Reviewerly/exHarmony huggingface dataset:
Generalized overlap quantum state tomography1

We propose and experimentally demonstrate a quantum state tomography protocol that generalizes and improves upon the Wallentowitz-Vogel-Banaszek-Wódkiewicz point-by-point Wigner function reconstruction. The full density operator of an arbitrary quantum state is efficiently reconstructed in the Fock basis, using semidefinite programming, after interference with a small set of calibrated coherent states. This protocol is resource- and computationally efficient, is robust against noise, does not rely on approximate state displacements, and ensures the physicality of results.Received 12 May 2020Revised 2 September 2020Accepted 14 September 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.042002Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum information processing with continuous variablesQuantum opticsQuantum tomographyQuantum InformationAtomic, Molecular & Optical
Weighted Fuzzy Production Rule Extraction Using Modified Harmony Search Algorithm and BP Neural Network Framework

Compared with rules in the form of `IF-THEN,' weighted fuzzy production rules (WFPRs) have more robust knowledge expression capabilities, but weighted fuzzy production rules are more difficult to obtain. The weighted fuzzy production rules obtained using traditional neural network methods have shortcomings, such as insufficient precision and insufficient knowledge extraction. Focusing on the mentioned shortages, a modified weighted fuzzy production rules extraction approach is proposed by combining the modified harmony search algorithm, and neural network. The method consists of three main stages. First, a global optimal adaptive harmony search algorithm (AGOHS) is proposed to overcome the traditional harmony search algorithm's existing poor adaptive ability. Then, the AGOHS algorithm is used to optimize the neural network's initial weights to improve the neural network's training efficiency. Finally, extract the WFPRs with IF-THEN from the trained neural network and give the corresponding fuzzy reasoning. Through the WFPRs extraction experiments using IRIS and PIMA data sets reveal the proposed rule extraction framework has some apparent highlights, such as high accuracy, the smaller number of generated rules, and low redundancy.
Assisting engineers extracting requirements on components from domain documents

Context: When entering an unfamiliar domain, organizations usually have to invest significant time and effort performing domain analysis with the purpose of acquiring system requirements. This process usually involves collecting domain documents extensively, retrieving and reviewing the related ones carefully, searching for the requirements knowledge, then extracting and specifying system requirements. Furthermore, the task must often be performed repeatedly throughout the early phases of projects. Depending on the nature of the domain and the availability of documentation, this process is extremely time-consuming and may require non-trivial human effort. Objective: In order to assist engineers identifying requirements knowledge from a collect of domain documents, previously we proposed an approach MaRK in the Conference RE’16 which ranks the domain documents by their relevance to components and highlights the content that are likely to contain component-related information. Experiments showed MaRK can almost identify the top and bottom documents in the reference list. However, it tends to underestimate the relevance of the domain documents that have a number of sections with medium knowledge density. Method: We improve the ranking algorithm in MaRK and propose MaRK-II. In addition, to assist engineers locating the relevant information in lengthy documents, we preserve the highlighting work in MaRK and strengthen MaRK-II by extracting the summary of component-related text. MaRK-II is evaluated with the documents in three domains. Results: We found that MaRK-II significantly outperforms MaRK and VSM on ranking the documents by their relevance. And a user study showed that MaRK-II is indeed helpful for engineers to extract requirements on components. Conclusions: Our approach provides three mechanisms including documents ranking, pertinent content highlighting and summarizing to help engineers obtaining requirements from a collection of domain documents.
Automatic Detection of Cervical Cells Using Dense-Cascade R-CNN

It is necessary to realize the automatic detection of cervical cells in Pap Smears. We present an automatic cervical cell detection approach based on the so-called Dense-Cascade R-CNN (Dense-Cascade Region-based Convolutional Neural Networks). The approach consists of three modules: data augmentation, training set balancing (TSB), and the Dense-Cascade R-CNN. The data augmentation module carries out operations such as rotation, scale, flip, etc. on input images to increase the samples. The TSB module is used to balance the number of cervical cell samples of various classes in the training set after data augmentation. As for the Dense-Cascade R-CNN module, the residual neural network (ResNet) with 101 layers in a Cascade R-CNN is replaced by a dense connected convolution neural network (DenseNet) with 121 layers so as to improve the detection performance of cervical cells. We evaluated the proposed method on the Herlev dataset. The results show that our approach can improve both mean average precision (mAP) and mean average recall (mAR) for Cascade R-CNN. Our cervical cell automatic detection approach can be used as an auxiliary diagnostic tool for cervical cancer screening.
Analyzing the Quality and Stability of a Streaming End-to-End On-Device Speech Recognizer

The demand for fast and accurate incremental speech recognition increases as the applications of automatic speech recognition (ASR) proliferate. Incremental speech recognizers output chunks of partially recognized words while the user is still talking. Partial results can be revised before the ASR finalizes its hypothesis, causing instability issues. We analyze the quality and stability of on-device streaming end-to-end (E2E) ASR models. We first introduce a novel set of metrics that quantify the instability at word and segment levels. We study the impact of several model training techniques that improve E2E model qualities but degrade model stability. We categorize the causes of instability and explore various solutions to mitigate them in a streaming E2E ASR system. Index Terms: ASR, stability, end-to-end, text normalization,on-device, RNN-T
Underwater Image Restoration and Enhancement via Residual Two-Fold Attention Networks

A C TUnderwater images or videos are common but essential information carrier for observation, fishery industry and intelligent analysis system in underwater vehicles.But underwater images are usually suffering from more complex imaging interfering impacts.This paper describes a novel residual two-fold attention networks for underwater image restoration and enhancement to eliminate the interference of color deviation and noise at the same time.In our network framework, nonlocal attention and channel attention mechanisms are respectively embedded to mine and enhance more features.Quantitative and qualitative experiment data demonstrates that our proposed approach generates more visually appealing images, and also provides higher objective evaluation index score.
CNN based Smart Surveillance System: A Smart IoT Application Post Covid-19 Era

There are many applications available in the face detection algorithm but very limited applications are identified for further processing.When it comes to identifying faces in the crowd and that too in all-weather conditions then it's too difficult a task to be conducted.Considering this challenge, most of the surveillance systems are not automated.In the sense the CCTV deployed are used only for bufferingpurposes.Very rarely an event is brought to the notice and later CCTV footage is used as a tool for legal issues.There are a lot of concerns about deploying CCTV's in Public places as well.S electing this system, our work have made sure that the CCTV's wil l be used to process the video being taken, and whenever an event triggers, notice(s) has to be provided for officials.Thus, the proposed system is for automatic detection and recognition of human faces for finding criminals/suspects/missing persons for surveillance.The intended method first detects a face in the video using a face detection algorithm and then it will search whether the face is present in the data centre.The method provides the ability to detect, extract features, and recognize a face from inputs taken by camera or video automatically.Recognizing faces under different natural conditions can be done by training the system on a limited number of facial images.Also, this system is tested for Covid-19 Post situations, wherein wearing masks in public places is compulsory thus face recognition in these aspects is also tested, and encouraging results have been achieved.
Improving Word Sense Disambiguation with Translations

It has been conjectured that multilingual information can help monolingual word sense disambiguation (WSD).However, existing WSD systems rarely consider multilingual information, and no effective method has been proposed for improving WSD by generating translations.In this paper, we present a novel approach that improves the performance of a base WSD system using machine translation.Since our approach is language independent, we perform WSD experiments on several languages.The results demonstrate that our methods can consistently improve the performance of WSD systems, and obtain state-ofthe-art results in both English and multilingual WSD.To facilitate the use of lexical translation information, we also propose BABALIGN, an precise bitext alignment algorithm which is guided by multilingual lexical correspondences from BabelNet.
Interview: Large-scale Modeling of Media Dialog with Discourse Patterns and Knowledge Grounding

In this work, we perform the first large-scale analysis of discourse in media dialog and its impact on generative modeling of dialog turns, with a focus on interrogative patterns and use of external knowledge.Discourse analysis can help us understand modes of persuasion, entertainment, and information elicitation in such settings, but has been limited to manual review of small corpora.We introduce I -a large-scale (105K conversations) media dialog dataset collected from news interview transcripts-which allows us to investigate such patterns at scale.We present a dialog model that leverages external knowledge as well as dialog acts via auxiliary losses and demonstrate that our model quantitatively and qualitatively outperforms strong discourse-agnostic baselines for dialog modeling-generating more specific and topical responses in interview-style conversations.
A Blockchain Prediction Model on Time, Value, and Purchase Based on Markov Chain and Queuing Theory in Stock Trade

With the continuous development of the blockchain, it has brought a subversive impact on the blossom of all fields with its characteristics of decentralization, trust-free, and tampering, especially in the financial field. It is of great significance to research the application of blockchain technology in the financial field. As an important part of the financial market, stock has the crucial influence, and the combination of stock and blockchain is becoming a growing trend. In recent years, many studies have focused on the prediction of the stock value, but they have not fully considered the combination of time, value, and purchase. To solve the above problem, we propose a preemption queuing model for multipriority service objects in the blockchain financial architecture according to different service priority. Meanwhile, a queuing-based resource scheduling model is established by using the Markov chain to find the optimal solution. The method in this paper can greatly improve the efficiency of the system and provide a basis for future scientific research in the healthy and sustainable development of the securities industry.
First Order Constrained Optimization in Policy Space

In reinforcement learning, an agent attempts to learn high-performing behaviors through interacting with the environment, such behaviors are often quantified in the form of a reward function. However some aspects of behavior-such as ones which are deemed unsafe and to be avoided-are best captured through constraints. We propose a novel approach called First Order Constrained Optimization in Policy Space (FOCOPS) which maximizes an agent's overall reward while ensuring the agent satisfies a set of cost constraints. Using data generated from the current policy, FOCOPS first finds the optimal update policy by solving a constrained optimization problem in the nonparameterized policy space. FOCOPS then projects the update policy back into the parametric policy space. Our approach has an approximate upper bound for worst-case constraint violation throughout training and is first-order in nature therefore simple to implement. We provide empirical evidence that our simple approach achieves better performance on a set of constrained robotics locomotive tasks.
Adapting Coreference Resolution to Twitter Conversations

The performance of standard coreference resolution is known to drop significantly on Twitter texts.We improve the performance of the (Lee et al., 2018) system, which is originally trained on OntoNotes, by retraining on manually-annotated Twitter conversation data.Further experiments by combining different portions of OntoNotes with Twitter data show that selecting text genres for the training data can beat the mere maximization of training data amount.In addition, we inspect several phenomena such as the role of deictic pronouns in conversational data, and present additional results for variant settings.Our best configuration improves the performance of the "out of the box" system by 21.6%.
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations

Abstract Current state-of-the-art object recognition models are largely based on convolutional neural network (CNN) architectures, which are loosely inspired by the primate visual system. However, these CNNs can be fooled by imperceptibly small, explicitly crafted perturbations, and struggle to recognize objects in corrupted images that are easily recognized by humans. Here, by making comparisons with primate neural data, we first observed that CNN models with a neural hidden layer that better matches primate primary visual cortex (V1) are also more robust to adversarial attacks. Inspired by this observation, we developed VOneNets, a new class of hybrid CNN vision models. Each VOneNet contains a fixed weight neural network front-end that simulates primate V1, called the VOneBlock, followed by a neural network back-end adapted from current CNN vision models. The VOneBlock is based on a classical neuroscientific model of V1: the linear-nonlinear-Poisson model, consisting of a biologically-constrained Gabor filter bank, simple and complex cell nonlinearities, and a V1 neuronal stochasticity generator. After training, VOneNets retain high ImageNet performance, but each is substantially more robust, outperforming the base CNNs and state-of-the-art methods by 18% and 3%, respectively, on a conglomerate benchmark of perturbations comprised of white box adversarial attacks and common image corruptions. Finally, we show that all components of the VOneBlock work in synergy to improve robustness. While current CNN architectures are arguably brain-inspired, the results presented here demonstrate that more precisely mimicking just one stage of the primate visual system leads to new gains in ImageNet-level computer vision applications.
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks

Many pairwise classification tasks, such as paraphrase detection and open-domain question answering, naturally have extreme label imbalance (e.g., 99.99% of examples are negatives).In contrast, many recent datasets heuristically choose examples to ensure label balance.We show that these heuristics lead to trained models that generalize poorly: State-of-the art models trained on QQP and WikiQA each have only 2.4% average precision when evaluated on realistically imbalanced test data.We instead collect training data with active learning, using a BERT-based embedding model to efficiently retrieve uncertain points from a very large pool of unlabeled utterance pairs.By creating balanced training data with more informative negative examples, active learning greatly improves average precision to 32.5% on QQP and 20.1% on WikiQA.
Flexible retrieval with NMSLIB and FlexNeuART

Our objective is to introduce to the NLP community an existing k-NN search library NMSLIB, a new retrieval toolkit FlexNeuART, as well as their integration capabilities.NMSLIB, while being one the fastest k-NN search libraries, is quite generic and supports a variety of distance/similarity functions.Because the library relies on the distance-based structure-agnostic algorithms, it can be further extended by adding new distances.FlexNeuART is a modular, extendible and flexible toolkit for candidate generation in IR and QA applications, which supports mixing of classic and neural ranking signals.FlexNeuART can efficiently retrieve mixed dense and sparse representations (with weights learned from training data), which is achieved by extending NMSLIB.In that, other retrieval systems work with purely sparse representations (e.g., Lucene), purely dense representations (e.g., FAISS and Annoy), or only perform mixing at the re-ranking stage.
Online Meta-Critic Learning for Off-Policy Actor-Critic Methods

Off-Policy Actor-Critic (Off-PAC) methods have proven successful in a variety of continuous control tasks. Normally, the critic's action-value function is updated using temporal-difference, and the critic in turn provides a loss for the actor that trains it to take actions with higher expected return. In this paper, we introduce a novel and flexible meta-critic that observes the learning process and meta-learns an additional loss for the actor that accelerates and improves actor-critic learning. Compared to the vanilla critic, the meta-critic network is explicitly trained to accelerate the learning process; and compared to existing meta-learning algorithms, meta-critic is rapidly learned online for a single task, rather than slowly over a family of tasks. Crucially, our meta-critic framework is designed for off-policy based learners, which currently provide state-of-the-art reinforcement learning sample efficiency. We demonstrate that online meta-critic learning leads to improvements in avariety of continuous control environments when combined with contemporary Off-PAC methods DDPG, TD3 and the state-of-the-art SAC.
Towards Reasonably-Sized Character-Level Transformer NMT by Finetuning Subword Systems

Applying the Transformer architecture on the character level usually requires very deep architectures that are difficult and slow to train.These problems can be partially overcome by incorporating a segmentation into tokens in the model.We show that by initially training a subword model and then finetuning it on characters, we can obtain a neural machine translation model that works at the character level without requiring token segmentation.We use only the vanilla 6-layer Transformer Base architecture.Our character-level models better capture morphological phenomena and show more robustness to noise at the expense of somewhat worse overall translation quality.Our study is a significant step towards highperformance and easy to train character-based models that are not extremely large.
THE EFFECT OF OUTDOOR LESSONS IN NATURAL SCIENCES ON STUDENTS’ KNOWLEDGE, THROUGH TABLETS AND EXPERIENTIAL LEARNING

Experiential learning was introduced to support a child’s concept development towards evolution scientific literacy. This study examined the effect of an experiential learning model (The Mobile Natural Science Learning - MNSL) on the knowledge of 4th grade primary school students in Slovenia, during natural science school lessons at the seashore through the use of tablets. Mobile technology provides authentic learning, assisting outdoor lessons providing material and environmental context in learning, which support the experience of a learner. In the experimental group (N = 95), outdoor learning in the seashore included Kolb’s experiential learning cycle with the integration of tablets. In the control group (N=97), the teachers used concrete experience for exploring the seashore, excluding the Kolb’s cycle. The data were collected by using two science knowledge pre and post – tests. The test items were classified into three TIMSS’ cognitive domains: 1) factual knowledge, 2) conceptual understanding, and 3) reasoning and analysis. Differences between groups were analysed through Mann-Whitney U-test and showed that the experimental group had better learning outcomes than the control group. Findings indicate that the MNSL-model had a positive effect on students’ achievement in science, more precisely in knowledge on marine organisms and life at the seashore. Keywords: Kolb’s experiential learning cycle, natural science, outdoor learning, seashore
Electro-Optical Hybrid Fourier Neural Network with Amplitude-Only Modulation

We demonstrate an electro-optical hybrid massively-parallel Fourier Neural Network exploiting Digital Micromirror Devices performing amplitude-only filtering, achieving ~10,000 2-Megapixel convolutions per second, preserving the inference accuracy level similar to phase-based approaches.
Could Virtual Reality Substitute the ‘Real’ Experience? Evidence from a UNESCO World Heritage Site in Northern Portugal

Virtual Reality could be useful for heritage management and preservation by complementing or, even, by replacing the ‘real’ visitation to more threatened destinations. The objective of this study was to empirically test the level of similarity perceived by a group of students between VR experience and the ‘real’ visit in a UNESCO World Heritage Cultural attraction in order to assess the capacity of VR to act as a substitute of the ‘real’ visit. Ridit analysis was conducted in order to rank the level of agreement perceived by respondents concerning to similarity between the VR experience and the ‘real’ visit. Results revealed that VR experience could act as a complement, rather than a substitute of the ‘real’ visitation. This is, the feelings and emotions derived from the ‘real’ visit could not be replaced by the VR experience. VR could be an effective marketing tool to encourage sustainable tourism behaviors, rather than to substitute the ‘real’ visit.
Behavioural Types for Memory and Method Safety in a Core Object-Oriented Language

We present a type-based analysis ensuring memory safety and object protocol completion in the Java-like language Mungo. Objects are annotated with usages, typestates-like specifications of the admissible sequences of method calls. The analysis entwines usage checking, controlling the order in which methods are called, with a static check determining whether references may contain null values. It prevents null pointer dereferencing in a typestate-aware manner and memory leaks and ensures that the intended usage protocol of every object is respected and completed. The type system admits an algorithm that infers the most general usage with respect to a simulation preorder. The type system is implemented in the form of a type checker and a usage inference tool.