Creating sentiment lexicon for sentiment analysis in Urdu: The case of a resource‐poor language

The sentiment analysis (SA) applications are becoming popular among the individuals and organizations for gathering and analysing user's sentiments about products, services, policies, and current affairs. Due to the availability of a wide range of English lexical resources, such as part‐of‐speech taggers, parsers, and polarity lexicons, development of sophisticated SA applications for the English language has attracted many researchers. Although there have been efforts for creating polarity lexicons in non‐English languages such as Urdu, they suffer from many deficiencies, such as lack of publically available sentiment lexicons with a proper scoring mechanism of opinion words and modifiers. In this work, we present a word‐level translation scheme for creating a first comprehensive Urdu polarity resource: “Urdu Lexicon” using a merger of existing resources: list of English opinion words, SentiWordNet, English–Urdu bilingual dictionary, and a collection of Urdu modifiers. We assign two polarity scores, positive and negative, to each Urdu opinion word. Moreover, modifiers are collected, classified, and tagged with proper polarity scores. We also perform an extrinsic evaluation in terms of subjectivity detection and sentiment classification, and the evaluation results show that the polarity scores assigned by this technique are more accurate than the baseline methods.     

Image compression approach with ridgelet transformation using modified neuro modeling for biomedical images

Image compression is applied to many fields such as television dissemination, remote sensing, image storage. Digitized images are compressed by a method which exploits the redundancy of the images so that the number of bits required to represent the image can be reduced with acceptable degradation of the decoded image. The humiliation of the image quality is limited with respect to the application used. There are various biomedical applications where accuracy is of major concern. To attain the objective of performance improvement with respect to decoded picture quality and compression ratios, in contrast to existing image compression techniques, an effective image coding technique which involves transforming the image into another domain with ridgelet function and then quantizing the coefficients with hybrid neural networks combining two different learning networks called auto-associative multilayer perceptron and self-organizing feature map is proposed. Ridge functions are effective in representing functions that have discontinuities along straight lines. Normal wavelet transforms not succeed to represent such functions effectively. The results obtained from the combination of finite ridgelet transform with hybrid neural networks found much better than that obtained from the JPEG2000 image compression system.     

A security-aware virtual machine placement in the cloud using hesitant fuzzy decision-making processes

The introduction of cloud computing systems brought with itself a solution for the dynamic scaling of computing resources leveraging various approaches for providing computing power, networking, and storage. On the other hand, it helped decrease the human resource cost by delegating the maintenance cost of infrastructures and platforms to the cloud providers. Nevertheless, the security risks of utilizing shared resources are recognized as one of the major concerns in using cloud computing environments. To be more specific, an intruder can attack a virtual machine and consequently extend his/her attack to other virtual machines that are co-located on the same physical machine. The worst situation is when the hypervisor is compromised in which all the virtual machines assigned to the physical node will be under security risk. To address these issues, we have proposed a security-aware virtual machine placement scheme to reduce the risk of co-location for vulnerable virtual machines. Four attributes are introduced to reduce the aforementioned risk including the vulnerability level of a virtual machine, the importance level of a virtual machine in the given context, the cumulative vulnerability level of a physical machine, and the capacity of a physical machine for the allocation of new virtual machines. Nevertheless, the evaluation of security risks, due to the various vulnerabilities’ nature as well as the different properties of deployment environments is not quite accurate. To manage the precision of security evaluations, it is vital to consider hesitancy factors regarding security evaluations. To consider hesitancy in the proposed method, hesitant fuzzy sets are used. In the proposed method, the priorities of the cloud provider for the allocation of virtual machines are also considered. This will allow the model to assign more weights to attributes that have higher importance for the cloud provider. Eventually, the simulation results for the devised scenarios demonstrate that the proposed method can reduce the overall security risk of the given cloud data center. The results show that the proposed approach can reduce the risk of attacks caused by the co-location of virtual machines up to 41% compared to the existing approaches.

     

Recent developments and progress of aerogel assisted environmental remediation: a review

Journal of Porous Materials - Aerogel is a special type of porous material with many outstanding physical and chemical properties, such as low density, high porosity, high surface area, and...     

An enriched game-theoretic framework for multi-objective clustering

The framework of multi-objective clustering can serve as a competent technique in nowadays human issues ranging from decision making process to machine learning and pattern recognition problems. Multi-objective clustering basically aims at placing similar objects into the same groups based on some conflicting objectives, which substantially supports the use of game theory to come to a resolution. Based on these understandings, this paper suggests Enriched Game Theory K-means, called EGTKMeans, as a novel multi-objective clustering technique based on the notion of game theory. EGTKMeans is specially designed to optimize two intrinsically conflicting objectives, named, compaction and equi-partitioning. The key contributions of the proposed approach are three folds. First, it formulates an elegant and novel payoff definition which considers both objectives with equal priority. The presented payoff function incorporates a desirable fairness into the final clustering results. Second, EGTKMeans performs better off by utilizing the advantages of mixed strategies as well as those of pure ones, considering the existence of mixed Nash Equilibrium in every game. The last but not the least is that EGTKMeans approaches the optimal solution in a very promising manner by optimizing both objectives simultaneously. The experimental results suggest that the proposed approach significantly outperforms other rival methods across real world and synthetic data sets with reasonable time complexity.     

Numerical and experimental investigation of pulsating blankholder effect on drawing of cylindrical part of aluminum alloy in deep drawing process

In this study, the effect of a new pulsating blankholder system has been investigated on improving the formability of aluminum 1050 alloy. By using this system, during each pulsating cycle, first, the metal was easily flowed into the die through removing the blankholder force, and then the blankholder force applied by springs was employed to prevent excessive metal flow and wrinkling. Deep drawing of cylindrical cup was simulated by ABAQUS6.7 software. Cup depth, tearing, and thickness distribution of the experimental and numerical analyses were then compared. The results indicated that by using the pulsating blankholder system coupled with proper frequency and gap, the cup depth can be increased and thickness distribution can be improved. Further, good agreement was observed between simulation and experimental results.     

Case Study: 17th Street Canal Breach Closure Procedures

On August 29, 2005 Hurricane Katrina resulted in several breaches in the levees and flood walls protecting New Orleans. Of the 20 breaches, the 17th Street Canal breach caused much of the city flooding. In this paper, the results of studies on a 1:50 scale hydraulic model of this breach based on the Froude similitude relationships are presented. It is assumed in the model that the bed is fixed and the levee below the flood wall remains intact during breach closure. This was the case in the 17th Street Canal breach. Because of the many uncertainties in the values of various variables, a range of conditions were run on the model in an attempt to bracket the results for the flooding depths and the initial failed attempts to close the breach. Then, various possible methods for breach closure were investigated utilizing the procedures developed for cofferdam closure for river diversion, e.g., toe dumping, transverse dumping, single- and multibarrier embankments, etc. Closures of the breach and the closure of the canal at the Old Hammond Highway Bridge were investigated. Results from the case study show that some of these methods could have been utilized for closing the Katrina breaches. However, special care should be exercised when extending them for breach closure at other sites.