Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Authentication and Trust Management Mechanism for Smart Cities

Security has always been the main concern for the internet of things (IoT)-based systems. Blockchain, with its decentralized and distributed design, prevents the risks of the existing centralized methodologies. Conventional security and privacy architectures are inapplicable in the spectrum of IoT due to its resource constraints. To overcome this problem, this paper presents a Blockchain-based security mechanism that enables secure authorized access to smart city resources. The presented mechanism comprises the ACE (Authentication and Authorization for Constrained Environments) framework-based authorization Blockchain and the OSCAR (Object Security Architecture for the Internet of Things) object security model. The Blockchain lays out a flexible and trustless authorization mechanism, while OSCAR makes use of a public ledger to structure multicast groups for authorized clients. Moreover, a meteor-based application is developed to provide a user-friendly interface for heterogeneous technologies belonging to the smart city. The users would be able to interact with and control their smart city resources such as traffic lights, smart electric meters, surveillance cameras, etc., through this application. To evaluate the performance and feasibility of the proposed mechanism, the authorization Blockchain is implemented on top of the Ethereum network. The authentication mechanism is developed in the node.js server and a smart city is simulated with the help of Raspberry Pi B+. Furthermore, mocha and chai frameworks are used to assess the performance of the system. Experimental results reveal that the authentication response time is less than 100 ms even if the average hand-shaking time increases with the number of clients.
Nonreciprocity and Faraday Rotation at Time Interfaces

Nonreciprocity is critically important in modern wave technologies, yet its general principles and practical implementations continue to raise intense research interest, in particular in the context of broken reciprocity based on spatiotemporal modulation. Abrupt changes in time of the electromagnetic properties of a material have also been shown to replace spatial boundaries, supporting highly unusual wave-matter interactions in so-called time metamaterials. Here, we introduce nonreciprocity for temporal boundaries, demonstrating Faraday polarization rotation in a magnetoplasma with material properties abruptly switched in time. Our findings open new opportunities for time metamaterials, yielding new avenues for nonreciprocity with broad applicability for wave engineering.
VoxLens: Making Online Data Visualizations Accessible with an Interactive JavaScript Plug-In

JavaScript visualization libraries are widely used to create online data visualizations but provide limited access to their information for screen-reader users. Building on prior findings about the experiences of screen-reader users with online data visualizations, we present VoxLens, an open-source JavaScript plug-in that—with a single line of code—improves the accessibility of online data visualizations for screen-reader users using a multi-modal approach. Specifically, VoxLens enables screen-reader users to obtain a holistic summary of presented information, play sonified versions of the data, and interact with visualizations in a “drill-down” manner using voice-activated commands. Through task-based experiments with 21 screen-reader users, we show that VoxLens improves the accuracy of information extraction and interaction time by 122% and 36%, respectively, over existing conventional interaction with online data visualizations. Our interviews with screen-reader users suggest that VoxLens is a “game-changer” in making online data visualizations accessible to screen-reader users, saving them time and effort.
Analog Quantum Control of Magnonic Cat States on a Chip by a Superconducting Qubit

We propose to directly and quantum-coherently couple a superconducting transmon qubit to magnons-the quanta of the collective spin excitations, in a nearby magnetic particle. The magnet's stray field couples to the qubit via a superconducting quantum interference device. We predict a resonant magnon-qubit exchange and a nonlinear radiation-pressure interaction that are both stronger than dissipation rates and tunable by an external flux bias. We additionally demonstrate a quantum control scheme that generates magnon-qubit entanglement and magnonic Schrödinger cat states with high fidelity.
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data

Retrieval-based methods have been shown to be effective in NLP tasks via introducing external knowledge.However, the indexing and retrieving of large-scale corpora bring considerable computational cost.Surprisingly, we found that REtrieving from the traINing datA (REINA) only can lead to significant gains on multiple NLG and NLU tasks.We retrieve the labeled training instances most similar to the input text and then concatenate them with the input to feed into the model to generate the output.Experimental results show that this simple method can achieve significantly better performance on a variety of NLU and NLG tasks, including summarization, machine translation, language modeling, and question answering tasks.For instance, our proposed method achieved state-ofthe-art results on XSum, BigPatent, and Com-monsenseQA.Our code is released. 1
Feminist Human-Robot Interaction

Human-Robot Interaction (HRI) is inherently a human-centric field of technology. The role of feminist theories in related fields (e.g. Human-Computer Interaction, Data Science) are taken as a starting point to present a vision for Feminist HRI which can support better, more ethical HRI practice everyday, as well as a more activist research and design stance. We first define feminist design for an HRI audience and use a set of feminist principles from neighboring fields to examine existent HRI literature, showing the progress that has been made already alongside some additional potential ways forward. Following this we identify a set of reflexive questions to be posed throughout the HRI design, research and development pipeline, encouraging a sensitivity to power and to individuals' goals and values. Importantly, we do not look to present a definitive, fixed notion of Feminist HRI, but rather demonstrate the ways in which bringing feminist principles to our field can lead to better, more ethical HRI, and to discuss how we, the HRI community, might do this in practice.
Competing to Share Expertise: the Taskcn Knowledge Sharing Community

"Witkeys" are websites in China that form a rapidly growing web-based knowledge market. A user who posts a task also offers a small fee, and many other users submit their answers to compete. The Witkey sites fall in-between aspects of the now-defunct Google Answers (vetted experts answer questions for a fee) and Yahoo Answers (anyone can answer or ask a question). As such, these sites promise new possibilities for knowledge-sharing online communities, perhaps fostering the freelance marketplace of the future. In this paper, we investigate one of the biggest Witkey websites in China, Taskcn.com. In particular, we apply social network prestige measures to a novel construction of user and task networks based on competitive outcomes to discover the underlying properties of both users and tasks. Our results demonstrate the power of this approach: Our analysis allows us to infer relative expertise of the users and provides an understanding of the participation structure in Taskcn. The results suggest challenges and opportunities for this kind of knowledge sharing medium.
Optimal stochastic modeling with unitary quantum dynamics

Identifying and extracting the past information relevant to the future behaviour of stochastic processes is a central task in the quantitative sciences. Quantum models offer a promising approach to this, allowing for accurate simulation of future trajectories whilst using less past information than any classical counterpart. Here we introduce a class of phase-enhanced quantum models, representing the most general means of causal simulation with a unitary quantum circuit. We show that the resulting constructions can display advantages over previous state-of-art methods - both in the amount of information they need to store about the past, and in the minimal memory dimension they require to store this information. Moreover, we find that these two features are generally competing factors in optimisation - leading to an ambiguity in what constitutes the optimal model - a phenomenon that does not manifest classically. Our results thus simultaneously offer new quantum advantages for stochastic simulation, and illustrate further qualitative differences in behaviour between classical and quantum notions of complexity.
Multi-Turn Video Question Answering via Hierarchical Attention Context Reinforced Networks

Multi-turn video question answering is a challenging task in visual information retrieval, which generates the accurate answer from the referenced video contents according to the visual conversation context and given question. However, the existing visual question answering methods mainly tackle the problem of single-turn video question answering, which may be ineffectively applied for multi-turn video question answering directly, due to the insufficiency of modeling the sequential conversation context. In this paper, we study the problem of multi-turn video question answering from the viewpoint of multi-stream hierarchical attention context reinforced network learning. We first propose the hierarchical attention context network for context-aware question understanding by modeling the hierarchically sequential conversation context structure. We then develop the multi-stream spatio-temporal attention network for learning the joint representation of the dynamic video contents and context-aware question embedding. We next devise a multi-step reasoning process to enhance the multi-stream hierarchical attention context network learning method. We finally predict the multiple-choice answer from the candidate answer set and further develop the reinforced decoder network to generate the open-ended natural language answer for multi-turn video question answering. We construct two large-scale multi-turn video question answering datasets. The extensive experiments show the effectiveness of our method.
Performance optimization research of reservoir computing system based on an optical feedback semiconductor laser under electrical information injection

Via Santa Fe time series prediction and nonlinear channel equalization tasks, the performances of a reservoir computing (RC) system based on an optical feedback semiconductor laser (SL) under electrical information injection are numerically investigated. The simulated results show that the feedback delay time and strength seriously affect the performances of this RC system. By adopting a current-related optimized feedback delay time and strength, the RC can achieve a good performance for an SL biased within a wide region of 1.1-3.5 times its threshold. The prediction errors are smaller than 0.01 when implementing the Santa Fe tests, and the symbol error rates (SERs) are very low on the order of 10-5 for accomplishing nonlinear channel equalization tests under a signal-to-noise ratio (SNR) of 32 dB. Moreover, under a given RC performance level, the information processing rate of the RC can be improved by increasing the SL current.
Tighter Security Proofs for Generic Key Encapsulation Mechanism in the Quantum Random Oracle Model

In (TCC 2017), Hofheinz, Hövelmanns and Kiltz provided a fine-grained and modular toolkit of generic key encapsulation mechanism (KEM) constructions, which were widely used among KEM submissions to NIST Post-Quantum Cryptography Standardization project. The security of these generic constructions in the quantum random oracle model (QROM) has been analyzed by Hofheinz, Hövelmanns and Kiltz (TCC 2017), Saito, Xagawa and Yamakawa (Eurocrypt 2018), and Jiang et al. (Crypto 2018). However, the security proofs from standard assumptions are far from tight. In particular, the factor of security loss is q and the degree of security loss is 2, where q is the total number of adversarial queries to various oracles. In this paper, using semi-classical oracle technique recently introduced by Ambainis, Hamburg and Unruh (ePrint 2018/904), we improve the results in (Eurocrypt 2018, Crypto 2018) and provide tighter security proofs for generic KEM constructions from standard assumptions. More precisely, the factor of security loss q is reduced to be $$\sqrt{q}$$ . In addition, for transformation T that turns a probabilistic public-key encryption (PKE) into a determined one by derandomization and re-encryption, the degree of security loss 2 is reduced to be 1. Our tighter security proofs can give more confidence to NIST KEM submissions where these generic transformations are used, e.g., CRYSTALS-Kyber etc.
On the Turing Completeness of Modern Neural Network Architectures

Alternatives to recurrent neural networks, in particular, architectures based on attention or convolutions, have been gaining momentum for processing input sequences. In spite of their relevance, the computational properties of these alternatives have not yet been fully explored. We study the computational power of two of the most paradigmatic architectures exemplifying these mechanisms: the Transformer (Vaswani et al., 2017) and the Neural GPU (Kaiser & Sutskever, 2016). We show both models to be Turing complete exclusively based on their capacity to compute and access internal dense representations of the data. In particular, neither the Transformer nor the Neural GPU requires access to an external memory to become Turing complete. Our study also reveals some minimal sets of elements needed to obtain these completeness results.
Weakly supervised instance segmentation using the bounding box tightness prior

This paper presents a weakly supervised instance segmentation method that consumes training data with tight bounding box annotations. The major difficulty lies in the uncertain figure-ground separation within each bounding box since there is no supervisory signal about it. We address the difficulty by formulating the problem as a multiple instance learning (MIL) task, and generate positive and negative bags based on the sweeping lines of each bounding box. The proposed deep model integrates MIL into a fully supervised instance segmentation network, and can be derived by the objective consisting of two terms, i.e., the unary term and the pairwise term. The former estimates the foreground and background areas of each bounding box while the latter maintains the unity of the estimated object masks. The experimental results show that our method performs favorably against existing weakly supervised methods and even surpasses some fully supervised methods for instance segmentation on the PASCAL VOC dataset.
Facial Expression Recognition Based on Improved LeNet-5 CNN

Aiming at the influence of local occlusion on expression recognition, this paper proposes an improved cross-connected multi-layer LeNet-5 Convolutional Neural Network model. Under occlusion conditions, traditional machine learning methods are not robust due to lack of image information and noise interference, and the recognition rate is poor. Based on the advantages of deep learning in feature extraction, this paper adds a convolution layer and a pooling layer based on the LeNet-5 model. The low-level features extracted from the network structure are combined with the high-level features to construct the classifier. The implicit features are extracted by using the trainable convolution kernel, and the extracted implicit features are reduced by the pooling layer. Finally, the Softmax classifier is used for classification and recognition. The contrast experiment between the occlusion determination and the occlusion uncertainty is carried out, and the occlusion robustness of the improved method is verified.
Duet: an expressive higher-order language and linear type system for statically enforcing differential privacy

During the past decade, differential privacy has become the gold standard for protecting the privacy of individuals. However, verifying that a particular program provides differential privacy often remains a manual task to be completed by an expert in the field. Language-based techniques have been proposed for fully automating proofs of differential privacy via type system design, however these results have lagged behind advances in differentially-private algorithms, leaving a noticeable gap in programs which can be automatically verified while also providing state-of-the-art bounds on privacy. We propose Duet, an expressive higher-order language, linear type system and tool for automatically verifying differential privacy of general-purpose higher-order programs. In addition to general purpose programming, Duet supports encoding machine learning algorithms such as stochastic gradient descent, as well as common auxiliary data analysis tasks such as clipping, normalization and hyperparameter tuning - each of which are particularly challenging to encode in a statically verified differential privacy framework. We present a core design of the Duet language and linear type system, and complete key proofs about privacy for well-typed programs. We then show how to extend Duet to support realistic machine learning applications and recent variants of differential privacy which result in improved accuracy for many practical differentially private algorithms. Finally, we implement several differentially private machine learning algorithms in Duet which have never before been automatically verified by a language-based tool, and we present experimental results which demonstrate the benefits of Duet's language design in terms of accuracy of trained machine learning models.
HUE-Span: Fast High Utility Episode Mining

High utility episode mining consists of finding episodes (sub-sequences of events) that have a high importance (e.g high profit) in a sequence of events with quantities and weights. Though it has important real-life applications, the current problem definition has two critical limitations. First, it underestimates the utility of episodes by not taking into account all timestamps of minimal occurrences for utility calculations, which can result in missing high utility episodes. Second, the state-of-the-art UP-Span algorithm is inefficient on large databases because it uses a loose upper bound on the utility to reduce the search space. This paper addresses the first issue by redefining the problem to guarantee that all high utility episodes are found. Moreover, an efficient algorithm named HUE-Span is proposed to efficiently find all patterns. It relies on a novel upper-bound to reduce the search space and a novel co-occurrence based pruning strategy. Experimental results show that HUE-Span not only finds all patterns but is also up to five times faster than UP-Span.
Research on News Keyword Extraction Technology Based on TF-IDF and TextRank

With the rapid development of information technology and the widespread use of the Internet, the Internet, as an information carrier, has gradually replaced the traditional media such as newspapers and television, and become the main channel for people to obtain information. This paper takes English news text as the research object of keyword extraction method. We combine TF-IDF and the TextRank algorithm to extract keywords from text by constructing word graph model, counting word frequency and inverse document frequency, and considering the weight of the positioning of headlines. A large number of experiments have been carried out with Sina News Corpus, and the performance of the algorithm is evaluated by recall rate, precision rate and macro average value. The results show that the integration of TF-IDF and the TextRank algorithm significantly outperforms the traditional algorithm in performance parameters and extraction effect.
A Pilot Study on Convolutional Neural Networks for Motion Estimation From Ultrasound Images

In recent years, deep learning (DL) has been successfully applied to the analysis and processing of ultrasound images. To date, most of this research has focused on segmentation and view recognition. This article benchmarks different convolutional neural network algorithms for motion estimation in ultrasound imaging. We evaluated and compared several networks derived from FlowNet2, one of the most efficient architectures in computer vision. The networks were tested with and without transfer learning, and the best configuration was compared against the particle imaging velocimetry method, a popular state-of-the-art block-matching algorithm. Rotations are known to be difficult to track from ultrasound images due to a significant speckle decorrelation. We thus focused on the images of rotating disks, which could be tracked through speckle features only. Our database consisted of synthetic and in vitro B-mode images after log compression and covered a large range of rotational speeds. One of the FlowNet2 subnetworks, FlowNet2SD, produced competitive results with a motion field error smaller than 1 pixel on real data after transfer learning based on the simulated data. These errors remain small for a large velocity range without the need for hyperparameter tuning, which indicates the high potential and adaptability of DL solutions to motion estimation in ultrasound imaging.
A novel approach to define the local region of dynamic selection techniques in imbalanced credit scoring problems

Lenders, such as banks and credit card companies, use credit scoring models to evaluate the potential risk posed by lending money to customers, and therefore to mitigate losses due to bad credit. The profitability of the banks thus highly depends on the models used to decide on the customer’s loans. State-of-the-art credit scoring models are based on machine learning and statistical methods. One of the major problems of this field is that lenders often deal with imbalanced datasets that usually contain many paid loans but very few not paid ones (called defaults). Recently, dynamic selection methods combined with ensemble methods and preprocessing techniques have been evaluated to improve classification models in imbalanced datasets presenting advantages over the static machine learning methods. In a dynamic selection technique, samples in the neighborhood of each query sample are used to compute the local competence of each base classifier. Then, the technique selects only competent classifiers to predict the query sample. In this paper, we evaluate the suitability of dynamic selection techniques for credit scoring problem, and we present Reduced Minority k-Nearest Neighbors (RMkNN), an approach that enhances state of the art in defining the local region of dynamic selection techniques for imbalanced credit scoring datasets. This proposed technique has a superior prediction performance in imbalanced credit scoring datasets compared to state of the art. Furthermore, RMkNN does not need any preprocessing or sampling method to generate the dynamic selection dataset (called DSEL). Additionally, we observe an equivalence between dynamic selection and static selection classification. We conduct a comprehensive evaluation of the proposed technique against state-of-the-art competitors on six real-world public datasets and one private one. Experiments show that RMkNN improves the classification performance of the evaluated datasets regarding AUC, balanced accuracy, H-measure, G-mean, F-measure, and Recall.