One-Class Convolutional Neural Networks for Water-Level Anomaly Detection.

Companies that own water systems to provide water storage and distribution services always strive to enhance and efficiently distribute water to different places for various purposes. However, these water systems are likely to face problems ranging from leakage to destruction of infrastructures, leading to economic and life losses. Thus, apprehending the nature of abnormalities that may interrupt or aggravate the service or cause the destruction is at the core of their business model. Normally, companies use sensor networks to monitor these systems and record operational data including any fluctuations in water levels considered abnormalities. Detecting abnormalities allows water companies to enhance the service's sustainability, quality, and affordability. This study investigates a 2D-CNN-based method for detecting water-level abnormalities as time-series anomaly pattern detection in the One-Class Classification (OCC) problem. Moreover, since abnormal data are usually scarce or unavailable, we explored a cheap method to generate synthetic temporal data and use them as a target class in addition to the normal data to train the CNN model for feature extraction and classification. These settings allow us to train a model to learn relevant pattern representations of the given classes in a binary classification fashion using cross-entropy loss. The ultimate goal of these investigations is to determine if any 2D-CNN-based model can be trained from scratch or if transfer learning of any pre-trained CNN model can be partially trained and used as the base network for one-class classification. The evaluation of the proposed One-Class CNN and previous approaches have shown that our approach has outperformed several state-of-the-art approaches by a significant margin. Additionally, in this paper, we mention two interesting findings: using synthetic data as the pseudo-class is a promising direction, and transfer learning should be dealt with considering that underfitting can happen because the transferred model is too complicated for training data.

A Review on Planted (l, d) Motif Discovery Algorithms for Medical Diagnose.

Personalized diagnosis of chronic disease requires capturing the continual pattern across the biological sequence. This repeating pattern in medical science is called "Motif". Motifs are the short, recurring patterns of biological sequences that are supposed signify some health disorder. They identify the binding sites for transcription factors that modulate and synchronize the gene expression. These motifs are important for the analysis and interpretation of various health issues like human disease, gene function, drug design, patient's conditions, etc. Searching for these patterns is an important step in unraveling the mechanisms of gene expression properly diagnose and treat chronic disease. Thus, motif identification has a vital role in healthcare studies and attracts many researchers. Numerous approaches have been characterized for the motif discovery process. This article attempts to review and analyze fifty-four of the most frequently found motif discovery processes/algorithms from different approaches and summarizes the discussion with their strengths and weaknesses.

Anomaly Detection of Water Level Using Deep Autoencoder.

Anomaly detection is one of the crucial tasks in daily infrastructure operations as it can prevent massive damage to devices or resources, which may then lead to catastrophic outcomes. To address this challenge, we propose an automated solution to detect anomaly pattern(s) of the water levels and report the analysis and time/point(s) of abnormality. This research's motivation is the level difficulty and time-consuming managing facilities responsible for controlling water levels due to the rare occurrence of abnormal patterns. Consequently, we employed deep autoencoder, one of the types of artificial neural network architectures, to learn different patterns from the given sequences of data points and reconstruct them. Then we use the reconstructed patterns from the deep autoencoder together with a threshold to report which patterns are abnormal from the normal ones. We used a stream of time-series data collected from sensors to train the model and then evaluate it, ready for deployment as the anomaly detection system framework. We run extensive experiments on sensor data from water tanks. Our analysis shows why we conclude vanilla deep autoencoder as the most effective solution in this scenario.

Uni-image: Universal image construction for robust neural model.

Deep neural networks have shown high performance in prediction, but they are defenseless when they predict on adversarial examples which are generated by adversarial attack techniques. In image classification, those attack techniques usually perturb the pixel of an image to fool the deep neural networks. To improve the robustness of the neural networks, many researchers have introduced several defense techniques against those attack techniques. To the best of our knowledge, adversarial training is one of the most effective defense techniques against the adversarial examples. However, the defense technique could fail against a semantic adversarial image that performs arbitrary perturbation to fool the neural networks, where the modified image semantically represents the same object as the original image. Against this background, we propose a novel defense technique, Uni-Image Procedure (UIP) method. UIP generates a universal-image (uni-image) from a given image, which can be a clean image or a perturbed image by some attacks. The generated uni-image preserves its own characteristics (i.e. color) regardless of the transformations of the original image. Note that those transformations include inverting the pixel value of an image, modifying the saturation, hue, and value of an image, etc. Our experimental results using several benchmark datasets show that our method not only defends well known adversarial attacks and semantic adversarial attack but also boosts the robustness of the neural network.

Myocardial-specific ablation of Jumonji and AT-rich interaction domain-containing 2 (Jarid2) leads to dilated cardiomyopathy in mice.

Cardiomyopathy is a common myocardial disease that can lead to sudden death. However, molecular mechanisms underlying cardiomyopathy remain unclear. Jumonji and AT-rich interaction domain-containing 2 (Jarid2) is necessary for embryonic heart development, but functions of Jarid2 after birth remain to be elucidated. Here, we report that myocardial-specific deletion of Jarid2 using αMHC::Cre mice (Jarid2αMHC) causes dilated cardiomyopathy (DCM) and premature death 6-9 months after birth. To determine functions of Jarid2 in the adult heart and DCM, we analyzed gene expression in the heart at postnatal day (p)10 (neonatal) and 7 months (DCM). Pathway analyses revealed that dysregulated genes in Jarid2αMHC hearts at p10, prior to cardiomyopathy, represented heart development and muscle contraction pathways. At 7 months, down-regulated genes in Jarid2αMHC hearts were enriched in metabolic process and ion channel activity pathways and up-regulated genes in extracellular matrix components. In normal hearts, expression levels of contractile genes were increased from p10 to 7 months but were not sufficiently increased in Jarid2αMHC hearts. Moreover, Jarid2 was also necessary to repress fetal contractile genes such as TroponinI1, slow skeletal type (Tnni1) and Actin alpha 2, smooth muscle (Acta2) in neonatal stages through ErbB2-receptor tyrosine kinase 4 (ErbB4) signaling. Interestingly, Ankyrin repeat domain 1 (Ankrd1) and Neuregulin 1 (Nrg1), whose expression levels are known to be increased in the failing heart, were already elevated in Jarid2αMHC hearts within 1 month of birth. Thus, we demonstrate that ablation of Jarid2 in cardiomyocytes results in DCM and suggest that Jarid2 plays important roles in cardiomyocyte maturation during neonatal stages.

Parallel MapReduce: Maximizing Cloud Resource Utilization and Performance Improvement Using Parallel Execution Strategies.

MapReduce is the preferred cloud computing framework used in large data analysis and application processing. MapReduce frameworks currently in place suffer performance degradation due to the adoption of sequential processing approaches with little modification and thus exhibit underutilization of cloud resources. To overcome this drawback and reduce costs, we introduce a Parallel MapReduce (PMR) framework in this paper. We design a novel parallel execution strategy of Map and Reduce worker nodes. Our strategy enables further performance improvement and efficient utilization of cloud resources execution of Map and Reduce functions to utilize multicore environments available with computing nodes. We explain in detail makespan modeling and working principle of the PMR framework in the paper. Performance of PMR is compared with Hadoop through experiments considering three biomedical applications. Experiments conducted for BLAST, CAP3, and DeepBind biomedical applications report makespan time reduction of 38.92%, 18.00%, and 34.62% considering the PMR framework against Hadoop framework. Experiments' results prove that the PMR cloud computing platform proposed is robust, cost-effective, and scalable, which sufficiently supports diverse applications on public and private cloud platforms. Consequently, overall presentation and results indicate that there is good matching between theoretical makespan modeling presented and experimental values investigated.

Biased Dropout and Crossmap Dropout: Learning towards effective Dropout regularization in convolutional neural network.

Training a deep neural network with a large number of parameters often leads to overfitting problem. Recently, Dropout has been introduced as a simple, yet effective regularization approach to combat overfitting in such models. Although Dropout has shown remarkable results on many deep neural network cases, its actual effect on CNN has not been thoroughly explored. Moreover, training a Dropout model will significantly increase the training time as it takes longer time to converge than a non-Dropout model with the same architecture. To deal with these issues, we address Biased Dropout and Crossmap Dropout, two novel approaches of Dropout extension based on the behavior of hidden units in CNN model. Biased Dropout divides the hidden units in a certain layer into two groups based on their magnitude and applies different Dropout rate to each group appropriately. Hidden units with higher activation value, which give more contributions to the network final performance, will be retained by a lower Dropout rate, while units with lower activation value will be exposed to a higher Dropout rate to compensate the previous part. The second approach is Crossmap Dropout, which is an extension of the regular Dropout in convolution layer. Each feature map in a convolution layer has a strong correlation between each other, particularly in every identical pixel location in each feature map. Crossmap Dropout tries to maintain this important correlation yet at the same time break the correlation between each adjacent pixel with respect to all feature maps by applying the same Dropout mask to all feature maps, so that all pixels or units in equivalent positions in each feature map will be either dropped or active during training. Our experiment with various benchmark datasets shows that our approaches provide better generalization than the regular Dropout. Moreover, our Biased Dropout takes faster time to converge during training phase, suggesting that assigning noise appropriately in hidden units can lead to an effective regularization.

Long Read Alignment with Parallel MapReduce Cloud Platform.

Genomic sequence alignment is an important technique to decode genome sequences in bioinformatics. Next-Generation Sequencing technologies produce genomic data of longer reads. Cloud platforms are adopted to address the problems arising from storage and analysis of large genomic data. Existing genes sequencing tools for cloud platforms predominantly consider short read gene sequences and adopt the Hadoop MapReduce framework for computation. However, serial execution of map and reduce phases is a problem in such systems. Therefore, in this paper, we introduce Burrows-Wheeler Aligner's Smith-Waterman Alignment on Parallel MapReduce (BWASW-PMR) cloud platform for long sequence alignment. The proposed cloud platform adopts a widely accepted and accurate BWA-SW algorithm for long sequence alignment. A custom MapReduce platform is developed to overcome the drawbacks of the Hadoop framework. A parallel execution strategy of the MapReduce phases and optimization of Smith-Waterman algorithm are considered. Performance evaluation results exhibit an average speed-up of 6.7 considering BWASW-PMR compared with the state-of-the-art Bwasw-Cloud. An average reduction of 30% in the map phase makespan is reported across all experiments comparing BWASW-PMR with Bwasw-Cloud. Optimization of Smith-Waterman results in reducing the execution time by 91.8%. The experimental study proves the efficiency of BWASW-PMR for aligning long genomic sequences on cloud platforms.