Reviewerly/exHarmony huggingface dataset:
Actor-Aware Alignment Network for Action Recognition

Action recognition has attracted growing interest recently. It suffers from the problem that complex and diverse environments may disturb the extraction of action features. Existing methods propose to explore the temporal associations to alleviate the issue. However, they cannot handle long-range frames, and the rigid techniques are powerless against the differences caused by the deformation of the actors. To this end, we propose the Actor-Aware Alignment Network (A <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> Net), which helps locate the action region. Specifically, through the intra-snippet correction, we afford the local segment alignment frames. The inter-snippet is designed to rectify the results, avoiding the occlusion situation that may appear in the local snippet. In addition, we consider intra-alignment short-range adjustive frames and long-range context frames between different snippets, which allows our A <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> Net network to achieve the effect of focusing on long-range frame information. Multiple Reasoning Attention (MRA) modules are introduced to integrate features along the temporal dimension to keep the video spatio-temporal consistent. Extensive experiments conducted on three widely-used public benchmarks, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UCF101</b> , <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HMDB51</b> , and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">InfAR</b> , indicate that the excellence of our approach over other state-of-the-art models in wild scenarios.
Mining Top-K High Utility Itemset Using Bio-Inspired Algorithms

High utility itemset (HUI) mining is a necessary research problem in the field of knowledge discovery and data mining. Many algorithms for Top-K HUI mining have been proposed. However, the principal issue with these algorithms is that they need to store potential top-k patterns in the memory anytime, and they request the minimum utility threshold to automatically rise when finding HUIs. Consequently, the performance of existing exact algorithms for Top-K HUIs mining tends to decrease when the database size and the number of distinct items in the databases rise. To address this issue, we suggest a Binary Particle Swarm Optimization (BPSO) based algorithm for mining Top-K HUIs effectively, namely TKO-BPSO (Top-K high utility itemset mining in One phase based on Binary Particle Swarm Optimization). The main idea of TKO-BPSO is not only to use a one-phase model and strategy Raising the threshold by the Utility of Candidates (RUC) to effectively increase the border thresholds for pruning the search space but also to adopt the sigmoid function in the updating process of the particles. This might significantly reduce the combinational problem in traditional HUIM when the database size and the number of distinct items in the databases rise. Consequently, its performance outperforms existing exact algorithms for mining Top-K HUIs because it efficiently overcomes the problem of the vast amount candidates. Substantial experiments conducted on publicly available several real and synthetic datasets show that the proposed algorithm has better results than existing state-of-the-art algorithms in terms of runtime, which can significantly reduce the combinational problem and memory usage.
DeepShadow: Neural Shape from Shadow

This paper presents 'DeepShadow', a one-shot method for recovering the depth map and surface normals from photometric stereo shadow maps. Previous works that try to recover the surface normals from photometric stereo images treat cast shadows as a disturbance. We show that the self and cast shadows not only do not disturb 3D reconstruction, but can be used alone, as a strong learning signal, to recover the depth map and surface normals. We demonstrate that 3D reconstruction from shadows can even outperform shape-from-shading in certain cases. To the best of our knowledge, our method is the first to reconstruct 3D shape-from-shadows using neural networks. The method does not require any pre-training or expensive labeled data, and is optimized during inference time.
JPEG Artifacts Removal via Contrastive Representation Learning

To meet the needs of practical applications, current deep learning-based methods focus on using a single model to handle JPEG images with different compression qualities, while few of them consider the auxiliary effects of the compression quality information. Recently, several methods estimate quality factors in a supervised learning manner to guide their network to remove JPEG artifacts. However, they may fail to estimate unseen compression types, affecting the subsequent restoration performance. To remedy this issue, we propose an unsupervised compression quality representation learning strategy for the blind JPEG artifacts removal. Specifically, we utilize contrastive learning to obtain discriminative compression quality representations in the latent feature space. Then, to fully exploit the learned representations, we design a compression-guided blind JPEG artifacts removal network, which integrates the discriminative compression quality representations in an information lossless way. In this way, our single network can flexibly handle various JPEG compression images. Experiments demonstrate that our method can adapt to different compression qualities to obtain discriminative representations and outperform state-of-art methods.
Improving Cross-Project Software Defect Prediction Method Through Transformation and Feature Selection Approach

In the traditional software defect prediction methodology, the historical record (dataset) of the same project is partitioned into training and testing data.In a practical situation where the project to be predicted is new, traditional software defect prediction cannot be employed.An alternative method is cross-project defect prediction, where the historical record of one project (source) is used to predict the defect status of another project (target).The cross-project defect prediction method solves the limitations of the historical records in the traditional software defect prediction method.However, the performance of cross-project defect prediction is relatively low because of the distribution differences between the source and target projects.Furthermore, the software defect dataset used for cross-project defect prediction is characterized by high-dimensional features, some of which are irrelevant and contribute to low performance.To resolve these two issues, this study proposes a transformation and feature selection approach to reduce the distribution difference and high-dimensional features in cross-project defect prediction.A comparative experiment was conducted on publicly available datasets from the AEEEM.Analysis of the results obtained shows that the proposed approach in conjugation with random forest as the classification model outperformed the other four state-of-the-art cross-project defect prediction methods based on the commonly used performance evaluation metric F1_score.
SofitMix: A Secure Offchain-Supported Bitcoin-Compatible Mixing Protocol

Privacy preservation is highly expected in the Bitcoin Network. However, only applying pseudonyms cannot completely ensure anonymity/unlinkability between payers and payees. Current approaches mainly depend on a mixer service, which obfuscates payer-payee relationships of transactions. While the mixer service improves transaction privacy, it still suffers from some severe security threats (e.g., DoS attack and collusion attack), and does not support effective and reliable off-chain payment in a parallel mode. In this article, we propose a mixing protocol for the Bitcoin Network based on zero-knowledge proof, called SofitMix. It is the first mixing protocol that can effectively resist both the DoS attack and the collusion attack. It can also support a set of parallel off-chain payments in a reliable way no matter whether some payers abort a transaction. We analyze and prove SofitMix security following the Universal Composability model with regard to fair exchange, unlinkability, collusion-resistance, DoS-resistance and Sybil-resistance. Through a proof-of-concept implementation, we demonstrate its validity and fairness. We also show its advance on off-chain payment reliability and DoS attack resistance, compared to TumbleBit.
Dynamic Linear Transformer for 3D Biomedical Image Segmentation

Transformer-based neural networks have surpassed promising performance on many biomedical image segmentation tasks due to a better global information modeling from the self-attention mechanism. However, most methods are still designed for 2D medical images while ignoring the essential 3D volume information. The main challenge for 3D Transformer-based segmentation methods is the quadratic complexity introduced by the self-attention mechanism [17]. In this paper, we are addressing these two research gaps, lack of 3D methods and computational complexity in Transformers, by proposing a novel Transformer architecture that has an encoder-decoder style architecture with linear complexity. Furthermore, we newly introduce a dynamic token concept to further reduce the token numbers for self-attention calculation. Taking advantage of the global information modeling, we provide uncertainty maps from different hierarchy stages. We evaluate this method on multiple challenging CT pancreas segmentation datasets. Our results show that our novel 3D Transformer-based segmentor could provide promising highly feasible segmentation performance and accurate uncertainty quantification using single annotation. Code is available https://github.com/freshman97/LinTransUNet.
YOLO-ORE: A Deep Learning-Aided Object Recognition Approach for Radar Systems

To enable intelligent vehicles and transportation systems, the vehicles and relevant systems need to have the ability to sense environment and recognize objects. In order to benefit from the robustness of radar for sensing, knowing how to use the radar system for effective object recognition is critical. Observing this, we in this paper propose a novel deep learning-aided object recognition system for radar systems by combining the You only look once (YOLO) system with a proposed object recheck system. Our proposed system is able to benefit from conventional YOLO and also mitigate the overlap errors and misclassification errors induced by using YOLO. We conduct extensive real-world experiments in realistic scenarios to evaluate our proposed object recognition system. Results validate that our system can provide good performance in complicated real-world scenarios. The results also show that our proposed object recognition system can outperform the state-of-the-art learning-based object recognition systems.
Giving Back: Contributions Congruent to Library Dependency Changes in a Software Ecosystem

The widespread adoption of third-party libraries for contemporary software development has led to the creation of large inter-dependency networks, where sustainability issues of a single library can have widespread network effects. Maintainers of these libraries are often overworked, relying on the contributions of volunteers to sustain these libraries. To understand these contributions, in this work, we leverage socio-technical techniques to introduce and formalise dependency-contribution congruence (DC congruence) at both ecosystem and library level, i.e., to understand the degree and origins of contributions congruent to dependency changes, analyze whether they contribute to library dormancy (i.e., a lack of activity), and investigate similarities between these congruent contributions compared to typical contributions. We conduct a large-scale empirical study to measure the DC congruence for the npm ecosystem using 1.7 million issues, 970 thousand pull requests (PRs), and over 5.3 million commits belonging to 107,242 npm libraries. We find that the most congruent contributions originate from contributors who can only submit (not commit) to both a client and a library. At the project level, we find that DC congruence shares an inverse relationship with the likelihood that a library becomes dormant. Specifically, a library is less likely to become dormant if the contributions are congruent with upgrading dependencies. Finally, by comparing the source code of contributions, we find statistical differences in the file path and added lines in the source code of congruent contributions when compared to typical contributions. Our work has implications to encourage dependency contributions, especially to support library maintainers in sustaining their projects.
Modal FRP for all: Functional reactive programming without space leaks in Haskell

Abstract Functional reactive programming (FRP) provides a high-level interface for implementing reactive systems in a declarative manner. However, this high-level interface has to be carefully reigned in to ensure that programs can in fact be executed in practice. Specifically, one must ensure that FRP programs are causal and can be implemented without introducing space leaks. In recent years, modal types have been demonstrated to be an effective tool to ensure these operational properties. In this paper, we present $\mathsf{Rattus}$ , a modal FRP language that extends and simplifies previous modal FRP calculi while still maintaining the operational guarantees for productivity, causality, and space leaks. The simplified type system makes $\mathsf{Rattus}$ a practical programming language that can be integrated with existing functional programming languages. To demonstrate this, we have implemented a shallow embedding of $\mathsf{Rattus}$ in Haskell that allows the programmer to write $\mathsf{Rattus}$ code in familiar Haskell syntax and seamlessly integrate it with regular Haskell code. Thus, $\mathsf{Rattus}$ combines the benefits enjoyed by FRP libraries such as Yampa, namely access to a rich library ecosystem (e.g., for graphics programming), with the strong operational guarantees offered by a bespoke type system. To establish the productivity, causality, and memory properties of the language, we prove type soundness using a logical relations argument fully mechanised in the Coq proof assistant.
Organic Priors in Non-rigid Structure from Motion

This paper advocates the use of organic priors in classical non-rigid structure from motion (NRSfM). By organic priors, we mean invaluable intermediate prior information intrinsic to the NRSfM matrix factorization theory. It is shown that such priors reside in the factorized matrices, and quite surprisingly, existing methods generally disregard them. The paper’s main contribution is to put forward a simple, methodical, and practical method that can effectively exploit such organic priors to solve NRSfM. The proposed method does not make assumptions other than the popular one on the low-rank shape and offers a reliable solution to NRSfM under orthographic projection. Our work reveals that the accessibility of organic priors is independent of the camera motion and shape deformation type. Besides that, the paper provides insights into the NRSfM factorization—both in terms of shape and motion—and is the first approach to show the benefit of single rotation averaging for NRSfM. Furthermore, we outline how to effectively recover motion and non-rigid 3D shape using the proposed organic prior based approach and demonstrate results that outperform prior-free NRSfM performance by a significant margin. Finally, we present the benefits of our method via extensive experiments and evaluations on several benchmark datasets.
Semantic Color Mapping: A Pipeline for Assigning Meaningful Colors to Text

Current visual text analytics applications do not regard color assignment as a prominent design consideration. We argue that there is a need for applying meaningful colors to text, enhancing comprehension and comparability. Hence, in this paper, we present a guideline to facilitate the choice of colors in text visualizations. The semantic color mapping pipeline is derived from literature and experiences in text visualization design and sums up design considerations, lessons learned, and best practices. The proposed pipeline starts by extracting labeled data from raw text, choosing an aggregation level to create an appropriate vector representation, then defining the unit of analysis to project the data into a low-dimensional space, and finally assigning colors based on the selected color space. We argue that applying such a pipeline enhances the understanding of attribute relations in text visualizations, as confirmed by two applications.
An Energy-and-Area-Efficient CNN Accelerator for Universal Powers-of-Two Quantization

CNN model computation on edge devices is tightly restricted to the limited resource and power budgets, which motivates the low-bit quantization technology to compress CNN models into 4-bit or lower format to reduce the model size and increase hardware efficiency. Most current low-bit quantization methods use uniform quantization that maps weight and activation values onto evenly-distributed levels, which usually results in accuracy loss due to distribution mismatch. Meanwhile, some non-uniform quantization methods propose specialized representation that can better match various distribution shapes but are usually difficult to be efficiently accelerated on hardware. In order to achieve low-bit quantization with high accuracy and hardware efficiency, this paper proposes Universal Power-of-Two (UPoT), a novel low-bit quantization method that represents values as the addition of multiple power-of-two values selected from a series of subsets. By updating the subset contents, UPoT can provide adaptive quantization levels for various distributions. For each CNN model layer, UPoT automatically searches for the optimized distribution that minimizes the quantization error. Moreover, we design an efficient accelerator system with specifically optimized power-of-two multipliers and requantization units. Evaluations show that the proposed architecture can provide high-performance CNN inference with reduced circuit area and energy, and outperforms several mainstream CNN accelerators with higher ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$65\times $ </tex-math></inline-formula> ) area efficiency and ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$19\times $ </tex-math></inline-formula> ) energy efficiency. Further experiments of 4/3/2-bit quantization on ResNet18/50, MobileNet_V2 and EfficientNet models show that our UPoT can achieve high model accuracy which greatly outperform other state-of-the-art low-bit quantization methods by 0.3%–6%. The results indicate that our approach provides a highly-efficient accelerator for low-bit CNN model quantization with low hardware overheads and good model accuracy.
Designing One Unified Framework for High-Fidelity Face Reenactment and Swapping

Face reenactment and swapping share a similar identity and attribute manipulating pattern, but most methods treat them separately, which is redundant and practical-unfriendly. In this paper, we propose an effective end-to-end unified framework to achieve both tasks. Unlike existing methods that directly utilize pre-estimated structures and do not fully exploit their potential similarity, our model sufficiently transfers identity and attribute based on learned disentangled representations to generate high-fidelity faces. Specifically, Feature Disentanglement first disentangles identity and attribute unsupervisedly. Then the proposed Attribute Transfer (AttrT) employs learned Feature Displacement Fields to transfer the attribute granularly, and Identity Transfer (IdT) explicitly models identity-related feature interaction to adaptively control the identity fusion. We joint AttrT and IdT according to their intrinsic relationship to further facilitate each task, i.e., help improve identity consistency in reenactment and attribute preservation in swapping. Extensive experiments demonstrate the superiority of our method. Code is available at https://github.com/xc-csc101/UniFace .
Scalable and Transparent Proofs over All Large Fields, via Elliptic Curves

Concretely efficient interactive oracle proofs (IOPs) are of interest due to their applications to scaling blockchains, their minimal security assumptions, and their potential future-proof resistance to quantum attacks. Scalable IOPs, in which prover time scales quasilinearly with the computation size and verifier time scales poly-logarithmically with it, have been known to exist thus far only over a set of finite fields of negligible density, namely, over “FFT-friendly” fields that contain a sub-group of size $$2^\mathsf{{k}} $$ . Our main result is to show that scalable IOPs can be constructed over any sufficiently large finite field, of size that is at least quadratic in the length of computation whose integrity is proved by the IOP. This result has practical applications as well, because it reduces the proving and verification complexity of cryptographic statements that are naturally stated over pre-defined finite fields which are not “FFT-friendly”. Prior state-of-the-art scalable IOPs relied heavily on arithmetization via univariate polynomials and Reed–Solomon codes over FFT-friendly fields. To prove our main result and extend scalability to all large finite fields, we generalize the prior techniques and use new algebraic geometry codes evaluated on sub-groups of elliptic curves (elliptic curve codes). We also show a new arithmetization scheme that uses the rich and well-understood group structure of elliptic curves to reduce statements of computational integrity to other statements about the proximity of functions evaluated on the elliptic curve to the new family of elliptic curve codes.
PreGLAM-MMM

We present and evaluate an application of affective adaptive generative music in a single-player, action-RPG video game. We create a score that serves as an audience to the gameplay, based on the output of PreGLAM, which models the emotional perception of a game audience. We use the Multi-track Music Machine to expand and extend a composed adaptive musical score, and we use industry-standard production techniques to synthesize and perform all of our musical scores. We evaluate our application of generative music in comparison to two composed scores, one adaptive and one linear. Our generative score is rated as nearly equivalent to a composed linear score in perceptions of emotional congruency, immersion, and preference.