Angle-resolved photoemission study of NbGeSb with non-symmorphic symmetry

We investigate the electronic structure of NbGeSb with non-symmorphic symmetry.We employ angle-resolved photoemission spectroscopy(ARPES)to observe and identify the bulk and surface states over the Brillouin zone.By utilizing high-energy photons,we identify the bulk Fermi surface and bulk nodal line along the direction X–R,while the Fermi surface of the surface state is observed by using low-energy photons.We observe the splitting of surface bands away from the high-symmetry point X.The density functional theory calculations on bulk and 1 to 5-layer slab models,as well as spin textures of NbGeSb,verify that the band splitting could be attributed to the Rashba-like spin–orbit coupling caused by space-inversion-symmetry breaking at the surface.These splitted surface bands cross with each other,forming two-dimensional Weyl-like crossings that are protected by mirror symmetry.Our findings provide insights into the two-dimensional topological and symmetry-protected band inversion of surface states.

Bidirectional transfer of quantum information for unknown photons via cross-Kerr nonlinearity and photon-number-resolving measurement

We propose an arbitrary controlled-unitary （CU） gate and a bidirectional transfer scheme of quantum information （BTQI） for unknown photons. The proposed CU gate utilizes quantum non-demolition photon-number-resolving measure- ment based on the weak cross-Kerr nonlinearities （XKNLs） and two quantum bus beams; the proposed CU gate consists of consecutive operations of a controlled-path gate and a gathering-path gate. It is almost deterministic and is feasible with current technology when a strong amplitude of the coherent state and weak XKNLs are employed. Compared with the existing optical multi-qubit or controlled gates, which utilize XKNLs and homodyne detectors, the proposed CU gate can increase experimental realization feasibility and enhance robustness against decoherence. According to the CU gate, we present a BTQI scheme in which the two unknown states of photons between two parties （Alice and Bob） are mutually swapped by transferring only a single photon. Consequently, by using the proposed CU gate, it is possible to experimentally implement the BTQI scheme with a certain probability of success.

Information-Centric IoT-Based Smart Farming with Dynamic Data Optimization

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Many researchers try to integrate IoT-based smart farming on cloud platforms effectively.They define various frameworks on smart farming and monitoring system and still lacks to define effective data management schemes.Since IoT-cloud systems involve massive structured and unstructured data,data optimization comes into the picture.Hence,this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization(ICISF-DDO),which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved lifetime.Here,a conceptual framework of the proposed scheme and statistical design model has beenwell defined.The information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data optimization.The simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%,reliability ratio of 98.63%,a coverage ratio of 99.67%,least sensor error rate of 8.96%,and efficient energy consumption ratio of 4.84%.

Novel Rifle Number Recognition Based on Improved YOLO in Military Environment

Deep neural networks perform well in image recognition,object recognition,pattern analysis,and speech recog-nition.In military applications,deep neural networks can detect equipment and recognize objects.In military equipment,it is necessary to detect and recognize rifle management,which is an important piece of equipment,using deep neural networks.There have been no previous studies on the detection of real rifle numbers using real rifle image datasets.In this study,we propose a method for detecting and recognizing rifle numbers when rifle image data are insufficient.The proposed method was designed to improve the recognition rate of a specific dataset using data fusion and transfer learningmethods.In the proposed method,real rifle images and existing digit images are fusedas trainingdata,andthe final layer is transferredto theYolov5 algorithmmodel.The detectionand recognition performance of rifle numbers was improved and analyzed using rifle image and numerical datasets.We used actual rifle image data(K-2 rifle)and numeric image datasets,as an experimental environment.TensorFlow was used as the machine learning library.Experimental results show that the proposed method maintains 84.42% accuracy,73.54% precision,81.81% recall,and 77.46% F1-score in detecting and recognizing rifle numbers.The proposed method is effective in detecting rifle numbers.

Attention-Enhanced Voice Portrait Model Using Generative Adversarial Network

Voice portrait technology has explored and established the relationship between speakers’ voices and their facialfeatures, aiming to generate corresponding facial characteristics by providing the voice of an unknown speaker.Due to its powerful advantages in image generation, Generative Adversarial Networks (GANs) have now beenwidely applied across various fields. The existing Voice2Face methods for voice portraits are primarily based onGANs trained on voice-face paired datasets. However, voice portrait models solely constructed on GANs facelimitations in image generation quality and struggle to maintain facial similarity. Additionally, the training processis relatively unstable, thereby affecting the overall generative performance of the model. To overcome the abovechallenges,wepropose a novel deepGenerativeAdversarialNetworkmodel for audio-visual synthesis, namedAVPGAN(Attention-enhanced Voice Portrait Model using Generative Adversarial Network). This model is based ona convolutional attention mechanism and is capable of generating corresponding facial images from the voice ofan unknown speaker. Firstly, to address the issue of training instability, we integrate convolutional neural networkswith deep GANs. In the network architecture, we apply spectral normalization to constrain the variation of thediscriminator, preventing issues such as mode collapse. Secondly, to enhance the model’s ability to extract relevantfeatures between the two modalities, we propose a voice portrait model based on convolutional attention. Thismodel learns the mapping relationship between voice and facial features in a common space from both channeland spatial dimensions independently. Thirdly, to enhance the quality of generated faces, we have incorporated adegradation removal module and utilized pretrained facial GANs as facial priors to repair and enhance the clarityof the generated facial images. Experimental results demonstrate that our AVP-GAN achieved a cosine similarity of0.511, outperforming the performance of our comparison model, and effectively achieved the generation of highqualityfacial images corresponding to a speaker’s voice.

An End-To-End Hyperbolic Deep Graph Convolutional Neural Network Framework

Graph Convolutional Neural Networks(GCNs)have been widely used in various fields due to their powerful capabilities in processing graph-structured data.However,GCNs encounter significant challenges when applied to scale-free graphs with power-law distributions,resulting in substantial distortions.Moreover,most of the existing GCN models are shallow structures,which restricts their ability to capture dependencies among distant nodes and more refined high-order node features in scale-free graphs with hierarchical structures.To more broadly and precisely apply GCNs to real-world graphs exhibiting scale-free or hierarchical structures and utilize multi-level aggregation of GCNs for capturing high-level information in local representations,we propose the Hyperbolic Deep Graph Convolutional Neural Network(HDGCNN),an end-to-end deep graph representation learning framework that can map scale-free graphs from Euclidean space to hyperbolic space.In HDGCNN,we define the fundamental operations of deep graph convolutional neural networks in hyperbolic space.Additionally,we introduce a hyperbolic feature transformation method based on identity mapping and a dense connection scheme based on a novel non-local message passing framework.In addition,we present a neighborhood aggregation method that combines initial structural featureswith hyperbolic attention coefficients.Through the above methods,HDGCNN effectively leverages both the structural features and node features of graph data,enabling enhanced exploration of non-local structural features and more refined node features in scale-free or hierarchical graphs.Experimental results demonstrate that HDGCNN achieves remarkable performance improvements over state-ofthe-art GCNs in node classification and link prediction tasks,even when utilizing low-dimensional embedding representations.Furthermore,when compared to shallow hyperbolic graph convolutional neural network models,HDGCNN exhibits notable advantages and performance enhancements.

Security in Wireless Mesh Neworks: Challenges and Solutions

Wireless mesh network is a new emerging field with its potential applications in extremely unpredictable and dynamic environments.However,it is particularly vulnerable due to its features of open medium,dynamic changing topology, cooperative routing algorithms.The article surveys the state of the art in security for wireless mesh networks.Firstly,we analyze various possible threats to security in wireless mesh networks.Secondly,we introduce some representative solutions to these threats,including solutions to the problems of key management,secure network routing,and intrusion detection.We also provide a comparison and discussion of their respective merits and drawbacks,and propose some improvements for these drawbacks.Finally,we also discuss the remaining challenges in the area.

Performance analysis of mobile ad hoc networks under flooding attacks

Due to their characteristics of dynamic topology, wireless channels and limited resources, mobile ad hoc networks are particularly vulnerable to a denial of service （DoS） attacks launched by intruders. The effects of flooding attacks in network simulation 2 （NS2） and measured performance parameters are investigated, including packet loss ratio, average delay, throughput and average number of hops under different numbers of attack nodes, flooding frequency, network bandwidth and network size. Simulation results show that with the increase of the flooding frequency and the number of attack nodes, network performance sharply drops. But when the frequency of flooding attacks or the number of attack nodes is greater than a certain value, performance degradation tends to a stable value.

On-line linear time construction of sequential binary suffix trees

Suffix trees are the key data structure for text string matching, and are used in wide application areas such as bioinformatics and data compression. Ukkonen algorithm is deeply investigated and a new algorithm, which decreases the number of memory operations in construction and keeps the result tree sequential, is proposed. The experiment result shows that both the construction and the matching procedure are more efficient than Ukkonen algorithm.

A Detailed Chunk-Level Performance Study of Web Page Retrieve Latency

It is a widely discussed question that where the web latency comes from. In this paper, we propose a novel chunk-level latency dependence model to give a better illustration of the web latency. Based on the fact that web content is delivered in chunk sequence, and clients care more about whole page retrieval latency, this paper carries out a detailed study on how the chunk sequence and relations affect the web retrieval latency. A series of thorough experiments are also conducted and data analysis are also made. The result is useful for further study on how to reduce the web latency.

Bidirectional quantum teleportation of unknown photons using path-polarization intra-particle hybrid entanglement and controlled-unitary gates via cross-Kerr nonlinearity

We propose an arbitrary controlled-unitary （CU） gate and a bidirectional quantum teleportation （BQTP） scheme. The proposed CU gate utilizes photonic qubits （photons） with cross-Kerr nonlinearities （XKNLs）, X-homodyne detectors, and linear optical elements, and consists of the consecutive operation of a controlled-path （C-path） gate and a gathering-path （G- path） gate. It is almost deterministic and feasible with current technology when a strong coherent state and weak XKNLs are employed. Based on the CU gate, we present a BQTP scheme that simultaneously teleports two unknown photons between distant users by transmitting only one photon in a path-polarization intra-particle hybrid entangled state. Consequently, it is possible to experimentally implement BQTP with a certain success probability using the proposed CU gate.

A distributed information retrieval method in peer-to-peer network

To avoid the scalability of the existing systems that employed centralized indexing,index flooding or query flooding,we proposed an efficient peer-to-peer information retrieval system SPIRS (Semantic P2P-based Information Retrieval System) that supported state-of-the-art content and semantic searches. SPIRS distributes document indices through P2P network hierarchically by Latent Semantic Indexing (LSI) and organizes nodes into a hierarchical overlay through CAN and TRIE. Comparing with other P2P search techniques,those based on simple keyword matching,SPIRS has better accuracy for considering the advanced relevance among documents. Given a query,only a small number of nodes are needed for SPIRS to identify the matching documents. Furthermore,both theoretical analysis and experimental results show that SPIRS possesses higher accuracy and less logic hops.

Analysis of Microdisk/Microring’s Surface Roughness Effect by Orthogonal Decomposition

Application of micro-resonator is limited by different types of surface inhomogeneity. The 1-th derivative of inhomogeneity (i.e. Δrˊ(φ)) affects the wave transport as well as the height of inhomogeneity (i.e. Δrˊ(φ)). A method based on orthogonal decomposition is proposed to analysis both scattering mechanism respectively. Then surface roughness effect on Q-factor of micro-disk waveguide gallery mode (WGM) resonator is investigated with our method and the analysis fits well with FDTD simulation results.

Joint Optimization Scheme for the Reconfigurable Intelligent Surface-Assisted Untrusted Relay Networks

To further improve the secrecy rate,a joint optimization scheme for the reconfigurable intelligent surface(RIS)phase shift and the power allocation is proposed in the untrusted relay(UR)networks assisted by the RIS.The eavesdropping on the UR is interfered by a source-based jamming strategy.Under the constraints of unit modulus and total power,the RIS phase shift,the power allocation between the confidential signal and the jamming signal,and the power allocation between the source node and the UR are jointly optimized to maximize the secrecy rate.The complex multivariable coupling problem is decomposed into three sub-problems,and the non-convexity of the objective function and the constraints is solved with semi-definite relaxation.Simulation results indicate that the secrecy rate is remarkably enhanced with the proposed scheme compared with the equal power allocation scheme,the random phase shift scheme,and the no-RIS scheme.

Robust and Reusable Fuzzy Extractors from Non-Uniform Learning with Errors Problem

Afuzzy extractor can extract an almost uniformrandom string from a noisy source with enough entropy such as biometric data.To reproduce an identical key from repeated readings of biometric data,the fuzzy extractor generates a helper data and a random string from biometric data and uses the helper data to reproduce the random string from the second reading.In 2013,Fuller et al.proposed a computational fuzzy extractor based on the learning with errors problem.Their construction,however,can tolerate a sub-linear fraction of errors and has an inefficient decoding algorithm,which causes the reproducing time to increase significantly.In 2016,Canetti et al.proposed a fuzzy extractor with inputs from low-entropy distributions based on a strong primitive,which is called digital locker.However,their construction necessitates an excessive amount of storage space for the helper data,which is stored in authentication server.Based on these observations,we propose a new efficient computational fuzzy extractorwith small size of helper data.Our scheme supports reusability and robustness,which are security notions that must be satisfied in order to use a fuzzy extractor as a secure authentication method in real life.Also,it conceals no information about the biometric data and thanks to the new decoding algorithm can tolerate linear errors.Based on the non-uniform learning with errors problem,we present a formal security proof for the proposed fuzzy extractor.Furthermore,we analyze the performance of our fuzzy extractor scheme and provide parameter sets that meet the security requirements.As a result of our implementation and analysis,we show that our scheme outperforms previous fuzzy extractor schemes in terms of the efficiency of the generation and reproduction algorithms,as well as the size of helper data.

Multi-Branch Deepfake Detection Algorithm Based on Fine-Grained Features

With the rapid development of deepfake technology,the authenticity of various types of fake synthetic content is increasing rapidly,which brings potential security threats to people’s daily life and social stability.Currently,most algorithms define deepfake detection as a binary classification problem,i.e.,global features are first extracted using a backbone network and then fed into a binary classifier to discriminate true or false.However,the differences between real and fake samples are often subtle and local,and such global feature-based detection algorithms are not optimal in efficiency and accuracy.To this end,to enhance the extraction of forgery details in deep forgery samples,we propose a multi-branch deepfake detection algorithm based on fine-grained features from the perspective of fine-grained classification.First,to address the critical problem in locating discriminative feature regions in fine-grained classification tasks,we investigate a method for locating multiple different discriminative regions and design a lightweight feature localization module to obtain crucial feature representations by augmenting the most significant parts of the feature map.Second,using information complementation,we introduce a correlation-guided fusion module to enhance the discriminative feature information of different branches.Finally,we use the global attention module in the multi-branch model to improve the cross-dimensional interaction of spatial domain and channel domain information and increase the weights of crucial feature regions and feature channels.We conduct sufficient ablation experiments and comparative experiments.The experimental results show that the algorithm outperforms the detection accuracy and effectiveness on the FaceForensics++and Celeb-DF-v2 datasets compared with the representative detection algorithms in recent years,which can achieve better detection results.

Chinese Cyber Threat Intelligence Named Entity Recognition via RoBERTa-wwm-RDCNN-CRF

In recent years,cyber attacks have been intensifying and causing great harm to individuals,companies,and countries.The mining of cyber threat intelligence(CTI)can facilitate intelligence integration and serve well in combating cyber attacks.Named Entity Recognition(NER),as a crucial component of text mining,can structure complex CTI text and aid cybersecurity professionals in effectively countering threats.However,current CTI NER research has mainly focused on studying English CTI.In the limited studies conducted on Chinese text,existing models have shown poor performance.To fully utilize the power of Chinese pre-trained language models(PLMs)and conquer the problem of lengthy infrequent English words mixing in the Chinese CTIs,we propose a residual dilated convolutional neural network(RDCNN)with a conditional random field(CRF)based on a robustly optimized bidirectional encoder representation from transformers pre-training approach with whole word masking(RoBERTa-wwm),abbreviated as RoBERTa-wwm-RDCNN-CRF.We are the first to experiment on the relevant open source dataset and achieve an F1-score of 82.35%,which exceeds the common baseline model bidirectional encoder representation from transformers(BERT)-bidirectional long short-term memory(BiLSTM)-CRF in this field by about 19.52%and exceeds the current state-of-the-art model,BERT-RDCNN-CRF,by about 3.53%.In addition,we conducted an ablation study on the encoder part of the model to verify the effectiveness of the proposed model and an in-depth investigation of the PLMs and encoder part of the model to verify the effectiveness of the proposed model.The RoBERTa-wwm-RDCNN-CRF model,the shared pre-processing,and augmentation methods can serve the subsequent fundamental tasks such as cybersecurity information extraction and knowledge graph construction,contributing to important applications in downstream tasks such as intrusion detection and advanced persistent threat(APT)attack detection.

CDR2IMG:A Bridge from Text to Image in Telecommunication Fraud Detection

Telecommunication fraud has run rampant recently worldwide.However,previous studies depend highly on expert knowledge-based feature engineering to extract behavior information,which cannot adapt to the fastchanging modes of fraudulent subscribers.Therefore,we propose a new taxonomy that needs no hand-designed features but directly takes raw Call DetailRecords(CDR)data as input for the classifier.Concretely,we proposed a fraud detectionmethod using a convolutional neural network(CNN)by taking CDR data as images and applying computer vision techniques like image augmentation.Comprehensive experiments on the real-world dataset from the 2020 Digital Sichuan Innovation Competition show that our proposed method outperforms the classic methods in many metrics with excellent stability in both the changes of quantity and the balance of samples.Compared with the state-of-the-art method,the proposed method has achieved about 89.98%F1-score and 92.93%AUC,improving 2.97%and 0.48%,respectively.With the augmentation technique,the model’s performance can be further enhanced by a 91.09%F1-score and a 94.49%AUC respectively.Beyond telecommunication fraud detection,our method can also be extended to other text datasets to automatically discover new features in the view of computer vision and its powerful methods.

Distributed intrusion detection for mobile ad hoc networks

Mobile ad hoc networking （MANET） has become an exciting and important technology in recent years, because of the rapid proliferation of wireless devices. Mobile ad hoc networks is highly vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, and lack of centralized monitoring and management point. The traditional way of protecting networks with firewalls and encryption software is no longer sufficient and effective for those features. A distributed intrusion detection approach based on timed automata is given. A cluster-based detection scheme is presented, where periodically a node is elected as the monitor node for a cluster. These monitor nodes can not only make local intrusion detection decisions, but also cooperatively take part in global intrusion detection. And then the timed automata is constructed by the way of manually abstracting the correct behaviours of the node according to the routing protocol of dynamic source routing （DSR）. The monitor nodes can verify the behaviour of every nodes by timed automata, and validly detect real-time attacks without signatures of intrusion or trained data. Compared with the architecture where each node is its own IDS agent, the approach is much more efficient while maintaining the same level of effectiveness. Finally, the intrusion detection method is evaluated through simulation experiments.