A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1315&context=electricalengineeringfacpub

A Survey on Smart Grid CommunicationInfrastructures: Motivations, Requirements andChallenges

Sarcasm identification on text documents is one of the most challenging tasks in natural language processing (NLP), has become an essential research direction, due to its prevalence on social media data. The purpose of our research is to present an effective sarcasm identification framework on social media data by pursuing the paradigms of neural language models and deep neural networks. To represent text documents, we introduce inverse gravity moment based term weighted word embedding model with trigrams. In this way, critical words/terms have higher values by keeping the word-ordering information. In our model, we present a three-layer stacked bidirectional long short-term memory architecture to identify sarcastic text documents. For the evaluation task, the presented framework has been evaluated on three-sarcasm identification corpus. In the empirical analysis, three neural language models (i.e., word2vec, fastText and GloVe), two unsupervised term weighting functions (i.e., term-frequency, and TF-IDF) and eight supervised term weighting functions (i.e., odds ratio, relevance frequency, balanced distributional concentration, inverse question frequency-question frequency-inverse category frequency, short text weighting, inverse gravity moment, regularized entropy and inverse false negative-true positive-inverse category frequency) have been evaluated. For sarcasm identification task, the presented model yields promising results with a classification accuracy of 95.30%.

A Term Weighted Neural Language Model and Stacked Bidirectional LSTM Based Framework for Sarcasm Identification

Model-based development is becoming an increasingly common development methodology. In important domains like embedded systems already major parts of the code are generated from models specified with domain-specific modelling languages. Hence, such models are nowadays an integral part of the software development and maintenance process and therefore have a major economic and strategic value for the software-developing organisations. Nevertheless almost no work has been done on a quality defect that is known to seriously hamper maintenance productivity in classic code-based development: Cloning. This paper presents an approach for the automatic detection of clones in large models as they are used in model-based development of control systems. The approach is based on graph theory and hence can be applied to most graphical data-flow languages. An industrial case study demonstrates the applicability of our approach for the detection of clones in Matlab/Simulink models that are widely used in model-based development of embedded systems in the automotive domain.

Clone Detection in Automotive Model-Based Development.

The impact of enterprise risk management on competitive advantage by moderating role of information technology

In the current turbulent market, firms spend lots of tangible and intangible resources to gain competitive advantage and superior performance. Prior studies have discussed several determinants of competitive advantage and performance, particularly in developed economies, whereas small- and medium-sized enterprises (SMEs) in emerging economies have received minor attention. This study examines the mediating role of competitive advantage between enterprise risk management practices and SME performance and the moderating role of financial literacy between enterprise risk management practices and competitive advantage. A structured questionnaire is used to collect data from 304 SMEs operating in the emerging market of Pakistan. The hypotheses of the proposed study are tested through Structural Equation Modeling (SEM) in Analysis of a Moment Structures (AMOS). The results indicate that enterprise risk management practices significantly influence competitive advantage and SME performance. Competitive advantage partially mediates the relationship between enterprise risk management practices and SME performance. Additionally, financial literacy significantly moderates the relationship between enterprise risk management practices and competitive advantage. Firms are advised to implement formal enterprise risk management practices to gain competitive advantage and superior performance. Top managers need to have enough financial education that they will be able to perform risk management practices in an efficient way to gain a competitive position in the market. Implications for practices have been discussed in detail.

https://www.econstor.eu/bitstream/10419/238894/1/1030127859.pdf

Enterprise Risk Management Practices and Firm Performance, the Mediating Role of Competitive Advantage and the Moderating Role of Financial Literacy

Purpose




Enterprise risk management (ERM) is a risk management approach that calls for integrating all the organization-wide risks and takes a portfolio view point of managing organizational risks. The purpose of this paper is to investigate the factor that influence a firm’s decision to adopt ERM.




Design/methodology/approach




The authors employ a particular technique of survival data analysis, the Cox proportional hazards model, to investigate the factors that lead towards the decision of initiating an ERM programme. The authors constructed a unique sample of French firms derived from the information in 315 corporate news announcements for the hiring of a chief risk officer and information retrieved from publicly available annual reports to identify firms that initiated an ERM programme, over the period from year 1999 to 2008.




Findings




The results suggest that besides the growing international and local regulatory pressure, factors that are internal to the organizations like the expected probability of financial distress and its explicit and implicit costs, poor earnings performance and the existence of growth opportunities play vital role in motivating firms to adopt ERM. It was also found that corporate governance practices such as the independence of the board may also lead towards an initiation of the ERM.




Originality/value




This study makes theoretical and methodological contribution the ERM literature by employing a novel methodology and presenting empirical evidence based on data form French firms.

Why do firms adopt enterprise risk management (ERM)? Empirical evidence from France

Breast Cancer is a common dangerous disease for women. Around the world, many women have died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues, there are several techniques and methods. The image processing, machine learning, and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to save a women's life. To detect the breast masses, microcalcifications, and malignant cells,different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for breast cancer survival, it is essential to improve the methods or techniques to diagnose it at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are also challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

Social media has become a platform of first choice where one can express his/her feelings with freedom. The sports and matches being played are also discussed on social media such as Twitter. In this article, efforts are made to investigate the feasibility of using collective knowledge obtained from microposts posted on Twitter to predict the winner of a Cricket match. For predictions, we use three different methods that depend on the total number of tweets before the game for each team, fans sentiments toward each team and fans score predictions on Twitter. By combining these three methods, we classify winning team prediction in a Cricket game before the start of game. Our results are promising enough to be used for winning team forecast. Furthermore, the effectiveness of supervised learning algorithms is evaluated where Support Vector Machine (SVM) has shown advantage over other classifiers.

Predicting The Cricket Match Outcome Using Crowd Opinions On Social Networks: A Comparative Study Of Machine Learning Methods

A survey of P2P content sharing in MANETs

Software Defined Networking (SDN) as an innovative network paradigm that separates the management and control planes from the data plane of forwarding devices by implementing both the management and control planes at a logically centralized entity, called controller. Therefore, it ensures simple network management and control. However, due to several reasons (e.g., deployment cost, fear of downtime) organizations are very reluctant to adopt SDN in practice. Therefore, a viable solution is to replace the legacy devices by SDN devices incrementally. This results in a new network architecture called hybrid SDN. In hybrid SDN, both SDN and legacy devices operate in such a way to achieve the maximum benefit of SDN. The legacy devices are running a traditional protocol and SDN devices are operating using Open-flow protocols. Network policies play an essential role to secure the entire network from several types of attacks like unauthorized access and port/protocol control. In a hybrid SDN, policy implementation is a tedious task that requires extreme care and attention due to the hybrid nature of network traffic. Network policies may be implemented at various positions in hybrid SDN, e.g., near the destination or source node, and at the egress or ingress ports of a router. Each of these schemes has some trade-offs. For example, if policies are implemented near the source nodes then each packet generated from the source must pass through the filter and, thus, requires more processing power, time, resources, etc. Similarly, if policies are installed near the destination nodes, then a lot of unwanted traffic generated causing network congestion. This is an NP-hard problem. To address these challenges, we propose a systematic design approach to implement network policies optimally by using decision tree and K-partite graph. By traversing all the policies, we built up the decision tree that identifies which source nodes can communicate with which destination. Then, we traverse the decision tree and constructs K-partite graph to find possible places (interfaces of the routers) where ACL policies are to be implemented based on the different criteria (i.e., the minimum number of ACL rules and the minimum number of transmissions for unwanted traffic). The edge weight represents the cost per criteria. Then, we traverse the K-partite graph to find the optimal place for ACL rules implementation according to the given criteria. The simulation results indicate that the proposed technique outperforms existing approaches in terms of computation time, traffic optimization and successful packet delivery, etc. The results also indicate that the proposed method improves network performance and efficiency by decreasing network congestion and providing ease of policy implementation.

https://www.mdpi.com/2079-9292/8/6/604/pdf

Waqar Mehmood

Papers

Why do firms adopt enterprise risk management (ERM)? Empirical evidence from France

Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

Predicting The Cricket Match Outcome Using Crowd Opinions On Social Networks: A Comparative Study Of Machine Learning Methods

A survey of P2P content sharing in MANETs

Enforcing Optimal ACL Policies Using K-Partite Graph in Hybrid SDN