Reviewerly/exHarmony huggingface dataset:
Supervised Short-Length Hashing

Hashing can compress high-dimensional data into compact binary codes, while preserving the similarity, to facilitate efficient retrieval and storage. However, when retrieving using an extremely short length hash code learned by the existing methods, the performance cannot be guaranteed because of severe information loss. To address this issue, in this study, we propose a novel supervised short-length hashing (SSLH). In this proposed SSLH, mutual reconstruction between the short-length hash codes and original features are performed to reduce semantic loss. Furthermore, to enhance the robustness and accuracy of the hash representation, a robust estimator term is added to fully utilize the label information. Extensive experiments conducted on four image benchmarks demonstrate the superior performance of the proposed SSLH with short-length hash codes. In addition, the proposed SSLH outperforms the existing methods, with long-length hash codes. To the best of our knowledge, this is the first linear-based hashing method that focuses on both short and long-length hash codes for maintaining high precision.
Microsoft Research Asia’s Systems for WMT19

We Microsoft Research Asia made submissions to 11 language directions in the WMT19 news translation tasks.We won the first place for 8 of the 11 directions and the second place for the other three.Our basic systems are built on Transformer, back translation and knowledge distillation.We integrate several of our rececent techniques to enhance the baseline systems: multi-agent dual learning (MADL), masked sequence-to-sequence pre-training (MASS), neural architecture optimization (NAO), and soft contextual data augmentation (SCA).
Cultural tourism and temples: Content construction and interactivity design

Cultural and creative industries have a crucial role in the postindustrial knowledge economy. However, our understanding of the importance of temples in connecting people with society is limited. To fill this gap, this study explores points of interest for tourists in Taiwan to analyse the design of cultural interest operation modes in temples' interactive kiosk interfaces. We also examine three cultural levels related to the design of interactive kiosks in temples. Results reveal that participants’ levels of interest vary depending on temple complexity. Most participants prefer animated presentations of content related to two- and three-dimensional murals and the history and origins of temples. We illustrate how to develop a process for designing cultural and creative digital products. We construct a flowchart for guided temple tours and present an effective and suitable design method and its prototype product. Implications for the revitalisation of historic sites to create new value are discussed.
Continuous variable direct secure quantum communication using Gaussian states

Continuous variable one-way and controlled two-way direct secure quantum communication schemes have been designed using Gaussian states. Specifically, a scheme for continuous variable quantum secure direct communication and another scheme for continuous variable controlled quantum dialogue are proposed using single-mode squeezed coherent states. The security of the proposed schemes against a set of attacks (e.g., Gaussian quantum cloning machine and intercept–resend attacks) has been proved. Further, it is established that the proposed schemes do not require two-mode squeezed states, which are essential for a set of existing proposals. The controlled two-way communication scheme is shown to be very general in nature as it can be reduced to schemes for various relatively simpler cryptographic tasks such as controlled deterministic secure communication, quantum dialogue, and quantum key distribution. In addition, it is briefly discussed that the proposed schemes can provide us tools to design quantum cryptographic solutions for several socioeconomic problems.
Automatic repair of regular expressions

We introduce RFixer, a tool for repairing complex regular expressions using examples and only consider regular expressions without non-regular operators (e.g., negative lookahead). Given an incorrect regular expression and sets of positive and negative examples, RFixer synthesizes the closest regular expression to the original one that is consistent with the examples. Automatically repairing regular expressions requires exploring a large search space because practical regular expressions: i) are large, ii) operate over very large alphabets---e.g., UTF-16 and ASCII---and iii) employ complex constructs---e.g., character classes and numerical quantifiers. RFixer's repair algorithm achieves scalability by taking advantage of structural properties of regular expressions to effectively prune the search space, and it employs satisfiability modulo theory solvers to efficiently and symbolically explore the sets of possible character classes and numerical quantifiers. RFixer could successfully compute minimal repairs for regular expressions collected from a variety of sources, whereas existing tools either failed to produce any repair or produced overly complex repairs.
Behaviour Suite for Reinforcement Learning

This paper introduces the Behaviour Suite for Reinforcement Learning, or bsuite for short. bsuite is a collection of carefully-designed experiments that investigate core capabilities of reinforcement learning (RL) agents with two objectives. First, to collect clear, informative and scalable problems that capture key issues in the design of general and efficient learning algorithms. Second, to study agent behaviour through their performance on these shared benchmarks. To complement this effort, we open source this http URL, which automates evaluation and analysis of any agent on bsuite. This library facilitates reproducible and accessible research on the core issues in RL, and ultimately the design of superior learning algorithms. Our code is Python, and easy to use within existing projects. We include examples with OpenAI Baselines, Dopamine as well as new reference implementations. Going forward, we hope to incorporate more excellent experiments from the research community, and commit to a periodic review of bsuite from a committee of prominent researchers.
Temporal Context Enhanced Feature Aggregation for Video Object Detection

Video object detection is a challenging task because of the presence of appearance deterioration in certain video frames. One typical solution is to aggregate neighboring features to enhance per-frame appearance features. However, such a method ignores the temporal relations between the aggregated frames, which is critical for improving video recognition accuracy. To handle the appearance deterioration problem, this paper proposes a temporal context enhanced network (TCENet) to exploit temporal context information by temporal aggregation for video object detection. To handle the displacement of the objects in videos, a novel DeformAlign module is proposed to align the spatial features from frame to frame. Instead of adopting a fixed-length window fusion strategy, a temporal stride predictor is proposed to adaptively select video frames for aggregation, which facilitates exploiting variable temporal information and requiring fewer video frames for aggregation to achieve better results. Our TCENet achieves state-of-the-art performance on the ImageNet VID dataset and has a faster runtime. Without bells-and-whistles, our TCENet achieves 80.3% mAP by only aggregating 3 frames.
Multi-objective evolutionary framework for non-linear system identification: A comprehensive investigation

The present study proposes a multi-objective framework for structure selection of nonlinear systems which are represented by polynomial NARX models. This framework integrates the key components of Multi-Criteria Decision Making (MCDM) which include preference handling, Multi-Objective Evolutionary Algorithms (MOEAs) and a posteriori selection. To this end, three well-known MOEAs such as NSGA-II, SPEA-II and MOEA/D are thoroughly investigated to determine if there exists any significant difference in their search performance. The sensitivity of all these MOEAs to various qualitative and quantitative parameters, such as the choice of recombination mechanism, crossover and mutation probabilities, is also studied. These issues are critically analyzed considering seven discrete-time and a continuous-time benchmark nonlinear system as well as a practical case study of non-linear wave-force modeling. The results of this investigation demonstrate that MOEAs can be tailored to determine the correct structure of nonlinear systems. Further, it has been established through frequency domain analysis that it is possible to identify multiple valid discrete-time models for continuous-time systems. A rigorous statistical analysis of MOEAs via performance sweet spots in the parameter space convincingly demonstrates that these algorithms are robust over a wide range of control parameters.
Looting Liars Masking Models

This paper does not raise objections but spells out problems that I consider at present unsolved within Priest’s view on logic. In light of the state of scientific and other theories (Sect. 8.2) and in light of the character of natural languages (Sect. 8.3), Priest’s central arguments do not seem convincing. Next, I offer some six independent obstacles for defining consistency, identifying models and describing the semantics and metatheory of $$\mathbf {LP}$$ (Sect. 8.4).
On the Optimal Pattern for Displacement Field Measurement: Random Speckle and DIC, or Checkerboard and LSA?

This paper deals with the optimal pattern that can be used to retrieve displacement fields by minimizing the optical residual calculated over small regions of contrasted images. This minimization is generally performed in the spatial domain by processing speckle patterns with DIC. Another option is also considered here. It consists in switching this minimization to the Fourier domain. The benefit is that periodic patterns can be processed, which is generally not possible with DIC. It turns out that the optimal pattern in terms of sensor noise propagation is theoretically the checkerboard if it is correctly sampled, and this pattern is periodic. The reason why checkerboard is optimal is that the image gradient is maximum in this case. In addition, the minimization of the image residual in this case has a quasi-direct solution, which considerably speeds up the calculations. We first recall the basics of the different techniques used in the paper, namely classic subset-based DIC, and a spectral method called Localized Spectrum Analysis (LSA). A recent deconvolution procedure introduced to enhance the metrological performance of DIC and LSA is also briefly recalled and used in this study. Synthetic images are considered to assess in different cases the displacement resolution, as well as other sources of spurious spatial fluctuations observed in the displacement fields such as the pattern-induced bias with DIC. The main conclusion is that using checkerboards instead of random speckles leads to measurements featuring a better compromise between spatial resolution and measurement resolution.
Assessment of university teachers on their digital competences

The potential of DTIC has brought new challenges to teachers, making it essential to acquire digital competences. The aim of this research is to assess Portuguese university teachers' digital competence level. The quantitative methodological approach emphasises the teachers’ perception of their digital competences in three dimensions: teachers´ professional and pedagogic competences and learners´ competences and involved 118 Portuguese University teachers. The main findings show that the digital competence level of teachers is moderate, and that subdimensions “Guidance”, “Analysing Evidence” and “Responsible Use”, are the weakest. On the other hand, the subdimension in which teachers perceive to have more competence is “Organisational Communication”. The results show the need for teachers to increase their digital competence level through specific training, especially as regards the pedagogical use of technology, in particular more practical, experimental training.
Personalising Explainable Recommendations: Literature and Conceptualisation

Explanations in intelligent systems aim to enhance a users’ understandability of their reasoning process and the resulted decisions and recommendations. Explanations typically increase trust, user acceptance and retention. The need for explanations is on the rise due to the increasing public concerns about AI and the emergence of new laws, such as the General Data Protection Regulation (GDPR) in Europe. However, users are different in their needs for explanations, and such needs can depend on their dynamic context. Explanations suffer the risk of being seen as information overload, and this makes personalisation more needed. In this paper, we review literature around personalising explanations in intelligent systems. We synthesise a conceptualisation that puts together various aspects being considered important for the personalisation needs and implementation. Moreover, we identify several challenges which would need more research, including the frequency of explanation and their evolution in tandem with the ongoing user experience.
Human-in-the-loop differential subspace search in high-dimensional latent space

Generative models based on deep neural networks often have a high-dimensional latent space, ranging sometimes to a few hundred dimensions or even higher, which typically makes them hard for a user to explore directly. We propose differential subspace search to allow efficient iterative user exploration in such a space, without relying on domain- or data-specific assumptions. We develop a general framework to extract low-dimensional subspaces based on a local differential analysis of the generative model, such that a small change in such a subspace would provide enough change in the resulting data. We do so by applying singular value decomposition to the Jacobian of the generative model and forming a subspace with the desired dimensionality spanned by a given number of singular vectors stochastically selected on the basis of their singular values, to maintain ergodicity. We use our framework to present 1D subspaces to the user via a 1D slider interface. Starting from an initial location, the user finds a new candidate in the presented 1D subspace, which is in turn updated at the new candidate location. This process is repeated until no further improvement can be made. Numerical simulations show that our method can better optimize synthetic black-box objective functions than the alternatives that we tested. Furthermore, we conducted a user study using complex generative models and the results show that our method enables more efficient exploration of high-dimensional latent spaces than the alternatives.
Unbiased Implicit Recommendation and Propensity Estimation via Combinational Joint Learning

This paper focuses on how to generate unbiased recommendations based on biased implicit user-item interactions. We propose a combinational joint learning framework to simultaneously learn unbiased user-item relevance and unbiased propensity. More specifically, we first present a new unbiased objective function for estimating propensity. We then show how a naïve joint learning approach faces an estimation-training overlap problem. Hence, we propose to jointly train multiple sub-models from different parts of the training dataset to avoid this problem. Finally, we show how to incorporate residual components trained by the complete training data to complement the relevance and propensity sub-models. Extensive experiments on two public datasets demonstrate the effectiveness of the proposed model with an improvement of 4% on average over the best alternatives.
Sentiment Analysis by Fusing Text and Location Features of Geo-Tagged Tweets

Twitter sentiment analysis provides valuable feedback from public emotion concerning certain events or products.Current research has been focused on obtaining sentiment features from vectorized lexical and syntactic feature from tweets, without further context.In this paper, we demonstrated how vectorized location information could be combined with word embeddings to produce a hybrid representation, which has resulted in an improvement on a tweet sentiment classification task.The location information of the geo-tagged tweets provided further context, which was useful for a sentiment classification task.The tweets investigated contained a set of geo-tagged tweets.The word embeddings of these tweets were combined with the geo-tagged tweets' vectorized location features to form a sentiment feature set of geo-tagged tweets.The sentiment feature set was incorporated into a convolution neural network and a bi-directional long shortterm memory network for the tasks of training and predicting of sentiment classification labels.This hybrid representation is compared with the baseline GloVe model through a few experiments, and the results have shown that the incorporation of vectorized location information has resulted in improvement of the accuracy for the twitter sentiment classification task.
Multi-variant differential evolution algorithm for feature selection

Abstract This work introduces a new population-based stochastic search technique, named multi-variant differential evolution (MVDE) algorithm for solving fifteen well-known real world problems from UCI repository and compared to four popular optimization methods. The MVDE proposes a new self-adaptive scaling factor based on cosine and logistic distributions as an almost factor-free optimization technique. For more updated chances, this factor is binary-mapped by incorporating an adaptive crossover operator. During the evolution, both greedy and less-greedy variants are managed by adjusting and incorporating the binary scaling factor and elite identification mechanism into a new multi-mutation crossover process through a number of sequentially evolutionary phases. Feature selection decreases the number of features by eliminating irrelevant or misleading, noisy and redundant data which can accelerate the process of classification. In this paper, a new feature selection algorithm based on the MVDE method and artificial neural network is presented which enabled MVDE to get a combination features’ set, accelerate the accuracy of the classification, and optimize both the structure and weights of Artificial Neural Network (ANN) simultaneously. The experimental results show the encouraging behavior of the proposed algorithm in terms of the classification accuracies and optimal number of feature selection.
A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences

Data sharing and reuse are crucial to enhance scientific progress and maximize return of investments in science. Although attitudes are increasingly favorable, data reuse remains difficult due to lack of infrastructures, standards, and policies. The FAIR (findable, accessible, interoperable, reusable) principles aim to provide recommendations to increase data reuse. Because of the broad interpretation of the FAIR principles, maturity indicators are necessary to determine the FAIRness of a dataset. In this work, we propose a reproducible computational workflow to assess data FAIRness in the life sciences. Our implementation follows principles and guidelines recommended by the maturity indicator authoring group and integrates concepts from the literature. In addition, we propose a FAIR balloon plot to summarize and compare dataset FAIRness. We evaluated the feasibility of our method on three real use cases where researchers looked for six datasets to answer their scientific questions. We retrieved information from repositories (ArrayExpress, Gene Expression Omnibus, eNanoMapper, caNanoLab, NanoCommons and ChEMBL), a registry of repositories, and a searchable resource (Google Dataset Search) via application program interfaces (API) wherever possible. With our analysis, we found that the six datasets met the majority of the criteria defined by the maturity indicators, and we showed areas where improvements can easily be reached. We suggest that use of standard schema for metadata and the presence of specific attributes in registries of repositories could increase FAIRness of datasets.
DoMoBOT

Domain modelling transforms domain problem descriptions written in natural language (NL) into analyzable and concise domain models (class diagrams) during requirements analysis or the early stages of design in software development. Since the practice of domain modelling requires time in addition to modelling skills and experience, several approaches have been proposed to automate or semi-automate the construction of domain models from problem descriptions expressed in NL. Despite the existing work on domain model extraction, some significant challenges remain unaddressed: (i) the extracted domain models are not accurate enough to be used directly or with minor modifications in software development, (ii) existing approaches do not facilitate the tracing of the rationale behind the modelling decisions taken by the model extractor, and (iii) existing approaches do not provide interactive interfaces to update the extracted domain models. Therefore, in this paper, we introduce a domain modelling bot called DoMoBOT, explain its architecture, and implement it in the form of a web-based prototype tool. The bot automatically extracts a domain model from a problem description written in NL with an accuracy higher than existing approaches. Furthermore, the bot enables modellers to update a part of the extracted domain model and in response the bot re-configures the other parts of the domain model pro-actively. To improve the accuracy of extracted domain models, we combine the techniques of Natural Language Processing and Machine Learning. Finally, we evaluate the accuracy of the extracted domain models.
Balanced Meta-Softmax for Long-Tailed Visual Recognition

Deep classifiers have achieved great success in visual recognition. However, real-world data is long-tailed by nature, leading to the mismatch between training and testing distributions. In this paper, we show that the Softmax function, though used in most classification tasks, gives a biased gradient estimation under the long-tailed setup. This paper presents Balanced Softmax, an elegant unbiased extension of Softmax, to accommodate the label distribution shift between training and testing. Theoretically, we derive the generalization bound for multiclass Softmax regression and show our loss minimizes the bound. In addition, we introduce Balanced Meta-Softmax, applying a complementary Meta Sampler to estimate the optimal class sample rate and further improve long-tailed learning. In our experiments, we demonstrate that Balanced Meta-Softmax outperforms state-of-the-art long-tailed classification solutions on both visual recognition and instance segmentation tasks.