Reviewerly/exHarmony huggingface dataset:
Random Surface Covariance Estimation by Shifted Partial Tracing

The problem of covariance estimation for replicated surface-valued processes is examined from the functional data analysis perspective. Considerations of statistical and computational efficiency often compel the use of separability of the covariance, even though the assumption may fail in practice. We consider a setting where the covariance structure may fail to be separable locally—either due to noise contamination or due to the presence of a nonseparable short-range dependent signal component. That is, the covariance is an additive perturbation of a separable component by a nonseparable but banded component. We introduce nonparametric estimators hinging on the novel concept of shifted partial tracing, enabling computationally efficient estimation of the model under dense observation. Due to the denoising properties of shifted partial tracing, our methods are shown to yield consistent estimators even under noisy discrete observation, without the need for smoothing. Further to deriving the convergence rates and limit theorems, we also show that the implementation of our estimators, including prediction, comes at no computational overhead relative to a separable model. Finally, we demonstrate empirical performance and computational feasibility of our methods in an extensive simulation study and on a real dataset. Supplementary materials for this article are available online.
Lessons Learned on the Interface Between Quantum and Conventional Networking

The future Quantum Internet is expected to be based on a hybrid architecture with core quantum transport capabilities complemented by conventional networking. Practical and foundational considerations indicate the need for conventional control and data planes that (i) utilize extensive existing telecommunications fiber infrastructure, and (ii) provide parallel conventional data channels needed for quantum networking protocols. We propose a quantum-conventional network (QCN) harness to implement a new architecture to meet these requirements. The QCN control plane carries the control and management traffic, whereas its data plane handles the conventional and quantum data communications. We established a local area QCN connecting three quantum laboratories over dedicated fiber and conventional network connections. We describe considerations and tradeoffs for layering QCN functionalities, informed by our recent quantum entanglement distribution experiments conducted over this network.
Structured nonnegative matrix factorization for traffic flow estimation of large cloud networks

Network traffic matrix estimation is an ill-posed linear inverse problem: it requires to estimate the unobservable origin destination traffic flows, X, given the observable link traffic flows, Y, and a binary routing matrix, A, which are such that Y = AX. This is a challenging but vital problem as accurate estimation of OD flows is required for several network management tasks. In this paper, we propose a novel model for the network traffic matrix estimation problem which maps high-dimension OD flows to low-dimension latent flows with the following three constraints: (1) nonnegativity constraint on the estimated OD flows, (2) autoregression constraint that enables the proposed model to effectively capture temporal patterns of the OD flows, and (3) orthogonality constraint that ensures the mapping between low-dimensional latent flows and the corresponding link flows to be distance preserving. The parameters of the proposed model are estimated with a training algorithm based on Nesterov accelerated gradient and generally shows fast convergence. We validate the proposed traffic flow estimation model on two real backbone IP network datasets, namely Internet2 and G'EANT. Empirical results show that the proposed model outperforms the state-of-the-art models not only in terms of tracking the individual OD flows but also in terms of standard performance metrics. The proposed model is also found to be highly scalable compared to the existing state-of-the-art approaches.
Research on English Writing Teaching of Tobacco Control Theme Public Welfare in Multimedia Environment

Objectives: On the basis of the statistics offered by World Health Organization annually, there are about 3 million people who die from smoking. Tobacco products have been produced and advertised in a large variety of ways, although the evidence on their safety and smoking cessation benefits is uncertain. So is the electronic cigarette. Active tobacco control measures should be taken. Public service tobacco control advertising plays an important role in tobacco control. In contemporary society, multimedia network technology has been widely used in the education industry, and the combination of internet and education has been increasingly significant. Methods: This inevitability is due to the fact because the traditional teaching method can no longer meet the needs of the education, the combination of internet and education has the inevitability. In this research, we will explore the English teaching method in multimedia environment to build a new ecological system of “artificial intelligence + writing education” in English Writing Teaching of tobacco control theme public welfare in multimedia environment. The field of writing in language education is a major challenge for artificial intelligence, especially writing assessment. In traditional English writing teaching, the emphasis of score evaluation is more common, and the assessment method is relatively simple. Achievement assessment, as an important part of teaching mode, plays an increasingly important role in improving the subjective initiative and ability. In addition to the traditional methods of literature research, questionnaire survey and case analysis, the research method based on design is also adopted to help learners establish effective learning strategies by carrying out reasonable functional design according to learners’ learning needs. Results: This study will have a really important impact on the language learning of Chinese English learners, and provide a new learning environment that will be more convenient and effective for learners. Conclusion: It is a useful attempt for artificial intelligence to spread widely in the field of human knowledge transmission.
Data Dimensionality Reduction based Geological Interpretation of AVIRIS-NG Hyperspectral Data

This work reviews the three basic linear data dimensionality reduction techniques for an airborne hyperspectral image and their applications in feature identification
Optimized for Addiction: Extending Product Liability Concepts to Defectively Designed Social Media Algorithms and Overcoming the Communications Decency Act

Over the past decade, social media has gained an ever more pervasive presence in our lives. But the social connection, real-time dissemination of information, and creative outlet it provides come with a cost that is invisible to most users: in order to increase site value, many platforms are designed to addict users and trap them in a cycle of dependence that can ravage their mental health and well-being. One of the most effective ways for platforms to maximize their value is by using recommendation systems, sophisticated software systems that utilize artificial intelligence to learn about the user and predict what kinds of content will keep them scrolling, clicking, and liking. This Comment sketches out a legal solution for a subset of the harms caused by these algorithms by arguing that product liability concepts should extend to social media platforms, who have managed to achieve a nearly impenetrable form of immunity due to § 230 of the Communications Decency Act (CDA). The first Part of this Comment provides an overview of relevant concepts, including the recommendation engines that drive social media, the mental health effects of social media use, and the origins and purpose of the CDA. Next, this Comment demonstrates that social media platforms qualify as products for the purposes of strict product liability and walks the reader through a hypothetical example of the risk-utility test. Finally, this Comment considers § 230 of the CDA and offers two avenues for circumventing its broad grant of immunity. Instead of joining the extensive body of literature lamenting the woes of the CDA, this Comment proposes an affirmative solution that enables victims to recover while holding the multi-billion-dollar social media industry to a higher standard and incentivizing greater levels of caution in its development process.
Sensing Anomalies as Potential Hazards: Datasets and Benchmarks

We consider the problem of detecting, in the visual sensing data stream of an autonomous mobile robot, semantic patterns that are unusual (i.e., anomalous) with respect to the robot's previous experience in similar environments. These anomalies might indicate unforeseen hazards and, in scenarios where failure is costly, can be used to trigger an avoidance behavior. We contribute three novel image-based datasets acquired in robot exploration scenarios, comprising a total of more than 200k labeled frames, spanning various types of anomalies. On these datasets, we study the performance of an anomaly detection approach based on autoencoders operating at different scales.
Frequency Based Feature Extraction Technique for Text Documents in Tamil Language

In Natural Language Processing, text classification involves the process of categorizing documents into organized groups. Classifiers are trained to analyze the documents and categorize them into groups based on pre-defined tags. In this paper, a framework has been devised to extract features from the text documents. The documents are preprocessed, tagged and then the features are extracted. Preprocessing includes removal of stop words, punctuations. Preprocessed Words were tagged under two categories, Noun and Verb. Tagging of words was based on morphophonemic rules pertaining to Tamil Language. Feature extraction is based on frequency of the occurrences of words tagged as Noun. Extraction of features through the proposed framework enhances dimensionality reduction of data considered for classification process.
Categorical Perception: A Groundwork for Deep Learning

Classification is one of the major tasks that deep learning is successfully tackling. Categorization is also a fundamental cognitive ability. A well-known perceptual consequence of categorization in humans and other animals, categorical perception, is notably characterized by a within-category compression and a between-category separation: two items, close in input space, are perceived closer if they belong to the same category than if they belong to different categories. Elaborating on experimental and theoretical results in cognitive science, here we study categorical effects in artificial neural networks. We combine a theoretical analysis that makes use of mutual and Fisher information quantities and a series of numerical simulations on networks of increasing complexity. These formal and numerical analyses provide insights into the geometry of the neural representation in deep layers, with expansion of space near category boundaries and contraction far from category boundaries. We investigate categorical representation by using two complementary approaches: one mimics experiments in psychophysics and cognitive neuroscience by means of morphed continua between stimuli of different categories, while the other introduces a categoricality index that, for each layer in the network, quantifies the separability of the categories at the neural population level. We show on both shallow and deep neural networks that category learning automatically induces categorical perception. We further show that the deeper a layer, the stronger the categorical effects. As an outcome of our study, we propose a coherent view of the efficacy of different heuristic practices of the dropout regularization technique. More generally, our view, which finds echoes in the neuroscience literature, insists on the differential impact of noise in any given layer depending on the geometry of the neural representation that is being learned, that is, on how this geometry reflects the structure of the categories.
Development Strategies for Higher Education Institutions Based on the Cultivation of Core Competitiveness

The development of higher education institutions is the inevitable path towards strong scientific research ability of higher education. Currently, under the impact of the strategies of developing world-class universities, and strengthening the core competitiveness of disciplines, higher education institutions in China are faced with immense challenges. Based on the cultivation of core competitiveness, this paper explores the evaluation and development strategies for the core competitiveness of higher education institutions. The results show that: core competitiveness is a comprehensive intangible ability, and improving core competitiveness helps to clarify the direction and enhancing the quality of talent training. The core competitiveness of China’s higher education institutions has continued to improve, as evidenced by the growth in educational resources, educational fund, and scientific results. The core competitiveness of higher education can be evaluated by the following indexes: school-running philosophy, academic production capacity, management model innovation capacity, campus culture construction capacity, human resource management innovation capacity, and training model innovation capacity. The research results lay a theoretical basis for the reform of higher education institutions.
K-Net: Towards Unified Image Segmentation

Semantic, instance, and panoptic segmentations have been addressed using different and specialized frameworks despite their underlying connections. This paper presents a unified, simple, and effective framework for these essentially similar tasks. The framework, named K-Net, segments both instances and semantic categories consistently by a group of learnable kernels, where each kernel is responsible for generating a mask for either a potential instance or a stuff class. To remedy the difficulties of distinguishing various instances, we propose a kernel update strategy that enables each kernel dynamic and conditional on its meaningful group in the input image. K-Net can be trained in an end-to-end manner with bipartite matching, and its training and inference are naturally NMS-free and box-free. Without bells and whistles, K-Net surpasses all previous state-of-the-art single-model results of panoptic segmentation on MS COCO and semantic segmentation on ADE20K with 52.1% PQ and 54.3% mIoU, respectively. Its instance segmentation performance is also on par with Cascade Mask R-CNNon MS COCO with 60%-90% faster inference speeds. Code and models will be released at this https URL.
Analysis of Natural Scene Derived Spatial Frequency Responses for Estimating Camera ISO12233 Slanted-edge Performance

The Natural Scene derived Spatial Frequency Response (NS-SFR) framework automatically extracts suitable step-edges from natural pictorial scenes and processes these edges via the edge-based ISO12233 (e-SFR) algorithm. Previously, a novel methodology was presented to estimate the standard e-SFR from NS-SFR data. This paper implements this method using diverse natural scene image datasets from three characterized camera systems. Quantitative analysis was carried out on the system e-SFR estimates to validate accuracy of the method. Both linear and non-linear camera systems were evaluated. To investigate how scene content and dataset size affect system e-SFR estimates, analysis was conducted on entire datasets, as well as subsets of various sizes and scene group types. Results demonstrate that system e-SFR estimates strongly correlate with results from test chart inputs, with accuracy comparable to that of the ISO12233. Further work toward improving and fine-tuning the proposed methodology for practical implementation is discussed.
A Self-attention Based Model for Offline Handwritten Text Recognition

Offline handwritten text recognition is an important part of document analysis and it has been receiving a lot of attention from numerous researchers for decades. In this paper, we present a self-attention-based model for offline handwritten textline recognition. The proposed model consists of three main components: a feature extractor by CNN; an encoder by a BLSTM network and a self-attention module; and a decoder by CTC. The self-attention module is complementary to RNN in the encoder and helps the encoder to capture long-range and multi-level dependencies across an input sequence. According to the extensive experiments on the two datasets of IAM Handwriting and Kuzushiji, the proposed model achieves better accuracy than the state-of-the-art models. The self-attention map visualization shows that the self-attention mechanism helps the encoder capture long-range and multi-level dependencies across an input sequence.
A Blockchain-Enabled Approach for Online Stream Sensor Data Protection in Cyber-Physical Manufacturing Systems

Abstract With the rapid development of the Internet of Things and information technologies, more and more manufacturing systems become cyber-enabled, which significantly improves the flexibility and productivity of manufacturing. Furthermore, a large variety of online sensors are also commonly incorporated in the manufacturing systems for online quality monitoring and control. However, the cyber-enabled environment may pose the collected online stream sensor data under high risks of cyber-physical attacks as well. Specifically, cyber-physical attacks could occur during the manufacturing process to maliciously tamper the sensor data, which could result in false alarms or failures of anomaly detection. In addition, the cyber-physical attacks may also illegally access the collected data without authorization and cause leakage of key information. Therefore, it becomes critical to develop an effective approach to protect online stream data from these attacks so that the cyber-physical security of the manufacturing systems could be assured. To achieve this goal, an integrative blockchain-enabled method, is proposed by leveraging both asymmetry encryption and camouflage techniques. A real-world case study that protects cyber-physical security of collected stream data in additive manufacturing is provided to demonstrate the effectiveness of the proposed method. The results demonstrate that malicious tampering could be detected in a relatively short time and the risk of unauthorized data access is significantly reduced as well.
Sequentially allocated merge-split samplers for conjugate Bayesian nonparametric models

We propose efficient Markov chain Monte Carlo samplers for posterior simulation in conjugate Bayesian nonparametric models. The Gibbs sampler is easily implemented but only updates cluster membership one item at a time and thus can be hampered by poor mixing. As such, the literature suggests augmenting Gibbs scans with merge-split proposals, potentially performing dramatic updates to move between posterior modes. In contrast to less efficient merge-split samplers, we introduce a class of merge-split samplers whose updates are computationally efficient and well supported by the posterior distribution. A key feature of our samplers is that they propose updates through the sequential allocation of items. To investigate the efficiency of existing and our novel samplers, we simulate multiple replications of six different dataset/model scenarios and show that our sequentially allocated merge-split samplers achieve the highest relative computational efficiency.
Neural Class Expression Synthesis

Many applications require explainable node classification in knowledge graphs. Towards this end, a popular “white-box” approach is class expression learning: Given sets of positive and negative nodes, class expressions in description logics are learned that separate positive from negative nodes. Most existing approaches are search-based approaches generating many candidate class expressions and selecting the best one. However, they often take a long time to find suitable class expressions. In this paper, we cast class expression learning as a translation problem and propose a new family of class expression learning approaches which we dub neural class expression synthesizers. Training examples are “translated” into class expressions in a fashion akin to machine translation. Consequently, our synthesizers are not subject to the runtime limitations of search-based approaches. We study three instances of this novel family of approaches based on LSTMs, GRUs, and set transformers, respectively. An evaluation of our approach on four benchmark datasets suggests that it can effectively synthesize high-quality class expressions with respect to the input examples in approximately one second on average. Moreover, a comparison to state-of-the-art approaches suggests that we achieve better F-measures on large datasets. For reproducibility purposes, we provide our implementation as well as pretrained models in our public GitHub repository at https://github.com/dice-group/NeuralClassExpressionSynthesis
Fluctuation-Based Fade Detection for Local Scene Changes

In recent years, fade detection algorithms can classify fade scenes in massive video libraries have been developed. However, these algorithms misclassify some non-fade scenes as fade scenes, especially dissolve scenes and scenes with captions or flashing light sources. This paper proposes a new fade detection algorithm that uses similarity tendencies of luminance transitions to overcome such obstacles. To prevent detection accuracy degradation by letterboxing and captions, video frames are simplified. Then, fade candidates are detected by transition boundary detection using the angular and curvature characteristics of the luminance vectors. Finally, luminance flipping detection improves the detection accuracy by extracting the luminance retrograde phenomenon that occurs with flashing or light source movements. Through objective evaluation using F1 score, the detection accuracy of the proposed algorithm was 0.884, which is an increase of 0.187 (21.2% improvement) compared with the average F1 score of existing high-performance methods.
A Geometric Structure of Acceleration and Its Role in Making Gradients Small Fast

Since Nesterov's seminal 1983 work, many accelerated first-order optimization methods have been proposed, but their analyses lacks a common unifying structure. In this work, we identify a geometric structure satisfied by a wide range of first-order accelerated methods. Using this geometric insight, we present several novel generalizations of accelerated methods. Most interesting among them is a method that reduces the squared gradient norm with $\mathcal{O}(1/K^4)$ rate in the prox-grad setup, faster than the $\mathcal{O}(1/K^3)$ rates of Nesterov's FGM or Kim and Fessler's FPGM-m.
Vision Inertial Fusion Based on BP Neural Network for Aircraft Autonomous Positioning

Long-term navigation for aircraft in global position system (GPS) denied environment is a very challenging task. In this thesis, a novel aircraft autonomous positioning method based on fusion multi-modal image matching and inertial navigation system (INS) is proposed. This method consists of three steps. First, the coordinates of ground control points were obtained by matching the real-time image with the reference image. Then, PnP algorithm is employed for calculating the position of the aircraft. Finally, aircraft gets the optimal position by using BP neural network to combine the position estimated by inertial navigation and the results of multi-modal image matching. Simulation result displays that our method can effectively improve the positioning accuracy of aircraft. The accuracy of this method proposed in this thesis is comparable with that of GPS. Therefore, the study of this data fusion method has certain effects, the positioning accuracy can be improved of the aircraft. It is a great significance for the aircraft to achieve autonomous navigation.