Reviewerly/exHarmony huggingface dataset:
Sensor-Chain: A Lightweight Scalable Blockchain Framework for Internet of Things

The Internet of Things (IoT), forming the foundation of Cyber Physical Systems (CPS), connects a huge number of ubiquitous sensing and mobile computing devices. The mobile IoT systems generate an enormous volume of a variety of dynamic context data and typically count on centralized architectures to process them. However, their inability to ensure security and decline in communication efficiency and response time with the increase in the size of IoT network are some of the many concerning weaknesses that are holding back the fast-paced growth of IoT. Realizing the limitations of centralized systems, recently blockchain-based decentralized architecture is being considered as the key to redesigning the IoT systems in a way that is designed to be secure, transparent, highly resistant to outages, auditable, and efficient. However, before realizing the new promise of blockchain for IoT, there are significant challenges to address. One fundamental challenge is the scale issue around data collection, storage, and analytic as IoT sensor devices possess limited computational power and storage capabilities. In particular, since the chain is always growing, IoT devices require more and more resources. Thus, an oversized chain poses storage and scalability problems. With this in mind, the overall goal of our research is to design a lightweight scalable blockchain framework for IoT of mobile devices. This framework, coined as "Sensor-Chain", promises a new generation of lightweight blockchain management with a superior reduction in resource consumption, and at the same time capable of retaining critical information about the IoT systems of mobile devices.
Reading Between the Guidelines

Voice-Activated Personal Assistants (VAPAs)-like Apple Siri and Amazon Alexa - have rapidly become common features on mobile devices and in homes of millions of people around the world. They have proven to be particularly valuable to people with disabilities, chiefly among people with visual impairments. Yet, we still know relatively little about the fundamental metaphors and guidelines for designing voice assistants, and how they might empower and constrain visually impaired users. To address this need, we conducted a qualitative document review of VAPA design guidelines published by top commercial vendors Amazon, Google, Microsoft, Apple and Alibaba. We found that guidelines have many commonalities that surface an underlying assumption that VAPA interfaces should be modeled after human-human conversation. We draw on prior work about needs of people with visual impairments to critique this taken-for-granted human-human conversation metaphor and offer amendments to prevailing design guidelines that can make this now-pervasive platform more fully achieve its potential to become universally usable.
Dilated Convolution Neural Network for Remaining Useful Life Prediction

Abstract Accurate prediction of remaining useful life (RUL) plays an important role in reducing the probability of accidents and lessening the economic loss. However, traditional model-based methods for RUL are not suitable when operating conditions and fault models are complicated. To deal with this problem, this paper proposes a novel data-driven method based on a deep dilated convolution neural networks (D-CNN). The novelties of the proposed method are triple folds. First, no feature engineering is required, and the raw sensor data are directly used as the input of the model. Second the dilated convolutional structure is used to enlarge the receptive field and further improve the accuracy of prediction. Finally, time sequences are encoded by a 2D-convolution to extract higher-level features. Extensive experiments on the C-MAPSS dataset demonstrate that the proposed D-CNN achieves high performance while requiring less training time.
Pano Popups: Indoor 3D Reconstruction with a Plane-Aware Network

In this work we present a method to train a plane-aware convolutional neural network for dense depth and surface normal estimation as well as plane boundaries from a single indoor 360 image. Using our proposed loss function, our network outperforms existing methods for single-view, indoor, omnidirectional depth estimation and provides an initial benchmark for surface normal prediction from 360 images. Our improvements are due to the use of a novel plane-aware loss that leverages principal curvature as an indicator of planar boundaries. We also show that including geodesic coordinate maps as network priors provides a significant boost in surface normal prediction accuracy. Finally, we demonstrate how we can combine our network's outputs to generate high quality 3D "pop-up" models of indoor scenes.
Jidar: A Jigsaw-like Data Reduction Approach Without Trust Assumptions for Bitcoin System

The pioneer blockchain platform Bitcoin has drawn massive attention from various sectors in society, with its promising decentralized, irreversible, and trust-free natures. Increasing volume of Bitcoin transactions makes it impractical for each user to store a full copy of whole ledger, especially for a normal user who makes few transactions and owns constrained storage space. There are already some works conducted to reduce the storage overhead by redesigning the system protocol, based on different trust assumptions. An example is running light nodes, which trust the full nodes to store its relevant data and reply to its data request timely. However, it introduces the risks of permanent data loss, as the data relevant to a light node may be tailored by all the full nodes later. Another attempt is conducted by organizing the nodes into several cooperative units based on a trust assumption, which is hard to satisfy in practice. In this paper, we propose a novel Jigsaw-like Data Reduction (Jidar) approach. Each node in Jidar only stores transactions of interest and relevant Merkle branches from the complete blocks, just like selecting several pieces from the jigsaw puzzles. A node can maintain and verify all its relevant data locally and safely without any trust assumptions. To verify the validity of a newly proposed transaction, Merkle branches relevant to the inputs are provided along with the transaction by the proposer. In view of the requirement that a user wants to cohere all the fragments stored in different nodes into a complete block, just like stitching all the pieces into a complete jigsaw-puzzle picture, a mechanism to query full data is added to Jidar. Experimental results demonstrate that Jidar can reduce a normal node's storage cost to about 1.03% of the original Bitcoin system at a low communication cost
Self-Supervised Representation Learning From Multi-Domain Data

We present an information-theoretically motivated constraint for self-supervised representation learning from multiple related domains. In contrast to previous self-supervised learning methods, our approach learns from multiple domains, which has the benefit of decreasing the build-in bias of individual domain, as well as leveraging information and allowing knowledge transfer across multiple domains. The proposed mutual information constraints encourage neural network to extract common invariant information across domains and to preserve peculiar information of each domain simultaneously. We adopt tractable upper and lower bounds of mutual information to make the proposed constraints solvable. The learned representation is more unbiased and robust toward the input images. Extensive experimental results on both multi-domain and large-scale datasets demonstrate the necessity and advantage of multi-domain self-supervised learning with mutual information constraints. Representations learned in our framework on state-of-the-art methods achieve improved performance than those learned on a single domain.
CIIDefence: Defeating Adversarial Attacks by Fusing Class-Specific Image Inpainting and Image Denoising

This paper presents a novel approach for protecting deep neural networks from adversarial attacks, i.e., methods that add well-crafted imperceptible modifications to the original inputs such that they are incorrectly classified with high confidence. The proposed defence mechanism is inspired by the recent works mitigating the adversarial disturbances by the means of image reconstruction and denoising. However, unlike the previous works, we apply the reconstruction only for small and carefully selected image areas that are most influential to the current classification outcome. The selection process is guided by the class activation map responses obtained for multiple top-ranking class labels. The same regions are also the most prominent for the adversarial perturbations and hence most important to purify. The resulting inpainting task is substantially more tractable than the full image reconstruction, while still being able to prevent the adversarial attacks. Furthermore, we combine the selective image inpainting with wavelet based image denoising to produce a non differentiable layer that prevents attacker from using gradient backpropagation. Moreover, the proposed nonlinearity cannot be easily approximated with simple differentiable alternative as demonstrated in the experiments with Backward Pass Differentiable Approximation (BPDA) attack. Finally, we experimentally show that the proposed Class-specific Image Inpainting Defence (CIIDefence) is able to withstand several powerful adversarial attacks including the BPDA. The obtained results are consistently better compared to the other recent defence approaches.
Analysis of Ontology Competency Questions and their formalizations in SPARQL-OWL

Competency Questions (CQs) are natural language questions outlining and constraining the scope of knowledge represented in an ontology. Despite that CQs are a part of several ontology engineering methodologies, the actual publication of CQs for the available ontologies is very limited and even scarcer is the publication of their respective formalizations in terms of, e.g., SPARQL queries. This paper aims to contribute to addressing the myriad of engineering hurdles to using CQs in ontology development. A prerequisite to this is to understand the relation between CQs and the queries over the ontology. We use a new dataset of 234 competency questions and their SPARQL-OWL queries for several ontologies in different domains developed by different groups, and analysed the CQs in two principal ways. The first stage focused on a linguistic analysis of the natural language text itself, i.e., a lexico-syntactic analysis without any presuppositions of ontology elements, and a subsequent step of semantic analysis in order to find patterns. This increased diversity of CQ sources resulted in a 4-5-fold increase of hitherto published patterns, to 106 distinct CQ patterns, which have a limited subset of few patterns shared across the CQ sets from the different ontologies. Next, we analysed the relation between the found CQ patterns and their respective SPARQL-OWL patterns, which revealed that one CQ pattern may be realized by more than one SPARQL-OWL query pattern, and vice versa. These insights may contribute to establishing common practices, templates, automation, and user tools that will support CQ formulation, formalization, execution, and general management.
Next and Next New POI Recommendation via Latent Behavior Pattern Inference

Next and next new point-of-interest (POI) recommendation are essential instruments in promoting customer experiences and business operations related to locations. However, due to the sparsity of the check-in records, they still remain insufficiently studied. In this article, we propose to utilize personalized latent behavior patterns learned from contextual features, e.g., time of day, day of week, and location category, to improve the effectiveness of the recommendations. Two variations of models are developed, including GPDM, which learns a fixed pattern distribution for all users; and PPDM, which learns personalized pattern distribution for each user. In both models, a soft-max function is applied to integrate the personalized Markov chain with the latent patterns, and a sequential Bayesian Personalized Ranking (S-BPR) is applied as the optimization criterion. Then, Expectation Maximization (EM) is in charge of finding optimized model parameters. Extensive experiments on three large-scale commonly adopted real-world LBSN data sets prove that the inclusion of location category and latent patterns helps to boost the performance of POI recommendations. Specifically, our models in general significantly outperform other state-of-the-art methods for both next and next new POI recommendation tasks. Moreover, our models are capable of making accurate recommendations regardless of the short/long duration or distance.
Mop Moiré Patterns Using MopNet

Moiré pattern is a common image quality degradation caused by frequency aliasing between monitors and cameras when taking screen-shot photos. The complex frequency distribution, imbalanced magnitude in colour channels, and diverse appearance attributes of moiré pattern make its removal a challenging problem. In this paper, we propose a Moiré pattern Removal Neural Network (MopNet) to solve this problem. All core components of MopNet are specially designed for unique properties of moire patterns, including the multi-scale feature aggregation addressing complex frequency, the channel-wise target edge predictor to exploit imbalanced magnitude among colour channels, and the attribute-aware classifier to characterize the diverse appearance for better modelling Moiré patterns. Quantitative and qualitative experimental comparison validate the state-of-the-art performance of MopNet.
Large-scale multi-label classification using unknown streaming images

In this paper, we investigate the large-scale multi-label image classification problem when images with unknown novel classes come in stream during the training stage. It coincides with the practical requirement that usually novel classes are detected and used to update an existing image recognition system. Most existing multi-label image classification methods cannot be directly applied in this scenario, where the training and testing stages must have the same label set. In this paper, we proposed to learn a multi-label classifier and a novel-class detector alternately to solve this problem. The multi-label classifier is learned using a convolutional neural network (CNN) from the images in the known classes. We proposed a recurrent novel-class detector which is learned in the supervised manner to detect the novel class by encoding image features with the multi-label information. In the experiment, our method is evaluated on several large-scale multi-label benchmarks including MS COCO. The results show the proposed method is comparable to most existing multi-label image classification methods, which validate its efficacy when encountering streaming images with unknown classes.
Omniring

Monero is the largest cryptocurrency with built-in cryptographic privacy features. The transactions are authenticated using zero-knowledge spend proofs, which provide a certain level of anonymity by hiding the source accounts from which the funds are sent among a set of other accounts. Due to its similarities to ring signatures, this core cryptographic component is called Ring Confidential Transactions (RingCT). Because of its practical relevance, several works attempt to analyze the security of RingCT. Since RingCT is rather complex, most of them are either informal, miss fundamental functionalities, or introduce undesirable trusted setup assumptions. Regarding efficiency, Monero currently deploys a scheme in which the size of the spend proof is linear in the ring size. This limits the ring size to only a few accounts, which in turn limits the acquired anonymity significantly and facilitates de-anonymization attacks. As a solution to these problems, we present the first rigorous formalization of RingCT as a cryptographic primitive. We then propose a generic construction of RingCT and prove it secure in our formal security model. By instantiating our generic construction with new efficient zero-knowledge proofs, we obtain Omniring, a fully-fledged RingCT scheme in the discrete logarithm setting that provides the highest concrete and asymptotic efficiency as of today. Omniring is the first RingCT scheme which 1) does not require a trusted setup or pairing-friendly elliptic curves, 2) has a proof size logarithmic in the size of the ring, and 3) allows to share the same ring between all source accounts in a transaction, thereby enabling significantly improved privacy level without sacrificing performance. Our zero-knowledge proofs rely on novel enhancements to the Bulletproofs framework (S&P 2018), which we believe are of independent interest.
An examination of the studies on foreign language teaching in pre-school education: a bibliometric mapping analysis

This study aims to provide a bibliometric mapping analysis of the studies on foreign language teaching in early childhood education in Web of Science. A total of 638 studies were reached however 596 studies were selected for the analysis. For bibliometric analysis, VOSViewer programme was used in order to reveal the most used keywords, words in the abstracts, citation analyses and co-citation analyses in the studies. In addition, whether any technology was adopted in the studies was examined within 76 studies. The results showed that the most used keywords were English language learning, bilingualism, English as a second language, and English learners. The most used words in the abstracts were teacher, acquisition and effect. Bialystok and Cummins, are the most cited authors in this field. The most cited journals are Journal of Educational Psychology and Applied Psycholinguistics. The studies have been mostly published in Spain, Sweden, and Israel. In addition, it is observed that the studies started in 2012 mainly in the United States, Canada, England and Germany. When technology use in the studies was examined, ICT in education, multimedia, digital technologies, instructional technology, technology integration were used in the studies. This study provides a guide for new studies, to identify the trends in the field and to compare the existing research on the topic. Consequently, it is suggested that future researches need focusing more on the pedagogical aspects and testing whether teaching environments supported by new technologies contribute to foreign language education.
Deep clustering for weakly-supervised semantic segmentation in autonomous driving scenes

Weakly-supervised semantic segmentation (WSSS) using only tags can significantly ease the label costing, because full supervision needs pixel-level labeling. It is, however, a very challenging task because it is not straightforward to associate tags to visual appearance. Existing researches can only do tag-based WSSS on simple images, where only two or three tags exist in each image, and different images usually have different tags, such as the PASCAL VOC dataset. Therefore, it is easy to relate the tags to visual appearance and supervise the segmentation. However, real-world scenes are much more complex. Especially, the autonomous driving scenes usually contain nearly 20 tags in each image and those tags can repetitively appear from image to image, which means the existing simple image strategy does not work. In this paper, we propose to solve the problem by using region based deep clustering. The key idea is that, since each tagged object is repetitively appearing from image to image, it allows us to find the common appearance through region clustering, and particular deep neural network based clustering. Later, we relate the clustered region appearance to tags and utilize the tags to supervise the segmentation. Furthermore, regions found by clustering with weak supervision can be very noisy. We further propose a mechanic to improve and refine the supervision in an iterative manner. To our best knowledge, it is the first time that image tags weakly-supervised semantic segmentation can be applied in complex autonomous driving datasets with still images. Experimental results on the Cityscapes and CamVid datasets demonstrate the effectiveness of our method.
Progress on Integrated Quantum Photonic Sources with Silicon

Abstract The high stability and scalability of integrated circuits make them a reliable and practical platform for photonic quantum information processing. In various platforms for quantum photonic integrated circuits, the silicon‐on‐insulator technology, with its strong nonlinear effect and mature fabrication technology, has gradually emerged in the preparation of quantum photonic sources. This report presents a review of this series of research advances in the preparation of a quantum photonic source, based on the spontaneously four‐wave mixing process in a silicon waveguide, especially chip‐scale entangled states that have been realized in recent years.
SPIR: A Secure and Privacy-Preserving Incentive Scheme for Reliable Real-Time Map Updates

The high-precision maps can provide additional information on roads and conditions, which plays an important role in autonomous vehicles (AVs) navigation. Compared with the existing map update methods, the real-time map updates based on crowdsensing have lower cost and higher accuracy. However, in the process of map update, the map service platform (MSP) cannot recruit enough vehicle users to obtain the sensing data due to a lack of incentive mechanism. Therefore, how to motivate more vehicle users to provide high-quality sensing data is the key for real-time map updates. In this article, we propose a secure and privacy-preserving incentive scheme for reliable real-time map updates, named SPIR. Specifically, under the condition of limited service platform budget and limited vehicle user's ability, an effective incentive mechanism based on reverse auction is presented, which can solve two core problems: i.e., payment control for MSP and completion quality for vehicle users. Meanwhile, a credit management and payment system based on the blockchain technique are designed. In addition, the partially blind signature technique is applied to guarantee the security of the incentive mechanism and protect the privacy of vehicle users. Both theoretical analysis and simulation results indicate that the proposed SPIR achieves near-optimal benefits, which can provide the fair reward for vehicle users and reasonable budget for the MSP. In the real-time map update services, SPIR can guarantee the computational efficiency and data reliability.
Collaborative Data Work Towards a Caring Democracy

Researchers in human-centered computing have surfaced a feminist ethic of care in interaction with technologies, in data collection, and in data work. Drawing on two years of ethnographic fieldwork, we consider how democratic caring might be enacted and sustained through collaborative data work. We employ philosopher Joan Tronto's theory of caring democracy to structure our analysis of a resident-led initiative that uses data to organize and address issues of neglect and abandonment in their neighborhood. Adding to the CSCW literature on sociotechnical systems of care, we look particularly at Tronto's concept of caring democracy where caring needs and the ways in which they are met are an ongoing and inclusive process of assigning and reassigning caring responsibilities, characterized by both equality of voice and freedom from domination. This work develops grounded insight into the practice of democratic caring and how collaborative data work is relevant to this caring practice. We discuss opportunities and challenges for a data-supported caring democracy and address how caring democracy technologies are different from other modern civic technology practices. We conclude with a call to researchers to identify and enact democratic caring experiments in the small.