Reviewerly/exHarmony huggingface dataset:
Consistent Low Latency Scheduler for Distributed Key-Value Stores

Nowadays, the distributed key-value stores have become the basic building block for large-scale cloud applications. In large-scale distributed key-value stores, many key-value access operations, which will be processed in parallel on different servers, are usually generated for a single end-user request. Accordingly, the completion time of an end-user request is determined by the last completed key-value access operation. Scheduling the order of serving key-value access operations can effectively reduce the completion times of end requests, thereby improving the user experience. However, existing scheduling algorithms hardly achieve consistent low latency due to the following challenges: the large overhead of cooperating clients and servers, the time-varying load and performance of servers, the traffic distribution can be either heavy-tailed or light-tailed and both the mean and the tail completion time are expected to be low. In this paper, we formalize the problem of scheduling key-value access operations and show it is NP-hard. Furthermore, we heuristically design the distributed adaptive scheduler (DAS), which distributively combines the largest remaining processing time last and the shortest remaining process time first algorithms. Theoretical analysis shows that DAS is adaptive to the time-varying traffic and server performance and can achieve consistent low mean and tail latency regardless of traffic distributions. Extensive simulations show that DAS reduces the mean request completion time by <inline-formula><tex-math notation="LaTeX">$17 \! \sim \! 50\%$</tex-math></inline-formula> with heavy-tailed traffic and <inline-formula><tex-math notation="LaTeX">$2 \! \sim 26 \! \%$</tex-math></inline-formula> with light-tailed traffic, while keeping the smallest tail completion time, compared to the default first come first served algorithm. Moreover, DAS outperforms the existing Rein-SBF algorithm under various scenarios.
Style Transfer of Thangka Images Highlighting Style Attributes

The HAA-GAN (Highlighting Artistic Attributes Generative Adversarial Net-work) style migration model is proposed to address the problem of poor expression of image artistic attributes and mismatch between semantic and stylistic features in images generated from Tangka image style transfer experiments. The image features extracted by the encoder are dual-channeled feature transfer and feature refinement according to the style domain attribute refinement channel and the self-attention semantic information matching channel. The former can highlight the artistic attributes of the images, while the latter can flexibly match the Tangka image style features to the content features according to the semantic space distribution of the content images. At the same time, adding a pooling layer based on wavelet multilevel transform to the codec structure of the generator can retain more key features of the image during the feature extraction process. Finally, in order to improve the quality of style transfer images, a multi-scale discriminative network structure is used to discriminate the image blocks, resulting in better visual effects of the generated images. The experimental results show that HAA-GAN conducted style transfer experiments on Thangka dataset and Impressionism dataset. Compared with SAFIN, DSTN and other style transfer models, the average score of FID and PSNR indicators increased by 9.06% and 12.7%, and the generation of stylized renderings achieved better results in structural detail maintenance, feature matching and artistic effect expression.
Multi-Document Summarization Using Selective Attention Span and Reinforcement Learning

Abstractive text summarization systems using recently improved RNN-based sequence-to-sequence architecture have shown great promise for single-document summarization. However, such neural models fail to perpetuate the performance in the multi-document summarization setting owing to the long-range dependencies within the documents, overlapping/contradicting facts and extrinsic model hallucinations. These shortcomings augment the model to generate inconsistent, repetitive and non-factual summaries. In this work, we introduce REISA, a sequence-to-sequence model with a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reinforced selective attention span</i> that attends over the input and recalibrates the local attention weights to focus on important segments while generating output at each time step. REISA utilizes a reinforcement learning-based policy gradient algorithm to reward the model and formulate attention distributions over the encoder input. We further benchmark REISA on two widely-used multi-document summarization corpora – Multinews and CQASumm, and observe an improvement of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$+2.91$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$+6.64$</tex-math></inline-formula> ROUGE-L scores, respectively. The qualitative analyses on semantic similarity by BERTScore, faithfulness by question-answer evaluation and human evaluation show significant improvement over the baseline-generated summaries.
Microwave Gaussian Quantum Sensing With a CNOT Gate Receiver

In <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">quantum illumination</i> (QI) the non-classical correlations between <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">continuous variable</i> (CV) entangled modes of radiation are exploited to detect the presence of a target embedded in thermal noise. The extreme environment where QI outperforms its optimal classical counterpart suggests that applications in the microwave domain would benefit the most from this new sensing paradigm. However all the proposed QI receivers rely on ideal photon counters or detectors, which are not currently feasible in the microwave domain. Here we propose a new QI receiver that utilises a CV <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">controlled not gate</i> (CNOT) in order to perform a joint measurement on a target return and its retained twin. Unlike other QI receivers, the entire detection process is carried out by homodyne measurements and square-law detectors. The receiver exploits two squeezed ancillary modes as a part of the gate’s operation. These extra resources are prepared offline and their overall gain is controlled passively by a single beamsplitter parameter. We compare our model to other QI receivers and demonstrate its operation regime where it outperforms others and achieves optimal performance. Although the main focus of this study is microwave quantum sensing applications, our proposed device can be built as well in the optical domain, thus rendering it as a new addition to the quantum sensing toolbox in a wider sense.
MilInst: Enhanced Instance Segmentation Framework for Military Camouflaged Targets Using Sparse Instance Activation

In this study, an improved end-to-end framework for instance segmentation of military camouflaged targets, referred to as MilInst, is proposed. The framework builds upon SparseInst method developed by Cheng <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al</i> . [27]. Several improvements are introduced to enhance the model’s performance. First, Receptive Field Enhancement Module (RFEM) is employed to capture broader contextual information. Additionally, Feature Merging Module (FMM) is utilized to eliminate feature noise through the implementation of a matrix decomposition method. Furthermore, a novel linear dynamic bipartite matching approach is proposed, facilitating a smooth transition from one-to-one matching to one-to-many, and eventually achieving accurate to one-to-one matching. Experimental results demonstrate the effectiveness of MilInst algorithm. Comparisons with a selected real-time instance segmentation baseline model reveal superior performance, with MilInst framework achieving the highest mean Average Precision (mAP) index of 84.8% on a self-made dataset.
SFA-guided mosaic transformer for tracking small objects in snapshot spectral imaging

This paper presents an end-to-end solution, the Spectral Filter Array (SFA)-guided Mosaic Transformer (SMT), designed for tracking small objects within mosaic spectral videos captured by snapshot spectral cameras. Tracking small objects amidst complex scenes poses greater challenges due to their variable appearances and limited feature representation. Spectral imaging, leveraging spectral and spatial information to characterize the material properties of objects, offers enhanced object feature discrimination compared to conventional visual imaging, making it an ideal choice for this task. Existing spectral tracking techniques, however, fall short in delivering satisfactory results for small objects due to their disruption of spatial-spectral aliasing correlations or disregard for small object characteristics. Hence, the proposed SMT leverages SFA guidance to model and aggregate multi-layer features effectively. Comprising the SFA-guided mosaic backbone (SMB), Multi-layer Feature Aggregation (MFA), and Prediction Head, SMT extracts hierarchical features directly from mosaic spectral images, amalgamates interdependencies between shallow-layer detail and deep-layer semantic information, and precisely locates small objects. Experiment results on our curated fully-annotated mosaic spectral small object tracking dataset, alongside a public normal-sized object tracking dataset, showcase SMT’s prowess in adeptly tracking small objects amidst challenging scenarios such as occlusion and drift. Specifically, our SMT achieves gains ranging from 0.3% to 5.3% in average precision rate and from 2.0% to 5.1% in average success rate over the second-ranked trackers across various challenging attributes. The dataset and code are available at: https://github.com/Chenlulu1993/SMT.
ChatSh*t and Other Conversations (That We Should Be Having, But Mostly Are Not)

Generative artificial intelligence (AI) is rapidly transforming people’s access to and attitudes toward knowledge. It is an extremely powerful technology, but this transformation presents numerous social, environmental, political, and, perhaps, in particular, educational considerations. There is a pressing need to have a profound and nuanced conversation about these considerations, without asking a chatbot for its opinion. Instead, we seem to be having mostly a distractive conversation about singularities, which is arguably a domain of sheer speculation, rather than a more pressing conversation about “the system” that produced the technology and what it is doing right now to people, society, and processes, in particular, the educational process. Therefore, starting from a viewpoint on education as the democratization of knowledge, this article presents six talking points as a contribution to the conversation about those considerations, especially with respect to education.
A Structure-Aware Framework of Unsupervised Cross-Modality Domain Adaptation via Frequency and Spatial Knowledge Distillation

Unsupervised domain adaptation (UDA) aims to train a model on a labeled source domain and adapt it to an unlabeled target domain. In medical image segmentation field, most existing UDA methods rely on adversarial learning to address the domain gap between different image modalities. However, this process is complicated and inefficient. In this paper, we propose a simple yet effective UDA method based on both frequency and spatial domain transfer under a multi-teacher distillation framework. In the frequency domain, we introduce non-subsampled contourlet transform for identifying domain-invariant and domain-variant frequency components (DIFs and DVFs) and replace the DVFs of the source domain images with those of the target domain images while keeping the DIFs unchanged to narrow the domain gap. In the spatial domain, we propose a batch momentum update-based histogram matching strategy to minimize the domain-variant image style bias. Additionally, we further propose a dual contrastive learning module at both image and pixel levels to learn structure-related information. Our proposed method outperforms state-of-the-art methods on two cross-modality medical image segmentation datasets (cardiac and abdominal). Codes are avaliable at https://github.com/slliuEric/FSUDA.
VulHunter: Hunting Vulnerable Smart Contracts at EVM Bytecode-Level via Multiple Instance Learning

With the economic development of Ethereum, the frequent security incidents involving smart contracts running on this platform have caused billions of dollars in losses. Consequently, there is a pressing need to identify the vulnerabilities in contracts, while the state-of-the-art (SOTA) detection methods have been limited in this regard as they cannot overcome three challenges at the same time. (i) Meet the requirements of detecting the source code, bytecode, and opcode of contracts simultaneously; (ii) reduce the reliance on manual pre-defined rules/patterns and expert involvement; (iii) assist contract developers in completing the contract lifecycle more safely, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , vulnerability repair and abnormal monitoring. With the development of machine learning (ML), using it to detect the contract runtime execution sequences (called instances) has made it possible to address these challenges. However, the lack of datasets with fine-grained sequence labels poses a significant obstacle, given the unreadability of bytecode/opcode. To this end, we propose a method named VulHunter that extracts the instances by traversing the Control Flow Graph built from contract opcodes. Based on the hybrid attention and multi-instance learning mechanisms, VulHunter reasons the instance labels and designs an optional classifier to automatically capture the subtle features of both normal and defective contracts, thereby identifying the vulnerable instances. Then, it combines the symbolic execution to construct and solve symbolic constraints to validate their feasibility. Finally, we implement a prototype of VulHunter with 15K lines of code and compare it with 9 SOTA methods on five open source datasets including 52,042 source codes and 184,289 bytecodes. The results indicate that VulHunter can detect contract vulnerabilities more accurately (90.04% accurate rate and 85.60% F1 score), efficiently (only took 4.4 seconds per contract), and robustly (0% analysis failed rate) than the SOTA methods. Also, it can focus on specific metrics such as precision and recall by employing different baseline models and hyperparameters to meet the various user requirements, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , vulnerability discovery and misreport mitigation. More importantly, compared with the previous ML-based arts, it can not only provide classification results, defective contract source code statements, key opcode fragments, and vulnerable execution paths, but also eliminate misreports and facilitate more operations such as vulnerability repair and attack simulation during the contract lifecycle.
Hypersphere-Based Remote Sensing Cross-Modal Text–Image Retrieval via Curriculum Learning

Remote sensing cross-modal text-image retrieval (RSCTIR) is a flexible and human-centered approach to retrieving rich information from different modalities, which has attracted plenty of attention in recent years. It remains challenging because the current methods usually ignore the varying difficulty levels of different sample pairs, stemming from the large image distribution difference and the high text similarity in the remote sensing (RS) field. Therefore, in this paper, we propose an innovative hypersphere-based visual semantic alignment (HVSA) network via curriculum learning. Specifically, we first design an adaptive alignment strategy based on curriculum learning, that aligns RS image-text pairs from easy to hard. Sample pairs with different levels of difficulty are treated unequally, and we obtain a better embedding representation when projecting the features onto the unit hypersphere. Then, to measure the robustness of cross-modal feature alignment on the unit hypersphere, we introduce the feature uniformity strategy. It reduces the occurrence of mismatching cases and improves generalization performance. Finally, we design the key-entity attention (KEA) mechanism to alleviate the problem of information imbalance among different modalities. KEA has the ability to extract information about the key entity which is aligned with textual information. Despite its conciseness, our framework achieves state-of-the-art performance on classical datasets of RSCTIR tasks while enjoying faster inference. The summed recall of HVSA on the RISCD and RSITMD is 120.97 and 198.94, 2.50 and 10.49 points ahead of the current best methods, respectively. Extensive experiments demonstrate the competitiveness of our method. The code has been released at https://github.com/ZhangWeihang99/HVSA.
Analysis of Fisherman Exploitation in Taiwan Distant Water Fishing

The analysis of fisherman exploitation in Taiwan’s distant water fishing (DWF) industry is a critical undertaking to address and combat labor abuses within this sector. DWF involves fishing activities conducted in waters beyond Taiwan’s territorial limits, and it is crucial to ensure fair and ethical treatment of the fishermen involved. The analysis of fisherman exploitation entails a comprehensive examination of various factors contributing to labor abuses. This includes investigating working conditions, working hours, payment practices, living conditions aboard fishing vessels, and overall treatment of the fishermen. Evaluating whether these practices comply with local and international labor standards, human rights principles, and relevant regulations is essential. Human action recognition (HAR) involves the use of computer vision algorithms to automatically identify and classify actions being performed by humans in videos or images. HAR technology can analyze footage from DWF vessels and identify actions indicating labor exploitation or abuse. It is essential to address the issue of exploitation and abuse in Taiwan’s DWF industry <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> . Lewis et al. <xref ref-type="bibr" rid="ref16" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[16]</xref> explored using “AI” and face recognition as both a speech act and a technological tool for monitoring workers or intimidating migrants and protesters. While this concept has been implemented in various systems, our primary focus is developing a system to detect and address long work-hour exploitation.
Adapting Detection in Blockchain-enabled Federated Learning for IoT Networks

Blockchain-enabled Federated Learning (FL) is a decentralized approach to coordinate nodes during the training of a machine learning model in order to enhance privacy and save network resources (e.g., bandwidth). However, malicious nodes may try to sabotage the training by carrying out poisoning attacks to hinder the performance of the model. An effective defense to have on the blockchain is a mechanism to monitor the behaviour of the nodes, detect the malicious nodes, and remove them from the training. This work proposes a multiobjective optimization algorithm that adapts local training and model sharing on the blockchain to the wireless and mobile environment of the nodes. The results show an improvement in terms of time, bandwidth, and energy consumption when the optimization of the selection of the proportion of miners responsible for the monitoring and consensus was adapted to take into account multiple factors related to the communication network.
A long simulation scenario for evaluation of multi-target tracking methods

Algorithms for multi-target tracking are typically evaluated by means of simulations. Typically, the simulated scenarios are fairly short and of very simple geometry. In contrast, this paper describes a scenario which was deliberately and explicitly designed to exhibit a large challenging behavior for several targets moving in close formation for about 23 minutes.The scenario is inspired by experiments with autonomous surface vehicles (ASVs). We use the scenario to study the performance differences between tracking methods in the Poisson Multi-Bernoulli Mixture (PMBM) family. A multiple models approach outperforms the single model approach, but is to a large extent outperformed by the multiple hypothesis approach.
Multi-Agent Collision Avoidance Method Using Fuzzy Risk Estimation and Information Sharing in Unknown Environments

Automated vehicles within Industry 4.0 are used as logistics units where they can move resources from one place to another safely and efficiently. The automated vehicles can be tasked to work in unknown environments where collision-free navigation is challenging due to uncertainty and lack of environmental information. Collisions can damage equipment and may even cause harm to human workers sharing the same space. In order to carry out tasks and avoid collisions in unknown environments, automated vehicles need means of self regulation and environmental awareness through sensors. This paper presents a multi-agent collision avoidance method using fuzzy risk estimation and information sharing for automated vehicles navigating unknown environments. The automated vehicles exchange information about their current state and GPS location with other automated vehicles and use fuzzy collision risk estimation based on sensor data to avoid collisions with static obstacles and each other. Additionally, the automated vehicles collectively learn about their work environment over time by creating a virtual map of the environment through shared information. To test and evaluate the method, a series of simulated tests are carried out. The results from tests show that the automated vehicles are able to avoid collisions while also accurately mapping an initially unknown work environment using the method.