Miracle-dz/UniFAR-FTUs huggingface dataset:
TaxiInt: Predicting the Taxi Flow at Urban Traffic Hotspots Using Graph Convolutional Networks and the Trajectory Data

Taxi flow is an important part of the urban intelligent transportation system. The accurate prediction of taxi flow provides an attractive way to find the potential traffic hotspots in the city, which helps to avoid serious traffic congestions by taking effective measures in advance. The current prediction of taxi flow and its impact on urban transportation are closely related to the passenger origin-destination (OD) information. However, high-quality OD information is not always available. To address this problem, a prediction model, named as TaxiInt, is proposed in this study. Different from other density-clustering-based approaches, neural network, or OD information based models, TaxiInt predicted the taxi flow using the trajectory data of taxis. The spatial features and temporal features of each road were extracted using a graph convolutional network, which was trained with the road network information and the trajectory data. The experiments carried on a real taxi dataset showed the validity of our model. It can predict the taxi flow at a given urban intersection with high accuracy.
Formation Mechanism of Cubic Mesoporous Carbon Monolith Synthesized by Evaporation-Induced Self-assembly

The formation mechanism of the cubic mesoporous carbon, FDU-16, synthesized by evaporation-induced self-assembly (EISA) was investigated at the molecular level by electron paramagnetic resonance (EPR) spectroscopic techniques. This material is synthesized using F127 pluronic block copolymer [poly(ethylene oxide)–poly(propylene oxide)–poly(ethylene oxide) (PEO106-PPO70-PEO106)] as a structure-directing agent (template) and phenolic resol as a carbon precursor. Using two spin probes derived from pluronics with PEO and PPO chains of different lengths that are designed to sense different regions of the system, we followed the evaporation and thermopolymerization stages of the synthesis in situ. To make such studies possible, we have used a polyurethane foam support, placed in the EPR tube, which allows for the efficient solvent evaporation as required for EISA. We focused on the evolution of the dynamics of the template and its interactions with the resol during the reaction. We observed that during the evapo.
Cooperation/Competition between Halogen Bonds and Hydrogen Bonds in Complexes of 2, 6-Diaminopyridines and X-CY3 (X = Cl, Br; Y = H, F)

The DFT calculations have been performed on a series of two-element complexes formed by substituted 2,6-diaminopyridine (R−PDA) and pyridine (R−Pyr) with X−CY3 molecules (where X = Cl, Br and Y = H, F). The primary aim of this study was to examine the intermolecular hydrogen and halogen bonds in the condition of their mutual coexistence. Symmetry/antisymmetry of the interrelation between three individual interactions is addressed. It appears that halogen bonds play the main role in the stabilization of the structures of the selected systems. However, the occurrence of one or two hydrogen bonds was associated with the favourable geometry of the complexes. Moreover, the impact of different substituent groups attached in the para position to the aromatic ring of the 2,6-diaminopyridine and pyridine on the character of the intermolecular hydrogen and halogen bonds was examined. The results indicate that the presence of electron-donating substituents strengthens the bonds. In turn, the presence of electron-withdrawing substituents reduces the strength of halogen bonds. Additionally, when hydrogen and halogen bonds lose their leading role in the complex formation, the nonspecific electrostatic interactions between dipole moments take their place. Analysis was based on geometric, energetic, and topological parameters of the studied systems.
Shoal: A Network Level Moving Target Defense Engine with Software Defined Networking

Moving Target Defense (MTD) was proposed as a promising defense paradigm to introduce various uncertainties into computer systems, which can greatly raise the bar for the attackers. Currently, there are two classes of MTD research over computer system, system level MTD and network level MTD. System level MTD research introduces uncertainties to various aspects of computer systems; while network level MTD research brings unpredictability of network properties to the target network. A lot of network level MTD research has been proposed, which covers various aspects of computer network. However, the existing MTD approaches usually target on one aspect of computer network, and most of them are designed against a certain network security threat. They can hardly defend against complex attacks or provide complicated protections. In this paper, we propose Shoal, a Moving Target Defense engine with multiple MTD strategies over SDN networks. By applying hybrid and multiple network level MTD methods, Shoal is capable of providing complicated protections and defending advanced attacks. We evaluate Shoal in two advanced protection scenarios, moving target surface and Crossfire attack. The evaluation results, in term of security effectiveness and performance cost, show the protection provided by Shoal’s hybrid MTD methods is effective and the performance cost is relatively low. Received on 25 March 2021; accepted on 09 May 2021; published on 01 June 2021
Computational Resource Consumption in Convolutional Neural Network Training - A Focus on Memory

c (cid:13) The Authors 2021. This paper is published with open access at SuperFri.org Deep neural networks (DNNs) have grown in popularity in recent years thanks to the increase in computing power and the size and relevance of data sets. This has made it possible to build more complex models and include more areas of research and application. At the same time, the amount of data generated during the training process of these models puts great pressure on the capacity and bandwidth of the memory subsystem and, as a direct consequence, has become one of the biggest bottlenecks for the scalability of neural networks. Therefore, the optimizing of the workloads produced by DNNs in the memory subsystem requires a detailed understanding of access to the memory and the interactions between the processor, accelerator devices, and the system memory hierarchy. However, contrary to what would be expected, most DNN proﬁlers work at a high level, so they only perform an analysis of the model and individual layers of the network leaving aside the complex interactions between all the hardware components involved in the training. This article shows the characterization performed using a convolutional neural network implemented in the two most popular frameworks: TensorFlow and Pytorch. Likewise, the behavior of the component interactions is discussed by varying the batch size for two sets of synthetic data and showing the results obtained by the proﬁler created for the study. Moreover, the results obtained when evaluating the AlexNet version on TensorFlow and its similarity in behavior when using a basic CNN are included.
A multi-division convolutional neural network-based plant identification system

Background Plants have an important place in the life of all living things. Today, there is a risk of extinction for many plant species due to climate change and its environmental impact. Therefore, researchers have conducted various studies with the aim of protecting the diversity of the planet’s plant life. Generally, research in this area is aimed at determining plant species and diseases, with works predominantly based on plant images. Advances in deep learning techniques have provided very successful results in this field, and have become widely used in research studies to identify plant species. Methods In this paper, a Multi-Division Convolutional Neural Network (MD-CNN)-based plant recognition system was developed in order to address an agricultural problem related to the classification of plant species. In the proposed system, we divide plant images into equal nxn-sized pieces, and then deep features are extracted for each piece using a Convolutional Neural Network (CNN). For each part of the obtained deep features, effective features are selected using the Principal Component Analysis (PCA) algorithm. Finally, the obtained effective features are combined and classification conducted using the Support Vector Machine (SVM) method. Results In order to test the performance of the proposed deep-based system, eight different plant datasets were used: Flavia, Swedish, ICL, Foliage, Folio, Flower17, Flower102, and LeafSnap. According to the results of these experimental studies, 100% accuracy scores were achieved for the Flavia, Swedish, and Folio datasets, whilst the ICL, Foliage, Flower17, Flower102, and LeafSnap datasets achieved results of 99.77%, 99.93%, 97.87%, 98.03%, and 94.38%, respectively.
Gait Symmetry Analysis Based on Dynamic Time Warping

Background: Gait deviations may negatively affect the articular surfaces of the lower extremity joints and lead to some minor degenerative changes. The simplest method for gait evaluation is to assess the symmetry of its parameters, assuming that each undisturbed gait should be symmetrical. This study aims to quantify the degree of asymmetry of kinematic and kinetic parameters caused by the presence of different ankle orthosis settings using Dynamic Time Warping (DTW). Methods: Barefoot gait and gait with four different walker settings were investigated in eighteen healthy persons. Kinematic and kinetic parameters were measured using the Vicon system and Kistler plates. Symmetry was assessed using the DTW method. Results: It was shown that the presence of different ankle orthosis settings significantly disturbs the symmetry of all lower limb kinematic parameters and only knee and hip torques. The highest values of asymmetry were noted for the walker set at 15° of dorsiflexion (15DF). Conclusions: The DTW method allowed us to quantify the degree of asymmetry throughout the gait cycle in relation to barefoot walking. Our results suggest that each orthosis position analysed in this study fulfills its protective function, but gait 15DF can lead to the overload of knee and hip joints.
Dataset on the Effects of Anti-Insect Nets of Different Porosity on Mineral and Organic Acids Profile of Cucurbita pepo L. Fruits and Leaves

The growing interest in healthy foods has driven the agricultural sector towards eco-friendly implementation to manage biotic and abiotic factors in protected environments. In this perspective, anti-insect nets are an effective tool for controlling harmful insect populations concomitantly with reducing chemicals’ interference. However, the low porosity of nets necessary to ensure high exclusion efficiency for a designated insect leads to reduced airflow, impacting the productivity and quality attributes of vegetables. The evidence presented in this dataset pertains to the content of total nitrogen, minerals (i.e., NO3, K, PO4, SO4, Ca, Mg, Cl, and Na), and organic acids (i.e., malate and citrate) of zucchini squash (Cucurbita pepo L. cv. Zufolo F1) in leaves and fruits grown with two anti-insect nets with different porosities (Biorete® 50 mesh and Biorete® 50 mesh AirPlus), is and analyzed by the Kjeldahl method and ion chromatography (ICS3000), respectively. Data of total nitrogen concentration, macronutrients, and organic acids provide in-depth information about plants’ physiological response to microclimate changes induced by anti-insect nets.
Lateralized Declarative-Like Memory for Conditional Spatial Information in Domestic Chicks (Gallus gallus)

Declarative memory is an explicit, long-term memory system, used in generalization and categorization processes and to make inferences and to predict probable outcomes in novel situations. Animals have been proven to possess a similar declarative-like memory system. Here, we investigated declarative-like memory representations in young chicks, assessing the roles of the two hemispheres in memory recollection. Chicks were exposed for three consecutive days to two different arenas (blue/yellow), where they were presented with two panels, each depicting a different stimulus (cross/square). Only one of the two stimuli was rewarded, i.e., it hid a food reward. The position (left/right) of the rewarded stimulus remained constant within the same arena, but it differed between the two arenas (e.g., reward always on the left in the blue context and on the right in the yellow one). At test, both panels depicted the rewarded stimulus, thus chicks had to remember food position depending on the previously experienced contextual rule. Both binocular and right-eye monocularly-tested chicks correctly located the reward, whereas left-eye monocularly-tested chicks performed at the chance level. We showed that declarative-like memory of integrated information is available at early stages of development, and it is associated with a left hemisphere dominance.
Increasing fault tolerance of data plane on the internet of things using the software-defined networks

Considering the Internet of Things (IoT) impact in today’s world, uninterrupted service is essential, and recovery has received more attention than ever before. Fault-tolerance (FT) is an essential aspect of network resilience. Fault-tolerance mechanisms are required to ensure high availability and high reliability in systems. The advent of software-defined networking (SDN) in the IoT plays a significant role in providing a reliable communication platform. This paper proposes a data plane fault-tolerant architecture using the concepts of software-defined networks for IoT environments. In this work, a mathematical model called Shared Risk Link Group (SRLG) calculates redundant paths as the primary and backup non-overlapping paths between network equipment. In addition to the fault tolerance, service quality was considered in the proposed schemes. Putting the percentage of link bandwidth usage and the rate of link delay in calculating link costs makes it possible to calculate two completely non-overlapping paths with the best condition. We compare our two proposed dynamic schemes with the hybrid disjoint paths (Hybrid_DP) method and our previous work. IoT developments, wireless and wired equipment are now used in many industrial and commercial applications, so the proposed hybrid dynamic method supports both wired and wireless devices; furthermore multiple link failures will be supported in the two proposed dynamic schemes. Simulation results indicate that, while reducing the error recovery time, the two proposed dynamic designs lead to improved service quality parameters such as packet loss and delay compared to the Hybrid_DP method. The results show that in case of a link failure in the network, the proposed hybrid dynamic scheme’s recovery time is approximately 12 ms. Furthermore, in the proposed hybrid dynamic scheme, on average, the recovery time, the packet loss, and the delay improved by 22.39%, 8.2%, 5.66%, compared to the Hybrid_DP method, respectively.
Removal of H3K27me3 by JMJ Proteins Controls Plant Development and Environmental Responses in Arabidopsis

Trimethylation of histone H3 lysine 27 (H3K27me3) is a highly conserved repressive histone modification that signifies transcriptional repression in plants and animals. In Arabidopsis thaliana, the demethylation of H3K27 is regulated by a group of JUMONJI DOMAIN-CONTANING PROTEIN (JMJ) genes. Transcription of JMJ genes is spatiotemporally regulated during plant development and in response to the environment. Once JMJ genes are transcribed, recruitment of JMJs to target genes, followed by demethylation of H3K27, is critically important for the precise control of gene expression. JMJs function synergistically and antagonistically with transcription factors and/or other epigenetic regulators on chromatin. This review summarizes the latest advances in our understanding of Arabidopsis H3K27me3 demethylases that provide robust and flexible epigenetic regulation of gene expression to direct appropriate development and environmental responses in plants.
The FAANG Data Portal: Global, Open-Access, “FAIR”, and Richly Validated Genotype to Phenotype Data for High-Quality Functional Annotation of Animal Genomes

The Functional Annotation of ANimal Genomes (FAANG) project is a worldwide coordinated action creating high-quality functional annotation of farmed and companion animal genomes. The generation of a rich genome-to-phenome resource and supporting informatic infrastructure advances the scope of comparative genomics and furthers the understanding of functional elements. The project also provides terrestrial and aquatic animal agriculture community powerful resources for supporting improvements to farmed animal production, disease resistance, and genetic diversity. The FAANG Data Portal (https://data.faang.org) ensures Findable, Accessible, Interoperable and Reusable (FAIR) open access to the wealth of sample, sequencing, and analysis data produced by an ever-growing number of FAANG consortia. It is developed and maintained by the FAANG Data Coordination Centre (DCC) at the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI). FAANG projects produce a standardised set of multi-omic assays with resulting data placed into a range of specialised open data archives. To ensure this data is easily findable and accessible by the community, the portal automatically identifies and collates all submitted FAANG data into a single easily searchable resource. The Data Portal supports direct download from the multiple underlying archives to enable seamless access to all FAANG data from within the portal itself. The portal provides a range of predefined filters, powerful predictive search, and a catalogue of sampling and analysis protocols and automatically identifies publications associated with any dataset. To ensure all FAANG data submissions are high-quality, the portal includes powerful contextual metadata validation and data submissions brokering to the underlying EMBL-EBI archives. The portal will incorporate extensive new technical infrastructure to effectively deliver and standardise FAANG's shift to single-cellomics, cell atlases, pangenomes, and novel phenotypic prediction models. The Data Portal plays a key role for FAANG by supporting high-quality functional annotation of animal genomes, through open FAIR sharing of data, complete with standardised rich metadata. Future Data Portal features developed by the DCC will support new technological developments for continued improvement for FAANG projects.
Effect of Sitting Position vs. Supine Position With the Head Turned to the Affected Side on Benign Paroxysmal Positional Vertigo Fatigue

Objective: In benign paroxysmal positional vertigo (BPPV), positional nystagmus becomes generally weaker when the Dix–Hallpike test is repeated. This phenomenon is termed BPPV fatigue. We previously reported that the effect of BPPV fatigue deteriorates over time (i.e., the positional nystagmus is observed again after maintaining a sitting head position). The aim of this study was to investigate whether the effect of BPPV fatigue attenuates after maintaining a supine position with the head turned to the affected side. Methods: Twenty patients with posterior-canal-type BPPV were assigned to two groups. Group A received Dix–Hallpike test, were returned to the sitting position (reverse Dix–Hallpike test) with a sitting head position for 10 min, and then received a second Dix–Hallpike test. Group B received Dix–Hallpike test, were kept in the supine position with the head turned to the affected side for 10 min, and then received reverse Dix–Hallpike test followed by the second Dix–Hallpike test. The maximum slow phase eye velocity (MSPEV) of positional nystagmus induced by the first, reverse, and second Dix–Hallpike test were analyzed. Results: The ratio of MSPEV of the positional nystagmus induced by the second Dix–Hallpike test relative to the first Dix–Hallpike test was significantly smaller in group B than that in group A. There was no difference in the MSPEV of the positional nystagmus induced by the reverse Dix–Hallpike test between group A and B. Conclusions: The effect of BPPV fatigue is continued by maintaining a supine position with the head turned to the affected side, while the effect is weakened by maintaining a sitting head position. On the basis of the most widely accepted theory of the pathophysiology of BPPV fatigue, in which the particles become dispersed along the canal during head movement in the Dix–Hallpike test, we found an inconsistency whereby the dispersed otoconial debris return to a mass during the sitting position but do not return to a mass in the supine position with the head turned to the affected side. Future studies are required to determine the exact pathophysiology of BPPV fatigue. Classification of Evidence: 2b.
Effects of Pasteurization and High-Pressure Processing of Camel and Bovine Cheese Quality, and Proteolysis Contribution to Camel Cheese Softness

The effects of high-pressure processing (HPP) compared to thermal treatments on the quality of camel vs. bovine cheeses were studied. The study showed that camel milk has a lower microbial load compared to bovine milk, which is maintained during 7 days' storage of the processed milk. The effect of three HPP treatments (350, 450, and 550 MPa for 5 min at 4°C) and two pasteurization treatments (65°C for 30 min and 75°C for 30 s) on the quality of soft unripened camel and bovine milk cheeses were accessed. The cheeses were evaluated for pH, yield, proximate composition, textural and rheological properties, microstructure, and protein profile by SDS-PAGE electrophoresis. The effects of the treatments on cheese's hardness were different between the camel and bovine cheeses; while heat treatment at 65°C for 30 min gave the hardest bovine milk cheese (1,253 ± 20), HPP treatment at 350 MPa for 5 min gave the highest value for camel milk cheese (519 ± 5) (p < 0.05). The hardness of the cheeses was associated with low yield and moisture content. SDS-PAGE electrophoresis revealed that extensive proteolysis might have contributed to the softness of camel cheeses compared to bovine and suggested the involvement of some residual enzyme activities.
Factors Affecting the Spatial Resolution in 2D Grating–Based X-Ray Phase Contrast Imaging

X-ray phase contrast imaging is a promising technique in X-ray biological microscopy, as it improves the contrast of images for materials with low electron density compared to traditional X-ray imaging. The spatial resolution is an important parameter to evaluate the image quality. In this paper, simulation of factors which may affect the spatial resolution in a typical 2D grating–based phase contrast imaging system is conducted. This simulation is based on scalar diffraction theory and the operator theory of imaging. Absorption, differential phase contrast, and dark-field images are retrieved via the Fourier transform method. Furthermore, the limitation of the grating-to-detector distance in the spatial harmonic method is discussed in detail.