Reviewerly/exHarmony huggingface dataset:
Quantum processor-inspired machine learning in the biomedical sciences

Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex aberrant molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired in part by recent advances in physical quantum processors, we evaluated several unconventional machine learning (ML) strategies on actual human tumor data. Here we show for the first time the efficacy of multiple annealing-based ML algorithms for classification of high-dimensional, multi-omics human cancer data from the Cancer Genome Atlas. To assess algorithm performance, we compared these classifiers to a variety of standard ML methods. Our results indicate the feasibility of using annealing-based ML to provide competitive classification of human cancer types and associated molecular subtypes and superior performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing architectures in the biomedical sciences.
Generalized two-dimensional linear discriminant analysis with regularization

Recent advances show that two-dimensional linear discriminant analysis (2DLDA) is a successful matrix based dimensionality reduction method. However, 2DLDA may encounter the singularity issue theoretically, and also is sensitive to outliers. In this paper, a generalized Lp-norm 2DLDA framework with regularization for an arbitrary p>0 is proposed, named G2DLDA. There are mainly two contributions of G2DLDA: one is G2DLDA model uses an arbitrary Lp-norm to measure the between-class and within-class scatter, and hence a proper p can be selected to achieve robustness. The other one is that the introduced regularization term makes G2DLDA enjoy better generalization performance and avoid singularity. In addition, an effective learning algorithm is designed for G2LDA, which can be solved through a series of convex problems with closed-form solutions. Its convergence can be guaranteed theoretically when 1≤p≤2. Preliminary experimental results on three contaminated human face databases show the effectiveness of the proposed G2DLDA.
Cross-GCN: Enhancing Graph Convolutional Network with k-Order Feature Interactions

Graph Convolutional Network (GCN) is an emerging technique that performs learning and reasoning on graph data. It operates feature learning on the graph structure, through aggregating the features of the neighbor nodes to obtain the embedding of each target node. Owing to the strong representation power, recent research shows that GCN achieves state-of-the-art performance on several tasks such as recommendation and linked document classification. Despite its effectiveness, we argue that existing designs of GCN forgo modeling cross features, making GCN less effective for tasks or data where cross features are important. Although neural network can approximate any continuous function, including the multiplication operator for modeling feature crosses, it can be rather inefficient to do so (i.e., wasting many parameters at the risk of overfitting) if there is no explicit design. To this end, we design a new operator named Cross-feature Graph Convolution, which explicitly models the arbitrary-order cross features with complexity linear to feature dimension and order size. We term our proposed architecture as Cross-GCN, and conduct experiments on three graphs to validate its effectiveness. Extensive analysis validates the utility of explicitly modeling cross features in GCN, especially for feature learning at lower layers.
GNNMark: A Benchmark Suite to Characterize Graph Neural Network Training on GPUs

Graph Neural Networks (GNNs) have emerged as a promising class of Machine Learning algorithms to train on non-euclidean data. GNNs are widely used in recommender systems, drug discovery, text understanding, and traffic forecasting. Due to the energy efficiency and high-performance capabilities of GPUs, GPUs are a natural choice for accelerating the training of GNNs. Thus, we want to better understand the architectural and system-level implications of training GNNs on GPUs. Presently, there is no benchmark suite available designed to study GNN training workloads. In this work, we address this need by presenting GNNMark, a feature-rich benchmark suite that covers the diversity present in GNN training workloads, datasets, and GNN frameworks. Our benchmark suite consists of GNN workloads that utilize a variety of different graph-based data structures, including homogeneous graphs, dynamic graphs, and heterogeneous graphs commonly used in a number of application domains that we mentioned above. We use this benchmark suite to explore and characterize GNN training behavior on GPUs. We study a variety of aspects of GNN execution, including both compute and memory behavior, highlighting major bottlenecks observed during GNN training. At the system level, we study various aspects, including the scalability of training GNNs across a multi-GPU system, as well as the sparsity of data, encountered during training. The insights derived from our work can be leveraged by both hardware and software developers to improve both the hardware and software performance of GNN training on GPUs.
Towards Privacy-Preserved Aging in Place: A Systematic Review

Owing to progressive population aging, elderly people (aged 65 and above) face challenges in carrying out activities of daily living, while placement of the elderly in a care facility is expensive and mentally taxing for them. Thus, there is a need to develop their own homes into smart homes using new technologies. However, this raises concerns of privacy and data security for users since it can be handled remotely. Hence, with advancing technologies it is important to overcome this challenge using privacy-preserving and non-intrusive models. For this review, 235 articles were scanned from databases, out of which 31 articles pertaining to in-home technologies that assist the elderly in living independently were shortlisted for inclusion. They described the adoption of various methodologies like different sensor-based mechanisms, wearables, camera-based techniques, robots, and machine learning strategies to provide a safe and comfortable environment to the elderly. Recent innovations have rendered these technologies more unobtrusive and privacy-preserving with increasing use of environmental sensors and less use of cameras and other devices that may compromise the privacy of individuals. There is a need to develop a comprehensive system for smart homes which ensures patient safety, privacy, and data security; in addition, robots should be integrated with the existing sensor-based platforms to assist in carrying out daily activities and therapies as required.
Points of Interest recommendations: Methods, evaluation, and future directions

The emergence of Location-based social networks (LBSNs) in recent years has boosted improvements in Recommender Systems for a new and specific task: the recommendation of points-of-interest (POI). Despite all the blunt advances recently observed, the area still lacks an updated and consolidated view about the main limitations, common assumptions, and directions we are following as a community. Thus, this paper aims to provide an updated picture of POI recommendation, identifying relevant efforts, results, contributions, and limitations. In this sense, through a systematic mapping, we selected 74 relevant papers published in the last three years (2017, 2018, and 2019) in the main conferences and journals of the area. Most of these studies have focused on the general POI recommendation problem, although we still could identify a significant number of efforts addressing specific problems in the area, such as Time-Aware, Next POI, and In/Out-of-Town Recommendations. Also, we found user contexts, such as social network and geolocation, as recurring data types used to improve preference elicitation when exploring different methods (e.g., collaborative filtering, factorization, probabilistic, link-based, and hybrid methods). As major limitations, first, we identified that these studies prioritize accuracy over other quality dimensions, despite the consensus in the RS community that accuracy alone is not enough to assess the practical effectiveness of Recommender Systems. Further, we found a low intersection of metrics and datasets used to evaluate the proposed solutions, along with a large number of metrics used in a few distinct studies. These observations point out the potential damage to reproducibility and straightforward comparison of results in the area, motivating us to propose an extensible POI recommendation benchmark. Through this benchmark, we showed that when proper evaluations are carried out, by considering different datasets, metrics, and baselines, identifying which algorithm is the best one becomes non-trivial. Finally, we highlight as a promising future work the in-depth exploitation of textual data since just a few evaluated studies marginally use this rich data source.
WGNCS: A robust hybrid cross-version defect model via multi-objective optimization and deep enhanced feature representation

For a constantly-evolving software project with multiple releasing versions, Cross-Version Defect Prediction (CVDP) can identify the potential defect in the latter one by mining historical defect information from the prior releasing software versions. Unfortunately, the imbalanced class distribution and the complex intrinsic structure in software projects make it challenging to obtain suitable defect features and construct a predominant CVDP model. To address these challenges, we propose a robust hybrid CVDP model named WGNCS based on WGAN-GP (Wasserstein GAN with Gradient Penalty), multi-objective NSGA-III (Non-dominated Sorting Genetic Algorithm - III) algorithm and hybrid CNN-SVM (Convolutional Neural Network – Support Vector Machine) in this study, which has three main merits: (1) employ a powerful deep learning generative model – WGAN-GP to conduct data augmentation tasks, thereby achieving defect class balance and generating more training instances. (2) utilize the multi-objective NSGA-III algorithm to select the fewest representative training feature subset for the minimum error. (3) construct a single powerful defect predictor CNN-SVM by cascading a high-level deep semantic feature extractor (CNN) and a classifier (SVM) with the fixed non-linear Gaussian kernel function. We verify the CVDP performance of WGNCS on 32 cross-version pairs derived from 45 software project versions. The experimental results demonstrate that the WGNCS model can exhibit encouraging performance.
Video salient object detection using dual-stream spatiotemporal attention

Video salient object detection plays an important role in many exciting applications in different areas. However, the existing deep learning-based video salient object detection methods still struggle in scenes of large salient object variabilities and great background scene diversity between and within frames. In this paper, we propose a dual-stream spatiotemporal attention network (DSSANet) for saliency detection in videos. It creatively introduces a multiplex attention mechanism to effectively extract and fuse spatiotemporal features of video salient object over frames in the video, thereby improving saliency detection performance. The DSSANet consists of: (1) A context feature path leverages a novel attention-augmented convolutional LSTM to effectively model the long-range dependency of the great temporal variation in the salient object over frames. (2) A content feature path creatively leverages an attention-based 1D dilated convolution to effectively model the local pixel correlation structure of each pixel in the salient object and the surrounding objects. (3) A refinement fusion module fuses these two features from their paths and further refines the fused feature by an attention-based feature selection. By integrating these three parts, DSSANet accurately detects the salient object from the video. The extensive experiments are performed on four public datasets and demonstrate the effectiveness of DSSANet and the superiority to five state-of-the-art video salient object detection methods. • Dual-stream framework for learning video context and content features. • Integrating of multi-attention for learning different representation subspaces. • Attention-augmented convolutional LSTM for enriching the temporal learning.
Grouped feature importance and combined features effect plot

Abstract Interpretable machine learning has become a very active area of research due to the rising popularity of machine learning algorithms and their inherently challenging interpretability. Most work in this area has been focused on the interpretation of single features in a model. However, for researchers and practitioners, it is often equally important to quantify the importance or visualize the effect of feature groups. To address this research gap, we provide a comprehensive overview of how existing model-agnostic techniques can be defined for feature groups to assess the grouped feature importance, focusing on permutation-based, refitting, and Shapley-based methods. We also introduce an importance-based sequential procedure that identifies a stable and well-performing combination of features in the grouped feature space. Furthermore, we introduce the combined features effect plot, which is a technique to visualize the effect of a group of features based on a sparse, interpretable linear combination of features. We used simulation studies and real data examples to analyze, compare, and discuss these methods.
You Keep Using That Word

CSCW researchers have long inquired into the ways that identity informs, and is informed by, the design of technological systems. Gender is a regularly considered aspect of identity, with extensive work documenting and exploring gendered experiences and designs with the aim of addressing inequalities in, or through, design. Recent work has questioned the way that we conceptualise and "measure" gender, advocating more nuanced classificatory schemes to avoid silencing or obscuring trans and/or non-binary experiences. Building on and extending this research, our work examines how gender is conceptualised more broadly. Drawing from a range of theoretical perspectives in gender studies, feminist and postcolonial theory, we argue for the treatment of gender as "multiplicitous" when we conceptualise and interpret research, in order to avoid unintentionally perpetuating silencing and inequality even as we work to tackle it. Illustrating our argument with examples from both within and without CSCW, we suggest both new research directions for CSCW scholars inquiring into gender, and sensitising questions that scholars can use when constructing and evaluating studies.
Selective Region-based Photo Color Adjustment for Graphic Designs

When adding a photo onto a graphic design, professional graphic designers often adjust its colors based on some target colors obtained from the brand or product to make the entire design more memorable to audiences and establish a consistent brand identity. However, adjusting the colors of a photo in the context of a graphic design is a difficult task, with two major challenges: (1) Locality: The color is often adjusted locally to preserve the semantics and atmosphere of the original image; and (2) Naturalness: The modified region needs to be carefully chosen and recolored to obtain a semantically valid and visually natural result. To address these challenges, we propose a learning-based approach to photo color adjustment for graphic designs, which maps an input photo along with the target colors to a recolored result. Our method decomposes the color adjustment process into two successive stages: modifiable region selection and target color propagation. The first stage aims to solve the core, challenging problem of which local image region(s) should be adjusted, which requires not only a common sense of colors appearing in our visual world but also understanding of subtle visual design heuristics. To this end, we capitalize on both natural photos and graphic designs to train a region selection network, which detects the most likely regions to be adjusted to the target colors. The second stage trains a recoloring network to naturally propagate the target colors in the detected regions. Through extensive experiments and a user study, we demonstrate the effectiveness of our selective region-based photo recoloring framework.
Multiframe-to-Multiframe Network for Video Denoising

Most existing studies performed video denoising by using multiple adjacent noisy frames to recover one clean frame; however, despite achieving relatively good quality for each individual frame, these approaches may result in visual flickering when the denoised frames are considered in sequence. In this paper, instead of separately restoring each clean frame, we propose a multiframe-to-multiframe (MM) denoising scheme that simultaneously recovers multiple clean frames from consecutive noisy frames. The proposed MM denoising scheme uses a training strategy that optimizes the denoised video from both the spatial and temporal dimensions, enabling better temporal consistency in the denoised video. Furthermore, we present an MM network (MMNet), which adopts a spatiotemporal convolutional architecture that considers both the interframe similarity and single-frame characteristics. Benefiting from the underlying parallel mechanism of the MM denoising scheme, MMNet achieves a highly competitive denoising efficiency. Extensive analyses and experiments demonstrate that MMNet outperforms the state-of-the-art video denoising methods, yielding temporal consistency improvements of at least 13.3 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and running more than 2 times faster than the other methods.
Resisting membership inference attacks through knowledge distillation

Recently, membership inference attacks (MIAs) against machine learning models have been proposed. Using MIAs, adversaries can inference whether a data record is in the training set of the target model. Defense methods which use differential privacy mechanisms or adversarial training cannot handle the trade-off between privacy and utility well. Other methods based on knowledge transfer to improve model utility need public unlabeled data in the same distribution as private data, and this requirement may not be satisfied in some scenarios. To handle the trade-off between privacy and utility better, we propose two algorithms of deep learning, i.e., complementary knowledge distillation (CKD) and pseudo complementary knowledge distillation (PCKD). In CKD, the transfer data of knowledge distillation all come from the private training set, but their soft targets are generated from the teacher model which is trained using their complementary set. With similar idea, we propose PCKD which reduces the training set of each teacher model and uses model averaging to generate soft targets of transfer data. Because smaller training set leads to less utility, PCKD utilizes pre-training to improve the utility of teacher models. Experimental results on widely used datasets show that CKD and PCKD can both averagely decrease attack accuracy by nearly 25% with negligible utility loss. The training time of PCKD is nearly 40% lower than that of CKD. Compared with existing defense methods such as DMP, adversarial regularization, dropout, and DP-SGD, CKD and PCKD have great advantages on handling the trade-off between privacy and utility.
Machine Learning Approaches for Early Diagnosis and Prediction of Fetal Abnormalities

Fetal Health denotes the health and growth of the fetal and frequent contacts in the uterus of the pregnant women during pregnancy. Maximum pregnancy period complexities leads fetal to a severe difficulty which limits right growth that causes deficiency or death. Harmless pregnancy period by predicting the risk levels before the occasion of difficulties boost right fetal growth. Forecasting the fetal health and growth state from a set of pre-classified patterns knowledge is vital in developing a predictive classifier model using Machine Learning Algorithms.
Teaching in times of COVID-19: determinants of teachers' educational technology use

We conduct a large and nationwide survey among German teachers to investigate the determinants of teachers' adaption to an increased use of educational technology during the COVID-19 school closures. We find that higher levels of technical affinity and higher perceived learning effectiveness of distance teaching are positively associated with using at least one (new) educational technology solution while teachers' age and the digital infrastructure of the school have no predictive power.
Linkable Privacy-Preserving Scheme for Location-Based Services

Query content privacy and location privacy of the user and point of interest (POI) of the service provider are a serious concern for location-based services (LBS). The current LBS schemes either protect the query content privacy or location privacy. To protect query content of the user and POI of the server, several schemes have been proposed based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -anonymity, Oblivious transfer (OT), private information retrieval (PIR), and homomorphic encryption. To protect location privacy, the existing schemes either use cryptographic schemes or need a trusted third party (TTP) between the server and the user. A change of location requires re-execution of the core scheme, which causes communication and computational overhead. To address these problems, we propose a linkable location-based services (L2BS) scheme that uses Modified Linkable Spontaneous Anonymous Group (MLSAG) signature scheme and the OT scheme. The proposed MLSAG scheme provides privacy-preserving authentication with privacy preserving service linking. The OT protocol protects the query content of the user and the POI of the server. When a user requests for service and sends a query to the same server second time onwards, the L2BS scheme does not require re-execution of the OT. This, L2BSWOT scheme, decreases the communication and computation costs in addition to privacy-preserving linking of past and current queries from the same user. Results show that L2BS scheme with or without the need for OT execution (L2BSWOT scheme) outperforms other existing LBS schemes in terms of communication and computation costs while satisfying all privacy requirements of an LBS.