Reviewerly/exHarmony huggingface dataset:
Measuring distance between quantum states on a quantum computer

We propose protocols for determining the distances in Hilbert space between pure and mixed quantum states prepared on a quantum computer. In the case of pure quantum states, the protocol is based on measuring the square of modulus of scalar product between certain states. Determination of the distance between mixed quantum states is reduced to measuring the squares of modules of scalar products between all pure states included in the mixed states. In addition, we develop a protocol that allows one to determine the speed of evolution of the spin system simulated by a quantum computer. These protocols we apply to measure distances and speeds of evolution of different quantum systems implemented on the ibmq-santiago quantum computer.
Learning Causal Bayesian Networks from Text

Causal relationships form the basis for reasoning and decision-making in Artificial Intelligence systems. To exploit the large volume of textual data available today, the automatic discovery of causal relationships from text has emerged as a significant challenge in recent years. Existing approaches in this realm are limited to the extraction of low-level relations among individual events. To overcome the limitations of the existing approaches, in this paper, we propose a method for automatic inference of causal relationships from human written language at conceptual level. To this end, we leverage the characteristics of hierarchy of concepts and linguistic variables created from text, and represent the extracted causal relationships in the form of a Causal Bayesian Network. Our experiments demonstrate superiority of our approach over the existing approaches in inferring complex causal reasoning from the text.
Multi-Object Tracking using Poisson Multi-Bernoulli Mixture Filtering for Autonomous Vehicles

The ability of an autonomous vehicle to perform 3D tracking is essential for safe planing and navigation in cluttered environments. The main challenges for multi-object tracking (MOT) in autonomous driving applications reside in the inherent uncertainties regarding the number of objects, when and where the objects may appear and disappear, and uncertainties regarding objects' states. Random finite set (RFS) based approaches can naturally model these uncertainties accurately and elegantly, and they have been widely used in radar-based tracking applications. In this work, we developed an RFS-based MOT framework for 3D LiDAR data. In partiuclar, we propose a Poisson multi-Bernoulli mixture (PMBM) filter to solve the amodal MOT problem for autonomous driving applications. To the best of our knowledge, this represents a first attempt for employing an RFS-based approach in conjunction with 3D LiDAR data for MOT applications with comprehensive validation using challenging datasets made available by industry leaders. The superior experimental results of our PMBM tracker on public Waymo and Argoverse datasets clearly illustrate that an RFS-based tracker outperforms many state-of-the-art deep learning-based and Kalman filter-based methods, and consequently, these results indicate a great potential for further exploration of RFS-based frameworks for 3D MOT applications.
Natural Deduction and Normalization Proofs for the Intersection Type Discipline

Refining and extending previous work by Retoré [8], we develop a systematic approach to intersection types via natural deduction.We show how a step of beta reduction can be seen as performing, at the level of typing derivations, Prawitz reductions in parallel.Then we derive as immediate consequences of Subject Reduction the main theorems about normalization for intersection types: for system D, strong normalization, for system DΩ, the leftmost reduction termination for terms typable without Ω.
Scene Text Recognition in the Wild with Motion Deblurring Using Deep Networks

In this paper, the problem of text detection and recognition in videos has been addressed. We address two major issues that make it difficult to extract information in a video captured by a moving vehicle. Video captured by a moving vehicle contains a lot of blurs caused by motion which is one of the major issues preventing accurate recognition of text. The second major issue is the orientation of the text being detected, which may not be in the same plane. We propose a novel end-to-end pipeline consisting of deep networks. Our pipeline consists of a fully convolution network to detect text, Generative Adversarial Network to remove motion blur, a rectification network which makes use of Thin Spline Transformations and a Spatial Transform network to handle text which is not straight i.e. perspective and curved, and a recognition network to recognize the text. We only deblur the region around text boxes instead of complete images. We also track the text boxes in each frame to avoid re-recognition of text in consecutive frames. This significantly improves the performance of the system, as proved by higher classification scores achieved as compared to state of the art.
Can Differential Privacy Practically Protect Collaborative Deep Learning Inference for the Internet of Things?

Collaborative inference has recently emerged as an attractive framework for applying deep learning to Internet of Things (IoT) applications by splitting a DNN model into several subpart models among resource-constrained IoT devices and the cloud. However, the reconstruction attack was proposed recently to recover the original input image from intermediate outputs that can be collected from local models in collaborative inference. For addressing such privacy issues, a promising technique is to adopt differential privacy so that the intermediate outputs are protected with a small accuracy loss. In this paper, we provide the first systematic study to reveal insights regarding the effectiveness of differential privacy for collaborative inference against the reconstruction attack. We specifically explore the privacy-accuracy trade-offs for three collaborative inference models with four datasets (SVHN, GTSRB, STL-10, and CIFAR-10). Our experimental analysis demonstrates that differential privacy can practically be applied to collaborative inference when a dataset has small intra-class variations in appearance. With the (empirically) optimized privacy budget parameter in our study, the differential privacy technique incurs accuracy loss of 0.476%, 2.066%, 5.021%, and 12.454% on SVHN, GTSRB, STL-10, and CIFAR-10 datasets, respectively, while thwarting the reconstruction attack.
A Novel Quantum Entanglement‐Inspired Meta‐heuristic Framework for Solving Multimodal Optimization Problems

To solve Multimodal optimization problems (MOPs), a Novel Quantum entanglement-inspired meta-heuristic framework (NMF-QE) is proposed. Its main inspirations are two concepts of quantum physics: quantum entanglement and quantum superposition. When given Proto-born particles (PBPs) of a population, these two concepts are mathematically developed to generate twin-born and combination-born particles, respectively. And if any elite-born particles would be created by a local re-searching strategy. These three or four groups of particles come together as a whole search population of NMF-QE to realize exploration and exploitation of algorithms. To guarantee dynamical optimization capability of NMF-QE, the individual evolutionary mechanism of some existing meta-heuristics will be adopted to iteratively create PBPs. A selected meta-heuristic is coupled with NMF-QE to present its improved variant. Numerical results show that the proposed NMF-QE can effectively improve optimization performance of meta-heuristics on MOPs.
한국과 일본의 초등학교 과학교과서 삽화 비교 분석

The purpose of this study was to compare and analyze the illustrations of elementary science textbooks in Korea and Japan, and to determine whether...
Efficiently computing runs on a trie

A maximal repetition, or run, in a string, is a maximal periodic substring whose smallest period is at most half the length of the substring. In this paper, we consider runs that correspond to a path on a trie, or in other words, on a rooted edge-labeled tree where the endpoints of the path must be a descendant/ancestor of the other. For a trie with $n$ edges, we show that the number of runs is less than $n$. We also show an asymptotic lower bound on the maximum density of runs in tries: $\lim_{n\rightarrow\infty}\rho_\mathcal{T}(n)/n \geq 0.993238$ where $\rho_{\mathcal{T}}(n)$ is the maximum number of runs in a trie with $n$ edges. Furthermore, we also show an $O(n\log \log n)$ time and $O(n)$ space algorithm for finding all runs.
Simpler is better: A comparative study of randomized algorithms for computing the CUR decomposition

The CUR decomposition is a technique for low-rank approximation that selects small subsets of the columns and rows of a given matrix to use as bases for its column and rowspaces. It has recently attracted much interest, as it has several advantages over traditional low rank decompositions based on orthonormal bases. These include the preservation of properties such as sparsity or non-negativity, the ability to interpret data, and reduced storage requirements. The problem of finding the skeleton sets that minimize the norm of the residual error is known to be NP-hard, but classical pivoting schemes such as column pivoted QR work tend to work well in practice. When combined with randomized dimension reduction techniques, classical pivoting based methods become particularly effective, and have proven capable of very rapidly computing approximate CUR decompositions of large, potentially sparse, matrices. Another class of popular algorithms for computing CUR de-compositions are based on drawing the columns and rows randomly from the full index sets, using specialized probability distributions based on leverage scores. Such sampling based techniques are particularly appealing for very large scale problems, and are well supported by theoretical performance guarantees. This manuscript provides a comparative study of the various randomized algorithms for computing CUR decompositions that have recently been proposed. Additionally, it proposes some modifications and simplifications to the existing algorithms that leads to faster execution times.
2015 개정 5~6학년군 초등 영어 교과서의 쓰기 활동 분석

The purpose of this study is to investigate the nature of writing activities represented in 5th and 6th grade English textbooks for the 2015 revised...
Named entity recognition based on semi-supervised ensemble learning with the improved tri-training algorithm

Named entity recognition is one of the hot topics in natural language processing. The purpose is to identify named entities in text and summarize them into corresponding entity types. The deep learning model has achieved good results for the task of named entity recognition. Due to the lack of annotated corpus data for named entity recognition tasks in specific domains, it is difficult for a single deep learning model to achieve excellent performance. To this end, by ensembling Conditional Random Field (CRF) model, Bidirectional Gated Recurrent Unit (BiGRU) network model, and Bidirectional Long Short-Term Memory (BiLSTM) network model, with collaborative training Tri -training algorithm, we proposed a named entity recognition method based on semi-supervised ensemble learning (NER-SSEL). It aims to improve model performance through iterative training with a small amount of labeled data and a large amount of unlabeled data. First, a small amount of labeled data is used for pre-training on the three base learners, and then a collaborative training tri-training algorithm is used to provide reliable labels for the unlabeled raw data. To avoid the introduction of noisy data, we propose a repeated labeling strategy to select high-confidence samples and iteratively expand the training set through the consistency evaluation function. Finally, the model is integrated through the weighted voting method. This article conducts experiments on the ATIS data set. Experimental results show that the model proposed in this paper can effectively use a large amount of unlabeled data and significantly improve the F1 measure in the task of named entity recognition.
Research on Centralized Matching Method of Teaching Knowledge Categories Based on Intelligent Language Recognition

In order to improve the accuracy and efficiency of teaching knowledge classification matching, a method of teaching knowledge classification matching based on intelligent language recognition is proposed. The deep learning network was constructed, the language files were pre-processed, and the extracted language feature vectors were used to train the network and optimise the deep learning network. The intelligent language recognition network model is established and the k-means clustering algorithm is used to acquire the model. Classification of teaching knowledge is processed centrally and the classification system of teaching knowledge is obtained. Matching problem of teaching knowledge classification system is modelled, and the corresponding undirected graph is constructed, which is converted into the matching problem with the greatest weight, and the optimal matching scheme under the category of teaching knowledge concentration is obtained. Experimental results show that the matching results based on intelligent language recognition are more accurate and more efficient.
Perseus: Characterizing Performance and Cost of Multi-Tenant Serving for CNN Models

Deep learning models are increasingly used for end-user applications, supporting both novel features such as facial recognition, and traditional features, e.g. web search. To accommodate high inference throughput, it is common to host a single pre-trained Convolutional Neural Network (CNN) in dedicated cloud-based servers with hardware accelerators such as Graphics Processing Units (GPUs). However, GPUs can be orders of magnitude more expensive than traditional Central Processing Unit (CPU) servers. These resources could also be under-utilized facing dynamic workloads, which may result in inflated serving costs. One potential way to alleviate this problem is by allowing hosted models to share the underlying resources, which we refer to as multi-tenant inference serving. One of the key challenges is maximizing the resource efficiency for multi-tenant serving given hardware with diverse characteristics, models with unique response time Service Level Agreement (SLA), and dynamic inference workloads. In this paper, we present Perseus, a measurement framework that provides the basis for understanding the performance and cost trade-offs of multi-tenant model serving. We implemented Perseus in Python atop a popular cloud inference server called Nvidia TensorRT Inference Server. Leveraging Perseus, we evaluated the inference throughput and cost for serving various models and demonstrated that multi-tenant model serving led to up to 12% cost reduction.
InferNet for Delayed Reinforcement Tasks: Addressing the Temporal Credit Assignment Problem

The temporal Credit Assignment Problem (CAP) is a well-known and challenging task in AI. While Reinforcement Learning (RL), especially Deep RL, works well when immediate rewards are available, it can fail when only delayed rewards are available or when the reward function is noisy. In this work, we propose delegating the CAP to a Neural Network-based algorithm named InferNet that explicitly learns to infer the immediate rewards from the delayed rewards. The effectiveness of InferNet was evaluated on two online RL tasks: a simple GridWorld and 40 Atari games; and two offline RL tasks: GridWorld and a real-life Sepsis treatment task. For all tasks, the effectiveness of using the InferNet inferred rewards is compared against the immediate and the delayed rewards with two settings: with noisy rewards and without noise. Overall, our results show that the effectiveness of InferNet is robust against noisy reward functions and is an effective add-on mechanism for solving temporal CAP in a wide range of RL tasks, from classic RL simulation environments to a real-world RL problem and for both online and offline learning.
Facial expression recognition in VGG network based on LBP feature extraction

LBP (Local binary mode) is a local spatial structure that describes an image. It has gray invariance and good robustness against background noise and visible light. In this paper, after image processing with LBP and VGG network, face facial expression recognition was studied. Experiments were carried out in CK+ data set, and the accuracy reached 96%. The experimental results show that this method is more advanced and effective than VGG network and traditional methods.
CCMN: A General Framework for Learning with Class-Conditional Multi-Label Noise

Class-conditional noise commonly exists in machine learning tasks, where the class label is corrupted with a probability depending on its ground-truth. Many research efforts have been made to improve the model robustness against the class-conditional noise. However, they typically focus on the single label case by assuming that only one label is corrupted. In real applications, an instance is usually associated with multiple labels, which could be corrupted simultaneously with their respective conditional probabilities. In this paper, we formalize this problem as a general framework of learning with Class-Conditional Multi-label Noise (CCMN for short). We establish two unbiased estimators with error bounds for solving the CCMN problems, and further prove that they are consistent with commonly used multi-label loss functions. Finally, a new method for partial multi-label learning is implemented with unbiased estimator under the CCMN framework. Empirical studies on multiple datasets and various evaluation metrics validate the effectiveness of the proposed method.
Research on Incentive Mechanism of UGC Community Based on Blockchain — Take Steemit for Example

With the development of the mobile Internet, the UGC (User Generated Content) community has played a very important role in people's daily lives. While the traditional UGC community stores content and information centrally, user data has security risks. In addition, it lacks an effective incentive mechanism, and the user viscosity is not very high. The use of blockchain technology can not only ensure the security of users' data, but the incentive mechanism contained therein can also well empower existing communities. This paper takes the blockchain-based community Steemit as an example, and focuses on the incentive mechanism. Moreover, we propose some improvement suggestions for the shortcomings in Steemit. We hope the UGC community based on blockchain can develop better in the future.
Trajectory planning for autonomous vehicles based on improved Hybrid A*

Trajectory planning plays an important role in autonomous driving technology. This paper presents an improved Hybrid A* algorithm by considering vehicle dynamics in planning. First, a vehicle dynamic model with three degrees of freedom and a nonlinear UniTire tyre model are developed to describe the vehicle dynamics. Then, in order to generate curvature-continuous trajectory, the method of expanding nodes based on the rotation speed of steering wheel is introduced. Then, heuristic functions are designed for trajectory searching. In addition, the feasibility of the planning result is verified in the driver-vehicle-road closed loop system. Simulation results show the proposed trajectory planning method performs well under a variety of working conditions and the planned trajectory is trackable even at extreme conditions, e.g., high speed and low-adhesion road surfaces. Specifically, on the icy road, the planned results are good and the maximum lateral tracking error of this trajectory is limited to 0.24 m.
An Adaptive and Dynamic Methodology for Detecting Phishing Email Based on Artificial Intelligence

Phishing is an exploit method that is used to steal information from a targeted individual or an organization by impersonating legitimate sites. This paper can detect such targets and warn the users. Classified mails are used to train the classifier for classifying the email and the others are used for testing the derived classifier model. The feature vector of the training set is trained by extracting and breaking down elements from the mail. Training is done so as to obtain required classifier model which is then used to classify the mail into an phishing email or not. The main objective of this project is detecting phishing through email address separation. This phishing detection method is found to be effective against various targeted phishing attacks. The proposed system is able to check and verify the phishing emails that usually target in large numbers by impersonation of a particular organization so as to be able to gain access to their resources by flagging them as phishing mail or not.
Embracing Ambiguity: Shifting the Training Target of NLI Models

Natural Language Inference (NLI) datasets contain examples with highly ambiguous labels. While many research works do not pay much attention to this fact, several recent efforts have been made to acknowledge and embrace the existence of ambiguity, such as UNLI and ChaosNLI. In this paper, we explore the option of training directly on the estimated label distribution of the annotators in the NLI task, using a learning loss based on this ambiguity distribution instead of the gold-labels. We prepare AmbiNLI, a trial dataset obtained from readily available sources, and show it is possible to reduce ChaosNLI divergence scores when finetuning on this data, a promising first step towards learning how to capture linguistic ambiguity. Additionally, we show that training on the same amount of data but targeting the ambiguity distribution instead of gold-labels can result in models that achieve higher performance and learn better representations for downstream tasks.
PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning

We propose a new framework of synthesizing data using deep generative models in a differentially private manner. Within our framework, sensitive data are sanitized with rigorous privacy guarantees in a one-shot fashion, such that training deep generative models is possible without re-using the original data. Hence, no extra privacy costs or model constraints are incurred, in contrast to popular approaches such as Differentially Private Stochastic Gradient Descent (DP-SGD), which, among other issues, causes degradation in privacy guarantees as the training iteration increases. We demonstrate a realization of our framework by making use of the characteristic function and an adversarial re-weighting objective, which are of independent interest as well. Our proposal has theoretical guarantees of performance, and empirical evaluations on multiple datasets show that our approach outperforms other methods at reasonable levels of privacy.
Consumer Image Quality Prediction using Recurrent Neural Networks for Spatial Pooling

Promising results for subjective image quality prediction have been achieved during the past few years by using convolutional neural networks (CNN). However, the use of CNNs for high resolution image quality assessment remains a challenge, since typical CNN architectures have been designed for small resolution input images. In this study, we propose an image quality model that attempts to mimic the attention mechanism of human visual system (HVS) by using a recurrent neural network (RNN) for spatial pooling of the features extracted from different spatial areas (patches) by a deep CNN-based feature extractor. The experimental study, conducted by using images with different resolutions from two recently published image quality datasets, indicates that the quality prediction accuracy of the proposed method is competitive against benchmark models representing the state-of-the-art, and the proposed method also performs consistently on different resolution versions of the same dataset.