Reviewerly/exHarmony huggingface dataset:
Quantum adiabatic algorithm design using reinforcement learning

Quantum algorithm design plays a crucial role in exploiting the computational advantage of quantum devices. Here we develop a deep-reinforcement-learning based approach for quantum adiabatic algorithm design. Our approach is generically applicable to a class of problems with solution hard-to-find but easy-to-verify, e.g., searching and NP-complete problems. We benchmark this approach in Grover-search and 3-SAT problems, and find that the adiabatic algorithm obtained by our RL approach leads to significant improvement in the resultant success probability. In application to Grover search, our RL design automatically produces an adiabatic quantum algorithm that has the quadratic speedup. We find for all our studied cases that quantitatively the RL-designed algorithm has a better performance compared to the analytically constructed nonlinear Hamiltonian path when the encoding Hamiltonian is solvable, and that this RL-design approach remains applicable even when the nonlinear Hamiltonian path is not analytically available. In 3-SAT we find RL design has fascinating transferability---the adiabatic algorithm obtained by training on a specific choice of clause number leads to better performance consistently over the linear algorithm on different clause numbers. These findings suggest the applicability of reinforcement learning for automated quantum adiabatic algorithm design. Further considering the established complexity equivalence of circuit and adiabatic quantum algorithms, we expect the RL-designed adiabatic algorithm to inspire novel circuit algorithms as well. Our approach is potentially applicable to different quantum hardware from trapped ions and optical lattices to superconducting-qubit devices.
SAL:Selection and Attention Losses for Weakly Supervised Semantic Segmentation

Training a fully supervised semantic segmentation network requires a large amount of expensive pixel-level annotations in manual labor. In this work, we focus on studying the semantic segmentation problem using only image-level supervision. An effective scheme for weakly supervised segmentation is employed to produce the proxy annotations via image tags firstly. Then the segmentation network is retrained on the generated noisy proxy annotations. However, learning from noisy annotations is risky, as proxy annotations of poor quality may deteriorate the performance of the baseline segmentation and classification networks. In order to train the segmentation network using noisy annotations more effectively, two novel loss functions are proposed in this paper, namely, the selection loss and attention loss. Firstly, a selection loss is designed by weighting the proxy annotations based on a coarse-to-fine strategy for evaluating the quality of segmentation masks. Secondly, an attention loss taking the clean image tags as supervision is utilized to correct the classification errors caused by ambiguous pixel-level labels. Finally, we propose an end-to-end semantic segmentation network SAL-Net guided by the above two losses. From the extensive experiments conducted on PASCAL VOC 2012 dataset, SAL-Net reaches state-of-the-art performance with mean IoU (mIoU) as 62.5% and 66.6% on the test set by taking VGG16 network and ResNet101 network as the baselines respectively, which demonstrates the superiority of the proposed algorithm over eight representative weakly supervised segmentation methods. The code and models are available at https://github.com/zmbhou/SALTMM.
SimpleMKKM: Simple Multiple Kernel K-Means

We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM). It extends the widely used supervised kernel alignment criterion to multi-kernel clustering. Our criterion is given by an intractable minimization-maximization problem in the kernel coefficient and clustering partition matrix. To optimize it, we equivalently rewrite the minimization-maximization formulation as a minimization of an optimal value function, prove its differenentiablity, and design a reduced gradient descent algorithm to decrease it. Furthermore, we prove that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the resultant solution of SimpleMKKM is the global optimum</i> . We theoretically analyze the performance of SimpleMKKM in terms of its clustering generalization error. After that, we develop extensive experiments to investigate the proposed SimpleMKKM from the perspectives of clustering accuracy, advantage on the formulation and optimization, variation of the learned consensus clustering matrix with iterations, clustering performance with varied number of samples and base kernels, analysis of the learned kernel weight, the running time and the global convergence. The experimental study demonstrates the effectiveness of the proposed SimpleMKKM by considerably and consistently outperforming state of the art multiple kernel clustering alternatives. In addition, the ablation study shows that the improved clustering performance is contributed by both the novel formulation and new optimization. Our work provides a more effective approach to integrate multi-view data for clustering, and this could trigger novel research on multiple kernel clustering. The source code and data for SimpleMKKM are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/xinwangliu/SimpleMKKMcodes/</uri> .
A Lexicon-Enhanced Attention Network for Aspect-Level Sentiment Analysis

Aspect-level sentiment classification is a fine-grained task in sentiment analysis. In recent years, researchers have realized the importance of the relationship between aspect term and sentence and many classification models based on deep learning network have been proposed. However, these end-to-end deep neural network models lack flexibility and do not consider the sentiment word information in existing methods. Therefore, we propose a lexicon-enhanced attention network (LEAN) based on bidirectional LSTM. LEAN not only can catch the sentiment words in a sentence but also concentrate on specific aspect information in a sentence. Moreover, leveraging lexicon information will enhance the model's flexibility and robustness. We experiment on the SemEval 2014 dataset and results find that our model achieves state-of-the-art performance on aspect-level sentiment classification.
The Case for a Learned Sorting Algorithm

Sorting is one of the most fundamental algorithms in Computer Science and a common operation in databases not just for sorting query results but also as part of joins (i.e., sort-merge-join) or indexing. In this work, we introduce a new type of distribution sort that leverages a learned model of the empirical CDF of the data. Our algorithm uses a model to efficiently get an approximation of the scaled empirical CDF for each record key and map it to the corresponding position in the output array. We then apply a deterministic sorting algorithm that works well on nearly-sorted arrays (e.g., Insertion Sort) to establish a totally sorted order. We compared this algorithm against common sorting approaches and measured its performance for up to 1 billion normally-distributed double-precision keys. The results show that our approach yields an average 3.38x performance improvement over C++ STL sort, which is an optimized Quicksort hybrid, 1.49x improvement over sequential Radix Sort, and 5.54x improvement over a C++ implementation of Timsort, which is the default sorting function for Java and Python.
Virtual and Augmented Reality Environments to Learn the Fundamentals of Crystallography

Nowadays, there are many situations in which information and communications technology (ICT) is used as a vehicle to transmit knowledge. The fast evolution of technology in recent decades has favored the development of virtual reality (VR) and augmented reality (AR) and with them the emergence of virtual laboratories (VLs) using VR or AR. Since such technologies can help students understand the atomic spatial distribution, crystallography is a discipline that has taken advantage of the use of VL in the teaching of crystal lattices, thus solving the usual educational problem of visualization in two- and three-dimensions. This paper presents a literature review that helps to identify the main features of VLs (based on VR or AR) that have been developed in the academic field to support the learning of crystallography concepts. Furthermore, this paper describes a VL developed by the authors where students can learn the main contents related to the 14 Bravais lattices (unit cells, directions, crystallographic planes, interstitial sites, etc.) by exploring the stands of a virtual museum. Such a VL uses non-immersive VR and has been designed based on the authors’ long-term research to achieve a high learning effectiveness educative platform.
Preserving Data Privacy in the Internet of Medical Things Using Dual Signature ECDSA

The disclosure of personal and private information is one of the main challenges of the Internet of Medical Things (IoMT). Most IoMT-based services, applications, and platforms follow a common architecture where wearables or other medical devices capture data that are forwarded to the cloud. In this scenario, edge computing brings new opportunities to enhance the operation of IoMT. However, despite the benefits, the inherent characteristics of edge computing require countermeasures to address the security and privacy issues that IoMT gives rise to. The restrictions of IoT devices in terms of battery, memory, hardware resources, or computing capabilities have led to a common agreement for the use of elliptic curve cryptography (ECC) with hardware or software implementations. As an example, the elliptic curve digital signature algorithm (ECDSA) is widely used by IoT devices to compute digital signatures. On the other hand, it is well known that dual signature has been an effective method to provide consumer privacy in classic e-commerce services. This article joins both approaches. It presents a novel solution to enhanced security and the preservation of data privacy in communications between IoMT devices and the cloud via edge computing devices. While data source anonymity is achieved from the cloud perspective, integrity and origin authentication of the collected data is also provided. In addition, computational requirements and complexity are kept to a minimum.
From Zero to Hero: Human-In-The-Loop Entity Linking in Low Resource Domains

Entity linking (EL) is concerned with disambiguating entity mentions in a text against knowledge bases (KB).It is crucial in a considerable number of fields like humanities, technical writing and biomedical sciences to enrich texts with semantics and discover more knowledge.The use of EL in such domains requires handling noisy texts, low resource settings and domain-specific KBs.Existing approaches are mostly inappropriate for this, as they depend on training data.However, in the above scenario, there exists hardly annotated data, and it needs to be created from scratch.We therefore present a novel domain-agnostic Human-In-The-Loop annotation approach: we use recommenders that suggest potential concepts and adaptive candidate ranking, thereby speeding up the overall annotation process and making it less tedious for users.We evaluate our ranking approach in a simulation on difficult texts and show that it greatly outperforms a strong baseline in ranking accuracy.In a user study, the annotation speed improves by 35 % compared to annotating without interactive support; users report that they strongly prefer our system.An open-source and ready-to-use implementation based on the text annotation platform INCEpTION 1 is made available 2 .
Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition

Interpretable rationales for model predictions play a critical role in practical applications.In this study, we develop models possessing interpretable inference process for structured prediction.Specifically, we present a method of instance-based learning that learns similarities between spans.At inference time, each span is assigned a class label based on its similar spans in the training set, where it is easy to understand how much each training instance contributes to the predictions.Through empirical analysis on named entity recognition, we demonstrate that our method enables to build models that have high interpretability without sacrificing performance.
Contrastive Self-Supervised Learning for Commonsense Reasoning

We propose a self-supervised method to solve Pronoun Disambiguation and Winograd Schema Challenge problems.Our approach exploits the characteristic structure of training corpora related to so-called "trigger" words, which are responsible for flipping the answer in pronoun disambiguation.We achieve such commonsense reasoning by constructing pairwise contrastive auxiliary predictions.To this end, we leverage a mutual exclusive loss regularized by a contrastive margin.Our architecture is based on the recently introduced transformer networks, BERT, that exhibits strong performance on many NLP benchmarks.Empirical results show that our method alleviates the limitation of current supervised approaches for commonsense reasoning.This study opens up avenues for exploiting inexpensive self-supervision to achieve performance gain in commonsense reasoning tasks.
Classification Models for Skin Tumor Detection Using Texture Analysis in Medical Images

Medical images have made a great contribution to early diagnosis. In this study, a new strategy is presented for analyzing medical images of skin with melanoma and nevus to model, classify and identify lesions on the skin. Machine learning applied to the data generated by first and second order statistics features, Gray Level Co-occurrence Matrix (GLCM), keypoints and color channel information—Red, Green, Blue and grayscale images of the skin were used to characterize decisive information for the classification of the images. This work proposes a strategy for the analysis of skin images, aiming to choose the best mathematical classifier model, for the identification of melanoma, with the objective of assisting the dermatologist in the identification of melanomas, especially towards an early diagnosis.
Interpretability and Analysis in Neural NLP

While deep learning has transformed the natural language processing (NLP) field and impacted the larger computational linguistics community, the rise of neural networks is stained by their opaque nature: It is challenging to interpret the inner workings of neural network models, and explicate their behavior.Therefore, in the last few years, an increasingly large body of work has been devoted to the analysis and interpretation of neural network models in NLP.This body of work is so far lacking a common framework and methodology.Moreover, approaching the analysis of modern neural networks can be difficult for newcomers to the field.This tutorial aims to fill this gap and introduce the nascent field of interpretability and analysis of neural networks in NLP.The tutorial will cover the main lines of analysis work, such as structural analyses using probing classifiers, behavioral studies and test suites, and interactive visualizations.We will highlight not only the most commonly applied analysis methods, but also the specific limitations and shortcomings of current approaches, in order to inform participants where to focus future efforts.
Digital Literacy in Formal Online Education: A Short Review

This study aims to explain the current findings of digital literacy in formal online education. The methods of research is a type of library descriptionÂ obtained from relevant scientific articles obtained from internet search media library engines: proquest, researchgate, google scholar and other search engines with keyword search "digital literacy" to find the publication of related scientific papers / relevant to the topic. Then the 41 articles found (listed in the references) are studied and then explained from the author's perspective. Digital literacy is multi-dimensional, built by multi construct hypotesis. In the previous research found a higher level of digital literacy that was positively related to the output of learning outcomes in the subjects studied. Online learnersÂ feel anxious because they do not understand and are accustomed to online classes, especially for those who have limited computer skills. Most importantly, independent students get the final grade of the program far better than non-independent students. The best predictor of academic success, measured by the final grades, namely components of academic skills, reading and writing abilities. Higher levels of digital literacy positively affected the high output of learning outcomes to student academic performance.
Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Electronic health records are used to extract patient's information instantly and remotely, which can help to keep track of patients' due dates for checkups, immunizations, and to monitor health performance.The Health Insurance Portability and Accountability Act (HIPAA) in the USA protects the patient data confidentiality, but it can be used if data is re-identified using 'HIPAA Safe Harbor' technique.Usually, this re-identification is performed manually, which is very laborious and time captivating exertion.Various techniques have been proposed for automatic extraction of useful information, and accurate diagnosis of diseases.Most of these methods are based on Machine Learning and Deep Learning Methods, while the auxiliary diagnosis is performed using Rule-based methods.This review focuses on recently published papers, which are categorized into Rule-Based Methods, Machine Learning (ML) Methods, and Deep Learning (DL) Methods.Particularly, ML methods are further categorized into Support Vector Machine Methods (SVM), Bayes Methods, and Decision Tree Methods (DT).DL methods are decomposed into Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Belief Network (DBN) and Autoencoders (AE) methods.The objective of this survey paper is to highlight both the strong and weak points of various proposed techniques in the disease diagnosis.Moreover, we present advantage, disadvantage, focused disease, dataset employed, and publication year of each category.
Modern Aspects of Cyber-Security Training and Continuous Adaptation of Programmes to Trainees

Nowadays, more-and-more cyber-security training is emerging as an essential process for the lifelong personnel education in organizations, especially for those which operate critical infrastructures. This is due to security breaches on popular services that become publicly known and raise people’s security awareness. Except from large organizations, small-to-medium enterprises and individuals need to keep their knowledge on the related topics up-to-date as a means to protect their business operation or to obtain professional skills. Therefore, the potential target-group may range from simple users, who require basic knowledge on the current threat landscape and how to operate the related defense mechanisms, to security experts, who require hands-on experience in responding to security incidents. This high diversity makes training and certification quite a challenging task. This study combines pedagogical practices and cyber-security modelling in an attempt to support dynamically adaptive training procedures. The training programme is initially tailored to the trainee’s needs, promoting the continuous adaptation to his/her performance afterwards. As the trainee accomplishes the basic evaluation tasks, the assessment starts involving more advanced features that demand a higher level of understanding. The overall method is integrated in a modern cyber-ranges platform, and a pilot training programme for smart shipping employees is presented.
TiVGAN: Text to Image to Video Generation With Step-by-Step Evolutionary Generator

Advances in technology have led to the development of methods that can create desired visual multimedia. In particular, image generation using deep learning has been extensively studied across diverse fields. In comparison, video generation, especially on conditional inputs, remains a challenging and less explored area. To narrow this gap, we aim to train our model to produce a video corresponding to a given text description. We propose a novel training framework, Text-to-Image-to-Video Generative Adversarial Network (TiVGAN), which evolves frame-by-frame and finally produces a full-length video. In the first phase, we focus on creating a high-quality single video frame while learning the relationship between the text and an image. As the steps proceed, our model is trained gradually on more number of consecutive frames. This step-by-step learning process helps stabilize the training and enables the creation of high-resolution video based on conditional text descriptions. Qualitative and quantitative experimental results on various datasets demonstrate the effectiveness of the proposed method.
Visual-LiDAR Based 3D Object Detection and Tracking for Embedded Systems

In recent years, persistent news updates on autonomous vehicles and the claims of companies entering the space, brace the notion that vehicular autonomy of level 5 is just around the corner. However, the main hindrance in asserting the full autonomy still boils down to environmental perception that affects the autonomous decisions. An efficient perceptual system requires redundancy in sensor modalities capable of performing in varying environmental conditions, and providing a reliable information using limited computational resources. This work addresses the task of 3D object detection and tracking in the vehicles' environment, using camera and 3D LiDAR as primary sensors. The proposed framework is designed to operate in an embedded system that visually classifies the objects using a lightweight neural network, while tracking is performed in 3D space using LiDAR information. The main contribution of this work is 3D LiDAR point cloud classification using visual object detector, and an IMM-UKF-JPDAF based object tracker that jointly performs 3D object detection and tracking. The performance evaluation is carried out using MOT16 metrics and ground truths provided by KITTI Datasets. Furthermore, the proposed tracker is evaluated and compared with state-of-the-art approaches. The experiments suggest that the proposed framework offers a suitable solution for embedded systems to solve 3D object detection and tracking problem, with added benefits.
Comparison of unfolding methods using RooFitUnfold

In this paper we describe RooFitUnfold, an extension of the RooFit statistical software package to treat unfolding problems, and which includes most of the unfolding methods that commonly used in particle physics. The package provides a common interface to these algorithms as well as common uniform methods to evaluate their performance in terms of bias, variance and coverage. In this paper we exploit this common interface of RooFitUnfold to compare the performance of unfolding with the Richardson–Lucy, Iterative Dynamically Stabilized, Tikhonov, Gaussian Process, bin-by-bin and inversion methods on several example problems.