Reviewerly/exHarmony huggingface dataset:
Shannon Perfect Secrecy in a Discrete Hilbert Space

The One-time-pad (OTP) was mathematically proven to be perfectly secure by Shannon in 1949. We propose to extend the classical OTP from an n-bit finite field to the entire symmetric group over the finite field. Within this context the symmetric group can be represented by a discrete Hilbert sphere (DHS) over an n-bit computational basis. Unlike the continuous Hilbert space defined over a complex field in quantum computing, a DHS is defined over the finite field GF(2). Within this DHS, the entire symmetric group can be completely described by the complete set of n-bit binary permutation matrices. Encoding of a plaintext can be done by randomly selecting a permutation matrix from the symmetric group to multiply with the computational basis vector associated with the state corresponding to the data to be encoded. Then, the resulting vector is converted to an output state as the ciphertext. The decoding is the same procedure but with the transpose of the pre-shared permutation matrix. We demonstrate that under this extension, the 1-to-1 mapping in the classical OTP is equally likely decoupled in Discrete Hilbert Space. The uncertainty relationship between permutation matrices protects the selected pad, consisting of M permutation matrices (also called Quantum permutation pad, or QPP). QPP not only maintains the perfect secrecy feature of the classical formulation but is also reusable without invalidating the perfect secrecy property. The extended Shannon perfect secrecy is then stated such that the ciphertext C gives absolutely no information about the plaintext P and the pad.
Dual Adversarial Network for Deep Active Learning

Active learning, reducing the cost and workload of annotations, attracts increasing attentions from the community. Current active learning approaches commonly adopted uncertainty-based acquisition functions for the data selection due to their effectiveness. However, data selection based on uncertainty suffers from the overlapping problem, i.e., the top-K samples ranked by the uncertainty are similar. In this paper, we investigate the overlapping problem of recent uncertainty-based approaches and propose to alleviate the issue by taking representativeness into consideration. In particular, we propose a dual adversarial network, namely DAAL, for this purpose. Different from previous hybrid active learning methods requiring multi-stage data selections i.e., step-by-step evaluating the uncertainty and representativeness using different acquisition functions, our DAAL learns to select the most uncertain and representative data points in one-stage. Extensive experiments conducted on three publicly available datasets, i.e., CIFAR10/100 and Cityscapes, demonstrate the effectiveness of our method—a new state-of-the-art accuracy is achieved.
Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes

We present a method to perform novel view and time synthesis of dynamic scenes, requiring only a monocular video with known camera poses as input. To do this, we introduce Neural Scene Flow Fields, a new representation that models the dynamic scene as a time-variant continuous function of appearance, geometry, and 3D scene motion. Our representation is optimized through a neural network to fit the observed input views. We show that our representation can be used for complex dynamic scenes, including thin structures, view-dependent effects, and natural degrees of motion. We conduct a number of experiments that demonstrate our approach significantly outperforms recent monocular view synthesis methods, and show qualitative results of space-time view synthesis on a variety of real-world videos.
A Statistical Theory of Language Translation Based on Communication Theory

We propose the first statistical theory of language translation based on communication theory.The theory is based on New Testament translations from Greek to Latin and to other 35 modern languages.In a text translated into another language, all linguistic variables do numerically change.To study the chaotic data that emerge, we model any translation as a complex communication channel affected by "noise", studied according to Communication Theory applied for the first time to this channel.This theory deals with aspects of languages more complex than those currently considered in machine translations.The input language is the "signal", the output language is a "replica" of the input language, but largely perturbed by noise, indispensable, however, for conveying the meaning of the input language to its readers.We have defined a noise-to-signal power ratio and found that channels are differently affected by translation noise.Communication channels are also characterized by channel capacity.The translation of novels has more constraints than the New Testament translations.We propose a global readability formula for alphabetical languages, not available for most of them, and conclude with a general theory of language translation which shows that direct and reverse channels are not symmetric.The general theory can also be applied to channels of texts belonging to the same language both to study how texts of the same author may have changed over time, or to compare texts of different authors.In conclusion, a common underlying mathematical structure governing human textual/verbal communication channels seems to emerge.Language does not play the only role in translation; this role is shared with reader's reading ability and short-term memory capacity.Different versions of New Testament within the same language can even seem, mathematically, to belong to different languages.These conclusions are everlasting because valid also for ancient Roman and Greek readers.
VoxSRC 2020: The Second VoxCeleb Speaker Recognition Challenge

We held the second installment of the VoxCeleb Speaker Recognition Challenge in conjunction with Interspeech 2020. The goal of this challenge was to assess how well current speaker recognition technology is able to diarise and recognize speakers in unconstrained or `in the wild' data. It consisted of: (i) a publicly available speaker recognition and diarisation dataset from YouTube videos together with ground truth annotation and standardised evaluation software; and (ii) a virtual public challenge and workshop held at Interspeech 2020. This paper outlines the challenge, and describes the baselines, methods used, and results. We conclude with a discussion of the progress over the first installment of the challenge.
Deep learning of cuneiform sign detection with weak supervision using transliteration alignment

The cuneiform script provides a glimpse into our ancient history. However, reading age-old clay tablets is time-consuming and requires years of training. To simplify this process, we propose a deep-learning based sign detector that locates and classifies cuneiform signs in images of clay tablets. Deep learning requires large amounts of training data in the form of bounding boxes around cuneiform signs, which are not readily available and costly to obtain in the case of cuneiform script. To tackle this problem, we make use of existing transliterations, a sign-by-sign representation of the tablet content in Latin script. Since these do not provide sign localization, we propose a weakly supervised approach: We align tablet images with their corresponding transliterations to localize the transliterated signs in the tablet image, before using these localized signs in place of annotations to re-train the sign detector. A better sign detector in turn boosts the quality of the alignments. We combine these steps in an iterative process that enables training a cuneiform sign detector from transliterations only. While our method works weakly supervised, a small number of annotations further boost the performance of the cuneiform sign detector which we evaluate on a large collection of clay tablets from the Neo-Assyrian period. To enable experts to directly apply the sign detector in their study of cuneiform texts, we additionally provide a web application for the analysis of clay tablets with a trained cuneiform sign detector.
Automatic Distractor Generation for Multiple Choice Questions in Standard Tests

To assess the knowledge proficiency of a learner, multiple choice question is an efficient and widespread form in standard tests.However, the composition of the multiple choice question, especially the construction of distractors is quite challenging.The distractors are required to both incorrect and plausible enough to confuse the learners who did not master the knowledge.Currently, the distractors are generated by domain experts which are both expensive and timeconsuming.This urges the emergence of automatic distractor generation, which can benefit various standard tests in a wide range of domains.In this paper, we propose a quEstion and answer guided Distractor GEneration (EDGE) framework to automate distractor generation.EDGE consists of three major modules: (1) the Reforming Question Module and the Reforming Passage Module apply gate layers to guarantee the inherent incorrectness of the generated distractors; (2) the Distractor Generator Module applies attention mechanism to control the level of plausibility.Experimental results on a large-scale public dataset demonstrate that our model significantly outperforms existing models and achieves a new state-of-the-art.
SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation.

We investigate how to adapt simultaneous text translation methods such as wait-k and monotonic multihead attention to end-to-end simultaneous speech translation by introducing a pre-decision module. A detailed analysis is provided on the latency-quality trade-offs of combining fixed and flexible pre-decision with fixed and flexible policies. We also design a novel computation-aware latency metric, adapted from Average Lagging.
Augmenting NLP models using Latent Feature Interpolations

Models with a large number of parameters are prone to over-fitting and often fail to capture the underlying input distribution.We introduce Emix, a data augmentation method that uses interpolations of word embeddings and hidden layer representations to construct virtual examples.We show that Emix shows significant improvements over previously used interpolation based regularizers and data augmentation techniques.We also demonstrate how our proposed method is more robust to sparsification.We highlight the merits of our proposed methodology by performing thorough quantitative and qualitative assessments.
Conversational Machine Comprehension: a Literature Review

Conversational Machine Comprehension (CMC), a research track in conversational AI, expects the machine to understand an open-domain natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text.While most of the research in Machine Reading Comprehension (MRC) revolves around single-turn question answering (QA), multi-turn CMC has recently gained prominence, thanks to the advancement in natural language understanding via neural language models such as BERT and the introduction of large-scale conversational datasets such as CoQA and QuAC.The rise in interest has, however, led to a flurry of concurrent publications, each with a different yet structurally similar modeling approach and an inconsistent view of the surrounding literature.With the volume of model submissions to conversational datasets increasing every year, there exists a need to consolidate the scattered knowledge in this domain to streamline future research.This literature review attempts at providing a holistic overview of CMC with an emphasis on the common trends across recently published models, specifically in their approach to tackling conversational history.The review synthesizes a generic framework for CMC models while highlighting the differences in recent approaches and intends to serve as a compendium of CMC for future researchers.
KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi

Recent progress in text classification has been focused on high-resource languages such as English and Chinese.For low-resource languages, amongst them most African languages, the lack of well-annotated data and effective preprocessing, is hindering the progress and the transfer of successful methods.In this paper, we introduce two news datasets (KINNEWS and KIRNEWS) for multi-class classification of news articles in Kinyarwanda and Kirundi, two low-resource African languages.The two languages are mutually intelligible, but while Kinyarwanda has been studied in Natural Language Processing (NLP) to some extent, this work constitutes the first study on Kirundi.Along with the datasets, we provide statistics, guidelines for preprocessing, and monolingual and cross-lingual baseline models.Our experiments show that training embeddings on the relatively higher-resourced Kinyarwanda yields successful cross-lingual transfer to Kirundi.In addition, the design of the created datasets allows for a wider use in NLP beyond text classification in future studies, such as representation learning, cross-lingual learning with more distant languages, or as base for new annotations for tasks such as parsing, POS tagging, and NER.The datasets, stopwords, and pre-trained embeddings are publicly available at https:
Navigating ambiguous negativity: A case study of Twitch.tv live chats

On Twitch.tv, the gaming-oriented live streaming platform, users interact by sharing and viewing gameplay and participating in live chats. Negativity in online gaming is often explored on a large scale using broad categories. This study offers a nuanced look at Twitch.tv communities dedicated to the Dark Souls game series to examine the descriptive and injunctive community norms surrounding both aggressive negativity and ambiguous negativity, which involves interactions where the valence is not obvious and must be interpreted based on community-specific meanings and rules. This study systematically analyzed excerpts of chats and stream recordings of 22 live streamed sessions. It found ambiguous negativity was prevalent in cases of cursing, game jargon, banter, spam, and sarcasm. Aggressive negativity was rare but manifested in exclusionary language and banter gone too far. The response of community members and collective acceptance or rejection of such negativity was not clearly defined.
Twin-Field Quantum Key Distribution with Fully Discrete Phase Randomization

Twin-field (TF) quantum key distribution (QKD) can overcome fundamental secret-key-rate bounds on point-to-point QKD links, allowing us to reach longer distances than ever before. Since its introduction, several TF-QKD variants have been proposed, and some of them have already been implemented experimentally. Most of them assume that the users can emit weak coherent pulses with a continuous random phase. In practice, this assumption is often not satisfied, which could open up security loopholes in their implementations. To close this loophole, we propose and prove the security of a TF-QKD variant that relies exclusively on discrete phase randomisation. Remarkably, our results show that it can also provide higher secret-key rates than counterpart protocols that rely on continuous phase randomisation.
Financial Development and Economic Growth: Evindence From Azerbaijan

The impact of financial development on economic growth has always been an important issue. Especially when financial crises occur, the relationship between financial markets and financial crises and economic activities is on the agenda. The main purpose of this study is to determine the relationship between financial development and economic growth in the Azerbaijani economy. For this purpose, VECM model estimation and Granger causality analysis was performed by taking monthly data between 2005-2019. As a result of the analysis, it was revealed that there is a two-way relationship between financial development and economic growth in Azerbaijan
MVHM: A Large-Scale Multi-View Hand Mesh Benchmark for Accurate 3D Hand Pose Estimation

Estimating 3D hand poses from a single RGB image is challenging because depth ambiguity leads the problem ill-posed. Training hand pose estimators with 3D hand mesh annotations and multi-view images often results in significant performance gains. However, existing multi-view datasets are relatively small with hand joints annotated by off-the-shelf trackers or automated through model predictions, both of which may be inaccurate and can introduce biases. Collecting a large-scale multi-view 3D hand pose images with accurate mesh and joint annotations is valuable but strenuous. In this paper, we design a spin match algorithm that enables a rigid mesh model matching with any target mesh ground truth. Based on the match algorithm, we propose an efficient pipeline to generate a large-scale multi-view hand mesh (MVHM) dataset with accurate 3D hand mesh and joint labels. We further present a multi-view hand pose estimation approach to verify that training a hand pose estimator with our generated dataset greatly enhances the performance. Experimental results show that our approach achieves the performance of 0.990 in AUC20-50 on the MHP dataset compared to the previous state-of-the-art of 0.939 on this dataset. Our datasset is available at https://github.com/Kuzphi/MVHM.
Detecting Anomaly and Its Sources in Activities of Daily Living

Abstract To support the independent living and improve the quality of life for the increasing ageing population, system for monitoring their daily routine and detecting anomalies in the routine is required. Existing anomaly detection systems are unable to identify the sources of the abnormalities, thereby hindering the development of adaptive monitoring systems with reduced false prediction rate. In this paper, an approach for identifying the sources of abnormalities in human activities of daily living is proposed. Anomalies are detected by modelling the existing activity data representing the usual behavioural routine of an individual to serve as a baseline model. Subsequent activities deviating from the baseline are then classified as outliers or anomalies. An ensemble of one-class support vector machine, isolation forest, robust covariance estimator and local outlier factor is utilised for the anomaly detection achieving an accuracy of $$98\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>98</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math> . The proposed approach for identifying anomaly sources is based on the concept of similarity measure using distance functions. Two methods for measuring the pairwise distance of the features of the activity data termed as one vs one similarity measure and one vs all similarity measure are proposed. Experimental evaluation of the proposed approach on activities of daily living datasets has shown the credibility of the proposed approach for utilisation in an in-home monitoring system.
How Artificial Intelligence Will Change the Future of Tourism Industry: The Practice in China

Abstract In the future, artificial intelligence (AI) is likely to substantially change both the tourism industry and tourist behavior. At present, research on artificial intelligence and tourism is receiving widespread attention, but most of them focus on a certain subject or a specific aspect of the tourism industry. For example, artificial intelligence influences the behavior of tourists and tourism enterprises. The analysis of the impact of artificial intelligence on the tourism industry as a system is still insufficient. Therefore, this research proposes a multi-dimensional framework from an industry perspective based on the existing definition of artificial intelligence. The framework involves three aspects: the level of intelligence, task types, and whether artificial intelligence is embedded in robots. The authors use a large number of Chinese practice cases to investigate how AI affects the tourism industry, then put forward a research agenda to analyze how destination government, tourism enterprises and tourist experience will change in the future. Finally, they highlight important issues related to privacy, prejudice and ethics.
Collaborative Deep Forest Learning for Recommender Systems

Collaborative filtering (CF) is one of the most practical approaches on recommendation systems by predicting users' preferences for items based on the user-item interaction information. Besides the connections between users and items, social networks among users can provide auxiliary information to improve the performance of recommender systems. Here, we propose an end-to-end deep learning framework by learning latent social features to embed in a CF approach. First, representation learning is employed on the rating matrix to extract the latent social features. Then, a novel deep learning approach based on cascade tree forest is used in the recommendation process. Experiments on real-world datasets from different domains demonstrate that the proposed Collaborative Deep Forest Learning (CDFL) outperforms the state-of-the-art CF recommendation methods.
Security of quantum key distribution with source and detection imperfections

Abstract In practice, the device imperfections might introduce deviations from the idealized models used in the security proofs of quantum key distribution (QKD). This requires the refined security analysis for practical QKD. However, in most of previous analysis, the imperfections are individually considered with different models. Here, we derive a security analysis which takes both the source and detection imperfections into account. Particularly, the efficiency mismatch in the detection and a number of flaws in the source (such as, inaccuracy of encoded quantum state, side-channel of source, distinguishable decoy states, Trojan-horse, and so on) are analyzed in a general security model. Then the performance of the QKD system with the devices imperfections is evaluated. Our results present an important step toward the practical security of QKD wit realistic devices.
Unpaired medical image colorization using generative adversarial network

Abstract We consider medical image transformation problems where a grayscale image is transformed into a color image. The colorized medical image should have the same features as the input image because extra synthesized features can increase the possibility of diagnostic errors. In this paper, to secure colorized medical images and improve the quality of synthesized images, as well as to leverage unpaired training image data, a colorization network is proposed based on the cycle generative adversarial network (CycleGAN) model, combining a perceptual loss function and a total variation (TV) loss function. Visual comparisons and experimental indicators from the NRMSE, PSNR, and SSIM metrics are used to evaluate the performance of the proposed method. The experimental results show that GAN-based style conversion can be applied to colorization of medical images. As well, the introduction of perceptual loss and TV loss can improve the quality of images produced as a result of colorization better than the result generated by only using the CycleGAN model.