Reviewerly/exHarmony huggingface dataset:
Cryptocurrency, a successful application of blockchain technology

Purpose The purpose of this paper is to identify the applications and contributions of blockchain technology in finance in general, and to identify areas where the technology can make a larger impact in payment systems. Design/methodology/approach The authors do an exhaustive review of blockchain technology and cryptocurrency, and examine the successful applications of blockchain technology in several finance disciplines including cryptocurrency. The authors critically evaluate the technical studies on behaviors in cryptocurrency prices. Findings Cryptocurrency is the first successful application of blockchain technology and can be used as the main fuel of the global money transfer network. Research limitations/implications Blockchain is a revolutionary technology that can change the world with its convenience, transparency, accuracy and efficiency in speed and cost. The growth of blockchain usage in finance depends on further familiarization and trust gained by an increasing number of proven successful usage cases and testimonials as well as appropriate legislative changes. Originality/value This paper provides a comprehensive review of the contributions that blockchain technology has made and is expected to make in the field of finance with the aim of adding value to corporate executives, investors, policy makers and a general audience.
Structured Graph Optimization for Unsupervised Feature Selection

Unsupervised feature selection has attracted more and more attention due to the rapid growth of the large amount of unlabelled and high-dimensional data. The performance of traditional spectral-based unsupervised methods always depends on the quality of constructed similarity matrix. However, real world data always contain a large number of noise samples and features that make the similarity matrix created by original data cannot be fully relied. We propose an unsupervised feature selection method which conducts feature selection and local structure learning simultaneously. Moreover, we add an important constraint on the similarity matrix to allow it to capture more accurate information of the data structure. To perform feature selection, orthogonal constraint and `2;p-norm are adopted on the projection matrix. An efficient and simple algorithm is derived to tackle the problem. We conduct comprehensive experiments on various benchmark data sets, including handwritten digit, face image, and biomedical data, to validate the effectiveness of the proposed approach.
General framework for verifying pure quantum states in the adversarial scenario

Bipartite and multipartite entangled states are of central interest in quantum information processing and foundational studies. Efficient verification of these states, especially in the adversarial scenario, is a key to various applications, including quantum computation, quantum simulation, and quantum networks. However, little is known about this topic in the adversarial scenario. Here we initiate a systematic study of pure-state verification in the adversarial scenario. In particular, we introduce a general method for determining the minimal number of tests required by a given strategy to achieve a given precision. In the case of homogeneous strategies, we can even derive an analytical formula. Furthermore, we propose a general recipe to verifying pure quantum states in the adversarial scenario by virtue of protocols for the nonadversarial scenario. Thanks to this recipe, the resource cost for verifying an arbitrary pure state in the adversarial scenario is comparable to the counterpart for the nonadversarial scenario, and the overhead is at most three times for high-precision verification. Our recipe can readily be applied to efficiently verify bipartite pure states, stabilizer states, hypergraph states, weighted graph states, and Dicke states in the adversarial scenario, even if only local projective measurements are accessible. This paper is an extended version of the companion paper Zhu and Hayashi, Phys. Rev. Lett. 123, 260504 (2019).
A Repository of Conversational Datasets

Matthew Henderson, Paweł Budzianowski, Iñigo Casanueva, Sam Coope, Daniela Gerz, Girish Kumar, Nikola Mrkšić, Georgios Spithourakis, Pei-Hao Su, Ivan Vulić, Tsung-Hsien Wen. Proceedings of the First Workshop on NLP for Conversational AI. 2019.
Learning to Extract Flawless Slow Motion From Blurry Videos

In this paper, we introduce the task of generating a sharp slow-motion video given a low frame rate blurry video. We propose a data-driven approach, where the training data is captured with a high frame rate camera and blurry images are simulated through an averaging process. While it is possible to train a neural network to recover the sharp frames from their average, there is no guarantee of the temporal smoothness for the formed video, as the frames are estimated independently. To address the temporal smoothness requirement we propose a system with two networks: One, DeblurNet, to predict sharp keyframes and the second, InterpNet, to predict intermediate frames between the generated keyframes. A smooth transition is ensured by interpolating between consecutive keyframes using InterpNet. Moreover, the proposed scheme enables further increase in frame rate without retraining the network, by applying InterpNet recursively between pairs of sharp frames. We evaluate the proposed method on several datasets, including a novel dataset captured with a Sony RX V camera. We also demonstrate its performance of increasing the frame rate up to 20 times on real blurry videos.
BDPL: A Boundary Differentially Private Layer Against Machine Learning Model Extraction Attacks

Machine learning models trained by large volume of proprietary data and intensive computational resources are valuable assets of their owners, who merchandise these models to third-party users through prediction service API. However, existing literature shows that model parameters are vulnerable to extraction attacks which accumulate a large number of prediction queries and their responses to train a replica model. As countermeasures, researchers have proposed to reduce the rich API output, such as hiding the precise confidence level of the prediction response. Nonetheless, even with response being only one bit, an adversary can still exploit fine-tuned queries with differential property to infer the decision boundary of the underlying model. In this paper, we propose boundary differential privacy ( $$\epsilon $$ -BDP) as a solution to protect against such attacks by obfuscating the prediction responses near the decision boundary. $$\epsilon $$ -BDP guarantees an adversary cannot learn the decision boundary by a predefined precision no matter how many queries are issued to the prediction API. We design and prove a perturbation algorithm called boundary randomized response that can achieve $$\epsilon $$ -BDP. The effectiveness and high utility of our solution against model extraction attacks are verified by extensive experiments on both linear and non-linear models.
Robust deep auto-encoding Gaussian process regression for unsupervised anomaly detection

Unsupervised anomaly detection (AD) is of great importance in both fundamental machine learning researches and industrial applications. Previous approaches have achieved great advance in improving the performance of unsupervised AD model recently. However, there are still some thorny issues unsolved, especially the problem of efficiency degradation when dealing with high-dimensional data and the inability to maintain robustness when dealing with contaminated data, which have not been addressed simultaneously in the existing models. In our work, we propose a novel hybrid unsupervised AD method, which first integrates convolutional auto-encoder and Gaussian process regression to extract features and to remove anomalies from noisy data as well. Our model behaves more effectively at modeling high-dimension data and more robust to variation of the anomaly rate in dataset. We evaluate its performance on four publicly benchmark datasets and show the state-of-the-art performance against competitive methods.
Label Distribution Learning with Label Correlations on Local Samples

Label distribution learning (LDL) is proposed for solving the label ambiguity problem in recent years, which can be seen as an extension of multi-label learning. To improve the performance of label distribution learning, some existing algorithms exploit label correlations in a global manner that assumes the label correlations are shared by all instances. However, the instances in different groups may share different label correlations, and few label correlations are globally applicable in real-world tasks. In this paper, two novel label distribution learning algorithms are proposed by exploiting label correlations on local samples, which are called GD-LDL-SCL and Adam-LDL-SCL, respectively. To utilize the label correlations on local samples, the influence of local samples is encoded, and a local correlation vector is designed as the additional features for each instance, which is based on the different clustered local samples. Then, the label distribution for an unseen instance can be predicted by exploiting the original features and the additional features simultaneously. Extensive experiments on some real-world data sets validate that our proposed methods can address the label distribution problems effectively and outperform state-of-the-art methods.
Path-based reasoning approach for knowledge graph completion using CNN-BiLSTM with attention mechanism

Knowledge graphs are valuable resources for building intelligent systems such as question answering or recommendation systems. However, most knowledge graphs are impaired by missing relationships between entities. Embedding methods that translate entities and relations into a low-dimensional space achieve great results, but they only focus on the direct relations between entities and neglect the presence of path relations in graphs. On the contrary, path-based embedding methods consider a single path to make inferences. It also relies on simple recurrent neural networks while highly efficient neural network models are available for processing sequence data. We propose a new approach for knowledge graph completion that combines bidirectional long short-term memory (BiLSTM) and convolutional neural network modules with an attention mechanism. Given a candidate relation and two entities, we encode paths that connect the entities into a low-dimensional space using a convolutional operation followed by BiLSTM. Then, an attention layer is applied to capture the semantic correlation between a candidate relation and each path between two entities and attentively extract reasoning evidence from the representation of multiple paths to predict whether the entities should be connected by the candidate relation. We extend our model to perform multistep reasoning over path representations in an embedding space. A recurrent neural network is designed to repeatedly interact with an attention module to derive logical inference from the representation of multiple paths. We perform link prediction tasks on several knowledge graphs and show that our method achieves better performance compared with recent state-of-the-art path-reasoning methods.
A Real-Time ATC Safety Monitoring Framework Using a Deep Learning Approach

A deep learning-based safety monitoring framework for air traffic control (ATC) systems is proposed in this paper to reduce human errors and relieve the controllers' workload by regulating the controlling procedure, eliminating communication misunderstanding, monitoring flight conformance, and detecting potential conflicts. The framework comprises automatic speech recognition (ASR), controlling intent inference (CII), and control safety monitoring (CSM) subsystems. The pipeline of the proposed framework can be described as follows: the ASR subsystem translates the pilot-controller voice communications (PCVCs) into texts, which are then converted to the predefined data structure by the CII subsystem. Three types of air traffic safety measures, including repetition check, flight conformance verification, and potential conflict detection, are finally validated by the CSM subsystem. An improved end-to-end ASR model with convolutional, bidirectional long short-term memory (BLSTM) and fully connected (FC) layers is trained using the connectionist temporal classification loss function. The BLSTM and FC combined CII model is designed to infer the controlling intent and slot filling. A language model is also trained in this subsystem to improve the overall performance of the framework. After converting the PCVCs to ATC data, the CSM subsystem checks the given safety monitoring tasks and sends warnings to the current system. The experimental results show that the proposed ASR model obtains a better performance than that of other approaches, and the tasks in the CII subsystem are fulfilled with a high classification precision. The CSM subsystem is also tested to confirm its safety monitoring function by playing back the data and several simulated instructions. To the best of our knowledge, this is pioneering work in the safety monitoring of flight control by recognizing the PCVCs with deep learning-based methods.
Conceptual Framework of the Use of Robots, Artificial Intelligence and Service Automation in Travel, Tourism, and Hospitality Companies

Purpose: The purpose of this chapter is to elaborate on the major conceptual and practical considerations of the use of robots, artificial intelligence and service automation (RAISA) in travel, tourism, and hospitality companies (TTH).Design/methodology/approach: The chapter develops a conceptual framework of the major issues related to the use of RAISA in the travel, tourism and hospitality context.Findings: The findings indicate that while there is a creeping incursion of RAISA into TTH, there are major concerns that the TTH industry has to consider in regard to automating TTH services.Practical implications: In a practical sense, the chapter identifies the decisions that TTH industry professionals need to take when dealing with RAISA technologies. Furthermore, the chapter elaborates on the impacts RAISA have on business operations, marketing management, human resources and financial management of TTH companies. The TTH industry has to adjust its practices and communicate with its workforce in ways as not to increase Luddite tendencies and resistance among employees.Social implications: The analysis shows that there is an upcoming era in which automation of services will be so advanced that wealthy countries may not need to import labour to make up with its own aging workforce, suggesting that RAISA and its further development has the potential for disrupting society and international relations.Originality/value: This chapter provides a comprehensive review of the issues related to the use of RAISA in the TTH industry, including the drivers of RAISA adoption in tourism, advantages and disadvantages of RAISA technologies compared to human employees, decisions that managers need to take, and the impacts of RAISA on business processes. It shows how macroenvironmental pressures shape the microeconomic decisions to use RAISA in a TTH context.
It Matters What Stories Tell Stories; It Matters Whose Stories Tell Stories

Aiming to craft symstories and symbiographies, this essay proposes several short instances of compost writing. First, retelling another’s personal and family stories foregrounds the question of who owns stories, who has access to whose stories, who is safe enough to tell their stories, and who lives and dies as a result. Second, represented by the Crochet Coral Reef, earth stories propose reconfiguring organisms as holobionts to foreground collective becoming-with. Third, an Iñupiak computer game shows the complexity of collaborations for telling decolonial geostories in continuing times of rapid destruction and extinction. Finally, the author’s science fiction [SF] story proposes speculative fiction to strengthen the difficult search for multispecies reproductive justice.
DiamondGAN: Unified Multi-modal Generative Adversarial Networks for MRI Sequences Synthesis

Synthesizing MR imaging sequences is highly relevant in clinical practice, as single sequences are often missing or are of poor quality (e.g. due to motion). Naturally, the idea arises that a target modality would benefit from multi-modal input, as proprietary information of individual modalities can be synergistic. However, existing methods fail to scale up to multiple non-aligned imaging modalities, facing common drawbacks of complex imaging sequences. We propose a novel, scalable and multi-modal approach called DiamondGAN. Our model is capable of performing flexible non-aligned cross-modality synthesis and data infill, when given multiple modalities or any of their arbitrary subsets, learning structured information in an end-to-end fashion. We synthesize two MRI sequences with clinical relevance (i.e., double inversion recovery (DIR) and contrast-enhanced T1 (T1-c)), reconstructed from three common sequences. In addition, we perform a multi-rater visual evaluation experiment and find that trained radiologists are unable to distinguish synthetic DIR images from real ones.
Learning to Coordinate Video Codec with Transport Protocol for Mobile Video Telephony

Despite the pervasive use of real-time video telephony services, the users' quality of experience (QoE) remains unsatisfactory, especially over the mobile Internet. Previous work studied the problem via controlled experiments, while a systematic and in-depth investigation in the wild is still missing. To bridge the gap, we conduct a large-scale measurement campaign on \appname, an operational mobile video telephony service. Our measurement logs fine-grained performance metrics over 1 million video call sessions. Our analysis shows that the application-layer video codec and transport-layer protocols remain highly uncoordinated, which represents one major reason for the low QoE. We thus propose \name, a machine learning based framework to resolve the issue. Instead of blindly following the transport layer's estimation of network capacity, \name reviews historical logs of both layers, and extracts high-level features of codec/network dynamics, based on which it determines the highest bitrates for forthcoming video frames without incurring congestion. To attain the ability, we train \name with the aforementioned massive data traces using a custom-designed imitation learning algorithm, which enables \name to learn from past experience. We have implemented and incorporated \name into \appname. Our experiments show that \name outperforms state-of-the-art solutions, improving video quality while reducing stalling time by multi-folds under various practical scenarios.
Privacy-enhanced multi-party deep learning

In multi-party deep learning, multiple participants jointly train a deep learning model through a central server to achieve common objectives without sharing their private data. Recently, a significant amount of progress has been made toward the privacy issue of this emerging multi-party deep learning paradigm. In this paper, we mainly focus on two problems in multi-party deep learning. The first problem is that most of the existing works are incapable of defending simultaneously against the attacks of honest-but-curious participants and an honest-but-curious server without a manager trusted by all participants. To tackle this problem, we design a privacy-enhanced multi-party deep learning framework, which integrates differential privacy and homomorphic encryption to prevent potential privacy leakage to other participants and a central server without requiring a manager that all participants trust. The other problem is that existing frameworks consume high total privacy budget when applying differential privacy for preserving privacy, which leads to a high risk of privacy leakage. In order to alleviate this problem, we propose three strategies for dynamically allocating privacy budget at each epoch to further enhance privacy guarantees without compromising the model utility. Moreover, it provides participants with an intuitive handle to strike a balance between the privacy level and the training efficiency by choosing different strategies. Both analytical and experimental evaluations demonstrate the promising performance of our proposed framework.
Creating a Vocabulary for Data Privacy

Managing privacy and understanding handling of personal data has turned into a fundamental right, at least within the European Union, with the General Data Protection Regulation (GDPR) being enforced since May 25th 2018. This has led to tools and services that promise compliance to GDPR in terms of consent management and keeping track of personal data being processed. The information recorded within such tools, as well as that for compliance itself, needs to be interoperable to provide sufficient transparency in its usage. Additionally, interoperability is also necessary towards addressing the right to data portability under GDPR as well as creation of user-configurable and manageable privacy policies. We argue that such interoperability can be enabled through agreement over vocabularies using linked data principles. The W3C Data Privacy Vocabulary and Controls Community Group (DPVCG) was set up to jointly develop such vocabularies towards interoperability in the context of data privacy. This paper presents the resulting Data Privacy Vocabulary (DPV), along with a discussion on its potential uses, and an invitation for feedback and participation.
Lossless Image Compression Techniques: A State-of-the-Art Survey

Modern daily life activities result in a huge amount of data, which creates a big challenge for storing and communicating them. As an example, hospitals produce a huge amount of data on a daily basis, which makes a big challenge to store it in a limited storage or to communicate them through the restricted bandwidth over the Internet. Therefore, there is an increasing demand for more research in data compression and communication theory to deal with such challenges. Such research responds to the requirements of data transmission at high speed over networks. In this paper, we focus on deep analysis of the most common techniques in image compression. We present a detailed analysis of run-length, entropy and dictionary based lossless image compression algorithms with a common numeric example for a clear comparison. Following that, the state-of-the-art techniques are discussed based on some bench-marked images. Finally, we use standard metrics such as average code length (ACL), compression ratio (CR), pick signal-to-noise ratio (PSNR), efficiency, encoding time (ET) and decoding time (DT) in order to measure the performance of the state-of-the-art techniques.