Reviewerly/exHarmony huggingface dataset:
Towards Robust CNN-based Object Detection through Augmentation with Synthetic Rain Variations

Convolutional Neural Networks (CNNs) achieve high accuracy in vision-based object detection tasks. For their usage in the automotive domain, CNNs have to be robust against various kinds of natural distortions caused by different weather conditions while state-of-the-art datasets like KITTI lack these challenging scenarios. Our approach automatically identifies corner cases where CNNs fail and improves their robustness by automated augmentation of the training data with synthetic rain variations including falling rain with brightness reduction as well as raindrops on the windshield. Our method achieves higher performance upon validation against a real rain dataset compared with state-of-the art data augmentation techniques like Gaussian noise (GN) or Salt-and-Pepper noise (SPN).
Zero-Shot Sketch-Based Image Retrieval via Graph Convolution Network

Zero-Shot Sketch-based Image Retrieval (ZS-SBIR) has been proposed recently, putting the traditional Sketch-based Image Retrieval (SBIR) under the setting of zero-shot learning. Dealing with both the challenges in SBIR and zero-shot learning makes it become a more difficult task. Previous works mainly focus on utilizing one kind of information, i.e., the visual information or the semantic information. In this paper, we propose a SketchGCN model utilizing the graph convolution network, which simultaneously considers both the visual information and the semantic information. Thus, our model can effectively narrow the domain gap and transfer the knowledge. Furthermore, we generate the semantic information from the visual information using a Conditional Variational Autoencoder rather than only map them back from the visual space to the semantic space, which enhances the generalization ability of our model. Besides, feature loss, classification loss, and semantic loss are introduced to optimize our proposed SketchGCN model. Our model gets a good performance on the challenging Sketchy and TU-Berlin datasets.
Automatic Generation of Medical Imaging Diagnostic Report with Hierarchical Recurrent Neural Network

Medical images are widely used in the medical domain for the diagnosis and treatment of diseases. Reading a medical image and summarizing its insights is a routine, yet nonetheless time-consuming task, which often represents a bottleneck in the clinical diagnosis process. Automatic report generation can relieve the issues. However, generating medical reports presents two major challenges: (i) it is hard to accurately detect all the abnormalities simultaneously, especially the rare diseases; (ii) a medical image report consists of many paragraphs and sentences, which are longer than natural image captions. We present a new framework to accurately detect the abnormalities and automatically generate medical reports. The report generation model is based on hierarchical recurrent neural network (HRNN). We introduce a topic matching mechanism to HRNN, so as to make generated reports more accurate and diverse. The soft attention mechanism is also introduced to HRNN model. Experimental results on two image-paragraph pair datasets show that our framework outperforms all the state-of-art methods.
Learning and Evaluating Contextual Embedding of Source Code

Recent research has achieved impressive results on understanding and improving source code by building up on machine-learning techniques developed for natural languages. A significant advancement in natural-language understanding has come with the development of pre-Trained contextual embeddings, such as BERT, which can be fine-Tuned for downstream tasks with less labeled data and training budget, while achieving better accuracies. However, there is no attempt yet to obtain a high-quality contextual embedding of source code, and to evaluate it on multiple program-understanding tasks simultaneously; that is the gap that this paper aims to mitigate. Specifically, first, we curate a massive, deduplicated corpus of 7.4M Python files from GitHub, which we use to pre-Train CuBERT, an open-sourced codeunderstanding BERT model; and, second, we create an open-sourced benchmark that comprises five classification tasks and one program-repair task, akin to code-understanding tasks proposed in the literature before. We fine-Tune CuBERT on our benchmark tasks, and compare the resulting models to different variants of Word2Vec token embeddings, BiLSTM and Transformer models, as well as published state-of-The-Art models, showing that CuBERT outperforms them all, even with shorter training, and with fewer labeled examples. Future work on source-code embedding can benefit from reusing our benchmark, and from comparing against CuBERT models as a strong baseline. Â© 2020 by the Authors.
Underwater Image Super-Resolution using Deep Residual Multipliers

We present a deep residual network-based generative model for single image super-resolution (SISR) of underwater imagery for use by autonomous underwater robots. We also provide an adversarial training pipeline for learning SISR from paired data. In order to supervise the training, we formulate an objective function that evaluates the perceptual quality of an image based on its global content, color, and local style information. Additionally, we present USR-248, a large-scale dataset of three sets of underwater images of `high' (640×480) and `low' (80 × 60, 160 × 120, and 320×240) resolution. USR-248 contains paired instances for supervised training of 2×, 4×, or 8× SISR models. Furthermore, we validate the effectiveness of our proposed model through qualitative and quantitative experiments and compare the results with several state-of-the-art models' performances. We also analyze its practical feasibility for applications such as scene understanding and attention modeling in noisy visual conditions.
MultiSpeech: Multi-Speaker Text to Speech with Transformer

Transformer-based text to speech (TTS) model (e.g., Transformer TTS~\cite{li2019neural}, FastSpeech~\cite{ren2019fastspeech}) has shown the advantages of training and inference efficiency over RNN-based model (e.g., Tacotron~\cite{shen2018natural}) due to its parallel computation in training and/or inference. However, the parallel computation increases the difficulty while learning the alignment between text and speech in Transformer, which is further magnified in the multi-speaker scenario with noisy data and diverse speakers, and hinders the applicability of Transformer for multi-speaker TTS. In this paper, we develop a robust and high-quality multi-speaker Transformer TTS system called MultiSpeech, with several specially designed components/techniques to improve text-to-speech alignment: 1) a diagonal constraint on the weight matrix of encoder-decoder attention in both training and inference; 2) layer normalization on phoneme embedding in encoder to better preserve position information; 3) a bottleneck in decoder pre-net to prevent copy between consecutive speech frames. Experiments on VCTK and LibriTTS multi-speaker datasets demonstrate the effectiveness of MultiSpeech: 1) it synthesizes more robust and better quality multi-speaker voice than naive Transformer based TTS; 2) with a MutiSpeech model as the teacher, we obtain a strong multi-speaker FastSpeech model with almost zero quality degradation while enjoying extremely fast inference speed.
Finite-size security of continuous-variable quantum key distribution with digital signal processing

Abstract In comparison to conventional discrete-variable (DV) quantum key distribution (QKD), continuous-variable (CV) QKD with homodyne/heterodyne measurements has distinct advantages of lower-cost implementation and affinity to wavelength division multiplexing. On the other hand, its continuous nature makes it harder to accommodate to practical signal processing, which is always discretized, leading to lack of complete security proofs so far. Here we propose a tight and robust method of estimating fidelity of an optical pulse to a coherent state via heterodyne measurements. We then construct a binary phase modulated CV-QKD protocol and prove its security in the finite-key-size regime against general coherent attacks, based on proof techniques of DV QKD. Such a complete security proof is indispensable for exploiting the benefits of CV QKD.
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-start Users

Static recommendation methods like collaborative filtering suffer from the inherent limitation of performing real-time personalization for cold-start users. Online recommendation, e.g., multi-armed bandit approach, addresses this limitation by interactively exploring user preference online and pursuing the exploration-exploitation (EE) trade-off. However, existing bandit-based methods model recommendation actions homogeneously. Specifically, they only consider the items as the arms, being incapable of handling the item attributes, which naturally provide interpretable information of user's current demands and can effectively filter out undesired items. In this work, we consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively. This important scenario was studied in a recent work. However, it employs a hand-crafted function to decide when to ask attributes or make recommendations. Such separate modeling of attributes and items makes the effectiveness of the system highly rely on the choice of the hand-crafted function, thus introducing fragility to the system. To address this limitation, we seamlessly unify attributes and items in the same arm space and achieve their EE trade-offs automatically using the framework of Thompson Sampling. Our Conversational Thompson Sampling (ConTS) model holistically solves all questions in conversational recommendation by choosing the arm with the maximal reward to play. Extensive experiments on three benchmark datasets show that ConTS outperforms the state-of-the-art methods Conversational UCB (ConUCB) and Estimation-Action-Reflection model in both metrics of success rate and average number of conversation turns.
Human capital dimensions and firm performance, mediating role of knowledge management

The study was aimed to examine the impact of human capital dimensions on form survivability. The study further investigated the mediating role of knowledge management in the said relationship. To measure human capital six dimensions (education, experience, health, personal attributes, skills, and training) were utilised. The data was collected through a structured questionnaire. The respondent's agreeableness on statements was measured by five-point Likert scale. The sample size of the study was 342 responses collected from the owners of SMEs in Iraq. A structural model was developed to relate the variables and these relationships were examined using partial least square structural equation modelling (PLS-SEM). SmartPLS 3.0 was used to analyse the data. The findings suggest the positive and significant impact of HC-dimension on firm survival. The results also suggest the mediating role of knowledge management on the relationship of HC-dimension (education, skills, and training) on firm survival. The results were supported by the existing literature. Moreover, the research implication, limitations and future research avenues were presented.
Prediction and identification of discrete-time dynamic nonlinear systems based on adaptive echo state network

In this paper, a new prediction and identification method based on adaptive echo state network (AESN) is proposed to identify a class of discrete-time dynamic nonlinear systems (DDNS). Firstly, according to the characteristics of input signals, the reservoir state update equation of AESN can be adaptively adjusted. In order to guarantee the echo state property of AESN, a sufficient condition for echo state property is given. Secondly, the reservoir parameters of AESN are optimized to improve the identification and prediction performance of AESN. Thirdly, an improved online output weights learning method based on historical reservoir state and output error is given. Finally, the effectiveness of the proposed method is verified by simulation examples.
Generating Distractors for Reading Comprehension Questions from Real Examinations

We investigate the task of distractor generation for multiple choice reading comprehension questions from examinations. In contrast to all previous works, we do not aim at preparing words or short phrases distractors, instead, we endeavor to generate longer and semantic-rich distractors which are closer to distractors in real reading comprehension from examinations. Taking a reading comprehension article, a pair of question and its correct option as input, our goal is to generate several distractors which are somehow related to the answer, consistent with the semantic context of the question and have some trace in the article. We propose a hierarchical encoderdecoder framework with static and dynamic attention mechanisms to tackle this task. Specifically, the dynamic attention can combine sentence-level and word-level attention varying at each recurrent time step to generate a more readable sequence. The static attention is to modulate the dynamic attention not to focus on question irrelevant sentences or sentences which contribute to the correct option. Our proposed framework outperforms several strong baselines on the first prepared distractor generation dataset of real reading comprehension questions. For human evaluation, compared with those distractors generated by baselines, our generated distractors are more functional to confuse the annotators.
Day-Ahead Hourly Forecasting of Solar Generation Based on Cluster Analysis and Ensemble Model

Accurate solar generation prediction is of great significance for grid dispatching and operation of photovoltaic power plants.In this paper, we propose a novel solar generation forecasting method based on cluster analysis and ensemble model.Two common ways to improve prediction accuracy are adopted.We first conduct cluster analysis based on solar generation to obtain a weather regime, which improves the computational efficiency and avoids the difficulty in selecting weather variables to participate in the clustering process.Then random forests with different parameters is established for different weather regimes, which is used as component models in the followed ensemble model.Finally, we weighted the predictions from different weather regimes to get the final results.To avoid manual design weights, ridge regression is used to calculate weights for each weather regime automatically.A large number of experiments have been carried out on freely available data sets to verify the performance of the proposed method.The experimental results show that our method predicts solar generation more accurately, which has broad prospects in practical application.
The Role of Interactivity in Local Differential Privacy

We study the power of interactivity in local differential privacy. First, we focus on the difference between fully interactive and sequentially interactive protocols. Sequentially interactive protocols may query users adaptively in sequence, but they cannot return to previously queried users. The vast majority of existing lower bounds for local differential privacy apply only to sequentially interactive protocols, and before this paper it was not known whether fully interactive protocols were more powerful. We resolve this question. First, we classify locally private protocols by their compositionality, the multiplicative factor by which the sum of a protocol's single-round privacy parameters exceeds its overall privacy guarantee. We then show how to efficiently transform any fully interactive compositional protocol into an equivalent sequentially interactive protocol with a blowup in sample complexity linear in this compositionality. Next, we show that our reduction is tight by exhibiting a family of problems such that any sequentially interactive protocol requires this blowup in sample complexity over a fully interactive compositional protocol. We then turn our attention to hypothesis testing problems. We show that for a large class of compound hypothesis testing problems - which include all simple hypothesis testing problems as a special case - a simple noninteractive test is optimal among the class of all (possibly fully interactive) tests.
Detecting Spoofing Attacks Using VGG and SincNet: BUT-Omilia Submission to ASVspoof 2019 Challenge

In this paper, we present the system description of the joint efforts of Brno University of Technology (BUT) and Omilia -- Conversational Intelligence for the ASVSpoof2019 Spoofing and Countermeasures Challenge. The primary submission for Physical access (PA) is a fusion of two VGG networks, trained on single and two-channels features. For Logical access (LA), our primary system is a fusion of VGG and the recently introduced SincNet architecture. The results on PA show that the proposed networks yield very competitive performance in all conditions and achieved 86\:\% relative improvement compared to the official baseline. On the other hand, the results on LA showed that although the proposed architecture and training strategy performs very well on certain spoofing attacks, it fails to generalize to certain attacks that are unseen during training.
Human Motion Prediction using Semi-adaptable Neural Networks

Human motion prediction is an important component to facilitate human robot interaction. Robots need to accurately predict human's future movement in order to safely plan its own motion trajectories and efficiently collaborate with humans. Many recent approaches predict human's movement using deep learning methods, such as recurrent neural networks. However, existing methods lack the ability to adapt to time-varying human behaviors, and many of them do not quantify uncertainties in the prediction. This paper proposes an approach that uses a semi-adaptable neural network for human motion prediction, and provides uncertainty bounds of the predictions in real time. In particular, a neural network is trained offline to represent the human motion transition model, and then recursive least square parameter adaptation algorithm (RLS-PAA) is adopted for online parameter adaptation of the neural network and for uncertainty estimation. Experiments on several human motion datasets verify that the proposed method significantly outperforms the state-of-the-art approach in terms of prediction accuracy and computation efficiency.
Action Recognition From Single Timestamp Supervision in Untrimmed Videos

Recognising actions in videos relies on labelled supervision during training, typically the start and end times of each action instance. This supervision is not only subjective, but also expensive to acquire. Weak video-level supervision has been successfully exploited for recognition in untrimmed videos, however it is challenged when the number of different actions in training videos increases. We propose a method that is supervised by single timestamps located around each action instance, in untrimmed videos. We replace expensive action bounds with sampling distributions initialised from these timestamps. We then use the classifier's response to iteratively update the sampling distributions. We demonstrate that these distributions converge to the location and extent of discriminative action segments. We evaluate our method on three datasets for fine-grained recognition, with increasing number of different actions per video, and show that single timestamps offer a reasonable compromise between recognition performance and labelling effort, performing comparably to full temporal supervision. Our update method improves top-1 test accuracy by up to 5.4%. across the evaluated datasets.
Scene Understanding and Recognition: Statistical Segmented Model using Geometrical Features and Gaussian Naïve Bayes

To examine the features of complex visual world, sensor technology merged with objects characteristics to scenes well. These scenes understanding are highly demanding task in different domains of visionary technologies like autonomous driving, generic object detection, sports scene identification and security. In this paper, we proposed a novel statistical segmented framework that can learn robust scene model and separate each object component. Then, each component is used to extract geometrical features that concatenate extreme points features, orientation and polygon displacement values. These features help in object detection and Gaussian Naïve Bayes is used for the scene recognition. The experimental evaluation demonstrated the proposed approach over UIUC Sports and 15 Scene datasets that achieved scene recognition rate of 85.09% and 82.65%. The proposed system should be applicable to different emerging technologies such as augmented reality scene integration, GPS location finder and visual surveillance which recognized different locations/objects to understand real world scenes.
Distributed Security Architecture Based on Blockchain for Connected Health: Architecture, Challenges, and Approaches

The ubiquitous nature of a connected health system imposes challenges with regard to the design of system architecture, security, and privacy. Different from traditional centralized systems, a distributed blockchain-based security protection architecture is proposed. In particular, smart contracts, as an intelligent protocol in the blockchain technology, are exploited to automatically achieve system confidentiality, integrity, and authenticity. Research challenges related to security and privacy issues in our proposed architecture are then analyzed, followed by potential solutions. Finally, security performance simulations and analyses are conducted to validate and evaluate the effectiveness of the proposed security architecture.
“You Sound Just Like Your Father” Commercial Machine Translation Systems Include Stylistic Biases

The main goal of machine translation has been to convey the correct content. Stylistic considerations have been at best secondary. We show that as a consequence, the output of three commercial machine translation systems (Bing, DeepL, Google) make demographically diverse samples from five languages “sound” older and more male than the original. Our findings suggest that translation models reflect demographic bias in the training data. This opens up interesting new research avenues in machine translation to take stylistic considerations into account.
ATHENA++

Natural Language Interfaces to Databases (NLIDB) systems eliminate the requirement for an end user to use complex query languages like SQL, by translating the input natural language (NL) queries to SQL automatically. Although a significant volume of research has focused on this space, most state-of-the-art systems can at best handle simple select-project-join queries. There has been little to no research on extending the capabilities of NLIDB systems to handle complex business intelligence (BI) queries that often involve nesting as well as aggregation. In this paper, we present Athena++, an end-to-end system that can answer such complex queries in natural language by translating them into nested SQL queries. In particular, Athena++ combines linguistic patterns from NL queries with deep domain reasoning using ontologies to enable nested query detection and generation. We also introduce a new benchmark data set ( FIBEN ), which consists of 300 NL queries, corresponding to 237 distinct complex SQL queries on a database with 152 tables, conforming to an ontology derived from standard financial ontologies (FIBO and FRO). We conducted extensive experiments comparing Athena++ with two state-of-the-art NLIDB systems, using both FIBEN and the prominent Spider benchmark. Athena++ consistently outperforms both systems across all benchmark data sets with a wide variety of complex queries, achieving 88.33% accuracy on FIBEN benchmark, and 78.89% accuracy on Spider benchmark, beating the best reported accuracy results on the dev set by 8%.