Customized Convolutional Neural Network for Accurate Detection of Deep Fake Images in Video Collections

The motivation for this study is that the quality of deep fakes is constantly improving,which leads to the need to develop new methods for their detection.The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection,which is then used as input to the CNN.The customized Convolutional Neural Network method is the date augmented-based CNN model to generate‘fake data’or‘fake images’.This study was carried out using Python and its libraries.We used 242 films from the dataset gathered by the Deep Fake Detection Challenge,of which 199 were made up and the remaining 53 were real.Ten seconds were allotted for each video.There were 318 videos used in all,199 of which were fake and 119 of which were real.Our proposedmethod achieved a testing accuracy of 91.47%,loss of 0.342,and AUC score of 0.92,outperforming two alternative approaches,CNN and MLP-CNN.Furthermore,our method succeeded in greater accuracy than contemporary models such as XceptionNet,Meso-4,EfficientNet-BO,MesoInception-4,VGG-16,and DST-Net.The novelty of this investigation is the development of a new Convolutional Neural Network(CNN)learning model that can accurately detect deep fake face photos.

Actor-Critic-Based UAV-Assisted Data Collection in the Wireless Sensor Network

Wireless Sensor Network(WSN)is widely utilized in large-scale distributed unmanned detection scenarios due to its low cost and flexible installation.However,WSN data collection encounters challenges in scenarios lacking communication infrastructure.Unmanned aerial vehicle(UAV)offers a novel solution for WSN data collection,leveraging their high mobility.In this paper,we present an efficient UAV-assisted data collection algorithm aimed at minimizing the overall power consumption of the WSN.Firstly,a two-layer UAV-assisted data collection model is introduced,including the ground and aerial layers.The ground layer senses the environmental data by the cluster members(CMs),and the CMs transmit the data to the cluster heads(CHs),which forward the collected data to the UAVs.The aerial network layer consists of multiple UAVs that collect,store,and forward data from the CHs to the data center for analysis.Secondly,an improved clustering algorithm based on K-Means++is proposed to optimize the number and locations of CHs.Moreover,an Actor-Critic based algorithm is introduced to optimize the UAV deployment and the association with CHs.Finally,simulation results verify the effectiveness of the proposed algorithms.

Reliable Data Collection Model and Transmission Framework in Large-Scale Wireless Medical Sensor Networks

Large-scale wireless sensor networks(WSNs)play a critical role in monitoring dangerous scenarios and responding to medical emergencies.However,the inherent instability and error-prone nature of wireless links present significant challenges,necessitating efficient data collection and reliable transmission services.This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs.The primary goal is to enhance the reliability of data collection and transmission services,ensuring a comprehensive and practical approach.Our approach focuses on refining the hop-count-based routing scheme to achieve fairness in forwarding reliability.Additionally,it emphasizes reliable data collection within clusters and establishes robust data transmission over multiple hops.These systematic improvements are designed to optimize the overall performance of the WSN in real-world scenarios.Simulation results of the proposed protocol validate its exceptional performance compared to other prominent data transmission schemes.The evaluation spans varying sensor densities,wireless channel conditions,and packet transmission rates,showcasing the protocol’s superiority in ensuring reliable and efficient data transfer.Our systematic end-to-end design successfully addresses the challenges posed by the instability of wireless links in large-scaleWSNs.By prioritizing fairness,reliability,and efficiency,the proposed protocol demonstrates its efficacy in enhancing data collection and transmission services,thereby offering a valuable contribution to the field of medical event-drivenWSNs.

Genetic Diversity Analysis and Constructing Core Collection Based on Phenotypes in Cotton

In that DNA diversity detected nowadays could not mean phenotypic diversity,it is the precondition of breeding project and basic research of crop improvement that genetic diversity analysis and sampling of core collection by phenotypes.Phenotyping and statistic analysis on 9 traits of 92 accessions of cotton germplasm resource from three species（Gossypium hirsutum L.,Gossypium barbadence L.and Gossypium arboreum L.）were conducted.And the statistics（variation coefficient,proportion of special accessions and Shannon-Weaver information index）indicated that initial collection had abundant phenotypic diversity;software NTSYS-pc and the unweighted pair group method of arithmetic（UPGMA）were used for the cluster analysis on genetic similarity coefficient and genetic distance matrix,and the result showed that the genetic relationship among accessions was highly consistent with the pedigree;22 accessions of core collection were selected by software QGAStation,four statistics,such as variance difference percentage（VD%）,mean difference percentage（MD%）,coincidence rate（CR%）and variable rate（VR%）,showed that the genetic diversity of core collection was approximately equal to the initial collection.The results of genetic diversity analysis based on phenotypic data and sampling of core collection would provide reference for breeding projects and basic research.

The USDA cucumber (Cucumis sativus L.) collection: genetic diversity, population structure, genome-wide association studies, and core collection development

Germplasm collections are a crucial resource to conserve natural genetic diversity and provide a source of novel traits essential for sustained crop improvement.Optimal collection,preservation and utilization of these materials depends upon knowledge of the genetic variation present within the collection.Here we use the high-throughput genotyping-by-sequencing(GBS)technology to characterize the United States National Plant Germplasm System(NPGS)collection of cucumber(Cucumis sativus L.).The GBS data,derived from 1234 cucumber accessions,provided more than 23 K high-quality single-nucleotide polymorphisms(SNPs)that are well distributed at high density in the genome(~1 SNP/10.6 kb).The SNP markers were used to characterize genetic diversity,population structure,phylogenetic relationships,linkage disequilibrium,and population differentiation of the NPGS cucumber collection.These results,providing detailed genetic analysis of the U.S.cucumber collection,complement NPGS descriptive information regarding geographic origin and phenotypic characterization.We also identified genome regions significantly associated with 13 horticulturally important traits through genome-wide association studies(GWAS).Finally,we developed a molecularly informed,publicly accessible core collection of 395 accessions that represents at least 96%of the genetic variation present in the NPGS.Collectively,the information obtained from the GBS data enabled deep insight into the diversity present and genetic relationships among accessions within the collection,and will provide a valuable resource for genetic analyses,gene discovery,crop improvement,and germplasm preservation.

Comparison of Copepod Collection Efficiencies by Three Commonly Used Plankton Nets: A Case Study in Bohai Sea, China

Abstract Selection of net with a suitable mesh size is a key concern in the quantitative assessment of zooplankton, which is cru- cial to understand pelagic ecosystem processes. This study compared the copepod collecting efficiency of three commonly used plankton nets, namely, the China standard coarse net （505 gm mesh）, the China standard fine net （77 gin）, and the WP-2 net （200 μm） The experiment was performed at six stations in the Bohai Sea during the autumn of 2012. The coarse net substantially un- der-sampled small individuals （body widths 〈 672 gm） and led to the lowest species number in each tow, whereas the fine net col- lected all small copepod species but failed to collect rare species. The WP-2 net appeared to be a compromise of the two other nets, collecting both small copepods and rare species. The abundance of copepods collected by the coarse net （126.4±86.5 indm-3） was one to two orders of magnitude lower than that by the WP-2 net （5802.4 ± 2595.4 indm3）, and the value of the fine net （11117.0±4563.41 indm-3） was nearly twice that of the WP-2 net. The abundance of large copepods （i.e., adult Calanus sinicus） in the three nets showed no significant differences, but the abundance of small copepods declined with decreasing mesh size. The dif- ference in abundance resulted from the under-sampling of small copepods with body widths 〈 672 μm and 〈 266μm by the coarse and WP-2 nets, respectively.

Genetic structure associated with diversity and geographic distribution in the USDA rice world collection

Cultivated rice (Oryza sativa L.) is structured into five genetic groups, indica, aus, tropical japonica, temperate japonica and aromatic. Genetic characterization of rice germplasm collections will enhance their utilization by the global research community for improvement of rice. The USDA world collection of rice germ-plasm that was initiated in 1904 has resulted in over 18,000 accessions from 116 countries, but their ancestry information is not available. A core subset, including 1,763 accessions repre-senting the collection, was genotyped using 72 genome-wide SSR markers, and analyzed for genetic structure, genetic relationship, global distribution and genetic diversity. Ancestry analysis proportioned this collection to 35% indica, 27% temperate japonica, 24% tropical japonica, 10% aus and 4% aromatic. Graphing model-based ancestry coefficients demon-strated that tropical japonica showed up mainly in the American continents and part of the South Pacific and Oceania, and temperate japonica in Europe and the North Pacific far from the equator, which matched the responses to tem-perature. Indica is adapted to the warm areas of Southern Asia, South China, Southeast Asia, South Pacific and Central Africa and around the equator while aus and aromatic are special types of rice that concentrates in Bangladesh and India. Indica and aus were highly diversified while temperate and tropical japonicas had low diversity, indicated by average alleles and pri-vate alleles per locus. Aromatic has the most polymorphic information content. Indica and aromatic were genetically closer to tropical ja-ponica than temperate japonica. This study of global rice has found significant population stratification generally corresponding to major geographic regions of the world.

Maximum Data Collection Rate in Rechargeable Wireless Sensor Networks with Multiple Sinks

In rechargeable wireless sensor networks, a sensor cannot be always benefi cial to conserve energy when a network can harvest excessive energy from the environment due to its energy replenished continually and limited energy storage capacity. Therefore, surplus energy of a node can be utilized for strengthening packet delivery efficiency and improving data collection rate. In this work, we propose an algorithm to compute an upper data generation rate that maximizes it as an optimization problem for a network with multiple sinks, which is formulated as a linear programming problem. Subsequently, a dual problem by introducing Lagrange multipliers is constructed, and subgradient algorithms are used to solve it in a distributed manner. The resulting algorithms are guaranteed to converge to an optimal data generation rate, which are illustrated by an example in which an optimum data generation rate is computed for a network of randomly distributed nodes. Through extensive simulation and experiments, we demonstrate our algorithm is efficient to maximize data collection rate in rechargeable wireless sensor networks.

Exploiting the diversity of tomato:the development of a phenotypically and genetically detailed germplasm collection

A collection of 163 accessions,including Solanum pimpinellifolium,Solanum lycopersicum var.cerasiforme and Solanum lycopersicum var.lycopersicum,was selected to represent the genetic and morphological variability of tomato at its centers of origin and domestication:Andean regions of Peru and Ecuador and Mesoamerica.The collection is enriched with S.lycopersicum var.cerasiforme from the Amazonian region that has not been analyzed previously nor used extensively.The collection has been morphologically characterized showing diversity for fruit,flower and vegetative traits.Their genomes were sequenced in the Varitome project and are publicly available(solgenomics.net/projects/varitome).The identified SNPs have been annotated with respect to their impact and a total number of 37,974 out of 19,364,146 SNPs have been described as high impact by the SnpEeff analysis.GWAS has shown associations for different traits,demonstrating the potential of this collection for this kind of analysis.We have not only identified known QTLs and genes,but also new regions associated with traits such as fruit color,number of flowers per inflorescence or inflorescence architecture.To speed up and facilitate the use of this information,F2 populations were constructed by crossing the whole collection with three different parents.This F2 collection is useful for testing SNPs identified by GWAs,selection sweeps or any other candidate gene.All data is available on Solanaceae Genomics Network and the accession and F2 seeds are freely available at COMAV and at TGRC genebanks.All these resources together make this collection a good candidate for genetic studies.

Genetic diversity and a population structure analysis of accessions in the Chinese cowpea [Vigna unguiculata(L.)Walp.]germplasm collection

Cowpea(Vigna imguicuiata) is an important legume crop with diverse uses. The species is presently a minor crop, and evaluation of its genetic diversity has been very limited. In this study, a total of 200 genic and 100 genomic simple sequence repeat(SSR) markers were developed from cowpea unigene and genome sequences, respectively. Among them, 27 genic and 27 genomic SSR markers were polymorphic and were used for assessment of genetic diversity and population structure in 105 selected cowpea accessions. A total of 155 alleles and 2.9 alleles per marker were identified, and the average polymorphic information content(PIC) value was 0.3615. The average PIC of genomic SSRs(0.3996) was higher than that of genic SSRs(0.3235), and most of the polymorphic genomic SSRs were composed of di-and trinucleotide repeats(51.9% and 37.0% of all loci, respectively). The low level of detected genetic diversity may be attributed to a severe genetic bottleneck that occurred during the cowpea domestication process. The accessions were classified by structure and cluster analysis into four subgroups that correlated well with their geographic origins or collection sites. The classification results were also consistent with the results from principal coordinate analysis and can be used as a guide during future germplasm collection and selection of accessions as breeding materials for cultivar improvement. The newly developed genic and genomic SSR markers described in this study will be valuable genomic resources for the assessment of genetic diversity, population structure, evaluation of germplasm accessions, construction of genetic maps, identification of genes of interest,and application of marker-assisted selection in cowpea breeding programs.

UAV trajectory planning algorithmfor data collection in wireless sensor networks

In order to maximize the value of information(VoI)of collected data in unmanned aerial vehicle(UAV)-aided wireless sensor networks(WSNs),a UAV trajectory planning algorithm named maximum VoI first and successive convex approximation(MVF-SCA)is proposed.First,the Rician channel model is adopted in the system and sensor nodes(SNs)are divided into key nodes and common nodes.Secondly,the data collection problem is formulated as a mixed integer non-linear program(MINLP)problem.The problem is divided into two sub-problems according to the different types of SNs to seek a sub-optimal solution with a low complexity.Finally,the MVF-SCA algorithm for UAV trajectory planning is proposed,which can not only be used for daily data collection in the target area,but also collect time-sensitive abnormal data in time when the exception occurs.Simulation results show that,compared with the existing classic traveling salesman problem(TSP)algorithm and greedy path planning algorithm,the VoI collected by the proposed algorithm can be improved by about 15%to 30%.

Endoscopic intramural cystogastrostomy for treatment of peripancreatic fluid collection: A viewpoint from a surgeon

Percutaneous or endoscopic drainage is the initial choice for the treatment of peripancreatic fluid collection in symptomatic patients.Endoscopic transgastric fenestration(ETGF)was first reported for the management of pancreatic pseu-docysts of 20 patients in 2008.From a surgeon’s viewpoint,ETGF is a similar procedure to cystogastrostomy in that they both produce a wide outlet orifice for the drainage of fluid and necrotic debris.ETGF can be performed at least 4 wk after the initial onset of acute pancreatitis and it has a high priority over the surgical approach.However,the surgical approach usually has a better success rate because surgical cystogastrostomy has a wider outlet(>6 cm vs 2 cm)than ETGF.However,percutaneous or endoscopic drainage,ETGF,and surgical approach offer various treatment options for peripancreatic fluid collection patients based on their conditions.

Core collection construction of tea plant germplasm in Anhui Province based on genetic diversity analysis using simple sequence repeat markers

The tea plant[Camellia sinensis(L.)O.Kuntze]is an industrial crop in China.The Anhui Province has a long history of tea cultivation and has a large resource of tea germplasm with abundant genetic diversity.To reduce the cost of conservation and utilization of germplasm resources,a core collection needs to be constructed.To this end,573 representative tea accessions were collected from six major tea-producing areas in Anhui Province.Based on 60 pairs of simple sequence repeat(SSR)markers,phylogenetic relationships,population structure and principal coordinate analysis(PCoA)were conducted.Phylogenetic analysis indicated that the 573 tea individuals clustered into five groups were related to geographical location and were consistent with the results of the PCoA.Finally,we constructed a core collection consisting of 115 tea individuals,accounting for 20%of the whole collection.The 115 core collections were considered to have a 90.9%retention rate for the observed number of alleles(Na),and Shannon’s information index(I)of the core and whole collections were highly consistent.Of these,39 individuals were preserved in the Huangshan area,accounting for 33.9%of the core collection,while only 10 individuals were reserved in the Jinzhai County,accounting for 8.9%of the core set.PCoA of the accessions in the tea plant core collection exhibited a pattern nearly identical to that of the accessions in the entire collection,further supporting the broad representation of the core germplasm in Anhui Province.The results demonstrated that the core collection could represent the genetic diversity of the original collection.Our present work is valuable for the high-efficiency conservation and utilization of tea plant germplasms in Anhui Province.

Genetics, Genetic Collection of Characters of Structural Shrub Characteristics of G. hirsutum L.

The structure and shape of a cotton shrub aredetermined in result of combining theirelements:length and amount internodes of amain stem and fruit brunches,amount and shapeof leaf blades.Some lines of genetic collectionhave been crossed between each other for

Energy-Efficient Mobile Data Collection Adopting Node Cooperation in an Underwater Acoustic Sensor Network

This paper considers an underwater acoustic sensor network with one mobile surface node to collect data from multiple underwater nodes,where the mobile destination requests retransmission from each underwater node individually employing traditional automatic-repeat-request(ARQ) protocol.We propose a practical node cooperation(NC) protocol to enhance the collection efficiency,utilizing the fact that underwater nodes can overhear the transmission of others.To reduce the source level of underwater nodes,the underwater data collection area is divided into several sub-zones,and in each sub-zone,the mobile surface node adopting the NC protocol could switch adaptively between selective relay cooperation(SRC) and dynamic network coded cooperation(DNC) .The difference of SRC and DNC lies in whether or not the selected relay node combines the local data and the data overheard from undecoded node(s) to form network coded packets in the retransmission phase.The NC protocol could also be applied across the sub-zones due to the wiretap property.In addition,we investigate the effects of different mobile collection paths,collection area division and cooperative zone design for energy saving.The numerical results showthat the proposed NC protocol can effectively save energy compared with the traditional ARQ scheme.

NOMA Empowered Energy Efficient Data Collection and Wireless Power Transfer in Space-Air-Ground Integrated Networks

As the sixth generation network(6G)emerges,the Internet of remote things(IoRT)has become a critical issue.However,conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT networks,and the Space-Air-Ground integrated network(SAGIN)holds promise.We propose a novel setup that integrates non-orthogonal multiple access(NOMA)and wireless power transfer(WPT)to collect latency-sensitive data from IoRT networks.To extend the lifetime of devices,we aim to minimize the maximum energy consumption among all IoRT devices.Due to the coupling between variables,the resulting problem is non-convex.We first decouple the variables and split the original problem into four subproblems.Then,we propose an iterative algorithm to solve the corresponding subproblems based on successive convex approximation(SCA)techniques and slack variables.Finally,simulation results show that the NOMA strategy has a tremendous advantage over the OMA scheme in terms of network lifetime and energy efficiency,providing valuable insights.

Optimizing Data Collection Path in Sensor Networks with Mobile Elements

Exploiting mobile elements （MEs） to accomplish data collection in wireless sensor networks （WSNs） can improve the energy efficiency of sensor nodes, and prolong network lifetime. However, it will lead to large data collection latency for the network, which is unacceptable for data-critical applications. In this paper, we address this problem by minimizing the traveling length of MEs. Our methods mainly consist of two steps： we first construct a virtual grid network and select the minimal stop point set （SPS） from it; then, we make optimal scheduling for the MEs based on the SPS in order to minimize their traveling length. Different implementations of genetic algorithm （GA） are used to solve the problem. Our methods are evaluated by extensive simulations. The results show that these methods can greatly reduce the traveling length of MEs, and decrease the data collection latency.

Traffic Scheduling Mechanism Based on Interference Avoidance for Meter Data Collection in Wireless Smart Grid Communication Networks

Meter Data Collection Building Area Network(MDCBAN) deployed in high rises is playing an increasingly important role in wireless multi-hop smart grid meter data collection. Recently, increasingly numerous application layer data traffic makes MDCBAN be facing serious communication pressure. In addition, large density of meter data collection devices scattered in the limited geographical space of high rises results in obvious communication interference. To solve these problems, a traffic scheduling mechanism based on interference avoidance for meter data collection in MDCBAN is proposed. Firstly, the characteristics of network topology are analyzed and the corresponding traffic distribution model is proposed. Next, a wireless multi-channel selection scheme for different Floor Gateways and a single-channel time unit assignment scheme for data collection devices in the same Floor Network are proposed to avoid interference. At last, a data balanced traffic scheduling algorithm is proposed. Simulation results show that balanced traffic distribution and highly efficient and reliable data transmission can be achieved on the basis of effective interference avoidance between data collection devices.

AUV-Aided Data Collection Considering Adaptive Ocean Currents for Underwater Wireless Sensor Networks

Autonomous underwater vehicle(AUV)-assisted data collection is an efficient approach to implementing smart ocean.However,the data collection in time-varying ocean currents is plagued by two critical issues:AUV yaw and sensor node movement.We propose an adaptive AUV-assisted data collection strategy for ocean currents to address these issues.First,we consider the energy consumption of an AUV in conjunction with the value of information(VoI)over the sensor nodes and formulate an optimization problem to maximize the VoI-energy ratio.The AUV yaw problem is then solved by deriving the AUV's reachable region in different ocean current environments and the optimal cruising direction to the target nodes.Finally,using the predicted VoI-energy ratio,we sequentially design a distributed path planning algorithm to select the next target node for AUV.The simulation results indicate that the proposed strategy can utilize ocean currents to aid AUV navigation,thereby reducing the AUV's energy consumption and ensuring timely data collection.

Anonymous data collection scheme for cloud-aided mobile edge networks

With the rapid spread of smart sensors,data collection is becoming more and more important in Mobile Edge Networks(MENs).The collected data can be used in many applications based on the analysis results of these data by cloud computing.Nowadays,data collection schemes have been widely studied by researchers.However,most of the researches take the amount of collected data into consideration without thinking about the problem of privacy leakage of the collected data.In this paper,we propose an energy-efficient and anonymous data collection scheme for MENs to keep a balance between energy consumption and data privacy,in which the privacy information of senors is hidden during data communication.In addition,the residual energy of nodes is taken into consideration in this scheme in particular when it comes to the selection of the relay node.The security analysis shows that no privacy information of the source node and relay node is leaked to attackers.Moreover,the simulation results demonstrate that the proposed scheme is better than other schemes in aspects of lifetime and energy consumption.At the end of the simulation part,we present a qualitative analysis for the proposed scheme and some conventional protocols.It is noteworthy that the proposed scheme outperforms the existing protocols in terms of the above indicators.