Reviewerly/exHarmony huggingface dataset:
Stability Analysis for Interval Type-2 Fuzzy Systems by Applying Homogenous Polynomially Membership Functions Dependent Matrices and Switching Technique

In this article, the homogenous polynomially membership functions dependent (HPMFD) matrices are used to study the interval type-2 Takagi-Sugeno fuzzy systems. First, some necessary notations of the HPMFD matrices are introduced. Next, based on these notations, the time derivative of the HPMFD matrices is discussed and a switching method is proposed to ensure that the time derivative of the HPMFD matrices is negative. Then, a HPMFD controller is designed and new stabilization conditions are obtained by using the HPMFD Lyapunov function. In the end, the simulations show that the method in this article is less conservative than the existing ones in the literatures.
'Yes, I comply!'

As science becomes increasingly data-intensive, the requirements for comprehensive Research Data Management (RDM) grow. This often overwhelms scientists, requiring more workload and training. The failure to conduct effective RDM leads to producing research artefacts that cannot be reproduced or reused. Past research placed high value on supporting data science workers, but focused mainly on data production, collection, processing, and sensemaking. In order to understand practices and needs of data science workers in relation to documentation, preservation, sharing, and reuse, we conducted a cross-domain study with 15 scientists and data managers from diverse scientific domains. We identified five core concepts which describe requirements, drivers, and boundaries in the development of commitment for RDM, essential for generating reproducible research artefacts: Practice, Adoption, Barriers, Education, and Impact. Based on those concepts, we introduce a stage-based model of personal RDM commitment evolution. The model can be used to drive the design of future systems that support a transition to open science. We discuss infrastructure, policies, and motivations involved at the stages and transitions in the model. Our work supports designers in understanding the constraints and challenges involved in designing for reproducibility in an age of data-driven science.
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification

Graph classification aims to extract accurate information from graph-structured data for classification and is becoming more and more important in the graph learning community. Although Graph Neural Networks (GNNs) have been successfully applied to graph classification tasks, most of them overlook the scarcity of labeled graph data in many applications. For example, in bioinformatics, obtaining protein graph labels usually needs laborious experiments. Recently, few-shot learning has been explored to alleviate this problem with only a few labeled graph samples of test classes. The shared sub-structures between training classes and test classes are essential in the few-shot graph classification. Existing methods assume that the test classes belong to the same set of super-classes clustered from training classes. However, according to our observations, the label spaces of training classes and test classes usually do not overlap in a real-world scenario. As a result, the existing methods don't well capture the local structures of unseen test classes. To overcome the limitation, in this paper, we propose a direct method to capture the sub-structures with a well initialized meta-learner within a few adaptation steps. More specifically, (1) we propose a novel framework consisting of a graph meta-learner, which uses GNNs based modules for fast adaptation on graph data, and a step controller for the robustness and generalization of meta-learner; (2) we provide quantitative analysis for the framework and give a graph-dependent upper bound of the generalization error based on our framework; (3) the extensive experiments on real-world datasets demonstrate that our framework gets state-of-the-art results on several few-shot graph classification tasks compared to baselines.
Coded Computing for Resilient, Secure, and Privacy-Preserving Distributed Matrix Multiplication

Coded computing is a new framework to address fundamental issues in large scale distributed computing, by injecting structured randomness and redundancy. We first provide an overview of coded computing and summarize some recent advances. Then we focus on distributed matrix multiplication and consider a common scenario where each worker is assigned a fraction of the multiplication task. In particular, by partitioning two input matrices into m-by-p and p-by-n subblocks, a single multiplication task can be viewed as computing linear combinations of pmn submatrix products, which can be assigned to pmn workers. Such block-partitioning-based designs have been widely studied under the topics of secure, private, and batch computation, where the state of the arts all require computing at least “cubic” (pmn) number of submatrix multiplications. Entangled polynomial codes, first presented for straggler mitigation, provides a powerful method for breaking the cubic barrier. It achieves a subcubic recovery threshold, i.e., recovering the final product from any subset of multiplication results with a size order-wise smaller than pmn. We show that entangled polynomial codes can be further extended to also include these three important settings, providing unified frameworks that order-wise reduce the total computational costs by achieving subcubic recovery thresholds.
EthReview: An Ethereum-based Product Review System for Mitigating Rating Frauds

On an e-commerce platform, a buyer's decision about the purchase of products is highly dependent on the existing reviews of that product. These reviews provide a judgment metric about the quality of the products and the credibility of the sellers. Popular e-commerce platforms (like eBay, Amazon, etc.) receive and store a lot of customer reviews regularly. However, these e-commerce platforms are prone to various kinds of rating frauds (intended towards bad-mouthing and ballot-stuffing) carried by the fake buyers and fraudulent sellers to promote or demote certain products. Moreover, these e-commerce platforms are under the control of a single authority and are prone to tampering of the reviews by the centralized authority itself to increase product sales. These fake and tampered reviews lead towards bad online buying experience and a lack of trust in e-commerce platforms. In this paper, we propose a decentralized Ethereum based product review system, EthReview, which maintains the integrity of product reviews and is resilient against rating frauds. Our solution proposes a consortium network of randomized peer-to-peer (P2P) endorser nodes for the validation and verification of the truthfulness of product reviews. The honest reviewers are given incentives in the form of discount tokens for encouraging honest behavior in the system while the fraudulent behavior is penalized. We also provide security and performance analysis of EthReview to evaluate its robustness and resilience against different forms of rating fraud.
Refining Microservices Placement Employing Workload Profiling Over Multiple Kubernetes Clusters

As cloud-native computing is becoming the de-facto paradigm in the cloud field, Microservices Architecture has attracted attention from industries and researchers for agility and efficiency.Moreover, with the popularity of the IoT in the context of edge computing, cloud-native applications that utilize geographically-distributed multiple resources are emerging.In line with this trend, there is an increasing demand for microservices placement that selectively use optimal resources.However, optimal microservices placement is a significant challenge because microservices are dynamic and complex, depending on diversified workloads.Besides, generalizing workloads' characteristics consisting of complex microservices is realistically challenging.Thus, microservices deployment with mathematically structured algorithms based on simulation is less practical.As an alternative, a microservices placement framework is required that can reflect the characteristics of workloads derived from empirical profiling.Therefore, in this research work, we propose a refinement framework for profiling-based microservices placement to identify and respond to workload characteristics in a practical way.To achieve this goal, we perform profiling experiments with selected workloads to derive delicate resource requirements.Then, we perform microservices placement with a greedy-based heuristic algorithm that considers application performance by using resource requirements derived from the profiled results.Finally, we verify the proposed concept by comparing the experimental results that use our work and those that don't.
Sequential Recommender via Time-aware Attentive Memory Network

Recommendation systems aim to assist users to discover most preferred contents from an ever-growing corpus of items. Although recommenders have been greatly improved by deep learning, they still face several challenges: (1) Behaviors are much more com- plex than words in sentences, so traditional attentive and recurrent models have limitations capturing the temporal dynamics of user preferences. (2) The preferences of users are multiple and evolving, so it is difficult to integrate long-term memory and short-term intent.
Relative Position and Posture Detection of Hydraulic Support Based on Particle Swarm Optimization

The position and posture of the hydraulic support seriously affect the efficiency of coal mining and the safety of coal production. However, most of the current detection technologies have poor reliability, and the detection methods are difficult to adapt to the complex environment of coal mines. To effectively monitor the hydraulic support, and thereby improve the efficiency of coal mining, we propose a new method to detect the relative position and posture of the hydraulic support. The method is based on the mathematical idea that three points can determine a plane. Firstly, we use angle sensors and displacement sensors to build the detection device, use STM32 microprocessor to collect data, and realize real-time display of the data on the PC. Then, we conduct a single point detection experiment and a plane moving experiment, and combined the particle swarm optimization (PSO) algorithm to optimize the data. Experiment results show that the relative error of the single-point detection can reach 0.53%. Thirdly, we carry out a detection experiment of three points on the plane. Experiment results show that the detection accuracy of the two planes can reach 0.2°. Finally, to test the monitoring effect of the detection device on the hydraulic support, we carry out the relative position and posture detection experiment of the canopy. The experiment results show that the device can effectively detect the posture change of the canopy when the hydraulic support are moving. The method we use is contact measurement, which has high reliability and strong stability. The research on the relative position and posture detection of hydraulic support provides a reliable method to monitor the support working status. It lays the foundation for the intelligent control of the mining working face straightness and the perception of the support posture.
A multimodal approach for multi-label movie genre classification

Movie genre classification is a challenging task that has increasingly attracted the attention of researchers. The number of movie consumers interested in taking advantage of automatic movie genre classification is overgrowing, thanks to media streaming service providers' popularization. In this paper, we addressed the multi-label classification of movie genres in a multimodal way. To this end, we created a dataset composed of trailer video clips, subtitles, synopses, and movie posters from 152,622 movie titles of the Movie Database (TMDb). Such a large dataset was carefully curated, organized, and made available as a contribution of this work. We labeled each movie of the dataset according to a set of eighteen genre labels. In the experimental evaluation performed in this paper, we computed different kinds of descriptors, such as Mel Frequency Cepstral Coefficients (MFCCs), Statistical Spectrum Descriptor (SSD), Local Binary Pattern (LBP) from spectrograms, Long-Short Term Memory (LSTM), and Convolutional Neural Networks (CNN). With these descriptors, we trained different monolithic classifiers using BinaryRelevance and ML-kNN techniques. Besides, we also explored the combination of classifiers/features using a late fusion strategy. The fusion of a LSTM trained on synopses and another LSTM trained on the movie subtitles provided our best results in F-Score (0.674) and AUC-PR (0.725) metrics. These results corroborate the existence of complementarity among classifiers trained on different sources of information in this field of application. As far as we know, this is the most comprehensive study developed in terms of diversity of multimedia sources of information to perform movie genre classification.
Dynamic-Hand-Gesture Authentication Dataset and Benchmark

In recent years, biometrics have received considerable attention for its reliability and usability. Dynamic-hand-gesture is one of the representative biometric modalities, with advantages of safety and template-replaceability, has huge potential value. However, due to the lack of large-scale dataset and comprehensive evaluation methods, few researches are intended to study the dynamic-hand-gesture authentication method. In this article, we introduce a new dataset SCUT-DHGA, which is the first large-scale Dynamic-Hand-Gestures-Authentication dataset. SCUT-DHGA contains 29,160 dynamic-hand-gesture video sequences and more than 1.86 million frames for both color and depth modalities acquired from 193 volunteers. Six kinds of dynamic-hand-gestures are carefully designed for researching two types of authentication tasks: gesture-predefined authentication and gesture-free authentication. To investigate the hypothesis that users' gestures would be variant after time-span, which will degrade the performance of a dynamic-hand-gesture authentication system, two separate sessions' data were acquired from 50 volunteers with an average interval of one week. Beside the SCUT-DHGA dataset, we also benchmark this dataset with our proposed DHGA-net. By releasing such a large-scale dataset and benchmark, we expect dynamic-hand-gesture authentication methods to gain further improvement and generalization.
Collective Organizing and Social Responsibility at CSCW

The CSCW community has long discussed the ethics and politics of sociotechnical systems and how they become embedded in society and public policy. In light of the Black Lives Matter protests and Hong Kong protests, technologies such as facial recognition and contact tracing have re-invigorated conversations about the ethical and social responsibility of tech corporations, tech workers, and academics in science and technology. The goal of this workshop is to move beyond a call for the usual suspects of participatory design and human-centered design by committing to concrete steps to transform society through advocacy and activism.
Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior

Uncertainty quantification in inverse medical imaging tasks with deep learning has received little attention. However, deep models trained on large data sets tend to hallucinate and create artifacts in the reconstructed output that are not anatomically present. We use a randomly initialized convolutional network as parameterization of the reconstructed image and perform gradient descent to match the observation, which is known as deep image prior. In this case, the reconstruction does not suffer from hallucinations as no prior training is performed. We extend this to a Bayesian approach with Monte Carlo dropout to quantify both aleatoric and epistemic uncertainty. The presented method is evaluated on the task of denoising different medical imaging modalities. The experimental results show that our approach yields well-calibrated uncertainty. That is, the predictive uncertainty correlates with the predictive error. This allows for reliable uncertainty estimates and can tackle the problem of hallucinations and artifacts in inverse medical imaging tasks.
One-Shot Unsupervised Cross-Domain Detection

Despite impressive progress in object detection over the last years, it is still an open challenge to reliably detect objects across visual domains. All current approaches access a sizable amount of target data at training time. This is a heavy assumption, as often it is not possible to anticipate the domain where a detector will be used, nor to access it in advance for data acquisition. Consider for instance the task of monitoring image feeds from social media: as every image is uploaded by a different user it belongs to a different target domain that is impossible to foresee during training. Our work addresses this setting, presenting an object detection algorithm able to perform unsupervised adaptation across domains by using only one target sample, seen at test time. We introduce a multi-task architecture that one-shot adapts to any incoming sample by iteratively solving a self-supervised task on it. We further enhance this auxiliary adaptation with cross-task pseudo-labeling. A thorough benchmark analysis against the most recent cross-domain detection methods and a detailed ablation study show the advantage of our approach.
Guided Semantic Flow

Establishing dense semantic correspondences requires dealing with large geometric variations caused by the unconstrained setting of images. To address such severe matching ambiguities, we introduce a novel approach, called guided semantic flow, based on the key insight that sparse yet reliable matches can effectively capture non-rigid geometric variations, and these confident matches can guide adjacent pixels to have similar solution spaces, reducing the matching ambiguities significantly. We realize this idea with learning-based selection of confident matches from an initial set of all pairwise matching scores and their propagation by a new differentiable upsampling layer based on moving least square concept. We take advantage of the guidance from reliable matches to refine the matching hypotheses through Gaussian parametric model in the subsequent matching pipeline. With the proposed method, state-of-the-art performance is attained on several standard benchmarks for semantic correspondence.
Ultrafast and Accurate Temperature Extraction via Kernel Extreme Learning Machine for BOTDA Sensors

Brillouin optical time-domain analyzer (BOTDA) is used to monitor the temperature and strain along a fiber. So far, neural network and machine learning methods have been successfully applied for temperature extraction. But for different frequency scanning steps, different networks should be designed and trained. Here, a BOTDA assisted by kernel extreme learning machine (K-ELM) with high generalization is proposed and experimentally demonstrated. By utilizing K-ELM, the raw Brillouin gain spectra measured from BOTDA system are classified into different temperature classes. The performance of K-ELM is investigated both in simulation and experiment under different cases of signal-to-noise ratios, pump pulse widths, and frequency scanning steps. Compared with curve fitting methods, the K-ELM algorithm has better measurement accuracy of 0.3 °C and it realizes great improvement of the processing speed over 120 times. The ultrafast processing speed, high accuracy and generality make K-ELM become a highly competitive candidate for the high-speed BOTDA sensing system.
Towards image-based cancer cell lines authentication using deep neural networks

Abstract Although short tandem repeat (STR) analysis is available as a reliable method for the determination of the genetic origin of cell lines, the occurrence of misauthenticated cell lines remains an important issue. Reasons include the cost, effort and time associated with STR analysis. Moreover, there are currently no methods for the discrimination between isogenic cell lines (cell lines of the same genetic origin, e.g. different cell lines derived from the same organism, clonal sublines, sublines adapted to grow under certain conditions). Hence, additional complementary, ideally low-cost and low-effort methods are required that enable (1) the monitoring of cell line identity as part of the daily laboratory routine and 2) the authentication of isogenic cell lines. In this research, we automate the process of cell line identification by image-based analysis using deep convolutional neural networks. Two different convolutional neural networks models (MobileNet and InceptionResNet V2) were trained to automatically identify four parental cancer cell line (COLO 704, EFO-21, EFO-27 and UKF-NB-3) and their sublines adapted to the anti-cancer drugs cisplatin (COLO-704 r CDDP 1000 , EFO-21 r CDDP 2000 , EFO-27 r CDDP 2000 ) or oxaliplatin (UKF-NB-3 r OXALI 2000 ), hence resulting in an eight-class problem. Our best performing model, InceptionResNet V2, achieved an average of 0.91 F1-score on tenfold cross validation with an average area under the curve (AUC) of 0.95, for the 8-class problem. Our best model also achieved an average F1-score of 0.94 and 0.96 on the authentication through a classification process of the four parental cell lines and the respective drug-adapted cells, respectively, on a four-class problem separately. These findings provide the basis for further development of the application of deep learning for the automation of cell line authentication into a readily available easy-to-use methodology that enables routine monitoring of the identity of cell lines including isogenic cell lines. It should be noted that, this is just a proof of principal that, images can also be used as a method for authentication of cancer cell lines and not a replacement for the STR method.
A Quest for Co-Located Mixed Reality: Aligning and Assessing SLAM Tracking for Same-Space Multi-User Experiences

Current solutions for creating co-located Mixed Reality (MR) experiences typically rely on platform-specific synchronisation of spatial anchors or Simultaneous Localisation and Mapping (SLAM) data across clients, often coupled to cloud services. This introduces significant costs (in development and deployment), constraints (with interoperability across platforms often limited), and privacy concerns. For practitioners, support is needed for creating platform-agnostic co-located MR experiences. This paper explores the utility of aligned SLAM solutions by 1) surveying approaches toward aligning disparate device coordinate spaces, formalizing their theoretical accuracy and limitations; 2) providing skeleton implementations for audience-based, small-scale and large-scale co-location using said alignment approaches; and 3) detailing how we can assess the accuracy and safety of 6DoF/SLAM tracking solutions for any arbitrary device and dynamic environment without the need for an expensive ground truth optical tracking, by using trilateration and a $30 laser distance meter. Through this, we hope to further democratise the creation of cross-platform co-located MR experiences.