Computational Experiments for Complex Social Systems:Experiment Design and Generative Explanation

Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system emergence.To address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating laws.Finally,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.

Study on Interfacial Bonding State of Ag-Cu in Cold Pressure Welding

The interfacial bonding of Ag-Cu (they are limited soluble) formed by the technology of cold pressure welding was discussed from the point of metallurgic view in this paper. Meanwhile, tensile test and microscopic test were adopted for studying the state of interfacial bonding, suggesting that the joint of Ag-Cu has not only strong welding joint but also atomic diffusion on the interface. For Ag-Cu, the interaction of dislocation caused by plastic deformation will cause the strain and the vibration of microconstructer defects, accompanied by emitting energy. The energy increases the atomic action and the amplitude of atomic vibration, and the result is that the atom can diffuse to several lattice parameters deep from interface to inner metals. Therefore, under the condition of chemical potential gradient, the special technique, cold pressure welding rather than basic requirements of diffusion should be taken into account. During the cold pressure welding, plastic deformation plays an important role for it causes the metals′ displacement, crystal defects, further activates the surface atoms. Finally, the fracture of atomic bonding leads to the atomic exchange and diffusion between the new metals′ surfaces.In other words the metals Ag,Cu can achieve solidate bonding by cold pressure welding accompanied by the atomic diffusion. Moreover, theoretical analysis and calculation on the basis of thermodynamics, crystallogy, so- lid physics,etc, have been applied to calculate the amount of atomic diffusion, which has further proved the testing results that joint Ag-Cu has strong bonding strength through the mechanism of atomic diffusion.

Hybrid Model Testing Technique for Deep-Sea Platforms Based on Equivalent Water Depth Truncation

In this paper, an inner turret moored FPSO which works in the water of 320 m depth, is selected to study the socalled ＂passively-truncated ＋ numerical-simulation＂ type of hybrid model testing technique while the tnmcated water depth is 160 m and the model scale ）, = 80. During the investigation, the optimization design of the equivalent-depth truncated system is performed by using the similarity of the static characteristics between the truncated system and the full depth one as the objective function. According to the truncated system, the corresponding physical test model is made. By adopting the coupling time domain simulation method, the tnmcated system model test is numerically reconstructed to carefully verify the computer simulation software and to adjust the corresponding hydrodynamic parameters. Based on the above work, the numerical extrapolation to the full depth system is performed by using the verified computer software and the adjusted hydrodyrmmic parameters. The full depth system model test is then performed in the basin and the results are compared with those from the numerical extrapolation. At last, the implementation procedure and the key technique of the hybrid model testing of the deep-sea platforms are summarized and printed. Through the above investigations, some beneficial conclusions are presented.

Network-Aided Intelligent Traffic Steering in 5G Mobile Networks

Recently,the fifth generation(5G)of mobile networks has been deployed and various ranges of mobile services have been provided.The 5G mobile network supports improved mobile broadband,ultra-low latency and densely deployed massive devices.It allows multiple radio access technologies and interworks them for services.5G mobile systems employ traffic steering techniques to efficiently use multiple radio access technologies.However,conventional traffic steering techniques do not consider dynamic network conditions efficiently.In this paper,we propose a network aided traffic steering technique in 5G mobile network architecture.5G mobile systems monitor network conditions and learn with network data.Through a machine learning algorithm such as a feed-forward neural network,it recognizes dynamic network conditions and then performs traffic steering.The proposed scheme controls traffic for multiple radio access according to the ratio of measured throughput.Thus,it can be expected to improve traffic steering efficiency.The performance of the proposed traffic steering scheme is evaluated using extensive computer simulations.

Adaptive Data Transmission Method According to Wireless State in Long Range Wide Area Networks

The Internet of Things(IoT)has enabled various intelligent services,and IoT service range has been steadily extended through long range wide area communication technologies,which enable very long distance wireless data transmission.End-nodes are connected to a gateway with a single hop.They consume very low-power,using very low data rate to deliver data.Since long transmission time is consequently needed for each data packet transmission in long range wide area networks,data transmission should be efficiently performed.Therefore,this paper proposes a multicast uplink data transmission mechanism particularly for bad network conditions.Transmission delay will be increased if only retransmissions are used under bad network conditions.However,employing multicast techniques in bad network conditions can significantly increase packet delivery rate.Thus,retransmission can be reduced and hence transmission efficiency increased.Therefore,the proposed method adopts multicast uplink after network condition prediction.To predict network conditions,the proposed method uses a deep neural network algorithm.The proposed method performance was verified by comparison with uplink unicast transmission only,confirming significantly improved performance.

Experience Availability： Tail-Latency Oriented Availability in Software-Defined Cloud Computing

Resource sharing, multi-tenant interference and bursty workloads in cloud computing lead to high tail-latency that severely affects user quality of experience （QoE）, where response latency is a critical factor. A lot of research efforts are dedicated to reducing high tail-latency and improving user QoE, such as software-defined cloud computing （SDC）. However, the traditional availability analysis of cloud computing captures the pure failure-repair behavior with user QoE ignored. In this paper, we propose a conceptual framework, experience availability, to properly assess the effectiveness of SDC while taking into account both availability and response latency simultaneously. We review the related work on availability models and methods of cloud systems, and discuss open problems for evaluating experience availability in SDC. We also show some of our preliminary results to demonstrate the feasibility of our ideas.

Generating IDS Attack Pattern Automatically Based on Attack Tree

Generating attack pattern automatically based on attack tree is studied. The extending definition of attack tree is proposed. And the algorithm of generating attack tree is presented. The method of generating attack pattern automatically based on attack tree is shown, which is tested by concrete attack instances. The results show that the algorithm is effective and efficient. In doing so, the efficiency of generating attack pattern is improved and the attack trees can be reused.

Transfer Learning Model to Indicate Heart Health Status Using Phonocardiogram

The early diagnosis of pre-existing coronary disorders helps to control complications such as pulmonary hypertension,irregular cardiac functioning,and heart failure.Machine-based learning of heart sound is an efficient technology which can help minimize the workload of manual auscultation by automatically identifying irregular cardiac sounds.Phonocardiogram(PCG)and electrocardiogram(ECG)waveforms provide the much-needed information for the diagnosis of these diseases.In this work,the researchers have converted the heart sound signal into its corresponding repeating pattern-based spectrogram.PhysioNet 2016 and PASCAL 2011 have been taken as the benchmark datasets to perform experimentation.The existing models,viz.MobileNet,Xception,Visual Geometry Group(VGG16),ResNet,DenseNet,and InceptionV3 of Transfer Learning have been used for classifying the heart sound signals as normal and abnormal.For PhysioNet 2016,DenseNet has outperformed its peer models with an accuracy of 89.04 percent,whereas for PASCAL 2011,VGG has outperformed its peer approaches with an accuracy of 92.96 percent.

Fully Differential Leapfrog Implementation for High-Pass Ladder Filters

This paper presents a novel leapfrog signal flow graph (SFG) implementation by fully differential Op amp integrators, which combines low sensitivity, high dynamic range with simple circuit configuration. The direct SFG simulation and leapfrog SFG simulation can yield integrator-based structures likewise, but both of them will lead to higher circuit complexity, noise density and sensitivity. Three Butterworth 5-order high-pass filters with a pass band edge frequency of 1.778 kHz are designed based on different SFGs. From the example, the noise density of the simplest leapfrog configuration is approximately 0.4 nV/Hz1/2 lower than those of the other two in the stop band, and shows the best noise density in the pass band. The sensitivity densities of two types of leapfrog filters are approximately equivalent, while that of the direct SFG simulation filter is much higher. However, the pass band response of the simplest configuration is not as good as those of the other two because of two parasitic zeros (at 708 kHz, -31.6 dB and 1.59 MHz, -20.55 dB) and a parasitic pole (at 4.57 MHz, 45.5 dB).

Study on the transaction linkage technique combined with the designated terminal for 5G-enabled IoT

With the growth of the scale of the market for Internet banking and e-commerce,the number of Internet-based financial markets has been increasing.Meanwhile,hacking incidents continuously affect Internet-banking services.For this reason,a countermeasure is required to improve the security of the online identification process.The current security and authentication mechanisms applied to financial services,such as Internet banking services for 5G-enabled IoT,do not ensure security.In this paper,a transaction-linkage technique with which the designated terminal is combined is proposed to solve this fundamental problem.The technique improves the security of online identification mechanisms because it is possible to counteract all of the existing security threats.The proposed technique supports mutual authentication and is safe from eavesdropping attacks,replay attacks,spoofing attacks,and service-denial attacks.Moreover,the technique supports non-repudiation by storing the transaction history in a transaction-linkage device.We believe that the security of Internet-banking services for 5G-enabled IoT will be increased through the utilization of the proposed technique.

Document Clustering Based on Constructing Density Tree

This paper focuses on document clustering by clustering algorithm based on a DEnsityTree (CABDET) to improve the accuracy of clustering. The CABDET method constructs a density-based treestructure for every potential cluster by dynamically adjusting the radius of neighborhood according to local density. It avoids density-based spatial clustering of applications with noise (DBSCAN) ′s global density parameters and reduces input parameters to one. The results of experiment on real document show that CABDET achieves better accuracy of clustering than DBSCAN method. The CABDET algorithm obtains the max F-measure value 0.347 with the root node's radius of neighborhood 0.80, which is higher than 0.332 of DBSCAN with the radius of neighborhood 0.65 and the minimum number of objects 6.

Pairings-Based Protocol for Group Key Agreement

An authenticated group key agreement protocol suite (PAGKA) based on pairings is presented in this paper. This suite extends an efficient Diffie-Hellman-based protocol suite by introducing parings on elliptic curves and public-key certificates. The result suite builds its security on the hardness of the bilinear Diffie-Hellman (BDH) problem in the random oracle model, and provides implicit key authentication, perfect forward secrecy and unknown key-share secrecy.

Efficient Algorithm for Energy-Aware Virtual Network Embedding

Network virtualization is a promising approach for resource management that allows customized Virtual Networks （VNs） to be multiplexed on a shared physical infrastructure. A key function that network virtualization can provide is Virtual Network Embedding （VNE）, which maps virtual networks requested by users to a shared substrate network maintained by an Internet service provider. Existing research has worked on this, but has primarily focused on maximizing the revenue of the Internet service provider. In this paper, we consider energy-aware virtual network embedding, which aims at minimizing the energy consumption for embedding virtual networks in a substrate network. In our optimization model, we consider energy consumption of both links and nodes. We propose an efficient heuristic to assign virtual nodes to appropriate substrate nodes based on priority, where existing activated nodes have higher priority for hosting newly arrived virtual nodes. In addition, our proposed algorithm can take advantage of activated links for embedding virtual links so as to minimize total energy consumption. The simulation results show that, for all the cases considered, our algorithm can improve upon previous work by an average of 12.6% on acceptance rate, while the consumed energy can be reduced by 12.34% on average.

BHONEM:Binary High-Order Network Embedding Methods for Net worked-Guarantee Loans

Networked-guarantee loans may cause systemic risk related concern for the government and banks in China.The prediction of the default of enterprise loans is a typical machine learning based classification problem, and the networked guarantee makes this problem very difficult to solve. As we know, a complex network is usually stored and represented by an adjacency matrix. It is a high-dimensional and sparse matrix, whereas machine-learning methods usually need lowdimensional dense feature representations. Therefore, in this paper, we propose a binary higher-order network embedding method to learn the low-dimensional representations of a guarantee network. We first set vertices of this heterogeneous economic network by binary roles (guarantor and guarantee), and then define high-order adjacent measures based on their roles and economic domain knowledge. Afterwards, we design a penalty parameter in the objective function to balance the importance of network structure and adjacency. We optimize it by negative sampling based gradient descent algorithms,which solve the limitation of stochastic gradient descent on weighted edges without compromising efficiency. Finally, we test our proposed method on three real-world network datasets. The result shows that this method outperforms other start-of-the-art algorithms for both classification accuracy and robustness, especially in a guarantee network.

Apparel Brand Overlap Based on Customer Perceived Value and Eye-Tracking Technology

Competition in the apparel market has developed from preliminary price competition, quality competition, and scale competition to the current state of brand competition. The phenomenon of brand overlap cannot be avoided by apparel enterprises during the process of building and manufacturing their brands. This paper has selected nine identification elements of three dimensions, which we used to construct an apparel brand overlap identification model. This model is based on the theories of customer perceived value and brand identity and was constructed by taking consumer perspectives as the starting point. Two apparel brands from the representative international E Company have been selected as our empirical research objects. An apparel brand overlap identification model has been constructed based on questionnaire analysis and a cognitive experiment involving eye-tracking technology. In addition, the overlap elements among apparel brands, as well as the cognitive situation of consumers with regard to brand overlap, have been analyzed.

A K self-adaptive SDN controller placement for wide area networks

As a novel architecture,software-defined networking(SDN) is viewed as the key technology of future networking.The core idea of SDN is to decouple the control plane and the data plane,enabling centralized,flexible,and programmable network control.Although local area networks like data center networks have benefited from SDN,it is still a problem to deploy SDN in wide area networks(WANs) or large-scale networks.Existing works show that multiple controllers are required in WANs with each covering one small SDN domain.However,the problems of SDN domain partition and controller placement should be further addressed.Therefore,we propose the spectral clustering based partition and placement algorithms,by which we can partition a large network into several small SDN domains efficiently and effectively.In our algorithms,the matrix perturbation theory and eigengap are used to discover the stability of SDN domains and decide the optimal number of SDN domains automatically.To evaluate our algorithms,we develop a new experimental framework with the Internet2 topology and other available WAN topologies.The results show the effectiveness of our algorithm for the SDN domain partition and controller placement problems.

Generating Circulation Designs Using Shape Grammars

Shape grammars are well-known tools to explore abstract systems of shapes and their relationships. Although there has been much research on the use of shape grammars in the field of architectural styles, there has been little investigation into the concept of circulation using shape grammars. Circulation is a tool used in architecture and interior design and is formed by connecting the points left by the movement of human beings through indoor or outdoor spaces. This study shows that shape grammars are also helpful in this particular field by presenting how a parametric shape grammar is applied to generate circulation designs, and the system automatically generates four basic types of complex circulations. According to the specific conditions and requirements of each commercial space, different forms of circulation can be specified and generated.