An Assisted Diagnosis of Alzheimer’s Disease Incorporating Attention Mechanisms Med-3D Transfer Modeling

Alzheimer’s disease(AD)is a complex,progressive neurodegenerative disorder.The subtle and insidious onset of its pathogenesis makes early detection of a formidable challenge in both contemporary neuroscience and clinical practice.In this study,we introduce an advanced diagnostic methodology rooted in theMed-3D transfermodel and enhanced with an attention mechanism.We aim to improve the precision of AD diagnosis and facilitate its early identification.Initially,we employ a spatial normalization technique to address challenges like clarity degradation and unsaturation,which are commonly observed in imaging datasets.Subsequently,an attention mechanism is incorporated to selectively focus on the salient features within the imaging data.Building upon this foundation,we present the novelMed-3D transfermodel,designed to further elucidate and amplify the intricate features associated withADpathogenesis.Our proposedmodel has demonstrated promising results,achieving a classification accuracy of 92%.To emphasize the robustness and practicality of our approach,we introduce an adaptive‘hot-updating’auxiliary diagnostic system.This system not only enables continuous model training and optimization but also provides a dynamic platform to meet the real-time diagnostic and therapeutic demands of AD.

基于C/S结构的分布式数字视频监控系统的设计和实现

结合当前视频技术、计算机网络技术、通信技术的发展,介绍了基于C/S结构的分布式数字视频监控系统的设计和实现,详细描述了各模块的功能与实现,对系统中的关键技术,如C/S结构、视频编码技术和网络实时传输技术等进行了讨论。

A Resistant Quantum Key Exchange Protocol and Its Corresponding Encryption Scheme

The emergence of quantum computer will threaten the security of existing public-key cryptosystems, including the Diffie Hellman key exchange protocol, encryption scheme and etc, and it makes the study of resistant quantum cryptography very urgent. This motivate us to design a new key exchange protocol and eneryption scheme in this paper. Firstly, some acknowledged mathematical problems was introduced, such as ergodic matrix problem and tensor decomposition problem, the two problems have been proved to NPC hard. From the computational complexity prospective, NPC problems have been considered that there is no polynomial-time quantum algorithm to solve them. From the algebraic structures prospective, non-commutative cryptography has been considered to resist quantum. The matrix and tensor operator we adopted also satisfied with this non-commutative algebraic structures, so they can be used as candidate problems for resisting quantum from perspective of computational complexity theory and algebraic structures. Secondly, a new problem was constructed based on the introduced problems in this paper, then a key exchange protocol and a public key encryption scheme were proposed based on it. Finally the security analysis, efficiency, recommended parameters, performance evaluation and etc. were also been given. The two schemes has the following characteristics, provable security,security bits can be scalable, to achieve high efficiency, quantum resistance, and etc.

System Architecture and Key Technologies of Network Security Situation Awareness System YHSAS

Network Security Situation Awareness System YHSAS acquires,understands and displays the security factors which cause changes of network situation,and predicts the future development trend of these security factors.YHSAS is developed for national backbone network,large network operators,large enterprises and other large-scale network.This paper describes its architecture and key technologies:Network Security Oriented Total Factor Information Collection and High-Dimensional Vector Space Analysis,Knowledge Representation and Management of Super Large-Scale Network Security,Multi-Level,Multi-Granularity and Multi-Dimensional Network Security Index Construction Method,Multi-Mode and Multi-Granularity Network Security Situation Prediction Technology,and so on.The performance tests show that YHSAS has high real-time performance and accuracy in security situation analysis and trend prediction.The system meets the demands of analysis and prediction for large-scale network security situation.

Design and Implementation of Weibo Sentiment Analysis Based on LDA and Dependency Parsing

Information content security is a branch of cyberspace security. How to effectively manage and use Weibo comment information has become a research focus in the field of information content security. Three main tasks involved are emotion sentence identification and classification,emotion tendency classification,and emotion expression extraction. Combining with the latent Dirichlet allocation(LDA) model,a Gibbs sampling implementation for inference of our algorithm is presented,and can be used to categorize emotion tendency automatically with the computer. In accordance with the lower ratio of recall for emotion expression extraction in Weibo,use dependency parsing,divided into two categories with subject and object,summarized six kinds of dependency models from evaluating objects and emotion words,and proposed that a merge algorithm for evaluating objects can be accurately evaluated by participating in a public bakeoff and in the shared tasks among the best methods in the sub-task of emotion expression extraction,indicating the value of our method as not only innovative but practical.

Detecting Hidden Malware Method Based on "In-VM" Model

Security tools are rapidly developed as network security threat is becoming more and more serious.To overcome the fundamental limitation of traditional host-based anti-malware system which is likely to be deceived and attacked by malicious codes,VMM-based anti-malware systems have recently become a hot research field.In this article,the existing malware hiding technique is analyzed,and a detecting model for hidden process based on "In-VM" idea is also proposed.Based on this detecting model,a hidden process detection technology which is based on HOOK SwapContext on the VMM platform is also implemented successfully.This technology can guarantee the detecting method not to be attacked by malwares and also resist all the current process hiding technologies.In order to detect the malwares which use remote injection method to hide themselves,a method by hijacking sysenter instruction is also proposed.Experiments show that the proposed methods guarantee the isolation of virtual machines,can detect all malware samples,and just bring little performance loss.

Digital chaotic sequence generator based on coupled chaotic systems

Chaotic systems perform well as a new rich source of cryptography and pseudo-random coding. Unfortunately their digital dynamical properties would degrade due to the finite computing precision. Proposed in this paper is a modified digital chaotic sequence generator based on chaotic logistic systems with a coupling structure where one chaotic subsystem generates perturbation signals to disturb the control parameter of the other one. The numerical simulations show that the length of chaotic orbits, the output distribution of chaotic system, and the security of chaotic sequences have been greatly improved. Moreover the chaotic sequence period can be extended at least by one order of magnitude longer than that of the uncoupled logistic system and the difficulty in decrypting increases 2^128＊2^128 times indicating that the dynamical degradation of digital chaos is effectively improved. A field programmable gate array （FPGA） implementation of an algorithm is given and the corresponding experiment shows that the output speed of the generated chaotic sequences can reach 571.4 Mbps indicating that the designed generator can he applied to the real-time video image encryption.

Depth Similarity Enhanced Image Summarization Algorithm for Hole-Filling in Depth Image-Based Rendering

In free viewpoint video(FVV)and 3DTV,the depth image-based rendering method has been put forward for rendering virtual view video based on multi-view video plus depth(MVD) format.However,the projection with slightly different perspective turns the covered background regions into hole regions in the rendered video.This paper presents a depth enhanced image summarization generation model for the hole-filling via exploiting the texture fidelity and the geometry consistency between the hole and the remaining nearby regions.The texture fidelity and the geometry consistency are enhanced by drawing texture details and pixel-wise depth information into the energy cost of similarity measure correspondingly.The proposed approach offers significant improvement in terms of 0.2dB PSNR gain,0.06 SSIM gain and subjective quality enhancement for the hole-filling images in virtual viewpoint video.

Dependence-Induced Risk: Security Metrics and Their Measurement Framework

Despite the tremendous effort made by industry and academia,we are still searching for metrics that can characterize Cyberspace and system security risks. In this paper,we study the class of security risks that are inherent to the dependence structure in software with vulnerabilities and exhibit a "cascading" effect. We present a measurement framework for evaluating these metrics,and report a preliminary case study on evaluating the dependence-induced security risks in the Apache HTTP Server. The experiment results show that our framework can not only clearly analyze the root cause of the security risks but also quantitatively evaluate the attack consequence of the risks.

FACOR：Flexible Access Control with Outsourceable Revocation in Mobile Clouds

Access control is a key mechanism to secure outsourced data in mobile clouds. Some existing solutions are proposed to enforce flexible access control on outsourced data or reduce the computations performed by mobile devices. However, less attention has been paid to the efficiency of revocation when there are mobile devices needed to be revoked. In this paper, we put forward a new solution, referred to as flexible access control with outsourceable revocation(FACOR) for mobile clouds. The FACOR applies the attribute-based encryption to enable flexible access control on outsourced data, and allows mobile users to outsource the time-consuming encryption and decryption computations to proxies, with only requiring attributes authorization to be fully trusted. As an advantageous feature, FACOR provides an outsourceable revocation for mobile users to reduce the complicated attribute-based revocation operations. The security analysis shows that our FACOR scheme achieves data security against collusion attacks and unauthorized accesses from revoked users. Both theoretical and experimental results confirm that our proposed scheme greatly reliefs the mobile devices from heavy encryption and decryption computations, as well as the complicated revocation of access rights in mobile clouds.

Pedestrian Attributes Recognition in Surveillance Scenarios with Hierarchical Multi-Task CNN Models

Pedestrian attributes recognition is a very important problem in video surveillance and video forensics. Traditional methods assume the pedestrian attributes are independent and design handcraft features for each one. In this paper, we propose a joint hierarchical multi-task learning algorithm to learn the relationships among attributes for better recognizing the pedestrian attributes in still images using convolutional neural networks(CNN). We divide the attributes into local and global ones according to spatial and semantic relations, and then consider learning semantic attributes through a hierarchical multi-task CNN model where each CNN in the first layer will predict each group of such local attributes and CNN in the second layer will predict the global attributes. Our multi-task learning framework allows each CNN model to simultaneously share visual knowledge among different groups of attribute categories. Extensive experiments are conducted on two popular and challenging benchmarks in surveillance scenarios, namely, the PETA and RAP pedestrian attributes datasets. On both benchmarks, our framework achieves superior results over the state-of-theart methods by 88.2% on PETA and 83.25% on RAP, respectively.

MAVP-FE:Multi-Authority Vector Policy Functional Encryption with Efficient Encryption and Decryption

In cloud,data access control is a crucial way to ensure data security.Functional encryption(FE) is a novel cryptographic primitive supporting fine-grained access control of encrypted data in cloud.In FE,every ciphertext is specified with an access policy,a decryptor can access the data if and only if his secret key matches with the access policy.However,the FE cannot be directly applied to construct access control scheme due to the exposure of the access policy which may contain sensitive information.In this paper,we deal with the policy privacy issue and present a mechanism named multi-authority vector policy(MAVP) which provides hidden and expressive access policy for FE.Firstly,each access policy is encoded as a matrix and decryptors can only obtain the matched result from the matrix in MAVP.Then,we design a novel function encryption scheme based on the multi-authority spatial policy(MAVPFE),which can support privacy-preserving yet non-monotone access policy.Moreover,we greatly improve the efficiency of encryption and decryption in MAVP-FE by shifting the major computation of clients to the outsourced server.Finally,the security and performance analysis show that our MAVP-FE is secure and efficient in practice.

CYBERSPACE SECURITY

With rapid development and wide application of information technology,mankind is entering into an information age.The world where people live and work has been changed to a brand new triple-dimensional space that consists of the physical world,the human society and the cyberspace.It is well known that the cyberspace is an essential environment of human existence and development and it

Super-Resolution for Face Image with an Improved K-NN Search Strategy

Recently, neighbor embedding based face super-resolution(SR) methods have shown the ability for achieving high-quality face images, those methods are based on the assumption that the same neighborhoods are preserved in both low-resolution(LR) training set and high-resolution(HR) training set. However, due to the "one-to-many" mapping between the LR image and HR ones in practice, the neighborhood relationship of the LR patch in LR space is quite different with that of the HR counterpart, that is to say the neighborhood relationship obtained is not true. In this paper, we explore a novel and effective re-identified K-nearest neighbor(RIKNN) method to search neighbors of LR patch. Compared with other methods, our method uses the geometrical information of LR manifold and HR manifold simultaneously. In particular, it searches K-NN of LR patch in the LR space and refines the searching results by re-identifying in the HR space, thus giving rise to accurate K-NN and improved performance. A statistical analysis of the influence of the training set size and nearest neighbor number is given, experimental results on some public face databases show the superiority of our proposed scheme over state-of-the-art face hallucination approaches in terms of subjective and objective results as well as computational complexity.

Cryptanalysis of Cryptosystems Based on General Linear Group

Advances in quantum computers threaten to break public key cryptosystems such as RSA, ECC, and EIGamal on the hardness of factoring or taking a discrete logarithm, while no quantum algorithms are found to solve certain mathematical problems on non-commutative algebraic structures until now. In this background, Majid Khan et al.proposed two novel public-key encryption schemes based on large abelian subgroup of general linear group over a residue ring. In this paper we show that the two schemes are not secure. We present that they are vulnerable to a structural attack and that, it only requires polynomial time complexity to retrieve the message from associated public keys respectively. Then we conduct a detailed analysis on attack methods and show corresponding algorithmic description and efficiency analysis respectively. After that, we propose an improvement assisted to enhance Majid Khan's scheme. In addition, we discuss possible lines of future work.

Formalization of Rules for the Cooperation between Goal and Process

To meet the end-user's dynamically changed requirements for software and achieve the on-demand changes of operational process in time, based on the Goal model and the Process model, the user's personalized operations of the related work were simplified and integrated, and rules for the cooperation between Goal and Process which fit for software engineers were proposed and formalized. The user's personalized operations of the related work were analyzed to conclude the pattern of Goal modification. By the direct drive of Goal modification, the corresponding changes of Process were induced and specified. Then rules for the cooperation between Goal and Process were concluded and the formalization was achieved in Semantic Web Rule Language (SWRL). In this paper, reasoning support was provided for the model modification based on ontology and the dynamic evolution of Process model.

Nanopore techniques as a potent tool in the diagnosis and treatment of endophthalmitis:a literature review

Endophthalmitis is a serious ophthalmic disease characterized by changes in the eye's posterior segment,such as hypopyon and intraocular inflammation,vitritis being a hallmark.Infection-caused endophthalmitis can lead to irreversible vision loss,accompanied by eye pain or eye distention,and in the most severe cases the removal of the eyeball.Microorganisms such as bacteria,fungi,viruses,and parasites typically account for the disease and the entry pathways of the microbial can be divided into either endogenous or exogenous approaches,according to the origin of the etiological agents.Exogenous endophthalmitis can be derived from various occasions(such as postoperative complications or trauma)while endogenous endophthalmitis results from the bloodstream which carries pathogens to the eye.This review aims to summarize the application of new technology in pathogen identification of endophthalmitis so as to prevent the disease and better guide clinical diagnosis and treatment.

Modified scaling function projective synchronization of chaotic systems

This paper investigates a kind of modified scaling function projective synchronization of uncertain chaotic systems using an adaptive controller. The given scaling function in the new method can be an equilibrium point; a periodic orbit, or even a chaotic attractor in the phase space. Based on LaSalle＇s invariance set principle, the adaptive control law is derived to make the states of two chaotic systems function projective synchronized. Some numerical examples are also given to show the effectiveness of the proposed method.

ANOTHER TYPE OF GENERALIZED BALL CURVES AND SURFACES

In 2000, Wu presented two new types of generalized Ball curves, one of which is called an NB1 curve located between the Wang-Ball curve and the Said-Ball curve. In this article, the authors aim to discuss properties of NB1 curves and surfaces, including the recursive algorithms, conversion algorithms between NB1 and Bezier curves and surfaces, etc. In addition the authors compare the computation efficiency of recursive algorithms for the NB1 and above mentioned two generalized Ball curves and surfaces.

Intrusion Detection Model Based on Incomplete Information Ga me in Wireless Mesh Networks

Wireless Mesh Networks （WMNs） have many applications in homes, schools, enterprises, and public places because of their useful characteristics, such as high bandwidth, high speed, and wide coverage. However, the security of wireless mesh networks is a precondition for practical use. Intrusion detection is pivotal for increasing network security. Considering the energy limitations in wireless mesh networks, we adopt two types of nodes： Heavy Intrusion Detection Node （HIDN） and Light Intrusion Detection Node （LIDN）. To conserve energy, the LIDN detects abnorrml behavior according to probability, while the HIDN, which has sufficient energy, is always operational. In practice, it is very difficult to acquire accurate information regarding attackers. We propose an intrusion detection model based on the incomplete inforrmtion game （ID-IIG）. The ID-IIG utilizes the Harsanyi transformation and Bayesian Nash equilibrium to select the best strategies of defenders, although the exact attack probability is unknown. Thus, it can effectively direct the deployment of defenders. Through experiments, we analyze the perforrmnce of ID-IIG and verify the existence and attainability of the Bayesian Nash equilibrium.