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Zubarev, Dmytro, Skarga-Bandurova, Inna.  2019.  Cross-Site Scripting for Graphic Data: Vulnerabilities and Prevention. 2019 10th International Conference on Dependable Systems, Services and Technologies (DESSERT). :154–160.

In this paper, we present an overview of the problems associated with the cross-site scripting (XSS) in the graphical content of web applications. The brief analysis of vulnerabilities for graphical files and factors responsible for making SVG images vulnerable to XSS attacks are discussed. XML treatment methods and their practical testing are performed. As a result, the set of rules for protecting the graphic content of the websites and prevent XSS vulnerabilities are proposed.

Zou, Shuai, Kuzushima, Kento, Mitake, Hironori, Hasegawa, Shoichi.  2017.  Conversational Agent Learning Natural Gaze and Motion of Multi-Party Conversation from Example. Proceedings of the 5th International Conference on Human Agent Interaction. :405–409.

Recent developments in robotics and virtual reality (VR) are making embodied agents familiar, and social behaviors of embodied conversational agents are essential to create mindful daily lives with conversational agents. Especially, natural nonverbal behaviors are required, such as gaze and gesture movement. We propose a novel method to create an agent with human-like gaze as a listener in multi-party conversation, using Hidden Markov Model (HMM) to learn the behavior from real conversation examples. The model can generate gaze reaction according to users' gaze and utterance. We implemented an agent with proposed method, and created VR environment to interact with the agent. The proposed agent reproduced several features of gaze behavior in example conversations. Impression survey result showed that there is at least a group who felt the proposed agent is similar to human and better than conventional methods.

Zola, Francesco, Eguimendia, Maria, Bruse, Jan Lukas, Orduna Urrutia, Raul.  2019.  Cascading Machine Learning to Attack Bitcoin Anonymity. 2019 IEEE International Conference on Blockchain (Blockchain). :10—17.

Bitcoin is a decentralized, pseudonymous cryptocurrency that is one of the most used digital assets to date. Its unregulated nature and inherent anonymity of users have led to a dramatic increase in its use for illicit activities. This calls for the development of novel methods capable of characterizing different entities in the Bitcoin network. In this paper, a method to attack Bitcoin anonymity is presented, leveraging a novel cascading machine learning approach that requires only a few features directly extracted from Bitcoin blockchain data. Cascading, used to enrich entities information with data from previous classifications, led to considerably improved multi-class classification performance with excellent values of Precision close to 1.0 for each considered class. Final models were implemented and compared using different machine learning models and showed significantly higher accuracy compared to their baseline implementation. Our approach can contribute to the development of effective tools for Bitcoin entity characterization, which may assist in uncovering illegal activities.

Zodik, Gabi.  2016.  Cognitive and Contextual Enterprise Mobile Computing: Invited Keynote Talk. Proceedings of the 9th India Software Engineering Conference. :11–12.

The second wave of change presented by the age of mobility, wearables, and IoT focuses on how organizations and enterprises, from a wide variety of commercial areas and industries, will use and leverage the new technologies available. Businesses and industries that don't change with the times will simply cease to exist. Applications need to be powered by cognitive and contextual technologies to support real-time proactive decisions. These decisions will be based on the mobile context of a specific user or group of users, incorporating location, time of day, current user task, and more. Driven by the huge amounts of data produced by mobile and wearables devices, and influenced by privacy concerns, the next wave in computing will need to exploit data and computing at the edge of the network. Future mobile apps will have to be cognitive to 'understand' user intentions based on all the available interactions and unstructured data. Mobile applications are becoming increasingly ubiquitous, going beyond what end users can easily comprehend. Essentially, for both business-to-client (B2C) and business-to-business (B2B) apps, only about 30% of the development efforts appear in the interface of the mobile app. For example, areas such as the collaborative nature of the software or the shortened development cycle and time-to-market are not apparent to end users. The other 70% of the effort invested is dedicated to integrating the applications with back-office systems and developing those aspects of the application that operate behind the scenes. An important, yet often complex, part of the solution and mobile app takes place far from the public eye-in the back-office environment. It is there that various aspects of customer relationship management must be addressed: tracking usage data, pushing out messaging as needed, distributing apps to employees within the enterprise, and handling the wide variety of operational and management tasks-often involving the collection and monitoring of data from sensors and wearable devices. All this must be carried out while addressing security concerns that range from verifying user identities, to data protection, to blocking attempted breaches of the organization, and activation of malicious code. Of course, these tasks must be augmented by a systematic approach and vigilant maintenance of user privacy. The first wave of the mobile revolution focused on development platforms, run-time platforms, deployment, activation, and management tools for multi-platform environments, including comprehensive mobile device management (MDM). To realize the full potential of this revolution, we must capitalize on information about the context within which mobile devices are used. With both employees and customers, this context could be a simple piece of information such as the user location or time of use, the hour of the day, or the day of the week. The context could also be represented by more complex data, such as the amount of time used, type of activity performed, or user preferences. Further insight could include the relationship history with the user and the user's behavior as part of that relationship, as well as a long list of variables to be considered in various scenarios. Today, with the new wave of wearables, the definition of context is being further extended to include environmental factors such as temperature, weather, or pollution, as well as personal factors such as heart rate, movement, or even clothing worn. In both B2E and B2C situations, a context-dependent approach, based on the appropriate context for each specific user, offers a superior tool for working with both employees and clients alike. This mode of operation does not start and end with the individual user. Rather, it takes into account the people surrounding the user, the events taking place nearby, appliances or equipment activated, the user's daily schedule, as well as other, more general information, such as the environment and weather. Developing enterprise-wide, context-dependent, mobile solutions is still a complex challenge. A system of real added-value services must be developed, as well as a comprehensive architecture. These four-tier architectures comprise end-user devices like wearables and smartphones, connected to systems of engagement (SoEs), and systems of record (SoRs). All this is needed to enable data analytics and collection in the context where it is created. The data collected will allow further interaction with employees or customers, analytics, and follow-up actions based on the results of that analysis. We also need to ensure end-to-end (E2E) security across these four tiers, and to keep the data and application contexts in sync. These are just some of the challenges being addressed by IBM Research. As an example, these technologies could be deployed in the retail space, especially in brick-and-mortar stores. Identifying a customer entering a store, detecting her location among the aisles, and cross-referencing that data with the customer's transaction history, could lead to special offers tailor-made for that specific customer or suggestions relevant to her purchasing process. This technology enables real-world implementation of metrics, analytics, and other tools familiar to us from the online realm. We can now measure visits to physical stores in the same way we measure web page hits: analyze time spent in the store, the areas visited by the customer, and the results of those visits. In this way, we can also identify shoppers wandering around the store and understand when they are having trouble finding the product they want to purchase. We can also gain insight into the standard traffic patterns of shoppers and how they navigate a store's floors and departments. We might even consider redesigning the store layout to take advantage of this insight to enhance sales. In healthcare, the context can refer to insight extracted from data received from sensors on the patient, from either his mobile device or wearable technology, and information about the patient's environment and location at that moment in time. This data can help determine if any assistance is required. For example, if a patient is discharged from the hospital for continued at-home care, doctors can continue to remotely monitor his condition via a system of sensors and analytic tools that interpret the sensor readings. This approach can also be applied to the area of safety. Scientists at IBM Research are developing a platform that collects and analyzes data from wearable technology to protect the safety of employees working in construction, heavy industry, manufacturing, or out in the field. This solution can serve as a real-time warning system by analyzing information gathered from wearable sensors embedded in personal protective equipment, such as smart safety helmets and protective vests, and in the workers' individual smartphones. These sensors can continuously monitor a worker's pulse rate, movements, body temperature, and hydration level, as well as environmental factors such as noise level, and other parameters. The system can provide immediate alerts to the worker about any dangers in the work environment to prevent possible injury. It can also be used to prevent accidents before they happen or detect accidents once they occur. For example, with sophisticated algorithms, we can detect if a worker falls based on a sudden difference in elevations detected by an accelerometer, and then send an alert to notify her peers and supervisor or call for help. Monitoring can also help ensure safety in areas where continuous exposure to heat or dangerous materials must be limited based on regulated time periods. Mobile technologies can also help manage events with massive numbers of participants, such as professional soccer games, music festivals, and even large-scale public demonstrations, by sending alerts concerning long and growing lines or specific high-traffic areas. These technologies can be used to detect accidents typical of large-scale gatherings, send warnings about overcrowding, and alert the event organizers. In the same way, they can alleviate parking problems or guide public transportation operators- all via analysis and predictive analytics. IBM Research - Haifa is currently involved in multiple activities as part of IBM's MobileFirst initiative. Haifa researchers have a special expertise in time- and location-based intelligent applications, including visual maps that display activity contexts and predictive analytics systems for mobile data and users. In another area, IBM researchers in Haifa are developing new cognitive services driven from the unique data available on mobile and wearable devices. Looking to the future, the IBM Research team is further advancing the integration of wearable technology, augmented reality systems, and biometric tools for mobile user identity validation. Managing contextual data and analyzing the interaction between the different kinds of data presents fascinating challenges for the development of next-generation programming. For example, we need to rethink when and where data processing and computations should occur: Is it best to leave them at the user-device level, or perhaps they should be moved to the back-office systems, servers, and/or the cloud infrastructures with which the user device is connected? New-age applications are becoming more and more distributed. They operate on a wide range of devices, such as wearable technologies, use a variety of sensors, and depend on cloud-based systems. As a result, a new distributed programming paradigm is emerging to meet the needs of these use-cases and real-time scenarios. This paradigm needs to deal with massive amounts of devices, sensors, and data in business systems, and must be able to shift computation from the cloud to the edge, based on context in close to real-time. By processing data at the edge of the network, close to where the interactions and processing are happening, we can help reduce latency and offer new opportunities for improved privacy and security. Despite all these interactions, data collection, and the analytic insights based upon them-we cannot forget the issues of privacy. Without a proper and reliable solution that offers more control over what personal data is shared and how it is used, people will refrain from sharing information. Such sharing is necessary for developing and understanding the context in which people are carrying out various actions, and to offer them tools and services to enhance their actions. In the not-so-distant future, we anticipate the appearance of ad-hoc networks for wearable technology systems that will interact with one another to further expand the scope and value of available context-dependent data.

Zobaed, S.M., ahmad, sahan, Gottumukkala, Raju, Salehi, Mohsen Amini.  2019.  ClustCrypt: Privacy-Preserving Clustering of Unstructured Big Data in the Cloud. 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). :609—616.
Security and confidentiality of big data stored in the cloud are important concerns for many organizations to adopt cloud services. One common approach to address the concerns is client-side encryption where data is encrypted on the client machine before being stored in the cloud. Having encrypted data in the cloud, however, limits the ability of data clustering, which is a crucial part of many data analytics applications, such as search systems. To overcome the limitation, in this paper, we present an approach named ClustCrypt for efficient topic-based clustering of encrypted unstructured big data in the cloud. ClustCrypt dynamically estimates the optimal number of clusters based on the statistical characteristics of encrypted data. It also provides clustering approach for encrypted data. We deploy ClustCrypt within the context of a secure cloud-based semantic search system (S3BD). Experimental results obtained from evaluating ClustCrypt on three datasets demonstrate on average 60% improvement on clusters' coherency. ClustCrypt also decreases the search-time overhead by up to 78% and increases the accuracy of search results by up to 35%.
Zhuo Lu, Wenye Wang, Wang, C..  2015.  Camouflage Traffic: Minimizing Message Delay for Smart Grid Applications under Jamming. Dependable and Secure Computing, IEEE Transactions on. 12:31-44.

Smart grid is a cyber-physical system that integrates power infrastructures with information technologies. To facilitate efficient information exchange, wireless networks have been proposed to be widely used in the smart grid. However, the jamming attack that constantly broadcasts radio interference is a primary security threat to prevent the deployment of wireless networks in the smart grid. Hence, spread spectrum systems, which provide jamming resilience via multiple frequency and code channels, must be adapted to the smart grid for secure wireless communications, while at the same time providing latency guarantee for control messages. An open question is how to minimize message delay for timely smart grid communication under any potential jamming attack. To address this issue, we provide a paradigm shift from the case-by-case methodology, which is widely used in existing works to investigate well-adopted attack models, to the worst-case methodology, which offers delay performance guarantee for smart grid applications under any attack. We first define a generic jamming process that characterizes a wide range of existing attack models. Then, we show that in all strategies under the generic process, the worst-case message delay is a U-shaped function of network traffic load. This indicates that, interestingly, increasing a fair amount of traffic can in fact improve the worst-case delay performance. As a result, we demonstrate a lightweight yet promising system, transmitting adaptive camouflage traffic (TACT), to combat jamming attacks. TACT minimizes the message delay by generating extra traffic called camouflage to balance the network load at the optimum. Experiments show that TACT can decrease the probability that a message is not delivered on time in order of magnitude.

Zhu, Z., Jiang, R., Jia, Y., Xu, J., Li, A..  2018.  Cyber Security Knowledge Graph Based Cyber Attack Attribution Framework for Space-ground Integration Information Network. 2018 IEEE 18th International Conference on Communication Technology (ICCT). :870–874.
Comparing with the traditional Internet, the space-ground integration information network has more complicated topology, wider coverage area and is more difficult to find the source of attacks. In this paper, a cyber attack attribution framework is proposed to trace the attack source in space-ground integration information network. First, we constructs a cyber security knowledge graph for space-ground integration information network. An automated attributing framework for cyber-attack is proposed. It attributes the source of the attack by querying the cyber security knowledge graph we constructed. Experiments show that the proposed framework can attribute network attacks simply, effectively, and automatically.
Zhu, Suwen, Lu, Long, Singh, Kapil.  2016.  CASE: Comprehensive Application Security Enforcement on COTS Mobile Devices. Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services. :375–386.

Without violating existing app security enforcement, malicious modules inside apps, such as a library or an external class, can steal private data and abuse sensitive capabilities meant for other modules inside the same apps. These so-called "module-level attacks" are quickly emerging, fueled by the pervasive use of third-party code in apps and the lack of module-level security enforcement on mobile platforms. To systematically thwart the threats, we build CASE, an automatic app patching tool used by app developers to enable module-level security in their apps built for COTS Android devices. During runtime, patched apps enforce developer-supplied security policies that regulate interactions among modules at the granularity of a Java class. Requiring no changes or special support from the Android OS, the enforcement is complete in covering inter-module crossings in apps and is robust against malicious Java and native app modules. We evaluate CASE with 420 popular apps and a set of Android's unit tests. The results show that CASE is fully compatible with the tested apps and incurs an average performance overhead of 4.9%.

Zhu, Meng, Yang, Xudong.  2019.  Chinese Texts Classification System. 2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT). :149–152.
In this article, we designed an automatic Chinese text classification system aiming to implement a system for classifying news texts. We propose two improved classification algorithms as two different choices for users to choose and then our system uses the chosen method for the obtaining of the classified result of the input text. There are two improved algorithms, one is k-Bayes using hierarchy conception based on NB method in machine learning field and another one adds attention layer to the convolutional neural network in deep learning field. Through experiments, our results showed that improved classification algorithms had better accuracy than based algorithms and our system is useful for making classifying news texts more reasonably and effectively.
Zhou, Z., Yang, Y., Cai, Z., Yang, Y., Lin, L..  2019.  Combined Layer GAN for Image Style Transfer*. 2019 IEEE International Conference on Computational Electromagnetics (ICCEM). :1—3.

Image style transfer is an increasingly interesting topic in computer vision where the goal is to map images from one style to another. In this paper, we propose a new framework called Combined Layer GAN as a solution of dealing with image style transfer problem. Specifically, the edge-constraint and color-constraint are proposed and explored in the GAN based image translation method to improve the performance. The motivation of the work is that color and edge are fundamental vision factors for an image, while in the traditional deep network based approach, there is a lack of fine control of these factors in the process of translation and the performance is degraded consequently. Our experiments and evaluations show that our novel method with the edge and color constrains is more stable, and significantly improves the performance compared with the traditional methods.

Zhou, Yiwen, Shen, Qili, Dong, Mianxiong, Ota, Kaoru, Wu, Jun.  2019.  Chaos-Based Delay-Constrained Green Security Communications for Fog-Enabled Information-Centric Multimedia Network. 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring). :1–6.
The Information-Centric Network possessing the content-centric features, is the innovative architecture of the next generation of network. Collaborating with fog computing characterized by its strong edge power, ICN will become the development trend of the future network. The emergence of Information-Centric Multimedia Network (ICMN) can meet the increasing demand for transmission of multimedia streams in the current Internet environment. The data transmission has become more delay-constrained and convenient because of the distributed storage, the separation between the location of information and terminals, and the strong cacheability of each node in ICN. However, at the same time, the security of the multimedia streams in the delivery process still requires further protection against wiretapping, interception or attacking. In this paper, we propose the delay-constrained green security communications for ICMN based on chaotic encryption and fog computing so as to transmit multimedia streams in a more secure and time-saving way. We adapt a chaotic cryptographic method to ICMN, implementing the encryption and decryption of multimedia streams. Meanwhile, the network edge capability to process the encryption and decryption is enhanced. Thanks to the fog computing, the strengthened transmission speed of the multimedia streams can fulfill the need for short latency. The work in the paper is of great significance to improve the green security communications of multimedia streams in ICMN.
Zhou, Lin, Feng, Jing, He, Haiguang, Mao, Zhijie, Chen, Yingmei, Gao, Mei, He, Zhuzhen.  2019.  A Construction Method of Security Mechanism Requirement for Wireless Access System Based on CC Standard. 2019 International Conference on Intelligent Transportation, Big Data Smart City (ICITBS). :369–372.

Aiming at the incomplete and incomplete security mechanism of wireless access system in emergency communication network, this paper proposes a security mechanism requirement construction method for wireless access system based on security evaluation standard. This paper discusses the requirements of security mechanism construction in wireless access system from three aspects: the definition of security issues, the construction of security functional components and security assurance components. This method can comprehensively analyze the security threats and security requirements of wireless access system in emergency communication network, and can provide correct and reasonable guidance and reference for the establishment of security mechanism.

Zhou, Liang, Ouyang, Xuan, Ying, Huan, Han, Lifang, Cheng, Yushi, Zhang, Tianchen.  2018.  Cyber-Attack Classification in Smart Grid via Deep Neural Network. Proceedings of the 2Nd International Conference on Computer Science and Application Engineering. :90:1–90:5.
Smart grid1 is a modern power transmission network. With its development, the computing, communication and physical processes is getting more and more connected. However, an adversary can destroy power production by attacking the power secondary equipment. Accurate and fast response to cyber-attacks is a prerequisite for stable grid operation. Therefore, it is critical to identify and classify attacks in the smart grid. In this paper, we propose a novel approach that utilizes machine learning algorithms to help classify cyber-attacks. We built a deep neural network (DNN) model and select the global optimal parameters to achieve high generalization performance. The evaluation result demonstrates that the proposed method can effectively identify cyber-attacks in smart grid with an accuracy as high as 96%.
Zhong, J., Yang, C..  2019.  A Compositionality Assembled Model for Learning and Recognizing Emotion from Bodily Expression. 2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM). :821–826.
When we are express our internal status, such as emotions, the human body expression we use follows the compositionality principle. It is a theory in linguistic which proposes that the single components of the bodily presentation as well as the rules used to combine them are the major parts to finish this process. In this paper, such principle is applied to the process of expressing and recognizing emotional states through body expression, in which certain key features can be learned to represent certain primitives of the internal emotional state in the form of basic variables. This is done by a hierarchical recurrent neural learning framework (RNN) because of its nonlinear dynamic bifurcation, so that variables can be learned to represent different hierarchies. In addition, we applied some adaptive learning techniques in machine learning for the requirement of real-time emotion recognition, in which a stable representation can be maintained compared to previous work. The model is examined by comparing the PB values between the training and recognition phases. This hierarchical model shows the rationality of the compositionality hypothesis by the RNN learning and explains how key features can be used and combined in bodily expression to show the emotional state.
Zhenqi Huang, University of Illinois at Urbana-Champaign, Yu Wang, University of Illinois at Urbana-Champaign, Sayan Mitra, University of Illinois at Urbana-Champaign, Geir Dullerud, University of Illinois at Urbana-Champaign.  2015.  Controller Synthesis for Linear Time-varying Systems with Adversaries.

We present a controller synthesis algorithm for a discrete time reach-avoid problem in the presence of adversaries. Our model of the adversary captures typical malicious attacks en- visioned on cyber-physical systems such as sensor spoofing, controller corruption, and actuator intrusion. After formu- lating the problem in a general setting, we present a sound and complete algorithm for the case with linear dynamics and an adversary with a budget on the total L2-norm of its actions. The algorithm relies on a result from linear control theory that enables us to decompose and precisely compute the reachable states of the system in terms of a symbolic simulation of the adversary-free dynamics and the total uncertainty induced by the adversary. With this de- composition, the synthesis problem eliminates the universal quantifier on the adversary’s choices and the symbolic con- troller actions can be effectively solved using an SMT solver. The constraints induced by the adversary are computed by solving second-order cone programmings. The algorithm is later extended to synthesize state-dependent controller and to generate attacks for the adversary. We present prelimi- nary experimental results that show the effectiveness of this approach on several example problems.

Zhenqi Huang, University of Illinois at Urbana-Champaign, Yu Wang, University of Illinois at Urbana-Champaign, Sayan Mitra, University of Illinois at Urbana-Champaign, Geir Dullerud, University of Illinois at Urbana-Champaign.  2014.  On the Cost of Privacy in Distributed Control Systems. 3rd ACM International Conference on High Confidence Networked Systems (HiCoNS).

Individuals sharing information can improve the cost or performance of a distributed control system. But, sharing may also violate privacy. We develop a general framework for studying the cost of differential privacy in systems where a collection of agents, with coupled dynamics, communicate for sensing their shared environment while pursuing individ- ual preferences. First, we propose a communication strategy that relies on adding carefully chosen random noise to agent states and show that it preserves differential privacy. Of course, the higher the standard deviation of the noise, the higher the cost of privacy. For linear distributed control systems with quadratic cost functions, the standard deviation becomes independent of the number agents and it decays with the maximum eigenvalue of the dynamics matrix. Furthermore, for stable dynamics, the noise to be added is independent of the number of agents as well as the time horizon up to which privacy is desired.

Zheng, Yan, Phillips, Jeff M..  2017.  Coresets for Kernel Regression. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. :645–654.
Kernel regression is an essential and ubiquitous tool for non-parametric data analysis, particularly popular among time series and spatial data. However, the central operation which is performed many times, evaluating a kernel on the data set, takes linear time. This is impractical for modern large data sets. In this paper we describe coresets for kernel regression: compressed data sets which can be used as proxy for the original data and have provably bounded worst case error. The size of the coresets are independent of the raw number of data points; rather they only depend on the error guarantee, and in some cases the size of domain and amount of smoothing. We evaluate our methods on very large time series and spatial data, and demonstrate that they incur negligible error, can be constructed extremely efficiently, and allow for great computational gains.
Zheng, Y., Zheng, S..  2015.  Cyber Security Risk Assessment for Industrial Automation Platform. 2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). :341–344.

Due to the fact that the cyber security risks exist in industrial control system, risk assessment on Industrial Automation Platform (IAP) is discussed in this paper. The cyber security assessment model for IAP is built based on relevant standards at abroad. Fuzzy analytic hierarchy process and fuzzy comprehensive evaluation method based on entropy theory are utilized to evaluate the communication links' risk of IAP software. As a result, the risk weight of communication links which have impacts on platform and the risk level of this platform are given for further study on protective strategy. The assessment result shows that the methods used can evaluate this platform efficiently and practically.

Zheng, Huanhuan, Qu, Yanyun, Zeng, Kun.  2016.  Coupled Autoencoder Network with Joint Regularizations for Image Super-resolution. Proceedings of the International Conference on Internet Multimedia Computing and Service. :114–117.
This paper aims at building a sparse deep autoencoder network with joint regularizations for image super-resolution. A map is learned from the low-resolution feature space to high-resolution feature space. In the training stage, two autoencoder networks are built for image representation for low resolution images and their high resolution counterparts, respectively. A neural network is constructed to learn a map between the features of low resolution images and high resolution images. Furthermore, due to the local smoothness and the redundancy of an image, the joint variation regularizations are unified with the coupled autoencoder network (CAN). For the local smoothness, steerable kernel variation regularization is designed. For redundancy, non-local variation regularization is designed. The joint regularizations improve the quality of the super resolution image. Experimental results on Set5 demonstrate the effectiveness of our proposed method.
Zhao, Zengzhen, Guan, Qingxiao, Zhao, Xianfeng.  2016.  Constructing Near-optimal Double-layered Syndrome-Trellis Codes for Spatial Steganography. Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security. :139–148.

In this paper, we present a new kind of near-optimal double-layered syndrome-trellis codes (STCs) for spatial domain steganography. The STCs can hide longer message or improve the security with the same-length message comparing to the previous double-layered STCs. In our scheme, according to the theoretical deduction we can more precisely divide the secret payload into two parts which will be embedded in the first layer and the second layer of the cover respectively with binary STCs. When embed the message, we encourage to realize the double-layered embedding by ±1 modifications. But in order to further decrease the modifications and improve the time efficient, we allow few pixels to be modified by ±2. Experiment results demonstrate that while applying this double-layered STCs to the adaptive steganographic algorithms, the embedding modifications become more concentrative and the number decreases, consequently the security of steganography is improved.

Zhao, Xuanyi, Cassella, Cristian.  2019.  On the Coupling Coefficient of ScyAl1-yN-based Piezoelectric Acoustic Resonators. 2019 Joint Conference of the IEEE International Frequency Control Symposium and European Frequency and Time Forum (EFTF/IFC). :1–4.
This work investigates the electromechanical coupling coefficient (kt2) attained by two available piezoelectric acoustic resonator technologies relying on Aluminum Scandium Nitride (ScyAl1-yN) films to operate. In particular, by using a theoretical approach, we extracted the maximum kt2-value attainable, for different scandium-doping concentrations (from 0% to 40%), by Film-Bulk-Acoustic-Resonators (FBARs) and Cross-Sectional-Lamé-Mode Resonators (CLMRs). For the first time, we show how the use of higher scandium doping concentrations can render the kt2 of CLMRs higher (35%) than the one attained by FBARs (28%). Such a unique feature renders CLMRs as ideal candidates to form lithographically defined resonators and filters for next-generation wideband radiofrequency (RF) front-ends.
Zhao, Min, Li, Shunxin, Xiao, Dong, Zhao, Guoliang, Li, Bo, Liu, Li, Chen, Xiangyu, Yang, Min.  2019.  Consumption Ability Estimation of Distribution System Interconnected with Microgrids. 2019 IEEE International Conference on Energy Internet (ICEI). :345–350.
With fast development of distributed generation, storages and control techniques, a growing number of microgrids are interconnected with distribution networks. Microgrid capacity that a local distribution system can afford, is important to distribution network planning and microgrids well-organized integration. Therefore, this paper focuses on estimating consumption ability of distribution system interconnected with microgrids. The method to judge rationality of microgrids access plan is put forward, and an index system covering operation security, power quality and energy management is proposed. Consumption ability estimation procedure based on rationality evaluation and interactions is built up then, and requirements on multi-scenario simulation are presented. Case study on a practical distribution system design with multi-microgrids guarantees the validity and reasonableness of the proposed method and process. The results also indicate construction and reinforcement directions for the distribution network.
Zhao, Jianfeng.  2018.  Case Study: Discovering Hardware Trojans Based on Model Checking. Proceedings of the 8th International Conference on Communication and Network Security. :64–68.

Hardware Trojan may cause changes in system functions, system information leakage, and system damage or system paralysis. According to the hardware Trojan classification method, this paper discusses the hardware Trojan that belongs to the design stage, the behavior level description, the internal trigger, and it changes the function of processor, it is a hardware Trojan of combinational logic. The domestic and foreign research institutions put forward a variety of methods for the detection of hardware Trojans. In this paper, based on the open source processor OR1200 RTL source code, Aiming at a kind of hardware Trojan, which is composed of combinational logic trigger, one of the formal methods, the model checking technique, is used to detect the hardware Trojan. The experiment uses the open source EBMC model detection tool, uses the RTL source code as the model input, and uses SVA to describe the property input. The experimental results show that the model checking technique can be used as an effective hardware Trojan detection method.

Zhao, J..  2017.  Composition Properties of Bayesian Differential Privacy. 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). :1–5.

Differential privacy is a rigorous privacy standard that has been applied to a range of data analysis tasks. To broaden the application scenarios of differential privacy when data records have dependencies, the notion of Bayesian differential privacy has been recently proposed. However, it is unknown whether Bayesian differential privacy preserves three nice properties of differential privacy: sequential composability, parallel composability, and post-processing. In this paper, we provide an affirmative answer to this question; i.e., Bayesian differential privacy still have these properties. The idea behind sequential composability is that if we have m algorithms Y1, Y2,łdots, Ym, where Y$\mathscrl$ is independently $ε\mathscrl$-Bayesian differential private for $\mathscrl$ = 1,2,łdots, m, then by feeding the result of Y1 into Y2, the result of Y2 into Y3, and so on, we will finally have an $Σ$m$\mathscrl$=;1 $ε\mathscrl$-Bayesian differential private algorithm. For parallel composability, we consider the situation where a database is partitioned into m disjoint subsets. The $\mathscrl$-th subset is input to a Bayesian differential private algorithm Y$\mathscrl$, for $\mathscrl$= 1, 2,łdots, m. Then the parallel composition of Y1, Y2,łdots, Ym will be maxm$\mathscrl$=;1=1 $ε\mathscrl$-Bayesian differential private. The postprocessing property means that a data analyst, without additional knowledge abo- t the private database, cannot compute a function of the output of a Bayesian differential private algorithm and reduce its privacy guarantee.

Zhao, H., Ren, J., Pei, Z., Cai, Z., Dai, Q., Wei, W..  2017.  Compressive Sensing Based Feature Residual for Image Steganalysis Detection. 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). :1096–1100.

Based on the feature analysis of image content, this paper proposes a novel steganalytic method for grayscale images in spatial domain. In this work, we firstly investigates directional lifting wavelet transform (DLWT) as a sparse representation in compressive sensing (CS) domain. Then a block CS (BCS) measurement matrix is designed by using the generalized Gaussian distribution (GGD) model, in which the measurement matrix can be used to sense the DLWT coefficients of images to reflect the feature residual introduced by steganography. Extensive experiments are showed that proposed scheme CS-based is feasible and universal for detecting stegography in spatial domain.