Petrophysical data prediction from seismic attributes using committee fuzzy inference system

This study presents an intelligent model based on fuzzy systems for making a quantitative formulation between seismic attributes and petrophysical data. The proposed methodology comprises two major steps. Firstly, the petrophysical data, including water saturation (S_w) and porosity, are predicted from seismic attributes using various fuzzy inference systems (FISs), including Sugeno (SFIS), Mamdani (MFIS) and Larsen (LFIS). Secondly, a committee fuzzy inference system (CFIS) is constructed using a hybrid genetic algorithms-pattern search (GA-PS) technique. The inputs of the CFIS model are the outputs and averages of the FIS petrophysical data. The methodology is illustrated using 3D seismic and petrophysical data of 11 wells of an Iranian offshore oil field in the Persian Gulf. The performance of the CFIS model is compared with a probabilistic neural network (PNN). The results show that the CFIS method performed better than neural network, the best individual fuzzy model and a simple averaging method.     

Side-channel attacks and learning-vector quantization

The security of cryptographic systems is a major concern for cryptosystem designers, even though cryptography algorithms have been improved. Side-channel attacks, by taking advantage of physical vulnerabilities of cryptosystems, aim to gain secret information. Several approaches have been proposed to analyze side-channel information, among which machine learning is known as a promising method. Machine learning in terms of neural networks learns the signature (power consumption and electromagnetic emission) of an instruction, and then recognizes it automatically. In this paper, a novel experimental investigation was conducted on field-programmable gate array (FPGA) implementation of elliptic curve cryptography (ECC), to explore the efficiency of side-channel information characterization based on a learning vector quantization (LVQ) neural network. The main characteristics of LVQ as a multi-class classifier are that it has the ability to learn complex non-linear input-output relationships, use sequential training procedures, and adapt to the data. Experimental results show the performance of multi-class classification based on LVQ as a powerful and promising approach of side-channel data characterization.     

System performances analysis of reservoir optimization–simulation model in application of artificial bee colony algorithm

In reservoir system operation, optimization is very much essential and the compatibility of different optimization techniques is essential to be checked by some performance checking indices. In this study, various types of performance-measuring index are used and compared to provide a complete knowledge on adopting different approaches. Here, the considered performance-measuring indicators will check the operation policy in terms of three different scenarios—how the method is efficient in achieving best results (reliability); how vulnerable the method is for different critical situation (vulnerability); and how capable it is to handle a failure of the model (resiliency). Therefore, the study proposed the artificial bee colony (ABC) optimization technique to develop an optimal water release policy for the well-known Aswan High Dam, Egypt. Particle swarm optimization, genetic algorithm and neural network-based stochastic dynamic programming are also used in a view of comparing model performances. A release curve is developed for every month as a guidance to the decision maker. Simulation has been done for each method using historical actual inflow data, and reliability, resiliency and vulnerability are measured. All model indicators proved that the release policy provided by ABC optimization outperforms in terms of achieving minimum water deficit, less waste of water and handling critical situations.

     

Ant-based service selection framework for a smart home monitoring environment

Selecting ambient media services in a smart home monitoring environment is challenging. Services in such an environment should be ubiquitous, adaptive, and robust with respect to access and delivery. Many different techniques exist for selecting services in smart environments, for example, dynamic programming, genetic algorithms, and fuzzy logic. However, existing approaches to service selection fail to address the dynamic nature of the services and the requirement of considering the user context and user satisfaction. We address this issue by proposing an ant-inspired service selection framework based on dynamic user preferences and satisfaction. This ant-inspired approach is robust to failures and adaptive to dynamic context. The proposed framework enables different categories of residents (e.g., elderly people, fathers with children, mothers, and so on) to access various media services in such a way that their experiences are optimized with regard to their surrounding environment. Experimental results demonstrate the viability of the proposed framework.     

A personal meeting scheduling agent

When setting up a meeting, meeting participants need to reach a mutual agreement to hold the meeting subject to their personal constraints and preferences. It is a time-consuming process, and a variety of calendaring applications are in use assisting users to schedule meetings. Software applications failed to overcome the constraints of the traditional-scheduling process and works as a supporting tool for managing meeting information. One of the main constraints in automated scheduling is the unavailability of a standard structured communication protocol. In addition, automated scheduling requires other issues to be considered such as automated decision-making paradigm, negotiation strategy selection mechanism, etc. This paper proposes a personal meeting scheduling agent (PMSA) and a personal meeting scheduling protocol (PMSP). PMSP is embedded in the PMSA for handling bilateral and multilateral negotiations. PMSA is designed using model-based, goal-based methodology. Additionally, PMSP is designed following a structured negotiation protocol influenced by simultaneous response protocol. To evaluate all meeting invitations and to make decisions subject to users’ preferences, participants’ profiles, and the schedule availability, this paper utilizes the naïve Bayes model of maximum likelihood Estimation. The PMSP goal is to automatically make decisions and select the appropriate negotiation strategies to avoid or resolve possible meeting conflicts. To demonstrate the feasibility of the proposed PMSP, a simulation environment with experimental results is presented.     

Texture databases – A comprehensive survey

Texture analysis is a very important area in the field of computer vision and related fields. There are a good number of databases developed by different research groups for various texture analysis, in the field of medical analysis, robotics, recognition, analysis, image processing, etc. However, till-to-date, there is no comprehensive works covering the important databases and analyze these in various perspectives. In this paper, we consider this important task so that it becomes helpful for a researcher to choose and evaluate having crucial evaluating aspects in mind. We categorize and critically survey based on many references of the state-of-the-art related to the databases and other texture works. We strongly believe that this elegant survey will be a great contribution for the vision community, especially in the arena of texture analysis.     

AR-based serious game framework for post-stroke rehabilitation

Stroke is considered one of the main causes of death around the world. Survivors often suffer different kinds of disabilities in terms of their cognitive and motor capabilities, and are therefore unable to perform their day-to-day activities. To regain some of their cognitive as well as motor abilities, they require rehabilitation. To this end, we present a serious game framework based on augmented reality technology that may motivate the patients’ involvement in the rehabilitation exercise. Additionally, we analyze the requirements for such a framework and describe the concept and implementation of the proposed approach. Furthermore, we designed a wireless vibrotactile output device that is attached to a tangible object. The tangible object that is connected to the framework can give haptic as well as audio-visual feedback to the patient in a more motivating and entertaining environment for rehabilitation exercises. The suitability and utility of the proposed framework was evaluated with real stroke patients and compared against the performance of a healthy control group, thus facilitating occupational therapists in assessing a patient’s progress. Our evaluations show that the serious games with vibrotactile feedback are well accepted by patients.     

Determining trust in media-rich websites using semantic similarity

Significant growth of multimedia content on the World Wide Web (or simply ??Web??) has made it an essential part of peoples lives. The web provides enormous amount of information, however, it is very important for the users to be able to gauge the trustworthiness of web information. Users normally access content from the first few links provided to them by search engines such as Google or Yahoo!. This is assuming that these search engines provide factual information, which may be popular due to criteria such as page rank but may not always be trustworthy from the factual aspects. This paper presents a mechanism to determine trust of websites based on the semantic similarity of their multimedia content with already established and trusted websites. The proposed method allows for dynamic computation of the trust level of websites of different domains and hence overcomes the dependency on traditional user feedback methods for determining trust. In fact, our method attempts to emulate the evolving process of trust that takes place in a user??s mind. The experimental results have been provided to demonstrate the utility and practicality of the proposed method.     

Can crisis management teams mitigate COVID-19's effects? A comparative case study between China and the United States

The COVID-19 pandemic has been the world's greatest challenge since World War II. As an unprecedented global public health crisis, crisis management teams (CMTs) in the infected countries need to rethink to cope with the similar uncertainty and urgency of the ongoing COVID-19 pandemic. The shared context of COVID-19 allows us to explore a cross-nation study of different constructs and CMT to communicate information about crises with the public effectively. Since the pandemic affected all countries, the comparison is warranted. Can CMTs mitigate the effects of COVID-19? Based on the analysis of China and the US cases, our study explores how shared and common knowledge cognition among crisis responders plays a pivotal role in effective CMTs' communication while technological failures and inadequate information disrupt the system, worsening pandemics like COVID-19. Furthermore, organizational dysfunction, such as institutional fragmentation, regulatory hurdles and bureaucratic arrogance, impede effective communication between CMTs. However, effective coordination and decisive leadership could improve coordination effectiveness and reduce crisis costs.