A Deep Learning Based Energy-Efficient Computational Offloading Method in Internet of Vehicles

With the emergence of advanced vehicular applications, the challenge of satisfying computational and communication demands of vehicles has become increasingly prominent. Fog computing is a potential solution to improve advanced vehicular services by enabling computational offloading at the edge of network. In this paper, we propose a fog-cloud computational offloading algorithm in Internet of Vehicles(IoV) to both minimize the power consumption of vehicles and that of the computational facilities. First, we establish the system model, and then formulate the offloading problem as an optimization problem, which is NP-hard. After that, we propose a heuristic algorithm to solve the offloading problem gradually. Specifically, we design a predictive combination transmission mode for vehicles, and establish a deep learning model for computational facilities to obtain the optimal workload allocation. Simulation results demonstrate the superiority of our algorithm in energy efficiency and network latency.

An integrated operation mode for green logistics of enterprises

The integrated operation mode of green logistics is a new enterprise＇s logistics operation and management mode concerning, environment issues based on the traditional integrated mode. Through comparative study on the integrated operation mode of logistics based on self.operation, the operation mode of an integrated logistics of enterprises based on TPL the green-supply chain management, and the operation mode of eco-iadustrial park （EIP）, a relatively complete objective system structure is put forward for the integrated operation mode of green logistics of enterprises. Furthermore, the correspondent practical mode is also set up to help enterprises, especially for the manufacturing companies, not only improve the environment effectively, but support the technological framework for the enterprise＇s economic performance and social benefits in order to realize economic, social and environmental benefits are concerned.

Numerical realization of the conditions of Max Nther's residual intersection theorem

The aim of this paper is to study numerical realization of the conditions of Max Nother＇s residual intersection theorem. The numerical realization relies on obtaining the inter- section of two algebraic curves by homotopy continuation method, computing the approximate places of an algebraic curve, getting the exact orders of a polynomial at the places, and determin- ing the multiplicity and character of a point of an algebraic curve. The numerical experiments show that our method is accurate, effective and robust without using multiprecision arithmetic, even if the coefficients of algebraic curves are inexact. We also conclude that the computational complexity of the numerical realization is polynomial time.

An Extension of Chebyshev's Maximum Principle to Several Variables

In this article, we generalize Chebyshev＇s maximum principle to several variables. Some analogous maximum formulae for the special integration functional are given. A sufficient condition of the existence of Chebyshev＇s maximum principle is also obtained.

FOG COMPUTING ENABLED INTERNET OF EVERYTHING

With the rapid development of ubiquitous networks and smart cities,the connection and communication of Internet of Everything(IoE)have drawn great attention from both academia and industry.The main challenge for constructing IoE is to enable real-time communication and high-efficiency computing among mobile devices.Mobile fog computing is promising to lower communication delay and offload network traffic.However,how to realize fog-enabled communication and computing in IoE with high-dynamic and heterogeneous network characters has not been fully investigated.Furthermore,deployment and reliable communications among fog nodes are also challenging.

Computing Numerical Singular Points of Plane Algebraic Curves

Given an irreducible plane algebraic curve of degree d 〉 3, we compute its numerical singular points, determine their multiplicities, and count the number of distinct tangents at each to decide whether the singular points are ordinary. The numerical procedures rely on computing numerical solutions of polynomial systems by homotopy continuation method and a reliable method that calculates multiple roots of the univariate polynomials accurately using standard machine precision. It is completely different from the traditional symbolic computation and provides singular points and their related properties of some plane algebraic curves that the symbolic software Maple cannot work out. Without using multiprecision arithmetic, extensive numerical experiments show that our numerical procedures are accurate, efficient and robust, even if the coefficients of plane algebraic curves are inexact.

An efficient QoS enhancement mechanism of VoIP using erasure array codes

In order to solve the problem of losing voice packets in voice over internet protocol(VoIP),a kind of lost packets double recovery algorithm is proposed. The algorithm is based on erasure coding technique which comes from highly available data storage systems. An efficient coding scheme with higher tolerance based on STAR and Reed-Solomon( RS) erasure code is described. An efficient method is also provided which could transform the voice data packets of one dimensional bit stream into two dimensional array according to given window size. If the lost rate has increased beyond the error correction capability,packet-loss concealment will be adopted. Under various conditions of packet-loss simulation during the experiments,the algorithm has proved its better performance on MOS rating and coding rate.

Summarizing Software Artifacts： A Literature Review

This paper presents a literature review in the field of summarizing software artifacts, focusing on bug reports, source code, mailing lists and developer discussions artifacts. From Jan. 2010 to Apr. 2016, numerous summarization techniques, approaches, and tools have been proposed to satisfy the ongoing demand of improving software performance and quality and facilitating developers in understanding the problems at hand. Since aforementioned artifacts contain both structured and unstructured data at the same time, researchers have applied different machine learning and data mining techniques to generate summaries. Therefore, this paper first intends to provide a general perspective on the state of the art, describing the type of artifacts, approaches for summarization, as well as the common portions of experimental procedures shared among these artifacts. Moreover, we discuss the applications of summarization, i.e., what tasks at hand have been achieved through summarization. Next, this paper presents tools that are generated for summarization tasks or employed during summarization tasks. In addition, we present different summarization evaluation methods employed in selected studies as well as other important factors that are used for the evaluation of generated summaries such as adequacy and quality. Moreover, we briefly present modern communication channels and complementarities with commonalities among different software artifacts. Finally, some thoughts about the challenges applicable to the existing studies in general as well as future research directions are also discussed. The survey of existing studies will allow future researchers to have a wide and useful background knowledge on the main and important aspects of this research field.

BC-PC-Share:Blockchain-Based Patient-Centric Data Sharing Scheme for PHRs in Cloud Computing

Sharing of personal health records(PHR)in cloud computing is an essential functionality in the healthcare system.However,how to securely,efficiently and flexibly share PHRs data of the patient in a multi-receiver setting has not been well addressed.For instance,since the trust domain of the cloud server is not identical to the data owner or data user,the semi-trust cloud service provider may intentionally destroy or tamper shared PHRs data of user or only transform partial ciphertext of the shared PHRs or even return wrong computation results to save its storage and computation resource,to pursue maximum economic interest or other malicious purposes.Thus,the PHRs data storing or sharing via the cloud server should be performed with consistency and integrity verification.Fortunately,the emergence of blockchain technology provides new ideas and prospects for ensuring the consistency and integrity of shared PHRs data.To this end,in this work,we leverage the consortiumblockchain technology to enhance the trustworthiness of each participant and propose a blockchain-based patient-centric data sharing scheme for PHRs in cloud computing(BC-PC-Share).Different from the state-of-art schemes,our proposal can achieve the following desired properties:(1)Realizing patient-centric PHRs sharing with a public verification function,i.e.,which can ensure that the returned shared data is consistent with the requested shared data and the integrity of the shared data is not compromised.(2)Supporting scalable and fine-grained access control and sharing of PHRs data with multiple domain users,such as hospitals,medical research institutes,and medical insurance companies.(3)Achieving efficient user decryption by leveraging the transformation key technique and efficient user revocation by introducing time-controlled access.The security analysis and simulation experiment demonstrate that the proposed BC-PC-Share scheme is a feasible and promising solution for PHRs data sharing via consortium blockchain.

Advanced DAG-Based Ranking(ADR)Protocol for Blockchain Scalability

In the past decade,blockchain has evolved as a promising solution to develop secure distributed ledgers and has gained massive attention.However,current blockchain systems face the problems of limited throughput,poor scalability,and high latency.Due to the failure of consensus algorithms in managing nodes’identities,blockchain technology is considered inappropriate for many applications,e.g.,in IoT environments,because of poor scalability.This paper proposes a blockchain consensus mechanism called the Advanced DAG-based Ranking(ADR)protocol to improve blockchain scalability and throughput.The ADR protocol uses the directed acyclic graph ledger,where nodes are placed according to their ranking positions in the graph.It allows honest nodes to use theDirect Acyclic Graph(DAG)topology to write blocks and verify transactions instead of a chain of blocks.By using a three-step strategy,this protocol ensures that the system is secured against doublespending attacks and allows for higher throughput and scalability.The first step involves the safe entry of nodes into the system by verifying their private and public keys.The next step involves developing an advanced DAG ledger so nodes can start block production and verify transactions.In the third step,a ranking algorithm is developed to separate the nodes created by attackers.After eliminating attacker nodes,the nodes are ranked according to their performance in the system,and true nodes are arranged in blocks in topological order.As a result,the ADR protocol is suitable for applications in the Internet of Things(IoT).We evaluated ADR on EC2 clusters with more than 100 nodes and achieved better transaction throughput and liveness of the network while adding malicious nodes.Based on the simulation results,this research determined that the transaction’s performance was significantly improved over blockchains like Internet of Things Applications(IOTA)and ByteBall.

An adaptive and robust secret key extraction scheme from high noise wireless channel in IIoT

The Sixth-Generation(6G)enhances the Industrial Internet of Things(IIoT)communication efficiency and further raises security requirements.It is crucial to construct a post-quantum security communication channel between any pair of industrial equipment.Recent work shows that two legitimate devices can directly extract symmetrical secret keys in information-theoretic secure by measuring their common wireless channels.However,existing schemes may cause a high bit mismatch rate in IIoT with high noise.In addition,the physical layer key extraction scheme widely uses passive attack assumptions,which also contradicts the high-security requirements of IIoT.By analyzing and modeling IIoT systems and channels,we propose an Adaptive and Robust Secret Key Extraction scheme(ARSKE)from high noise wireless channels in 6G-enabled IIoT.To eliminate the non-reciprocity of the wireless channel,we design a smoothing method as the preprocessing module.Then,we propose a Robust Secure Reconciliation technique that can effectively resist active attacks by jointly designing the information reconciliation and privacy amplification phases.Extensive real-world experiments are conducted to test the effectiveness and robustness of our scheme.

From a projective invariant to some new properties of algebraic hypersurfaces

Projective invariants are not only important objects in mathematics especially in geometry,but also widely used in many practical applications such as in computer vision and object recognition. In this work,we show a projective invariant named as characteristic number,from which we obtain an intrinsic property of an algebraic hypersurface involving the intersections of the hypersurface and some lines that constitute a closed loop. From this property,two high-dimensional generalizations of Pascal's theorem are given,one establishing the connection of hypersurfaces of distinct degrees,and the other concerned with the intersections of a hypersurface and a simplex.

Improved artificial bee colony algorithm with mutual learning

The recently invented artificial bee colony （ABC） al- gorithm is an optimization algorithm based on swarm intelligence that has been used to solve many kinds of numerical function optimization problems. It performs well in most cases, however, there still exists an insufficiency in the ABC algorithm that ignores the fitness of related pairs of individuals in the mechanism of find- ing a neighboring food source. This paper presents an improved ABC algorithm with mutual learning （MutualABC） that adjusts the produced candidate food source with the higher fitness between two individuals selected by a mutual learning factor. The perfor- mance of the improved MutualABC algorithm is tested on a set of benchmark functions and compared with the basic ABC algo- rithm and some classical versions of improved ABC algorithms. The experimental results show that the MutualABC algorithm with appropriate parameters outperforms other ABC algorithms in most experiments.

NOMA-Based Energy-Efficient Task Scheduling in Vehicular Edge Computing Networks: A Self-Imitation Learning-Based Approach

Mobile Edge Computing(MEC)is promising to alleviate the computation and storage burdens for terminals in wireless networks.The huge energy consumption of MEC servers challenges the establishment of smart cities and their service time powered by rechargeable batteries.In addition,Orthogonal Multiple Access(OMA)technique cannot utilize limited spectrum resources fully and efficiently.Therefore,Non-Orthogonal Multiple Access(NOMA)-based energy-efficient task scheduling among MEC servers for delay-constraint mobile applications is important,especially in highly-dynamic vehicular edge computing networks.The various movement patterns of vehicles lead to unbalanced offloading requirements and different load pressure for MEC servers.Self-Imitation Learning(SIL)-based Deep Reinforcement Learning(DRL)has emerged as a promising machine learning technique to break through obstacles in various research fields,especially in time-varying networks.In this paper,we first introduce related MEC technologies in vehicular networks.Then,we propose an energy-efficient approach for task scheduling in vehicular edge computing networks based on DRL,with the purpose of both guaranteeing the task latency requirement for multiple users and minimizing total energy consumption of MEC servers.Numerical results demonstrate that the proposed algorithm outperforms other methods.

Unique optimal solution instance and computational complexity of backbone in the graph bi-partitioning problem

As an important tool for heuristic design of NP-hard problems, backbone analysis has become a hot spot in theoretical computer science in recent years. Due to the difficulty in the research on computa- tional complexity of the backbone, many researchers analyzed the backbone by statistic ways. Aiming to increase the backbone size which is usually very small by the existing methods, the unique optimal solution instance construction (UOSIC) is proposed for the graph bi-partitioning problem (GBP). Also, we prove by using the UOSIC that it is NP-hard to obtain the backbone, i.e. no algorithm exists to obtain the backbone of a GBP in polynomial time under the assumption that P ≠ NP. Our work expands the research area of computational complexity of the backbone. And the UOSIC provides a new way for heuristic design of NP-hard problems.

An Efficient Information Hiding Scheme Based on Closest Paired Tree Structure Vector Quantization

Information hiding schemes based on vector quantization （VQ） usually require lengthy VQ encoding and decoding processes. In this paper, we propose an efficient information hiding method based on closest paired tree structure vector quantization （CPTSVQ）. The simulation result shows that the execution time of the proposed scheme is much shorter than that attained by previous approaches.

ARAP++:an extension of the local/global approach to mesh parameterization

Mesh parameterization is one of the fundamental operations in computer graphics(CG) and computeraided design(CAD). In this paper, we propose a novel local/global parameterization approach, ARAP++, for singleand multi-boundary triangular meshes. It is an extension of the as-rigid-as-possible(ARAP) approach, which stitches together 1-ring patches instead of individual triangles. To optimize the spring energy, we introduce a linear iterative scheme which employs convex combination weights and a fitting Jacobian matrix corresponding to a prescribed family of transformations. Our algorithm is simple, efficient, and robust. The geometric properties(angle and area)of the original model can also be preserved by appropriately prescribing the singular values of the fitting matrix. To reduce the area and stretch distortions for high-curvature models, a stretch operator is introduced. Numerical results demonstrate that ARAP++ outperforms several state-of-the-art methods in terms of controlling the distortions of angle, area, and stretch. Furthermore, it achieves a better visualization performance for several applications, such as texture mapping and surface remeshing.

Quadratic nonconforming finite element method for 3D Stokes equations on cuboid meshes

In this paper, the quadratic nonconforming brick element （MSLK element） intro- duced in [10] is used for the 3D Stokes equations. The instability for the mixed element pair MSLK-P1 is analyzed, where the vector-valued MSLK element approximates the velocity and the piecewise P1 element approximates the pressure. As a cure, we adopt the piecewise P1 macroelement to discretize the pressure instead of the standard piecewise P1 element on cuboid meshes. This new pair is stable and the optimal error estimate is achieved. Numerical examples verify our theoretical analysis.

Homonyms Discovery in Folksonomy Based on User Community Analysis

to the capability of reflecting social perception on semantic of resources, folksonomy has been proposed to improve the social learning for education and scholar researching. However, its actual impact is significantly influenced by the semantic ambiguity problem of tags. So, in this paper, we proposed a novel way of detecting homonyms, one of the main sources of tag＇s semantic ambiguity problem, in noisy folksonomies. The study is based on two hypotheses： 1） Users having different interests tend to have different understanding of the same tag. 2） Users having similar interest tend to have common understanding of the same tag. Therefore, we firstly discover user communities according to users＇ interests. Then, tag contexts are discovered in subsets of folksonomy on the basis of user communities. The experimental results show that our method is effective and outperform the method finding tag contexts using all tags in folksonomy with overlapping clustering algorithm especially when various users having different interests are contained by the folksonomyo

A Novel(n,n)Secret Image Sharing Scheme Based on Sudoku

Recently, Chang et al. proposed a Sudoku-based secret image sharing scheme. They utilized the Sudoku grid to generate meaningful shadow images, and their scheme satisfied all essential requirements. Based on Chang et al.＇s scheme, we propose a novel （n, n） secret image sharing scheme based on Sudoku. In the proposed scheme, a secret image can be shared among n different cover images by generating n shadow images, and the secret image can be reconstructed without distortion using only these n shadow images. Also, the proposed scheme can solve the overflow and underflow problems. The experimental results show that the visual quality of the shadow images is satisfactory. Furthermore, the proposed scheme allows for a large embedding capacity.