Reviewerly/exHarmony huggingface dataset:
mNet2FPGA: A Design Flow for Mapping a Fixed-Point CNN to Zynq SoC FPGA

The convolutional neural networks (CNNs) are a computation and memory demanding class of deep neural networks. The field-programmable gate arrays (FPGAs) are often used to accelerate the networks deployed in embedded platforms due to the high computational complexity of CNNs. In most cases, the CNNs are trained with existing deep learning frameworks and then mapped to FPGAs with specialized toolflows. In this paper, we propose a CNN core architecture called mNet2FPGA that places a trained CNN on a SoC FPGA. The processing system (PS) is responsible for convolution and fully connected core configuration according to the list of prescheduled instructions. The programmable logic holds cores of convolution and fully connected layers. The hardware architecture is based on the advanced extensible interface (AXI) stream processing with simultaneous bidirectional transfers between RAM and the CNN core. The core was tested on a cost-optimized Z-7020 FPGA with 16-bit fixed-point VGG networks. The kernel binarization and merging with the batch normalization layer were applied to reduce the number of DSPs in the multi-channel convolutional core. The convolutional core processes eight input feature maps at once and generates eight output channels of the same size and composition at 50 MHz. The core of the fully connected (FC) layer works at 100 MHz with up to 4096 neurons per layer. In a current version of the CNN core, the size of the convolutional kernel is fixed to 3×3. The estimated average performance is 8.6 GOPS for VGG13 and near 8.4 GOPS for VGG16/19 networks.
Experience-Based Approach for Cognitive Vehicle Research

This study discusses an experience-based approach to cognitive vehicle research. The author recruited six subjects who have different individual driving experiences and skills. This paper made them drive their own cars on the same test course. This research collected the drivers' mental status and operations as the driving data using a simple brainwave sensor and the acceleration sensor of a smart phone. The authors analyzed the data and developed models using the cognitive qualitative analysis and modeling tool (QCAM) that the authors have developed. The authors experimentally verified how different the results of the analysis and modeling were. The experiment results identified a parameter that indicated the degree of experience and skill of each driver. Moreover, the models derived the rules that explain the experience of each driver. The experience-based approach enables an understanding of unconscious operations and situated cognition based on the drivers' experiences, and it also allows novice or senior drivers to record their driving experiences and watch them later to maintain their driving skills.
Automatic Modulation Classification in Time-Varying Channels Based on Deep Learning

Automatic modulation classification (AMC) is an important technology in military signal reconnaissance and civilian communications such as cognitive radios. Most of the existing works focused on the AMC in additional white Gaussian noise channels, but the AMC in time-varying wireless channels is more practical and challenging. In this article, we investigate the AMC in time-varying channels by using the deep learning method for high classification accuracy. Specifically, we take the modulation constellation diagram (CD) as the key feature and propose a slotted constellation diagram (slotted-CD) scheme in order to extract the feature of the time-evolution of the CD due to channel variation. We then develop an advanced neural network for modulation classification, where the output sub-images from the slotted-CD feature extractor are first processed separately by a number of parallel convolutional neural networks and then further processed by a recurrent neural network for exploring their time relationship. Experimental results show that the proposed AMC scheme achieves higher classification accuracy in both slow and fast fading channels when compared with the traditional deep learning based AMC schemes. Such performance improvement can be clearly illustrated by visualizing the outputs of the convolutional layers of the classifier. We also show that visualization can help optimize the parameters of the AMC neural networks.
QBSUM: A large-scale query-based document summarization dataset from real-world applications

Query-based document summarization aims to extract or generate a summary of a document which directly answers or is relevant to the search query. It is an important technique that can be beneficial to a variety of applications such as search engines, document-level machine reading comprehension, and chatbots. Currently, datasets designed for query-based summarization are short in numbers and existing datasets are also limited in both scale and quality. Moreover, to the best of our knowledge, there is no publicly available dataset for Chinese query-based document summarization. In this paper, we present QBSUM, a high-quality large-scale dataset consisting of 49,000+ data samples for the task of Chinese query-based document summarization. We also propose multiple unsupervised and supervised solutions to the task and demonstrate their high-speed inference and superior performance via both offline experiments and online A/B tests. The QBSUM dataset is released in order to facilitate future advancement of this research field.
A Robust Medical Images Watermarking Using FDCuT-DCT-SVD

One way to prevent image duplication is by applying watermarking techniques. In this work, the watermarking process is applied to medical images using the Fast Discrete Curvelet Transforms (FDCuT), Discrete Cosine Transform (DCT), and Singular Value Decomposition (SVD) methods. The medical image of the host is transformed using FDCuT so that three subbands are obtained. High Frequency (HF) subband selected for DCT and SVD applications. Meanwhile, SVD was also applied to the watermark image. The singular value on the host image is exchanged with the singular value on the watermark. Insertion of tears by exchanging singular values does not cause the quality of medical images to decrease significantly. The experimental results prove that the proposed FDCuT-DCT-SVD algorithm produces good imperceptibility. The proposed algorithm is also resistant to various types of attacks, including JPEG compression, noise enhancement attacks, filtering attacks, and other common attacks.
Practical I-Voting on Stellar Blockchain

In this paper, we propose a privacy-preserving i-voting system based on the public Stellar Blockchain network. We argue that the proposed system satisfies all requirements stated for a robust i-voting system including transparency, verifiability, and voter anonymity. The practical architecture of the system abstracts a voter from blockchain technology used underneath. To keep user privacy, we propose a privacy-first protocol that protects voter anonymity. Additionally, high throughput and low transaction fees allow handling large scale voting at low costs. As a result we built an open-source, cheap, and secure system for i-voting that uses public blockchain, where everyone can participate and verify the election process without the need to trust a central authority. The main contribution to the field is a method based on a blind signature used to construct reliable voting protocol. The proposed method fulfills all requirements defined for i-voting systems, which is challenging to achieve altogether.
Approximating Edit Distance Within Constant Factor in Truly Sub-quadratic Time

Edit distance is a measure of similarity of two strings based on the minimum number of character insertions, deletions, and substitutions required to transform one string into the other. The edit distance can be computed exactly using a dynamic programming algorithm that runs in quadratic time. Andoni, Krauthgamer, and Onak (2010) gave a nearly linear time algorithm that approximates edit distance within approximation factor poly(log n ). In this article, we provide an algorithm with running time Õ( n 2−2/7 ) that approximates the edit distance within a constant factor.
An Asymmetric Chaotic Competitive Swarm Optimization Algorithm for Feature Selection in High-Dimensional Data

This paper presents a method for feature selection in a high-dimensional classification context. The proposed method finds a candidate solution based on quality criteria using subset searching. In this study, the competitive swarm optimization (CSO) algorithm was implemented to solve feature selection problems in high-dimensional data. A new asymmetric chaotic function was proposed and used to generate the population and search for a CSO solution. Its histogram is right-skewed. The proposed method is named an asymmetric chaotic competitive swarm optimization algorithm (ACCSO). According to the asymmetrical property of the proposed chaotic map, ACCSO prefers zero than one. Therefore, the solution is very compact and can achieve high classification accuracy with a minimal feature subset for high-dimensional datasets. The proposed method was evaluated on 12 datasets, with dimensions ranging from 4 to 10,304. ACCSO was compared to the original CSO algorithm and other metaheuristic algorithms. Experimental results show that the proposed method can increase accuracy and it reduces the number of selected features. Compared to different optimization algorithms with other wrappers, the proposed method exhibits excellent performance.
No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension

The Natural Questions (NQ) benchmark set brings new challenges to Machine Reading Comprehension: the answers are not only at different levels of granularity (long and short), but also of richer types (including no-answer, yes/no, single-span and multi-span).In this paper, we target at this challenge and handle all answer types systematically.In particular, we propose a novel approach called Reflection Net which leverages a two-step training procedure to identify the no-answer and wrong-answer cases.Extensive experiments are conducted to verify the effectiveness of our approach.At the time of paper writing (May.20, 2020), our approach achieved the top 1 on both long and short answer leaderboard * , with F1 scores of 77.2 and 64.1, respectively.
Canonical forms for single-qutrit Clifford+<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e19" altimg="si5.gif"><mml:mi>T</mml:mi></mml:math> operators

We introduce canonical forms for single qutrit Clifford+T circuits and prove that every single-qutrit Clifford+T operator admits a unique such canonical form. We show that our canonical forms are T-optimal in the sense that among all the single-qutrit Clifford+T circuits implementing a given operator our canonical form uses the least number of T gates. Finally, we provide an algorithm which inputs the description of an operator (as a matrix or a circuit) and constructs the canonical form for this operator. The algorithm runs in time linear in the number of T gates. Our results provide a higher-dimensional generalization of prior work by Matsumoto and Amano who introduced similar canonical forms for single-qubit Clifford+T circuits.
Deep Weighted MaxSAT for Aspect-based Opinion Extraction

Though deep learning has achieved significant success in various NLP tasks, most deep learning models lack the capability of encoding explicit domain knowledge to model complex causal relationships among different types of variables.On the other hand, logic rules offer a compact expression to represent the causal relationships to guide the training process.Logic programs can be cast as a satisfiability problem which aims to find truth assignments to logic variables by maximizing the number of satisfiable clauses (MaxSAT).We adopt the MaxSAT semantics to model logic inference process and smoothly incorporate a weighted version of MaxSAT that connects deep neural networks and a graphical model in a joint framework.The joint model feeds deep learning outputs to a weighted MaxSAT layer to rectify the erroneous predictions and can be trained via end-to-end gradient descent.Our proposed model associates the benefits of highlevel feature learning, knowledge reasoning, and structured learning with observable performance gain for the task of aspect-based opinion extraction.
Learning from Unlabelled Data for Clinical Semantic Textual Similarity

Domain pretraining followed by task finetuning has become the standard paradigm for NLP tasks, but requires in-domain labelled data for task fine-tuning.To overcome this, we propose to utilise unlabelled domain data by assigning pseudo-labels from a general model.We evaluate the approach on two clinical STS datasets, and achieve r = 0.80 on N2C2-STS.Further investigation reveals that if the data distribution of unlabelled sentence pairs is closer to the test data, we can obtain better performance.By leveraging a large general-purpose STS dataset and small-scale in-domain training data, we obtain further improvements to r = 0.90, a new SOTA.
Hashtags, Emotions, and Comments: A Large-Scale Dataset to Understand Fine-Grained Social Emotions to Online Topics

This paper studies social emotions to online discussion topics. While most prior work focus on emotions from writers, we investigate readers’ responses and explore the public feelings to an online topic. A large-scale dataset is collected from Chinese microblog Sina Weibo with over 13 thousand trending topics, emotion votes in 24 fine-grained types from massive participants, and user comments to allow context understanding. In experiments, we examine baseline performance to predict a topic’s possible social emotions in a multilabel classification setting. The results show that a seq2seq model with user comment modeling performs the best, even surpassing human prediction. More analyses shed light on the effects of emotion types, topic description lengths, contexts from user comments, and the limited capacity of the existing models.
Reconstructing veriT Proofs in Isabelle/HOL

Automated theorem provers are now commonly used within interactive theorem provers to discharge an increasingly large number of proof obligations.To maintain the trustworthiness of a proof, the automatically found proof must be verified inside the proof assistant.We present here a reconstruction procedure in the proof assistant Isabelle/HOL for proofs generated by the satisfiability modulo theories solver veriT which is part of the smt tactic.We describe in detail the architecture of our improved reconstruction method and the challenges we faced in designing it.Our experiments show that the veriT-powered smt tactic is regularly suggested by Sledgehammer as the fastest method to automatically solve proof goals.
Impact of requirements volatility on software architecture: How do software teams keep up with ever‐changing requirements?

Abstract Requirements volatility is a major issue in software development, causing problems such as higher defect density, project delays, and cost overruns. Software architecture that guides the overall vision of software product is one of the areas that is greatly affected by requirements volatility. Since critical architecture decisions are made based on the requirements at hand, changes in requirements can result significant changes in architecture. With the wide adoption of agile software development, software architectures are designed to accommodate possible future changes. However, the changes has to be carefully managed as unnecessary and excessive changes can bring negative consequences. An exploratory case study was conducted to study the impact of requirements volatility on software architecture. Fifteen semistructured, thematic interviews were conducted in a European software company. The research revealed poor communication, information distortion, and external dependencies as the main factors that cause requirement volatility and inadequate architecture documentation, inability to trace design rationale, and increased complexity as the main implications of requirements volatility on software architecture. Insights from software teams' awareness of the requirement volatility, factors contribute to it, and possible ways to mitigate its implications will be utilized to improve the management of requirement volatility during software architecting process.
Not-so-adiabatic quantum computation for the shortest vector problem

This paper describes adiabatic-inspired algorithms to solve the approximate shortest vector problem. Numerical simulations varying algorithm sweep-time suggest the existence of a Goldilocks zone for maximizing likelihood of success.
Dual-pump approach to photon-pair generation: demonstration of enhanced characterization and engineering capabilities

We experimentally study the generation of photon pairs via spontaneous four-wave mixing with two distinct laser pulses.We find that the dual-pump technique enables new capabilities: 1) a new characterization methodology to measure noise contributions, source brightness and photoncollection efficiencies directly from raw photon-count measurements; 2) an enhanced ability to generate heralded single photons in a pure quantum state; and 3) the ability to derive upper and lower bounds on heralded-photon quantum state purity from measurements of photon-number statistics even in the presence of noise.Such features are highly valuable in photon-pair sources for quantum applications.
Topological dynamical decoupling

We show that topological equivalence classes of circles in a two-dimensional square lattice can be used to design dynamical decoupling procedures to protect qubits attached on the edges of the lattice. Based on the circles of the topologically trivial class in the original and the dual lattices, we devise a procedure which removes all kinds of local Hamiltonians from the dynamics of the qubits while keeping information stored in the homological degrees of freedom unchanged. If only the linearly independent interaction and nearest-neighbor two-qubit interactions are concerned, a much simpler procedure which involves the four equivalence classes of circles can be designed. This procedure is compatible with Eulerian and concatenated dynamical decouplings, which make it possible to implement the procedure with bounded-strength controls and for a long time period. As an application, it is shown that our method can be directly generalized to finite square lattices to suppress uncorrectable errors in surface codes.
A Spectral Method for Unsupervised Multi-Document Summarization

Multi-document summarization (MDS) aims at producing a good-quality summary for several related documents.In this paper, we propose a spectral-based hypothesis, which states that the goodness of summary candidate is closely linked to its so-called spectral impact.Here spectral impact considers the perturbation to the dominant eigenvalue of affinity matrix when dropping the summary candidate from the document cluster.The hypothesis is validated by three theoretical perspectives: semantic scaling, propagation dynamics and matrix perturbation.According to the hypothesis, we formulate the MDS task as the combinatorial optimization of spectral impact and propose an accelerated greedy solution based on a surrogate of spectral impact.The evaluation results on various datasets demonstrate:(1) The performance of the summary candidate is positively correlated with its spectral impact, which accords with our hypothesis; (2) Our spectral-based method has a competitive result as compared to state-of-the-art MDS systems.
Quantum approximate Bayesian computation for NMR model inference

Recent technological advances may lead to the development of small-scale quantum computers that are capable of solving problems that cannot be tackled with classical computers. A limited number of algorithms have been proposed and their relevance to real-world problems is a subject of active investigation. Analysis of many-body quantum systems is particularly challenging for classical computers due to the exponential scaling of the Hilbert space dimension with the number of particles. Hence, solving the problems relevant to chemistry and condensed-matter physics is expected to be the first successful application of quantum computers. In this Article, we propose another class of problems from the quantum realm that can be solved efficiently on quantum computers: model inference for nuclear magnetic resonance (NMR) spectroscopy, which is important for biological and medical research. Our results are based on three interconnected studies. First, we use methods from classical machine learning to analyse a dataset of NMR spectra of small molecules. We perform stochastic neighbourhood embedding and identify clusters of spectra, and demonstrate that these clusters are correlated with the covalent structure of the molecules. Second, we propose a simple and efficient method, aided by a quantum simulator, to extract the NMR spectrum of any hypothetical molecule described by a parametric Heisenberg model. Third, we propose a simple variational Bayesian inference procedure for estimating the Hamiltonian parameters of experimentally relevant NMR spectra. Currently available quantum hardware is limited by noise, so practical implementations often involve a combination with classical approaches. Sels et al. identify a promising application for such a quantum–classic hybrid approach, namely inferring molecular structure from NMR spectra, by employing a range of machine learning tools in combination with a quantum simulator.
A computer vision approach to study surface deformation of materials

Characterization of the deformation of materials across different length scales has continuously attracted enormous attention from the mechanics and materials communities. In this study, the possibility of utilizing a computer vision algorithm to extract deformation information of materials has been explored, which greatly expands the use of computer vision approaches to studying mechanics of materials and potentially opens new dialogues between the two communities. The computer vision algorithm is first developed and tested on computationally deformed images, before evaluating experimentally collected images on speckle painted samples before and after deformation. Moreover, a virtual experiment has also shown the feasibility of mapping surface strain of a sample based on its natural pattern with significantly improved accuracy compared to DIC result, which provides new opportunities in experimentation and computer algorithms to study deformation mechanics of materials. Validation experiments include evaluating the performance of strain mapping using the computer vision approach in the uniaxial tensile test and three-point bending test, compared with extensometer reading and digital image correlation respectively.
To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging

Leveraging large amounts of unlabeled data using Transformer-like architectures, like BERT, has gained popularity in recent times owing to their effectiveness in learning general representations that can then be further fine-tuned for downstream tasks to much success.However, training these models can be costly both from an economic and environmental standpoint.In this work, we investigate how to effectively use unlabeled data: by exploring the task-specific semi-supervised approach, Cross-View Training (CVT) and comparing it with task-agnostic BERT in multiple settings that include domain and task relevant English data.CVT uses a much lighter model architecture and we show that it achieves similar performance to BERT on a set of sequence tagging tasks, with lesser financial and environmental impact.