Reviewerly/exHarmony huggingface dataset:
Adaptive Tabu Dropout for Regularization of Deep Neural Networks

Dropout is an effective strategy for the regularization of deep neural networks. Applying tabu to the units that have been dropped in the recent epoch and retaining them for training ensures diversification in dropout. In this paper, we improve the Tabu Dropout mechanism for training deep neural networks in two ways. Firstly, we propose to use tabu tenure, or the number of epochs a particular unit will not be dropped. Different tabu tenures provide diversification to boost the training of deep neural networks based on the search landscape. Secondly, we propose an adaptive tabu algorithm that automatically selects the tabu tenure based on the training performances through epochs. On several standard benchmark datasets, the experimental results show that the adaptive tabu dropout and tabu tenure dropout diversify and perform significantly better compared to the standard dropout and basic tabu dropout mechanisms.
Detection and Estimation of Randomly Phase Modulated Signals

The design of detectors for weak sinusoidal signals with random varying phase and frequency has been studied for many decades and continues to be a topic of significant theoretical and practical interest. For a random process, the likelihood ratio detector is known to have an estimator-correlator structure that requires generation of the causal minimum mean square error (MMSE) estimate of the random signal, which generally is very challenging for non-Gaussian signals. In this paper we present methods to project an infinite dimensional solution to the stochastic differential equations generating the required MMSE estimate onto a finite dimensional space, closely approximating the exact solution. Simulations of our proposed detectors are presented and compared with existing approaches, specifically optimized quadratic detectors and extended Kalman filter based methods.
A Fast and Efficient Algorithm for Filtering the Training Dataset

The goal of this paper is to present a new algorithm that filters out inconsistent instances from the training dataset for further usage with machine learning algorithms or learning of neural networks. The idea of this algorithm is based on the previous state-of-the-art algorithm, which uses the concept of local sets. Sophisticated modification of the definition of local sets changes the merits of the algorithm. It is additionally supported by locality-sensitive hashing used for searching for nearest neighbors, composing a new efficient ( $$O(n\log n)$$ ), and an accurate algorithm. Results prepared on many benchmarks show that the algorithm is as accurate as previous but strongly reduces the time complexity.
Correlation Based Semantic Transfer with Application to Domain Adaptation

In this paper, we introduce a multifaceted contribution. First, we propose a definition, specific to convolutional neural networks (CNN’s), for the notion of semantically similar features. Second, using this definition, we introduce a new loss, which semantically transfers features from one domain to another domain, where the features of both domains are learnt by two CNN’s. Our transfer loss, named CBT, constrains the responses of the corresponding convolutional kernels of the two CNN’s to correlate in similar contexts. When the features of the source domain are discriminative, with respect to a classifier, CBT helps to maintain in the target domain, the semantics of the feature space imposed by that classifier. Third, we show that CBT can be used for unsupervised domain adaptation (UDA) by proposing a novel approach for this problem.
Cost-Sensitive Online Adaptive Kernel Learning for Large-Scale Imbalanced Classification

Imbalanced classification is a challenging task in the fields of machine learning, data mining and pattern recognition. Cost-sensitive online algorithms are very important methods for large-scale imbalanced classification problems. At present, most of the cost-sensitive classification algorithms focus on the accuracy of the minority class and ignore the accuracy of the majority class. In order to better balance the accuracy between the minority class and the majority class, in this article, a misclassification cost is presented to ensure that the cost-sensitive online algorithm can better deal with the imbalanced classification problems without signifificantly reducing the accuracy of the majority class. Based on the proposed misclassification cost, a novel cost-sensitive online adaptive kernel learning algorithm is proposed to boost the adaptability of kernel function when data arrives one by one. According to the essential characteristics of the imbalanced binary classification, a cost-sensitive online adaptive kernel learning algorithm is given to handle the large-scale imbalanced multi-class classification problems. Theoretical analysis of the proposed algorithms are provided. Extensive experiments demonstrate that compared with the state-of-the-art imbalanced classification algorithms, the proposed algorithms can significantly improve the classification performances on most of the large-scale imbalanced data sets.
Directed Acyclic Graph Learning on Attributed Heterogeneous Network

Learning the directed acyclic graph (DAG) among causal variables is a fundamental pre-task in causal discovery. Available DAG learning solutions canonically focus on homogeneous nodes with multiple variables and assume i.i.d. samples, how to learn DAG on typical attributed heterogeneous network (AHN) composed with different types of inter-dependent nodes and diverse attributes is a practical but more difficult task. In this paper, we propose HetDAG to identify DAG among nodes from heterogeneous network. HetDAG first embeds different types of node attributes and aggregates these embeddings as the node's raw representation. Then it uses contrastive learning with prior network structure to explore latent relationships between nodes and update the representation. Next, HetDAG introduces an attention-based DAG learning module that takes node representations as input to search DAG and orient edges between nodes. To the best of our knowledge, HetDAG is the first study to learn DAG on heterogeneous networks. Extensive experiments on both semi-synthetic and real data show that HetDAG can learn DAG in an efficacy way and outperforms the state-of-the-art approaches. The results on real biological networks confirm that HetDAG can find out the causal relations between lncRNAs and miRNAs.
Construction of Near-Optimal Frequency Hopping Sequence Set with Low-Hit-Zone

The low-hit-zone (LHZ) frequency hopping sequence (FHS) sets are widely applicable in quasi-synchronous frequency hopping multiple-access (QS-FHMA) systems. In order to reduce mutual interference (MI) in the zone around the signal origin between different users, we recommend the LHZ FHS set instead of the conventional FHS set. In this letter, we propose a design of LHZ FHS sets via interleaving techniques. The obtained sequences can be confirmed that they are near-optimal in relation to the Peng-Fan-Lee bound.
PrigSim: Towards Privacy-Preserving Graph Similarity Search as a Cloud Service

Graphs are widely used to model complex structured data in many applications. With the proliferation of cloud computing, it is popular to store and query graphs in the cloud. Among others, graph similarity search, which aims to retrieve from a graph database graphs similar to a query graph, has received wide attentions and benefited various domains such as cheminformatics, computer vision, and more. Deploying graph similarity search services on the cloud, however, raises critical privacy concerns on the information-rich graphs. In this article, we initiate the first study on privacy-preserving graph similarity search in cloud computing. We design, implement, and evaluate PrigSim, a novel system allowing the cloud to host an outsourced encrypted graph database and support secure graph similarity search, where the graph similarity is measured by the well-known metric called graph edit distance. PrigSim is built from a customized and delicate synergy of insights on graph modelling, lightweight cryptography, and data encoding and padding, providing protections for the confidentiality of data content associated with graphs, as well as hiding the connections among vertices. Extensive experiments demonstrate that the security design of PrigSim is accuracy-preserving, and presents modest performance overheads (with <inline-formula><tex-math notation="LaTeX">$9\times$</tex-math></inline-formula> - <inline-formula><tex-math notation="LaTeX">$15\times$</tex-math></inline-formula> higher query latency than the plaintext baseline).
FCNet: Learning Noise-free Features for Point Cloud Denoising

The acquisition of point clouds is usually accompanied by noise due to imperfect laser scanning or image-based reconstruction techniques. Deep learning-based methods have achieved impressive performance in point cloud denoising. However, the features captured by a denoising network from noisy point clouds are usually contaminated by noise during training. The feature noise will lead to the oscillation of back-propagated gradients, which interferes with parameter optimization and reduces the denoising performance. In this paper, we propose to explicitly clean up feature noise for point cloud denoising from two aspects: feature noise cleaning and network training. From the first aspect, we propose the feature clean network (FCNet for short) to explicitly clean up the feature noise. From the second aspect, we train FCNet by a teacher-student learning model to learn the noise-free features under the guidance of feature domain losses. Specifically, FCNet is designed with emphasis on two modules: non-local self-similarity (NSS) and weighted average pooling (WAP). NSS module smooths features through a non-local filter based on the inherent non-local self-similarity of point clouds. WAP module applies original weights calculated by the statistical outlier removal algorithm to suppress the feature noise induced by outliers. In the teacher-student learning model, we introduce a clean input using the noisy point and its clean neighbors. The teacher network accepts the clean input to capture noise-free features. The student network is trained to imitate the teacher network to learn noise-free features by minimizing the feature loss. The experiments on synthetic and real scanned point clouds show that FCNet outperforms state-of-the-art point cloud denoising methods.
Matrix factorization-based multi-objective ranking–What makes a good university?

Non-negative matrix factorization (NMF) efficiently reduces high dimensionality for many-objective ranking problems. In multi-objective optimization, as long as only three or four conflicting viewpoints are present, an optimal solution can be determined by finding the Pareto front. When the number of the objectives increases, the multi-objective problem evolves into a many-objective optimization task, where the Pareto front becomes oversaturated. The key idea is that NMF aggregates the objectives so that the Pareto front can be applied, while the Sum of Ranking Differences (SRD) method selects the objectives that have a detrimental effect on the aggregation, and validates the findings. The applicability of the method is illustrated by the ranking of 1176 universities based on 46 variables of the CWTS Leiden Ranking 2020 database. The performance of NMF is compared to principal component analysis (PCA) and sparse non-negative matrix factorization-based solutions. The results illustrate that PCA incorporates negatively correlated objectives into the same principal component. On the contrary, NMF only allows non-negative correlations, which enable the proper use of the Pareto front. With the combination of NMF and SRD, a non-biased ranking of the universities based on 46 criteria is established, where Harvard, Rockefeller and Stanford Universities are determined as the first three. To evaluate the ranking capabilities of the methods, measures based on Relative Entropy (RE) and Hypervolume (HV) are proposed. The results confirm that the sparse NMF method provides the most informative ranking. The results highlight that academic excellence can be improved by decreasing the proportion of unknown open-access publications and short distance collaborations. The proportion of gender indicators barely correlate with scientific impact. More authors, long-distance collaborations, publications that have more scientific impact and citations on average highly influence the university ranking in a positive direction.
Relationship between a deep learning model and liquid‐based cytological processing techniques

Artificial intelligence (AI)-based cytopathology studies conducted using deep learning have enabled cell detection and classification. Liquid-based cytology (LBC) has facilitated the standardisation of specimen preparation; however, cytomorphology varies according to the LBC processing technique used. In this study, we elucidated the relationship between two LBC techniques and cell detection and classification using a deep learning model.Cytological specimens were prepared using the ThinPrep and SurePath methods. The accuracy of cell detection and cell classification was examined using the one- and five-cell models, which were trained with one and five cell types, respectively.When the same LBC processing techniques were used for the training and detection preparations, the cell detection and classification rates were high. The model trained on ThinPrep preparations was more accurate than that trained on SurePath. When the preparation types used for training and detection were different, the accuracy of cell detection and classification was significantly reduced (P < 0.01). The model trained on both ThinPrep and SurePath preparations exhibited slightly reduced cell detection and classification rates but was highly accurate.For the two LBC processing techniques, cytomorphology varied according to cell type; this difference affects the accuracy of cell detection and classification by deep learning. Therefore, for highly accurate cell detection and classification using AI, the same processing technique must be used for both training and detection. Our assessment also suggests that a deep learning model should be constructed using specimens prepared via a variety of processing techniques to construct a globally applicable AI model.
Learning to Learn Functions

Humans can learn complex functional relationships between variables from small amounts of data. In doing so, they draw on prior expectations about the form of these relationships. In three experiments, we show that people learn to adjust these expectations through experience, learning about the likely forms of the functions they will encounter. Previous work has used Gaussian processes-a statistical framework that extends Bayesian nonparametric approaches to regression-to model human function learning. We build on this work, modeling the process of learning to learn functions as a form of hierarchical Bayesian inference about the Gaussian process hyperparameters.
Improved accuracy and less fault prediction errors via modified sequential minimal optimization algorithm

The benefits and opportunities offered by cloud computing are among the fastest-growing technologies in the computer industry. Additionally, it addresses the difficulties and issues that make more users more likely to accept and use the technology. The proposed research comprised of machine learning (ML) algorithms is Naïve Bayes (NB), Library Support Vector Machine (LibSVM), Multinomial Logistic Regression (MLR), Sequential Minimal Optimization (SMO), K Nearest Neighbor (KNN), and Random Forest (RF) to compare the classifier gives better results in accuracy and less fault prediction. In this research, the secondary data results (CPU-Mem Mono) give the highest percentage of accuracy and less fault prediction on the NB classifier in terms of 80/20 (77.01%), 70/30 (76.05%), and 5 folds cross-validation (74.88%), and (CPU-Mem Multi) in terms of 80/20 (89.72%), 70/30 (90.28%), and 5 folds cross-validation (92.83%). Furthermore, on (HDD Mono) the SMO classifier gives the highest percentage of accuracy and less fault prediction fault in terms of 80/20 (87.72%), 70/30 (89.41%), and 5 folds cross-validation (88.38%), and (HDD-Multi) in terms of 80/20 (93.64%), 70/30 (90.91%), and 5 folds cross-validation (88.20%). Whereas, primary data results found RF classifier gives the highest percentage of accuracy and less fault prediction in terms of 80/20 (97.14%), 70/30 (96.19%), and 5 folds cross-validation (95.85%) in the primary data results, but the algorithm complexity (0.17 seconds) is not good. In terms of 80/20 (95.71%), 70/30 (95.71%), and 5 folds cross-validation (95.71%), SMO has the second highest accuracy and less fault prediction, but the algorithm complexity is good (0.3 seconds). The difference in accuracy and less fault prediction between RF and SMO is only (.13%), and the difference in time complexity is (14 seconds). We have decided that we will modify SMO. Finally, the Modified Sequential Minimal Optimization (MSMO) Algorithm method has been proposed to get the highest accuracy & less fault prediction errors in terms of 80/20 (96.42%), 70/30 (96.42%), & 5 fold cross validation (96.50%).
Prior skeleton based online deep reinforcement learning for coronary artery centerline extraction

Coronary centerline extraction is an essential technique for X-ray coronary angiography (XCA) image analysis, which provides qualitative and quantitative guidance for percutaneous coronary intervention (PCI). In this paper, an online deep reinforcement learning method for coronary centerline extraction is proposed based on the prior vascular skeleton. Firstly, with XCA image preprocessing (foreground extraction and vessel segmentation) results, the improved ZhangSuen image thinning algorithm is used to rapidly extract the preliminary vascular skeleton network. On this basis, according to the spatial-temporal and morphological continuity of the angiography image sequence, the connectivity of different branches is determined using k-means clustering, and the vessel segments are then grouped, screened, and reconnected to obtain the aorta and its major branches. Finally, using the previous results as prior information, an online Deep Q-Network (DQN) reinforcement learning method is proposed to optimize each branch simultaneously. It comprehensively considers grayscale intensity and eigenvector continuity to achieve the combination of data-driven and model-driven without pre-training. Experimental results on clinical images and the third-party dataset demonstrate that the proposed method can accurately extract, restructure, and optimize the centerline of XCA images with a higher overall accuracy than the existing state-of-the-art methods.
Multiagent Trust Region Policy Optimization

We extend trust region policy optimization (TRPO) to cooperative multiagent reinforcement learning (MARL) for partially observable Markov games (POMGs). We show that the policy update rule in TRPO can be equivalently transformed into a distributed consensus optimization for networked agents when the agents’ observation is sufficient. By using a local convexification and trust-region method, we propose a fully decentralized MARL algorithm based on a distributed alternating direction method of multipliers (ADMM). During training, agents only share local policy ratios with neighbors via a peer-to-peer communication network. Compared with traditional centralized training methods in MARL, the proposed algorithm does not need a control center to collect global information, such as global state, collective reward, or shared policy and value network parameters. Experiments on two cooperative environments demonstrate the effectiveness of the proposed method.
A multi-dimensional analysis of CBEC English genre variation in South Asia: Based on Daraz

The purpose of this study is to investigate how genres vary and figure out the factors that generate genre variation. The quantitative multi-dimensional analysis is used to examine genre variation of cross-border e-commerce English in South Asia. The texts in the observed corpus collected from four country websites of Daraz, a significant cross-border e-commerce platform, were tagged and analyzed using Multidimensional Analysis Tagger and SPSS statistical software. The results of linear regression analysis, independent sample t-test and Analysis of Variance show salient differences between the observed corpus and reference corpus. The research also indicates that four sub-corpora from country Websites are brought into line with each other. They show salient differences (p<0.05) with the general corpus in two dimensions of linguistic variation. The findings indicate that variables that lead to the cross-border e-commerce English genre variation might be attributable to cultural backgrounds, regional market sizes, and fundamental internet facilities. In conclusion, these findings lend significant empirical support to systemic functional theories and suggest further research in the application of a multi-dimensional analysis approach in the cross-border e-commerce English genre.
Reinforcement Learning for Enhancing Human Security Resource Allocation in Protecting Assets with Heterogeneous Losses

Given the rising cyber threats on today's cyber-physical systems (CPS), enhancing security resource allocation is highly desirable. We consider a security resource allocation setting that involves a human defender who is required to invest her (limited) security in protecting a given set of assets against attacks. Each asset has a certain loss valuation to the defender, along with a probability of being successfully compromised. The success probability of each asset is a function of the resource allocation in that asset by the defender. In this setting, we first model the impacts of behavioral probability weighting that is identified by behavioral economics to be a common feature of human decision-making on the security resource allocation strategies. We then propose a reinforcement learning (RL) technique for enhancing security resource allocation in multi-round setups. We illustrate the effect of probability weighting on the security investments and the benefits of our RL algorithm via numerical simulations. In particular, we show that RL can guide human behavioral defender in enhancing her security investments to make the system more secure.
Remote Attestation Assurance Arguments for Trusted Execution Environments

Remote attestation (RA) is emerging as an important security mechanism for cyber-physical systems with strict security requirements. Trusted computing at large and Trusted Execution Environments (TEEs) in particular have been identified as key technologies to enable RA since they ideally allow retaining some element of control over remote devices despite them being compromised at the OS level. Unfortunately, sometimes it is claimed that TEEs provide RA support without really substantiating how this support is provided. In this paper we build the assurance arguments for RA to carefully map how secure RA depends on underlying security properties and how these in turn can be provided by TEE capabilities. We base our security analysis of RA on existing literature on security requirements for RA and use Goal Structuring Notation (GSN) as the method to build the security arguments. Our analysis identifies the set of TEE properties (as described in the GlobalPlatform standard) that are needed to support RA, and which goals that cannot be mapped to TEE implementations, and therefore require other forms of evidence for RA to be trusted at the top level.