Reviewerly/exHarmony huggingface dataset:
Structure-aware deep clustering network based on contrastive learning

Recently, deep clustering has been extensively employed for various data mining tasks, and it can be divided into auto-encoder (AE)-based and graph neural networks (GNN)-based methods. However, existing AE-based methods fall short in effectively extracting structural information, while GNN suffer from smoothing and heterophily. Although methods that combine AE and GNN achieve impressive performance, there remains an inadequate balance between preserving the raw structure and exploring the underlying structure. Accordingly, we propose a novel network named Structure-Aware Deep Clustering network (SADC). Firstly, we compute the cumulative influence of non-adjacent nodes at multiple depths and, thus, enhance the adjacency matrix. Secondly, an enhanced graph auto-encoder is designed. Thirdly, the latent space of AE is endowed with the ability to perceive the raw structure during the learning process. Besides, we design self-supervised mechanisms to achieve co-optimization of node representation learning and topology learning. A new loss function is designed to preserve the inherent structure while also allowing for exploration of latent data structure. Extensive experiments on six benchmark datasets validate that our method outperforms state-of-the-art methods.
Comparative Study of Variations in Quantum Approximate Optimization Algorithms for the Traveling Salesman Problem

The traveling salesman problem (TSP) is one of the most often-used NP-hard problems in computer science to study the effectiveness of computing models and hardware platforms. In this regard, it is also heavily used as a vehicle to study the feasibility of the quantum computing paradigm for this class of problems. In this paper, we tackle the TSP using the quantum approximate optimization algorithm (QAOA) approach by formulating it as an optimization problem. By adopting an improved qubit encoding strategy and a layer-wise learning optimization protocol, we present numerical results obtained from the gate-based digital quantum simulator, specifically targeting TSP instances with 3, 4, and 5 cities. We focus on the evaluations of three distinctive QAOA mixer designs, considering their performances in terms of numerical accuracy and optimization cost. Notably, we find that a well-balanced QAOA mixer design exhibits more promising potential for gate-based simulators and realistic quantum devices in the long run, an observation further supported by our noise model simulations. Furthermore, we investigate the sensitivity of the simulations to the TSP graph. Overall, our simulation results show that the digital quantum simulation of problem-inspired ansatz is a successful candidate for finding optimal TSP solutions.
Beyond NeRF Underwater: Learning Neural Reflectance Fields for True Color Correction of Marine Imagery

Underwater imagery often exhibits distorted coloration as a result of light-water interactions, which complicates the study of benthic environments in marine biology and geography.In this research, we propose an algorithm to restore the true color (albedo) in underwater imagery by jointly learning the effects of the medium and neural scene representations.Our approach models water effects as a combination of light attenuation with distance and backscattered light.The proposed neural scene representation is based on a neural reflectance field model, which learns albedos, normals, and volume densities of the underwater environment.We introduce a logistic regression model to separate water from the scene and apply distinct light physics during training.Our method avoids the need to estimate complex backscatter effects in water by employing several approximations, enhancing sampling efficiency and numerical stability during training.The proposed technique integrates underwater light effects into a volume rendering framework with end-to-end differentiability.Experimental results on both synthetic and realworld data demonstrate that our method effectively restores true color from underwater imagery, outperforming existing approaches in terms of color consistency.Our code and data are released at https://github.com/tyz1030/neuralsea.git
NoisyTwins: Class-Consistent and Diverse Image Generation Through StyleGANs

StyleGANs are at the forefront of controllable image generation as they produce a latent space that is semantically disentangled, making it suitable for image editing and manipulation. However, the performance of StyleGANs severely degrades when trained via class-conditioning on large-scale long-tailed datasets. We find that one reason for degradation is the collapse of latents for each class in the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{W}$</tex> latent space. With NoisyTwins, we first introduce an effective and inexpensive augmentation strategy for class embeddings, which then decorrelates the latents based on self-supervision in the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{W}$</tex> space. This decorrelation mitigates collapse, ensuring that our method preserves intra-class diversity with class-consistency in image generation. We show the effectiveness of our approach on large-scale real-world long-tailed datasets of ImageNet-LT and iNaturalist 2019, where our method outperforms other methods by ∼ 19% on FID, establishing a new state-of-the-art.
Constrained Evolutionary Diffusion Filter for Monocular Endoscope Tracking

Stochastic filtering is widely used to deal with nonlinear optimization problems such as 3-D and visual tracking in various computer vision and augmented reality applications. Many current methods suffer from an imbalance between exploration and exploitation due to their particle degeneracy and impoverishment, resulting in local optimums. To address this imbalance, this work proposes a new constrained evolutionary diffusion filter for nonlinear optimization. Specifically, this filter develops spatial state constraints and adaptive history-recall differential evolution embedded evolutionary stochastic diffusion instead of sequential resampling to resolve the degeneracy and impoverishment problem. With application to monocular endoscope 3-D tracking, the experimental results show that the proposed filtering significantly improves the balance between exploration and exploitation and certainly works better than recent 3-D tracking methods. Particularly, the surgical tracking error was reduced from 4.03 mm to 2.59 mm.
Discriminator-Cooperated Feature Map Distillation for GAN Compression

Despite excellent performance in image generation, Generative Adversarial Networks (GANs) are notorious for its requirements of enormous storage and intensive computation. As an awesome “performance maker”, knowledge distillation is demonstrated to be particularly efficacious in exploring low-priced GANs. In this paper, we investigate the irreplaceability of teacher discriminator and present an inventive discriminator-cooperated distillation, abbreviated as DCD, towards refining better feature maps from the generator. In contrast to conventional pixel-to-pixel match methods in feature map distillation, our DCD utilizes teacher discriminator as a transformation to drive intermediate results of the student generator to be perceptually close to corresponding outputs of the teacher generator. Furthermore, in order to mitigate mode collapse in GAN compression, we construct a collaborative adversarial training paradigm where the teacher discriminator is from scratch established to co-train with student generator in company with our DCD. Our DCD shows superior results compared with existing GAN compression methods. For instance, after reducing over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$40\times MACs$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$80\times$</tex> parameters of CycleGAN, we well decrease FID metric from 61.53 to 48.24 while the current SoTA method merely has 51.92. This work's source code has been made accessible at https://github.com/poopit/DCD-official.
MaskSketch: Unpaired Structure-guided Masked Image Generation

Recent conditional image generation methods produce images of remarkable diversity, fidelity and realism. However, the majority of these methods allow conditioning only on labels or text prompts, which limits their level of control over the generation result. In this paper, we introduce MaskSketch, an image generation method that allows spatial conditioning of the generation result using a guiding sketch as an extra conditioning signal during sampling. MaskSketch utilizes a pretrained masked generative transformer, requiring no model training or paired supervision, and works with input sketches of different levels of abstraction. We show that intermediate self-attention maps of a masked generative transformer encode important structural information of the input image, such as scene layout and object shape, and we propose a novel sampling method based on this observation to enable structure-guided generation. Our results show that MaskSketch achieves high image realism and fidelity to the guiding structure. Evaluated on standard benchmark datasets, MaskSketch outperforms state-of-the-art methods for sketch-to-image translation, as well as unpaired image-to-image translation approaches. The code can be found on our project website: https://masksketch.github.io/
Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow

Optical flow has achieved great success under clean scenes, but suffers from restricted performance under foggy scenes. To bridge the clean-to-foggy domain gap, the existing methods typically adopt the domain adaptation to transfer the motion knowledge from clean to synthetic foggy domain. However, these methods unexpectedly neglect the synthetic-to-real domain gap, and thus are erroneous when applied to real-world scenes. To handle the practical optical flow under real foggy scenes, in this work, we propose a novel unsupervised cumulative domain adaptation optical flow (UCDA-Flow) framework: depth-association motion adaptation and correlation-alignment motion adaptation. Specifically, we discover that depth is a key ingredient to in-fluence the optical flow: the deeper depth, the inferior optical flow, which motivates us to design a depth-association motion adaptation module to bridge the clean-to-foggy domain gap. Moreover, we figure out that the cost volume correlation shares similar distribution of the synthetic and real foggy images, which enlightens us to devise a correlation-alignment motion adaptation module to distill motion knowledge of the synthetic foggy domain to the real foggy domain. Note that synthetic fog is designed as the intermediate domain. Under this unified framework, the proposed cumulative adaptation progressively transfers knowledge from clean scenes to real foggy scenes. Extensive experiments have been performed to verify the superiority of the proposed method.
A Measurement and Accuracy Evaluation Method for Internal Dam Settlement

Dams are an important part of the transportation infrastructure. There are many deficiencies in the traditional methods for monitoring the internal deformation of rockfill dams, and it is difficult for the traditional methods to meet the needs of 300m ultra-high rockfill dams. In this paper, we propose a method of internal settlement measurement and accuracy evaluation of rockfill dam based on a pipeline robot. The results of the accuracy evaluation show that the method of internal settlement measurement and accuracy evaluation of rockfill dam based on a pipeline robot has great internal precision and external accuracy, reaching 10.53 ppm and 11.9 ppm, respectively. This method has better validity, stability, and accuracy than the traditional monitoring method based on the water level settlement gauge and is a new internal deformation monitoring method for rockfill dams with good application prospects.
Adjustable spatial filter for optimal free-space quantum communication round the clock

Free-space quantum communication in daylight relies crucially on spatial filtering. The optimal filter configuration, however, depends on ever-changing link conditions. To this end, we developed an adjustable spatial filter that can be used to change the system field of view on the fly. We demonstrate its use in quantum key distribution over a 1.7-km free-space link. Furthermore, we compare it to filtering with multi-mode fiber coupling. Finally, we extrapolate our results to a broader range of realistic link conditions and show that active field-of-view optimization has the potential to substantially improve the overall secure key output of the system.
Corporate Concentration, Market Structures, and Inflationary Persistence in Nigeria

It is now reasonably undebatable that inflation is not only a monetary phenomenon but has real and structural aspects. What seems to be opaquer and more debatable is the role of market structures and corporate power in the persistence of inflation worldwide. Consequently, this study examines the role of evolving corporate concentration in Nigeria’s inflation dynamics using various listed companies on the Nigerian Stock Exchange. Our contribution to this debate is four-fold. First, we generate a “Corporate Concentration Index (CCI)” for Nigeria, which enables us to evaluate the extent of market concentration and by extension the engendering of monopolistic and oligopolistic structures within the country. Second, we empirically demonstrate that this index is a significant explanatory variable to persisting inflation in Nigeria. In particular, we find that on average a one percent reduction in market competition leads to a two percent rise in inflation. Third, we find that this index offers higher out-of-sample forecast gains, hence we call for its inclusion in the predictive model for Nigeria’s inflation. Fourth, we render some scenario analysis and projections on how an improved competitive business environment can help moderate the level of inflation in Nigeria. Results from this study strongly suggest that for the Central Bank of Nigeria to fight inflation more effectively, it must take a more than passive role in understanding market structures and in promoting a more competitive business environment in Nigeria.
SENTIMENT ANALYSIS ON REVIEWS OF WOMEN'S TOPS ON SHOPEE MARKETPLACE USING NAIVE BAYES ALGORITHM

Reviews of women's tops in the market are valuable information if processed properly. Merchants can conduct product review analysis to obtain information that can be used to evaluate products and services. Product review analysis activities are not enough just to see the number of stars, it is necessary to see the entire content of the review comments to be able to know the intent of the review. Sentiment analysis system is a system used to automatically analyze online product reviews to obtain information including sentiment information that is part of online reviews. The data is classified using Naive Bayes. The data collected were 1,000 product reviews of women's tops as samples. The purpose of this study is to determine the sentiment analysis of female top product reviews using the Naive Bayes algorithm. The stages of this research include data collection, labeling, pre-processing, sentiment classification, and evaluation. In the pre-processing stage there are 6 stages, namely Cleaning emoticons &amp; symbols, Case folding, Word Normalizer, Tokenize, Stopword Removal and Stemming. TF-IDF (Term Frequency - Inverse Document Frequency) method is used for word weighting. The data will be classified into 3 (three) classes, namely negative, positive and neutral. The data will then be evaluated using accuracy parameter testing. The test results show an accuracy value of 89%, this result shows that the product reviews of women's tops are more positive.
Orientation-Aware Hierarchical, Adaptive-Resolution A* Algorithm for UAV Trajectory Planning

Successful path planning for Unmanned Aerial Vehicles (UAVs) in challenging environments with narrow openings, such as disaster areas, requires attitude to be considered. State-of-the-art methods incorporate attitude only in the refinement stage. We introduce a first-of-a-kind global minimum cost path search method based on A* that considers attitude along the path. To make the problem tractable, our method exploits an adaptive and coarse-to-fine approach using global and local A* runs, plus an efficient method to introduce the UAV attitude in the process. We integrate our method with an SE(3) trajectory optimisation method based on a safe-flight-corridor, yielding a complete path planning pipeline. Extensive evaluation is undertaken using the AirSim flight simulator under closed loop control in a set of randomised maps, allowing us to quantitatively assess our method. We show that it achieves significantly higher success rates than the baselines, at a reduced computational burden.
Smoking behavior detection algorithm based on YOLOv8-MNC

Introduction The detection of smoking behavior is an emerging field faced with challenges in identifying small, frequently occluded objects like cigarette butts using existing deep learning technologies. Such challenges have led to unsatisfactory detection accuracy and poor model robustness. Methods To overcome these issues, this paper introduces a novel smoking detection algorithm, YOLOv8-MNC, which builds on the YOLOv8 network and includes a specialized layer for small target detection. The YOLOv8-MNC algorithm employs three key strategies: (1) It utilizes NWD Loss to mitigate the effects of minor deviations in object positions on IoU, thereby enhancing training accuracy; (2) It incorporates the Multi-head Self-Attention Mechanism (MHSA) to bolster the network’s global feature learning capacity; and (3) It implements the lightweight general up-sampling operator CARAFE, in place of conventional nearest-neighbor interpolation up-sampling modules, minimizing feature information loss during the up-sampling process. Results Experimental results from a customized smoking behavior dataset demonstrate significant improvement in detection accuracy. The YOLOv8-MNC model achieved a detection accuracy of 85.887%, signifying a remarkable increase of 5.7% in the mean Average Precision (mAP@0.5) when compared to the previous algorithm. Discussion The YOLOv8-MNC algorithm represents a valuable step forward in resolving existing problems in smoking behavior detection. Its enhanced performance in both detection accuracy and robustness indicates potential applicability in related fields, thus illustrating a meaningful advancement in the sphere of smoking behavior detection. Future efforts will focus on refining this technique and exploring its application in broader contexts.
Distributed Ledger Technologies for Food Sustainability indexing

The agro-food industry currently faces increasing environmental, social, and economic challenges, emphasizing the need for an effective assessment of food sustainability. While existing certification schemes serve to evaluate food production systems' sustainability, they are often complex, laborious, and prone to errors and result in a transparency deficit and a lack of trust in the process. This paper introduces a novel solution using Distributed Ledger Technology (DLT) to facilitate certificate generation and verification, with the goal of contributing to the attainment of the European Sustainable Development Goals (SDGs). Leveraging Blockchain technology, the solution creates an unalterable record of food sustainability certificates that can be verified with ease, enhancing efficiency, transparency, and trustworthiness in the certification process. The feasibility of the proposed DLT approach is demonstrated through a proof-of-concept implementation, which shows that the proposed solution can significantly improve the certificate generation and verification process. The resultant framework presents a secure and reliable solution for food sustainability certification, which can benefit various stakeholders in the food industry, such as producers, retailers, and consumers. The proposed DLT solution promises a significant transformation in generating and verifying food sustainability certificates, fostering more sustainable food production systems.
Elimination of constraints for parallel analysis of feature models

Cross-tree constraints give feature models maximal expressive power since any interdependency between features can be captured through arbitrary propositional logic formulas. However, the existence of these constraints increases the complexity of reasoning about feature models, both for using SAT solvers or compiling the model to a binary decision diagram for efficient analyses. Although some works have tried to refactor constraints to eliminate them, they deal only with simple constraints (i.e., requires and excludes) or require the introduction of an additional set of features, increasing the complexity of the resulting feature model. This paper presents an approach that eliminates all the cross-tree constraints present in regular boolean feature models, including arbitrary constraints, in propositional logic formulas. Our approach for removing constraints consists of splitting the semantics of feature models into orthogonal disjoint feature subtrees, which are then analyzed in parallel to alleviate the exponential blow-up in memory of the resulting feature tree.
Existence of pencils with nonblocking hypersurfaces

We prove that there is a pencil of hypersurfaces in Pn of any given degree over a finite field Fq such that every Fq-member of the pencil is not blocking with respect to Fq-lines.
PERANCANGAN MODEL PEMBELAJARAN TEAMS GAME TOURNAMENT MONOPOLI BUDAYA NTT MENGGUNAKAN GENIALLY

This study aims to introduce the cultural diversity of East Nusa Tenggara (NTT) Province through the TGT (Team Game Tournament) NTT cultural monopoly in Elementary School Students of East Nusa Tenggara. This application was designed using the type of research and development (R&amp;D) with the GDLC (Game Development Life Cycle) software development method. There were some stages applied in this research. It was started from Initiation, preproduction, production, Testing (Alpha testing, Beta testing), and release. The NTT Cultural Monopoly Game ran according to its function and it was acceptable to be used by teachers and students. This game increased new concept, new insight and references related to Interactive Learning media.&#x0D; Keywords: Learning Model, Teams Game Tournament, Cultural Monopoly, Game Development Life Cycle, Interactive Learning Media.
Machine Learning and Statistics in Clinical Research—Bridging the Gap

To the Editor We are writing in response to the Viewpoint by Finlayson et al.1 While the authors highlight the potential of machine learning (ML) in clinical research, we would like to address some points that warrant further discussion.