Low-Dimensional Halide Perovskites and Their Advanced Optoelectronic Applications

Nano-Micro Letters - A NiFe2O4/expanded graphite (NiFe2O4/EG) nanocomposite was prepared via a simple and inexpensive synthesis method. Its lithium storage properties were studied with the goal of...     

Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain tumors. These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas. The dataset consists of 120 patients. The pre-trained models have been used to classify tumor or no tumor images, while integrated models are applied to segment the tumor region correctly. We have evaluated their performance in terms of loss, accuracy, intersection over union, Jaccard distance, dice coefficient, and dice coefficient loss. From pre-trained models, the U-Net model achieves higher performance than other models by obtaining 95% accuracy. In contrast, U-Net with ResNet-50 outperforms all other models from integrated pre-trained models and correctly classified and segmented the tumor region.     

Learning from demonstration in robots: Experimental comparison of neural architectures

Robots have played an important role in the automation of computer aided manufacturing. The classical robot control implementation involves an expensive key step of model-based programming. An intuitive way to reduce this expensive exercise is to replace programming with machine learning of robot actions from demonstration where a (learner) robot learns an action by observing a demonstrator robot performing the same. To achieve this learning from demonstration (LFD) different machine learning techniques such as Artificial Neural Networks (ANN), Genetic Algorithms, Hidden Markov Models, Support Vector Machines, etc. can be used. This piece of work focuses exclusively on ANNs. Since ANNs have many standard architectural variations divided into two basic computational categories namely the recurrent networks and feed-forward networks, representative networks from each have been selected for study, i.e. Feed Forward Multilayer Perceptron (FF) network for feed-forward networks category and Elman (EL), and Nonlinear Autoregressive Exogenous Model (NARX) networks for the recurrent networks category. The main objective of this work is to identify the most suitable neural architecture for application of LFD in learning different robot actions. The sensor and actuator streams of demonstrated action are used as training data for ANN learning. Consequently, the learning capability is measured by comparing the error between demonstrator and corresponding learner streams. To achieve fairness in comparison three steps have been taken. First, Dynamic Time Warping is used to measure the error between demonstrator and learner streams, which gives resilience against translation in time. Second, comparison statistics are drawn between the best, instead of weight-equal, configurations of competing architectures so that learning capability of any architecture is not forced handicap. Third, each configuration's error is calculated as the average of ten trials of all possible learning sequences with random weight initialization so that the error value is independent of a particular sequence of learning or a particular set of initial weights. Six experiments are conducted to get a performance pattern of each architecture. In each experiment, a total of nine different robot actions were tested. Error statistics thus obtained have shown that NARX architecture is most suitable for this learning problem whereas Elman architecture has shown the worst suitability. Interestingly the computationally lesser MLP gives much lower and slightly higher error statistics compared to the computationally superior Elman and NARX neural architectures, respectively.     

A survey on search results diversification techniques

The quantity of information placed on the web has been greater than before and is increasing rapidly day by day. Searching through the huge amount of data and finding the most relevant and useful result set involves searching, ranking, and presenting the results. Most of the users probe into the top few results and neglect the rest. In order to increase user’s satisfaction, the presented result set should not only be relevant to the search topic, but should also present a variety of perspectives, that is, the results should be different from one another. The effectiveness of web search and the satisfaction of users can be enhanced through providing various results of a search query in a certain order of relevance and concern. The technique used to avoid presenting similar, though relevant, results to the user is known as a diversification of search results. This article presents a survey of the approaches used for search result diversification. To this end, this article not only provides a technical survey of existing diversification techniques, but also presents a taxonomy of diversification algorithms with respect to the types of search queries.     

A new region aware invisible robust blind watermarking approach

Multimedia Tools and Applications - The multimedia revolution has made a strong impact on our society. The explosive growth of Internet access to this digital information has generated new...     

ELF-Miner: using structural knowledge and data mining methods to detect new (Linux) malicious executables

Linux malware can pose a significant threat—its (Linux) penetration is exponentially increasing—because little is known or understood about Linux OS vulnerabilities. We believe that now is the right time to devise non-signature based zero-day (previously unknown) malware detection strategies before Linux intruders take us by surprise. Therefore, in this paper, we first do a forensic analysis of Linux executable and linkable format (ELF) files. Our forensic analysis provides insight into different features that have the potential to discriminate malicious executables from benign ones. As a result, we can select a features’ set of 383 features that are extracted from an ELF headers. We quantify the classification potential of features using information gain and then remove redundant features by employing preprocessing filters. Finally, we do an extensive evaluation among classical rule-based machine learning classifiers—RIPPER, PART, C4.5 Rules, and decision tree J48—and bio-inspired classifiers—cAnt Miner, UCS, XCS, and GAssist—to select the best classifier for our system. We have evaluated our approach on an available collection of 709 Linux malware samples from vx heavens and offensive computing. Our experiments show that ELF-Miner provides more than 99% detection accuracy with less than 0.1% false alarm rate.     

Light microscopy of Pakistani Berberis leaf cuticles and its taxonomic implications

Taxonomy of the genus Berberis is quite complex, due to overlapping morphological characters, making it very difficult to differentiate the species within the genus. In order to resolve this taxonomic complexity, the foliar anatomy of 10 Berberis L. species was carried out, for the first time from Pakistan, using light microscopy (LM). Significant variation in terms of epidermal cells shape, size, cell wall pattern, and stomata type was observed. B. baluchistanica has the largest epidermal cells, Adaxial: length = 45–(53.9 ± 3.6)–62.5 μm; and width = 22.5–(26.3 ± 1.3)–30 μm; Abaxial: length = 37.5–(43.25 ± 2.5)–50 μm; and width = 20–(22.6 ± 0.8)–25. The highest number of stomata was observed in B. glaucocarpa as 62 on the abaxial surface while the lowest number of stomata was recorded in B. baluchistanica as 8 on the adaxial surface. Of 10 investigated species, 6 possess anomocytic type stomata, while 2 species that is, B. aitchisonii and B. parkeriana have both anomocytic and anisocytic stomata while B. baluchistanica and B. calliobotrys have only paracytic type stomata. The highest number of cells per unit area was present on the adaxial surface of B. calliobotrys ranging from 245–(252.4)–260 followed by B. parkeriana with 209–(227.8)–250 on the abaxial surface. Stomatal index (SI) also varied considerably and was the lowest (2.6) percentage in B. baluchistanica and highest (31.9) percentage in B. kunawurensis. A taxonomic key based on micro‐morphological characters is provided for species identification.     

Throughput-efficient coalition formation of selfish/altruistic nodes in ad hoc networks: a hedonic game approach

In this paper, we analyze the problem of throughput-efficient distributed coalition formation (CF) of selfish/altruistic nodes in ad hoc radio networks. We formulate the problem as a hedonic CF game with non-transferable utility and propose different preference relations (CF rules) based on individual/group rate improvement of distributed nodes. We develop a hedonic CF algorithm, through which distributed nodes may self-organize into stable throughput-efficient disjoint coalitions. We apply the concept of frequency reuse over different coalitions, such that the members of each coalition will transmit over orthogonal sub-bands with the available spectrum being optimally allocated among them. We study the computational complexity and convergence properties of the proposed hedonic CF algorithm under selfish and altruistic preferences, and present means to guarantee Nash-stability. In addition, we identify the scenarios in which a CF process might lead to instability (CF cycle), and we propose methods to avoid cycles and define different exit procedures if a CF cycle is inevitable. Performance analysis shows that the proposed algorithm with optimal bandwidth allocation provides a substantial gain, in terms of average payoff per link, over existing coalition formation algorithms for a wide SNR range.     

Heat transfer enhancement in hydromagnetic dissipative flow past a moving wedge suspended by H2O-aluminum alloy nanoparticles in the presence of thermal radiation

This letter investigates the two dimensional magnetohydrodynamic Falkner Skan flow of water saturated with AA7072–H₂O and AA7075–H₂O nanoparticles over a moving wedge. Influence of viscous dissipation and thermal radiation is also under consideration. The model is reduced into nondimensional form by using defined self-similar transformations. Then, numerical study (Runge-Kutta numerical scheme) is carried out for solution purpose. The effects of pertinent flow parameters are discussed in the flow regimes by means of graphical aid. Graphical results for special cases (static wedge and when the movement of flow and wedge is in opposite direction) of the model are also elucidated graphically. It is noted that fluid velocity decreases for volumetric fraction and magnetic force favor the velocity. Significant effects of thermal radiation and nanoparticles volume fraction pointed for thermal filed and the influence of Eckert number is almost inconsequential. For more radiative fluid heat transfer coefficient decreases and nanoparticles volumetric fraction favor the local rate of heat transfer. In the presence of AA7075 nanoparticles, rate of heat transfer is quite rapid as compared to that of AA7072 nanoparticles. To validate the present computations, some present results are compared with already existing results in the literature and found better agreement between them. Finally, major points of the study ingrained in the last section of the letter.