Reviewerly/exHarmony huggingface dataset:
Evaluating Interpretability in Deep Learning using Breast Cancer Histopathological Images

Breast cancer is the most common cancer type and is the leading cause of death among females worldwide. Despite these negative statistics, early diagnosis gives patients a high probability of survival. In literature, diagnostic techniques based on histopathological images were proposed for early diagnosis. However, they are limited because they depend on the pathologist’s work and experience. In other words, a patient may receive a different diagnosis from different pathologists, or inexperienced pathologists may misdiagnose. In this work, we implement five Deep Neural Networks (DNNs) and evaluate classification accuracy and interpretability from real tumour images. To evaluate our models, we propose a metric to assess the interpretability of Deep Neural Networks (DNNs). The experiments with the BreCaHAD annotated dataset have shown that MobileNetV2 presented a higher accuracy in classifying histopathological images and interpreting their features, leading the way to improve the pathologist’s work
Robust Network Architecture Search via Feature Distortion Restraining

The vulnerability of Deep Neural Networks, i.e., susceptibility to adversarial attacks, severely limits the application of DNNs in security-sensitive domains. Most of existing methods improve model robustness from weight optimization, such as adversarial training. However, the architecture of DNNs is also a key factor to robustness, which is often neglected or underestimated. We propose Robust Network Architecture Search (RNAS) to obtain a robust network against adversarial attacks. We observe that an adversarial perturbation distorting the non-robust features in latent feature space can further aggravate misclassification. Based on this observation, we search the robust architecture through restricting feature distortion in the search process. Specifically, we define a network vulnerability metric based on feature distortion as a constraint in the search process. This process is modeled as a multi-objective bilevel optimization problem and a novel algorithm is proposed to solve this optimization. Extensive experiments conducted on CIFAR-10/100 and SVHN show that RNAS achieves the best robustness under various adversarial attacks compared with extensive baselines and SOTA methods.
A Naive Bayes Approach to the Identification of Subjectivity based on Optimized Subjective Clues

In natural language, the condition(aspect) of language that represents the contextual impressions as subjective or objective is referred to as subjectivity analysis. Subjectivity analysis briefly focuses on the subjective clues and phrases to detect sentences' contextual formulation. The purpose of this research was to study the aspects of subjective clues and to create a subjectivity classification model based on a secondary dataset of movie reviews. Identifying and analysing subjective information from a given text was the key criteria of the study. The subjectivity clues were analysed using the traditional NLP methods. The classification model was designed based on the Naive Bayes approach for classifying a sentence as a subjective or objective sentence. The performance of the model was satisfactory with an accuracy of 95% for the subjective dataset.
Machine Learning-based Weibo user group profiling under hot events

The construction of Weibo user profiles in hot events can help to grasp the characteristics of Weibo users involved in the events, which is conductive to the relevant department to strengthen public opinion guidance and propaganda education. Taking the “Viya's tax evasion” case as an example, firstly, the Latent Dirichlet allocation (LDA) topic model is used to construct a topic model ofWeibo content in the case, and the optimal number of topics is determined by perplexity. Then, the k-prototype algorithm and the improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm are respectively used to cluster Weibo users and analyze the similarities and differences between the various categories of users. At last, the clustering results of the two algorithms are compared. The experiments show that the topic generation method based on the LDA topic model has a good effect on describing discussion topics. In the process of data containing mixed attributes, the k-Prototype algorithm and the improved DBSCAN algorithm have their respective advantages, and the combined results of the two algorithms can obtain a more complete user group portrait.
Developing games with data-oriented design

Data-Oriented Design (DOD) is a software design process used in the commercial game industry for medium to large scale (AAA) games that revolves around data as the central design focus. Since DOD grew organically from industry practices, it has been viewed primarily as a set of tools and design patterns without a cohesive core. After studying data-oriented design, it has become clear that there is one core motivating concept behind DOD and that is that software exists to input, transform, and output data. In order to demonstrate the core concept and tools of DOD, examples are shown. The history and focus of DOD are discussed as well as how DOD is used in current commercial software.
A Knowledge Representation Method for Multiple Pattern Embeddings Based on Entity-Relation Mapping Matrix

The representation learning in knowledge graph aims to embed the semantic information of the research object into dense low dimensional distributed real valued vectors. The classical knowledge representation model, TransR, uses the relation specific matrix to represent the entities and relations in different semantic spaces, so as to solve the separation problem of semantic space. However, the TransR model only considers the different types of relations, ignores the entity types, and has defects in the processing of inverse relations. To solve the model problems, a novel knowledge representation method for multiple pattern embeddings based on entity-relation mapping matrix (M-TransER) is presented. The M-TransER model introduces the entity-relation mapping matrix, which makes the model consider the entity type while considering the relation type. On the basis that entities and relations are modeled as positive translation geometric distance, the inverse translation geometric distance model and the symmetrical relation model are fused. Eliminate the defects of the model in dealing with inverse relations, and improve the efficiency of symmetric relation processing. Experimental results on open datasets show that the M-TransER model has made great progress and demonstrated its superiority over the existing models and methods in both link prediction and triple classification tasks.
Learning When to Say “I Don’t Know"

We propose a new Reject Option Classification technique to identify and remove regions of uncertainty in the decision space for a given neural classifier and dataset. Such existing formulations employ a learned rejection (remove)/selection (keep) function and require either a known cost for rejecting examples or strong constraints on the accuracy or coverage of the selected examples. We consider an alternative formulation by instead analyzing the complementary reject region and employing a validation set to learn per-class softmax thresholds. The goal is to maximize the accuracy of the selected examples subject to a natural randomness allowance on the rejected examples (rejecting more incorrect than correct predictions). We provide results showing the benefits of the proposed method over naïvely thresholding calibrated/uncalibrated softmax scores with 2-D points, imagery, and text classification datasets using state-of-the-art pretrained models. Source code is available at https://github.com/osu-cvl/learning-idk .
Humans are Poor Few-Shot Classifiers for Sentinel-2 Land Cover

Learning to predict accurately from a few data samples is a central challenge in modern data-hungry machine learning. On natural images, human vision typically outperforms deep learning approaches on few-shot learning. However, we hypothesize that aerial and satellite images are more challenging to the human eye. This applies particularly when the image resolution is comparatively low, as with the 10m ground sampling distance of Sentinel-2. In this study, we benchmark model-agnostic meta-learning (MAML) algorithms against human participants on few-shot land cover classification with Sentinel-2 imagery on the Sen12MS dataset. We find that categorization of land cover from globally distributed regions is a difficult task for the participants, who classified the given images less accurately than the MAML-trained model and with a highly variable success rate. This suggest that hand-labeling land cover directly on Sentinel-2 imagery is not optimal when tackling a new land cover classification problem. Labeling only a few images and employing a trained meta-learning model to this task may lead to more accurate and consistent solutions compared to hand labeling by multiple individuals.
Using Hierarchically Connected Nodes and Multiple GNN Message Passing Steps to Increase the Contextual Information in Cell-Graph Classification

Graphs are useful in analysing histopathological images as they are able to represent neighbourhood interactions and spatial relationships. Typically graph nodes represent cells and the vertices are constructed by applying a nearest neighbor algorithm to cell's locations. When passing these graphs through one graph neural network (GNN) message passing step, each node can only utilise features from nodes within its immediate neighbourhood to make a classification. To overcome this, we introduce two levels of hierarchically connected nodes that we term "supernodes". These supernodes, used in conjunction with at least four GNN message passing steps, allow for cell node classifications to be influenced by a wider area, enabling the entire graph to learn tissue-level structures. The method is evaluated on a supervised task to classify individual cells as belonging to a specific tissue class. Results demonstrate that the inclusion of supernodes with multiple GNN message passing steps increases model accuracy.
Multi-Scale Cascaded Generator for Music-driven Dance Synthesis

Dance is a creative performance art and must keep coherent with the rhythm and style of music. To address these issues, most of the existing music-driven dance synthesis methods utilize deep generative models and capture the dynamic characteristics of dance motions. However, we observe that dance motions contain big-scale body part movements and small-scale joint movements that are mutually coordinated and related, and the generated dance motions, particularly affected by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{1}$</tex> loss, are too restrictive and conservative. In this paper, we propose a multi-scale cascaded music-driven dance synthesis network (MC-MDSN) that first generates big-scale body motions conditioned on music and then further refines local small-scale joint motions. Furthermore, we design a multi-scale feature loss to capture the dynamic characteristics of each scale motions and the relations between different scale motion joints. Experimental results show that our method generates better dance motions than the baselines.
Two Case Studies on Guiding Human Behavior Using Virtualization Technologies in Distributed Virtual Shopping Malls

This paper reports two case studies that use virtualization technologies on guiding human behavior for supporting distributed virtual shopping malls. The first case study is to portray a virtual crowd in front of exhibitions and stores to guide human behavior by creating interest in the exhibitions and stores. The second case study is to investigate whether it is possible to change a customer's interest in a target store by displaying an alternative stores' advertisements. We investigate the opportunities to adopt virtualization technologies in our future real world digital services from these two case studies.
Area-Specific Convolutional Neural Networks for Single Image Super-Resolution

The implementation of deep convolutional neural networks (CNN) in single image super-resolution (SISR) has been successful at improving restoration quality. However, due to analysis made in previous works, it is observed that missing details in low-resolution (LR) images mostly exist in the high-frequency regions. Since CNN operates all regions of the low-resolution (LR) image equally, the computation redundancy is observed in the low-frequency area. We generate a gradient-based binary mask (decision mask) to discriminate the high-frequency areas from the low-frequency areas and apply two kinds of convolution to them separately. We propose an Area-Specific CNN (ASCNN) for super-resolution. It consists of high parameter convolutions and low parameter convolutions to process the high-frequency areas and low-frequency areas separately, which efficiently reduces the FLOPs (floating-point operation) while maintaining restoration quality. The settings for reduction are configurable and experimental results show that ASCNN achieves state-of-the-art performance with FLOPs reduction up to 40.1% / 37.0% / 34.0% for ×2/×3/×4 scale factors.
Designing Monitoring Dashboard based on ERP System to Support Sustainable Production

The leather tanning industry produces a large amount of waste in the production process that can pollute the environment if not handled properly. This research design a monitoring dashboard based on an ERP system to support sustainable production, so the leather tanning industry becomes an eco-friendly industry. The ERP system makes the production process more effective. The ERP-based monitoring dashboard displays all the information related to production, especially the aspects that impact the environment. So it will be easier to monitor and control the production process and reduce the environmental pollution potential.
A Light Causal Feature Selection Approach to High-Dimensional Data

Causal feature selection has received increasing attention in recent years. However, the state-of-the-art causal feature selection algorithms use the conditional independence tests, which require enumerating conditioning sets, leading to an exponential increase in computational complexity along with an increase in feature space. To address this problem, in this paper, we theoretically analyze the unique performance of causal features in mutual information, and propose a novel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> ausal <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u> eature <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> election algorithm using <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> utual <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u> nformation, called CFS-MI. Specifically, CFS-MI separately instantiates the pairwise comparison of mutual information in two stages to reduce computational complexity, and thus improves the efficiency on high-dimensional data. Extensive experiments on 5 benchmark Bayesian networks and 16 real-world datasets validate that CFS-MI has comparable accuracy compared to 7 state-of-the-art causal feature selection algorithms, while presenting more superior computational efficiency.
Dynamic Population Cooperative

Particle swarm optimization (PSO) has attracted wide attention in the recent decade. Although PSO is an efficient and simple evolutionary algorithm and has been successfully applied to solve optimization problems in many real-world fields, premature maturation and poor local search capability remain two critical issues for PSO. Therefore, to alleviate these disadvantages, a dynamic population cooperative particle swarm optimization for global optimization problems (DPCPSO) is proposed. Firstly, to enhance local search capability, an elite neighborhood learning strategy is constructed by leveraging information from elite particles. Meanwhile, to make the particle easily jump out of the local optimum, a crossover-mutation mechanism is utilized. Finally, a dynamic population partitioning mechanism is designed to balance exploration and exploitation capabilities. 16 classic benchmark functions and 1 real-world optimization problem are used to test the proposed algorithm against with 6 typical PSO algorithms. The experimental results show that DPCPSO is statistically and significantly better than the compared algorithms for most of the test problems. Moreover, the convergence speed and convergence accuracy of DPCPSO are also significantly improved. Therefore, the algorithm is highly competitive in solving global optimization problems.
Open-Vocabulary One-Stage Detection with Hierarchical Visual-Language Knowledge Distillation

Open- vocabulary object detection aims to detect novel object categories beyond the training set. The advanced open- vocabulary two-stage detectors employ instance-level visual-to- visual knowledge distillation to align the visual space of the detector with the semantic space of the Pre-trained Visual-Language Model (PVLM). However, in the more efficient one-stage detector, the absence of class-agnostic object proposals hinders the knowledge distil-lation on unseen objects, leading to severe performance degradation. In this paper, we propose a hierarchical visual-language knowledge distillation method, i.e., Hi-erKD, for open-vocabulary one-stage detection. Specifi-cally, a global-level knowledge distillation is explored to transfer the knowledge of unseen categories from the PVLM to the detector. Moreover, we combine the proposed global-level knowledge distillation and the common instance-level knowledge distillation to learn the knowledge of seen and unseen categories simultaneously. Extensive experiments on MS-COCO show that our method significantly surpasses the previous best one-stage detector with 11.9% and 6.7% AP50 gains under the zero-shot detection and generalized zero-shot detection settings, and reduces the AP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> performance gap from 14% to 7.3% compared to the best two-stage detector. Code will be released at this url <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://qithub.com/menqqiDyanqqe/HierKD.
A Gap Between Blockchain and General Data Protection Regulation: A Systematic Review

As a service platform, blockchain has faced compliance issues since the General Data Protection Regulation (GDPR) came into effect in May 2018. Although many technical solutions have been proposed to solve the compatibility issues between blockchain and the GDPR, unresolved challenges remain. This study presents the gaps between the blockchain and the GDPR and explores solutions to bridge the gap.We review 91 previously published articles using a systematic literature review methodology. Then, we answer the following research questions: 1) Which solutions have been explored to allow the blockchain to comply with the GDPR? 2) What are the research gaps in the blockchain compliance field? Finally, we present five research gaps in this field: 1) development of a consent ontology model; 2) development of a methodology for monitoring fairness in the blockchain; 3) resolution of the contradiction between auditing and obfuscation; 4) development of a methodology for tracking controllers in the blockchain; and 5) integration of the different-purposed technical solutions without conflicts. Our research can raise the compatibility level of the blockchain and GDPR and guide the company adopting a blockchain to comply with the GDPR. Furthermore, it can advise the regulator to embrace new technologies into the GDPR while protecting a blockchain’s nature.
Self-Attention Neural Bag-of-Features

In this work, we propose several attention formulations for multi-variate sequence data. We build on top of the recently introduced 2D-Attention and reformulate the attention learning methodology by quantifying the relevance of feature/temporal dimensions through latent spaces based on self-attention rather than learning them directly. In addition, we propose a joint feature-temporal attention mechanism that learns a joint 2D attention mask highlighting relevant information without treating feature and temporal representations independently. The proposed approaches can be used in various architectures and we specifically evaluate their application together with Neural Bag of Features feature extraction module. Experiments on several sequence data analysis tasks show the improved performance yielded by our approach compared to standard methods.
Attention mechanisms with UNet3+ on brain tumor MRI segmentation

Brain tumor is one of the most dangerous disease of human brain. Often treated by radiotherapy, chemotherapy and surgery, timely detection and diagnosis is crucial to patient survival and prognosis. To speed up diagnosis process and improve overall accuracy, researchers keep on finding new techniques that can be implemented in the field of medical image analysis. Recently, deep learning image segmentation models are widely used in brain tumor image analysis, and contests such as BraTS are held annually to facilitate research in this field. In this paper, three attention mechanisms, convolutional block attention module, attention gate and efficient channel attention were combined with full scaled skip connection Unet, and transfer learning from pre-trained Unet's effect was also explored.
Research on the Application and Risk Prevention of Metaverse in Vocational Education

The Metaverse is a new concept born with the rapid development of information technology. The state has begun to lay out the applications of the Metaverse in various fields. Education is the main application scenario and innovation channel of the Metaverse. The features of the Metaverse, such as immersion, constructiveness, interactivity, decentralization and openness, are highly compatible with the future educational form. The era of Metaverse needs vocational schools to cognize the Metaverse objectively and make use of the Metaverse technology to empower vocational education, to promote the reform of teaching methods in vocational education, and to solve the difficulties in vocational education. At the same time, we should calmly analyze the possible challenges of privacy risk, ethical risk, addiction risk, and consider the effective measures to prevent the risk from occurring, with a view to providing some reference and inspiration for the high-quality development of the vocational education.
Fuzzy Bat Algorithm for improved Clustering of Convolutional Neural Network Data

This article is expanded the technique for clustering the images from Convolutional Neural Network (CNN) with Bat Algorithm (BA). Varian images extract the features from CNN for identifying each image data. BA categorize those image data for improved clustering, assist with fuzzy logic systems. Computational result are comparative experiment results between CNN and Fuzzy BA, mean squared error and root mean squared error respectively.