Miracle-dz/UniFAR-FTUs huggingface dataset:
MONARCH: Hierarchical Storage Management for Deep Learning Frameworks

Due to convenience and usability, many deep learning (DL) jobs resort to the available shared parallel file system (PFS) for storing and accessing training data when running in HPC environments. Under such a scenario, however, where multiple I/O-intensive applications operate concurrently, the PFS can quickly get saturated with simultaneous storage requests and become a critical performance bottleneck, leading to throughput variability and performance loss.We present MONARCH, a framework-agnostic middleware for hierarchical storage management. This solution leverages the existing storage tiers present at modern supercomputers (e.g., compute node’s local storage, PFS) to improve DL training performance and alleviate the current I/O pressure of the shared PFS.We validate the applicability of our approach by developing and integrating an early prototype with the TensorFlow DL framework. Results show that MONARCH can reduce I/O operations submitted to the shared PFS by up to 45%, decreasing training time by 24% and 12%, for I/O-intensive models, namely LeNet and AlexNet.
Evaluation of Xgboost and Lgbm Performance in Tree Species Classification with Sentinel-2 Data

Tree species classification with satellite data has become more and more popular since Sentinel-2 launch. We compared efficacy and effectiveness of Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LGBM) with widely used in remote sensing Random Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) algorithms. Analyses were performed over an area in Portugal with multi-temporal Sentinel-2 data registered in April, June, August and October 2018. The selected classes were: cork oak, holm oak, eucalyptus, other broadleaved, maritime pine, stone pine and other coniferous. Algorithm efficacy was measured through F1-score and accuracy while efficiency was measured through the median time needed for each fit. XGBoost and LGBM outperformed efficacy of other algorithms, which was already high (above 90% for the best variant of each algorithm). In terms of efficacy, LGBM overcame all algorithms, including XGBoost.
SSH and Telnet Protocols Attack Analysis Using Honeypot Technique : *Analysis of SSH AND TELNET Honeypot

Generally, the defense measures taken against new cyber-attack methods are insufficient for cybersecurity risk management. Contrary to classical attack methods, the existence of undiscovered attack types called’ zero-day attacks’ can invalidate the actions taken. It is possible with honeypot systems to implement new security measures by recording the attacker’s behavior. The purpose of the honeypot is to learn about the methods and tools used by the attacker or malicious activity. In particular, it allows us to discover zero-day attack types and develop new defense methods for them. Attackers have made protocols such as SSH (Secure Shell) and Telnet, which are widely used for remote access to devices, primary targets. In this study, SSHTelnet honeypot was established using Cowrie software. Attackers attempted to connect, and attackers record their activity after providing access. These collected attacker log records and files uploaded to the system are published on Github to other researchers1. We shared the observations and analysis results of attacks on SSH and Telnet protocols with honeypot.
Privileged Knowledge Distillation for SAR Building Extraction

Automatic building footprint extraction from SAR imagery is one of the critical tasks in the remote sensing community. CNN has been recently explored in building extraction tasks and achieved improved performance. However, due to the scarcity of training data, it suffers from overfitting problem. This paper presents a novel knowledge distillation based framework consisting of teacher and student networks. Regarding EO image as privileged information, the teacher network learns to extract the rich EO and SAR image pair features. The student network then learns to estimate the building footprints from only SAR images based on the privileged knowledge from the teacher network. Experimental results on SpaceNet-6 benchmark demonstrate the effectiveness of our framework, which explicitly improves the performance of SAR segmentation network.
Retrieval of Forest Water Potential from L-Band Vegetation Optical Depth

A retrieval methodology for forest water potential from ground-based L-band radiometry is proposed. It contains the estimation of the gravimetric and the relative water content of a forest stand and tests in situ- and model-based functions to transform these estimates into forest water potential. The retrieval is based on vegetation optical depth data from a tower-based experiment of the SMAPVEX 19–21 campaign for the period from April to October 2019 at Harvard Forest, MA, USA. In addition, comparison and validation with in situ measurements on leaf and xylem water potential as well as on leaf wetness and complex permittivity are foreseen to understand limitations and potentials of the proposed approach. As a first result the radiometer-based water potential estimates of the forest stand are concurrent in time and similar in value with their in situ (xylem) counterparts from single trees in the radiometer footprint.
Viewpoint Selection for Rover Relative Pose Estimation Driven by Minimal Uncertainty Criteria

Pose estimation is critical for mobile robots to fulfill various tasks, such as path following or mapping the environment. This is usually accomplished by simultaneous localization and mapping (SLAM). However, computationally constrained systems, such as planetary rovers, rely on less intensive guidance navigation and control (GNC) solutions generally derived solely from visual odometry (VO), wheel odometry, and the onboard inertial measurement unit. Although providing adequate localization performances, the drift accumulated overtime is not compensated by loop closing capabilities, typical of SLAM. Usually, rovers send surface images to the ground station, and these images are used for multiple purposes, such as scientific and operational planning. The number of images is constrained by the communication bandwidth and power budget. The set of transmitted images can be used as a means to correct the robot’s trajectory in an off-line manner. In this work, a solution is presented to the problem of selecting the optimal set of viewpoints belonging to the planned path from which to capture and transmit images: 1) it guarantees accurate trajectory correction and 2) complies with the maximum number of images that can be transmitted to ground control given the available data budget. To this end, it is proposed: 1) a delocalized/decentralized sensor fusion approach based on pose graph optimization and structure from motion and 2) a strategy to select a minimal set of viewpoints along the trajectory that, given a tentative geometry of the environment and the global path that the rover must follow, minimizes the uncertainty of all the robot poses. Optimal camera viewpoint positions are selected as a function of the planned trajectory, the approximate scene geometry, and the maximum transmittable number of images. The proposed method has been tested on a dataset of stereo-images collected in a representative Martian environment, the ALTEC Mars Terrain Simulator (MTS), with the ExoMars testing rover (ExoTeR—European Space Agency, Paris, France, property). Rover stereo-images ground truth was given with millimetric accuracy by a motion capture (MC) system.
EDLAD: An Encoder-Decoder Long Short-Term Memory Network-Based Anomaly Detector for Hyperspectral Images

In this paper, an encoder-decoder long short-term memory network-based anomaly detector (denoted as EDLAD) is proposed for hyperspectral images. The proposed EDLAD aims to simultaneously alleviate anomaly contamination and build a stable background component for anomaly detection. To reduce anomaly contamination, the EDLAD first utilizes a well-designed encoder-decoder LSTM to reconstruct the hyperspectral image. Based on the concept that the anomaly pixels occupy an extremely small fraction of the image, the well-designed encoder-decoder LSTM network tends to maintain the background and alleviate anomaly during the reconstruction process since the whole image is employed for training the network. Then the dimension reduction is used to further alleviate the anomaly contamination and build a stable background component. Finally, the EDLAD applies the Mahalanobis distance differences to detect the probable anomalies. The experiments on two benchmark hyperspectral images demonstrate the superiority of the EDLAD in anomaly detection.
Eciton: Very Low-Power LSTM Neural Network Accelerator for Predictive Maintenance at the Edge

This paper presents Eciton, a very low-power LSTM neural network accelerator for low-power edge sensor nodes, demonstrating real-time processing on predictive maintenance applications with a power consumption of 17 mW under load. Eciton reduces memory and chip resource requirements via 8-bit quantization and hard sigmoid activation, allowing the accelerator as well as the LSTM model parameters to fit in a lowcost, low-power Lattice iCE40 UP5K FPGA. Eciton demonstrates real-time processing at a very low power consumption with minimal loss of accuracy on two predictive maintenance scenarios with differing characteristics, while achieving competitive power efficiency against the state-of-the-art of similar scale. We also show that the addition of this accelerator actually reduces the power budget of the sensor node by reducing power-hungry wireless transmission.The resulting power budget of the sensor node is small enough to be powered by a power harvester, potentially allowing it to run indefinitely without a battery or periodic maintenance.
Deep Neural Network Training Using Synthetic Signatures for Rare Target Detection in SWIR Hyperspectral Imagery

Deep learning techniques have enjoyed tremendous success in the vision community, where large labeled datasets are available for training. This has been problematic for hyperspectral target detection applications, as labeled hyperspectral datasets are scarce, particularly for detection applications involving rarely occurring targets. In this work, we present a deep neural network architecture for hyperspectral detection of rare targets and a technique to train such network based on synthetic signature modeling. Using the proposed approach, our network reaches an accuracy competitive with commonly-used statistical detectors, while improving computation time by over 90 % on a realistic target detection problem. Evaluation is conducted using a large dataset of over 600 airborne short-wave infrared (SWIR) hyperspectral images.
Copernicus SENTINEL-2 Geometric Calibration Status

The Sentinel-2 mission is a key element of the Copernicus Earth monitoring program of the European Union. The mission is currently composed of two satellites and provides a continuous observation of land and coastal areas at high spatial resolution and with a revisit time of 5 days at the equator. The geometric uncertainty of the Sentinel-2 product is a critical contributor to the performance of the mission. We present the approach used to calibrate the geometric performance of Sentinel-2 data and latest activities, especially related to the co-registration with the Global Reference Image (GRI). Generation of the GRI, coregistration algorithm, called geometric refinement, and preliminary results are presented.
Electricity, ICT Infrastructure and Economic Growth of Gambia, Ghana and Nigeria

The paper considered electricity, ICT infrastructure and their growth impact on Gambia, Ghana and Nigeria for the period 1990 and 2017. There were mixed results among the variants of ICT infrastructure used. The negative impact of electricity access to economic growth of Nigeria may have a spillover effect to the other West African countries in the estimation and a two-way effect on economic growth. It may increase the income of the sellers of electricity generator and that of solar energy panel installers through increased demand thereby impacts positively on economic growth. On the other hand, it may reduce purchasing power of the buyers, hence the negative effects on economic growth may outweigh the positive effect, thereby impact of economic growth negatively. The control variables have positive and significant effect on economic growth. The country specific estimations showed no consistent results among the variants of infrastructure. Among others, government should ensure that the bottleneck and corruption in the electricity sector be controlled. Internet usage should be encouraged through price reduction in all the countries except Gambia where internet usage has negative link with economic growth. Official development assistant should be encouraged.
The Relationship between Product Quality, E-Service Quality and Brand Image on Customer Satisfaction: Preliminary Investigation in Perlis

This study focusing on The Effect of Product Quality, E-Service Quality and Brand Image on Customer Satisfaction which focusing among Perlis’s online shopping. The sample comprised of 250 online consumers at Perlis. The sampling method used in this research is the random method. The questionnaires were distributed by using google form. The data collected was analyzed by using software named as IBM Statistical Package for the Social Sciences (SPSS) version 25. The findings presented that product quality (β=.213, p=.001), e-service quality (β=.142, p=.039) and brand image (β=.553, p=.000) are the factors that significantly related to customer satisfaction. The analytical results confirmed that there is a significant positive correlation between product quality, e- service quality and brand image and online customer satisfaction. The study involves some management implications. In addition to this, the study has some limitations. Therefore, some suggestions are put forward for future research.
Primary Umbilical Endometriotic Nodule with an Ultrasound Incidental Finding of Ovarian Endometrioma and a Solitary Intramural Uterine Myoma: A Case Report

Endometriosis is the presence of endometrial stroma outside the uterine cavity. Endometrioma (chocolate cyst) is the most common form of endometriosis. Primary umbilical endometriosis (PUE) is rare, with a prevalence of < 1% of endometriosis cases. Our case is a 25-year-old nulliparous female with a three-year history of infertility presenting with primary umbilical endometriosis and an abdomino-pelvic ultrasonography finding of a right endometrioma, and a solitary intramural uterine myoma. These three findings in a patient are a rare occurrence. Abdominopelvic ultrasound and excision biopsy was performed to arrive at a diagnosis.
Comment on tc-2021-175

General comments 1. The commentary in this manuscript is focussed on a single multi-model comparison (ISMIP6 Greenland). Many of the suggestions would similarly apply to other initiatives in the ice sheet modelling community (e.g., ISMIP6 Antarctica, ABUMIP, and LARMIP2), outside of it (e.g., GlacierMIP or even CMIP), and equally to many individual projections. While ISMIP6-Greenland can still be used as an example, we would appreciate clarification on why the authors did not undertake a broader approach (for example, using both ice sheets).
Analysis of the Effectiveness of Reception of Restaurant, Cafe and Dining House Taxes and Their Effect on Regional Original Income (Pad) In Toraja Utara District

This study aims to examine and analyze the effectiveness of tax revenues from restaurants, cafes and restaurants as well as their effects on local authentic revenue (pad) in North Toraja Regency. This research is a descriptive and explanatory study. The type of data used in this study is quantitative data. Quantitative data is data that is measured on a numerical or numeric scale. Meanwhile, based on the source, the data used are primary data and secondary data. Primary data were obtained through field surveys, while secondary data were obtained through the publication of the Central Statistics Agency (BPS) and the Central Toraja District Revenue Data Center. The analysis in this study used the Ordinary Least Square (OLS) method. Based on the results of potential calculations that have been carried out, it is known that the potential for restaurant and restaurant taxes in North Toraja Regency in 2014-2018 continues to increase while the potential for cafe tax tends to fluctuate. While in the calculation of effectiveness it is obtained that restaurant tax revenues during 2014-2018 are very effective, Restaurant tax receipts during 2014-2018 were very effective and cafe tax receipts during 2014-2016 were very effective. Based on OLS estimation results, it was found that simultaneous restaurant, cafe and restaurant tax had a significant effect on PAD of North Toraja Regency, and partially only restaurant and restaurant taxes which have a significant effect on the PAD of North Toraja Regency.
Evaluation of dopaminergic neuronal loss and synaptic changes in an -synuclein overexpressing model of PD

Introduction Parkinson’s disease (PD) is the second most common age-related neurodegenerative disorder, pathologically characterized by a loss of dopaminergic neurons in the substantia nigra (SN). The resulting dopamine deficiency in the striatum underlies some of the observed motor symptoms such as rigidity and tremor. A histopathological hallmark of PD is the presence of Lewy Bodies in the brain; aggregates containing, amongst others, misfolded α-synuclein protein[1]. In the present study, we aim to evaluate neuronal loss and synaptic compensation mechanisms in a rodent PD model that unilaterally over-expresses mutant (A53T) human α-synuclein, a missense mutation found in familiar cases of PD[2]. Materials & Methods Two cohorts of rats (ntotal=16) were unilaterally injected in the SN with AAV2/6 viral vector coding for mutated (A53T) human alpha-synuclein. Animals were evaluated at early (n=8;6-8 weeks post-injection (wpi)) or late time-point (n=8;10-12wpi). At both time-points, all animals underwent in vivo behaviour and PET imaging, and post mortem immunohistological and qPCR analysis.
EFFECTS OF CREATINE ON THE QUALITY AND FERTILITY OF CHICKEN SEMEN DURING LIQUID STORAGE

Creatine plays the role as adenosine triphosphate synthesis substrate and antioxidant factor of sperm metabolism. Thus, it was hypothesised that the addition of creatine (0.1 mM, 1.0 mM, 10.0 mM) on diluent could enhance chicken semen quality in liquid storage. Semen collected from twelve Vietnamese native roosters was used in the experiment. The pooled semen of all roosters was diluted in Lake diluent at a ratio of 1:3 (v/v) and stored in 4C for 24 and 48 h. Each stored semen was inseminated into five Leghorn hens to evaluate the fertility. The results show that the chicken sperm mass activity, motility, and viability of 24 and 48 h stored semen was improved (P < 0.05) by the addition of 1.0 mM creatine. However, the sperm abnormality of 24 and 48 h stored semen was not different among treatments. The fertility of 24 and 48 h stored semen was highest for 1.0 mM creatine treatment (P < 0,05). The addition of 1.0 mM creatine on diluent could improve chicken semen quality in liquid storage.
Mental Disorders Detection via Online Social Media Mining using Machine Learning Framework

: online social networks provide relevant information on users' opinions and posts on various topics. So applications, such as monitoring and detection systems can collect and analyze this data. This paper presents a knowledge-based system, which includes an emotional health monitoring system to detect users with possible psychological disorders. Symptoms Of these psychological disorder are usually observed passively. In this situation, author argue that online social behaviour extraction offers an opportunity to actively identify disorder at an early stage. It is difficult to identify the disorder because the psychological factors considered in standard diagnostic criteria questionnaire cannot be observed by the registers of online social activities. Our approach, New and innovative for the practice of disorder detection, it does so do not trust the self-disclosure of those psychological factors through the questionnaires. Instead, propose a deep learning technique that is detection of mental disorder in social networks which exploits the features extracted from social network data for identify with precision possible cases of disorder detection. We perform an analysis of the characteristics and we also apply deep learning in large-scale data sets and analyse features of the various types of disorders.
A Framework to Predict Fine-Grained Sustainable Consumption Behavior Levels of Individuals

In order to encourage individuals to adopt more sustainable behaviors, it is crucial to know their current levels of consumption in specific domains (e.g., mobility) before exposing them to personalized incentives. Although various theoretical models exist, there is currently no technological solution that automatically estimates individual’s consumption sustainability levels. This short paper aims at addressing this gap and presents the design of a framework that enables to estimate these levels based on multiple features (e.g., demographics). It also presents a preliminary validation of a part of the framework through two empirical comparative studies related to the mobility consumption domain. These studies evaluate the performance of six classifiers using a large-scale survey of approximately 3000 representative individuals living in Switzerland. The results highlight that the gradient boosting trees and the multinomial logistic regression models are promising, and accommodation, habits and demographic variables are the most decisive features to estimate mobility behaviors .
Roof-top nesting gull study: concerning the population of gulls that breed within the Birmingham boundary

Prior to the mid 1950s, Lesser Black-backed Gull Larus fuscus and Herring Gull Larus argentatus were regarded as very scarce passage migrants or rare visitors to this region (Harrison & Harrison 2005). During the period mid 1950s through to 1986 the regional status of both gulls changed. They were now associated as being resident birds of winter months with large roosts becoming established at major reservoirs; at the same time they were also being recognised as becoming increasingly more numerous on passage. Both species are still to be found in large numbers during the winter period, scavenging at land fill sites, feeding and loafing on farmland and recreation areas and continuing to roost at the region’s larger water expanses. As from 1986 the regional status of Lesser Blackbacked Gull changed once again, now being regarded as an increasingly numerous breeding species (WMBC Checklist Emley 2011). Since 1999 the same breeding status also applies to Herring Gull, albeit in much smaller numbers than Lesser Black-backed Gull. The recent regional history of both species of gull is thoroughly documented in the West Midland Bird Club publication “The New Birds of the West Midlands” (Harrison & Harrison 2005).