On the design of secure primitives for real world applications

This editorial paper provides an overview of new advances on hardware security challenges and promising solutions for real world applications. It first presents the increasing hardware vulnerabilities with the emergence of diverse computing platforms. Then, it details the technical challenges, research opportunities, and potential solutions to cyber-attacks on -physical- devices.     

Simulation of dance performance analysis with microprocessor and machine vision interaction

Dance is the expression of artists' favourite art forms such as express emotions, their body language, and the combination of dance art and stage effects in dance performance. In almost all genres of the art form, the effectiveness of the dance performance is largely due to the quality of the individual dancer's and group dance performance. Performing arts, particularly dance, it is one of the most important of intangible cultural heritage. However, due to the preservation, documentation, analysis, and visualization understanding of dance mode, it is difficult because of technical difficulties relations. The Proposed Machine Learning Support Decision Vector Machine (SDVM) algorithm and Field Programmable Gate Array (FPGA) is a dance expert watching dance due to the recognition task, the task knowledge of professional forecasters, gestures, and facial expressions and face-to-face conditions led to better synchronization of timing. In the proposed Machine learning SDVM algorithm, the results show that positive and dancers in the audience increased negative emotions; acceleration rate and body movement also increased. SDVM is classified as dancer performance based on the artist's facial expressions, stage performance, emotions. The simulation results show good results compared to other methods.     

An integrated optimization modeling approach for planning emission trading and clean-energy development under uncertainty

The growing concern for global warming caused by the increased atmospheric concentration of carbon dioxide (CO₂) has a significant effect on environmental and energy policies and economic activities, due to the ever-increasing use of fossil fuels such as coal, oil and natural gas throughout the world. A variety of complexities and uncertainties exist in CO₂-emission-related processes and various impact factors, such as CO₂-emission inventory, mitigation measure, and cost parameter. Decision makers face problems of how many clean-energy resources (or carbon credits) are needed to be replaced (or bought) by measuring electric-power benefits and uncertain economic penalties from random excess CO₂ exceeding to given discharge permits. In this study, an integrated optimization modeling approach is developed for planning CO₂ abatement through emission trading scheme (ETS) and clean development mechanism (CDM), where uncertainties presented in terms of fuzzy sets, interval values, and random variables can be addressed. The developed model is also applied to a case study of planning CO₂-emission mitigation for an electric-power system (EPS) that involves three fossil-fueled power plants (i.e., gas, oil and coal-power plants). Different trading schemes and clean-energy development plans corresponding to different CO₂-emission management policies have been analyzed. The results demonstrate that CO₂-emission reduction program can be performed cost-effective through emission trading and clean-energy development projects. Violation analyses are also conducted to demonstrate that different violation levels for model’s objective and constraints have different effects on system benefit and satisfaction degree as well as emission trading and clean-energy development.     

Hap-Cp-Abe Based Encryption Technique With Hashed Access Policy Based Authentication Scheme For Privacy Preserving Of Phr

As a novel abridged record of patients' medical data, PHR would be accessed online. Nevertheless, this very sensitive private information might be attained through illegal parties as of the cloud. Numerous existent algorithms utilized CP-ABE to protect the data as of unapproved users, however, it loses the high-efficiency against the conventional methods on account of the security parameters’ requirement of large size. To surmount these issues, a HAP-CP-ABE-based encryption technique with HAP based authentication scheme intended for PHR is proposed. The proposed work encompasses '3′ phases: (A) the authentication phase, which includes verification before the user uploads the PHR files. (B) Secure upload phase: initially, the features are all extracted as of the PHR files. Then, the features are lessened as of the extracted features utilizing ENT-LDA. Then, HAP will be created using SHA-512. Lastly, securely upload PHR to cloud utilizing HAP-CP-ABE. (C) In the secure download phase, primarily, the system corroborates the HAP for the requested user. If it is matched, then it permits them to access, or else the system declined permission. After authentication, the system permits the requested user to download the file. Extensive analysis along with investigational outcomes shows the proposed system's security together with efficiency.     

Artificial neural network modeling of thin layer drying behavior of municipal sewage sludge

The back-propagation (BP) and generalized regression neural network models (GRNN) were investigated to predict the thin layer drying behavior in municipal sewage sludge during hot air forced convection. The accuracy of the BP model to predict the moisture content of the sewage sludge thin layer during hot air forced convective drying was far higher than that of the GRNN model. The GRNN models could automatically determine the best smoothing parameters, which were 0.6 and 0.3 for predicting the moisture content and average temperature, respectively. The model type for predicting the average temperature of the sewage sludge thin layer was selected for different sample groups by comparing their MSE values or R² values. The GRNN model was suitable for predicting the average temperature corresponding to the sample groups at hot air velocity of 0.6 m/s, and drying temperatures of 100 °C, 160 °C; hot air velocity of 1.4 m/s, and drying temperatures of 130 °C, 140 °C; hot air velocity of 2.0 m/s, and drying temperatures of 150 °C, 160 °C. The average temperature for the other sample groups was best predicted by the BP model.     

A case based reasoning approach on supplier selection in petroleum enterprises

Petroleum is an important strategic material which is connected with the vitals and safety of the national economy, and the supplier selections are related to the safety of petroleum production and supply. However, the traditional approaches for supplier selections are limited in subjective evaluation of weights, inaccurate assessing rules, and inefficient decision-making. Although most of the current methods are widely applied in corporation management, a more efficient approach needs to be proposed for supplier selection of oil enterprise.This paper summarizes the particular characteristics of the supply chain of Chinese petroleum enterprises, analyzes the limitations of the traditional methods of supplier selection, and brought forward the method based on case reasoning system (CBR) for petroleum enterprises. The method based on data mining techniques which solves three key problems of CBR, includes calculating the weights of the attributes with information entropy in case warehouse organizing process objectively, evaluating the similarities with k-prototype clustering between the original and target cases in case retrieving process exactly, and extracting the potential rules with back propagation neural networks from conclusions in maintenance and revising process efficiently. It demonstrates the advantages, practicability and validity of this method via case study finally.     

Non-restrictive computational reflection

Adaptable software systems and architectures give the programmer the ability to create applications that might customize themselves to runtime-emerging requirements. Computational reflection is a programming language technique that is commonly used to achieve the development of this kind of systems. Most runtime reflective systems use Meta-Object Protocols (MOPs). However, MOPs restrict the amount of features an application can customize, and the way they can express its own adaptation. Furthermore, this kind of systems uses a fixed programming language: they develop an interpreter, not a whole language-independent platform.What we present in this paper a non-restrictive reflective platform, called nitrO, that achieves a real computational-environment jump, making every application and language feature adaptable at runtime—without any previously defined restriction. Moreover, the platform has been built using a generic interpreter, in which the reflection mechanism is independent of the language selected by the programmer. Different applications may dynamically adapt each other, regardless of the programming language they use.     

The Digital Fingerprinting Analysis of Generic Twitter Sessions from Internet of Things Security Aspect

Nowadays, the unparalleled growth of Internet of Things (IoT) is a new digital disruption which intelligent devices are connected and working together. IoT connects personal computers, tablets, and smart phones in our daily communications from leisure purposes to business tasks. However, the security concern of IoT can’t be overemphasized due to the connectivity among communication gadgets. Sustainably, Twitter is one of the most popular Instant Message (IM) toolkits today. When Twitter is being utilized, there will be some negligible and imperceptible digital remnants left in the computing devices, which could be probative digital evidences in a court of law when IoT application is involved. Hence, the digital forensics of IM has been a relatively rigorous, competitive, and novel research field by the law enforcement agency officers and the information security staffs with respect to some cybercrimes concerning IoT. Consequently, this research is targeting on the utilization of Twitter on the desktop PC under Windows 7 operating system via the acquisition of volatile digital bread crumbs inside physical memory of the computing device for the purpose of presenting supportive digital evidences for some information security concerns. The proposed various scenarios in the design of the experiment could be the paradigms for digital forensics specialists or law enforcement agencies to follow in order to reconstruct the previous operations in the Twitter sessions of a certain user under the IoT ecosystems.

     

An integrated preventive maintenance and production planning model with sequence‐dependent setup costs and times

This paper considers the integration of preventive maintenance and tactical production planning in a multiproduct production system, where setup costs and times are sequence dependent. A set of products needs to be produced in lots during a finite planning horizon, where preventive maintenance is conducted periodically at the end of some production periods and corrective maintenance is always performed when there is a failure. The system downtime—as caused by maintenance and setup—affects the system's available production capacity. We use a sequence‐oriented method to search for the optimal setup sequence and develop some steps to prune the searching tree. Our objective is to find the optimal preventive maintenance interval, production lot size, and production sequence in every period, by minimizing the sum of maintenance, production, inventory, and setup costs within the planning horizon. Numerical examples are presented to illustrate our model.     

Measuring scale efficiency and returns to scale on large commercial rooftop photovoltaic systems in California

Electricity generated by solar Photovoltaic (PV) power systems is emerging as one of the most promising cleaner alternatives to replace conventional energy sources, such as coal, oil, and natural gas. This study examines managerial sources of operational efficiency or inefficiency on 855 large commercial rooftop PV power systems in California by examining both scale efficiency and Returns to Scale (RTS). For the research purpose, this study utilizes Data Envelopment Analysis (DEA) as a methodology to assess the scale measures. A difficulty in applying DEA to the performance analysis of PV power systems is that it contains uncontrollable variables such as ambient temperature and solar irradiation, all of which are influenced by weather. It is also widely known that PV power systems cannot produce electricity during night, so being unable to serve as a base load. Thus, these uncontrollable factors often influence the performance of PV power systems. Paying attention to the effects of those uncontrollable factors, this study discusses how to measure scale efficiency and RTS within the framework of DEA. Then, as an application of the proposed approach, this study examines the performance of large commercial rooftop PV power systems in California. This study finds that they belong to increasing (48), constant (807) and decreasing (0) RTS, implying that most of the PV power systems in California have been operating in their appropriate sizes. This further indicates that the operational inefficiency of PV power systems is due to managerial inappropriateness, not their sizes for generation. Thus, there is a space to improve operational efficiency of rooftop PV systems under constant RTS, through managerial efforts such as increase in transmission capacity and operational flexibility to improve solar penetration, and routine maintenance to minimize detrimental effects of dust deposition.