Knowledge-empowered agent information system for privacy payoff in eCommerce

Today, many online companies are gathering information and assembling sophisticated databases that know a great deal of information about many people, generally without the knowledge of those people. Such endeavor has resulted in the unprecedented attrition of individual??s right to informational self-determination. On the one hand, Consumers are powerless to prevent the unauthorized dissemination of their personal information, and on the other, they are excluded from its profitable commercial exchange. This paper focuses on developing knowledge-empowered agent information system for privacy payoff as a means of rewarding consumers for sharing their personal information with online businesses. The design of this system is driven by the following argument: if consumers?? personal information is a valuable asset, should they not be entitled to benefit from their asset as well? The proposed information system is a multi-agent system where several agents employ various knowledge and requirements for personal information valuation and interaction capabilities that most users cannot do on their own. The agents in the information system bear the responsibility of working on behalf of consumers to categorize their personal data objects, report to consumers on online businesses?? trust and reputation, determine the value of their compensation using risk-based financial models, and finally negotiate for a payoff value in return for the dissemination of users?? information. The details of the system as well as a proof-of-concept implementation using JADE (Java Agent Development Environment) are presented here.     

Body Workspace of Quadruped Walking Robot and its Applicability in Legged Locomotion

This paper discusses on determination of the workspace of the body of a quadruped walking robot, called “body workspace”, and its applicability in legged locomotion. The body workspace represents the set of all valid body configurations for a next step by considering three constraints of a body position: existence of the inverse kinematic solutions, reach-ability of the next swing leg to the next desired foothold, and static equilibrium of the robot when the next swing leg is lifted. The space contains all the body positions that ensure the existence of inverse kinematic solutions, is calculated in the first. Then, a subspace inside the determined space that allows the robot to reach the next desired foothold is analyzed. Finally, the workspace is obtained by excluding all the positions inside the subspace that do not ensure the equilibrium of the robot when the next swing leg is lifted. Therefore, the workspace shows all possible solutions for choosing the next body configuration of a given static walking problem. It is significant in improving the robot’s performances since moving body takes an intrinsic role in static walking, besides swinging a leg. The algorithm runs fast in real-time because it is a pure geometric method. The body workspace of a quadruped walking robot is visualized to help the understanding of the algorithm. In addition, applications of using the body workspace in improving the robot’s ability are presented to show potential applicability of the workspace.     

Knowledge Discovery from Series of Interval Events

Knowledge discovery from data sets can be extensively automated by using data mining software tools. Techniques for mining series of interval events, however, have not been considered. Such time series are common in many applications. In this paper, we propose mining techniques to discover temporal containment relationships in such series. Specifically, an item A is said to contain an item B if an event of type B occurs during the time span of an event of type A, and this is a frequent relationship in the data set. Mining such relationships provides insight about temporal relationships among various items. We implement the technique and analyze trace data collected from a real database application. Experimental results indicate that the proposed mining technique can discover interesting results. We also introduce a quantization technique as a preprocessing step to generalize the method to all time series.     

Robust PID controller design for processes with stochastic parametric uncertainties

The stability and performance of a system can be inferred from the evolution of statistical characteristics of the system's states. Wiener's polynomial chaos can provide an efficient framework for the statistical analysis of dynamical systems, computationally far superior to Monte Carlo simulations. This work proposes a new method of robust PID controller design based on polynomial chaos for processes with stochastic parametric uncertainties. The proposed method can greatly reduce computation time and can also efficiently handle both nominal and robust performance against stochastic uncertainties by solving a simple optimization problem. Simulation comparison with other methods demonstrated the effectiveness of the proposed design method.     

Generative adversarial network (GAN) and enhanced root mean square error (ERMSE): deep learning for stock price movement prediction

The prediction of stock price movement direction is significant in financial circles and academic. Stock price contains complex, incomplete, and fuzzy information which makes it an extremely difficult task to predict its development trend. Predicting and analysing financial data is a nonlinear, time-dependent problem. With rapid development in machine learning and deep learning, this task can be performed more effectively by a purposely designed network. This paper aims to improve prediction accuracy and minimizing forecasting error loss through deep learning architecture by using Generative Adversarial Networks. It was proposed a generic model consisting of Phase-space Reconstruction (PSR) method for reconstructing price series and Generative Adversarial Network (GAN) which is a combination of two neural networks which are Long Short-Term Memory (LSTM) as Generative model and Convolutional Neural Network (CNN) as Discriminative model for adversarial training to forecast the stock market. LSTM will generate new instances based on historical basic indicators information and then CNN will estimate whether the data is predicted by LSTM or is real. It was found that the Generative Adversarial Network (GAN) has performed well on the enhanced root mean square error to LSTM, as it was 4.35% more accurate in predicting the direction and reduced processing time and RMSE by 78 s and 0.029, respectively. This study provides a better result in the accuracy of the stock index. It seems that the proposed system concentrates on minimizing the root mean square error and processing time and improving the direction prediction accuracy, and provides a better result in the accuracy of the stock index.

     

Tailoring Vanadium Dioxide Film Orientation Using Nanosheets: a Combined Microscopy,Diffraction, Transport,and Soft X‐Ray in Transmission Study

Vanadium dioxide (VO₂) is a much‐discussed material for oxide electronics and neuromorphic computing applications. Here, heteroepitaxy of VO₂ is realized on top of oxide nanosheets that cover either the amorphous silicon dioxide surfaces of Si substrates or X‐ray transparent silicon nitride membranes. The out‐of‐plane orientation of the VO₂ thin films is controlled at will between (011)_M1/(110)_R and (?402)_M1/(002)_R by coating the bulk substrates with Ti_0.87O₂ and NbWO₆ nanosheets, respectively, prior to VO₂ growth. Temperature‐dependent X‐ray diffraction and automated crystal orientation mapping in microprobe transmission electron microscope mode (ACOM‐TEM) characterize the high phase purity, the crystallographic and orientational properties of the VO₂ films. Transport measurements and soft X‐ray absorption in transmission are used to probe the VO₂ metal–insulator transition, showing results of a quality equal to those from epitaxial films on bulk single‐crystal substrates. Successful local manipulation of two different VO₂ orientations on a single substrate is demonstrated using VO₂ grown on lithographically patterned lines of Ti_0.87O₂ and NbWO₆ nanosheets investigated by electron backscatter diffraction. Finally, the excellent suitability of these nanosheet‐templated VO₂ films for advanced lensless imaging of the metal–insulator transition using coherent soft X‐rays is discussed.     

Social-Aware Caching and Resource Sharing Maximized Video Delivery Capacity in 5G Ultra-Dense Networks

Mobile Networks and Applications - In 5G networks, a massive number of connections of high data rate services, e.g., video streaming services, certainly make the networks deteriorated because of...     

Molybdenum and Phosphorous Dual Doping in Cobalt Monolayer Interfacial Assembled Cobalt Nanowires for Efficient Overall Water Splitting

The necessity for better water splitting requires speedy development of efficient catalysts with high activity, long‐term stability, and cost effectiveness. In this work, a bifunctional catalyst originating from the interfacial assembly of a thin Mo,P‐codoped Co layer (≈50 nm) shelled Co nanowire (Co‐Mo‐P/CoNWs) network is fabricated via a facile approach. The catalyst exhibits low overpotentials of 0.08 and 0.27 V to reach a current response of 20 mA cm^?2 for the hydrogen evolution reaction and oxygen evolution reaction, respectively, together with long‐term stability in 1.0 m KOH medium. The outstanding performance is further demonstrated by a Co‐Mo‐P/CoNWs‐based electrolyzer, which enables a cell voltage of only 1.495 V to reach 10 mA cm^?2, superior to one derived from commercial (Pt/C + RuO₂/C) as well as to various reports recently published elsewhere. It is recognized that the formation of multiactive centers together with the increased active site number caused by Mo and P dual doping synergistically promote both hydrogen and oxygen evolution performance. Such a hybrid material opens a new approach for developing efficient and cost‐effective catalysts for water splitting application.     

Quasi-orthogonal space-time-frequency codes in MB-OFDM UWB

Quasi-orthogonal space-time-frequency codes (QOSTFCs) will be examined in this paper to advance either data rate or error performance in recently proposed space-time-frequency coded multiband OFDM Ultra-wideband (STFC MB-OFDM UWB) communication systems. It is shown that QOSTFCs can provide significantly better error performance, compared to the conventional MB-OFDM UWB (without STFCs) and to the orthogonal STFCs (OSTFCs) of the same order, at the same data rate, without increasing the total transmission power. Another form of the enhancement would be that QOSTFCs can provide higher data rates with the same error performance, compared to OSTFCs.     

Pool-based anonymous communication framework for high-performance computing

We propose and analyze in details the revised model of XPROB, an infinite family of pool-based anonymous communication systems that can be used in various applications including high performance computing environments. XPROB overcomes the limitations of APROB Channel that only resists a global delaying adversary (GDA). Each instance of XPROB uses a pool mix as its core component to provide resistance against a global active adversary (GAA), a stronger yet more practical opponent than a GDA. For XPROB, a GAA can drop messages from users but cannot break the anonymity of the senders of messages. Analysis and experimental evaluations show that each instance of XPROB provides greater anonymity than APROB Channel for the same traffic load and user behaviors (rate and number of messages sent). In XPROB, any message can be delivered with high probability within a few rounds after its arrival into the system; thus, an opponent cannot be certain when a message will be delivered. Furthermore, users can choose their own preference balance between anonymity and delay. Through the evaluation, we prove that XPROB can provide anonymity for users in high-performance computing environments.