Reviewerly/exHarmony huggingface dataset:
User Dial Test Campus Network Quality Monitoring System Based on Big Data

Aiming at the issues and characteristic of existing campus network monitoring system, this paper designs a campus network monitoring system to assess network quality issues from the user's perspective based on big data, proposes a scoring algorithm to evaluate different outlet network access experience, and builds data analysis based on data warehouse. The system can meet most of the campus network quality management needs and put forward new ideas for network monitoring of campus networks.
Pedagogical tasks of forming the rules of perception of art works on the example of painting

The necessity to foster a sense of beauty in a person, to foster creative thinking and abilities in youngsters and future instructors, determines the article's importance. The article discusses the findings of a study on how students at pedagogical universities specifically perceive works of fine art. The article's goal is to provide a summary of the approaches to pedagogical problems involving the development of skills for perceiving paintings. The study discusses the role that national art can play in the spiritual development of young people and defines strategies for incorporating it into educational settings. On the basis of analytical and synthetic methods, the general spiritual and moral approach to the organization of cognitive activity of the training youth; the authors of the article offer a description of the ways of perception and understanding of works of artistic creativity, while both content content and methodical support of the work are taken into account in the implementation of pedagogical tasks. In the study's practical component, 54 students from O.Zhanibekov South Kazakhstan Pedagogical University prepared an essay on the theme "Basic principle of perception of artistic text." The students completed a content analysis essay in which they analyzed the concepts that, in their opinion, are crucial to the perception of the image. The majority of students identified the principle "ethical principle" as the fundamental principle. The selection of the leading principles of visual art by future teachers is critical, because it determines what message students use to understand life through the language of art. The article was supported by the Ministry of Science and Higher Education&nbsp; RK АР09259731 "Didactic potential of national painting in the implementation of the program of spiritual revival of the nation". Key words: psychology, artistic creativity, painting, spirituality formation, influence, perception, awareness.
TransFTTA: A Knowledge Representation Learning Model Based on Flexible Translation and Temporal Attributes

Knowledge graph is a structured semantic knowledge base that has gained widespread application across diverse domains. Nevertheless, traditional models for knowledge graph representation learning, such as TransE and TransH, exhibit constraints in terms of representation capacity and accuracy when it comes to tasks like link prediction and triple classification. To overcome these issues and further enhance the performance of knowledge graph representation models, this study introduces a knowledge representation learning model based on dynamic translation and temporal attributes. The experimental findings substantiate that our proposed model exhibits a significant improvement in performance compared to the TransE and TransH models.
Utilization of Augmented Reality for Introducing Tongkonan Toraja Traditional House

This paper discusses the implementation of Augmented Reality (AR) as a form to introduce the culture of Tongkonan Toraja House that holds cultural and historical values of the Toraja tribe. However, many people have an absence of understanding about Torajan culture. The purpose of this AR is to obtain knowledge and recognition by giving experiences and interactions through virtual representation of the Tongkonan Toraja House. Development uses the Waterfall approach, starting with using good 3D models, designing the layout, implementing the marker, adding interactivity, testing, and maintenance of the application. The analysis of User Experience Questionnaire (UEQ) as a measurement method obtains positive evaluations by entering data from 27 respondents. The positive evaluations were obtained of the Tongkonan Toraja AR, which its values surpass 0,8. Stimulation aspect indicates a good level of excitement from the users with the mean value of 2.204, the highest mean value among other aspects. On the contrary, Efficiency received the lowest mean value of 1.981, despite the fact that its mean value is above 0.8 which indicates positive evaluation. The UEQ dimensions are categorized into attractiveness, pragmatic quality, and hedonic quality. Pragmatic quality received the lowest score, while hedonic quality obtained the highest score, with attractiveness falling in between. Overall, the findings demonstrate positive user evaluations of the Tongkonan Toraja AR across dimensions, with Stimulation receiving particular praise and Efficiency showing potential for enhancement. These results indicate that the AR experience is generally well-received, providing valuable insights for further improvements and development.
Matrix NTRU Cryptosystem over Integral Domain

NTRU is one of the cryptosystems which has efficient public keys. Lattice L is a set of vectors in $\mathbb{R}^{n}$ which is generated by linearly independent vectors with a linear combination of integer coefficients. Nayak et al. introduced an NTRU based on matrices and Nanda et al. observed an NTRU based on matrices over Gaussian integer $\mathbb{Z}[i]$ as the continuation. In this paper, we observe an NTRU based on matrices over an integral domain $\mathbb{Z}[\sqrt{-3}]$. Moreover, we propose the algorithm of key choice, computational aspects, and the restriction of parameters and show that the number of decryption failures of the proposed algorithm is very low for the proposed parameters.
A ROS-based Robot-Aided Defect Detection System for Mirror-Like Surfaces

Detecting defects on free-form mirror-like surfaces has long been a challenging engineering problem. By capturing sinusoidal patterns from reflective surfaces, the three-dimensional surface profile can be solved by analyzing the variation in the ray patterns. This technique is termed phase measuring deflectometry, or PMD. With proper system design, this technique can measure 3D surface defects up to nanometer scale. In general, multiple shifts in phases are required to reproduce accurate surface profiles. This means that the camera will need to capture a sequence of images for each surface under testing (SUT). Catering the system for online defect detection will require fast imaging and robust defect detection algorithm. This study presents an innovative system design that uses ROS as the backbone of the software architecture, incorporates a real time synchronization imaging system, and deploys a novel calibration and marking approach that can be used on any free-form mirror-like surfaces. A YOLO5 AI network is trained to achieve fast online recognition of 7 classes of defects. Decent results are achieved on defect detection and localization with testing on real cars.
Self-Similarity Attention Module for Skeleton-Based Fine-Grained Action Recognition

Fine-grained action recognition poses a significant challenge as it involves distinguishing subtle and distinctive motion variations within fine-grained action categories. Previous methods have improved performance by enhancing the network's ability to capture variations in the action space and temporal changes. However, they face limitations in effectively distinguishing subtle differences within actions when they involve varying numbers of repetitions. In this paper, we propose an effective module called the Self-similarity Attention Module (SAM). This module represents the self-similarity of actions using the Temporal Self-similarity Matrix (TSM) and utilizes channel-wise excitation to capture the periodicity information in actions. The Self-similarity Attention Module can be embedded into any 3D convolutional neural network. Our approach outperforms previous skeleton-based action recognition methods on the wildly used FineGym dataset, which confirms its effectiveness and efficiency.
SAIR: Learning Semantic-aware Implicit Representation

Implicit representation of an image can map arbitrary coordinates in the continuous domain to their corresponding color values, presenting a powerful capability for image reconstruction. Nevertheless, existing implicit representation approaches only focus on building continuous appearance mapping, ignoring the continuities of the semantic information across pixels. As a result, they can hardly achieve desired reconstruction results when the semantic information within input images is corrupted, for example, a large region misses. To address the issue, we propose to learn semantic-aware implicit representation (SAIR), that is, we make the implicit representation of each pixel rely on both its appearance and semantic information (\eg, which object does the pixel belong to). To this end, we propose a framework with two modules: (1) building a semantic implicit representation (SIR) for a corrupted image whose large regions miss. Given an arbitrary coordinate in the continuous domain, we can obtain its respective text-aligned embedding indicating the object the pixel belongs. (2) building an appearance implicit representation (AIR) based on the SIR. Given an arbitrary coordinate in the continuous domain, we can reconstruct its color whether or not the pixel is missed in the input. We validate the novel semantic-aware implicit representation method on the image inpainting task, and the extensive experiments demonstrate that our method surpasses state-of-the-art approaches by a significant margin.
A Privacy-Preserving Cross-Domain Recommendation Algorithm for Industrial IoT Devices

Recommendation algorithms have been initially applied on the online business platform of industrial Internet of Things (IoT) devices. However, traditional recommendation algorithms are often difficult to solve the data sparsity problem. In fact, online shoppers are often accompanied by consumption behavior of other heterogeneous products, so we combine the consumer behavior of other heterogeneous products in the auxiliary domain to improve the recommendation performance of industrial IoT devices in the target domain. Due to privacy-preserving requirements, the original scoring information of the auxiliary domain is often not allowed to be directly shared with the target domain. Therefore, we propose a Privacy-Preserving Cross-Domain Recommendation algorithm for industrial IoT devices. First, the non-privacy preference features are extracted through the auxiliary domain scoring data. Next, the extracted preference features are fused with the target domain information. Extensive experiments have been conducted on the Amazon dataset to verify the effectiveness of our method.
Wave interference network with a wave function for traffic sign recognition

&lt;abstract&gt; &lt;p&gt;In this paper, we successfully combine convolution with a wave function to build an effective and efficient classifier for traffic signs, named the wave interference network (WiNet). In the WiNet, the feature map extracted by the convolutional filters is refined into many entities from an input image. Each entity is represented as a wave. We utilize Euler's formula to unfold the wave function. Based on the wave-like information representation, the model modulates the relationship between the entities and the fixed weights of convolution adaptively. Experiment results on the Chinese Traffic Sign Recognition Database (CTSRD) and the German Traffic Sign Recognition Benchmark (GTSRB) demonstrate that the performance of the presented model is better than some other models, such as ResMLP, ResNet50, PVT and ViT in the following aspects: 1) WiNet obtains the best accuracy rate with 99.80% on the CTSRD and recognizes all images exactly on the GTSRB; 2) WiNet gains better robustness on the dataset with different noises compared with other models; 3) WiNet has a good generalization on different datasets.&lt;/p&gt; &lt;/abstract&gt;
A representation points based method for object detection on the surface of electroplated diamond wire saw

Electroplated diamond wire saw (EDWS) is the main tool for slicing semiconductor materials such as monocrystal silicon, polycrystal silicon, and sapphire. The distribution density of abrasive and agglomeration on the surface of EDWS is an important index to evaluate its manufacturing quality. Limited by the diversity of object morphology in EDWS images, designing a unified feature extractor by traditional methods is pretty hardship. In this paper, a method named electroplated diamond wire saw detection network (EDWSDN) is proposed based on deep learning, which can adaptively extract the shape and posture of objects by using representation points. In addition, an adaptive positive points selection (APPS) module is designed to realize the quality evaluation and redistribution of positive samples. Extensive comparative experiments are implemented to verify the performance of the proposed method. Put the confidence threshold into 0.5, the proposed method can achieve 89.5 mean of Average Precision (mAP) while ensuring an inference speed of 21.5 Frame Per Second (FPS). The promising results demonstrate that the proposed method has good enough performance to be applied to the online detection of EDWS manufacturing quality.
Eliciting Model Steering Interactions from Users via Data and Visual Design Probes

Domain experts increasingly use automated data science tools to incorporate machine learning (ML) models in their work but struggle to "debug" these models when they are incorrect. For these experts, semantic interactions can provide an accessible avenue to guide and refine ML models without having to programmatically dive into its technical details. In this research, we conduct an elicitation study using data and visual design probes to examine if and how experts with a spectrum of ML expertise use semantic interactions to update a simple classification model. We use our design probes to facilitate an interactive dialogue with 20 participants and codify their interactions as a set of target-interaction pairs. Interestingly, our findings revealed that many targets of semantic interactions do not directly map to ML model parameters, but instead aim to augment the data a model uses for training. We also identify reasons that participants would hesitate to interact with ML models, including burdens of cognitive load and concerns of injecting bias. Unexpectedly participants also saw the value of using semantic interactions to work collaboratively with members of their team. Participants with less ML expertise found this to be a useful mechanism for communicating their concerns to ML experts. This was an especially important observation, as our study also shows the different needs that correspond to diverse ML expertise. Collectively, we demonstrate that design probes are effective tools for proactively gathering the affordances that should be offered in an interactive machine learning system.
Robustness and weight resource measures without convexity restriction: Multicopy witness and operational advantage in static and dynamical quantum resource theories

Quantum resource theories (QRTs) provide a unified framework to analyze quantum properties as resources for achieving advantages in quantum information processing. The generalized robustness and the weight of resource have been gaining increasing attention as useful resource quantifiers. However, the existing analyses of these measures were restricted to the cases where convexity of the set of free states is assumed, and physically motivated resources do not necessarily satisfy this restriction. In this paper, we give characterizations of robustness- and weight-based measures in general QRTs without convexity restriction through two different yet related approaches. On the one hand, we characterize the generalized robustness and the weight of resource by introducing a non-linear witness. We show a general construction of a new witness observable that detects the resourcefulness of a given state from multiple copies of the state and, using this witness, we provide operational interpretations of the above resource measures even without any convexity assumption. On the other hand, we find that the generalized robustness and the weight of resource can also be interpreted as the worst-case maximum advantage in variants of channel-discrimination and channel-exclusion tasks, respectively, where the set of free states consists of several convex subsets corresponding to multiple restrictions. We further extend these results to QRTs for quantum channels and quantum instruments. These characterizations show that every quantum resource exhibits an advantage for the corresponding tasks, even in general QRTs without convexity assumption. Thus, we establish the usefulness of robustness-based and weight-based techniques beyond the conventional scope of convex QRTs, leading to a better understanding of the general structure of QRTs.
Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task

Modern generative models exhibit unprecedented capabilities to generate extremely realistic data. However, given the inherent compositionality of the real world, reliable use of these models in practical applications requires that they exhibit the capability to compose a novel set of concepts to generate outputs not seen in the training data set. Prior work demonstrates that recent diffusion models do exhibit intriguing compositional generalization abilities, but also fail unpredictably. Motivated by this, we perform a controlled study for understanding compositional generalization in conditional diffusion models in a synthetic setting, varying different attributes of the training data and measuring the model's ability to generate samples out-of-distribution. Our results show: (i) the order in which the ability to generate samples from a concept and compose them emerges is governed by the structure of the underlying data-generating process; (ii) performance on compositional tasks exhibits a sudden "emergence" due to multiplicative reliance on the performance of constituent tasks, partially explaining emergent phenomena seen in generative models; and (iii) composing concepts with lower frequency in the training data to generate out-of-distribution samples requires considerably more optimization steps compared to generating in-distribution samples. Overall, our study lays a foundation for understanding capabilities and compositionality in generative models from a data-centric perspective.
Uncertainty Quantification using Generative Approach

We present the Incremental Generative Monte Carlo (IGMC) method, designed to measure uncertainty in deep neural networks using deep generative approaches. IGMC iteratively trains generative models, adding their output to the dataset, to compute the posterior distribution of the expectation of a random variable. We provide a theoretical guarantee of the convergence rate of IGMC relative to the sample size and sampling depth. Due to its compatibility with deep generative approaches, IGMC is adaptable to both neural network classification and regression tasks. We empirically study the behavior of IGMC on the MNIST digit classification task.
LL-VQ-VAE: Learnable Lattice Vector-Quantization For Efficient Representations

In this paper we introduce learnable lattice vector quantization and demonstrate its effectiveness for learning discrete representations. Our method, termed LL-VQ-VAE, replaces the vector quantization layer in VQ-VAE with lattice-based discretization. The learnable lattice imposes a structure over all discrete embeddings, acting as a deterrent against codebook collapse, leading to high codebook utilization. Compared to VQ-VAE, our method obtains lower reconstruction errors under the same training conditions, trains in a fraction of the time, and with a constant number of parameters (equal to the embedding dimension $D$), making it a very scalable approach. We demonstrate these results on the FFHQ-1024 dataset and include FashionMNIST and Celeb-A.
Exact Verification of ReLU Neural Control Barrier Functions

Control Barrier Functions (CBFs) are a popular approach for safe control of nonlinear systems. In CBF-based control, the desired safety properties of the system are mapped to nonnegativity of a CBF, and the control input is chosen to ensure that the CBF remains nonnegative for all time. Recently, machine learning methods that represent CBFs as neural networks (neural control barrier functions, or NCBFs) have shown great promise due to the universal representability of neural networks. However, verifying that a learned CBF guarantees safety remains a challenging research problem. This paper presents novel exact conditions and algorithms for verifying safety of feedforward NCBFs with ReLU activation functions. The key challenge in doing so is that, due to the piecewise linearity of the ReLU function, the NCBF will be nondifferentiable at certain points, thus invalidating traditional safety verification methods that assume a smooth barrier function. We resolve this issue by leveraging a generalization of Nagumo's theorem for proving invariance of sets with nonsmooth boundaries to derive necessary and sufficient conditions for safety. Based on this condition, we propose an algorithm for safety verification of NCBFs that first decomposes the NCBF into piecewise linear segments and then solves a nonlinear program to verify safety of each segment as well as the intersections of the linear segments. We mitigate the complexity by only considering the boundary of the safe region and by pruning the segments with Interval Bound Propagation (IBP) and linear relaxation. We evaluate our approach through numerical studies with comparison to state-of-the-art SMT-based methods. Our code is available at https://github.com/HongchaoZhang-HZ/exactverif-reluncbf-nips23.
Surveying the Landscape of Text Summarization with Deep Learning: A Comprehensive Review

In recent years, deep learning has revolutionized natural language processing (NLP) by enabling the development of models that can learn complex representations of language data, leading to significant improvements in performance across a wide range of NLP tasks. Deep learning models for NLP typically use large amounts of data to train deep neural networks, allowing them to learn the patterns and relationships in language data. This is in contrast to traditional NLP approaches, which rely on hand-engineered features and rules to perform NLP tasks. The ability of deep neural networks to learn hierarchical representations of language data, handle variable-length input sequences, and perform well on large datasets makes them well-suited for NLP applications. Driven by the exponential growth of textual data and the increasing demand for condensed, coherent, and informative summaries, text summarization has been a critical research area in the field of NLP. Applying deep learning to text summarization refers to the use of deep neural networks to perform text summarization tasks. In this survey, we begin with a review of fashionable text summarization tasks in recent years, including extractive, abstractive, multi-document, and so on. Next, we discuss most deep learning-based models and their experimental results on these tasks. The paper also covers datasets and data representation for summarization tasks. Finally, we delve into the opportunities and challenges associated with summarization tasks and their corresponding methodologies, aiming to inspire future research efforts to advance the field further. A goal of our survey is to explain how these methods differ in their requirements as understanding them is essential for choosing a technique suited for a specific setting.