Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Layer Thickness Retrieval Over the Qinghai-Tibet Plateau Using Sentinel-1 Multitemporal InSAR Monitored Permafrost Subsidence and Temporal-Spatial Multilayer Soil Moisture Data

Increasing near-surface temperature over the Qinghai-Tibet Plateau (QTP) has led to permafrost degradation and increasing active layer thickness (ALT). In this study, the ALT was estimated based on ground subsidence monitored by multitemporal interferometric synthetic aperture radar (MT-InSAR) and temporal-spatial multilayer soil moisture data. For the ground subsidence monitoring, a modified Stefan piecewise elevation change model based on air temperature data was integrated into a new small baseline subset (NSBAS) chain. A total of 33 scenes of Sentinel-1 data (S-1) were collected over one year to build the MT-InSAR analysis network. Moreover, both soil moisture active/passive (SMAP) L4 surface and root zone soil moisture data and ERA-Interim reanalysis data were used to build an ALT retrieval model. In particular, the global-scaled soil moisture data (SMAP and ERA-Interim) fraction was separated based on the Sentinel-1 amplitude-based land cover classification results and in situ soil moisture data. A typical ALT estimation method based on the point scale groundwater information was also performed to evaluate the performance of the proposed method. Based on the validation of the ground-based ALT observations, the proposed method outperformed the traditional point scale groundwater information-based method, with a correlation coefficient of 0.67, RMSE of 0.70 and ubRMSE of 0.51, respectively. The ERA-Interim-based estimation results were underestimated due to the overestimation of the ERA-Interim soil moisture data. Obvious differences were observed between the ALT of the alpine meadow areas and alpine desert areas. Our results demonstrate that the combination of temporal-spatial multilayer soil moisture data and the MT-InSAR method with S-1 images is a promising approach for the large-scale characterization of ALT.
A Hybrid Artificial Intelligence Model for Predicting the Strength of Foam-Cemented Paste Backfill

Foam-cemented paste backfill (FCPB) has become a trend to solve the problem of roof-contacted filling. In order to solve the time-consuming and labor-intensive disadvantages of laboratory uniaxial compressive strength (UCS) tests, a hybrid artificial intelligence model which combines random forest (RF) algorithm and grid search optimizer (GSO) was proposed for FCPB strength prediction. Moreover, the effects of foaming agent on cement hydration and pore structure characteristics were studied. The results showed that GSO can effectively tune the hyper-parameters of the proposed GSO-RF model and the developed model is an efficient and accurate tool to predict the UCS for FCPB. Though the foaming agent will not change the influence trend of cement-tailings ratio, solid content and curing time, the influence degree will be weakened by the foaming agent. The ranking of the relative importance of influencing variables is: cement-tailings ratio > curing time > foaming agent dosage > solid content. In addition, the foaming agent has no significant effect on the hydration of cement. The foaming agent mainly changes the strength by changing the pore structure characteristics (especially the large pore volume). This research can provided some guidance for developing UCS prediction model for FCPB.
A Study of Emerging Areas in Adoption of Blockchain Technology and it’s Prospective Challenges in India

Blockchain technology is a decentralized distributed system meant for secure computation and information sharing platform free from any central authority that enables multiple authoritative domain which do not trust each other, to cooperate, coordinate and collaborate in rational decision-making process. Bitcoin is pioneer crypto currency platform that uses blockchain technology but over the time it has moved beyond Bitcoin and has successfully bypassed to other application domain. Beside Bitcoin, Ethereum, Hyperledger and Corda, Quorum have emerged as a successful distributive computing platform. Major consortium like Enterprise Ethereum Alliance, Hyperledger and R3 are formed with aim to come up with business solution. Today we have different blockchain application in public sector, finance, supply-chain, healthcare and IoT. In many industries people think blockchain will be transformative. Financial sector is most enthusiastic in adopting blockchain followed by supply chain management. Blockchain is closely looked at in India too. In 2018 Reserve Bank of India (RBI) has issued a white paper in order to identify the potential application areas of blockchain in Indian banking. NITI Aayog is working on national strategy for blockchain which will identify the area where country can implement blockchain. Blockchain is still new and has issues like scalability, security and privacy breaches, limited transaction loads, and high (computational) costs, besides its technical problem, adopting blockchain into Indian market system has its own specific challenges. This paper discusses the prospect of blockchain technology in India, its current position and likely problem in its adoptation to Indian market place. This paper also give overview about blockchain and its fundamental technologies and prominent blockchain platform, it also gives account of application area’s in which blockchain is implemented successfully around the world.
Morphological changes of Giardia lamblia cysts after treatment with ozone and chlorine

Morphological changes of Giardia lamblia cysts after treatment with ozone, free chlorine, or combinations of oxidants were documented using transmission electron microscopy. Experiments were conducted using in vitro-derived G. lamblia cysts. Nucleic acid staining employing LIVE/DEAD BacLightTM was used for the evaluation of cyst viability. Cyst wall damage, plasmalemma breakage, lysis of peripheral vacuoles, and nuclear degradation were main features of cyst injury elicited by ozone or chlorine. Cell death pathways were found to be oxidant-specific. Morphological changes in G. lamblia cysts treated with both ozone and chlorine showed that the preconditioning of the cyst wall by exposure to the first oxidant allowed for easier penetration and increased damage induced by the second oxidant. Taken together, our results indicate that pretreatment of G. lamblia cysts with low doses of one oxidant significantly increases the susceptibility of the parasite to the sequentially applied second oxidant. Key words: o.
Stochastic Joint Bandwidth and Computational Allocation for Multi-Users and Multi-Edge-Servers in 5G D-RANs

Mobile-edge computing (MEC) is now regarded as a promising paradigm for cellular networks, which aims at decreasing end-to-end delay and improving the quality of services. This article innovatively envisions a MEC offloading model for multi-users and collaborative MEC servers for 5G DRANs, where the tasks can be exchanged feasibly between MEC base stations. Firstly, we formulate the joint computational and radio resource management problem to minimize the power consumption of collaborative MEC servers and terminals, which takes into account the constraint of the task buffer stability. And then, an online Lyapunov optimization method is proposed to solve it to get a trade-off between the average weighted power consumption and the execution delay for all tasks distributed in MEC-enabled 5G D-RANs. The numerical results demonstrate that the proposed method has lower power consumption compared with the non-collaborative case. The reason is that the tasks are more reasonably allocated to each collaborative MEC servers with the acceptable execution delay. The control parameter V and collaboration impact factors in the online algorithm are also proved to be efficient and practical to balance the power consumption and execution delay for MEC-enabled 5G D-RANs.
Application Development for Searching Drugs Information

The high need for drugs to cure various diseases make the drugs development grow rapidly and make a collection of drugs data. That collection of drugs data will make it difficult to search. Therefore, a searching application is need to help searching drugs data.In this study, a searching application is developed that can handle a collection of drugs data so that it can be easily searched. Searching application are developed on the mobile platform with Android operating system and use drugs documents in PDF form as the data source. Data from document is extracted, indexed and stored in the database. The process of searching for drugs data can be done by entering keywords of drugs name, drugs generic name, properties and content of the drug.After testing, it was found that data storage method without classification is more suitable than with classification proved by the execution time which is much faster, which is around 0.45-0.56 ms. For the data searching method, it is found that searching with SQL queries is more suitable than FTS3 as proven by the value of F1-Score which is 0.882 for SQL queries and 0.832 for FTS3.
Using SAT for verification in the presence of unknowns

In order to debug IP-based designs in early stages, people often need to verify partial implementation. In this paper, an efficient approach based on Boolean satisfiability (SAT) for verification in the presence of unknowns is presented. We use universally quantified conjunctive normal form (CNF) formula to represent the miter network with unknown constraints, our method achieves higher performance gains, and obtains one to three orders of magnitude performance improvement on ISCAS 85 benchmark circuits than other previous methods.
An Efficient Clustering Algorithm to Simultaneously Detect Multiple Planes in a Point Cloud

Plane detection in a point cloud is one of the primary step for various applications, such as computer vision, ground plane detection for autonomous navigation, obstacle detection, indoor scene reconstruction, etc. In this paper, a new algorithm for simultaneous detection of multiple planes in a point cloud is proposed. The proposed method is a two-step process. In the first step, the surface normals are automatically clustered into probable plane orientations (angular clusters) within a user specified angle threshold, without a priori knowledge of number of planes. In the second step, the angular clusters are further clustered into separate planes, within a user specified distance threshold, based on the normal distances of the points in an angular cluster. In contrast to popular random sampling based methods, the proposed method uses deterministic approach to simultaneously detect all possible planes and has comparable results with the existing methods and is two times faster. The proposed method is implemented using Open3d point cloud library and evaluated on datasets having variety of indoor scenes.
Dynamic Priority-Based Efficient Resource Allocation and Computing Framework for Vehicular Multimedia Cloud Computing

In intelligent transportation system, smart vehicles are equipped with a variety of sensing devices those offer various multimedia applications and services related to smart driving assistance, weather forecasting, traffic congestion information, road safety alarms, and many entertainment and comfort-related applications. These smart vehicles produce a massive amount of multimedia related data that required fast and real-time processing which cannot be fully handled by the standalone onboard computing devices due to their limited computational power and storage capacities. Therefore, handling such multimedia applications and services demanded changes in the underlaying networking and computing models. Recently, the integration of vehicles with cloud computing is emerged as a challenging computing paradigm. However, there are certain challenges related to multimedia contents processing, (i.e., resource cost, fast service response time, and quality of experience) that severely affect the performance of vehicular communication. Thus, in this paper, we propose an efficient resource allocation and computation framework for vehicular multimedia cloud computing to overcome the aforementioned challenges. The performance of the proposed scheme is evaluated in terms of quality of experience, service response time, and resource cost by using the Cloudsim simulator.
Social Network Analysis Based on Graph

Social network node classification has important application value in real life. Traditional iterative application classifiers and random walk based tag propagation algorithms do not fully consider the interaction information between nodes. Graphsage is an effective graph neural network model that can be used for social network analysis. This paper uses the graphsage model to analyze social networks, compares them with traditional social network analysis methods, discusses the values of some parameters of the model, and makes some improvements in the model sampling part.
Research and Design of a High Isolation 5G Antenna for Smart Phone

A multi-band 8×8 MIMO antenna array operating in the Sub-6 GHz band is designed and studied in this paper. Eight T-slot antenna elements with excitable dual-resonant mode are integrated on the ground surface of the substrate, and eight F-shaped feeding belts are set on the surface of the substrate for coupling feeding. The operating bandwidth of the antenna covers LTE42and LTE46 according to the simulated results. It basically meets the acceptable standard of 5G MIMO antenna array in mobile phones (return loss is greater than 6dB, isolation is greater than 10dB). It can be used in large-scale MIMO applications of 5G smart phones.
Deep Learning Cross-Phase Style Transfer for Motion Artifact Correction in Coronary Computed Tomography Angiography

Motion artifacts may occur in coronary computed tomography angiography (CCTA) due to the heartbeat and impede the clinician’s diagnosis of coronary arterial diseases. Thus, motion artifact correction of the coronary artery is required to quantify the risk of disease more accurately. We present a novel method based on deep learning for motion artifact correction in CCTA. Because the image of the coronary artery without motion (the ground-truth data required in supervised deep learning) is medically unattainable, we apply a style transfer method to 2D image patches cropped from full-phase 4D computed tomography (CT) to synthesize these images. We then train a convolutional neural network (CNN) for motion artifact correction using this synthetic ground-truth (SynGT). During testing, the output motion-corrected 2D image patches of the trained network are reinserted into the 3D CT volume with volumetric interpolation. The proposed method is evaluated using both phantom and clinical data. A phantom study demonstrates comparable results to other methods in quantitative performance and outperforms those methods in computation time. For clinical data, a quantitative analysis based on metric measurements is presented that confirms the correction of motion artifacts. Moreover, an observer study finds that by applying the proposed method, motion artifacts are markedly reduced, and boundaries of the coronary artery are much sharper, with a strong inter-observer agreement ( $\kappa = 0.78$ ). Finally, evaluations using commercial software on the original and resulting CT volumes of the proposed method reveal a considerable increase in tracked coronary artery length.
Multi-morbidity Analysis using Community Detection Approach: a Comparative study

Multi-morbidity, or the co-occurrence of two or more diseases at the same time for the same patient, is a major modern health problem in medicine. It can be modeled in network perspective and we can make use of advances in network analysis studies to get insight about this phenomenon. Despite recent trials of applying theoretical network analysis in co-morbidity context, community detection theory has not been enough explored. In this paper, we propose a step forward investigation of the use of community detection algorithms, as a mesoscopic approach of multi-morbidity analysis, to help physicians and computer scientist to tackle this phenomenon in collaborative manner. Firstly, we formulate a model of multi-morbidity in community detection framework. Secondly, we conduct a discussion based on comparative analysis, of some methodological elements from community detection literature. Four algorithms are performed in a constructed real diseases graph. Then we conclude by a discussion based on the obtained results.
Decomposition of Four Species of Leaf Litter in Response to Nitrate Manipulation

A bstract. Input of leaf litter and other allochthonous debris constitute the biological energy base of small watershed streams. The relationship of decomposition rate to nitrate inputs of a 3-4 fold magnitude was tested in three replicate experimental streams. Such a magnitude of nitrate inputs simulated possible input effects of nitrogen fertilization or logging, two common forest practices. At the levels tested, no significant increase in decomposition rate was observed on any of four litter species as a result of nitrate addition. Common parameters measured included weight loss, changes in carbon quality, absolute changes in nitrogen content, and microbial respiration measured as 02 consumption. Results were partially confounded by growth of filamentous green algae during spring. As a result of chemical 02 demand or leaf pack envelopment by the decaying algal mat, ? 13 of the total 02 consumption was not related to litter decomposition. Although not related to increases in nitrate concentration in water, decomposing litter did exhibit a twofold increase in nitrogen content following initial leaching. The mechanism of this increase, whether by biological or chemical means, remains uninvestigated.
Quantitative measure for mosaics with dynamic elements using dense optical flow and the structure similarity index measure.

Image mosaicking is a combination of multiple algorithms that use a series of images or a video to obtain as a result a single image with a larger field of view. However, in some cases the input data contains dynamic elements that change their position from a frame to another and this results in the appearance of errors like ghosting and parallax effects. In this paper, we will present a method to obtain a quantitative measure for mosaics with dynamic elements and how to determine the areas that create these errors. In fact, this was done by using dense optical flow to estimate the motion of all pixels and by comparing the Structure Similarity Index Measure of these areas obtained between the result mosaic and the input images. The obtained quantitative measures show better results and reflect better the obtained visual results.
Semantic Segmentation and Contextual Information Based Image Scene Interpretation: A Review

The images in the scene consist of several objects that depict different relationships among themselves. Interpretation understands those relationships. Thus, scene interpretation is a scene description in which the scene models are consistent with the evidence, context information, and world knowledge. On one side, images in scene interpretation are useful in extracting the information that is related to the physical world and is meant for human operators. On the other side, it has always constituted a great challenge because of the varieties of complex objects due to which computer vision is not much capable to comprehend the information regarding the images in the scene. It also requires many efforts to extract the deeper meaning of the scene. So in review paper, we are going to summarize the methodology proposed by various researchers in terms of semantic segmentation and contextual information to interpret the images and highlight their contributions and challenges which still persist. Our analysis of the different methods proposed is also provided to draw some conclusions.
GDPR Compliant Consent Driven Data Protection in Online Social Networks: A Blockchain-Based Approach

The enforcement of the General Data Protection Regulation (GDPR) represents a great challenge for online social networks (OSNs). Several OSNs are making significant changes to their systems to achieve compliance with GDPR. OSNs are required to obtain meaningful consent from users to achieve GDPR compliance. GDPR recognizes user's consent as a legitimate ground for personal data processing in the context of online social networks. This article presents a comparative study about the criteria for valid consent under GDPR and existing consent seeking practices of OSNs. In order to simplify the comparative process, Facebook is taken as a case study for online social networks. In conclusion of the comparative study, we argue that existing consent mechanisms in OSNs are not GDPR compliant. To achieve GDPR compliance in online social networks, we advocate a blockchain-based approach for consent management. This paper paves the way for designing a blockchain-based GDPR compliant consent management model for personal data processing in online social networks.
Multichannel Auction Strategies in Online Advertising With a Profit Model

In the online advertising market that has increasingly emerged with the Internet’s rapid development, supply side platforms (SSP) use multichannel auctions to sell demand side platform (DSP) advertisements. To explore which channel is dominant, we analyze three channels including direct bidding, bidding with costly information and proxy bidding. We develop an analytical model for studying the SSP’s channel decisions and expected profit that will be generated in the context of such multichannel auctions. First, we construct the SSP profit models for these different channels. Then, comparing the profits, we analyze the formulation of strategies for these different situations. Importantly, for the three channels, we study how the probability that bidders will attach a higher value to advertisements influences the multichannel strategy of the SSP. The results show the following: (1) each channel has a market share; (2) it is better for SSPs to use the costly bidding channel if the probability that the bidders are higher value bidders is low or very high; and (3) the SSPs should consider the use of proxy bidding when the reserve price is high and the probability of having higher value bidders is not high.