Reviewerly/exHarmony huggingface dataset:
Which Cryptocurrencies Are Mostly Traded in Distressed Times?

This paper investigates the level of liquidity of digital currencies during the very intense bearish phase in their markets. The data employed span the period from April 2018 until January 2019, which is the second phase of bearish times with almost constant decreases. The Amihud’s illiquidity ratio is employed in order to measure the liquidity of these digital assets. Findings indicate that the most popular cryptocurrencies exhibit higher levels of liquidity during stressed periods. Thereby, it is revealed that investors’ preferences for trading during highly risky times are favorable for well-known virtual currencies in the detriment of less-known ones. This enhances findings of relevant literature about strong and persistent positive or negative herding behavior of investors based on Bitcoin, Ethereum and highly-capitalized cryptocurrencies in general. Notably though, a tendency towards investing in the TrueUSD stablecoin has also emerged.
SLTFNet: A spatial and language-temporal tensor fusion network for video moment retrieval

This paper focuses on temporal retrieval of activities in videos via sentence queries. Given a sentence query describing an activity, temporal moment retrieval aims at localizing the temporal segment within the video that best describes the textual query. This is a general yet challenging task as it requires the comprehending of both video and language. Existing research predominantly employ coarse frame-level features as the visual representation, obfuscating the specific details (e.g., the desired objects “girl”, “cup” and action “pour”) within the video which may provide critical cues for localizing the desired moment. In this paper, we propose a novel Spatial and Language-Temporal Tensor Fusion (SLTF) approach to resolve those issues. Specifically, the SLTF method first takes advantage of object-level local features and attends to the most relevant local features (e.g., the local features “girl”, “cup”) by spatial attention. Then we encode the sequence of the local features on consecutive frames by employing LSTM network, which can capture the motion information and interactions among these objects (e.g., the interaction “pour” involving these two objects). Meanwhile, language-temporal attention is utilized to emphasize the keywords based on moment context information. Thereafter, a tensor fusion network learns both the intra-modality and inter-modality dynamics, which can enhance the learning of moment-query representation. Therefore, our proposed two attention sub-networks can adaptively recognize the most relevant objects and interactions in the video, and simultaneously highlight the keywords in the query for retrieving the desired moment. Experimental results on three public benchmark datasets (obtained from TACOS, Charades-STA, and DiDeMo) show that the SLTF model significantly outperforms current state-of-the-art approaches, and demonstrate the benefits produced by new technologies incorporated into SLTF.
Continuous-variable Quantum Phase Estimation based on Machine Learning

Abstract Making use of the general physical model of the Mach-Zehnder interferometer with photon loss which is a fundamental physical issue, we investigate the continuous-variable quantum phase estimation based on machine learning approach, and an efficient recursive Bayesian estimation algorithm for Gaussian states phase estimation has been proposed. With the proposed algorithm, the performance of the phase estimation may be improved distinguishably. For example, the physical limits (i.e., the standard quantum limit and Heisenberg limit) for the phase estimation precision may be reached in more efficient ways especially in the situation of the prior information being employed, the range for the estimated phase parameter can be extended from [0, π /2] to [0, 2 π ] compared with the conventional approach, and influences of the photon losses on the output parameter estimation precision may be suppressed dramatically in terms of saturating the lossy bound. In addition, the proposed algorithm can be extended to the time-variable or multi-parameter estimation framework.
NCUEE at MEDIQA 2019: Medical Text Inference Using Ensemble BERT-BiLSTM-Attention Model

This study describes the model design of the NCUEE system for the MEDIQA challenge at the ACL-BioNLP 2019 workshop. We use the BERT (Bidirectional Encoder Representations from Transformers) as the word embedding method to integrate the BiLSTM (Bidirectional Long Short-Term Memory) network with an attention mechanism for medical text inferences. A total of 42 teams participated in natural language inference task at MEDIQA 2019. Our best accuracy score of 0.84 ranked the top-third among all submissions in the leaderboard.
Denoising based Sequence-to-Sequence Pre-training for Text Generation

This paper presents a new sequence-tosequence (seq2seq) pre-training method PoDA (Pre-training of Denoising Autoencoders), which learns representations suitable for text generation tasks.Unlike encoder-only (e.g., BERT) or decoder-only (e.g., OpenAI GPT) pre-training approaches, PoDA jointly pretrains both the encoder and decoder by denoising the noise-corrupted text, and it also has the advantage of keeping the network architecture unchanged in the subsequent fine-tuning stage.Meanwhile, we design a hybrid model of Transformer and pointer-generator networks as the backbone architecture for PoDA.We conduct experiments on two text generation tasks: abstractive summarization, and grammatical error correction.Results on four datasets show that PoDA can improve model performance over strong baselines without using any task-specific techniques and significantly speed up convergence. 1
Benchmarking Collective Perception: New Task Difficulty Metrics for Collective Decision-Making

This paper presents nine different visual patterns for a Collective Perception scenario as new benchmark problems, which can be used for the future development of more efficient collective decision-making strategies. The experiments using isomorphism and three of the well-studied collective decision-making mechanisms are conducted to validate the performance of the new scenarios. The results on a diverse set of problems show that the real task difficulty lies not only in the quantity ratio of the features in the environment but also in their distributions and the clustering levels. Given this, two new metrics for the difficulty of the task are additionally proposed and evaluated on the provided set of benchmarks.
Program Synthesis and Semantic Parsing with Learned Code Idioms

Program synthesis of general-purpose source code from natural language specifications is challenging due to the need to reason about high-level patterns in the target program and low-level implementation details at the same time. In this work, we present Patois, a system that allows a neural program synthesizer to explicitly interleave high-level and low-level reasoning at every generation step. It accomplishes this by automatically mining common code idioms from a given corpus, incorporating them into the underlying language for neural synthesis, and training a tree-based neural synthesizer to use these idioms during code generation. We evaluate Patois on two complex semantic parsing datasets and show that using learned code idioms improves the synthesizer's accuracy.
A lightweight CLAS scheme with complete aggregation for healthcare mobile crowdsensing

The vast increase in the deployment of smart devices and the ubiquity of the Internet have evolved a new technology referred to as mobile crowdsensing (MCS). In healthcare MCS (HMCS), mobile devices can collect and upload medical data to the cloud server where authorized healthcare providers can access the relevant data for proper diagnosis and treatments. However, since this process is directly connected with patient’s sensitive health information, any unauthorized disclosure may have serious consequences on the patient’s wellbeing, hence privacy and security are serious issues in HMCS. To achieve data privacy and integrity, some certificateless aggregate signature (CLAS) schemes have been proposed. Very recently, four independent CLAS schemes were proposed for healthcare application. The authors claimed that their schemes were semantically secure in the security model. In this work, we analyze these schemes and find them to be insecure since there exists an adversary who can always forge a valid signature. Afterwards, we put forward a new CLAS scheme for HMCS application based on Elliptic Curve Cryptography (ECC) and hash function. In the proposed scheme, an aggregator can perform complete aggregation of certificateless signatures, resulting in improved performance. The proposed scheme satisfies all the security and privacy requirements of HMCS and can prevent possible attacks. Moreover, we show that the scheme is semantically secure with the assumption that the Discrete Logarithm Problem (DLP) is intractable. Extensive performance analysis and comparison show that the scheme is much more efficient than the state-of-the-art schemes.
A Multistep Lyapunov Approach for Finite-Time Analysis of Biased Stochastic Approximation

Motivated by the widespread use of temporal-difference (TD-) and Q-learning algorithms in reinforcement learning, this paper studies a class of biased stochastic approximation (SA) procedures under a mild "ergodic-like" assumption on the underlying stochastic noise sequence. Building upon a carefully designed multistep Lyapunov function that looks ahead to several future updates to accommodate the stochastic perturbations (for control of the gradient bias), we prove a general result on the convergence of the iterates, and use it to derive non-asymptotic bounds on the mean-square error in the case of constant stepsizes. This novel looking-ahead viewpoint renders finite-time analysis of biased SA algorithms under a large family of stochastic perturbations possible. For direct comparison with existing contributions, we also demonstrate these bounds by applying them to TD- and Q-learning with linear function approximation, under the practical Markov chain observation model. The resultant finite-time error bound for both the TD- as well as the Q-learning algorithms is the first of its kind, in the sense that it holds i) for the unmodified versions (i.e., without making any modifications to the parameter updates) using even nonlinear function approximators; as well as for Markov chains ii) under general mixing conditions and iii) starting from any initial distribution, at least one of which has to be violated for existing results to be applicable.
IT–IST at the SIGMORPHON 2019 Shared Task: Sparse Two-headed Models for Inflection

This paper presents the Instituto de Telecomunicac ¸ões-Instituto Superior Técnico submission to Task 1 of the SIGMORPHON 2019 Shared Task.Our models combine sparse sequence-to-sequence models with a two-headed attention mechanism that learns separate attention distributions for the lemma and inflectional tags.Among submissions to Task 1, our models rank second and third.Despite the low data setting of the task (only 100 in-language training examples), they learn plausible inflection patterns and often concentrate all probability mass into a small set of hypotheses, making beam search exact.
Smart home technology—comparing householder expectations at the point of installation with experiences 1 year later

Abstract This study, with 19 households, set out to analyse the expected benefits and anticipated challenges to the introduction of smart home technology (SHT), and to compare these to post-use experiences after living with the technology for 1 year. Contextual interviews were undertaken with householders during the installation of a range of SHT, and again approximately 1 year later, when householders had the option to keep the technology or have some or all of it removed. This identified whether initial expectations were met, whether initial concerns persisted and whether new issues, concerns, and benefits (that had not been initially anticipated) arose after an extended period of use. Initial expectations from all households were high—related to comfort, convenience, improved control, energy demand reduction, and integration of technologies. There were also many initial concerns, including reliability, usability, and how these systems would be ‘domesticated’ to fit existing routines. After living with the systems for 1 year, many expectations were met, but the added value (and hence the match between expectations and reality) was higher for households with varied routines and large, partially occupied houses. Some of the anticipated challenges—such as concerns over aesthetics—largely disappeared, issues to do with the awkward location of network components persisted and new issues arose relating to the usability of some components and the time and effort required to configure them. Recommendations are given to maximise the added value that SHT can provide to householders.
Exploring the Moderating Role of Gender in Juvenile Hacking Behaviors

Criminological research has demonstrated the significant relationship between deviant peer associations, a lack of self-control, and individual delinquency. These relationships also account for involvement in cybercrime, though these results are based largely on adult samples. There is less research considering juvenile offending online, particularly examining involvement in property-based offenses such as computer hacking. This study utilized an international sample of 48,327 juvenile respondents in the Second International Self-Report of Delinquency (ISRD-2) study to examine the role of gender as a moderating factor in the relationship between deviant peer associations, self-control, opportunity, and self-reported computer hacking behavior. The findings demonstrated different correlates associated with hacking for males and females, as well as differences on the basis of urban and rural residency.
Multi-Turn Chatbot Based on Query-Context Attentions and Dual Wasserstein Generative Adversarial Networks

To generate proper responses to user queries, multi-turn chatbot models should selectively consider dialogue histories. However, previous chatbot models have simply concatenated or averaged vector representations of all previous utterances without considering contextual importance. To mitigate this problem, we propose a multi-turn chatbot model in which previous utterances participate in response generation using different weights. The proposed model calculates the contextual importance of previous utterances by using an attention mechanism. In addition, we propose a training method that uses two types of Wasserstein generative adversarial networks to improve the quality of responses. In experiments with the DailyDialog dataset, the proposed model outperformed the previous state-of-the-art models based on various performance measures.
Multimodal optimization using whale optimization algorithm enhanced with local search and niching technique

For some real-world problems, it is desirable to find multiple global optima as many as possible. The multimodal optimization approach which finds multiple optima in a single run shows significant difference with the single modal optimization approach.The whale optimization algorithm (WOA) is a newly emerging reputable optimization algorithm. Its global search ability has been verified in many benchmark functions and real-world applications. In this paper, we propose a multimodal version of whale optimization algorithm (MMWOA). MMWOA enhances the multimodal search ability of WOA by using the niching technique and improves the local search efficiency of WOA by combining the Gaussian sampling technique. The algorithm has been tested on multimodal optimization benchmark functions recommended by CEC'2013 and on a multimodal optimization problem with non-linear constraints. Experimental results indicate that MMWOA has competitive performance compared with other state-of-the-art multimodal optimization algorithms.
Regularization for Deep Learning

Regularization is a technique for reducing the variance in the validation set, thus preventing the model from overfitting during training. In doing so, the model can better generalize to new examples. When training deep neural networks, a couple of strategies exist for use as a regularizer.
A Survey of Across Social Networks User Identification

With the popularization of the Internet and the arrival of the big data era, numerous different social networks (SNs) have emerged to satisfy users’ social needs and offer them rich content and convenient services. Under these circumstances, identifying multiple social accounts belonging to the same user across different SNs is of great importance for many applications. Across social networks user identification (ASNUI) can help perfect user information, offer personalized service recommendation, and data mining, as well as provide support for scientific research. This paper first systematically introduces the application of ASNUI in the field of social computing, then states its applications and challenges, and reviews the adopted models, frameworks, and performance comparison state-of-the-art techniques used in ASNUI. Finally, we also identify a few future research directions in ASNUI, such as weight allocation of user attribute information, the fusion of multi-dimensional information, and large-scale user identification.
Regression Analysis With Differential Privacy Preserving

In the field of data mining, protecting sensitive data from being leaked is part of the focuses of current research.As a strict and provable definition of privacy model, differential privacy provides an excellent solution to the problem of privacy leakage.Numerous methods have been suggested to enforce differential privacy in various data mining tasks, such as regression analysis.However, existing solutions for regression analysis is less than satisfactory since the amount of noise added is excessive.What's worse, the adversary can launch model inversion attacks to infer sensitive information with the published regression model.Motivated by this, we propose a differential privacy budget allocation model.We optimize the regression model by adjusting the privacy budget allocation within the objective function.Extensive evaluation results show the superiority of the proposed model in terms of noise reduction, model inversion attack proof, and the trade-off between privacy protection and data utility.
Hierarchical Species Sampling Models

This paper introduces a general class of hierarchical nonparametric prior distributions which includes new hierarchical mixture priors such as the hierarchical Gnedin measures, and other well-known prior distributions such as the hierarchical Pitman-Yor and the hierarchical normalized random measures.The random probability measures are constructed by a hierarchy of generalized species sampling processes with possibly non-diffuse base measures.The proposed framework provides a probabilistic foundation for hierarchical random measures, and allows for studying their properties under the alternative assumptions of diffuse, atomic and mixed base measure.We show that hierarchical species sampling models have a Chinese Restaurants Franchise representation and can be used as prior distributions to undertake Bayesian nonparametric inference.We provide a general sampling method for posterior approximation which easily accounts for non-diffuse base measures such as spike-and-slab.
Syntactically Meaningful and Transferable Recursive Neural Networks for Aspect and Opinion Extraction

In fine-grained opinion mining, extracting aspect terms (a.k.a. opinion targets) and opinion terms (a.k.a. opinion expressions) from user-generated texts is the most fundamental task in order to generate structured opinion summarization. Existing studies have shown that the syntactic relations between aspect and opinion words play an important role for aspect and opinion terms extraction. However, most of the works either relied on predefined rules or separated relation mining with feature learning. Moreover, these works only focused on single-domain extraction, which failed to adapt well to other domains of interest where only unlabeled data are available. In real-world scenarios, annotated resources are extremely scarce for many domains, motivating knowledge transfer strategies from labeled source domain(s) to any unlabeled target domain. We observe that syntactic relations among target words to be extracted are not only crucial for single-domain extraction, but also serve as invariant “pivot” information to bridge the gap between different domains. In this article, we explore the constructions of recursive neural networks based on the dependency tree of each sentence for associating syntactic structure with feature learning. Furthermore, we construct transferable recursive neural networks to automatically learn the domain-invariant fine-grained interactions among aspect words and opinion words. The transferability is built on an auxiliary task and a conditional domain adversarial network to reduce domain distribution difference in the hidden spaces effectively in word level through syntactic relations. Specifically, the auxiliary task builds structural correspondences across domains by predicting the dependency relation for each path of the dependency tree in the recursive neural network. The conditional domain adversarial network helps to learn domain-invariant hidden representation for each word conditioned on the syntactic structure. In the end, we integrate the recursive neural network with a sequence labeling classifier on top that models contextual influence in the final predictions. Extensive experiments and analysis are conducted to demonstrate the effectiveness of the proposed model and each component on three benchmark data sets.
Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer

Style transfer is a field with growing interest and use cases in deep learning. Recent work has shown Generative Adversarial Networks (GANs) can be used to create realistic images of virtually stained slide images in digital pathology with clinically validated interpretability. Digital pathology images are typically of extremely high resolution, making tilewise analysis necessary for deep learning applications. It has been shown that image generators with instance normalization can cause a tiling artifact when a large image is reconstructed from the tilewise analysis. We introduce a novel perceptual embedding consistency loss significantly reducing the tiling artifact created in the reconstructed whole slide image (WSI). We validate our results by comparing virtually stained slide images with consecutive real stained tissue slide images. We also demonstrate that our model is more robust to contrast, color and brightness perturbations by running comparative sensitivity analysis tests.
Content Enhanced BERT-based Text-to-SQL Generation

We present a simple methods to leverage the table content for the BERT-based model to solve the text-to-SQL problem. Based on the observation that some of the table content match some words in question string and some of the table header also match some words in question string, we encode two addition feature vector for the deep model. Our methods also benefit the model inference in testing time as the tables are almost the same in training and testing time. We test our model on the WikiSQL dataset and outperform the BERT-based baseline by 3.7% in logic form and 3.7% in execution accuracy and achieve state-of-the-art.