Reviewerly/exHarmony huggingface dataset:
Defence against adversarial attacks using classical and quantum-enhanced Boltzmann machines <sup>†</sup>

We provide a robust defence to adversarial attacks on discriminative algorithms. Neural networks are naturally vulnerable to small, tailored perturbations in the input data that lead to wrong predictions. On the contrary, generative models attempt to learn the distribution underlying a dataset, making them inherently more robust to small perturbations. We use Boltzmann machines for discrimination purposes as attack-resistant classifiers, and compare them against standard state-of-the-art adversarial defences. We find improvements ranging from 5% to 72% against attacks with Boltzmann machines on the MNIST dataset. We furthermore complement the training with quantum-enhanced sampling from the D-Wave 2000Q annealer, finding results comparable with classical techniques and with marginal improvements in some cases. These results underline the relevance of probabilistic methods in constructing neural networks and highlight a novel scenario of practical relevance where quantum computers, even with limited hardware capabilites, could provide advantages over classical computers. This work is dedicated to the memory of Peter Wittek.
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning

Paraphrase generation (PG) is of great importance to many downstream tasks in natural language processing.Diversity is an essential nature to PG for enhancing generalization capability and robustness of downstream applications.Recently, neural sequence-to-sequence (Seq2Seq) models have shown promising results in PG.However, traditional model training for PG focuses on optimizing model prediction against single reference and employs cross-entropy loss, which objective is unable to encourage model to generate diverse paraphrases.In this work, we present a novel approach with multi-objective learning to PG.We propose a learning-exploring method to generate sentences as learning objectives from the learned data distribution, and employ reinforcement learning to combine these new learning objectives for model training.We first design a sample-based algorithm to explore diverse sentences.Then we introduce several reward functions to evaluate the sampled sentences as learning signals in terms of expressive diversity and semantic fidelity, aiming to generate diverse and high-quality paraphrases.To effectively optimize model performance satisfying different evaluating aspects, we use a GradNorm-based algorithm that automatically balances these training objectives.Experiments and analyses on Quora and Twitter datasets demonstrate that our proposed method not only gains a significant increase in diversity but also improves generation quality over several state-of-the-art baselines.
Quantum self-learning Monte Carlo and quantum-inspired Fourier transform sampler

The self-learning metropolis-Hastings algorithm is a powerful Monte Carlo method that, with the help of machine learning, adaptively generates an easy-to-sample probability distribution for approximating a given hard-to-sample distribution. This paper provides a new self-learning Monte Carlo method that utilizes a quantum computer to output a proposal distribution. In particular, we show a novel subclass of this general scheme based on the quantum Fourier transform circuit; when the dimension of the input to QFT corresponding to the low-frequency components is not large, this sampler is classically simulable while having a certain advantage over conventional methods. The performance of this quantum-inspired algorithm is demonstrated by some numerical simulations.Received 2 June 2020Accepted 14 December 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.043442Published 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 AreasMachine learningQuantum algorithmsQuantum computationQuantum Information
Revisiting the ‘Wrong Kind of Object’ Problem

Any uniform semantic treatment of fictional names (e.g., 'Frodo') across parafictional statements (e.g., 'In The Lord of the Rings, Frodo was born in the Shire') and metafictional statements (e.g., 'Frodo was invented by Tolkien') runs into a variation of the 'wrong kind of object' problem.The problem arises when an analysis of one of these statements inappropriately attributes a property to an object.For example, it would be problematic if an analysis implied that flesh and blood individuals are invented by someone, and similarly problematic if an analysis implied that abstract objects are born in a certain region.Abstract object theory has provided a solution to this conundrum by distinguishing two modes of predication: encoding and exemplifying.Recently Klauk has argued that the problem reappears for the analysis of explicit parafictional statements in this theory.In this paper we formalize the objection and show that one can distinguish three issues in connection with the 'wrong kind of object' problem.We then address them in turn.
The Implementation of Blended Learning Model on Motivation and Students’ Learning Achievement

This study aims to determine differences in student motivation and learning achievement in KKPI subjects between students who use the face-to-face learning model and students who use blended learning models, increased motivation and student achievement in KKPI subjects due to the application of the blended model learning, and the interaction of the influence of the application of learning models and motivation on KKPI student learning achievement at SMK 1 Kamal Bangkalan. This quasi-experimental study uses two groups, namely the experimental group using the blended learning model and the control group using the face-to-face learning model. The study population was students of class XI SMKN 1 Kamal Bangkalan. The research sample consisted of 57 students divided into 30 students in the control group and 27 students in the experimental group. Data collection using questionnaires and multiple choice test questions. The results showed that there were significant differences between motivation and student achievement using the blended learning model and students using the face-to-face learning model, there was a significant increase in student motivation and learning achievement due to the application of the blended learning model, and there was no interaction of influence the application of learning models and motivation to student learning achievement.
Implementation of Efficient Distributed Crawler through Stepwise Crawling Node Allocation

Various websites have been created due to the increased use of the Internet, and the number of documents distributed through these websites has increased proportionally. However, it is not easy to collect newly updated documents rapidly. Web crawling methods have been used to continuously collect and manage new documents, whereas existing crawling systems applying a single node demonstrate limited performances. Furthermore, crawlers applying distribution methods exhibit a problem related to effective node management for crawling.<BR> This study proposes an efficient distributed crawler through stepwise crawling node allocation, which identifies websites
Quantumness of channels

The reliability of quantum channels for transmitting information is of profound importance from the perspective of quantum information. This naturally leads to the question as how well a quantum state is preserved when subjected to a quantum channel. We propose a measure of quantumness of channels based on non-commutativity of quantum states that is intuitive and easy to compute. We apply the proposed measure to some well known noise channels, both Markovian as well as non-Markovian and find that the results are in good agreement with those from a recently introduced $l_1$-norm coherence based measure.
A Novel Edge Computing Based Area Navigation Scheme

The area navigation system, discussed in this paper, is composed of ground responders and a navigation terminal and can position a high-velocity aircraft and measure its velocity. This navigation system is silent at ordinary times. It sends out a request signal when positioning is required for an aircraft, and then the ground responders send a signal for resolving the aircraft. Combining the direct sequence spread spectrum and frequency hopping, the concealed communication mode is used in the whole communication process, with short communication pulses as much as possible, so the system has strong concealment and anti-interference characteristics. As the transmission delay is apparent in the response communication mode, there is a big error where the common navigation algorithm is used for the positioning and velocity measurement of a high-velocity aircraft. In this paper, a new algorithm is proposed, which can eliminate the influence of response communication delay on the positioning of a high-velocity moving target to realize the precise positioning and velocity measurement of a high-velocity aircraft.
Simple vs Oversampling-based Classification Methods for Fine Grained Arabic Dialect Identification in Twitter

In this paper, we present a description of our experiments on country-level Arabic dialect identification. A comparison study between a set of classifiers has been carried out. The best results were achieved using the Linear Support Vector Classification (LSVC) model by applying a Random Over Sampling (ROS) process yielding an F1-score of 18.74% in the post-evaluation phase.In the evaluation phase, our best submitted system has achieved an F1-score of 18.27%, very close to the average F1-score (18.80%) obtained for all the submitted systems.
The Dichotomy of Evaluating Homomorphism-Closed Queries on Probabilistic Graphs

We study the problem of query evaluation on probabilistic graphs, namely, tuple-independent probabilistic databases over signatures of arity two. We focus on the class of queries closed under homomorphisms, or, equivalently, the infinite unions of conjunctive queries. Our main result states that the probabilistic query evaluation problem is #P-hard for all unbounded queries from this class. As bounded queries from this class are equivalent to a union of conjunctive queries, they are already classified by the dichotomy of Dalvi and Suciu (2012). Hence, our result and theirs imply a complete data complexity dichotomy, between polynomial time and #P-hardness, on evaluating homomorphism-closed queries over probabilistic graphs. This dichotomy covers in particular all fragments of infinite unions of conjunctive queries over arity-two signatures, such as negation-free (disjunctive) Datalog, regular path queries, and a large class of ontology-mediated queries. The dichotomy also applies to a restricted case of probabilistic query evaluation called generalized model counting, where fact probabilities must be 0, 0.5, or 1. We show the main result by reducing from the problem of counting the valuations of positive partitioned 2-DNF formulae, or from the source-to-target reliability problem in an undirected graph, depending on properties of minimal models for the query.
FID: Frame Interpolation and DCT-based Video Compression

In this paper, we present a hybrid video compression technique that combines the advantages of residual coding techniques found in traditional DCT-based video compression and learning-based video frame interpolation to reduce the amount of residual data that needs to be compressed. Learning-based frame interpolation techniques use machine learning algorithms to predict frames but have difficulty with uncovered areas and non-linear motion. This approach uses DCT-based residual coding only on areas that are difficult for video interpolation and provides tunable compression for such areas through an adaptive selection of data to be encoded. Experimental data for both PSNR and the newer video multi-method assessment fusion (VMAF) metrics are provided. Our results show that we can reduce the amount of data required to represent a video stream compared with traditional video coding while outperforming video frame interpolation techniques in quality.
Wiring diagrams as normal forms for computing in symmetric monoidal categories

Applications of category theory often involve symmetric monoidal categories (SMCs), in which abstract processes or operations can be composed in series and parallel. However, in 2020 there remains a dearth of computational tools for working with SMCs. We present an "unbiased" approach to implementing symmetric monoidal categories, based on an operad of directed, acyclic wiring diagrams. Because the interchange law and other laws of a SMC hold identically in a wiring diagram, no rewrite rules are needed to compare diagrams. We discuss the mathematics of the operad of wiring diagrams, as well as its implementation in the software package Catlab.
Study of latencies in ThingSpeak

IoT platforms play an important role on modern measurement systems because they allow the ingestion and processing of huge amounts of data (big data).Given the increasing use of these platforms, it is important to characterize their performance and robustness in real application scenarios.The paper analyzes the ThingSpeak platform by measuring the latencies associated to data packets sent to cloud and replied back, and by checking the consistency of the returned data.Several experiments were done considering different ways to access the platform: REST API, MQTT API, and MQTT broker alone.For each experiment, the methodology is explained, results are presented, and conclusions are extracted.The REST and MQTT APIs have similar performances, with roundtrip times between 1 s and 3 s.The MQTT broker alone is more agile, with roundtrip times below 250 ms.In all cases, the up and down links are far from being symmetric, with the uplink delay showing higher variance than the downlink delay.The obtained results can serve as a reference for other IoT platforms and provide guidelines for application development.
Modulation of saccade trajectories during sequential saccades

The planning and execution of sequential saccades can overlap in time, and abrupt changes in neural activity in the oculomotor system can alter the normal trajectory of saccades. In this study, we analyzed saccade trajectories to assess the combined programming of sequential saccades. In two separate psychophysical experiments, subjects were instructed to make a sequence of two saccades. The results showed modulation of saccade curvature by the direction and amplitude of both the preceding and following saccade: saccade curvature is modulated in the direction of preceding saccades and away from the direction of following saccades. Moreover, larger preceding and following saccades have stronger effects on curvature. These results support the idea that sequential saccades are programmed concurrently. Finally, the amount of saccade curvature is correlated with the deviation of saccade start and end points, and the time of maximum deviation of saccade trajectories is highly consistent in both experiments. Based on this, we propose a novel benefit for the modulation of saccade trajectories by the oculomotor system: minimizing the saccadic error in sequential saccades.NEW & NOTEWORTHY We show that in saccade sequences, saccade trajectory is modulated in the direction of the preceding saccade and away from the following saccade. The magnitude of this effect is correlated with preceding and following saccade amplitude. This confirms that programming of sequential saccades overlaps. Curvature is also correlated with the deviation of saccade start and end points. Thus, we propose a novel benefit for the modulation of saccade trajectories: minimizing end point error in sequential saccades.
On the Complexity of Finding the Maximum Entropy Compatible Quantum State

Herein we study the problem of recovering a density operator from a set of compatible marginals, motivated from limitations of physical observations. Given that the set of compatible density operators is not singular, we adopt Jaynes' principle and wish to characterize a compatible density operator with maximum entropy. We first show that comparing the entropy of compatible density operators is QSZK-complete, even for the simplest case of 3-chains. Then, we focus on the particular case of quantum Markov chains and trees and establish that for these cases, there exists a quantum polynomial circuit that constructs the maximum entropy compatible density operator. Finally, we extend the Chow-Liu algorithm to the same subclass of quantum states.
A new route toward orthogonality

We revisit the problem of determining conditions under which a pure state, that evolves under an arbitrary unitary transformation, reaches an orthogonal state in a finite amount of the transformation parameter. Simple geometric considerations disclose the existence of a fundamental limit for the minimal amount required, providing, in particular, an intuitive hint of the Mandelstam-Tamm bound. The geometric considerations leads us to focus on a particular, yet relevant, family of states that evolve towards orthogonality. Several dynamical features are discussed, which include the (relative) entropy production during transformation, and special attention is paid to multipartite systems of $N$ bosons that are allowed to tunnel between two sites. The effects of the tunneling in the amount of transformation required for the system to attain an orthogonal state are revealed, and the relation between the latter, the tunneling intensity and the mode-entanglement is explored.
Quantum Zeno approach for molecular energies with maximum commuting initial Hamiltonians

We propose to use a quantum adiabatic and simulated-annealing framework to compute the ground state of small molecules. The initial Hamiltonian of our algorithms is taken to be the maximum commuting Hamiltonian that consists of a maximal set of commuting terms in the full Hamiltonian of molecules in the Pauli basis. We consider two variants. In the first method, we perform the adiabatic evolution on the obtained time- or path-dependent Hamiltonian with the initial state as the ground state of the maximum commuting Hamiltonian. However, this method does suffer from the usual problems of adiabatic quantum computation due to degeneracy and energy-level crossings along the Hamiltonian path. This problem is mitigated by a Zeno method, i.e., via a series of eigenstate projections used in the quantum simulated annealing, with the path-dependent Hamiltonian augmented by a sum of Pauli X terms, whose contribution vanishes at the beginning and the end of the path. In addition to the ground state, the low lying excited states can be obtained using this quantum Zeno approach with equal accuracy to that of the ground state.7 MoreReceived 22 June 2020Revised 27 October 2020Accepted 21 January 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.013104Published 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 AreasAdiabatic quantum optimizationMolecular spectraQuantum algorithmsPhysical SystemsMoleculesTechniquesNumerical techniquesQuantum chemistry methodsAtomic, Molecular & OpticalQuantum Information
Joint Pre-Trained Chinese Named Entity Recognition Based on Bi-Directional Language Model

The current named entity recognition (NER) is mainly based on joint convolution or recurrent neural network. In order to achieve high performance, these networks need to provide a large amount of training data in the form of feature engineering corpus and lexicons. Chinese NER is very challenging because of the high contextual relevance of Chinese characters, that is, Chinese characters and phrases may have many possible meanings in different contexts. To this end, we propose a model that leverages a pre-trained and bi-directional encoder representations-from-transformers language model and a joint bi-directional long short-term memory (Bi-LSTM) and conditional random fields (CRF) model for Chinese NER. The underlying network layer embeds Chinese characters and outputs character-level representations. The output is then fed into a bidirectional long short-term memory to capture contextual sequence information. The top layer of the proposed model is CRF, which is used to take into account the dependencies of adjacent tags and jointly decode the optimal chain of tags. A series of extensive experiments were conducted to research the useful improvements of the proposed neural network architecture on different datasets without relying heavily on handcrafted features and domain-specific knowledge. Experimental results show that the proposed model is effective, and character-level representation is of great significance for Chinese NER tasks. In addition, through this work, we have composed a new informal conversation message corpus called the autonomous bus information inquiry dataset, and compared to the advanced baseline, our method has been significantly improved.
AI Explainability. A Bridge Between Machine Vision and Natural Language Processing

This paper attempts to present an appraisal review of explainable Artificial Intelligence research, with a focus on building a bridge between image processing community and natural language processing (NLP) community. The paper highlights the implicit link between the two disciplines as exemplified from the emergence of automatic image annotation systems, visual question-answer systems. Text-To-Image generation and multimedia analytics. Next, the paper identified a set of natural language processing fields where the visual-based explainability can boost the local NLP task. This includes, sentiment analysis, automatic text summarization, system argumentation, topical analysis, among others, which are highly expected to fuel prominent future research in the field.
Saliency-Aware Subtle Augmentation Improves Human Visual Search Performance in VR

Visual search becomes challenging when the time to find the target is limited. Here we focus on how performance in visual search can be improved via a subtle saliency-aware modulation of the scene. Specifically, we investigate whether blurring salient regions of the scene can improve participant’s ability to find the target faster when the target is located in non-salient areas. A set of real-world omnidirectional images were displayed in virtual reality with a search target overlaid on the visual scene at a pseudorandom location. Participants performed a visual search task in three conditions defined by blur strength, where the task was to find the target as fast as possible. The mean search time, and the proportion of trials where participants failed to find the target, were compared across different conditions. Furthermore, the number and duration of fixations were evaluated. A significant effect of blur on behavioral and fixation metrics was found using linear mixed models. This study shows that it is possible to improve the performance by a saliency-aware subtle scene modulation in a challenging realistic visual search scenario. The current work provides an insight into potential visual augmentation designs aiming to improve user’s performance in everyday visual search tasks.