tree wood quality. We also analyzed the best predictors for MOEd estimation, using variables easily measurable in the field, such as tree diameter, tree height, or their transformations (number of trees per hectare, basal area per hectare). We provide, here, a useful tool for predicting the wood stiffness in relation to stand parameters easily measurable in forest inventories.
The Influence of Entrepreneurship Knowledge on Entrepreneurial Interest in Medan Market Center with Self Efficacy as an intervening Variable

The author's purpose of conducting research is to determine the effect of Knowledge of Entrepreneurship on Interest in Entrepreneurship, to determine the effect of Self Efficacy on Interest in Entrepreneurship, to determine the effect of Knowledge of Entrepreneurship on Self Efficacy, to determine the effect of Knowledge of Entrepreneurship on Interest in Entrepreneurship with Self Efficacy as a Moderating Variable at the Medan Market Center . The sample in this study is 100 people who are entrepreneurs in the Medan Market Center. This research was collected through a questionnaire which was processed and analyzed using Path Analysis. Then test the quality of the data used is the validity test using Significant and reliability test using Cronbach Alpha. To test the hypothesis in this study. Researchers used the t test, and F test and did a determination test. The results of this study indicate that there is a positive influence of entrepreneurial knowledge on interest in entrepreneurship, there is a positive influence of self-efficacy on interest in entrepreneurship, there is a positive influence of entrepreneurial knowledge on self-efficacy and there is an indirect effect of entrepreneurial knowledge on interest in entrepreneurship through self-efficacy. Thus the interest in entrepreneurship becomes an integral part of entrepreneurial knowledge as an intermediary, so that entrepreneurial knowledge can indirectly affect self-efficacy.
CONSTRUCTION OF A DUAL-TASK MODEL FOR INDOOR SCENE RECOGNITION AND SEMANTIC SEGMENTATION BASED ON POINT CLOUDS

Abstract. Indoor scene recognition remains a challenging problem in the fields of artificial intelligence and computer vision due to the complexity, similarity, and spatial variability of indoor scenes. The existing research is mainly based on 2D data, which lacks 3D information about the scene and cannot accurately identify scenes with a high frequency of changes in lighting, shading, layout, etc. Moreover, the existing research usually focuses on the global features of the scene, which cannot represent indoor scenes with cluttered objects and complex spatial layouts. To solve the above problems, this paper proposes a dual-task model for indoor scene recognition and semantic segmentation based on point cloud data. The model expands the data loading method by giving the dataset loader the ability to return multi-dimensional labels and then realizes the dual-task model of scene recognition and semantic segmentation by fine-tuning PointNet++, setting task state control parameters, and adding a common feature layer. Finally, in order to solve the problem that the similarity of indoor scenes leads to the wrong scene recognition results, the rules of scenes and elements are constructed to correct the scene recognition results. The experimental results showed that with the assistance of scene-element rules, the overall accuracy of scene recognition with the proposed method in this paper is 82.4%, and the overall accuracy of semantic segmentation is 98.9%, which is better than the comparison model in this paper and provides a new method for cognition of indoor scenes based on 3D point clouds.
Integrated trophic position as a proxy for food‐web complexity

There are two distinct approaches to describing the distributions of biomass and species in food webs: one to consider them as discrete trophic levels (TLs); and the other to consider them as continuous trophic positions (TPs). Bridging the gap between these two perspectives presents a nontrivial challenge in integrating biodiversity and food‐web structure. Food network unfolding (FNU) is a technique used to bridge this gap by partitioning the biomass of species into integer TLs to compute three complexity indices, namely vertical (DV), horizontal (DH) and range (DR) diversity (D indices), through decomposition of Shannon's index H′. Using FNU, the food web (a network of species with unique TPs) is converted to a linear food chain (a biomass distribution at discrete TLs). This enables us to expect that the unfolded biomass within species decreases exponentially as the TL increases. Under this condition, the mean TL value in unfolded food chains is hypothesized to have an exponential relationship with the vertical diversity, DV. To explore this, we implemented FNU and calculated D indices for food webs publicly available at EcoBase (n = 158) and calculated the integrated TP (iTP), defined as the biomass‐weighted average TP of a given food web. The iTP corresponds to the mean TL in unfolded food chains and can be empirically measured through compound‐specific isotope analysis of amino acids (CSIA‐AA). Although our analysis is biased towards marine ecosystems, we revealed an exponential relationship between iTP and DV, suggesting that iTP can serve as a measurable proxy for DV. Furthermore, we found a positive correlation between the iTP observed in the total communities (total iTP) and the iTPs of partial communities consisting only of species with 2.0 ≤ TP < 3.0 (partial iTP; r2 = 0.48), suggesting that DV can be predicted using partial iTP. Our findings suggest that the net effect of species diversity, excluding the effect of biomass (corresponding to H′ − DV), on food‐web complexity can be revealed by combining CSIA‐AA with biodiversity analysis (e.g. environmental DNA).
How the Author's Group Came Up with Ideas in Analog/Mixed-Signal Circuit and System Area

SUMMARY This article reviews the author’s group research achievements in analog/mixed-signal circuit and system area with introduction of how they came up with the ideas. Analog/mixed-signal circuits and systems have to be designed as well-balanced in many aspects, and coming up ideas needs some experiences and discussions with researchers. It is also heavily dependent on researchers. Here, the author’s group own experiences are presented as well as their research motivations.
Comparison between the Effect of Adding MSGNP and B4C-WC Nanoparticles on the Metallurgical and Mechanical Characterization of Al-7075 Composites Produced by Stir Casting

to (35 nm) at different weight fractions percentages as reinforcements using stir-casting method and compared with Al7075/B4C-WC nanocomposites. These experiments aim to select the appropriate type and percentage of reinforcement particles for producing Al-7075 composites with the best mechanical properties. The mechanical properties, as well as the metallurgical features for analyzing the microstructure and the distribution of (MSGNP, SiC-WC) into the composite alloy specimens, have been studied utilizing the mechanical tests and the Optical Microscopy (OM), Scanning Electron Microscope (SEM), Energy Dispersive Spectroscopy (EDS), and X-Ray Diffraction (XRD) SEM pictures. By reviewing the mechanical test results, they manifested that the value of the composite's ultimate strength was enhanced with the additive concentration of nanoglass. The maximum tensile strength was obtained for the sample comprising 4% MSGNP—the composite with 6 wt. % B4C and WC have the highest hardness value, which means the increase of the added composite material beyond 10% will decrease the hardness. The X-ray diffraction (XRD) examination results illustrate the various phases of the two-theta value-generated diffraction patterns for the Al-7075/MSGNP and Al-7075/B4C-WC workpieces materials. These examinations show that the matrix of Al and the clear glass are the two major composite constituents and contain B4C, WC, and aluminum matrix, which are consistent with what was shown by the optical microstructure of the composite. The Al-7075 microstructure analysis also demonstrated a virtuous metallic bonding between the particles of Al and the uniformly dispersed and transparent glass particles at the optimal addition of 8wt%. Such enhancement was ascribed to the reinforcement's sufficiency and the uniform dispersion of MSGNP. Therefore, the addition of 8 wt. % was chosen in the present work.
ANALYSIS OF THE SPATIOTEMPORAL HETEROGENEITY OF DRIVERS ON PROVINCE-LEVEL SYNERGY OF AIR POLLUTION CONTROL AND CARBON MITIGATION IN CHINA

Abstract. Confronting the dual challenges of air pollution control and carbon mitigation, China is seeking a "win-win" pathway to achieve the co-reduction of air pollution and CO2 emission. In this study, taking 31 provinces in China as research objects, a novel index system, characterized by total emission, emission intensity and emission per capita, was established to evaluate the coupling coordination degree between the air pollution control (AP) subsystem and carbon mitigation (CM) subsystem at the provincial scale from 2013 to 2017. Local Geary’s C Index was performed to determine where the coupling coordination was correlated to its neighbour provinces. Considering the discrepancy of the socio-economic status, industrial and energy structure among provinces, Geographically and Temporally Weighted Regression (GTWR) was employed to measure the spatiotemporal heterogeneity of the influences of the driving factors on the coupling coordination degree, aiming to reduce uncertainty in the interpretation of spatial and temporal variability. Results show that: (1) Since the implementation of "Clean Air Action" in 2013, there has been an obvious improving trend of synergistic governance of air pollution control and carbon mitigation. (2) The synergy of air pollution control and carbon mitigation did not achieve good joint governance, locally. (3) The influence of drivers presents significant spatiotemporal heterogeneity among provinces. Generally, the improvement of the coupling coordination is greatly affected by the local energy structure and economic level. This conclusion has propounded practical significance for synergistic governance of air quality improvement and low-carbon development in the context of carbon neutrality achievement.
Data report: the Late Quaternary fossil record of holoplanktonic gastropods at IODP Sites U1395 and U1394

1Wall-Palmer, D. , Smart, C.W. , and Hart, M.B. , 2015. Data report: the Late Quaternary fossil record of holoplanktonic gastropods at IODP Sites U1395 and U1394. In Le Friant, A. , Ishizuka, O. , Stroncik, N.A. , and the Expedition 340 Scientists, Proceedings of the Integrated Ocean Drilling Program, 340: Tokyo (Integrated Ocean Drilling Program Management International, Inc.). doi:10.2204/iodp.proc.340.203. 2015 2School of Geography, Earth and Environmental Sciences, Plymouth University, Plymouth, PL4 8AA, UK. Correspondence author: deborah.wall-palmer@plymouth.ac. uk Data report: the Late Quaternary fossil record of holoplanktonic gastropods at IODP Sites U1395 and U1394
Grain Growth and Yield of Wheat as Influenced by Variety and Sowing Date

A field experiment was conducted at the Agronomy field, Sher-e-Bangla Agricultural University, Dhaka during November 2012 to April 2013 to evaluate the influence of variety and sowing date on grain growth and yield of wheat. The experiment consisted of four wheat variety viz., BARI Gom-21, BARI Gom-24, BARI Gom-25 and BARI Gom-26 and three sowing date viz., 20 November, 01December and 12 December. Grain growth, yield contributing characters and yield of wheat were significantly influenced by different variety and sowing date. Among the variety, BARI Gom-24 and BARI Gom-25 showed initial lower lag phase duration of 8 days after anthesis (DAA) and 12 DAA, respectively than BARI Gom-21 (16 DAA) sown at 01 December. The maximum grain growth rate whole over the period was maintained by BARI Gom-26 sown at 01 December. It reached peak at 20 DAA (1.17 mg /grain/day). The minimum growth rate was maintained by BARI Gom-21 and BARI Gom-24 sown at 12 December (0.15 mg /grain/day). BARI Gom-25 sown at 01December gave the highest yield (4.6 t ha −1 ) whereas BARI Gom-21 sown at 20 November gave the lowest (2.67 t ha −1 ). All the wheat varieties sown at 01 December yielded better than 20 November and 12 December sowing. Bangladesh Agron. J. 2015, 18(2): 97-104