Reviewerly/exHarmony huggingface dataset:
Depth Estimation for Light-Field Images Using Stereo Matching and Convolutional Neural Networks

The paper presents a novel depth-estimation method for light-field (LF) images based on innovative multi-stereo matching and machine-learning techniques. In the first stage, a novel block-based stereo matching algorithm is employed to compute the initial estimation. The proposed algorithm is specifically designed to operate on any pair of sub-aperture images (SAIs) in the LF image and to compute the pair's corresponding disparity map. For the central SAI, a disparity fusion technique is proposed to compute the initial disparity map based on all available pairwise disparities. In the second stage, a novel pixel-wise deep-learning (DL)-based method for residual error prediction is employed to further refine the disparity estimation. A novel neural network architecture is proposed based on a new structure of layers. The proposed DL-based method is employed to predict the residual error of the initial estimation and to refine the final disparity map. The experimental results demonstrate the superiority of the proposed framework and reveal that the proposed method achieves an average improvement of 15.65% in root mean squared error (RMSE), 43.62% in mean absolute error (MAE), and 5.03% in structural similarity index (SSIM) over machine-learning-based state-of-the-art methods.
Unsupervised Acoustic Unit Representation Learning for Voice Conversion Using WaveNet Auto-Encoders

Unsupervised representation learning of speech has been of keen interest in recent years, which is for example evident in the wide interest of the ZeroSpeech challenges. This work presents a new method for learning frame level representations based on WaveNet auto-encoders. Of particular interest in the ZeroSpeech Challenge 2019 were models with discrete latent variable such as the Vector Quantized Variational Auto-Encoder (VQVAE). However these models generate speech with relatively poor quality. In this work we aim to address this with two approaches: first WaveNet is used as the decoder and to generate waveform data directly from the latent representation; second, the low complexity of latent representations is improved with two alternative disentanglement learning methods, namely instance normalization and sliced vector quantization. The method was developed and tested in the context of the recent ZeroSpeech challenge 2020. The system output submitted to the challenge obtained the top position for naturalness (Mean Opinion Score 4.06), top position for intelligibility (Character Error Rate 0.15), and third position for the quality of the representation (ABX test score 12.5). These and further analysis in this paper illustrates that quality of the converted speech and the acoustic units representation can be well balanced.
On the Interplay Between Fine-tuning and Sentence-level Probing for Linguistic Knowledge in Pre-trained Transformers

Fine-tuning pre-trained contextualized embedding models has become an integral part of the NLP pipeline.At the same time, probing has emerged as a way to investigate the linguistic knowledge captured by pre-trained models.Very little is, however, understood about how fine-tuning affects the representations of pre-trained models and thereby the linguistic knowledge they encode.This paper contributes towards closing this gap.We study three different pre-trained models: BERT, RoBERTa, and ALBERT, and investigate through sentence-level probing how finetuning affects their representations.We find that for some probing tasks fine-tuning leads to substantial changes in accuracy, possibly suggesting that fine-tuning introduces or even removes linguistic knowledge from a pre-trained model.These changes, however, vary greatly across different models, fine-tuning and probing tasks.Our analysis reveals that while finetuning indeed changes the representations of a pre-trained model and these changes are typically larger for higher layers, only in very few cases, fine-tuning has a positive effect on probing accuracy that is larger than just using the pre-trained model with a strong pooling method.Based on our findings, we argue that both positive and negative effects of finetuning on probing require a careful interpretation.
A New Mechanism of Open System Evolution and Its Entropy Using Unitary Transformations in Noncomposite Qudit Systems

The evolution of an open system is usually associated with the interaction of the system with an environment. A new method to study the open-type system evolution of a qubit (two-level atom) state is established. This evolution is determined by a unitary transformation applied to the qutrit (three-level atom) state, which defines the qubit subsystems. This procedure can be used to obtain different qubit quantum channels employing unitary transformations into the qutrit system. In particular, we study the phase damping and spontaneous-emission quantum channels. In addition, we mention a proposal for quasiunitary transforms of qubits, in view of the unitary transform of the total qutrit system. The experimental realization is also addressed. The probability representation of the evolution and its information-entropic characteristics are considered.
Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection

Few-shot Intent Detection is challenging due to the scarcity of available annotated utterances.Although recent works demonstrate that multi-level matching plays an important role in transferring learned knowledge from seen training classes to novel testing classes, they rely on a static similarity measure and overly fine-grained matching components.These limitations inhibit generalizing capability towards Generalized Few-shot Learning settings where both seen and novel classes are co-existent.In this paper, we propose a novel Semantic Matching and Aggregation Network where semantic components are distilled from utterances via multi-head self-attention with additional dynamic regularization constraints.These semantic components capture high-level information, resulting in more effective matching between instances.Our multi-perspective matching method provides a comprehensive matching measure to enhance representations of both labeled and unlabeled instances.We also propose a more challenging evaluation setting that considers classification on the joint all-class label space.Extensive experimental results demonstrate the effectiveness of our method.Our code and data are publicly available 1 .
Cross-Media Keyphrase Prediction: A Unified Framework with Multi-Modality Multi-Head Attention and Image Wordings

Social media produces large amounts of contents every day.To help users quickly capture what they need, keyphrase prediction is receiving a growing attention.Nevertheless, most prior efforts focus on text modeling, largely ignoring the rich features embedded in the matching images.In this work, we explore the joint effects of texts and images in predicting the keyphrases for a multimedia post.To better align social media style texts and images, we propose: (1) a novel Multi-Modality Multi-Head Attention (M 3 H-Att) to capture the intricate cross-media interactions; (2) image wordings, in forms of optical characters and image attributes, to bridge the two modalities.Moreover, we design a novel unified framework to leverage the outputs of keyphrase classification and generation and couple their advantages.Extensive experiments on a large-scale dataset 1 newly collected from Twitter show that our model significantly outperforms the previous state of the art based on traditional co-attentions.Further analyses show that our multi-head attention is able to attend information from various aspects and boost classification or generation in diverse scenarios.
Self-Induced Curriculum Learning in Self-Supervised Neural Machine Translation

Self-supervised neural machine translation (SSNMT) jointly learns to identify and select suitable training data from comparable (rather than parallel) corpora and to translate, in a way that the two tasks support each other in a virtuous circle.In this study, we provide an in-depth analysis of the sampling choices the SSNMT model makes during training.We show how, without it having been told to do so, the model self-selects samples of increasing (i) complexity and (ii) task-relevance in combination with (iii) performing a denoising curriculum.We observe that the dynamics of the mutual-supervision signals of both system internal representation types are vital for the extraction and translation performance.We show that in terms of the Gunning-Fog Readability index, SSNMT starts extracting and learning from Wikipedia data suitable for high school students and quickly moves towards content suitable for first year undergraduate students.
Natural Language Processing for Achieving Sustainable Development: the Case of Neural Labelling to Enhance Community Profiling

In recent years, there has been an increasing interest in the application of Artificial Intelligence – and especially Machine Learning – to the field of Sustainable Development (SD). However, until now, NLP has not been systematically applied in this context. In this paper, we show the high potential of NLP to enhance project sustainability. In particular, we focus on the case of community profiling in developing countries, where, in contrast to the developed world, a notable data gap exists. Here, NLP could help to address the cost and time barrier of structuring qualitative data that prohibits its widespread use and associated benefits. We propose the new extreme multi-class multi-label Automatic UserPerceived Value classification task. We release Stories2Insights, an expert-annotated dataset of interviews carried out in Uganda, we provide a detailed corpus analysis, and we implement a number of strong neural baselines to address the task. Experimental results show that the problem is challenging, and leaves considerable room for future research at the intersection of NLP and SD.
Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising

Self-supervised frameworks that learn denoising models with merely individual noisy images have shown strong capability and promising performance in various image denoising tasks. Existing self-supervised denoising frameworks are mostly built upon the same theoretical foundation, where the denoising models are required to be J-invariant. However, our analyses indicate that the current theory and the J-invariance may lead to denoising models with reduced performance. In this work, we introduce Noise2Same, a novel self-supervised denoising framework. In Noise2Same, a new self-supervised loss is proposed by deriving a self-supervised upper bound of the typical supervised loss. In particular, Noise2Same requires neither J-invariance nor extra information about the noise model and can be used in a wider range of denoising applications. We analyze our proposed Noise2Same both theoretically and experimentally. The experimental results show that our Noise2Same remarkably outperforms previous self-supervised denoising methods in terms of denoising performance and training efficiency. Our code is available at this https URL.
Domain Adaptation of Thai Word Segmentation Models using Stacked Ensemble

Like many Natural Language Processing tasks, Thai word segmentation is domain-dependent.Researchers have been relying on transfer learning to adapt an existing model to a new domain.However, this approach is inapplicable to cases where we can interact with only input and output layers of the models, also known as "black boxes".We propose a filter-and-refine solution based on the stackedensemble learning paradigm to address this black-box limitation.We conducted extensive experimental studies comparing our method against state-of-the-art models and transfer learning.Experimental results show that our proposed solution is an effective domain adaptation method and has a similar performance as the transfer learning method.
A Two-Stage Method for Target Searching in the Path Planning for Mobile Robots

The path planning for target searching in mobile robots is critical for many applications, such as warehouse inspection and caring and surveillance for elderly people in the family scene. To ensure visual complete coverage from the camera equipped in robots is one of the most challenging tasks. To tackle this issue, we propose a two-stage optimization model to efficiently obtain an approximate optimal solution. In this model, we first develop a method to determine the key locations for visual complete coverage of a two-dimensional grid map, which is constructed by drawing lessons from the method of corner detection in the image processing. Then, we design a planning problem for searching the shortest path that passes all key locations considering the frequency of target occurrence. The testing results show that the proposed algorithm can achieve the significantly shorter search path length and the shorter target search time than the current Rule-based Algorithm and Genetic Algorithm (GA) in various simulation cases. Furthermore, the results show that the improved optimization algorithm with the priori known frequency of occurrence of the target can further improve the searching with shorter searching time. We also set up a test in a real environment to verify the feasibility of our algorithm.
Contour-Aware Recurrent Cross Constraint Network for Salient Object Detection

Recently, fully convolutional neural networks have been adopted for salient object detection and object contour detection, and have achieved impressive performance. Closely related contours are employed to help supervise low-level features, rather than being simultaneously trained as associated tasks as in most methods. This study proposes a coarse-to-fine architecture for a contour-aware recurrent cross constraint network (CARCCNet) for salient object detection. At the coarse stage, we design a contour-aware recurrent constraint network (CARCNet) with a recurrent structure that consists of a set of contour-aware constraint modules (CACMs), saliency-aware constraint modules (SACMs), and double supervised prediction modules (DSPMs). These modules can simultaneously generate saliency maps and contour maps and alternately constrain them at each recurrent step. In the refining stage, we propose a contour knowledge transfer residual (CKTR) module to transfer the contour knowledge from the low-level branch into the saliency features to obtain the final saliency map with complete objects and accurate contours. Our CARCCNet also finally generates the object contour map at the same time without post-processing. Extensive experiments on five saliency detection benchmark datasets demonstrate the effectiveness and robustness of the proposed method.
A Unified Network for Arbitrary Scale Super-Resolution of Video Satellite Images

Super-resolution (SR) has attracted increasing attention as it can improve the quality of video satellite images. Most previous studies only consider several integer magnification factors and focus on obtaining a specific SR model for each scale factor. However, in the real world, it is a common requirement to zoom the videos arbitrarily by rolling the mouse wheel. In this article, we propose a unified network for arbitrary scale SR (ASSR) of video satellite images. The proposed ASSR consists of two modules, i.e., feature learning module and arbitrary upscale module. The feature learning module accepts multiple low-resolution (LR) frames and extracts useful features of those frames by using many 3-D residual blocks. The arbitrary upscale module takes the extracted features as input and enhances the spatial resolution by subpixel convolution and bicubic-based adjustment. Different from existing video satellite image SR methods, ASSR can continuously zoom LR video satellite images with arbitrary integer and noninteger scale factors in a single model. Experiments have been conducted on real video satellite images acquired by Jilin-1 and OVS-1. Quantitative and qualitative results have demonstrated that ASSR has superior reconstruction performance compared with the state-of-the-art SR methods.
Attack Path Analysis for Cyber Physical Systems

The identification and analysis of potential paths that an adversary may exploit to attack Cyber Physical Systems comprising sub-systems enables the comprehensive understanding of the attacks and the impact that may have to the overall system, thus facilitating the definition of appropriate countermeasures that will satisfy the pertinent security requirements. To this end, several attack modelling techniques can be employed, the attack graph being the most prevalent among them. Unfortunately, the discovery and analysis of all possible attack paths in an attack graph is not possible in systems even of a moderate size. In this work we propose a novel systematic method for discovering and analyzing attack paths in real-world scale interconnected Cyber Physical Systems. The method considers the criticality of each sub-system in discovering paths and the risk to the overall system that each path presents to analyze and prioritize paths. We illustrate the workings of the method by applying to the navigational Cyber Physical Systems of the Cyber-Enabled Ship to identify and analyze highly critical attack paths originating from the Automatic Identification System (AIS) and targeting the Autonomous Navigation System (ANS).
Effect of virtual reality technology on the teaching of urban railway vehicle engineering

Abstract The real key structures of urban railway vehicle are costly and dangerous, but students majoring in relevant fields have to acquire skills needed to operate the key structures to get good jobs or do academic research easily. The virtual reality (VR) technology, which has been used in many fields, can provide students with a unique learning experience. The purpose of this paper is to investigate the impact of VR on the operational skills and cognitive load in students studying urban railway vehicle engineering. A virtual reality learning environment (VRLE) was designed to help students understand how a bogie is dismantled or assembled, thus enabling them to dismantle or assemble bogie. The experimental subjects were randomly divided into an experimental group and a control group, and each group contained 31 male and 9 female freshmen majoring in urban railway vehicle engineering. Corresponding pretests, teaching activities, and posttests were carried out according to the proposed teaching and experimental designs. The VRLE was used to teach the students in the experimental group, whereas the other group was taught by traditional teaching methods. The experimental results show that VR played a positive part in the students' acquisition of operational skills and the VRLE was helpful in reducing the cognitive load in the students when they were performing experiments. The results reveal that VR technology has a positive impact on the education in urban railway vehicle engineering.
TEEMon

Trusted Execution Environments (TEEs), such as Intel Software Guard eXtensions (SGX), are considered as a promising approach to resolve security challenges in clouds. TEEs protect the confidentiality and integrity of application code and data even against privileged attackers with root and physical access by providing an isolated secure memory area, i.e., enclaves. The security guarantees are provided by the CPU, thus even if system software is compromised, the attacker can never access the enclave's content. While this approach ensures strong security guarantees for applications, it also introduces a considerable runtime overhead in part by the limited availability of protected memory (enclave page cache). Currently, only a limited number of performance measurement tools for TEE-based applications exist and none offer performance monitoring and analysis during runtime.
Un-Weyl-ing the Clifford Hierarchy

The teleportation model of quantum computation introduced by Gottesman and Chuang (1999) motivated the development of the Clifford hierarchy. Despite its intrinsic value for quantum computing, the widespread use of magic state distillation, which is closely related to this model, emphasizes the importance of comprehending the hierarchy. There is currently a limited understanding of the structure of this hierarchy, apart from the case of diagonal unitaries (Cui et al., 2017; Rengaswamy et al. 2019). We explore the structure of the second and third levels of the hierarchy, the first level being the ubiquitous Pauli group, via the Weyl (i.e., Pauli) expansion of unitaries at these levels. In particular, we characterize the support of the standard Clifford operations on the Pauli group. Since conjugation of a Pauli by a third level unitary produces traceless Hermitian Cliffords, we characterize their Pauli support as well. Semi-Clifford unitaries are known to have ancilla savings in the teleportation model, and we explore their Pauli support via symplectic transvections. Finally, we show that, up to multiplication by a Clifford, every third level unitary commutes with at least one Pauli matrix. This can be used inductively to show that, up to a multiplication by a Clifford, every third level unitary is supported on a maximal commutative subgroup of the Pauli group. Additionally, it can be easily seen that the latter implies the generalized semi-Clifford conjecture, proven by Beigi and Shor (2010). We discuss potential applications in quantum error correction and the design of flag gadgets.
Using Stack Overflow to Assess Technical Debt Identification on Software Projects

Context. The accumulation of technical debt (TD) items can lead to risks in software projects, such a gradual decrease in product quality, difficulties in their maintenance, and ultimately the cancellation of the project. To mitigate these risks, developers need means to identify TD items, which enable better documentation and improvements in TD management. Recent literature has proposed different indicator-based strategies for TD identification. However, there is limited empirical evidence to support that developers use these indicators to identify TD in practice. In this context, data from Q&A websites, such as Stack Overflow (SO), have been extensively leveraged in recent studies to investigate software engineering practices from a developers' point of view. Goal. This paper seeks to investigate, from the point of view of practitioners, how developers commonly identify TD items in their projects. Method. We mined, curated, and selected a total of 140 TD-related discussions on SO, from which we performed both quantitative and qualitative analyses. Results. We found that SO's practitioners commonly discuss TD identification, revealing 29 different low-level indicators for recognizing TD items on code, infrastructure, architecture, and tests. We grouped low-level indicators based on their themes, producing an aggregated set of 13 distinct high-level indicators. We then classified all low- and high-level indicators into three different categories according to which type of debt each of them is meant to identify. Conclusions. We organize the empirical evidence on the low- and high-level indicators and their relationship to types of TD in a conceptual framework, which may assist developers and serve as guidance for future research, shedding new light on TD identification state-of-practice.