Reviewerly/exHarmony huggingface dataset:
FastGraphTTS: An Ultrafast Syntax-Aware Speech Synthesis Framework

This paper integrates graph-to-sequence into an end-to-end text-to-speech framework for syntax-aware modelling with syntactic information of input text. Specifically, the input text is parsed by a dependency parsing module to form a syntactic graph. The syntactic graph is then encoded by a graph encoder to extract the syntactic hidden information, which is concatenated with phoneme embedding and input to the alignment and flow-based decoding modules to generate the raw audio waveform. The model is experimented on two languages, English and Mandarin, using single-speaker, few samples of target speakers, and multi-speaker datasets, respectively. Experimental results show better prosodic consistency performance between input text and generated audio, and also get higher scores in the subjective prosodic evaluation, and show the ability of voice conversion. Besides, the efficiency of the model is largely boosted through the design of the AI chip operator with 5x acceleration.
PA-Sketch: A Fast and Accurate Sketch for Differentiated Flow Estimation

Due to the ability to maintain good accuracy and high throughput with limited memory resources, sketch has gained wide deployment and application for approximate flow estimation. However, most existing sketch approaches ignore the distinctions between flow priorities, though the high-priority flows are relatively scarce but hold significant information. Therefore, a class of priority-aware sketches has appeared recently to provide differentiated measurement accuracy for flows with different priorities. Unfortunately, it is challenging for these priority-aware sketches to strike a good balance between accuracy and throughput. To address this issue, we propose a priority-adaptive architecture PA-Sketch, which utilizes priority-aware hash to dynamically allocate appropriate numbers of hash functions for different flows according to their priorities. For the scenarios we experimented, we observed that PA-Sketch significantly improves accuracy while minimizing the hash overhead. Compared to the state-of-the-art priority-aware sketches, PA-Sketch achieves around 4.83x higher accuracy and 1.83x higher F1 score for high-priority flows on average, meanwhile maintaining slight accuracy loss for low-priority flows.
Task-Oriented Communication with Reliability-Driven Retransmission Request

The advanced Deep Learning (DL) techniques have enabled the development of semantic communication systems by their remarkable information processing and end - to-end optimization capabilities. However, the lack of performance guarantee of these DL-based methods also brings concerns on the reliability of semantic communication systems. To address this issue, we propose a semantic communication scheme with reliability-driven retransmission requests in order to improve the transmission efficiency and guarantee the reliability of the inference result at the same time. In particular, the transmitter first sends a basic amount of the information, and the receiver infers with this information, and assesses the reliability of the results. If the derived reliability is below a given threshold, a retransmission request is sent to the sender for more information to be transmitted. We exploit the entropy of the output logits to quantify the reliability of the classification results. More specifically, lower entropy corresponds to higher confidence, indicating higher reliability. Experimental results validate the effectiveness of the proposed scheme in terms of improving transmission efficiency and computational efficiency while maintaining the reliability of the inference results.
Conditional Data Synthesis with Deep Generative Models for Imbalanced Dataset Oversampling

The problem of data imbalance is defined as the uneven distribution of the training examples to the existing classes of a dataset. Among a wide variety of solutions, the oversampling techniques try to mitigate the problem by synthesizing artificial examples associated with the minority class. The huge success of Generative Adversarial Networks (GANs) rendered them an attractive choice for oversampling and numerous researchers proposed modifications of GANs for imbalanced datasets. Nevertheless, the existing models employ the entire minority class for sample generation, thus being vulnerable to outliers and noisy data instances. In addition, the majority of the relevant research concerns image classification tasks, leaving a large gap for research with tabular data. Finally, another powerful and popular generative model, the Variational Autoencoder (VAE) has been rather overlooked by the community in class imbalance solutions. In this paper we present SB-GAN and SB-VAE, two generative models that identify borderline and noisy samples before they are trained. In this manner SB-GAN and SB-VAE learn better class distributions that are not distorted by the existence of outliers. The experimental evaluation of SB-GAN and SB-VAE with 4 tabular datasets revealed a superior performance against 8 state-of-the-art oversampling techniques.
How They Relate and Leave: Understanding Atoms of Confusion in Open-Source Java Projects

Software comprehension is essential to improve understanding and avoid mistakes in the software development lifecycle. Code confusion occurs when a developer and the computer reach different interpretations about the behavior of the same piece of code. Such pieces of code can be represented as small and isolated code patterns called Atoms of Confusion (ACs). In this study, we empirically investigated the effects of ACs in the software development lifecycle of 21 open-source Java projects. We built a dataset linking more than 8,000 commits, 4,000 reported issues, and 7,000 ACs from the subject projects. Our findings showed a positive correlation between the number of ACs and the number of reported bugs and improvements. We also investigated changes in commits, looking forward to gathering a better understanding of in what context ACs are removed. As each commit is linked to at least one reported issue (e.g., bug and improvement), we were able to compare the ratio of ACs removal regarding each kind of commit and use it as a proxy to indicate whether ACs are likely to be the cause behind a reported issue. We found a higher ratio of removed ACs in bug-fix and improvement commits than in the other kind of commits (task, sub-task, new feature, wish, and test) for 14 of the 19 studied projects, which had ACs removed in commits. Finally, to support our quantitative results, we conducted a qualitative analysis to understand better how often atoms of confusion contributed to the occurrence of a bug or improvement. We inspected ACs removed in these types of commits with up to ten lines removed, analyzing the source code, messages of each involved commit, and the title, description, and comments of related Jira issues. Out of a universe of 8,641 commits from 21 analyzed projects, 391 removed ACs. Among them, 53 met the condition for our qualitative analysis. In 7 of these commits, 9 removed ACs were likely to contributed directly to the occurrence of a bug or improvement. To the best of our knowledge, our research is the first to investigate the connection between Atoms of Confusion and the source of bugs or the cause of improvements in Java projects.
Window Attention with Multiple Patterns for Single Image Super-Resolution

The non-local attention (NLA) is a generic method-ology that has proven its effectiveness in solving various image processing and computer vision tasks. Unfortunately, the NLA over the entire input image requires extremely high computational burden. A potential solution is to use the window attention instead, which restricts the NLA computation within a square window. However, this will sacrifice the capacity of capturing long-range dependencies (LRDs). To overcome the difficulty, a novel attention module termed the window attention with multiple patterns (WAMP) is proposed. In our WAMP, three window patterns are jointly exploited, including the square, horizontal, and vertical patterns, by which the attention receptive field can be expanded to the entire image for measuring LRDs with favorably low computational complexity. To cope with image features at different granularity levels, our WAMP is further performed with multiple window granularities. Moreover, by blending our WAMP with shift convolutions, a novel architectural element is also proposed, which incorporates non-local attention with local image information for boosting the model performance. To verify the effectiveness of our proposed attention module, a WAMP network is finally developed for conducting single image super-resolution (SISR). Extensive experimental results clearly demonstrate the superiority of our WAMP network over the state-of-the-art SISR methods.
Disjointness axioms between top-level ontology concepts as a heuristic for word similarity evaluation

Word semantic similarity is essential to numerous natural language processing and computational linguistics applications, including word sense disambiguation, machine translation, and information retrieval. Evaluating word similarity better and faster remains a complex problem due to the limitations of semantic similarity algorithms. In this paper, we propose a novel approach for computing word semantic similarity that uses the disjoint axioms between top-level ontology concepts as a heuristic for evaluating the semantic similarity between domain entities that represent the senses of those words. Our method leverages the disjointness information in top-level ontologies to reduce the number of comparisons needed to evaluate the semantic similarity between words. We evaluate our approach on benchmark datasets for the word similarity task. The results show that our method outperforms the traditional algorithms for knowledge-based word similarity regarding correlation with human judgments and time performance. Our proposed method can potentially facilitate the development of more effective and efficient applications.
Change Pattern Detection for Optimising Incremental Static Analysis

Static analyses can be used by developers to compute properties of a program, enabling e.g., bug detection and program verification. However, reanalysing a program from scratch upon every change is time-consuming, especially in settings where code changes often, such as within IDEs. To avoid such full reanalyses, incremental analyses instead reuse parts of the previous analysis result, and reanalyse the changed code as necessary. While incrementality improves the analysis time, we introduce a complementary approach that further reduces the analysis time. A traditional incremental analysis updates previous analysis results without domain-specific knowledge. However, the effect of particular source code changes on analysis results can be predicted. Performing a traditional incremental analysis of the changed code might therefore be unnecessary. Instead, we propose to detect code change patterns of which the effect on analysis results can be predicted and to update these results accordingly, saving potentially expensive computations. In this paper, we explore the idea of adapting the analysis results for behaviour-preserving change patterns. In particular, we consider consistent renamings, inverted conditionals, and moved function definitions within Scheme programs. We implemented our approach and evaluated it on 30 programs. We show decreases in incremental analysis time between 3% and 99% on 25 programs that contain at least one behaviour-preserving change pattern.
Zero-shot Bilingual App Reviews Mining with Large Language Models

App reviews from app stores are crucial for improving software requirements. A large number of valuable reviews are continually being posted, describing software problems and expected features. Effectively utilizing user reviews necessitates the extraction of relevant information, as well as their subsequent summarization. Due to the substantial volume of user reviews, manual analysis is arduous. Various approaches based on natural language processing (NLP) have been proposed for automatic user review mining. However, the majority of them requires a manually crafted dataset to train their models, which limits their usage in real-world scenarios. In this work, we propose Mini-BAR, a tool that integrates large language models (LLMs) to perform zero-shot mining of user reviews in both English and French. Specifically, Mini-BAR is designed to (i) classify the user reviews, (ii) cluster similar reviews together, (iii) generate an abstractive summary for each cluster and (iv) rank the user review clusters. To evaluate the performance of Mini-BAR, we created a dataset containing 6,000 English and 6,000 French annotated user reviews and conducted extensive experiments. Preliminary results demonstrate the effectiveness and efficiency of Mini-BAR in requirement engineering by analyzing bilingual app reviews.
Domain Adaptation in Machine Learning: A Practical Simulation Study

Domain adaptation (DA) is a critical technique in machine learning, designed to alleviate distribution differences between training and test sets by leveraging information from similar datasets. This paper presents a practical simulation of both widely-used and recent DA techniques, with a specific focus on unsupervised learning scenarios where labels are only available in the source domain. We thoroughly investigate the impact of these methods on various publicly available datasets, providing detailed explanations of our findings. Furthermore, we conduct an ablation study and elaborate extensively on the simulation results. Our study offers valuable information on the resilience of selected DA algorithms, highlighting the importance of appropriate datasets and neural network architectures with training methodologies to achieve optimal performance in DA tasks.
MC-Net: Multi-Scale Feature Fusion and Cross-Level Information Interaction Network for Traffic Sign Detection

Traffic sign detection is an important topic in autonomous driving and intelligent transportation, as it is widely applied in real-life scenarios to detect crucial road information for autonomous devices. However, existing detection methods face challenges due to significant variations in the scale of target objects and limited preservation of contextual information. To address these challenges, we propose MC-Net, a novel traffic sign detection network that enhances the model’s receptive field and enriches contextual information features. To overcome the sensitivity of detectors on the target scale variation, we first introduce the Multi-Scale Feature Fusion (MFF) module. By incorporating dilation convolution and group convolution, the MFF module effectively expands the network’s receptive field and reduces sensitivity to scale variations. Additionally, we incorporate a cross-layer information interaction (CLII) module into the MC-Net model, which facilitates the extraction of feature information across diverse network layers and effectively addresses the issue of contextual information loss. Experimental results on two real-world datasets (TT100K and STSD), demonstrate that MC-Net significantly improves detection efficiency compared to existing methods, achieving mAP of 85.7% and 94.8% respectively.
TDCGL: Two-Level Debiased Contrastive Graph Learning for Recommendation

As a milestone research combining recommender systems and knowledge graphs (KG), Knowledge Graph Attention Network (KGAT) has achieved great success in the field of recommender systems by proposing a new approach that implements explicit end-to-end modeling of higher-order relationships in a graph neural network framework to provide better item-assisted information recommendations. A series of methods following KGAT provide more solutions for KG-based recommendations. However, over-reliance on high-quality knowledge graphs is a bottleneck for such methods. Specifically, the long-tailed distribution of entities of KG and noise issues in the real world will make item-entity dependent relations deviate from reflecting true characteristics and significantly harm the performance of modeling user preference. Contrastive learning, as a novel method that is employed for data augmentation and denoising, provides inspiration to fill this research gap. However, the mainstream work only focuses on the long-tail properties of the number of items clicked, while ignoring that the long-tail properties of total number of clicks per user may also affect the performance of the recommendation model. Therefore, to tackle these problems, motivated by the Debiased Contrastive Learning of Unsupervised Sentence Representations (DCLR), we propose Two-Level Debiased Contrastive Graph Learning (TDCGL) model. Specifically, we design the Two-Level Debiased Contrastive Learning (TDCL) and deploy it in the KG, which is conducted not only on User-Item pairs but also on User-User pairs for modeling higher-order relations. Also, to reduce the bias caused by random sampling in contrastive learning, with the exception of the negative samples obtained by random sampling, we add a noise-based generation of negation to ensure spatial uniformity. Considerable experiments on open-source datasets demonstrate that our method has excellent anti-noise capability and significantly outperforms state-of-the-art baselines. In addition, ablation studies about the necessity for each level of TDCL are conducted.
Metrological Analysis of HoloLens 2 for Visual Marker-Based Surgical Navigation

Although the research on medical navigation systems dates back to the second half of the 20th century, the emergence of modern technologies offers new prospects for their application. One of the technologies that has been investigated in recent years for its potential in clinician support systems is augmented reality. Many AR solutions utilize head-mounted displays, allowing for the integration during medical procedures. These displays are equipped with cameras that can provide data for marker-based navigation. However, before implementing such approaches in real-world applications, it is crucial to assess the precision of marker detection. Therefore, in this paper we present a metrological analysis of the detection of two most common marker types, namely QR codes and ArUco markers, using Hololens 2 and dedicated algorithms designed for this specific purpose. Our study consisted of two different measuring setups: one to investigate the effect of different distances and the other to investigate the effect of angles on position error. The results showed that QR codes can be detected from a maximum distance of 750mm (for a 75mm size marker), while ArUco markers can be detected from distances as far as 1250mm, even for a marker as small as 2.3 cm. However, the standard deviation of the average measurement of ArUco marker size is greater than that of QR codes. The results obtained suggest that it is possible to detect ArUco markers in sizes and distances suitable for medical settings using HoloLens 2 and dedicated software. Therefore, the results of this study could potentially pave the way for the development of a marker-based method for medical navigation in the future.
Connectable and Independent Junction Tree-Based Compilation Technique of Object-Oriented Bayesian Networks

Object-oriented Bayesian network (OOBN) is a method for building compositional and hierarchical Bayesian network (BN) models that promote reuse and simple maintenance. Reasoning with both BNs and OOBNs entails the computational job of inference, the computation of new posterior probability distributions based on a set of evidence. A widely used inference strategy in conventional BN is to compile the BN into a junction tree (JT) before conducting standard inference. In the case of OOBN, it is first flattened into the underlying BN before performing the JT-based compilation. However, large OOBNs flatten to complex and larger BNs can be computationally intensive to compile into JTs due to the complexity of compilation being exponential to the size of BNs. To cope with these performance issues, techniques like Incremental Compilation (IC) avoid reconstructing JT from scratch after each modification of a BN. However, none of the existing works were able to reduce the computational complexity of compilation. Hence, in this paper, we propose a new compilation algorithm that compiles the OOBN without flattening it and re-using the existing JTs of embedded components of the OOBN. Evaluation results show that our proposed algorithm effectively reduces the computation time for JT construction of OOBN.
Societal Versus Encoded Stereotypes in Text Encoders

Text encoders have recently been convicted of en-coding unjustified social stereotypes, which lead models to make biased and prejudiced predictions when trained on downstream tasks such as sentiment analysis or question answering. The presence of bias in NLP models is dangerous since it promotes the divide between different social groups. Thus, attempts at mitigating biases from NLP models constitute an active line of research. However, these methods assume that models replicate exactly the same stereotypes ingrained in society’s impression, leading to potential inaccuracies in the normative framing of bias. In this work we confirm that text encoders are indeed biased. Nonetheless, we show that encoded biases are slightly different from survey-based biases proper to human prejudice. We ground our findings on the Stereotype Content Model, an acclaimed framework to interpret stereotypes, prejudice and inter-group relations in social psychology.
Efficient HTN to STRIPS Encodings for Concurrent Planning

The Hierarchical Task Network (HTN) formalism is used to express a wide variety of planning problems and many techniques have been proposed to solve them. A particular technique is to encode hierarchical planning problems as classical STRIPS planning problems. One advantage of this technique is to benefit directly from the constant improvements made by STRIPS planners. However, there are still few effective and expressive encodings. In this paper, we present the first HTN to STRIPS encodings allowing to generate concurrent plans. We show experimentally that these encodings not only outperform previous approaches on hierarchical IPC benchmarks but also produce more expressive solution plans.
Welcome Message from the VIS 2023 General Chairs

We are thrilled this year to welcome the IEEE VIS conference for its first ever visit to Australia. It is, for that matter, the first time for this conference to come to the Southern Hemisphere, as well as its first time in an Asian time zone. We acknowledge Traditional Owners of Narrm, the Boon Wurrung and Wurundjeri Woi Wurrung people of the Kulin nation, on whose land VIS 2023 will be held. We pay our respects to their Elders past, present and emerging. We know Melbourne (Narrm) is a long way from Europe and the US, as we’ve been doing the reverse trip ourselves for many years. But we are grateful to all from those regions who are able to join us, and we are delighted to welcome many new participants from the Asia-Pacific region who are now able to attend in-person for the first time. This sojourn down-under marks a significant milestone for IEEE VIS towards becoming a truly global conference.