Reviewerly/exHarmony huggingface dataset:
NT-ARS-RRT: A novel non-threshold adaptive region sampling RRT algorithm for path planning

Rapidly-Exploring Random Tree (RRT) algorithm is a widely used path planning method. However, it suffers from low solution efficiency, no search guidance, poor quality of the obtained path and the problem of obvious reduction of search efficiency in the narrow exit environment, which greatly reduces its performance. To overcome these issues, this paper proposes a novel non-threshold adaptive region sampling RRT (NT-ARS-RRT) algorithm for path planning. First, a map preprocessing method is proposed to reduce the explore space of the random tree and improves the path search efficiency especially at narrow exits. Second, a branch-and-leaf backtracking method is proposed to optimize the selection of the nearest node. Third, a distance threshold connection constraints optimization method between the newest node and the target point is proposed to reduce the generation of unnecessary nodes and thus further improves the algorithm's speed. Fourth, the initial path is optimized by using a rewiring method based on triangular inequality to improve the path quality. Experimental results show the effectiveness of the proposed algorithm.
A coevolutionary constrained multi-objective algorithm with a learning constraint boundary

When solving constrained multi-objective optimization problems, the balance of convergence, diversity, and feasibility plays a pivotal role. To address this issue, this paper proposes a coevolutionary constrained multi-objective algorithm with learning constraint boundary (CCMOLCB). Firstly, the constrained multi-objective problems are transformed by adding an additional objective using the constraint violation degree. Then, the transformed problem is solved by an improved coevolutionary framework which employs two populations. The main population explores the objective space and repairs infeasible solutions to maintain the feasibility of population. Meanwhile, the feasibility and diversity of solutions are balanced by using a dynamic weight coefficient during the evolution, it changes as the number of iterations increases. The subordinate population selects solutions by taking into consideration the learning constraint boundary (LCB). This boundary guarantees convergence of solutions by constraining the search range of the main population, thereby enhancing the environmental selection pressure. The performance of CCMOLCB is compared with seven state-of-the-art constrained multi-objective evolutionary algorithms on five test suites. The experimental results illustrate that CCMOLCB exhibits competitive performance when dealing with this family of problems.
Impossibility of bosonic autonomous entanglement engines in the weak-coupling limit

Entanglement is a fundamental feature of quantum physics and a key resource for quantum communication, computing, and sensing. Entangled states are fragile and maintaining coherence is a central challenge in quantum information processing. Nevertheless, entanglement can be generated and stabilized through dissipative processes. In fact, entanglement has been shown to exist in the steady state of certain interacting quantum systems subject solely to incoherent coupling to thermal baths. This has been demonstrated in a range of bi- and multipartite settings using systems of finite dimension. Here we focus on the steady state of infinite-dimensional bosonic systems. Specifically, we consider any set of bosonic modes undergoing excitation-number-preserving interactions of arbitrary strength and divided between an arbitrary number of parties that each couple weakly to thermal baths at different temperatures. We show that a unique steady state is always separable.
A novel deep-learning image condition for locating earthquake

SUMMARY Migration-based earthquake location methods may encounter the polarity reversal issue due to the non-explosive components of seismic sources, leading to an unfocused migration image. Such a problem usually makes it difficult to accurately retrieve the optimal location from the migrated source image. In this study, by taking advantage of the general pattern recognition ability of the convolutional neural network, we propose a novel deep-learning image condition (DLIC) to address this issue. The proposed DLIC measures the goodness of waveform alignments for both P and S waves, and it follows the geophysical principle of seismic imaging that the best-aligned waveforms represent fully a best-imaged source location. A synthetic test shows that the DLIC can effectively overcome the polarity reversal issues. Real data applications to southern California show that the DLIC can enhance the focusing of the migrated source image over the classic source scanning algorithm. Further tests show that the DLIC applies to continuous seismic data, to regions with few previously recorded earthquakes, and has the potential to locate small earthquakes. The proposed DLIC shall benefit the migration-based source location methods.
Multiscale convolutional neural networks for in-loop video restoration

Incorporating neural networks into a video codec as an in-loop filter has been shown to provide significant improvements in coding efficiency. Unfortunately, the computational complexity associated with the neural network, specifically the number of multiply-accumulate (MAC) operations, makes these approaches intractable in practice. In this paper, we consider using a multiscale approach to reduce complexity while maintaining coding efficiency. Experimental results demonstrate a 5.4× reduction in MAC operations while achieving an average bit rate savings of 6.4% and 6.3% for all intra and random access coding, respectively, when compared to the evolving AV2 standard. Ablation studies are also provided and show that the approach achieves all but 0.2% of the coding efficiency of full resolution processing.
An Automated Literature Review Tool (LiteRev) for Streamlining and Accelerating Research Using Natural Language Processing and Machine Learning: Descriptive Performance Evaluation Study

Literature reviews (LRs) identify, evaluate, and synthesize relevant papers to a particular research question to advance understanding and support decision-making. However, LRs, especially traditional systematic reviews, are slow, resource-intensive, and become outdated quickly.LiteRev is an advanced and enhanced version of an existing automation tool designed to assist researchers in conducting LRs through the implementation of cutting-edge technologies such as natural language processing and machine learning techniques. In this paper, we present a comprehensive explanation of LiteRev's capabilities, its methodology, and an evaluation of its accuracy and efficiency to a manual LR, highlighting the benefits of using LiteRev.Based on the user's query, LiteRev performs an automated search on a wide range of open-access databases and retrieves relevant metadata on the resulting papers, including abstracts or full texts when available. These abstracts (or full texts) are text processed and represented as a term frequency-inverse document frequency matrix. Using dimensionality reduction (pairwise controlled manifold approximation) and clustering (hierarchical density-based spatial clustering of applications with noise) techniques, the corpus is divided into different topics described by a list of the most important keywords. The user can then select one or several topics of interest, enter additional keywords to refine its search, or provide key papers to the research question. Based on these inputs, LiteRev performs a k-nearest neighbor (k-NN) search and suggests a list of potentially interesting papers. By tagging the relevant ones, the user triggers new k-NN searches until no additional paper is suggested for screening. To assess the performance of LiteRev, we ran it in parallel to a manual LR on the burden and care for acute and early HIV infection in sub-Saharan Africa. We assessed the performance of LiteRev using true and false predictive values, recall, and work saved over sampling.LiteRev extracted, processed, and transformed text into a term frequency-inverse document frequency matrix of 631 unique papers from PubMed. The topic modeling module identified 16 topics and highlighted 2 topics of interest to the research question. Based on 18 key papers, the k-NNs module suggested 193 papers for screening out of 613 papers in total (31.5% of the whole corpus) and correctly identified 64 relevant papers out of the 87 papers found by the manual abstract screening (recall rate of 73.6%). Compared to the manual full text screening, LiteRev identified 42 relevant papers out of the 48 papers found manually (recall rate of 87.5%). This represents a total work saved over sampling of 56%.We presented the features and functionalities of LiteRev, an automation tool that uses natural language processing and machine learning methods to streamline and accelerate LRs and support researchers in getting quick and in-depth overviews on any topic of interest.
Three filters for the enhancement of the images acquired from fluorescence microscope and weak-light-sources and the image compression

Fluorescence images enhancement is important as it can provide more information for medical diagnosis. In this work, we design three simple yet useful filters based on the combinations of mathematical functions, which are proved to be effective in strengthening the images acquired from the fluorescence microscope. Using these filters, detailed objects can be found in the dark sections of the fluorescence images. In addition, these filters can be used to enhance the low-light image, which provide satisfactory visual information and marginal profile for the blurred objects in the image. Moreover, these filters have been used to enhance the image with high degradation by the Gaussian noise, where clear edge profile can be extracted. Finally, we have shown that these filters can be utilized for the image compression. Compression ratio can be obtained to be 0.9688. This study shows the making of the filters with dual functions for the image enhancement and the image compression. Our designed filters are showing the potentials in the field of biomedical imaging and pattern identification.
MAXFormer: Enhanced transformer for medical image segmentation with multi-attention and multi-scale features fusion

Convolutional neural networks(CNN), especially U-shaped networks, have become the mainstream approach for medical image segmentation. However, due to the intrinsic locality of convolutional operations, CNN has inherent limitations in capturing long-range dependencies. Although Transformer-based methods have demonstrated remarkable performance in computer vision by modeling long-range dependencies, their high computational complexity and reliance on large-scale pre-training present challenges, particularly for higher-resolution medical images. In this paper, we introduce MAXFormer, a U-shaped hierarchical network that effectively leverages global context within individual samples and relationships between different samples. Our Transformer module reformulates the self-attention mechanism into two parts: local–global attention and external attention. The local–global attention provides an efficient alternative to self-attention with linear complexity, employing a parallel architecture that allows local–global spatial interactions. The local attention branch captures high-frequency local information, while the global attention branch captures low-frequency global information. Furthermore, we have designed the Refined Fused Connection module to effectively merge feature outputs from each encoder block with the decoder output, mitigating spatial detail loss due to downsampling. Extensive experiments on two different medical image segmentation datasets show that our proposed method outperforms other state-of-the-art methods without requiring pre-training weights. Code will be available at https://github.com/zhiwei-liang/MAXFormer.
Boosting multi-objective just-in-time software defect prediction by fusing expert metrics and semantic metrics

Just-in-time software defect prediction (JIT-SDP) aims to predict whether a code commit is defect-inducing or defect-clean immediately after developers submit their code commits. In our previous study, we modeled JIT-SDP as a multi-objective optimization problem by designing two potential conflict optimization objectives. By only considering expert metrics for code commits, our proposed multi-objective just-in-time software defect prediction (MOJ-SDP) approach can significantly outperform state-of-the-art supervised and unsupervised baselines. Recent studies have shown that deep learning techniques can be used to automatically extract semantic metrics from code commits and achieved promising performance for JIT-SDP. However, it is unclear how well MOJ-SDP performs when semantic metrics are used, and whether these two types of metrics are complementary and can be boosted by fusing them for MOJ-SDP. We conducted an extensive experiment using 27,319 code commits from 21 real-world open-source projects. Our results show that when using semantic features, the performance of MOJ-SDP can be slightly decreased for Popt, but greatly improved for Recall@20%Effort. However, when these two types of metrics are fused based on the model-level fusion with the maximum rule, the performance can be boosted by a large margin and outperform state-of-the-art JIT-SDP baselines.
Single-/Multi-Source Domain Adaptation via domain separation: A simple but effective method

Domain adaptation (DA) aims to reduce knowledge gap between domains and improve the prediction ability of models in the target domain. However, the representations learned from feature extraction network often contain redundant information, which is harmful to domain alignment. In addition, many methods only focus on either single-source DA task or multi-source DA task, which limits their real-world applications. In this paper, we propose a simple but effective method called Category and Domain Features Augmentation (CDFA), which consists of two components: Contrastive Classifier Network (CCN) and Domain-specific Learning Network (DSLN). CDFA can remove the specific representation at the feature extraction stage to alleviate transfer difficulty, where CCN is used to increase the probabilistic output of samples and avoid misclassification, and DSLN facilitates the separation of redundant information from all representations by learning domain-specific representations. Empirical evaluations on several cross-domain benchmarks under single-source and multi-source DA scenarios illustrate the competitive performance of CDFA with respect to the state-of-the-art.
CSCMOT: Multi-object tracking based on channel spatial cooperative attention mechanism

Multi-object tracking has made good progress in recent years. Most mainstream methods use the fusion method of detection and Re-ID to complete the multi-target tracking technology. However, the current multi-tracking algorithm is slow and cannot meet the real-time requirements, which makes it difficult to implement in actual scenarios. In addition, the current mainstream multi-target tracking technology often has the problem of identity information jumping. Such frequent identity information hopping can lead to serious problems in some demanding practical applications, resulting in poor tracking performance. To solve these problems, we propose a simple framework CSCMOT. A non-parametric attention mechanism is adopted to focus on some feature points of the target without increasing the amount of computation, so as to reduce the amount of computation and improve the real-time performance of the algorithm. In addition, the jumping problem of identity information can be reduced by random simulation occlusion to improve tracking performance. Experiments show that the real-time performance of the proposed CSCMOT framework reaches 32.5 FPS, which exceeds most of the mainstream methods. In addition, the ID-switch was reduced to 2493 on the MOT17 dataset. Made a great breakthrough, better to solve the problem of identity information jump. The tracking accuracy is also 71.5, a competitive result that exceeds most of the mainstream methods. Effective data show that the framework improves the real-time performance of the algorithm, solves the problem of identity jump between targets, and is more conducive to experiment landing, which is easy to combine with the mobile robot platform.
Path Planning for Mobile Robot Based on Improved Genetic Algorithm

To deal with these issues such as low initial population quality and slow convergence in the traditional algorithm, this article improves the genetic algorithm and applies it to the path planning for mobile robot. In order to improve the quality of the initial pathways, it generates paths via heuristic strategies; The fitness function is improved by a multi-objective function. The main operators of the algorithm are improved in this article, and the numerical values of crossover and mutation probabilities are adjusted by an adaptive adjustment method to better fit the problem. A catastrophe factor is also introduced to increase the diversity of algorithms and the ability to search globally. (Abstract)
Temporal Noise Shaping on MDCT Subband Signals for Transform Audio Coding

A common problem when coding transient signals in transform audio codecs is the temporal unmasking of quantization noise leading to artifacts such as pre-echoes. In this work, a new method for temporal shaping of quantization noise is introduced. Subband merging/splitting with overlapping windows is applied on the MDCT (Modified Discrete Cosine Transform) spectrum in the encoder and decoder. This allows direct modification of the temporal subband signals for different frequency ranges in a way that results in a flattening of the temporal envelopes in the encoder before quantization and a reshaping to their original envelope at the decoder. The reshaping process at the decoder not only restores the envelopes of the original subband signals, but also shapes the quantization noise accordingly, which results in quantization noise temporally shaped similarly to the original subband signal. This avoids temporal unmasking of quantization noise and the pre-echo artifact. For the temporal flattening/reshaping process the application of gains transmitted via Autoregressive model as well as a low side information approach via companding are proposed and evaluated regarding their noise shaping capabilities in an experimental codec system.
ExploreFault: Identifying Exploitable Fault Models in Block Ciphers with Reinforcement Learning

Exploitable fault models for block ciphers are typically cipher-specific, and their identification is essential for evaluating and certifying fault attack-protected implementations. However, identifying exploitable fault models has been a complex manual process. In this work, we utilize reinforcement learning (RL) to identify exploitable fault models generically and automatically. In contrast to the several weeks/months of tedious analyses required from experts, our RL-based approach identifies exploitable fault models for protected/unprotected AES and GIFT ciphers within 12 hours. Notably, in addition to all existing fault models, we identify/discover a novel fault model for GIFT, illustrating the power and promise of our approach in exploring new attack avenues.
Performance Analysis of Hyperledger Fabric on Multiple Infrastructure Setup

Blockchain technology has transformed traditional data storage and organization by introducing decentralized ledgers, ensuring data integrity through its resistance to tampering and alteration. Hyperledger Fabric, a popular industry platform, effectively manages high volumes of transactions on the blockchain. However, concerns about throughput and the factors influencing its value persist. This research paper conducts a performance analysis of Hyperledger Fabric on two distinct infrastructure setups: physical servers and virtual servers. The goal of this evaluation is to establish a baseline configuration derived from the performance results, which can be beneficial for future blockchain enhancement research. The experimental outcomes, based on 18,000 transactions, demonstrate that physical servers generally outperform virtual servers in terms of throughput, success rate, average latency, and resource consumption. These findings can guide practitioners in optimizing their blockchain platform adoption, tailored to their specific application requirements.
Improved Crossing Pedestrian Detection Model for Intelligent Vehicles in Complex Traffic Scenes

Detecting crossing pedestrians in complicated traffic situations is a critical problem for intelligent and connected cars (ICVs) in order to minimize traffic accidents. To address this issue, an improved model based on YOLOv5 named Alpha-YOLOv5 is proposed to detect the crossing pedestrian. With the aim of enhancing pedestrian detection performance in crowded and occluded scenes, the native BottleneckCSP module is replaced by the C3TR module, using the TransformerBlock. The Nearest Upsample is replaced with CARAFE to increase global upsampling and training efficiency. In order to address the issue of accurately locating targets in complex traffic scenes, the ESE attention mechanism module is fused in the YOLOv5 model, which improves the model’s ability to capture local information and increases the receptive field. In addition, the Alpha IoU loss is employed to improve the loss and regression accuracy of high IoU targets. Experimental results show that the proposed Alpha-YOLOv5s model outperforms the original YOLOv5 model by achieving a 3.32% increase in mAP for crossing pedestrian detection in real traffic scenes, with an mAP of 95.12%.
Smart Energy Meter Implementation: Security Challenges and Opportunities

The digital age has accelerated the development of smart energy monitoring technology (smart meter) that is crucial in our ever complex grid system. Smart energy meters are crucial element of the development of intelligent networking, measuring energy flow and communicating information on energy use between users and service providers, as well as monitoring and managing home appliances and gadgets using consumer data. They monitor consumption and generation of power in real time enabling consumer to be prosumer in energy. However, there are a number of obstacles that this technology may face. In this paper, the architecture, security concerns, and potential solutions to smart energy meter security are presented. In particular, a concise review of Internet of Things (IoT) security vulnerabilities that might possibly cause harm to the community is given, as well as some opportunities in this area. In addition, we explore the challenges and propose several options for researchers to make a more significant contribution. This research area can be considered as a sunrise development in most regions of the world and will continue to be a major development domain. In the future, researchers should see this research area as “greenfield” features of research from a variety of perspectives, including hackability, criminology, and implementation standards.