Reviewerly/exHarmony huggingface dataset:
Transfer Learning Framework for Low-Resource Text-to-Speech using a Large-Scale Unlabeled Speech Corpus

Training a text-to-speech (TTS) model requires a large scale text labeled speech corpus, which is troublesome to collect. In this paper, we propose a transfer learning framework for TTS that utilizes a large amount of unlabeled speech dataset for pre-training. By leveraging wav2vec2.0 representation, unlabeled speech can highly improve performance, especially in the lack of labeled speech. We also extend the proposed method to zero-shot multi-speaker TTS (ZS-TTS). The experimental results verify the effectiveness of the proposed method in terms of naturalness, intelligibility, and speaker generalization. We highlight that the single speaker TTS model fine-tuned on the only 10 minutes of labeled dataset outperforms the other baselines, and the ZS-TTS model fine-tuned on the only 30 minutes of single speaker dataset can generate the voice of the arbitrary speaker, by pre-training on unlabeled multi-speaker speech corpus.
Earthquake Nowcasting with Deep Learning

We review previous approaches to nowcasting earthquakes and introduce new approaches based on deep learning using three distinct models based on recurrent neural networks and transformers. We discuss different choices for observables and measures presenting promising initial results for a region of Southern California from 1950–2020. Earthquake activity is predicted as a function of 0.1-degree spatial bins for time periods varying from two weeks to four years. The overall quality is measured by the Nash Sutcliffe efficiency comparing the deviation of nowcast and observation with the variance over time in each spatial region. The software is available as open source together with the preprocessed data from the USGS.
Using consumer feedback from location-based services in PoI recommender systems for people with autism

When suggesting Points of Interest (PoIs) to people with autism spectrum disorders, we must take into account that they have idiosyncratic sensory aversions to noise, brightness and other features that influence the way they perceive places. Therefore, recommender systems must deal with these aspects. However, the retrieval of sensory data about PoIs is a real challenge because most geographical information servers fail to provide this data. Moreover, ad-hoc crowdsourcing campaigns do not guarantee to cover large geographical areas and lack sustainability. Thus, we investigate the extraction of sensory data about places from the consumer feedback collected by location-based services, on which people spontaneously post reviews from all over the world. Specifically, we propose a model for the extraction of sensory data from the reviews about PoIs, and its integration in recommender systems to predict item ratings by considering both user preferences and compatibility information. We tested our approach with autistic and neurotypical people by integrating it into diverse recommendation algorithms. For the test, we used a dataset built in a crowdsourcing campaign and another one extracted from TripAdvisor reviews. The results show that the algorithms obtain the highest accuracy and ranking capability when using TripAdvisor data. Moreover, by jointly using these two datasets, the algorithms further improve their performance. These results encourage the use of consumer feedback as a reliable source of information about places in the development of inclusive recommender systems.
Learning multi-level representations for affective image recognition

Abstract Images can convey intense affective experiences and affect people on an affective level. With the prevalence of online pictures and videos, evaluating emotions from visual content has attracted considerable attention. Affective image recognition aims to classify the emotions conveyed by digital images automatically. The existing studies using manual features or deep networks mainly focus on low-level visual features or high-level semantic representation without considering all factors. To better understand how deep networks are working for affective recognition tasks, we investigate the convolutional features by visualization them in this work. Our research shows that the hierarchical CNN model mainly relies on deep semantic information while ignoring the shallow visual details, which are essential to evoke emotions. To form a more general and discriminative representation, we propose a multi-level hybrid model that learns and integrates the deep semantics and shallow visual representations for sentiment classification. In addition, this study shows that class imbalance would affect performance as the main category of the affective dataset will overwhelm training and degenerate the deep networks. Therefore, a new loss function is introduced to optimize the deep affective model. Experimental results on several affective image recognition datasets show that our model outperforms various existing studies. The source code is publicly available.
Large-scale analysis of query logs to profile users for dataset search

Purpose With an explosion of datasets available on the Web, dataset search has gained attention as an emerging research domain. Understanding users' dataset behaviour is imperative for providing effective data discovery services. In this paper, the authors present a study on users' dataset search behaviour through the analysis of search logs from a research data discovery portal. Design/methodology/approach Using query and session based features, the authors apply cluster analysis to discover distinct user profiles with different search behaviours. One particular behavioural construct of our interest is users' expertise that the authors generate via computing semantic similarity between users' search queries and the title of metadata records in the displayed search results. Findings The findings revealed that there are six distinct classes of user behaviours for dataset search, namely; Expert Research, Expert Search, Expert Explore, Novice Research, Novice Search and Novice Explore. Research limitations/implications The user profiles are derived based on analysis of the search log of the research data catalogue in this study. Further research is needed to generalise the user profiles to other dataset search settings. Future research can take on a confirmatory approach to verify these user groups and establish a deeper understanding of their information needs. Practical implications The findings in this paper have implications for designing search systems that tailor search results matching the diverse information needs of different user groups. Originality/value We propose for the first time a taxonomy of users for dataset search based on their domain expertise and search behaviour.
Analysis between different Decentralized Lending and Borrowing Protocols

DeFi or decentralised finance is defined as financial services running on blockchain and controlled by the smart contract, often known as “code is law” in DeFi space, all set of operations run under the rules present on it. Once deployed on the blockchain, smart contract cannot be changed or modified. DeFi tends to make a new financial institution which is not regulated and is open to anyone, irrespective of who they are. They are generally governed by an autonomous entity known as DAO (Decentralised autonomous organisation). Where common people using the protocol could vote or propose the change in protocol.
PRE-NAS

Neural architecture search (NAS) aims to automate architecture engineering in neural networks. This often requires a high computational overhead to evaluate a number of candidate networks from the set of all possible networks in the search space during the search. Prediction of the networks' performance can alleviate this high computational overhead by mitigating the need for evaluating every candidate network. Developing such a predictor typically requires a large number of evaluated architectures which may be difficult to obtain. We address this challenge by proposing a novel evolutionary-based NAS strategy, Predictor-assisted E-NAS (PRE-NAS), which can perform well even with an extremely small number of evaluated architectures. PRE-NAS leverages new evolutionary search strategies and integrates high-fidelity weight inheritance over generations. Unlike one-shot strategies, which may suffer from bias in the evaluation due to weight sharing, offspring candidates in PRE-NAS are topologically homogeneous, which circumvents bias and leads to more accurate predictions. Extensive experiments on NAS-Bench-201 and DARTS search spaces show that PRE-NAS can outperform state-of-the-art NAS methods. With only a single GPU searching for 0.6 days, competitive architecture can be found by PRE-NAS which achieves 2.40% and 24% test error rates on CIFAR-10 and ImageNet respectively.
Learning and controlling the source-filter representation of speech with a variational autoencoder

Understanding and controlling latent representations in deep generative models is a challenging yet important problem for analyzing, transforming and generating various types of data. In speech processing, inspiring from the anatomical mechanisms of phonation, the source-filter model considers that speech signals are produced from a few independent and physically meaningful continuous latent factors, among which the fundamental frequency $f_0$ and the formants are of primary importance. In this work, we start from a variational autoencoder (VAE) trained in an unsupervised manner on a large dataset of unlabeled natural speech signals, and we show that the source-filter model of speech production naturally arises as orthogonal subspaces of the VAE latent space. Using only a few seconds of labeled speech signals generated with an artificial speech synthesizer, we propose a method to identify the latent subspaces encoding $f_0$ and the first three formant frequencies, we show that these subspaces are orthogonal, and based on this orthogonality, we develop a method to accurately and independently control the source-filter speech factors within the latent subspaces. Without requiring additional information such as text or human-labeled data, this results in a deep generative model of speech spectrograms that is conditioned on $f_0$ and the formant frequencies, and which is applied to the transformation speech signals. Finally, we also propose a robust $f_0$ estimation method that exploits the projection of a speech signal onto the learned latent subspace associated with $f_0$.
Does the relationship between brand attitude, brand attachment and purchase intention vary based on the type of prosocial expression-based brand emoji?

Purpose This study aims to analyze the most effective type of emoji for deriving positive marketing results by determining whether the relationship between brand attitude, brand attachment and purchase intention is moderated by the various types of prosocial expression-based brand emojis used. Design/methodology/approach An online survey was administered to Koreans in their 20s. Starbucks brand emojis were classified into three types: static gesture, animated gesture and a combined animated gesture and displayed word. A moderated–mediation analysis was performed to verify the research hypotheses. Gender, age, region of residence, frequency of Starbucks use and Starbucks favorability were used as control variables. Findings Animations in emojis were shown to strengthen the marketing effect. Further, combining animated prosocial gestures and displayed words had a more positive marketing effect than merely applying animated prosocial gestures. Originality/value This study closely examined the role of verbal, in the form of colloquial words, and nonverbal aspects, in the form of emojis, in the creation of positive business outcomes. Additionally, the positive marketing effect of animated emojis is discussed from a mechanistic point of view by linking research results to those in the field of neuroscience (mirroring by mirror neurons).
Shortcuts to the quantum approximate optimization algorithm

The quantum approximate optimization algorithm (QAOA) is a quantum-classical hybrid algorithm intending to find the ground state of a target Hamiltonian. Theoretically, QAOA can obtain the approximate solution if the quantum circuit is deep enough. Actually, the performance of QAOA decreases practically if the quantum circuit is deep since near-term devices are not noise free, and the errors caused by noise accumulate as the quantum circuit increases. In order to reduce the depth of quantum circuits, we propose an ansatz dubbed as ``Shortcuts to QAOA'' (S-QAOA), S-QAOA provides shortcuts to the ground state of the target Hamiltonian by including more two-body interactions and releasing the parameter freedoms. To be specific, besides the existing $ZZ$ interaction in the QAOA ansatz, other two-body interactions are introduced in the S-QAOA ansatz such that the approximate solutions could be obtained with smaller circuit depth. Considering the Max-Cut problem and the Sherrington-Kirkpatrick model, numerically computation shows the $YY$ interaction has the best performance. The reason for this might arise from the counterdiabatic effect generated by $YY$ interaction. On top of this, we release the freedom of parameters of two-body interactions, which a priori do not necessarily have to be fully identical, and numerical results show that it is worth paying the extra cost of having more parameter freedom since one has a greater improvement on success rate.
Privacy-Preserving Machine Learning Using Cryptography

Data scientists require an extensive training set to train an accurate and reliable machine learning model – the bigger and diverse the training set, the better. However, acquiring such a vast training set can be difficult, especially when sensitive user data is involved. The General Data Protection Regulation (GDPR) and similar regulations may prohibit the gathering and processing of this sensitive data. Privacy-preserving cryptographic protocols and primitives, like secure multi-party computation (MPC) and fully homomorphic encryption (FHE), may provide a solution to this problem. They allow us to perform calculations on private and unknown data and can, therefore, be used to classify and train on GDPR protected data sets. While still considered very inefficient, privacy-preserving machine learning using MPC and FHE has been heavily researched in recent years. In this chapter, we give an introduction to MPC and FHE, how they can be used, their limitations, and describe how state-of-the-art publications apply them to machine learning algorithms.
Pulse-Engineered Controlled-V Gate and Its Applications on Superconducting Quantum Device

In this paper, we demonstrate that, by employing OpenPulse design kit for IBM superconducting quantum devices, the controlled-V gate (CV gate) can be implemented in about half the gate time to the controlled-X (CX or CNOT gate) and consequently 65.5\% reduced gate time compared to the CX-based implementation of CV. Then, based on the theory of Cartan decomposition, we characterize the set of all two-qubit gates implemented with only two or three CV gates; using pulse-engineered CV gates enables us to implement these gates with shorter gate time and possibly better gate fidelity than the CX-based one, as actually demonstrated in two examples. Moreover, we showcase the improvement of linearly-coupled three-qubit Toffoli gate, by implementing it with the pulse-engineered CV gate, both in gate time and the averaged output-state fidelity. These results imply the importance of our CV gate implementation technique, which, as an additional option for the basis gate set design, may shorten the overall computation time and consequently improve the precision of several quantum algorithms executed on a real device.
Nonlinear quantum gates for a Bose-Einstein condensate

Quantum interferometry and quantum information processing have been proposed for Bose-Einstein condensates (BECs), but BECs are described in complicated ways such as using quantum field theory or using a nonlinear differential equation. Nonlinear quantum mechanics does not mesh well with the superposition principle at the heart of interferometry and quantum information processing but could be compatible. Thus, we develop a rigorous foundation for quantum gates, obtained by solving the equation for evolution, and then we employ this foundation, combined with quantum-control techniques and appropriate state-sampling techniques, to devise feasible nonlinear Hadamard gates and thereby feasible, i.e., high-contrast, nonlinear Ramsey interferometry. Our approach to BEC interferometry and quantum logic shifts the paradigm by enlarging to the case of nonlinear quantum mechanics, which we apply to the cases of BEC interferometry and quantum information processing.
Generalized conditional expectations for quantum retrodiction and smoothing

The inference of a hidden variable's historical value, based on observations before and after the fact, is a controversial subject in quantum mechanics. Here I address the controversy by proposing a formalism that unifies and generalizes some of the previous proposals for the task, including the quantum minimum-mean-square-error estimators proposed by Ohki, the generalized conditional expectation proposed by Accardi and Cecchini, the quantum smoothing theory proposed by Tsang, the optimal observables for parameter estimation proposed by Personick, Belavkin, and Grishanin, and the weak values proposed by Aharonov, Albert, and Vaidman. The formalism is based on Ohki's suggestion of a distance between two observables in the Heisenberg picture, which remains well defined for incompatible observables and serves as a more general foundation for quantum inference than Belavkin's nondemolition principle.
Generative Linguistic Steganography: A Comprehensive Review

Text steganography is one of the most imminent and promising research interests in the information security field.With the unprecedented success of the neural network and natural language processing (NLP), the last years have seen a surge of research on generative linguistic steganography (GLS).This paper provides a thorough and comprehensive review to summarize the existing key contributions, and creates a novel taxonomy for GLS according to NLP techniques and steganographic encoding algorithm, then summarizes the characteristics of generative linguistic steganographic methods properly to analyze the relationship and difference between each type of them.Meanwhile, this paper also comprehensively introduces and analyzes several evaluation metrics to evaluate the performance of GLS from diverse perspective.Finally, this paper concludes the future research work, which is more conducive to the follow-up research and innovation of researchers.
Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Data

This paper studies a novel pre-training technique with unpaired speech data, Speech2C, for encoder-decoder based automatic speech recognition (ASR). Within a multi-task learning framework, we introduce two pre-training tasks for the encoder-decoder network using acoustic units, i.e., pseudo codes, derived from an offline clustering model. One is to predict the pseudo codes via masked language modeling in encoder output, like HuBERT model, while the other lets the decoder learn to reconstruct pseudo codes autoregressively instead of generating textual scripts. In this way, the decoder learns to reconstruct original speech information with codes before learning to generate correct text. Comprehensive experiments on the LibriSpeech corpus show that the proposed Speech2C can relatively reduce the word error rate (WER) by 19.2% over the method without decoder pre-training, and also outperforms significantly the state-of-the-art wav2vec 2.0 and HuBERT on fine-tuning subsets of 10h and 100h. We release our code and model at https://github.com/microsoft/SpeechT5/tree/main/Speech2C.
Topological-Graph Dependencies and Scaling Properties of a Heuristic Qubit-Assignment Algorithm

The qubit-mapping problem aims to assign and route qubits of a quantum circuit onto an noisy intermediate-scale quantum (NISQ) device in an optimized fashion, with respect to some cost function.Finding an optimal solution to this problem is known to scale exponentially in computational complexity; as such, it is imperative to investigate scalable qubit-mapping solutions for NISQ computation.In this work, a noise-aware heuristic qubit-assignment algorithm (which assigns initial placements for qubits in a quantum algorithm to qubits on an NISQ device, but does not route qubits during the quantum algorithm's execution) is presented and compared against the optimal brute-force solution, as well as a trivial qubit assignment, with the aim to quantify the performance of our heuristic qubit-assignment algorithm.We find that for small, connected-graph algorithms, our heuristic-assignment algorithm faithfully lies in between the effective upper and lower bounds given by the brute-force and trivial qubit-assignment algorithms.Additionally, we find that the topological-graph properties of quantum algorithms with over six qubits play an important role in our heuristic qubit-assignment algorithm's performance on NISQ devices.Finally, we investigate the scaling properties of our heuristic algorithm for quantum processors with up to 100 qubits; here, the algorithm was found to be scalable for quantum-algorithms that admit path-like graphs.Our findings show that as the size of the quantum processor in our simulation grows, so do the benefits from utilizing the heuristic qubitassignment algorithm, under particular constraints for our heuristic algorithm.This work, thus, characterizes the performance of a heuristic qubit-assignment algorithm with respect to the topological-graph and scaling properties of a quantum algorithm that one may wish to run on a given NISQ device.
What makes effective leadership in agile software development teams?

Effective leadership is one of the key drivers of business and project success, and one of the most active areas of management research. But how does leadership work in agile software development, which emphasizes self-management and self-organization and marginalizes traditional leadership roles? To find out, this study examines agile leadership from the perspective of thirteen professionals who identify as agile leaders, in different roles, at ten different software development companies of varying sizes. Data from semi-structured interviews reveals that leadership: (1) is dynamically shared among team members; (2) engenders a sense of belonging to the team; and (3) involves balancing competing organizational cultures (e.g. balancing the new agile culture with the old milestone-driven culture). In other words, agile leadership is a property of a team, not a role, and effectiveness depends on agile team members' identifying with the team, accepting responsibility, and being sensitive to cultural conflict.
Hybrid classical-quantum autoencoder for anomaly detection

We propose a Hybrid classical-quantum Autoencoder (HAE) model, which is a synergy of a classical autoencoder (AE) and a parametrized quantum circuit (PQC) that is inserted into its bottleneck. The PQC augments the latent space, on which a standard outlier detection method is applied to search for anomalous data points within a classical dataset. Using this model and applying it to both standard benchmarking datasets, and a specific use-case dataset which relates to predictive maintenance of gas power plants, we show that the addition of the PQC leads to a performance enhancement in terms of precision, recall, and F1 score. Furthermore, we probe different PQC Ans\"atze and analyse which PQC features make them effective for this task.