Reviewerly/exHarmony huggingface dataset:
Voice In Ear

With the rapid growth of wearable computing and increasing demand for mobile authentication scenarios, voiceprint-based authentication has become one of the prevalent technologies and has already presented tremendous potentials to the public. However, it is vulnerable to voice spoofing attacks (e.g., replay attacks and synthetic voice attacks). To address this threat, we propose a new biometric authentication approach, named EarPrint, which aims to extend voiceprint and build a hidden and secure user authentication scheme on earphones. EarPrint builds on the speaking-induced body sound transmission from the throat to the ear canal, i.e., different users will have different body sound conduction patterns on both sides of ears. As the first exploratory study, extensive experiments on 23 subjects show the EarPrint is robust against ambient noises and body motions. EarPrint achieves an Equal Error Rate (EER) of 3.64% with 75 seconds enrollment data. We also evaluate the resilience of EarPrint against replay attacks. A major contribution of EarPrint is that it leverages two-level uniqueness, including the body sound conduction from the throat to the ear canal and the body asymmetry between the left and the right ears, taking advantage of earphones' paring form-factor. Compared with other mobile and wearable biometric modalities, EarPrint is a low-cost, accurate, and secure authentication solution for earphone users.
Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts

A few-shot font generation (FFG) method has to satisfy two objectives: the generated images should preserve the underlying global structure of the target character and present the diverse local reference style. Existing FFG methods aim to disentangle content and style either by extracting a universal representation style or extracting multiple component-wise style representations. However, previous methods either fail to capture diverse local styles or cannot be generalized to a character with unseen components, e.g., unseen language systems. To mitigate the issues, we propose a novel FFG method, named Multiple Localized Experts Few-shot Font Generation Network (MX-Font). MX-Font extracts multiple style features not explicitly conditioned on component labels, but automatically by multiple experts to represent different local concepts, e.g., left-side sub-glyph. Owing to the multiple experts, MX-Font can capture diverse local concepts and show the generalizability to unseen languages. During training, we utilize component labels as weak supervision to guide each expert to be specialized for different local concepts. We formulate the component assign problem to each expert as the graph matching problem, and solve it by the Hungarian algorithm. We also employ the independence loss and the content-style adversarial loss to impose the content-style disentanglement. In our experiments, MX-Font outperforms previous state-of-the-art FFG methods in the Chinese generation and cross-lingual, e.g., Chinese to Korean, generation. Source code is available at https://github.com/clovaai/mxfont.
Learning disentangled user representation with multi-view information fusion on social networks

User representation learning is one prominent and critical task of user analysis on social networks, which derives conceptual user representations to improve the inference of user intentions and behaviors. Previous efforts have shown its substantial value in multifarious real-world applications, including product recommendation, textual content modeling, link prediction, and many more. However, existing studies either underutilize multi-view information, or neglect the stringent entanglement among underlying factors that govern user intentions, thus deriving deteriorated representations. To overcome these shortages, this paper proposes an adversarial fusion framework to fully exploit substantial multi-view information for user representation, consisting of a generator and a discriminator. The generator learns representations with a variational autoencoder, and is forced by the adversarial fusion framework to pay specific attention to substantial informative signs, thus integrating multi-view information. Furthermore, the variational autoencoder used in the generator is novelly designed to capture and disentangle the latent factors behind user intentions. By fully utilizing multi-view information and achieving disentanglement, our model learns robust and interpretable user representations. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of our proposed model.
Feature learning for stacked ELM via low-rank matrix factorization

Extreme-learning-machine based auto-encoder (ELM-AE) is regarded as a useful architecture with fast learning speed and general approximation ability, and stacked ELM is used to develop efficient and effective deep learning networks. However, considering features learned from conventional ELM-AEs have issues of weak nonlinear representation ability and random factors in feature projection, this paper proposes an improved ELM-AE architecture which utilize low-rank matrix factorization to learn optimal low-dimensional features. Two superiorities can be obtained compared to conventional ELM-AEs. One is the dimensionality of the hidden layer in ELM-AE could be set arbitrarily, e.g. a higher-dimension hidden layer could lower the random effect in feature learning and enhance features representation ability. The other is enhancing features nonlinear ability, since features are learned directly from the nonlinear outputs of hidden layer. Finally, comparison experiments on numerical and image datasets are implemented in this paper to verify the superior performance of the proposed ELM-AE in this paper.
Sparse Common Feature Representation for Undersampled Face Recognition

This work investigates the problem of undersampled face recognition (i.e., insufficient training data) encountered in practical Internet-of-Things (IoT) applications. Insufficient and uncertain samples captured by IoT devices may include background and facial disguise that makes face recognition more challenging than that with sufficient and reliable images. Many models work well in face recognition on a big data set, but when training data are insufficient, they achieve unsatisfactory performance. This work proposes a novel method named sparse common feature-based representation (SCFR) that provides a unique and stable result and completely avoids very time-consuming training required by a deep learning model. Specially, it constructs a common feature dictionary using both training and test images. Thereinto, a common feature is based on a discriminative common vector and learned by a Gaussian mixture model for both training and test images in a semisupervised learninig manner, which would reduce the difference among samples in each class. In the optimization, the latent indicator of test data is initialized by the estimated label. This can avoid learning invalid information and lead to good prototype images. A new variation dictionary characterizes variables that can be shared by different classes. Finally, this work adopts minimum reconstruction residuals to recognize test images, thus bringing about a substantial improvement in SCFR's performance. Extensive results on benchmark face databases demonstrate that the proposed method is better than the state-of-the-art methods handling undersampled face recognition.
Overcoming the rate–distance limit of device-independent quantum key distribution

Device-independent quantum key distribution (DIQKD) exploits the violation of a Bell inequality to extract secure key even if the users' devices are untrusted. Currently, all DIQKD protocols suffer from the secret key capacity bound, i.e., the secret key rate scales linearly with the transmittance of two users. Here we propose a heralded DIQKD scheme based on entangled coherent states to improve entangling rates whereby long-distance entanglement is created by single-photon-type interference. The secret key rate of our scheme can significantly outperform the traditional two-photon-type Bell-state measurement scheme and, importantly, surpass the above capacity bound. Our protocol therefore is an important step towards a realization of DIQKD and can be a promising candidate scheme for entanglement swapping in future quantum internet.
Using Blockchain-Enabled Solutions as SDG Accelerators in the International Development Space

As the world is striving to recover from the shockwaves triggered by the COVID-19 crisis, all hands are needed on deck to transition towards green recovery and make peace with nature as prerequisites of a global sustainable development pathway. In this paper, we examine the blockchain hype, the gaps in the knowledge, and the tools needed to build promising use cases for blockchain technology to accelerate global efforts in this decade of action towards achieving the SDGs. We attempt to break the “hype cycle” portraying blockchain’s superiority by navigating a rational blockchain use case development approach. By prototyping an SDG Acceleration Scorecard to use blockchain-enabled solutions as SDG accelerators, we aim to provide useful insights towards developing an integrated approach that is fit-for-purpose to guide organizations and practitioners in their quest to make informed decisions to design and implement blockchain-backed solutions as SDG accelerators. Acknowledging the limitations in prototyping such tools, we believe these are minimally viable products and should be considered as living tools that can further evolve as the blockchain technology matures, its pace of adoption increases, lessons are learned, and good practices and standards are widely shared and internalized by teams and organizations working on innovation for development.
Joint feature representation learning and progressive distribution matching for cross-project defect prediction

Cross-Project Defect Prediction (CPDP) aims to leverage the knowledge from label-rich source software projects to promote tasks in a label-poor target software project. Existing CPDP methods have two major flaws. One is that previous CPDP methods only consider global feature representation and ignores local relationship between instances in the same category from different projects, resulting in ambiguous predictions near the decision boundary. The other one is that CPDP methods based on pseudo-labels assume that the conditional distribution can be well matched at one stroke, when instances of target project are correctly annotated pseudo labels. However, due to the great gap between projects, the pseudo-labels seriously deviate from the real labels. To address above issues, this paper proposed a novel CPDP method named Joint Feature Representation with Double Marginalized Denoising Autoencoders (DMDA_JFR). Our method mainly includes two parts: joint feature representation learning and progressive distribution matching. We utilize two novel autoencoders to jointly learn the global and local feature representations simultaneously. To achieve progressive distribution matching, we introduce a repetitious pseudo-labels strategy, which makes it possible that distributions are matched after each stack layer learning rather than in one stroke. The effectiveness of the proposed method was evaluated through experiments conducted on 10 open-source projects, including 29 software releases from PROMISE repository. Overall, experimental results show that our proposed method outperformed several state-of-the-art baseline CPDP methods. It can be concluded that (1) joint deep representations are promising for CPDP compared with only considering global feature representation methods, (2) progressive distribution matching is more effective for adapting probability distributions in CPDP compared with existing CPDP methods based on pseudo-labels. • This paper proposed a novel CPDP method named Joint Feature Representation with Double Marginalized Denoising Autoencoders (DMDA-JFR). • Our method mainly includes two parts: joint feature representation learning and progressive distribution matching. • We utilize two novel autoencoders to jointly learn the global and local feature representations simultaneously. • We introduce a repetitious pseudo-labels strategy to progressively match distribution.
Strategic visualization: the (real) usefulness of cognitive mapping in smart city conceptualization

Purpose The concept of smart city has recently become more topical in academic and policy discussions. This idea is considered a complex, non-consensual subject since its definition has not yet been agreed upon by most authors in the relevant fields. The need to identify and measure smart city indicators has also given rise to many different evaluation procedures. However, the available frameworks have yet to overcome challenges in structuring and measuring all the evaluation parameters of the cities in question. Thus, methods still need to be developed and applied that can structure criteria used to assess smart city success. Design/methodology/approach This study sought to show cognitive mapping's tangible usefulness as an expedient tool for strategic analysis, using smart cities as a complex object of study. To this end, various cognitive maps were constructed and compared using the Strategic Options Development and Analysis (SODA) approach. Findings Cognitive mapping's advantages and limitations in the strategic visualization research context are analyzed and discussed. Originality/value The authors know of no prior work reporting comparative analysis of this methodological approach in the same research context.
Answering multi-dimensional range queries under local differential privacy

In this paper, we tackle the problem of answering multi-dimensional range queries under local differential privacy. There are three key technical challenges: capturing the correlations among attributes, avoiding the curse of dimensionality, and dealing with the large domains of attributes. None of the existing approaches satisfactorily deals with all three challenges. Overcoming these three challenges, we first propose an approach called Two-Dimensional Grids (TDG). Its main idea is to carefully use binning to partition the two-dimensional (2-D) domains of all attribute pairs into 2-D grids that can answer all 2-D range queries and then estimate the answer of a higher dimensional range query from the answers of the associated 2-D range queries. However, in order to reduce errors due to noises, coarse granularities are needed for each attribute in 2-D grids, losing fine-grained distribution information for individual attributes. To correct this deficiency, we further propose Hybrid-Dimensional Grids (HDG), which also introduces 1-D grids to capture finer-grained information on distribution of each individual attribute and combines information from 1-D and 2-D grids to answer range queries. To make HDG consistently effective, we provide a guideline for properly choosing granularities of grids based on an analysis of how different sources of errors are impacted by these choices. Extensive experiments conducted on real and synthetic datasets show that HDG can give a significant improvement over the existing approaches.
Cascade RNN-Transducer: Syllable Based Streaming On-Device Mandarin Speech Recognition with a Syllable-To-Character Converter

End-to-end models are favored in automatic speech recognition (ASR) because of its simplified system structure and superior performance. Among these models, recurrent neural network transducer (RNN-T) has achieved significant progress in streaming on-device speech recognition because of its high-accuracy and low-latency. RNN-T adopts a prediction network to enhance language information, but its language modeling ability is limited because it still needs paired speech-text data to train. Further strengthening the language modeling ability through extra text data, such as shallow fusion with an external language model, only brings a small performance gain. In view of the fact that Mandarin Chinese is a character-based language and each character is pronounced as a tonal syllable, this paper proposes a novel cascade RNN-T approach to improve the language modeling ability of RNN-T. Our approach firstly uses an RNN-T to transform acoustic feature into syllable sequence, and then converts the syllable sequence into character sequence through an RNN-T-based syllable-to-character converter. Thus a rich text repository can be easily used to strengthen the language model ability. By introducing several important tricks, the cascade RNN-T approach surpasses the character-based RNN-T by a large margin on several Mandarin test sets, with much higher recognition quality and similar latency.
Deep analysis of word sense disambiguation via semi-supervised learning and neural word representations

Word Sense Disambiguation (WSD) aims to determine the meaning of a word in context. Different approaches have been proposed in supervised and unsupervised domains. In most cases, supervised learning provides superior WSD performance. Since sense-annotated corpora can be difficult or time-consuming to obtain, which must be repeated for new domains, languages, and sense inventories, semi-supervised learning (SSL) methods, that combine a small amount of sense-annotated data, start to be pre-eminent. In SSL, graph-based methods are common, because they capture the relationships between terms using an undirected graph. This paper aims to investigate semi-supervised WSD by considering different graph-based SSL algorithms with features generated by word embeddings from Word2Vec, FastText, GloVe, BERT and ELECTRA models combined with parts-of-speech tags and word context. We test several combinations of word-embedding models, similarity measures for graph construction and SSL classification algorithms to disambiguate classical lexical sample WSD datasets. The results indicate our SSL algorithms achieved competitive results compared to supervised ones and the ELECTRA models performed better than other embeddings for SSL.
Document-level relation extraction with Entity-Selection Attention

Document-level relation extraction is a complex natural language processing task that predicts relations of entity pairs by capturing the critical semantic features on entity pairs from the document. However, current methods usually consider that the entity pairs contain the vast majority of information which can represent relational facts, and thus focus on modeling the entity pair, ignoring features on whole document and sentences. In the document-level relation extraction, the distance between entity pairs is relatively long. Judging the relation between entities usually requires reading many sentences or the whole document. Therefore, sentences and documents are particularly crucial for document-level relation extraction. In order to make full use of the multi-level information of sentences and documents, this paper proposes a document-level relation extraction framework with two advantages. First, we use the encoder to obtain the semantic features about the document and use the inter-sentence attention based on entity pairs to dynamically capture the features of multiple vital sentences. Second, we design a document gating that combines sentence-level features with document-level features to predict relations. Extensive experiments on a benchmark dataset have well-validated the effectiveness of the proposed method.
Wireless 3D Surgical Navigation and Tracking System With 100μm Accuracy Using Magnetic-Field Gradient-Based Localization

This paper describes a high-resolution 3D navigation and tracking system using magnetic field gradients, that can replace X-Ray fluoroscopy in high-precision surgeries. Monotonically varying magnetic fields in X, Y and Z directions are created in the field-of-view (FOV) to produce magnetic field gradients, which encode each spatial point uniquely. Highly miniaturized, wireless and battery-less devices, capable of measuring their local magnetic field, are designed to sense the gradient field. One such device can be attached to an implant inside the body and another to a surgical tool, such that both can simultaneously measure and communicate the magnetic field at their respective locations to an external receiver. The relative location of the two devices on a real-time display can enable precise surgical navigation without using X-Rays. A prototype device is designed consisting of a micro-chip fabricated in 65nm CMOS technology, a 3D magnetic sensor and an inductor-coil. Planar electromagnetic coils are designed for creating the 3D magnetic field gradients in a 20×20×10cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> of scalable FOV. Unambiguous and orientation-independent spatial encoding is achieved by: (i) using the gradient in the total field magnitude instead of only the Z-component; and (ii) using a combination of the gradient fields to correct for the non-linearity and non-monotonicity in X and Y gradients. The resultant X and Y FOV yield ≥90% utilization of their respective coil-span. The system is tested in vitro to demonstrate a localization accuracy of <; 100 μ m in 3D, the highest reported to the best of our knowledge.
EBRB cascade classifier for imbalanced data via rule weight updating

In recent years, data imbalance in the conventional classification problem has raised great interest in the industry. However, concerning the rule-based systems, this problem has been rarely investigated. We propose a Belief Rule-Based (BRB) reasoning model based on the evidential reasoning algorithm to fill in the gap by solving the problem caused by imbalanced data with regards to the rule-based systems. It utilizes the data-driven characteristics of Extended Belief Rule-Based (EBRB) and incorporates the data class rebalancing technique with the ensemble learning method of EBRB. In order to increase the number of rules that can promote the correct classification of minority classes in EBRB, we apply an adaptive data resampling strategy to expand the proportion of minority classes. On this basis, the data is divided into several EBRB base learners with different classification characteristics. During the iterative process, the rule weight of the base learner is dynamically adjusted according to the correct and incorrect classification, such that the classifier can pay more attention to the minority classes and reduce the bias of classification. Finally, the optimized EBRB base learner is selected to build an ensemble classifier with excellent classification performance. Experiments on 27 binary class and 12 multi-class imbalanced datasets show that our approach significantly improves F-value and MACC compared with other imbalanced classification algorithms. By building a cascaded EBRB model, the activation weight is taken as the basis of model evolution, the application limitations of the EBRB model in the ensemble learning are solved innovatively, and the classification performance of the EBRB model for imbalanced data is improved significantly.