Reviewerly/exHarmony huggingface dataset:
AWANet: Attentive-Aware Wide-Kernels Asymmetrical Network with Blended Contour Information for Salient Object Detection

Although deep learning-based techniques for salient object detection have considerably improved over recent years, estimated saliency maps still exhibit imprecise predictions owing to the internal complexity and indefinite boundaries of salient objects of varying sizes. Existing methods emphasize the design of an exemplary structure to integrate multi-level features by employing multi-scale features and attention modules to filter salient regions from cluttered scenarios. We propose a saliency detection network based on three novel contributions. First, we use a dense feature extraction unit (DFEU) by introducing large kernels of asymmetric and grouped-wise convolutions with channel reshuffling. The DFEU extracts semantically enriched features with large receptive fields and reduces the gridding problem and parameter sizes for subsequent operations. Second, we suggest a cross-feature integration unit (CFIU) that extracts semantically enriched features from their high resolutions using dense short connections and sub-samples the integrated information into different attentional branches based on the inputs received for each stage of the backbone. The embedded independent attentional branches can observe the importance of the sub-regions for a salient object. With the constraint-wise growth of the sub-attentional branches at various stages, the CFIU can efficiently avoid global and local feature dilution effects by extracting semantically enriched features via dense short-connections from high and low levels. Finally, a contour-aware saliency refinement unit (CSRU) was devised by blending the contour and contextual features in a progressive dense connected fashion to assist the model toward obtaining more accurate saliency maps with precise boundaries in complex and perplexing scenarios. Our proposed model was analyzed with ResNet-50 and VGG-16 and outperforms most contemporary techniques with fewer parameters.
Constructing Seamless Learning Through Game-Based Learning Experiences

The goal of this paper is to explore how game-based experiences can be leveraged through mobile technology to activate learner engagement and achieve a seamless connection between formal and informal learning. The paper presents a mobile game authoring tool that enables educators to create gamified lesson paths, drawing on the concept of atomic learning. Preliminary evaluation revealed three main findings. First, mobile games constitute a key driver in seamlessly blending informal, unguided learning that is driven by natural human curiosity with learning experiences which are driven by defined, formal learning objectives. Second, ensuring learning elements are suitable for direct use, and reuse, within game-based tools, requiring learning content with high granularity. Third, the success of gamified learning depends on visual and audio impact, along with an appropriate blend of challenges, rewards, learning content, and assessment units that form the narrative backbone.
Wavelet-Based Learned Scalable Video Coding

Scalability is an important requirement for video coding when coded videos stream over dynamic-bandwidth networks. The state-of-the-art scalable video coding schemes adopt layer-based methods upon H.265, represented by the SHVC standard. Compared to layer-based schemes, wavelet-based schemes were suspected less efficient for a long while. We try to improve the compression efficiency of wavelet-based scalable video coding by leveraging the recent progresses of deep learning. First, we propose an entropy coding method, using trained convolutional neural networks (CNNs) for probability estimation, to compress the wavelet-transformed subbands. Second, we design a CNN-based method for inverse temporal wavelet transform. We integrate the two proposed methods into a traditional wavelet-based scalable video coding scheme, named Interframe-EZBC. The two methods together achieve more than 20% bits savings. Then, our scheme outperforms the SHVC reference software by 9.09%, 6.55%, and 8.66% BD-rate reductions in YUV respectively.
VPTR: Efficient Transformers for Video Prediction

In this paper, we propose a new Transformer block for video future frames prediction based on an efficient local spatial-temporal separation attention mechanism. Based on this new Transformer block, a fully autoregressive video future frames prediction Transformer is proposed. In addition, a non-autoregressive video prediction Transformer is also proposed to increase the inference speed and reduce the accumulated inference errors of its autoregressive counterpart. In order to avoid the prediction of very similar future frames, a contrastive feature loss is applied to maximize the mutual information between predicted and ground-truth future frame features. This work is the first that makes a formal comparison of the two types of attention-based video future frames prediction models over different scenarios. The proposed models reach a performance competitive with more complex state-of-the-art models. The source code is available at https://github.com/XiYe20/VPTR.
ApacheJIT

In this paper, we present ApacheJIT, a large dataset for Just-In-Time (JIT) defect prediction. ApacheJIT consists of clean and bug-inducing software changes in 14 popular Apache projects. ApacheJIT has a total of 106,674 commits (28,239 bug-inducing and 78,435 clean commits). Having a large number of commits makes ApacheJIT a suitable dataset for machine learning JIT models, especially deep learning models that require large training sets to effectively generalize the patterns present in the historical data to future data.
Making Blockchain Validators Honest

The importance of honesty among blockchain validators can not be overemphasized, especially as blockchain is used by many as an underlying technology for the development of various Industry 4.0 solutions. In the blockchain consensus process, validators validate the correctness of transactions and propose new blocks for addition to the blockchain. They are rewarded for this task with the blockchain native token (e.g., ETH on the Ethereum blockchain). This reward is often distributed among validators with respect to their staked amount. An increasing number of validators joining the network leads to a decreasing chance of a validator being chosen for the validation task and thus a reduction in the validation reward. This situation results in some form of competition among the validators, leading them to carry out various malicious actions to influence the blockchain validator selection protocol in order to be chosen. In this paper, we examine the competitive interactions between validators in a blockchain consensus process and propose a model using an infinitely repeated game model that ensures that the validators are deterred from behaving maliciously while also encouraging a self-policing notion due to the extra incentive mechanism of an improved reputation score when a validator can verifiably report malicious activities by others. Further, we discuss the factors that can incentivize or disincentivize a validator to either continue to behave honestly or switch to dishonest behavior.
Validation of AI models for ITCZ Detection from Climate Data

The Climate Change topic itself and associated life adjustments became a top global problem of the XXI century. It requires immediate attention and long-term solutions. Physical tracing of weather changes, performed without Artificial Intelligence (AI), is not capable of detection in a timely manner and correctly classifying dangerous situations, which is crucial for making rapid decisions and taking immediate actions on the ground. AI and Machine Learning (ML) systems are currently dominating solutions in Computer Science research fields and the issues of their testing and validation remain a critical open problem due to their uncertain outcomes. We use test automation to validate and assure the quality of various AI systems for one kind of weather prediction. The subject of our study is Inter-Tropical Convergence Zones (ITCZs). ITCZs play an important role in the global circulation system and even small changes in their patterns can cause severe droughts or flooding as well as other disasters like hurricanes. Global warming is causing more ITCZ scenarios as shown in weather data, which makes physical detection infeasible. Our research initially discovered that ITCZ detection based on a physical model alone could misclassify some unexpected situations when the double bands occur at different places with similar intensity or at the same places with different intensities. We then designed experiments to train AI models to detect ITCZs with test automation to collect results. We further trained several AI models with focus on VGG-16, VGG-19, Xception and MobileNETV2 models, collected and compared their results through test automation. Our exhaustive trials eventually achieved a 96.8% accuracy, which might be the best AI model to detect ITCZs with test automation and without human intervention. These results show that test automation can contribute to the selection of optimum AI models.
Cooperative Multi-Robot Hierarchical Reinforcement Learning

Recent advances in multi-robot deep reinforcement learning have made it possible to perform efficient exploration in problem space, but it remains a significant challenge in many complex domains.To alleviate this problem, a hierarchical approach has been designed in which agents can operate at many levels to complete tasks more efficiently.This paper proposes a novel technique called Multi-Agent Hierarchical Deep Deterministic Policy Gradient that combines the benefits of multiple robot systems with the hierarchical system used in Deep Reinforcement Learning.Here, agents acquire the ability to decompose a problem into simpler subproblems with varying time scales.Furthermore, this study develops a framework to formulate tasks into multiple levels.The upper levels function to learn policies for defining lower levels' subgoals, whereas the lowest level depicts robot's learning policies for primitive actions in the real environment.The proposed method is implemented and validated in a modified Multiple Particle Environment (MPE) scenario.
Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection

Image restoration algorithms such as super resolution (SR) are indispensable pre-processing modules for object detection in low quality images. Most of these algorithms assume the degradation is fixed and known a priori. However, in practical, either the real degradation or optimal up-sampling ratio rate is unknown or differs from assumption, leading to a deteriorating performance for both the pre-processing module and the consequent high-level task such as object detection. Here, we propose a novel self-supervised framework to detect objects in degraded low resolution images. We utilizes the downsampling degradation as a kind of transformation for self-supervised signals to explore the equivariant representation against various resolutions and other degradation conditions. The Auto Encoding Resolution in Self-supervision (AERIS) framework could further take the advantage of advanced SR architectures with an arbitrary resolution restoring decoder to reconstruct the original correspondence from the degraded input image. Both the representation learning and object detection are optimized jointly in an end-to-end training fashion. The generic AERIS framework could be implemented on various mainstream object detection architectures with different backbones. The extensive experiments show that our methods has achieved superior performance compared with existing methods when facing variant degradation situations. Code is available at this link https://github.com/cuiziteng/ECCV_AERIS .
DRCNet: Dynamic Image Restoration Contrastive Network

Image restoration aims to recover images from spatially-varying degradation. Most existing image-restoration models employed static CNN-based models, where the fixed learned filters cannot fit the diverse degradation well. To this end, we propose a novel Dynamic Image Restoration Contrastive Network (DRCNet) to address this issue. The principal block in DRCNet is the Dynamic Filter Restoration module (DFR), which mainly consists of the spatial filter branch and the energy-based attention branch. Specifically, the spatial filter branch suppresses spatial noise for varying spatial degradation; the energy-based attention branch guides the feature integration for better spatial detail recovery. To make degraded images and clean images more distinctive in the representation space, we develop a novel Intra-class Contrastive Regularization (Intra-CR) to serve as a constraint in the solution space for DRCNet. Meanwhile, our theoretical derivation proved Intra-CR owns less sensitivity towards hyper-parameter selection than previous contrastive regularization. DRCNet outperforms previous methods on the ten widely used benchmarks in image restoration. Besides, the ablation studies investigate the impact of the DFR module and Intra-CR, respectively.
YogaTube: A Video Benchmark for Yoga Action Recognition

Yoga can be seen as a set of fitness exercises involving various body postures. Most of the available pose and action recognition datasets are comprised of easy-to-moderate body pose orientations and do not offer much challenge to the learning algorithms in terms of the complexity of pose. In order to observe action recognition from a different perspective, we introduce YogaTube, a new large-scale video benchmark dataset for yoga action recognition. YogaTube aims at covering a wide range of complex yoga postures, which consist of 5484 videos belonging to a taxonomy of 82 classes of yoga asanas. Also, a three-stream architecture has been designed for yoga asanas pose recognition using two modules, feature extraction, and classification. Feature extraction comprises three parallel components. First, pose is estimated using the part affinity fields model to extract meaningful cues from the practitioner. Second, optical flow is used to extract temporal features. Third, raw RGB videos are used for extracting the spatiotemporal features. Finally in the classification module, pose, optical flow, and RGB streams are fused to get the final results of the yoga asanas. To the best of our knowledge, this is the first attempt to establish a video benchmark yoga recognition dataset. The code and dataset will be released soon.
Extraction and Sequential Recognition of MFR Pulse Groups in Intercepted Pulse Trains

A pulse group is defined as a pulse sequence performing a certain task, such as searching or tracking, in a specific range and azimuth. It serves as a basic information unit of MFR pulse trains. In passive radar data processing, the pulse group structure provides vital support for many applications, such as sequential pulse deinterleaving, pulse group recognition, and working mode identification. In recent years, research related to pulse groups has attracted much attention. In this paper, we propose a pulse group extraction method based on semantic coding and temporal feature clustering. The method can automatically extract pulse group structures from intercepted pulse trains polluted by missing pulses and interferential ones. Furthermore, a temporal pattern recognition method based on the finite state automaton (FSA) is also proposed. Based on extracted pulse group structures, the FSA model achieves sequential recognition of pulse groups in newly intercepted pulse streams. The simulation section verifies the performance of the proposed methods in pulse group extraction and sequential recognition.
A Cultural-History Analysis on Malay-Islamic Heritage of Siak Sri Indrapura through the Historical Urban Landscape Approach in Pekanbaru City

&lt;p&gt;&lt;em&gt;The Sultanate of Siak Sri Indrapura plays a vital role in the history of the development of Islam and Malay culture in Pekanbaru City. Pekanbaru City is a cultural-historical tourist attraction through the Malay-Islamic landscape, which can be seen from its historical heritage. The historical heritage is a Malay-Islamic cultural heritage building that still exists today. This study will explain the analysis of cultural history and its preservation management. The method used is qualitative with a Historic Urban Landscape (HUL) approach. The study's results showed 16 cultural heritage sites with varying levels of management, five sites in very well-maintained condition, three well maintained, and eight poorly maintained. Several challenges are faced: damaged buildings, incomplete historical data, regulations, and the absence of tourist programs. In addition, the distance between cultural heritage and the enthusiasm of the Malay community is a potential that needs to be optimized. The vision is to make Pekanbaru City the center of Malay-Islamic identity based on the waterfront city through the concept of preservation. The proposed preservation management is a historical tourism development program to connect cultural heritage buildings based on tourist trails that prioritize cultural and historical interpretation effectiveness and efficiency.&lt;/em&gt;&lt;/p&gt;
Robust Traffic Sign Recognition Against Camera Failures

Failures of the vehicle camera may compromise the correct acquisition of frames, that are subsequently used by autonomous driving tasks.A clear understanding of the behavior of the autonomous driving tasks under such failure conditions, together with strategies to avoid safety is jeopardized, are indeed necessary.This study analyses and improve the performance of Traffic Sign Recognition (TSR) systems for road vehicles under the possible occurrence of camera failures.Our experimental assessment relies on three public datasets, which are commonly used for benchmarking TSR systems.We artificially inject 13 different types of camera failures into the three datasets.Then, we exploit three deep neural networks (DNNs) to classify either a single frame of a traffic sign or a sequence (i.e., a sliding window) of frames.We show that sliding windows significantly improves the robustness of the classifier against altered frames.We confirm our observations through explainable AI, which allows understanding why different classifiers have different performance in case of camera failures.
A Novel Method for Few-Shot Specific Emitter Identification in Non-Cooperative Scenarios

Obtaining larger category-label-containing training signal datasets in non-cooperative scenarios is difficult.Moreover, employing smaller labeled signal datasets for specific emitter identification is technically challenging.Therefore, we propose a novel method for few-shot SEI.We first design a bispectral analysis and Radon transformation-based signal preprocessing scheme to obtain feature vectors that effectively characterize the radio frequency fingerprints.The feature vectors are then fed to a network model for feature learning.Moreover, a meta-learning algorithm is applied to the network model to adapt to few-shot feature learning.The conventional meta-learning algorithm is improved to develop a novel algorithm involving latent embedding optimization for meta-learning.The proposed method extracts lowdimensional key features from high-dimensional input data and evaluates the distance and degree of feature dispersion.The resulting information is employed in sample point prediction.The algorithm effectively achieves few-shot SEI and offers stable and efficient recognition after training with a minimum of forty samples.This method identifies emitter individuals under multiple modulation types and exhibits scalability in identifying the emitter numbers.Moreover, it offers adaptability in identifying the emitter individuals under multiple propagation channel types.
Exploration of Fault Effects on Formal RISC-V Microarchitecture Models

This paper introduces a formal workflow for modeling software/hardware systems in order to explore the effects of fault injections and evaluate the robustness to fault injection attacks. We illustrate this workflow on four versions of a PIN authentication code, embedding different software countermeasures. The code is symbolically evaluated on two implementations of the RISC-V CV32E40P core: the original implementation from the OpenHW group and an implementation that integrates protection of the pipeline control signals. On the original, unprotected core, our formal workflow exposes various vulnerabilities, including previously unknown ones, whereas, on the protected core, it confirms the effectiveness of the proposed countermeasures.
Closing Pandora’s Box on Naver: Toward Ending Cyber Harassment

In reaction to the high-profile celebrity suicides of Sulli and Goo Hara in 2019, attributed to cyberbullying, Korea's most popular internet platform Naver introduced a range of self-regulatory measures to curtail targeted hate comments online in 2020. These regulations include removing the entertainment comment section and pseudonymizing users by revealing comment history but maintaining user anonymity. To take a closer look at celebrity cyberbullying and analyze the effects of Naver's novel self-regulatory measures, we collected data about comments and emoticons from Naver News. In the celebrity space, we find that Sulli and Goo Hara indeed received negative comments and expressions using emoticons. However, our analyses of the self-regulatory measures demonstrate that while user interaction on Naver has decreased, the percentage of negative comments has also fallen, showing that Naver's regulations have had favorable effects.
A Sanitizer-centric Analysis to Detect Cross-Site Scripting in PHP Programs

A large number of PHP applications suffer from Cross-Site Scripting (XSS) attacks every year. Static taint analysis is a prevalent way to detect taint-style vulnerabilities like XSS. However, the precision of current tools suffers severely due to dynamic features of PHP programs and the incomplete recognition of user-defined sanitizers, which lead to false negatives and a large number of false positives. In this paper, we present PAT, a PHP static Analysis Tool for effective XSS vulnerability detection. A new concept of “inner” source and sink is introduced for the first time to shorten the taint paths needed to be traced statically, which therefore mitigates the broken path problem induced by dynamic language features to a certain extent. A sanitizer-centric approach is proposed to automatically identify them. Moreover, PAT leverages both data flow analysis and NLP technique to accurately identify user-defined sanitizers with a precision 82.8%. Lastly, PAT performs a classical taint analysis with the enhanced taint specifications (i.e., sources, sinks and sanitizers). Evaluations on 5 large, real-world PHP web applications and 5 popular WordPress plugins show that PAT performs better in XSS detection compared with 3 existing tools. Besides, 8 zero-day bugs are detected and confirmed by the developers.
Teacher-free Distillation via Regularizing Intermediate Representation

Feature distillation always leads to significant performance improvements, but requires extra training budgets. To address the problem, we propose TFD, a simple and effective Teacher-Free Distillation framework, which seeks to reuse the privileged features within the student network itself. Specifically, TFD squeezes feature knowledge in the deeper layers into the shallow ones by minimizing feature loss. Thanks to the narrow gap of these self-features, TFD only needs to adopt a simple l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> loss without complex transformations. Extensive experiments on recognition benchmarks show that our framework can achieve superior performance than teacher-based feature distillation methods. On the ImageNet dataset, our approach achieves 0.8% gains for ResNet18, which surpasses other state-of-the-art training techniques.