Reviewerly/exHarmony huggingface dataset:
Practical multi-party private collaborative k-means clustering

k-means clustering is widely used in many fields such as data mining, machine learning, and information retrieval. In many cases, users need to cooperate to perform k-means clustering tasks. How to perform clustering without revealing privacy has become a hot research topic. However, the existing k-means scheme based on secure multi-party computation cannot effectively protect the privacy of the output results. The multi-party k-means scheme based on differential privacy may lead to loss of data availability. In this article, we propose a practical protocol for k-means clustering in a collaborative manner, while protecting the privacy of each data record. Our protocol is the first to combine secure multi-party computing and differential privacy technology to train a privacy-preserving k-means clustering model. We design a novel algorithm, which is suitable for multi-party collaboration to update cluster centers without leaking data privacy. The algorithm guarantees that noise is added only once in each iteration, regardless of the number of participants. The protocol achieve the ”best of both worlds”, which can simultaneously achieves both the input privacy and the output privacy in the k-means clustering scheme. Evaluation of real data sets shows that our scheme has comparable running time compared with the k-means clustering scheme without privacy protection.
Domain-Invariant Disentangled Network for Generalizable Object Detection

We address the problem of domain generalizable object detection, which aims to learn a domain-invariant detector from multiple "seen" domains so that it can generalize well to other "unseen" domains. The generalization ability is crucial in practical scenarios especially when it is difficult to collect data. Compared to image classification, domain generalization in object detection has seldom been explored with more challenges brought by domain gaps on both image and instance levels. In this paper, we propose a novel generalizable object detection model, termed Domain-Invariant Disentangled Network (DIDN). In contrast to directly aligning multiple sources, we integrate a disentangled network into Faster R-CNN. By disentangling representations on both image and instance levels, DIDN is able to learn domain-invariant representations that are suitable for generalized object detection. Furthermore, we design a cross-level representation reconstruction to complement this two-level disentanglement so that informative object representations could be preserved. Extensive experiments are conducted on five benchmark datasets and the results demonstrate that our model achieves state-of-the-art performances on domain generalization for object detection.
Keypoints-Based Deep Feature Fusion for Cooperative Vehicle Detection of Autonomous Driving

Sharing collective perception messages (CPM) between vehicles is investigated to decrease occlusions so as to improve the perception accuracy and safety of autonomous driving. However, highly accurate data sharing and low communication overhead is a big challenge for collective perception, especially when real-time communication is required among connected and automated vehicles. In this letter, we propose an efficient and effective keypoints-based deep feature fusion framework built on the 3D object detector PV-RCNN, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fusion</i> PV-RCNN ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FPV-RCNN</b> for short), for collective perception. We introduce a high-performance bounding box proposal matching module and a keypoints selection strategy to compress the CPM size and solve the multi-vehicle data fusion problem. Besides, we also propose an effective localization error correction module based on the maximum consensus principle to increase the robustness of the data fusion. Compared to a bird’s-eye view (BEV) keypoints feature fusion, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FPV-RCNN</b> achieves improved detection accuracy by about 9% at a high evaluation criterion (IoU 0.7) on the synthetic dataset COMAP dedicated to collective perception. In addition, its performance is comparable to two raw data fusion baselines that have no data loss in sharing. Moreover, our method also significantly decreases the CPM size to less than 0.3 KB, and is thus about 50 times smaller than the BEV feature map sharing used in previous works. Even with further decreased CPM feature channels, i. e., from 128 to 32, the detection performance does not show apparent drops. The code of our method is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/YuanYunshuang/FPV_RCNN</uri> .
Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation

Deep Learning has revolutionized our ability to solve complex problems such as Vision-and-Language Navigation (VLN). This task requires the agent to navigate to a goal purely based on visual sensory inputs given natural language instructions. However, prior works formulate the problem as a navigation graph with a discrete action space. In this work, we lift the agent off the navigation graph and propose a more complex VLN setting in continuous 3D reconstructed environments. Our proposed setting, Robo-VLN, more closely mimics the challenges of real world navigation. Robo-VLN tasks have longer trajectory lengths, continuous action spaces, and challenges such as obstacles. We provide a suite of baselines inspired by state-of-the-art works in discrete VLN and show that they are less effective at this task. We further propose that decomposing the task into specialized high- and low-level policies can more effectively tackle this task. With extensive experiments, we show that by using layered decision making, modularized training, and decoupling reasoning and imitation, our proposed Hierarchical Cross-Modal (HCM) agent outperforms existing baselines in all key metrics and sets a new benchmark for Robo-VLN.
"How advertiser-friendly is my video?": YouTuber's Socioeconomic Interactions with Algorithmic Content Moderation

To manage user-generated harmful video content, YouTube relies on AI algorithms (e.g., machine learning) in content moderation and follows a retributive justice logic to punish convicted YouTubers through demonetization, a penalty that limits or deprives them of advertisements (ads), reducing their future ad income. Moderation research is burgeoning in CSCW, but relatively little attention has been paid to the socioeconomic implications of YouTube's algorithmic moderation. Drawing from the lens of algorithmic labor, we describe how algorithmic moderation shapes YouTubers' labor conditions through algorithmic opacity and precarity. YouTubers coped with such challenges from algorithmic moderation by sharing and applying practical knowledge they learned about moderation algorithms. By analyzing video content creation as algorithmic labor, we unpack the socioeconomic implications of algorithmic moderation and point to necessary post-punishment support as a form of restorative justice. Lastly, we put forward design considerations for algorithmic moderation systems.
Unispeech-Sat: Universal Speech Representation Learning With Speaker Aware Pre-Training

Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled data and avoids extensive human labeling. Recent years have witnessed great successes in applying self-supervised learning in speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are introduced for enhancing the unsupervised speaker information extraction. First, we apply multi-task learning to the current SSL framework, where we integrate utterance-wise contrastive loss with the SSL objective function. Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where additional overlapped utterances are created unsupervisely and incorporated during training. We integrate the proposed methods into the HuBERT framework. Experiment results on the SUPERB benchmark show that the proposed system achieves state-of-the-art performance in universal representation learning, especially for speaker identification oriented tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up the training dataset to 94 thousand hours of public audio data and achieve further performance improvement in all SUPERB tasks.
IrradianceNet: Spatiotemporal deep learning model for satellite-derived solar irradiance short-term forecasting

The presence of clouds is widely identified as the primary uncertainty in current surface solar global horizontal irradiance (GHI) forecasts. Despite a wealth of historical satellite-derived irradiance observations, only limited research has investigated this problem from a purely data-driven perspective, something that has seen tremendous success in related domains such as radar- and satellite-based precipitation short-term forecasting. This paper presents IrradianceNet, a novel satellite-based neural network for spatiotemporal forecasting of surface solar irradiance up to 4 h in the future over Europe. Our method is fully data-driven and needs no post-processing or calibration based on sparse ground-based measurements of irradiance. We demonstrate superior forecasting performance compared to several persistence models, the TV-L1 algorithm, and ERA5 reanalysis data for satellite-derived solar irradiance using the European SARAH-2.1 dataset. We also validate these results using ground-based pyranometer observations from the Baseline Surface Radiation Network. Our conclusions remain unchanged when we account for hourly and monthly seasonality. Finally, applying a simple cloud mask scheme, we demonstrate that our performance improvement arises due to a considerable reduction in cloudy pixel errors. This is initial evidence that purely data-driven methods might better approximate and infer future cloud dynamics and their impact on surface solar irradiance.
FedScale

research-article Share on FedScale: Benchmarking Model and System Performance of Federated Learning Authors: Fan Lai University of Michigan University of MichiganView Profile , Yinwei Dai University of Michigan University of MichiganView Profile , Xiangfeng Zhu University of Michigan University of MichiganView Profile , Harsha V. Madhyastha University of Michigan University of MichiganView Profile , Mosharaf Chowdhury University of Michigan University of MichiganView Profile Authors Info & Claims ResilientFL '21: Proceedings of the First Workshop on Systems Challenges in Reliable and Secure Federated LearningOctober 2021 Pages 1–3https://doi.org/10.1145/3477114.3488760Online:25 October 2021Publication History 0citation252DownloadsMetricsTotal Citations0Total Downloads252Last 12 Months252Last 6 weeks40 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
ICT adoption, innovation, and SMEs’ access to finance

This study examines the association between ICT adoption, innovation, and SMEs' access to finance by exploiting the World Bank Enterprise Survey (WBES) data of 171,000 SMEs from 149 countries between 2006 and 2020. We decompose SMEs' access to finance into two components, i.e., basic and advanced access to finance. Basic access to finance is captured from two indicators; namely, the firm's bank account and line of credit from a financial institution, while advanced access is captured by whether the firm's investments and working capital is financed by the bank. Consistent with the information asymmetry hypothesis, our findings confirm that ICT adoption reduces information asymmetry between SMEs and banks. Therefore, banks are more likely to grant credit and to finance projects and the working capital needs of innovative SMEs. Moreover, this study complements the argument of resource-based theory (RBT) and suggests that innovation can provide a competitive advantage with regard to a firm's access to credit. These results imply that SMEs with greater access to and use of new technologies are more likely to acquire financial resources from banks. The results hold to various robustness checks and identification strategies. Our findings provide important policy implications for SMEs around the globe.
Effect of customer's perception on service robot acceptance

Abstract A service robot is a physical entity integrated with information technology (IT) which can autonomously provide customized services to people. While the use of robots is regarded as one of the most important trends in service marketing, customer acceptance is still a major barrier to their application in service scenarios. This study examines robot characteristics (anthropomorphism, autonomy) and customer characteristics (role clarity, ability) as antecedents affecting customer acceptance of service robots from the perspective of service encounters. Subsequently, the study develops a conceptual model based on the technology acceptance model (TAM). A sample of 406 respondents from an online survey in China was used to test the proposed conceptual model. The results show that anthropomorphism, autonomy, and ability are positively related to perceived usefulness, while autonomy, ability, and role clarity are positively related to perceived ease of use. Both perceived usefulness and perceived ease of use are significant antecedents of customer attitude. Customer attitude determines customer acceptance of service robots in service encounters. Our findings have implications for both researchers and practitioners.
Multi-view Clustering via Deep Matrix Factorization and Partition Alignment

Multi-view clustering (MVC) has been extensively studied to collect multiple source information in recent years. One typical type of MVC methods is based on matrix factorization to effectively perform dimension reduction and clustering. However, the existing approaches can be further improved with following considerations: i) The current one-layer matrix factorization framework cannot fully exploit the useful data representations. ii) Most algorithms only focus on the shared information while ignore the view-specific structure leading to suboptimal solutions. iii) The partition level information has not been utilized in existing work. To solve the above issues, we propose a novel multi-view clustering algorithm via deep matrix decomposition and partition alignment. To be specific, the partition representations of each view are obtained through deep matrix decomposition, and then are jointly utilized with the optimal partition representation for fusing multi-view information. Finally, an alternating optimization algorithm is developed to solve the optimization problem with proven convergence. The comprehensive experimental results conducted on six benchmark multi-view datasets clearly demonstrates the effectiveness of the proposed algorithm against the SOTA methods. The code address for this algorithm is https://github.com/ZCtalk/MVC-DMF-PA.
An Exploration of Self-Supervised Pretrained Representations for End-to-End Speech Recognition

Self-supervised pretraining on speech data has achieved a lot of progress. High-fidelity representation of the speech signal is learned from a lot of untranscribed data and shows promising performance. Recently, there are several works focusing on evaluating the quality of self-supervised pretrained representations on various tasks with-out domain restriction, e.g. SUPERB. However, such evaluations do not provide a comprehensive comparison among many ASR benchmark corpora. In this paper, we focus on the general applications of pretrained speech representations, on advanced end-to-end automatic speech recognition (E2E-ASR) models. We select sev-eral pretrained speech representations and present the experimental results on various open-source and publicly available corpora for E2E-ASR. Without any modification of the back-end model archi-tectures or training strategy, some of the experiments with pretrained representations, e.g., WSJ, WSJ0-2mix with HuBERT, reach or out-perform current state-of-the-art (SOTA) recognition performance. Moreover, we further explore more scenarios for whether the pre-training representations are effective, such as the cross-language or overlapped speech. The scripts, configuratons and the trained mod-els have been released in ESPnet to let the community reproduce our experiments and improve them.
The Impact of Artificial Intelligence on Data System Security: A Literature Review

Diverse forms of artificial intelligence (AI) are at the forefront of triggering digital security innovations based on the threats that are arising in this post-COVID world. On the one hand, companies are experiencing difficulty in dealing with security challenges with regard to a variety of issues ranging from system openness, decision making, quality control, and web domain, to mention a few. On the other hand, in the last decade, research has focused on security capabilities based on tools such as platform complacency, intelligent trees, modeling methods, and outage management systems in an effort to understand the interplay between AI and those issues. the dependence on the emergence of AI in running industries and shaping the education, transports, and health sectors is now well known in the literature. AI is increasingly employed in managing data security across economic sectors. Thus, a literature review of AI and system security within the current digital society is opportune. This paper aims at identifying research trends in the field through a systematic bibliometric literature review (LRSB) of research on AI and system security. the review entails 77 articles published in the Scopus® database, presenting up-to-date knowledge on the topic. the LRSB results were synthesized across current research subthemes. Findings are presented. the originality of the paper relies on its LRSB method, together with an extant review of articles that have not been categorized so far. Implications for future research are suggested.
Quantum computing for chemical and biomolecular product design

Chemical process design has for long been benefiting from computer-aided methods and tools to develop new processes and services that can meet the needs of society. Chemical and biomolecular product design could also benefit from the use of computer-aided solution strategies and computational power to efficiently solve the problems at various scales as the complexity and size of problems grow. In this context, new modes of computation such as quantum computing are receiving increasing attention. While quantum computing has been in development for quite some time, the development of the technology to the point of making commercial use of such resources is quite recent, and still quite limited in scope. However, projections point to a rapid development of quantum computing resources becoming available to academia and industry, which opens potential application areas in chemical and biomolecular product design. With the advent of hybrid algorithms that are able to take advantage of both classical computing and quantum computing resources, as quantum computing grows, more and more problems relevant for chemical product design will become solvable. In this paper, some perspectives are given by identifying a set of needs and challenges for a selected set of opportunities, such as quantum chemistry-based property prediction, protein folding, complex multi-step chemical reactions, and molecular reaction dynamics.
Deep Learning and Zero-Day Traffic Classification: Lessons Learned From a Commercial-Grade Dataset

The increasing success of Machine Learning (ML) and Deep Learning (DL) has recently re-sparked interest towards traffic classification. While supervised techniques provide satisfactory performance when classifying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">known</i> traffic, the detection of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">zero-day</i> (i.e., unknown) traffic is a more challenging task. At the same time, zero-day detection, generally tackled with unsupervised techniques such as clustering, received less coverage by the traffic classification literature which focuses more on deriving DL models via supervised techniques. Moreover, the combination of supervised and unsupervised techniques poses challenges not fully covered by the traffic classification literature. In this paper, we share our experience on a commercial-grade DL traffic classification engine that combines supervised and unsupervised techniques to identify known and zero-day traffic. In particular, we rely on a dataset with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hundreds</i> of very fine grained application labels, and perform a thorough assessment of two state of the art traffic classifiers in commercial-grade settings. This pushes the boundaries of traffic classifiers evaluation beyond the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">few tens</i> of classes typically used in the literature. Our main contribution is the design and evaluation of GradBP, a novel technique for zero-day applications detection. Based on gradient backpropagation and tailored for DL models, GradBP yields superior performance with respect to state of the art alternatives, in both accuracy and computational cost. Overall, while ML and DL models are both equally able to provide excellent performance for the classification of known traffic, the non-linear feature extraction process of DL models backbone provides sizable advantages for the detection of unknown classes over classical ML models.
Service Robots: A Systematic Literature Review

A service robot performs various professional services and domestic/personal services useful for organizations and humans in many application domains. Currently, the service robot industry is growing rapidly along with the technological advances of the Fourth Industrial Revolution. In light of the great interest and potential of service robots, this study conducts a systematic review of the past and current research in service robots. This study examines the development activities for service robots across applications and industries and categorizes the service robots into four types. The categorization provides us with insights into the unique research activities and practices in each category of service robots. Then, this study analyzes the technological foundation that applies to all four categories of service robots. Finally, this study discusses opportunities and challenges that are understudied but potentially important for the future research of service robots.