Biblio

The goal of neural style transfer is to transform images by the deep learning method, such as changing oil paintings into sketch-style images. The Generative Adversarial Network (GAN) has made remarkable achievements in neural style transfer in recent years. At first, this paper introduces three typical neural style transfer methods, including StyleGAN, StarGAN, and Transparent Latent GAN (TL-GAN). Then, we discuss the advantages and disadvantages of these models, including the quality of the feature axis, the scale, and the model's interpretability. In addition, as the core of this paper, we put forward innovative improvements to the above models, including how to fully exploit the advantages of the above three models to derive a better style conversion model.

Arbitrary style transfer means that stylized images can be generated from a set of arbitrary input image pairs of content images and style images. Recent arbitrary style transfer algorithms lead to distortion of content or incompletion of style transfer because network need to make a balance between the content structure and style. In this paper, we introduce a dual attention network based on style attention and channel attention, which can flexibly transfer local styles, pay more attention to content structure, keep content structure intact and reduce unnecessary style transfer. Experimental results show that the network can synthesize high quality stylized images while maintaining real-time performance.

The highly dynamic nature of the cyber domain demands that cyber operators are capable of rapidly evolving and adapting with exquisite timing. These forces, in turn, pressure acquisition specialists to accoutre cyber warfighters to keep pace with both cyber domain advancement and adversary progression. However, in the Department of Defense (DoD), a vigorous tug of war exists between time and risk pressures. Risk reduction is a crucial element of managing any complex enterprise and this is particularly true for the DoD and its acquisition program [1]. This risk aversion comes at significant cost, as obsolescence by risk minimization is a real phenomenon in DoD acquisition programs and significantly limits the adaptability of its operational cyber forces. Our previous research generated three recommendations for reforming policy to deliver performance at the “speed of relevance” [3]. In this paper we focus on one of the recommendations: “Manage rather than avoid risk—especially time-based risks”. While this advice can apply to many areas of human endeavor, it has elevated urgency in cyberspace. Incomplete risk metrics lead to overly conservative acquisition efforts that imperil timely procurement of advanced cyber capabilities and repel innovators. Effective cyber defense operations require acquisition risk models to be extended beyond fiscal and technical risk metrics of performance, to include risks associated with the cost of failing to meet immediate mission requirements. This paper proposes a time-shifting approach to simultaneously (a) accelerate capability delivery while maintaining traditional rigor, and (b) achieve optimal balance between fiscal, performance, and time risks.

K-nearest neighbor algorithm that searches the K closest samples in a high dimensional feature space is one of the most fundamental tasks in machine learning and image retrieval applications. Computational storage device that combines computing unit and storage module on a single board becomes popular to address the data bandwidth bottleneck of the conventional computing system. In this paper, we propose a nearest neighbor search acceleration platform based on computational storage device, which can process a large-scale image dataset efficiently in terms of speed, energy, and cost. We believe that the proposed acceleration platform is promising to be deployed in cloud datacenters for data-intensive applications.

Named Data Network (NDN) is considered to be the future of Internet architecture. The nature of NDN is to disseminate data based on the naming scheme rather than the location of the node. This feature caters to the need of vehicular applications, resulting in Vehicular Named Data Networks (VNDN). Although it is still in the initial stages of research, the collaboration has assured various advantages which attract the researchers to explore the architecture further. VNDN face challenges such as intermittent connectivity, mobility of nodes, design of efficient forwarding and naming schemes, among others. In order to develop effective forwarding strategies, behavior of data and interest packets under various circumstances needs to be studied. In this paper, propagation behavior of data and interest packets is analyzed by considering metrics such as Interest Satisfaction Ratio (ISR), Hop Count Difference (HCD) and Copies of Data Packets Processed (CDPP). These metrics are evaluated under network conditions such as varying network size, node mobility and amount of interest produced by each node. Simulation results show that data packets do not follow the reverse path of interest packets.

Recently, methods are studied to overcome various problems for Named Data Networking(NDN). Among them, a new method which can overcome content storm problem is required to reduce network congestion and deliver content packet to consumer reliably. According to the various studies, the content storm problems could be overcame by scoped interest flooding. However, because these methods do not considers not only network congestion ratio but also the number another different paths, the correspond content packets could be transmitted unnecessary and network congestion could be worse. Therefore, in this paper, we propose a new content forwarding method for NDN to overcome the content storm problem. In the proposed method, if the network is locally congested and another paths are generated, an intermediate node could postpone or withdraw the content packet transmission to reduce congestion.

The fabricated circuitries are getting massive and denser with every passing year due to which a normal automatic test pattern generation technique to detect only the single stuck-at faults will overlook the multiple stuck-at faults. But generating test patterns that can detect all possible multiple stuck-at fault is practically not possible. Hence, this paper proposes a method, where multiple faults can be detected by using test vectors for detecting single stuck-at faults. Here, the patterns for detecting single faults are generated and their ability to detect multiple stuck-at faults is also analyzed. From the experimental results it was observed that, the generated vectors for single faults cover maximum number of the multiple faults and then new test vectors are generated for the undetermined faults. The generated vectors are optimized for the compact test patterns in order to reduce the test power.

This paper presents a method to improve the diagnosability of power network under multiple faults. In this paper, the steps of fault diagnosis are as follows: first, constructing finite automata model of the diagnostic system; then, a fault diagnoser model is established through coupling operation and trajectory reasoning mechanism; finally, the diagnosis results are obtained through this model. In this paper, the judgment basis of diagnosability is defined. Then, based on the existing diagnosis results, the information available can be increased by adding sensor devices, to achieve the purpose of diagnosability in the case of multiple faults of the system.

The cluster-featured conurbation cyber-physical power system (CPPS) interconnected with tie-lines facing the hazards from catastrophic cascading failures. To achieve better real-time performance, enhance the autonomous ability and improve resilience for the clustered conurbation CPPS, the decentralized cyber structure and the corresponding distributed security control strategy is proposed. Facing failures, the real-time security control is incorporated to mitigate cascading failures. The distributed security control problem is solved reliably based on alternating direction method of multipliers (ADMM). The system overall resilience degradation index(SORDI) adopted reflects the influence of cascading failures on both the topological integrity and operational security. The case study illustrates the decentralized cyber layer and distributed control will decrease the data congestion and enhance the autonomous ability for clusters, thus perform better effectiveness in mitigating the cascading failures, especially in topological perspective. With the proposed distributed security control strategy, curves of SORDI show more characteristics of second-order percolation transition and the cascading failure threshold increase, which is more efficient when the initial failure size is near the threshold values or step-type inflection point. Because of the feature of geological aggregation under cluster-based attack, the efficiency of the cluster-focused distributed security control strategy is more obvious than other nodes attack circumstances.

This paper develops an adaptive neural network (NN) asymptotic tracking control scheme for nonstrict-feedback switched nonlinear systems with unknown nonlinearities. The NNs are used to dispose the unknown nonlinearities. Different from the published results, the asymptotic convergence character is achieved based on the bound estimation method. By combining some smooth functions with the adaptive backstepping scheme, the asymptotic tracking control strategy is presented. It is proved that the fabricated scheme can guarantee that the system output can asymptotically follow the desired signal, and also that all signals of the entire system are bounded. The validity of the devised scheme is evaluated by a simulation example.

This paper focuses on the typical business scenario of intelligent factory, it includes the manufacturing process, carries out hierarchical security protection, forms a full coverage industrial control security protection network, completes multi-means industrial control security direct protection, at the same time, it utilizes big data analysis, dynamically analyzes the network security situation, completes security early warning, realizes indirect protection, and finally builds a self sensing and self-adjusting industrial network security protection system It provides a reliable reference for the development of intelligent manufacturing industry.

The transportation sector is undergoing rapid changes to reduce pollution and increase life quality in urban areas. One of the most effective approaches is flexible car rental and sharing to reduce traffic congestion and parking space issues. In this paper, we envision a flexible car sharing framework where vehicle owners want to make their vehicles available for flexible rental to other users. The owners delegate the management of their vehicles to intermediate services under certain policies, such as municipalities or authorized services, which manage the due infrastructure and services that can be accessed by users. We investigate the design of an accountable solution that allow vehicles owners, who want to share their vehicles securely under certain usage policies, to control that delegated services and users comply with the policies. While monitoring users behavior, our approach also takes care of users privacy, preventing tracking or profiling procedures by other parties. Existing approaches put high trust assumptions on users and third parties, do not consider users' privacy requirements, or have limitations in terms of flexibility or applicability. We propose an accountable protocol that extends standard delegated authorizations and integrate it with Security Credential Management Systems (SCMS), while considering the requirements and constraints of vehicular networks. We show that the proposed approach represents a practical approach to guarantee accountability in realistic scenarios with acceptable overhead.

In the current cloud service development, during the widely used of cloud service, it can self organize and respond on demand when the cloud service in phenomenon of failure or violation, but it may still cause violation. The first step in forecasting or accountability for this situation, is to generate a dynamic structure of cloud services in a timely manner. In this research, it has presented a method to generate the time-varying structure of cloud service. Firstly, dependencies between tasks and even instances within a job of cloud service are visualized to explore the time-varying characteristics contained in the cloud service structure. And then, those dependencies are discovered quantitatively using CNN (Convolutional Neural Networks). Finally, it structured into an event network of cloud service for tracing violation and other usages. A validation to this approach has been examined by an experiment based on Alibaba’s dataset. A function integrity of this approach may up to 0.80, which is higher than Bai Y and others which is no more than 0.60.

Wireless relay network is a solution for transmitting information from a source node to a sink node far away by installing a relay in between. The broadcasting nature of wireless communication allows the sink node to receive part of the data sent by the source node. In this way, the relay does not need to receive the whole piece of data from the source node and it does not need to forward everything it received. In this paper, we consider the application of batched network coding, a practical form of random linear network coding, for a better utilization of such a network. The amount of innovative information at the relay which is not yet received by the sink node, called the innovative rank, plays a crucial role in various applications including the design of the transmission scheme and the analysis of the throughput. We present a visualization of the innovative rank which allows us to understand and derive formulae related to the innovative rank with ease.

Middleware for IoT (Internet of Things) helps application developers face challenges, such as device heterogeneity, service interoperability, security and scalability. While extensively adopted nowadays, IoT middleware systems are static because, after deployment, updates are only possible by stopping the thing. Therefore, adaptive capabilities can improve existing solutions by allowing their dynamic adaptation to changes in the environmental conditions, evolve provided functionalities, or fix bugs. This paper presents AMoT, an adaptive publish/subscribe middleware for IoT whose design and implementation adopt software architecture principles and evolutive adaptation mechanisms. The experimental evaluation of AMoT helps to measure the impact of the proposed adaptation mechanisms while also comparing the performance of AMoT with a widely adopted MQTT (Message Queuing Telemetry Transport) based middleware. In the end, adaptation has an acceptable performance cost and the advantage of tunning the middleware functionality at runtime.

Recent advances in natural language generation have introduced powerful language models with high-quality output text. However, this raises concerns about the potential misuse of such models for malicious purposes. In this paper, we study natural language watermarking as a defense to help better mark and trace the provenance of text. We introduce the Adversarial Watermarking Transformer (AWT) with a jointly trained encoder-decoder and adversarial training that, given an input text and a binary message, generates an output text that is unobtrusively encoded with the given message. We further study different training and inference strategies to achieve minimal changes to the semantics and correctness of the input text.AWT is the first end-to-end model to hide data in text by automatically learning -without ground truth- word substitutions along with their locations in order to encode the message. We empirically show that our model is effective in largely preserving text utility and decoding the watermark while hiding its presence against adversaries. Additionally, we demonstrate that our method is robust against a range of attacks.

Internet of Things (IoT) based applications face an increasing number of potential security risks, which need to be systematically assessed and addressed. Expert-based manual assessment of IoT security is a predominant approach, which is usually inefficient. To address this problem, we propose an automated security assessment framework for IoT networks. Our framework first leverages machine learning and natural language processing to analyze vulnerability descriptions for predicting vulnerability metrics. The predicted metrics are then input into a two-layered graphical security model, which consists of an attack graph at the upper layer to present the network connectivity and an attack tree for each node in the network at the bottom layer to depict the vulnerability information. This security model automatically assesses the security of the IoT network by capturing potential attack paths. We evaluate the viability of our approach using a proof-of-concept smart building system model which contains a variety of real-world IoT devices and poten-tial vulnerabilities. Our evaluation of the proposed framework demonstrates its effectiveness in terms of automatically predicting the vulnerability metrics of new vulnerabilities with more than 90% accuracy, on average, and identifying the most vulnerable attack paths within an IoT network. The produced assessment results can serve as a guideline for cybersecurity professionals to take further actions and mitigate risks in a timely manner.

Security analysts conduct continuous evaluations of cyber-defense tools to keep pace with advanced and persistent threats. Cyber agility has become a critical proactive security resource that makes it possible to measure defense adjustments and reactions to rising threats. Subsequently, machine learning has been applied to support cyber agility prediction as an essential effort to anticipate future security performance. Nevertheless, apt and treacherous actors motivated by economic incentives continue to prevail in circumventing machine learning-based protection tools. Adversarial learning, widely applied to computer security, especially intrusion detection, has emerged as a new area of concern for the recently recognized critical cyber agility prediction. The rationale is, if a sophisticated malicious actor obtains the cyber agility parameters, correct prediction cannot be guaranteed. Unless with a demonstration of white-box attack failures. The challenge lies in recognizing that unconstrained adversaries hold vast potential capabilities. In practice, they could have perfect-knowledge, i.e., a full understanding of the defense tool in use. We address this challenge by proposing an adversarial machine learning approach that achieves accurate cyber agility forecast through mapped nefarious influence on static defense tools metrics. Considering an adversary would aim at influencing perilous confidence in a defense tool, we demonstrate resilient cyber agility prediction through verified attack signatures in dynamic learning windows. After that, we compare cyber agility prediction under negative influence with and without our proposed dynamic learning windows. Our numerical results show the model's execution degrades without adversarial machine learning. Such a feigned measure of performance could lead to incorrect software security patching.

The popular federated edge learning (FEEL) framework allows privacy-preserving collaborative model training via frequent learning-updates exchange between edge devices and server. Due to the constrained bandwidth, only a subset of devices can upload their updates at each communication round. This has led to an active research area in FEEL studying the optimal device scheduling policy for minimizing communication time. However, owing to the difficulty in quantifying the exact communication time, prior work in this area can only tackle the problem partially by considering either the communication rounds or per-round latency, while the total communication time is determined by both metrics. To close this gap, we make the first attempt in this paper to formulate and solve the communication time minimization problem. We first derive a tight bound to approximate the communication time through cross-disciplinary effort involving both learning theory for convergence analysis and communication theory for per-round latency analysis. Building on the analytical result, an optimized probabilistic scheduling policy is derived in closed-form by solving the approximate communication time minimization problem. It is found that the optimized policy gradually turns its priority from suppressing the remaining communication rounds to reducing per-round latency as the training process evolves. The effectiveness of the proposed scheme is demonstrated via a use case on collaborative 3D objective detection in autonomous driving.

Unforeseen situations on the shopfloor cause the assembly process to divert from its expected progress. To be able to overcome these deviations in a timely manner, assembly process monitoring and early deviation detection are necessary. However, legal regulations and union policies often limit the direct monitoring of human-intensive assembly processes. Grounded in an industry use case, this paper outlines a novel approach that, based on indirect privacy-respecting monitored data from the shopfloor, enables the near real-time detection of multiple types of process deviations. In doing so, this paper specifically addresses uncertainties stemming from indirect shopfloor observations and how to reason in their presence.

Blockchain technology (BCT) has paved a way for new potentials of handling serious data privacy, integrity and security issues in healthcare. To curb the increasing challenges in healthcare industry, healthcare organizations need to apply blockchain technology to better improve patient safety and protect patients records from counterfeiting and fraud. The purpose of this research paper was to define BCT can assist in improving pharmaceutical industries in Saudi Arabia upon utilization of its application. This study adopted quantitative methods to gather the study data. Based on healthcare leaders perception and Internet connection, lack of cooperation, and economic inequality were found to be leading factors hindering the application of blockchain technology in the pharmaceutical industries, Saudi Arabia. Factors facilitating the application of blockchain technology in the pharmaceutical industries, Saudi Arabia were found as system robustness of BCT, increased data safety and decentralization, need for enhanced supply chain management and interoperability, and government laws and policies. Adopting interventions that are targeted to specific patient population medications, effective delivery systems, transit provider reimbursement far from intensity and volume of services towards value and quality was found to compromise the pre-existent challenges and real capacity in healthcare system. Although the relationship between implementation of blockchain technology and cost spending is negative in the short-term, in the long run, the relationship is positive Blockchain helps in managing multiple levels in a more secure way, reduces paper work and amplifies verification inefficiency.

In order to suppress the spread of zero-day virus in the network effectively, a zero-day virus suppression strategy was proposed. Based on the mechanism of zero-day virus transmission and the idea of platform dynamic defense, the corresponding methods of virus transmission suppression are put forward. By changing the platform switching frequency, the scale of zero-day virus transmission and its inhibition effect are simulated in a small-world network model. Theory and computer simulation results show that the idea of platform switching can effectively restrain the spread of virus.

We present a novel approach for the collaborative training of neural network models in decentralized federated environments. In the iterative process a group of autonomous peers run multiple training rounds to train a common model. Thereby, participants perform all model training steps locally, such as stochastic gradient descent optimization, using their private, e.g. mission-critical, training datasets. Based on locally updated models, participants can jointly determine a common model by averaging all associated model weights without sharing the actual weight values. For this purpose we introduce a simple n-out-of-n secret sharing schema and an algorithm to calculate average values in a peer-to-peer manner. Our experimental results with deep neural networks on well-known sample datasets prove the generic applicability of the approach, with regard to model quality parameters. Since there is no need to involve a central service provider in model training, the approach can help establish trustworthy collaboration platforms for businesses with high security and data protection requirements.

The steady growing trend of user demand in using various 4G mobile broadband applications obligates telecom operators to thoroughly plan a precise Quality of Service (QoS) contract with its subscribers. This directly reveals a challenge in figuring out a sophisticated behavior of radio resources (RBs) at the base station to effectively handle the oscillated loads to fulfill their QoS profiles. This paper elaborates on the above issue by analyzing the behavior of the downlink packet scheduling scheme and proposes a solution to bridge between the two major QoS indicators for Real-Time (RT) services, that are, throughput and delay. The proposed scheduling scheme emphasizes that a prior RBs planning indeed has an immense impact on the behavior of the deployed scheduling rule, particularly, when heterogeneous flows share the channel capacity. System-level simulations are performed to evaluate the proposed scheduling scheme in a comparative manner. The numerical results of throughput and delay assured that diverse QoS profiles can be satisfied in case of considering RBs planning.

The objective of this work is to study, using an analytical approach and a numerical simulation, the dynamic behavior of an oscillating liquid inside a fixed U-tube with open ends used as wave energy converter. By establishing a detailed liquid's motion equation and developing a numerical simulation, based on volume of fluid formulation, we quantified the available power that could be extracted for our configuration. A parametrical study using the analytical model showed the effect of each significant parameter on first peak power and subsequent dampening of this peak power, which constitutes a tool for choosing optimal designs. The numerical simulation gave a more realistic model, the obtained results are in good agreements with those of the analytical approach that underestimates the dampening of oscillations. We focused after on influence of the numerical model formulation, mesh type and mesh size on simulation results: no noticeable effect was observed.