Reviewerly/exHarmony huggingface dataset:
BSense: A parallel Bayesian hyperparameter optimized Stacked ensemble model for breast cancer survival prediction

Breast Cancer is a disease with high risk and mortality rate associated with female health. The multi-omics data having genome, proteome, transcriptome, metabolome data, and pathological images are available for analysis. Artificial intelligence has huge scope in developing models to predict the occurrence, reoccurrence, and survival in breast cancer. In this paper, we proposed a parallel Bayesian hyperparameter optimized Stacked ensemble (BSense) model. The model is developed using stacking of machine learning models, i.e., Deep Neural Network (DNN), Gradient Boosting Machine (GBM), and Distributed Random Forest (DRF). Bayesian optimization with Gaussian Processes is used to find the best hyperparameters for the machine learning models. The parallelism is coupled with Bayesian optimization to address the high computational time. Artificial Bee Colony (ABC) algorithm is used to select the most effective features from a dataset. The proposed model is evaluated on breast cancer dataset available on the TCGA (The Cancer Genome Atlas) portal and is validated on the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium), Metabolomics, and RNA-seq datasets. It is evident from the experimental results that proposed BSense model outperforms DNN, GBM, and DRF models for breast cancer survival prediction. The BSense model achieved 83.9%, 87.3%, 91.1%, and 80.1% Area Under Curve (AUC) for TCGA, METABRIC, Metabolomics, and RNA-seq dataset, respectively, which is more in comparison to the existing models taken into consideration.
Teaching in times of disruption: Faculty digital literacy in higher education during the COVID-19 pandemic

This study identifies how instructors from higher education institutions experienced digital literacy during emergency remote teaching (ERT) as a result of the COVID-19 pandemic. A total of 239 full- and part-time instructors completed an electronic questionnaire with closed and open-ended items. Analysis at the item level provided specific shifts and variations of digital literacy challenges experienced, tools used, tasks performed, and the resources employed. This investigation helps researchers inform the academic community of the digital pedagogy and technologies instructors used during the COVID-19 pandemic. This investigation also highlights some challenges that professional development addressing instructors’ digital literacy can help mitigate.
Fine-grained Image Classification via Multi-scale Selective Hierarchical Biquadratic Pooling

How to extract distinctive features greatly challenges the fine-grained image classification tasks. In previous models, bilinear pooling has been frequently adopted to address this problem. However, most bilinear pooling models neglect either intra or inter layer feature interaction. This insufficient interaction brings in the loss of discriminative information. In this article, we devise a novel fine-grained image classification approach named M ulti-scale S elective H ierarchical bi Q uadratic P ooling (MSHQP). The proposed biquadratic pooling simultaneously models intra and inter layer feature interactions and enhances part response by integrating multi-layer features. The subsequent coarse-to-fine multi-scale interaction structure captures the complementary information within features. Finally, the active interaction selection module adaptively learns the optimal interaction subset for a specific dataset. Consequently, we obtain a robust image representation with coarse-to-fine semantics. We conduct experiments on five benchmark datasets. The experimental results demonstrate that MSHQP achieves competitive or even match the state-of-the-art methods in terms of both accuracy and computational efficiency, with 89.0%, 94.9%, 93.4%, 90.4%, and 91.5% top-1 classification accuracy on CUB-200-2011, Stanford-Cars, FGVC-Aircraft, Stanford-Dog, and VegFru, respectively.
Topic Modeling Techniques for Text Mining Over a Large-Scale Scientific and Biomedical Text Corpus

Topic models are efficient in extracting central themes from large-scale document collection and it is an active research area. The state-of-the-art techniques like Latent Dirichlet Allocation, Correlated Topic Model (CTM), Hierarchical Dirichlet Process (HDP), Dirichlet Multinomial Regression (DMR) and Hierarchical Pachinko Allocation (HPA) model is considered for comparison. . The abstracts of articles were collected between different periods from PUBMED library by keywords adolescence substance use and depression. A lot of research has happened in this area and thousands of articles are available on PubMed in this area. This collection is huge and so extracting information is very time-consuming. To fit the topic models this extracted text data is used and fitted models were evaluated using both likelihood and non-likelihood measures. The topic models are compared using the evaluation parameters like log-likelihood and perplexity. To evaluate the quality of topics topic coherence measures has been used.
Performance optimization of photovoltaic systems: Reassessment of political optimization with a quantum Nelder-mead functionality

Evaluating the PV system parameters is crucial for the more efficient utilization of sustainable solar energy. Due to PV systems' nonlinear and multimodal nature, efficient evaluation of PV system parameters is still a very challenging task. This paper proposes an improved political optimization algorithm, namely the QNMPO algorithm, which combines quantum rotation gate and Nelder-Mead simplex (NMs). It can be used to evaluate unknown parameters of PV systems and solar cells efficiently. In QNMPO, the quantum rotation gate method can rotate the population of agents to a more favorable position for finding the optimal solution, making the individuals in the middle of the population communicate more frequently and enhancing the population's diversity. In addition, the NMs method can improve the solution quality by better searching the neighborhoods near the optimal solution and then strengthen the advantage of local development of the algorithm so that it can converge to the global optimal solution faster. The developed QNMPO algorithm extracts the unknown parameters for single diode, double diode, triple diode and photovoltaic modules in this study. It is compared with some other representative algorithms. The experimental results show that QNMPO outperforms similar algorithms in convergence speed, solution accuracy, and better solution performance. QNMPO was then used to successfully extract the best parameter values for three real PV models (three commercial PV models (thin-film ST40, monocrystalline SM55, and multi crystalline KC200GT)) at different temperatures and different irradiance levels. The results also show that QNMPO performs consistently and accurately enough under varying temperatures and irradiation. Therefore, the QNMPO algorithm proposed in this paper can be a promising parameter extraction method for solar PV systems with good performance.
Learning to rank: An intelligent system for person reidentification

Person reidentification (P-Reid) is an emerging research domain in the field of information retrieval that has gained exponential growth due to its wide range of applications in pedestrian tracking and crime prevention. The primary goal of P-Reid is to recognize a person based on previous appearance in multiview surveillance videos. The mainstream approaches apply fully supervised learning techniques that have poor scalability when deployed in complex real-world scenes, due to the overfitting problem, caused by the lack of sufficient annotated data. Further, optimization of these models for unlabeled data in real-time surveillance is a challenging task. To tackle these issues, an intelligent framework (LR-Net) is proposed, consisting of three tiers including fine-tuning (FT), siamese network (SN), and fusion strategy (FS). In the first tier, a deep learning model is fine-tuned for P-Reid that can handle both labeled and unlabeled data. Next, with the assistance of transfer learning, an SN is proposed that has a strong discriminative capability in terms of similarity between a pair of images. Finally, a learning-to-rank strategy is applied to optimize the learning capability of the SN, in which a triplet network extracts spatial-temporal patterns from unlabeled samples. In addition, a bayesian fusion model (BFM) is introduced to integrate the spatiotemporal and visual features, which yields 4.4%, 9.3%, and 0.8% improvement in the matching score over Market-1501, DukeMCMT-reID, and CUHK03 data sets, respectively. The conducted experiments and ablation study on the benchmark data sets empirically validate the proposed system, which obtains a high Rank-1 score as compared with the state-of-the-art (SOTA) methods.
Engineering In-place (Shared-memory) Sorting Algorithms

We present new sequential and parallel sorting algorithms that now represent the fastest known techniques for a wide range of input sizes, input distributions, data types, and machines. Somewhat surprisingly, part of the speed advantage is due to the additional feature of the algorithms to work in-place, i.e., they do not need a significant amount of space beyond the input array. Previously, the in-place feature often implied performance penalties. Our main algorithmic contribution is a blockwise approach to in-place data distribution that is provably cache-efficient. We also parallelize this approach taking dynamic load balancing and memory locality into account. Our new comparison-based algorithm In-place Parallel Super Scalar Samplesort ( IPS 4 o ) , combines this technique with branchless decision trees. By taking cases with many equal elements into account and by adapting the distribution degree dynamically, we obtain a highly robust algorithm that outperforms the best previous in-place parallel comparison-based sorting algorithms by almost a factor of three. That algorithm also outperforms the best comparison-based competitors regardless of whether we consider in-place or not in-place, parallel or sequential settings. Another surprising result is that IPS 4 o even outperforms the best (in-place or not in-place) integer sorting algorithms in a wide range of situations. In many of the remaining cases (often involving near-uniform input distributions, small keys, or a sequential setting), our new In-place Parallel Super Scalar Radix Sort ( IPS 2 Ra ) turns out to be the best algorithm. Claims to have the – in some sense – “best” sorting algorithm can be found in many papers which cannot all be true. Therefore, we base our conclusions on an extensive experimental study involving a large part of the cross product of 21 state-of-the-art sorting codes, 6 data types, 10 input distributions, 4 machines, 4 memory allocation strategies, and input sizes varying over 7 orders of magnitude. This confirms the claims made about the robust performance of our algorithms while revealing major performance problems in many competitors outside the concrete set of measurements reported in the associated publications. This is particularly true for integer sorting algorithms giving one reason to prefer comparison-based algorithms for robust general-purpose sorting.
Generative adversarial network based adaptive data augmentation for handwritten Arabic text recognition

Training deep learning based handwritten text recognition systems needs a lot of data in terms of text images and their corresponding annotations. One way to deal with this issue is to use data augmentation techniques to increase the amount of training data. Generative Adversarial Networks (GANs) based data augmentation techniques are popular in literature especially in tasks related to images. However, specific challenges need to be addressed in order to effectively use GANs for data augmentation in the domain of text recognition. Text data is inherently imbalanced in terms of frequency of different characters appearing in training samples and the training data as a whole. GANs trained on the imbalanced dataset leads to augmented data that does not represent the minority characters well. In this paper, we present an adaptive data augmentation technique using GANs that deals with the issue of class imbalance arising in text recognition problems. We show, using experimental evaluations on two publicly available datasets for handwritten Arabic text recognition, that the GANs trained using the presented technique is effective in dealing with class imbalanced problem by generating augmented data that is balanced in terms of character frequencies. The resulting text recognition systems trained on the balanced augmented data improves the text recognition accuracy as compared to the systems trained using standard techniques.
Multi-category intuitionistic fuzzy twin support vector machines with an application to plant leaf recognition

The intuitionistic fuzzy twin support vector machine for multi-categorization is developed in this study, which incorporates both structural and empirical risk concepts. In this method, each training pattern is first aggregated with the appropriate membership and non-membership degrees, which describe the position of a pattern in relation to its class centre and surrounding circumstances in input or feature space, and then the separating hyperplane is constructed using the kernel function and convex quadratic programming. Empirical findings on an artificial and thirteen UCI standard datasets show that it outperforms well-known existing methods including improved support vector machines, K-nearest neighbour, logistic regression, decision trees, random forests, and multilayer perceptrons. Furthermore, the suggested classifier with linear, polynomial, and Gaussian kernels has been used to identify the leaves of various plants, where the shape, texture, and margin data are extracted from the leaf in order to categorize the plant species. The method's generalization capacity is demonstrated by the classification results on two leaf datasets of thirty and one hundred species, respectively. To compare the suggested method's prediction capacity with others, statistical analysis is performed using two non-parametric tests, Friedman and Wilcoxon, with a 5% threshold of significance. The results show that the proposed method yields better performance for both linear and non-linear kernels.
Self-trained prediction model and novel anomaly score mechanism for video anomaly detection

Video anomaly detection is important in various practical applications. This paper proposes an unsupervised method for video anomaly detection. In the core of the method lies a new prediction model for anomaly detection with novel anomaly score mechanism and self-training mechanism combined with prediction model. In the first stage, we use two conventional unsupervised anomaly detection methods to obtain pseudo normal and anomalous frames from the original unlabeled data. In the second stage, we train the prediction model with the pseudo normal frames to learn normal patterns. In the last stage, a three-branch decision module is constructed using prediction model and decision function to calculate the anomaly score of frames and update the pseudo frames for subsequent iterative training. The model then enters the second stage, until the last iterative training is completed. After several iterative training and evaluations, the optimal anomaly scores of the original unlabeled data are finally obtained, and a stable model is generated at the same time. Experimental results on four real-world video datasets demonstrate that the proposed method outperforms state-of-the-art methods without labeled data by a significant margin.
Learning to Navigate Sidewalks in Outdoor Environments

Outdoor navigation on sidewalks in urban environments is the key technology behind important human assistive applications, such as last-mile delivery or neighborhood patrol. This letter aims to develop a quadruped robot that follows a route plan generated by public map services, while remaining on sidewalks and avoiding collisions with obstacles and pedestrians. We devise a two-staged learning framework, which first trains a teacher agent in an abstract world with privileged ground-truth information, and then applies Behavior Cloning to teach the skills to a student agent who only has access to realistic sensors. The main research effort of this letter focuses on overcoming challenges when deploying the student policy on a quadruped robot in the real world. We propose methodologies for designing sensing modalities, network architectures, and training procedures to enable zero-shot policy transfer to unstructured and dynamic real outdoor environments. We evaluate our learning framework on a quadrupedal robot navigating sidewalks in the city of Atlanta, USA ( <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fig. 1</xref> ). Using the learned navigation policy and its onboard sensors, the robot is able to walk 3.2 kilometers with a limited number of human interventions.
CloudChain: A Cloud Blockchain Using Shared Memory Consensus and RDMA

Blockchain technologies can enable secure computing environments among mistrusting parties. Permissioned blockchains are particularly enlightened by companies, enterprises, and government agencies due to their efficiency, customizability, and governance-friendly features. Obviously, seamlessly fusing blockchain and cloud computing can significantly benefit permissioned blockchains; nevertheless, most blockchains implemented on clouds are originally designed for loosely-coupled networks where nodes communicate asynchronously, failing to take advantages of the closely-coupled nature of cloud servers. In this paper, we propose an innovative cloud-oriented blockchain -- CloudChain, which is a modularized three-layer system composed of the network layer, consensus layer, and blockchain layer. CloudChain is based on a shared-memory model where nodes communicate synchronously by direct memory accesses. We realize the shared-memory model with the Remote Direct Memory Access technology, based on which we propose a shared-memory consensus algorithm to ensure presistence and liveness, the two crucial blockchain security properties countering Byzantine nodes. We also implement a CloudChain prototype based on a RoCEv2-based testbed to experimentally validate our design, and the results verify the feasibility and efficiency of CloudChain.
Tracking of a Fixed-Shape Moving Object Based on the Gradient Descent Method

Tracking moving objects is one of the most promising yet the most challenging research areas pertaining to computer vision, pattern recognition and image processing. The challenges associated with object tracking range from problems pertaining to camera axis orientations to object occlusion. In addition, variations in remote scene environments add to the difficulties related to object tracking. All the mentioned challenges and problems pertaining to object tracking make the procedure computationally complex and time-consuming. In this paper, a stochastic gradient-based optimization technique has been used in conjunction with particle filters for object tracking. First, the object that needs to be tracked is detected using the Maximum Average Correlation Height (MACH) filter. The object of interest is detected based on the presence of a correlation peak and average similarity measure. The results of object detection are fed to the tracking routine. The gradient descent technique is employed for object tracking and is used to optimize the particle filters. The gradient descent technique allows particles to converge quickly, allowing less time for the object to be tracked. The results of the proposed algorithm are compared with similar state-of-the-art tracking algorithms on five datasets that include both artificial moving objects and humans to show that the gradient-based tracking algorithm provides better results, both in terms of accuracy and speed.
Privacy and security-aware workflow scheduling in a hybrid cloud

Increasing numbers of workflow applications are being deployed on cloud platforms. When deploying workflows in a hybrid cloud environment, additional concerns emerge: 1. privacy-sensitive data and tasks cannot be exposed to a public cloud platform; 2. security and bandwidth of data transmission across cloud platforms should be guaranteed as it goes through the ‘unpredictable’ Internet. This paper investigates how to effectively reduce monetary cost when deploying a workflow in a hybrid cloud under the deadline and privacy constraints. A three-level data privacy and security model integrating hybrid encryption is devised to guarantee privacy and security of workflow applications. Two scheduling heuristics are then proposed to tackle the considered constrained optimization problem: privacy and security-aware list scheduling (PSLS) and simulated annealing (PSSA). PSLS distributes the user-defined deadline to each task and allocates each task to a resource in the hybrid cloud that meets constraints and incurs a minimum cost. PSSA permutes task lists for iterative improvement based on simulated annealing and PSLS. Simulation experiments are conducted and experimental results demonstrate that PSLS generally performs better than four existing algorithms to optimize monetary costs and PSSA achieves an even higher performance at the expense of runtime.
Global-Reasoned Multi-Task Learning Model for Surgical Scene Understanding

Global and local relational reasoning enable scene understanding models to perform human-like scene analysis and understanding. Scene understanding enables better semantic segmentation and object-to-object interaction detection. In the medical domain, a robust surgical scene understanding model allows the automation of surgical skill evaluation, real-time monitoring of surgeon’s performance and post-surgical analysis. This letter introduces a globally-reasoned multi-task surgical scene understanding model capable of performing instrument segmentation and tool-tissue interaction detection. Here, we incorporate global relational reasoning in the latent interaction space and introduce multi-scale local (neighborhood) reasoning in the coordinate space to improve segmentation. Utilizing the multi-task model setup, the performance of the visual-semantic graph attention network in interaction detection is further enhanced through global reasoning. The global interaction space features from the segmentation module are introduced into the graph network, allowing it to detect interactions based on both node-to-node and global interaction reasoning. Our model reduces the computation cost compared to running two independent single-task models by sharing common modules, which is indispensable for practical applications. Using a sequential optimization technique, the proposed multi-task model outperforms other state-of-the-art single-task models on the MICCAI endoscopic vision challenge 2018 dataset. Additionally, we also observe the performance of the multi-task model when trained using the knowledge distillation technique. The official code implementation is made available in GitHub.
MCformer: A Transformer Based Deep Neural Network for Automatic Modulation Classification

In this paper, we propose MCformer - a novel deep neural network for the automatic modulation classification task of complex-valued raw radio signals. MCformer architecture leverages convolution layer along with self-attention based encoder layers to efficiently exploit temporal correlation between the embeddings produced by convolution layer. MCformer provides state of the art classification accuracy at all signal-to-noise ratios in the RadioML2016.10b data-set with significantly less number of parameters which is critical for fast and energy-efficient operation.