Reviewerly/exHarmony huggingface dataset:
Feedback search algorithm for multi-particle quantum walks over a ring based on permutation groups

In quantum computing science, much attention has been paid to how to construct quantum search algorithms better, moreover, searching for new search algorithms based on quantum walk is still attracting scholars' continuous in-depth research and exploration. In this paper, a multi-particle quantum walk search algorithm based on permutation group is proposed by considering many aspects, such as reducing time consumption and increasing the accuracy and controllability of algorithm search. Firstly, the permutation group can be regarded as a closed loop in space, and the permutation set is defined. The data set of data points is mapped to the defined permutation set by isomorphism mapping, which makes the element data points in the permutation set form a one-to-one correspondence. Secondly, according to the given initial state and coin operator, the target data search is carried out on the ring by using the quantum walk of multiple particles in the search space of the data point set and the permutation set. Finally, the target data is found according to the function &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$\varPhi(w)=1 $\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20211000_M3.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20211000_M3.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;, and the value is stored by the quantum state, which is used to form the feedback control of the search algorithm. At the same time, the direction of quantum walking on the ring is controlled by controlling the coin operator, thus increasing the operability and accuracy of the search. In this paper, the quantum walk of multiple particles is used to search, and the analysis shows that the particle number parameter &lt;i&gt;j&lt;/i&gt; is negatively correlated with the time complexity, but not negatively linear. The proposed quantum walk search algorithm conforms to the zero condition and the lower bound condition, and is not affected by the variable parameter &lt;i&gt;j&lt;/i&gt;. Through numerical analysis, it is found that the time complexity of the quantum walk search algorithm is equivalent to &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$O(\sqrt[3]{N})$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20211000_M4.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20211000_M4.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;, which improves the search efficiency compared with the Grover search algorithm.
Development of an Extractive Clinical Question Answering Dataset with Multi-Answer and Multi-Focus Questions

Background: Extractive question-answering (EQA) is a useful natural language processing (NLP) application for answering patient-specific questions by locating answers in their clinical notes. Realistic clinical EQA can have multiple answers to a single question and multiple focus points in one question, which are lacking in the existing datasets for development of artificial intelligence solutions. Objective: Create a dataset for developing and evaluating clinical EQA systems that can handle natural multi-answer and multi-focus questions. Methods: We leveraged the annotated relations from the 2018 National NLP Clinical Challenges (n2c2) corpus to generate an EQA dataset. Specifically, the 1-to-N, M-to-1, and M-to-N drug-reason relations were included to form the multi-answer and multi-focus QA entries, which represent more complex and natural challenges in addition to the basic one-drug-one-reason cases. A baseline solution was developed and tested on the dataset. Results: The derived RxWhyQA dataset contains 96,939 QA entries. Among the answerable questions, 25% require multiple answers, and 2% ask about multiple drugs within one question. There are frequent cues observed around the answers in the text, and 90% of the drug and reason terms occur within the same or an adjacent sentence. The baseline EQA solution achieved a best f1-measure of 0.72 on the entire dataset, and on specific subsets, it was: 0.93 on the unanswerable questions, 0.48 on single-drug questions versus 0.60 on multi-drug questions, 0.54 on the single-answer questions versus 0.43 on multi-answer questions. Discussion: The RxWhyQA dataset can be used to train and evaluate systems that need to handle multi-answer and multi-focus questions. Specifically, multi-answer EQA appears to be challenging and therefore warrants more investment in research.
Playing cards and bidding calls detection for automatic registration of a duplicate bridge game

In this work, the implementation of a playing cards and bidding calls detection system for the automatic registration of a duplicate bridge game is presented. For this purpose, two YOLOv4 deep convolutional neural networks were used. During the training process, which was carried out using an automatically generated dataset, we obtained a detector characterised by more than 99.9% accuracy on the test set. The prepared system has been evaluated on videos collected during bridge competitions and is characterised by high accuracy. The application has been implemented and evaluated on a CPU and an embedded GPU
Learning from many trajectories

We initiate a study of supervised learning from many independent sequences ("trajectories") of non-independent covariates, reflecting tasks in sequence modeling, control, and reinforcement learning. Conceptually, our multi-trajectory setup sits between two traditional settings in statistical learning theory: learning from independent examples and learning from a single auto-correlated sequence. Our conditions for efficient learning generalize the former setting--trajectories must be non-degenerate in ways that extend standard requirements for independent examples. Notably, we do not require that trajectories be ergodic, long, nor strictly stable. For linear least-squares regression, given $n$-dimensional examples produced by $m$ trajectories, each of length $T$, we observe a notable change in statistical efficiency as the number of trajectories increases from a few (namely $m \lesssim n$) to many (namely $m \gtrsim n$). Specifically, we establish that the worst-case error rate of this problem is $\Theta(n / m T)$ whenever $m \gtrsim n$. Meanwhile, when $m \lesssim n$, we establish a (sharp) lower bound of $\Omega(n^2 / m^2 T)$ on the worst-case error rate, realized by a simple, marginally unstable linear dynamical system. A key upshot is that, in domains where trajectories regularly reset, the error rate eventually behaves as if all of the examples were independent, drawn from their marginals. As a corollary of our analysis, we also improve guarantees for the linear system identification problem.
Polynomial-time quantum algorithm for solving the hidden subgroup problem

The hidden subgroup problem~(HSP) is one of the most important problems in quantum computation. Many problems for which quantum algorithm achieves exponential speedup over its classical counterparts can be reduced to the Abelian HSP. However, there is no efficient quantum algorithm for solving the non-Abelian HSP. We find that the HSP can be reduced to a nested structured search problem that is solved efficiently by using a quantum algorithm via multistep quantum computation. Then we solve the HSP and problems that can be reduced to both the Abelian and the non-Abelian HSP in polynomial time by using this algorithm.
A Fast Row-Stochastic Decentralized Method for Distributed Optimization Over Directed Graphs

In this paper, we introduce a fast row-stochastic decentralized algorithm, referred to as FRSD, to solve consensus optimization problems over directed communication graphs. The proposed algorithm only utilizes row-stochastic weights, leading to certain practical advantages in broadcast communication settings over those requiring column-stochastic weights. Under the assumption that each node-specific function is smooth and strongly convex, we show that the FRSD iterate sequence converges with a linear rate to the optimal consensus solution. In contrast to the existing methods for directed networks, FRSD enjoys linear convergence without employing a gradient tracking (GT) technique explicitly, rather it implements GT implicitly with the use of a novel momentum term, which leads to a significant reduction in communication and storage overhead for each node when FRSD is implemented for solving high-dimensional problems over small-to-medium scale networks. In the numerical tests, we compare FRSD with other state-of-the-art methods, which use row-stochastic and/or column-stochastic weights.
A Cost-effective 2-tier Security Paradigm to Safeguard Cloud Data with Faster Authentication: A Recent Study

Recent technological advancements have elevated cloud computing (CC) infrastructure to a significant level, with increasing research focus on cost-effective storage management. It lacks efficiency in fully preserving the integrity of user data attributes due to the potential distributed and pervasive storage facility. As a result, the collaborative sharing of user data creates a situation that opens up various types of security loopholes. In the cloud, consumer’s data are obtained to cloud service providers which can be trusted or untrusted. Various forms of security protocols have been observed in the past that attempted to solve this similar issue with cryptographic solutions but lacks sustainability and robustness from a computational standpoint. As a result, the study introduces a 2-tier framework that provides a higher level of access control as well as Virtual Machine (VM) storage security. The study basically optimizes the model's performance by speeding up the authentication process. The system's performance has been validated in relation to conventional encryption standards. The results show that the proposed solution outperforms existing security standards in terms of processing time, time to generate a secret key, and key size for encryption.
NILC-Metrix: assessing the complexity of written and spoken language in Brazilian Portuguese

This paper presents and makes publicly available the NILC-Metrix, a computational system comprising 200 metrics proposed in studies on discourse, psycholinguistics, cognitive and computational linguistics, to assess textual complexity in Brazilian Portuguese (BP). These metrics are relevant for descriptive analysis and the creation of computational models and can be used to extract information from various linguistic levels of written and spoken language. The metrics in NILC-Metrix were developed during the last 13 years, starting in 2008 with Coh-Metrix-Port, a tool developed within the scope of the PorSimples project. Coh-Metrix-Port adapted some metrics to BP from the Coh-Metrix tool that computes metrics related to cohesion and coherence of texts in English. After the end of PorSimples in 2010, new metrics were added to the initial 48 metrics of Coh-Metrix-Port. Given the large number of metrics, we present them following an organisation similar to the metrics of Coh-Metrix v3.0 to facilitate comparisons made with metrics in Portuguese and English. In this paper, we illustrate the potential of NILC-Metrix by presenting three applications: (i) a descriptive analysis of the differences between children's film subtitles and texts written for Elementary School I and II (Final Years); (ii) a new predictor of textual complexity for the corpus of original and simplified texts of the PorSimples project; (iii) a complexity prediction model for school grades, using transcripts of children's story narratives told by teenagers. For each application, we evaluate which groups of metrics are more discriminative, showing their contribution for each task.
ShinRL: A Library for Evaluating RL Algorithms from Theoretical and  Practical Perspectives

We present ShinRL, an open-source library specialized for the evaluation of reinforcement learning (RL) algorithms from both theoretical and practical perspectives. Existing RL libraries typically allow users to evaluate practical performances of deep RL algorithms through returns. Nevertheless, these libraries are not necessarily useful for analyzing if the algorithms perform as theoretically expected, such as if Q learning really achieves the optimal Q function. In contrast, ShinRL provides an RL environment interface that can compute metrics for delving into the behaviors of RL algorithms, such as the gap between learned and the optimal Q values and state visitation frequencies. In addition, we introduce a flexible solver interface for evaluating both theoretically justified algorithms (e.g., dynamic programming and tabular RL) and practically effective ones (i.e., deep RL, typically with some additional extensions and regularizations) in a consistent fashion. As a case study, we show that how combining these two features of ShinRL makes it easier to analyze the behavior of deep Q learning. Furthermore, we demonstrate that ShinRL can be used to empirically validate recent theoretical findings such as the effect of KL regularization for value iteration and for deep Q learning, and the robustness of entropy-regularized policies to adversarial rewards. The source code for ShinRL is available on GitHub: https://github.com/omron-sinicx/ShinRL.
Deep Latent-Variable Models for Text Generation

Text generation aims to produce human-like natural language output for down-stream tasks. It covers a wide range of applications like machine translation, document summarization, dialogue generation and so on. Recently deep neural network-based end-to-end architectures have been widely adopted. The end-to-end approach conflates all sub-modules, which used to be designed by complex handcrafted rules, into a holistic encode-decode architecture. Given enough training data, it is able to achieve state-of-the-art performance yet avoiding the need of language/domain-dependent knowledge. Nonetheless, deep learning models are known to be extremely data-hungry, and text generated from them usually suffer from low diversity, interpretability and controllability. As a result, it is difficult to trust the output from them in real-life applications. Deep latent-variable models, by specifying the probabilistic distribution over an intermediate latent process, provide a potential way of addressing these problems while maintaining the expressive power of deep neural networks. This dissertation presents how deep latent-variable models can improve over the standard encoder-decoder model for text generation.
Trap of Feature Diversity in the Learning of MLPs

In this paper, we discover a two-phase phenomenon in the learning of multi-layer perceptrons (MLPs). I.e., in the first phase, the training loss does not decrease significantly, but the similarity of features between different samples keeps increasing, which hurts the feature diversity. We explain such a two-phase phenomenon in terms of the learning dynamics of the MLP. Furthermore, we propose two normalization operations to eliminate the two-phase phenomenon, which avoids the decrease of the feature diversity and speeds up the training process.
SDPO: An Approach Towards Software Defect Prediction Using Ontology Driven Intelligence

AbstractSoftware defects arise from various modules and are of various categorical types. Complex software debugging process use up a lot of indispensable computational resources, hence there is an urgent need to optimize the defect detection process. Software defect Classification has been a widely researched topic since a long time, concentrating on predicting the type of software defect from recorded metrics at the time of defect generation. However, the models never concentrated on the sematic understanding of the defects. This chapter proposes a hybrid classifier, that tracks and collects defect metrics by a bug tracker tool, and uses an ontology of software defects to get a semantic understanding of the features of the defects. These features and metrics are passed into a deep learning model to classify and recommend software defect categories. The proposed model significantly improves accuracy of classification compared to notable software defect classification models.KeywordsSoftware defectsOntologySemantic similarityGRU modelSoftware defect classification
Latent Space based Memory Replay for Continual Learning in Artificial Neural Networks

Memory replay may be key to learning in biological brains, which manage to learn new tasks continually without catastrophically interfering with previous knowledge. On the other hand, artificial neural networks suffer from catastrophic forgetting and tend to only perform well on tasks that they were recently trained on. In this work we explore the application of latent space based memory replay for classification using artificial neural networks. We are able to preserve good performance in previous tasks by storing only a small percentage of the original data in a compressed latent space version.
Bayesian inference via sparse Hamiltonian flows

A Bayesian coreset is a small, weighted subset of data that replaces the full dataset during Bayesian inference, with the goal of reducing computational cost. Although past work has shown empirically that there often exists a coreset with low inferential error, efficiently constructing such a coreset remains a challenge. Current methods tend to be slow, require a secondary inference step after coreset construction, and do not provide bounds on the data marginal evidence. In this work, we introduce a new method -- sparse Hamiltonian flows -- that addresses all three of these challenges. The method involves first subsampling the data uniformly, and then optimizing a Hamiltonian flow parametrized by coreset weights and including periodic momentum quasi-refreshment steps. Theoretical results show that the method enables an exponential compression of the dataset in a representative model, and that the quasi-refreshment steps reduce the KL divergence to the target. Real and synthetic experiments demonstrate that sparse Hamiltonian flows provide accurate posterior approximations with significantly reduced runtime compared with competing dynamical-system-based inference methods.
Inf-CP: A Reliable Channel Pruning based on Channel Influence

One of the most effective methods of channel pruning is to trim on the basis of the importance of each neuron. However, measuring the importance of each neuron is an NP-hard problem. Previous works have proposed to trim by considering the statistics of a single layer or a plurality of successive layers of neurons. These works cannot eliminate the influence of different data on the model in the reconstruction error, and currently, there is no work to prove that the absolute values of the parameters can be directly used as the basis for judging the importance of the weights. A more reasonable approach is to eliminate the difference between batch data that accurately measures the weight of influence. In this paper, we propose to use ensemble learning to train a model for different batches of data and use the influence function (a classic technique from robust statistics) to learn the algorithm to track the model's prediction and return its training parameter gradient, so that we can determine the responsibility for each parameter, which we call "influence", in the prediction process. In addition, we theoretically prove that the back-propagation of the deep network is a first-order Taylor approximation of the influence function of the weights. We perform extensive experiments to prove that pruning based on the influence function using the idea of ensemble learning will be much more effective than just focusing on error reconstruction. Experiments on CIFAR shows that the influence pruning achieves the state-of-the-art result.
On solutions of the distributional Bellman equation

In distributional reinforcement learning not only expected returns but the complete return distributions of a policy are taken into account. The return distribution for a fixed policy is given as the solution of an associated distributional Bellman equation. In this note we consider general distributional Bellman equations and study existence and uniqueness of their solutions as well as tail properties of return distributions. We give necessary and sufficient conditions for existence and uniqueness of return distributions and identify cases of regular variation. We link distributional Bellman equations to multivariate affine distributional equations. We show that any solution of a distributional Bellman equation can be obtained as the vector of marginal laws of a solution to a multivariate affine distributional equation. This makes the general theory of such equations applicable to the distributional reinforcement learning setting.
Investigations of c-Differential Uniformity of Permutations with Carlitz Rank 3

The $c$-differential uniformity is recently proposed to reflect resistance against some variants of differential attack. Finding functions with low $c$-differential uniformity is attracting attention from many researchers. For even characteristic, it is known that permutations of low Carlitz rank have good cryptographic parameters, for example, low differential uniformity, high nonlinearity, etc. In this paper we show that permutations with low Carlitz rank have low $c$-differential uniformity. We also investigate $c$-differential uniformity of permutations with Carlitz rank 3 in detail.
Smart Surveillance Based on Video Summarization: A Comprehensive Review, Issues, and Challenges

In order to ensure the protection and safety of the public, intelligent video surveillance systems are used in a wide range of applications to monitor events and analyze activities. A huge data is being generated, processed, and stored for security reasons from a single or multi-monitoring camera. Owing to a time limit, an analyst is going through a laborious process of examining the most important incidents from vast video databases. As a result, Video Summarization is a smart solution to summarize the salient events by removing the redundant frames which facilitate fast searching, indexing, and quick sharing of the content automatically. This paper investigates existing video summarization Techniques and a comparison of various existing approaches and methods in the surveillance domain. It also presents insight into video summarization in traffic surveillance which can be used to address the ever-growing needs of Intelligent Transportation System (ITS). The present study will be useful in integrating essential research findings and data for quick reference, identifying existing work, and investigating possible directions for future research in video summarization for traffic surveillance.
On the Road to Online Adaptation for Semantic Image Segmentation

We propose a new problem formulation and a corresponding evaluation framework to advance research on unsupervised domain adaptation for semantic image segmentation. The overall goal is fostering the development of adaptive learning systems that will continuously learn, without supervision, in ever-changing environments. Typical protocols that study adaptation algorithms for segmentation models are limited to few domains, adaptation happens offline, and human intervention is generally required, at least to annotate data for hyper-parameter tuning. We argue that such constraints are incompatible with algorithms that can continuously adapt to different real-world situations. To address this, we propose a protocol where models need to learn online, from sequences of temporally correlated images, requiring continuous, frame-by-frame adaptation. We accompany this new protocol with a variety of baselines to tackle the proposed formulation, as well as an extensive analysis of their behaviors, which can serve as a starting point for future research.