Reviewerly/exHarmony huggingface dataset:
Metric entropy limits on recurrent neural network learning of linear dynamical systems

One of the most influential results in neural network theory is the universal approximation theorem [1], [2], [3] which states that continuous functions can be approximated to within arbitrary accuracy by single-hidden-layer feedforward neural networks. The purpose of this paper is to establish a result in this spirit for the approximation of general discrete-time linear dynamical systems—including time-varying systems—by recurrent neural networks (RNNs). For the subclass of linear time-invariant (LTI) systems, we devise a quantitative version of this statement. Specifically, measuring the complexity of the considered class of LTI systems through metric entropy according to [4], we show that RNNs can optimally learn—or identify in system-theory parlance—stable LTI systems. For LTI systems whose input-output relation is characterized through a difference equation, this means that RNNs can learn the difference equation from input-output traces in a metric-entropy optimal manner.
Moving QoE for monitoring DASH video streaming: models and a study of multiple mobile clients

Abstract Objective Quality of Experience (QoE) for Dynamic Adaptive Streaming over HTTP (DASH) video streaming has received considerable attention in recent years. While there are a number of objective QoE models, a limitation of the current models is that the QoE is provided after the entire video is delivered; also, the models are on a per client basis. For content service providers, QoE observed is important to monitor to understand ensemble performance during streaming such as for live events or concurrent streaming when multiple clients are streaming. For this purpose, we propose Moving QoE (MQoE, in short) models to measure QoE during periodically during video streaming for multiple simultaneous clients. Our first model MQoE_RF is a nonlinear model considering the bitrate gain and sensitivity from bitrate switching frequency. Our second model MQoE_SD is a linear model that focuses on capturing the standard deviation in the bitrate switching magnitude among segments along with the bitrate gain. We then study the effectiveness of both models in a multi-user mobile client environment, with the mobility patterns being based on traces from a train, a car, or a ferry. We implemented the study on the GENI testbed. Our study shows that our MQoE models are more accurate in capturing the QoE behavior during transmission than static QoE models. Furthermore, our MQoE_RF model captures the sensitivity due to bitrate switching frequency more effectively while MQoE_SD captures the sensitivity due to the magnitude of the bitrate switching. Either models are suitable for content service providers for monitoring video streaming based on their preference.
Segmentation Of Document Images Using Different Methods Like K-Mean Clustering And Fuzzy Clustering

In image processing, clustering or data grouping is the main initial process. In today's scenario, company data base size has increased significantly, with large amount of text and picture in these databases. As a picture, various pixels are gathered. The two pixels for grouping are hard to consider. The idea of image segmentation therefore plays a very significant influence in clustering, because it saves time and is also efficient. This paper explores the implementation in the field of image segmentation of standard k-mean and fuzzy k-mean clustering algorithms. Correct protocols have been introduced in order to evaluate and compare the two variants of the k-means and the fuzzy k-means. Exploratory outcomes show that fluorescent K-means algorithms that are logically optimized demonstrate helpfulness in the field of image analysis, providing equivalent and significantly progressive segmented results.
Extension of Mathematical Morphology in Riemannian Spaces

Mathematical morphology remains an efficient image analysis tool due to its morphological scale-spaces capability. It can be formulated using partial differential equations that are in fact a particular case of the first order Hamilton-Jacobi equations for which viscosity solutions exist and are given by Hopf-Lax-Oleinik (HLO) formulas. In this study, we propose an extension of HLO formulas in Riemannian manifolds by considering a general Cauchy problem, and prove the existence of a unique viscosity solution. Some properties are derived, and example on the hyperbolic ball is also provided.
Towards Efficient Distributed Subgraph Enumeration Via Backtracking-Based Framework

Finding or monitoring subgraph instances that are isomorphic to a given pattern graph in a data graph is a fundamental query operation in many graph analytic applications, such as network motif mining and fraud detection. Existing distributed methods are inefficient in communication. They have to shuffle partial matching results during the distributed multiway join. The partial matching results may be much larger than the data graph itself. To overcome the drawback, we develop the Batch-BENU framework for distributed subgraph enumeration on static data graphs. Batch-BENU executes a group of local search tasks in parallel. Each task enumerates subgraphs around a vertex in the data graph, guided by a backtracking-based execution plan. To handle large-scale data graphs that may exceed the memory capacity of a single machine, Batch-BENU stores the data graph in a distributed database. Each task queries adjacency sets of the data graph on demand, shuffling the data graph instead of partial matching results. To support incremental subgraph enumeration on dynamic data graphs, we propose the Streaming-BENU framework. Streaming-BENU turns the problem of enumerating incremental matching results into enumerating all matching results of incremental pattern graphs at each time step. We implement Batch-BENU and Streaming-BENU with the local database cache and the load balance optimization to improve their efficiency. Extensive experiments show that Batch-BENU and Streaming-BENU can scale to big graphs and complex pattern graphs. They outperform the state-of-the-art distributed methods by up to one and two orders of magnitude, respectively.
Considering the geological significance in data preprocessing and improving the prediction accuracy of hot springs by deep learning

Abstract The geothermal gradient in the eastern area of Liaoning Province is very low, but hot springs resources are variable. The reason is not clear till now but leads to the fact that a few strong influence factors can cause imbalances in the results of many prediction algorithms. It can be found as a black-box algorithm, deep learning will obtain a more unbalanced result with the fault influence factors. To tackle this issue, the role of preprocessing during the process of profound learning was enhanced and four comparative experiments were carried out. The results show that compared with the unprocessed experiment, the accuracy rate of the experiment with fully processed data increased by 11.9 p.p., and the area under the curve increased by 0.086 (0.796–0.882). This inspires us that even though the deep learning method can achieve high accuracy in the prediction of geological resources, we still need to pay attention to the analysis and pretreatment of data with expertise according to local conditions.
Leveraging State Sparsity for More Efficient Quantum Simulations

High-performance techniques to simulate quantum programs on classical hardware rely on exponentially large vectors to represent quantum states. When simulating quantum algorithms, the quantum states that occur are often sparse due to special structure in the algorithm or even in the underlying problem. We thus introduce a new simulation method that exploits this sparsity to reduce memory usage and simulation runtime. Moreover, our prototype implementation includes optimizations such as gate (re)scheduling, which amortizes data structure accesses and reduces memory usage. To benchmark our implementation, we run quantum algorithms for factoring, for computing integer and elliptic curve discrete logarithms, and for chemistry. Our simulator successfully runs a factoring instance of a 20-bit number using 102 qubits, and elliptic curve discrete logarithm over a 10-bit curve with 110 qubits. While previous work needed a supercomputer to simulate such instances of factoring, our approach succeeds in less than four minutes using a single core and less than 100 MB of memory. To the best of our knowledge, we are the first to fully simulate a quantum algorithm to compute elliptic curve discrete logarithms.
Adversarial sample detection framework based on autoencoder

Despite the great success of deep neural networks (DNN) in many tasks, they are often fooled by examples of confrontation created by adding small and purposeful distortions to natural examples. Previous research has mainly focused on improving DNN models, but either results are limited or expensive calculations are required. This paper studies an integrated feature noise reduction method: by using Gaussian filtering, mean filtering, and median filtering, the automatic encoder integrates a neural network to prevent the generation of adaptive adversarial samples. By comparing the reconstruction error of the autoencoder to detect adversarial samples, these simple strategies are not only low-cost, but also complementary to other defensive measures. In this paper, a new autoencoder is created as a modifier to make the two combine to form an adversarial The joint detection framework of the sample to achieve a high detection rate for the latest attacks. For several methods with high attack success rates, FGSM, BIM, PGD and CW attacks. For larger disturbances, the black box attacks against the MNIST data set the undetected rate of CW non-target attacks is 23%, and the detection rate of other attacks is 100%. The undetected rate of CIFAR-10 black box attacks except CW non-target attacks is 25%, and the undetected rate of other attacks is below 5%. In the case of black box attacks with small disturbances, the classification accuracy of the MNIST protected network has reached more than 90%, and the classification accuracy of the CIFAR-10 protected network has reached more than 80% in addition to the CW attack classification. The accuracy rate has also reached a high level.
Large deviations in the perceptron model and consequences for active learning

Abstract Active learning (AL) is a branch of machine learning that deals with problems where unlabeled data is abundant yet obtaining labels is expensive. The learning algorithm has the possibility of querying a limited number of samples to obtain the corresponding labels, subsequently used for supervised learning. In this work, we consider the task of choosing the subset of samples to be labeled from a fixed finite pool of samples. We assume the pool of samples to be a random matrix and the ground truth labels to be generated by a single-layer teacher random neural network. We employ replica methods to analyze the large deviations for the accuracy achieved after supervised learning on a subset of the original pool. These large deviations then provide optimal achievable performance boundaries for any AL algorithm. We show that the optimal learning performance can be efficiently approached by simple message-passing AL algorithms. We also provide a comparison with the performance of some other popular active learning strategies.
Imbalanced dataset classification algorithm based on NDSVM

Abstract Because of uneven distribution and indistinct boundary in imbalanced dataset, imbalanced dataset classification algorithm based on neighbors density support vector machine (NDSVM)is proposed. In this algorithm, the neighbor range density of each sample in the majority class is calculated firstly. According to the density value, the data which on the majority class border or close to the border is equal to the minority samples in quantity, which are selected, then the minority class complete SVM initial classification. Then the resulting support vector machine and residual data in the majority class optimize the initial classifier. The simulation results of experiments on the manual and UCI dataset show that compared with WSVM、 ALSMOTE-SVM and SVM, NDSVM has better classification performance, which effectively improve the classification performance of SVM algorithm on the uneven distribution and indistinct boundary in imbalanced dataset.
Global and local feature alignment for video object detection

Extending image-based object detectors into video domain suffers from immense inadaptability due to the deteriorated frames caused by motion blur, partial occlusion or strange poses. Therefore, the generated features of deteriorated frames encounter the poor quality of misalignment, which degrades the overall performance of video object detectors. How to capture valuable information locally or globally is of importance to feature alignment but remains quite challenging. In this paper, we propose a Global and Local Feature Alignment (abbreviated as GLFA) module for video object detection, which can distill both global and local information to excavate the deep relationship between features for feature alignment. Specifically, GLFA can model the spatial-temporal dependencies over frames based on propagating global information and capture the interactive correspondences within the same frame based on aggregating valuable local information. Moreover, we further introduce a Self-Adaptive Calibration (SAC) module to strengthen the semantic representation of features and distill valuable local information in a dual local-alignment manner. Experimental results on the ImageNet VID dataset show that the proposed method achieves high performance as well as a good trade-off between real-time speed and competitive accuracy.
Reinforcement Learning Based Smart Data Agent for Location Privacy

A "privacy by design" approach is presented for location management on a smartphone that is based on the concept of "smart data". Extensive use of location-based services has given rise to various privacy concerns such as processing and selling of personal information. The proposed work addresses these concerns by developing an intelligent agent that controls the release of the user's information in accordance with its preferences, context, nature of situation. The proposed model uses reinforcement learning to provide an on-the-go smart data agent that learns the user's privacy policy in a manner that is interactive and adaptive, enabling it to adjust itself to changes in user preferences over time. The agent aimed at mimicking the user, deciding when and where exact location information should be revealed and evolving over time to become the user's single trusted virtual proxy in cyberspace, managing its interactions with various applications in different environments.
Language models for some extensions of the Lambek calculus

We investigate language interpretations of two extensions of the Lambek calculus: with additive conjunction and disjunction and with additive conjunction and the unit constant. For extensions with additive connectives, we show that conjunction and disjunction behave differently. Adding both of them leads to incompleteness due to the distributivity law. We show that with conjunction only no issues with distributivity arise. In contrast, there exists a corollary of the distributivity law in the language with disjunction only which is not derivable in the non-distributive system. Moreover, this difference keeps valid for systems with permutation and/or weakening structural rules, that is, intuitionistic linear and affine logics and affine multiplicative-additive Lambek calculus. For the extension of the Lambek calculus with the unit constant, we present a calculus which reflects natural algebraic properties of the empty word. We do not claim completeness for this calculus, but we prove undecidability for the whole range of systems extending this minimal calculus and sound w.r.t. language models. As a corollary, we show that in the language with the unit there exists a sequent that is true if all variables are interpreted by regular language, but not true in language models in general.
Soft Label and Discriminant Embedding Estimation for Semi-Supervised Classification

In recent times, graph-based semi-supervised learning proved to be a powerful paradigm for processing and mining large datasets. The main advantage relies on the fact that these methods can be useful in propagating a small set of known labels to a large set of unlabeled data. The scarcity of labeled data may affect the performance of the semi-learning. This paper introduces a new semi-supervised framework for simultaneous linear feature extraction and label propagation. The proposed method simultaneously estimates a discriminant transformation and the unknown label by exploiting both labeled and unlabeled data. In addition, the unknowns of the learning model are estimated by integrating two types of graph-based smoothness constraints. The resulting semi-supervised model is expected to learn more discriminative information. Experiments are conducted on six public image datasets. These experimental results show that the performance of the proposed method can be better than that of many state-of-the-art graph-based semi-supervised algorithms.
Activity and Relationship Modeling Driven Weakly Supervised Object Detection

This paper presents a weakly supervised object detection method based on activity label and relationship modeling, which is motivated by the assumption that configuration of human and object are similar in same activity, and joint modeling of human, active object and activity could leverage the recognition of them. Compared to most weakly supervised method taking object as independent instance, firstly, active human and object proposals are learned and filtered based on class activation map of multi-label classification. Secondly, a spatial relationship prior including relative position, scale, overlaps etc are learned dependent on action. Finally, a multi-stream object detection framework integrating the spatial prior and pairwise ROI pooling are proposed to jointly learn the object and action class. Experiments are conducted on HICO-DET dataset, and our approach outperforms the state of the art weakly supervised object detection methods.
Modified Fully Homomorphic Encryption based on Parallel Processing in Cloud Computing

Cloud Computing is widely regarded as the most radically altering trend in information technology. However, great benefits come with great challenges, especially in the area of data security and privacy protection. Since standard cloud computing uses plaintext, certain encryption algorithms were implemented in the cloud for security reasons, and ‘encrypted' data was then stored in the cloud. Homomorphic Encryption (HE), a modern kind of encryption strategy, is born as a result of this change. Primarily, the paper will focus on implementing a successful Homomorphic Encryption (HE) scheme for polynomials. Furthermore, the objective of the paper is to propose, produce and implement a method to convert the already implemented sequentially processing Homomorphic Encryption into parallel processing Homomorphic Encryption (HE) using a Parallel Processing concept (Partitioning, Assigning, Scheduling, etc) and thereby producing a better performing Homomorphic Encryption (HE) called Fully Homomorphic Encryption (FHE). Fully Homomorphic Encryption (FHE) is an encryption technique that can perform specific analytical operations, functions and methods on normal or encrypted data and can still perform traditional encryption results as performed on plaintext. The three major reasons for implementing Fully Homomorphic Encryption (FHE) are advantages like no involvement of third parties, trade-off elimination between privacy and security and quantum safety.
DC-AC: Deep Correlation-Based Adaptive Compression of Feature Map Planes in Convolutional Neural Networks

Deep learning has been successfully deployed to a broad range of applications with its outstanding performance. Supporting an efficient hardware architecture is critical to making effective use of a deep learning approach with proven algorithm performance. One challenge in implementation of deep learning algorithm is to reduce memory bandwidth because a single memory access normally consumes 100* more energy than an arithmetic operation. To reduce the memory bandwidth, deep learning data could be compressed and decompressed before memory write/read operations. Especially, feature maps, which account for a significant portion of the convolutional neural network (CNN), could be compressed further by reducing the correlations between feature map planes. This paper proposes a compression method for feature maps in CNN that adaptively exploits the varying correlation between feature map planes. For every feature map plane, the proposed method searches the most similar plane among nearby planes in the same layer, and compresses the residual of the two planes instead of the plane itself. Experimental results show that the average bit length to store feature maps is reduced by 14.2% compared to the compression without correlation reduction, and the CNN accuracy does not change and additional training is also not required because the proposed method applies lossless compression.
<scp>Text2Plot</scp>: Sentiment Analysis by Creating <scp>2D</scp> Plot Representations of Texts

Abstract We introduce a novel approach for generating 2D RGB color images with a plot from the micro text (tweet) to be used for the overall polarity classification process of sentiment analysis. Researchers generally use word embedding and external resource‐based embedding techniques for text preprocessing of sentiment analysis through machine learning, neural networks, and natural language processing approaches. We sought to identify alternative ways to represent tweets for text classification. According to the experimental results, using the new ‘Text2Plot’ representation method could increase F1 scores by 27.2% for Convolutional neural networks (CNNs), 10.3% for support vector machine, and 4.4% for random forest models compared to using simple vectors as features for sentiment analysis. Hence, we propose this new method as a useful text representation approach for sentiment analysis, natural language processing tasks, and image processing problems. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Augmenting Case Based Learning With Dynamic Language Models

This paper describes a novel supporting tool for the case-based learning (CBL). Recent advances in deep-learning based language models (LMs) have enabled highly dynamic interactivity in dialog services and story generation. We leverage the progress in modelling language to develop a technologically augmented CBL pedagogy which we analyze with a standardized assessment. Our assessment shows reasonable case interactivity, low rates of factual inaccuracy, and no inappropriate machine-sourced responses. We also compare our assessment results across the case categories of Ethics, Chemistry, Biology, and Medicine, but find no statistically significant differences. In summary, we develop a framework for analyzing the ability of LMs to augment CBL, apply this framework to the GPT-3 LM, and discuss some of the challenges and potential solutions to ensuring proper usage in the classroom environment.