Reviewerly/exHarmony huggingface dataset:
A Home for Principal Component Analysis (PCA) as part of a Multi-Agent Safety System (MASS) for Human-Robot Collaboration (HRC) within the Industry 5.0 Enterprise Architecture (EA)

Industry 5.0 is here, and human interaction experts claim that in the process of augmenting a high production/manufacturing workplace, a safety critical situation is created with the introduction of “Cobots”. A Multi-Agent Safety System (MASS) is presented as a solution in this paper which uses commercial, wearable technologies with high data sharing acceptance rates such as the Apple watch to collect and share real time ECG signals with the Cobot. Principal Component Analysis (PCA) is selected as a dimension reduction tool because it is well established and meets the requirements for reliability in the development of a human-centric, safety system. Five Machine Learning (ML) classifiers (KNN, NB, RF, DT and GBM) are used with binary classification to predict whether the human is Distracted (Event 1) or Not Distracted (Event 0) to determine if this will pose a safety risk to the Human Robot Collaboration (HRC) System. Decision Tree (DT) classifier with 4 Principal Components (PCs) is evaluated at 98% Accuracy and 99%AUC and is the recommended model for future development of the MASS. A road map is also presented to ensure the longevity of MASS while signifying the inclusion of real time data which can close the demographic data gap and help to improve the privacy, efficiency and contextual reliability of the MASS model in the Industry 5.0 workplace.
Attentive Reinforcement Learning for Scheduling Problem with Node Auto-scaling

The distributed system Ray has attracted much attention in decision-making applications, which could greatly accelerate the training efficiency for intelligent algorithms. Task scheduling is one of the critical technologies in Ray, in which the number of resource nodes could be auto-scaling, i.e., automatically increasing or decreasing according to the workload. The adopted scheduling strategy is simple, which leaves much space to be optimized. In this paper, we consider designing a reinforcement learning method to optimize the scheduling problem in Ray. We propose an attentive reinforcement learning method, designing an attention-based state encoder that could efficiently extract the system state in the situation of the varying number of resource nodes. At the same time, an action mask mechanism filters invalid actions. Further, to improve the learning efficiency in the environment with the varied number of nodes, we design a curriculum learning method, which trains the method by gradually increasing the number of nodes in the scheduling process. Finally, we use the real data generated by the Alibaba Cluster Trace Program to test in the simulation platform CloudSim. The experimental results show that the proposed method effectively scales down the completion time of tasks compared to the original algorithm in Ray.
Disentangled Facial Expressions Editing in Trained Latent Space

In recent years, Generative Adversarial Networks (GANs) have gained attention in image synthesis mapping from the latent space onto image space. Trained latent space carries the visual semantics for generated images. Past studies observed that arithmetic operation and linear interpolation in latent space could change the visible facial attributes, such as beards and glasses, in image space. In this work, the visual concepts in the latent space are observed, allowing to change the emotion attribute per facial expressions in the image space. We observed interpolation of a sample while disentangling the emotional attributes to edit the emotion-related facial expressions in the synthesized images. For the experiment, the Deep Convolution Generative Adversarial Networks (DCGANs) are utilized for image synthesis, and Extended Cohn Kanade (CK +) facial expression dataset is applied as the input. Our results showed that manipulating the latent space of the well-trained GANs can edit the emotional aspects of the image space. Moreover, editing facial expressions in the latent space is helpful for the recognition task to improve accuracy. Empirical results showed that the facial expressions classifier improved its performance in the recognition sadness class from 20% to 80% on the imbalance dataset.
Membership Inference Attack in Face of Data Transformations

Membership inference attacks (MIAs) on machine learning models, which try to infer whether a sample is in the training dataset of a target model, have been widely studied over recent years as data privacy attracts increasing attention. One unignorable problem in the current MIA threat model is that it assumes the attacker always obtains exactly the same samples as in the training set. In reality, however, the attacker is more likely to gather only a transformed version of the training samples. For instance, portraits downloadable from a social networking website usually are re-scaled and compressed, while the website owner can train models with RAW images. We believe a transformed training sample still causes privacy leakage if the transformation is semantic-preserving. Therefore, we broaden the concept of membership inference into more realistic scenarios by considering data transformations. We introduce two strategies for designing MIAs in face of data transformations: one adapts current MIAs to transformations, and the other tries to reverse the transformations approximately. We demonstrated the effectiveness of our strategies and the significance of considering data transformations by extensive evaluations of multiple datasets with several common data transformations and by comparisons with six state-of-the-art attacks. Moreover, we conduct evaluations on data-augmented and privacy-preserving models protected by three state-of-the-art defenses.
A Novel GAN based on Progressive Growing Transformer with Capsule Embedding

Generative Adversarial Networks (GANs) have achieved great improvement after using Convolutional Neural Networks (CNNs) instead of Multi-Layer Perceptrons (MLPs) to build network architecture. Recently, since Transformer architecture has performed well in compute vision, building a Transformer-based image generation network helps solve some of problems caused by CNNs e.g. CNNs-based GANs are difficult to train. On the other hand, the learning of positional encoding in the Transformer structure is often ignored in Transformer-based GANs. Capsule networks are usually considered to be able to learn position information in image features. Therefore, this paper constructs a Progressive Growing Transformer network with Capsule Embedding GAN (PGTCEGAN). The results from the proposed approach are promising with 3.59 FID and 3.92 FID on CelebA and LSUN-Church datasets respectively in image generation task.
Statistics Without Measurement

Measurement theory (scaling theory) severely constrains what statistical procedures can meaningfully tell us. Nonparametric statistical procedures employ nominal, ordinal, and (some) interval scaling procedures to provide actual meaningful information in the human sciences, whereas using parametric procedures based upon ratio scaling provides misleading results because such procedures add in much information that cannot actually be supported by the data. Better to use a less powerful procedure that does work than to use a powerful one commonly used on physical phenomena that cannot work with the available functional realm data.
A Distributed Ledger Technology Design using Hyperledger Fabric and a Clinical Trial Use Case

Industry continues to be challenged when attempting to share data among organizations, especially when the data comes from different database management systems (DBMS) and different DBMS schemas. Another concern is that privacy laws may require some types of data to be protected under local access policies. We describe a secure data sharing solution using Next-generation Database Access Control (NDAC) and the Data Block Matrix (DBM). A clinical trial data use case is discussed, as well as a description of a proof-of-concept implementation of the DBM using Hyperledger Fabric. The solution described allows data access where the data resides, rather than exchanging or being centrally stored. Additionally, it solves the conflict between conventional blockchain use and privacy regulations, by using a form of distributed ledger technology that meets ‘right to erasure’ requirements.
Neural Model for Generating Method Names from Combined Contexts

The names given to methods within a software system are critical to the success of both software development and maintenance. Meaningful and concise method names save developers both time and effort when attempting to understand and use the code. Our study focuses on learning concise and meaningful method names from word tokens found within the contexts of a method, including the method documentation, input parameters, return type, method body, and enclosing class. Combining the approaches of previous studies, we constructed both an RNN encoder-decoder model with attention as well as a Transformer model, each tested using different combinations of contextual information as input. Our experiments demonstrate that a model that uses all of the mentioned contexts will have a higher performance than a model that uses any subset of the contexts. Furthermore, we demonstrate that the Transformer model outperforms the RNN model in this scenario.
Generalized class polynomials

Abstract The Hilbert class polynomial has as roots the j -invariants of elliptic curves whose endomorphism ring is a given imaginary quadratic order. It can be used to compute elliptic curves over finite fields with a prescribed number of points. Since its coefficients are typically rather large, there has been continued interest in finding alternative modular functions whose corresponding class polynomials are smaller. Best known are Weber’s functions, which reduce the size by a factor of 72 for a positive density subset of imaginary quadratic discriminants. On the other hand, Bröker and Stevenhagen showed that no modular function will ever do better than a factor of 100.83. We introduce a generalization of class polynomials, with reduction factors that are not limited by the Bröker–Stevenhagen bound. We provide examples matching Weber’s reduction factor. For an infinite family of discriminants, their reduction factors surpass those of all previously known modular functions by a factor at least 2.
Multi-layered InterCriteria Analysis as a Digital Tool for Studying the Dependencies of Some Key Indicators of Mortality During the Pandemic in the European Union

Intelligence healthcare expert systems (HCISs) assist decision-makers diagnose accurately and treat patients’ diseases. In a pandemic, they can also, by identifying the causes of higher mortality, predict a higher number of hospitalizations in intensive care units. The theory of intuitionistic fuzzy sets (IFSs), which is one of the first extensions of fuzzy sets of Zadeh, analyzes uncertain data. Uncertainty in data determines the need for the introduction of intuitionistic fuzzy HCISs (IFHCISs) for digital transformation of these processes which analyze uncertain data. The main determinant of the effectiveness of IFHCISs for diagnosing and treatment of diseases, prediction of ICU admission of patients during the pandemic and determining the main factors for higher mortality in pandemics is the optimal multicriteria system. An intuitionistic fuzzy interpretation of the classical rank correlation analyzes under the form of intercriteria analysis (ICrA) is proposed for optimizing each evaluation system of criteria in IFHCISs, based on the theories of index matrices and IFSs. The purpose of the chapter is to develop a model for successful optimization of multicriteria systems embedded in IFHCISs by expanding three-dimensional ICrA to multilayer ICrA (3-D MLICrA). The current pandemic situation raises problems about the dependence of pandemy deaths on certain factors. These problems have to process large data sets related to Covid’s mortality, some of which differ in the various reports. The effectiveness of 3-D MLICrA approach for optimizing the criteria of the IFHCIS is also demonstrated by an application on a dataset of total deaths attributed to COVID-19 and 15 key indicators of the European Union countries determining this mortality, provided by European Centre for DPC for 2021.
Public Grievance Redressal for Urban e-Governance in Guntur, Andhra Pradesh

DIGIT- Digital Infrastructure for Governance Impact & Transformation, is a platform developed by the eGovernments Foundation (eGov Foundation) that aims to transform urban governance in India. So far, the organization has digitized many Urban Local Bodies (ULBs) from different states. Andhra Pradesh was one of the first states to standardize the municipal governance processes across all 110 ULBs through integration using DIGIT, in partnership with the Commissioner and Director of Municipal Administration (CDMA), Government of Andhra Pradesh. The state of Andhra Pradesh provides online service delivery through its integrated DIGIT applications, which are CDMA Website, and PuraSeva Mobile Application.
The Role of Statistics in Promoting Data Reusability and Research Transparency

The value of research data has grown as the emphasis on research transparency and data-intensive research has increased. Data sharing is now required by funders and publishers and is becoming a disciplinary expectation in many fields. However, practices promoting data reusability and research transparency are poorly understood, making it difficult for statisticians and other researchers to reframe study methods to facilitate data sharing. This article reviews the larger landscape of open research and describes contextual information that data reusers need to understand, evaluate, and appropriately analyze shared data. The article connects data reusability to statistical thinking by considering the impact of the type and quality of shared research artifacts on the capacity to reproduce or replicate studies and examining quality evaluation frameworks to understand the nature of data errors and how they can be mitigated prior to sharing. Actions statisticians can take to update their research approaches for their own and collaborative investigations are suggested.
ViLoN—a multi-layer network approach to data integration demonstrated for patient stratification

With more and more data being collected, modern network representations exploit the complementary nature of different data sources as well as similarities across patients. We here introduce the Variation of information fused Layers of Networks algorithm (ViLoN), a novel network-based approach for the integration of multiple molecular profiles. As a key innovation, it directly incorporates prior functional knowledge (KEGG, GO). In the constructed network of patients, patients are represented by networks of pathways, comprising genes that are linked by common functions and joint regulation in the disease. Patient stratification remains a key challenge both in the clinic and for research on disease mechanisms and treatments. We thus validated ViLoN for patient stratification on multiple data type combinations (gene expression, methylation, copy number), showing substantial improvements and consistently competitive performance for all. Notably, the incorporation of prior functional knowledge was critical for good results in the smaller cohorts (rectum adenocarcinoma: 90, esophageal carcinoma: 180), where alternative methods failed.
Fully automated cardiac MRI segmentation using dilated residual network

Cardiac ventricle segmentation from cine magnetic resonance imaging (CMRI) is a recognized modality for the noninvasive assessment of cardiovascular pathologies. Deep learning based algorithms achieved state-of-the-art result performance from CMRI cardiac ventricle segmentation. However, most approaches received less attention at the bottom layer of UNet, where main features are lost due to pixel degradation. To increase performance, it is important to handle the bottleneck layer of UNet properly. Considering this problem, we enhanced the performance of main features at the bottom layer of network.We developed a fully automatic pipeline for segmenting the right ventricle (RV), myocardium (MYO), and left ventricle (LV) by incorporating short-axis CMRI sequence images. We propose a dilated residual network (DRN) to capture the features at full resolution in the bottleneck of UNet. Thus, it significantly increases spatial and temporal information and maintains the localization accuracy. A data-augmentation technique is employed to avoid overfitting and class imbalance problems. Finally, output from each expanding path is added pixel-wise to improve the training response.We used and evaluated our proposed method on automatic cardiac diagnosis challenge (ACDC). The test set consists of 50 patient records. The overall dice similarity coefficient (DSC) we achieved for our model is 0.924 ± 0.03, 0.907 ± 0.01, and 0.949 ± 0.05 for RV, MYO, and LV, respectively. Similarly, we obtained hausdorff distance (HD) scores of 10.09 ± 0.01, 7.25 ± 0.05, and 6.86 ± 0.02 mm for RV, MYO, and LV, respectively. The results show superior performance and outperformed state-of-the-art methods in terms of accuracy and reached expert-level segmentation. Consequently, the overall DSC and HD result improved by 1.0% and 1.5%, respectively.We designed a dilated residual UNet (DRN) for cardiac ventricle segmentation using short-axis CMRI. Our method has the advantage of restoring and capturing spatial and temporal information by expanding the receptive field without degrading the image main features in the bottleneck of UNet. Our method is highly accurate and quick, taking 0.28 s on average to process 2D MR images. Also, the network was designed to work on predictions of individual MR images to segment the ventricular region, for which our model outperforms many state-of-the-art methods.
MBNet: Detecting Salient Object in Low-Light Scenes

Benefiting from the powerful features created by deep learning techniques, salient object detection has recently made significant progress. Compared with the task of detecting salient object in well-light images, the detection of salient object in low-light scenes requires not only acquiring the spatial visual saliency of images under low light conditions, but also accurately identifying the multi-scale objects of interest. Mountain Basin Network (MBNet) is proposed for salient object detection to discriminate the pixel-level saliency of low-light images. To further refine the object localization and pixel classification performance, the proposed model integrates a high-low feature aggregation module (HLFA) to synergize the information from a high level branch (named Bal-Net) and a low level branch (named Mol-Net) to fuse the global and local context, and the hierarchical supervision modules (HSM) is embedded to assist in obtaining accurate salient objects, especially the small ones. Furthermore, multi-supervised integration strategy is leveraged to optimize the structure and boundaries of salient objects. Meanwhile, to facilitate further research and evaluation of the visual saliency models, we construct a new low-light dataset, which includes 13 categories with a total of 1000 low-light images. The experimental results show that the proposed model has state-of-the-art low-light saliency detection performance compared with seven existing methods.
Cluster-aware Diversity Samples Mining for Unsupervised Person Re-Identification

Recently, state-of-the-art unsupervised re-ID methods train the neural network by calculating cluster-level or instance-level contrastive loss for learning discriminative features with unlabeled data. However, due to the divergence of the individual cluster, the previous methods did not fully utilize inherent feature of clustered samples for contrastive learning, which merged an unreliable instance into a wrong cluster by simply using cluster centroid or hard instance. To solve this issue, we propose a novel Cluster-aware Diversity Samples Mining (CDSM) framework based on the compactness and independence of individual cluster to generate diverse samples for updating memory dictionary of each cluster, so as to reduce the effect of noisy labels and improve the robustness of the model performance. Significantly, the proposed Cluster-aware Diversity Samples Mining method gradually creates more reliable clusters to generate more robust pseudo labels by refining the memory which is of central importance to our outstanding performance. Extensive experiments demonstrate that the proposed CDSM framework achieves performances of 85.6%, 73.7% and 31.0% in mAP on Market1501, DukeMTMC-reID and MSMT17, respectively. Code is available at https://github.com/colinzhaoxp/CDSM.
On Attacking Deep Image Quality Evaluator Via Spatial Transform

Adversarial examples fool the neural networks by adding slightly-perturbed noise to the original image, which barriers the usability of deep models. Most of the works focused on the adversarial attack on the classification task. We, in this work, attempt to develop an adversarial example generation method for attacking neural-based image quality assessment (IQA). Specifically, instead of employing conventional additive adversarial noise generation methods, we propose an image content deformation approach, avoiding the loss of adversarial noise after compression. The deformation component is designed as neural layers. The given image is firstly deformed and then undergoes compression; an existing IQA evaluates the compressed image. The deformation layers are trained by back-propagating the differences between the targeted IQA score and the originally-evaluated one. Experimental results demonstrate that the proposed method can produce compression-resistant adversarial images for image quality evaluators. The generated adversarial examples could effectively attack neural-based image quality evaluators with less distortion. The data and code of this work are available at https://github.com/luning409/Attack_IQA.
Motion Arc Analysis in Virtual Reality Environment

With the popularity of VR, the body interaction in VR has not received enough corresponding attention, and the interaction design that violates the law of the human body happens a lot. Therefore, we study the action interaction in virtual reality through motion arc, an important parameter of human action, to get the factors that affect the user’s body interaction in virtual reality. A within-subject experiment (n=18) was conducted, in which all participants played three VR games and responded to the post-game questionnaire. Through video recordings of the front and right side of them, 3D skeleton modeling was reconstructed by using OpenPose and the conversion relationship between the four coordinate systems under computer vision. After the results of operations such as skeleton standardization and motion segmentation, clustering algorithms are used to cluster similar users, and Spearman’s Rank Correlation Coefficient is used to study the influence of user characteristics on motion arc. Our results indicated that in an unconstrained game, the tutorial makes the motion arc increase, while in a constrained game, the change of motion arc is more complex. It is also found that the participants’ instructions learning has the greatest impact on the average motion arc, followed by individual factors (including age gender, etc.) and sports experience.