Reviewerly/exHarmony huggingface dataset:
Automated Discovery of Autonomous Quantum Error Correction Schemes

We can encode a qubit in the energy levels of a quantum system. Relaxation and other dissipation processes lead to decay of the fidelity of this stored information. Is it possible to preserve the quantum information for a longer time by introducing additional drives and dissipation? The existence of autonomous quantum error correcting codes answers this question in the positive. Nonetheless, discovering these codes for a real physical system, i.e., finding the encoding and the associated driving fields and bath couplings, remains a challenge that has required intuition and inspiration to overcome. In this work, we develop and demonstrate a computational approach based on adjoint optimization for discovering autonomous quantum error correcting codes given a Hamiltonian description of a physical system. We implement an optimizer that searches for a logical subspace and control parameters to better preserve quantum information. We demonstrate our method on a system of a harmonic oscillator coupled to a lossy qubit, and find that varying the Hamiltonian distance in Fock space—a proxy for the control hardware complexity—leads to discovery of different and new error correcting schemes. We discover what we call the 3 code, realizable with a Hamiltonian distance d=2, and propose a hardware-efficient implementation based on superconducting circuits.1 MoreReceived 11 August 2021Revised 4 December 2021Accepted 7 March 2022DOI:https://doi.org/10.1103/PRXQuantum.3.020302Published 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 optimizationMachine learningQuantum error correctionQuantum information processing with continuous variablesPhysical SystemsSuperconducting qubitsQuantum Information
InverseNet: Augmenting Model Extraction Attacks with Training Data Inversion

Cloud service providers, including Google, Amazon, and Alibaba, have now launched machine-learning-as-a-service (MLaaS) platforms, allowing clients to access sophisticated cloud-based machine learning models via APIs. Unfortunately, however, the commercial value of these models makes them alluring targets for theft, and their strategic position as part of the IT infrastructure of many companies makes them an enticing springboard for conducting further adversarial attacks. In this paper, we put forth a novel and effective attack strategy, dubbed InverseNet, that steals the functionality of black-box cloud-based models with only a small number of queries. The crux of the innovation is that, unlike existing model extraction attacks that rely on public datasets or adversarial samples, InverseNet constructs inversed training samples to increase the similarity between the extracted substitute model and the victim model. Further, only a small number of data samples with high confidence scores (rather than an entire dataset) are used to reconstruct the inversed dataset, which substantially reduces the attack cost. Extensive experiments conducted on three simulated victim models and Alibaba Cloud's commercially-available API demonstrate that InverseNet yields a model with significantly greater functional similarity to the victim model than the current state-of-the-art attacks at a substantially lower query budget.
Towards Scalable Complete Verification of Relu Neural Networks via Dependency-based Branching

We introduce an efficient method for the complete verification of ReLU-based feed-forward neural networks. The method implements branching on the ReLU states on the basis of a notion of dependency between the nodes. This results in dividing the original verification problem into a set of sub-problems whose MILP formulations require fewer integrality constraints. We evaluate the method on all of the ReLU-based fully connected networks from the first competition for neural network verification. The experimental results obtained show 145% performance gains over the present state-of-the-art in complete verification.
Optimization Over Trace Polynomials

Motivated by recent progress in quantum information theory, this article aims at optimizing trace polynomials, i.e., polynomials in noncommuting variables and traces of their products. A novel Positivstellensatz certifying positivity of trace polynomials subject to trace constraints is presented, and a hierarchy of semidefinite relaxations converging monotonically to the optimum of a trace polynomial subject to tracial constraints is provided. This hierarchy can be seen as a tracial analog of the Pironio, Navascués and Acín scheme (Pironio et al. in New J. Phys. 10(7):073013, 2008) for optimization of noncommutative polynomials. The Gelfand–Naimark–Segal (GNS) construction is applied to extract optimizers of the trace optimization problem if flatness and extremality conditions are satisfied. These conditions are sufficient to obtain finite convergence of our hierarchy. The results obtained are applied to violations of polynomial Bell inequalities in quantum information theory. The main techniques used in this paper are inspired by real algebraic geometry, operator theory, and noncommutative algebra.
Understanding Characteristics and System Implications of DAG-Based Blockchain in IoT Environments

Blockchain is starting to be deployed in the Internet of Things (IoT) to enable autonomous device-to-device transactions. However, traditional block-based blockchain techniques, such as Bitcoin and Ethereum, are not suitable for IoT environments due to their low throughput, high computation overhead, and costly transaction fee. To satisfy the requirements of IoT environments, directed-acyclic-graph (DAG)-based approaches, aiming to provide cheap blockchain services with low latency and high throughput, are emerging. This article presents a set of comprehensive experimental studies on IOTA, a representative DAG-based blockchain. We aim to exhibit its unique characteristics mainly from three aspects: 1) performance; 2) security; and 3) system robustness. We have developed a series of benchmark tools and judiciously selected typical configurations to perform experimental examinations with a real private IOTA network. Our studies reveal several interesting findings: 1) the throughput of IOTA is higher than the traditional block-based blockchain but far less than the reported thousands of transactions per second (TPS) in its whitepaper, even with scaling-up configurations; 2) the database query heavily impacts the performance of IOTA, even more than its mining [i.e., Proof of Work (PoW)] process; and 3) the system robustness of IOTA is closely related to the frequency of the incoming transactions while the milestone sent by the centralized coordinator has little effect on the system robustness. We make our benchmark tools public and expect our works can inspire system architects, application designers, and practitioners with new optimization directions and potential application cases for further exploration.
Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering

Knowledge-based visual question answering (VQA) requires answering questions with external knowledge in addition to the content of images.One dataset that is mostly used in evaluating knowledge-based VQA is OK-VQA, but it lacks a gold standard knowledge corpus for retrieval.Existing work leverage different knowledge bases (e.g., ConceptNet and Wikipedia) to obtain external knowledge.Because of varying knowledge bases, it is hard to fairly compare models' performance.To address this issue, we collect a natural language knowledge base that can be used for any VQA system.Moreover, we propose a Visual Retriever-Reader pipeline to approach knowledge-based VQA.The visual retriever aims to retrieve relevant knowledge, and the visual reader seeks to predict answers based on given knowledge.We introduce various ways to retrieve knowledge using text and images and two reader styles: classification and extraction.Both the retriever and reader are trained with weak supervision.Our experimental results show that a good retriever can significantly improve the reader's performance on the OK-VQA challenge.The code and corpus are provided in this link.
Cryptocurrency Market Reactions to Regulatory News

Cryptocurrencies are often thought to operate out of the reach of national regulation, but in fact their valuations, transaction volumes and user bases react substantially to news about regulatory actions.The impact depends on the specific regulatory category to which the news relates: events related to general bans on cryptocurrencies or to their treatment under securities law have the greatest adverse effect, followed by news on combating money laundering and the financing of terrorism, and on restricting the interoperability of cryptocurrencies with regulated markets.News pointing to the establishment of specific legal frameworks tailored to cryptocurrencies and initial coin offerings coincides with strong market gains.These results suggest that cryptocurrency markets rely on regulated financial institutions to operate and that these markets are segmented across jurisdictions.
Unsupervised Conversation Disentanglement through Co-Training

Conversation disentanglement aims to separate intermingled messages into detached sessions, which is a fundamental task in understanding multi-party conversations.Existing work on conversation disentanglement relies heavily upon human-annotated datasets, which are expensive to obtain in practice.In this work, we explore to train a conversation disentanglement model without referencing any human annotations.Our method is built upon a deep co-training algorithm, which consists of two neural networks: a messagepair classifier and a session classifier.The former is responsible for retrieving local relations between two messages while the latter categorizes a message to a session by capturing context-aware information.Both networks are initialized respectively with pseudo data built from an unannotated corpus.During the deep co-training process, we use the session classifier as a reinforcement learning component to learn a session assigning policy by maximizing the local rewards given by the messagepair classifier.For the message-pair classifier, we enrich its training data by retrieving message pairs with high confidence from the disentangled sessions predicted by the session classifier.Experimental results on the large Movie Dialogue Dataset demonstrate that our proposed approach achieves competitive performance compared to the previous supervised methods.Further experiments show that the predicted disentangled conversations can promote the performance on the downstream task of multi-party response selection.
Contradictory Structure Learning for Semi-supervised Domain Adaptation

Current adversarial adaptation methods attempt to align the cross-domain features, whereas two challenges remain unsolved: 1) the conditional distribution mismatch and 2) the bias of the decision boundary towards the source domain.To solve these challenges, we propose a novel framework for semi-supervised domain adaptation by unifying the learning of opposite structures (UODA).UODA consists of a generator and two classifiers (i.e., the sourcescattering classifier and the target-clustering classifier), which are trained for contradictory purposes.The target-clustering classifier attempts to cluster the target features to improve intra-class density and enlarge inter-class divergence.Meanwhile, the source-scattering classifier is designed to scatter the source features to enhance the decision boundary's smoothness.Through the alternation of source-feature expansion and target-feature clustering procedures, the target features are well-enclosed within the dilated boundary of the corresponding source features.This strategy can make the cross-domain features to be precisely aligned against the source bias simultaneously.Moreover, to overcome the model collapse through training, we progressively update the measurement of feature's distance and their representation via an adversarial training paradigm.Extensive experiments on the benchmarks of DomainNet and Office-home datasets demonstrate the superiority of our approach over the state-of-the-art methods.
CNN Based Features Extraction for Age Estimation and Gender Classification

This paper proposes automatic age and gender predictions based on feature extraction from human facial images. In contrast to the other traditional methods on the unfiltered benchmarks show their failure to manage large degrees of variation in these types of facial images. In this work, we show that by learning representations through the use of deep convolutional neural networks (CNN), a significant increase in performance can be obtained on these tasks. The novel CNN approach used in this research is made to classify and achieve robust age group and gender classification of unconstrained images. This study has been evaluated and tested on both Essex face dataset and Adience benchmark for gender prediction and age estimation. The results obtained show that the proposed method provide a significant improvement in performance, our model obtains the state-of-the-art performance in both age and gender classification.
How much pretraining data do language models need to learn syntax?

Transformers-based pretrained language models achieve outstanding results in many wellknown NLU benchmarks.However, while pretraining methods are very convenient, they are expensive in terms of time and resources.This calls for a study of the impact of pretraining data size on the knowledge of the models.We explore this impact on the syntactic capabilities of RoBERTa, using models trained on incremental sizes of raw text data.First, we use syntactic structural probes to determine whether models pretrained on more data encode a higher amount of syntactic information.Second, we perform a targeted syntactic evaluation to analyze the impact of pretraining data size on the syntactic generalization performance of the models.Third, we compare the performance of the different models on three downstream applications: part-of-speech tagging, dependency parsing and paraphrase identification.We complement our study with an analysis of the cost-benefit trade-off of training such models.Our experiments show that while models pretrained on more data encode more syntactic knowledge and perform better on downstream applications, they do not always offer a better performance across the different syntactic phenomena and come at a higher financial and environmental cost.
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints

We study the problem of generating arithmetic math word problems (MWPs) given a math equation that specifies the mathematical computation and a context that specifies the problem scenario.Existing approaches are prone to generating MWPs that are either mathematically invalid or have unsatisfactory language quality.They also either ignore the context or require manual specification of a problem template, which compromises the diversity of the generated MWPs.In this paper, we develop a novel MWP generation approach that leverages i) pre-trained language models and a context keyword selection model to improve the language quality of the generated MWPs and ii) an equation consistency constraint for math equations to improve the mathematical validity of the generated MWPs.Extensive quantitative and qualitative experiments on three realworld MWP datasets demonstrate the superior performance of our approach compared to various baselines.
Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data

Generating high quality question-answer pairs is a hard but meaningful task.Although previous works have achieved great results on answer-aware question generation, it is difficult to apply them into practical application in the education field.This paper for the first time addresses the question-answer pair generation task on the real-world examination data, and proposes a new unified framework on RACE.To capture the important information of the input passage we first automatically generate (rather than extracting) keyphrases, thus this task is reduced to keyphrase-question-answer triplet joint generation.Accordingly, we propose a multi-agent communication model to generate and optimize the question and keyphrases iteratively, and then apply the generated question and keyphrases to guide the generation of answers.To establish a solid benchmark, we build our model on the strong generative pre-training model.Experimental results show that our model makes great breakthroughs in the question-answer pair generation task.Moreover, we make a comprehensive analysis on our model, suggesting new directions for this challenging task.
TS-Perf: General Performance Measurement of Trusted Execution Environment and Rich Execution Environment on Intel SGX, Arm TrustZone, and RISC-V Keystone

A trusted execution environment (TEE) is a new hardware security feature that is isolated from a normal OS (i.e., rich execution environment (REE)). The TEE enables us to run a critical process, but the behavior is invisible for security, which makes it difficult to debug and tune the performance. In addition, the hardware/software architectures of TEE are different for CPUs. For example, Intel SGX allows user-mode only, although ARM TrustZone and RISC-V Keystone run a trusted OS. In addition, each TEE has each SDK for programming and cannot write a common program. These features make it difficult to compare the performance fairly between TEE and REE on different CPUs. To obtain precise performance and behavior in TEE, we propose TS-perf which is a compiler-based performance measurement method. TS-perf accesses the hardware timestamp counter in TEE as well as REE and keeps a precise log. The codes for measurement are inserted in a TEE binary by the compiler options (i.e., profile option, constructor, and destructor). Furthermore, we utilize the separate compilation technique, and the same benchmark binary is used for a fair comparison between TEE and REE. TS-perf is implemented for three TEE architectures (ARM TrustZone, Intel SGX, and RISC-V Keystone) and measures the performance of GlobalPlatform’s TEE internal APIs, matrix multiplication, memory access, and storage access. The comparisons showthe difference in performance between TEE and REE and the unusual behavior of trusted applications (TAs).
A Hybrid Approach Based on Grey Wolf and Whale Optimization Algorithms for Solving Cloud Task Scheduling Problem

In the context of cloud computing, one problem that is frequently encountered is task scheduling. This problem has two primary implications, which are the planning of tasks on virtual machines and the attenuation of performance. In order to address the problem of task scheduling in cloud computing, requisite nontraditional optimization attitudes to attain the optima of the problem, the present paper puts forth a hybrid multiple-objective approach called hybrid grey wolf and whale optimization (HGWWO) algorithms, that integrates two algorithms, namely, the grey wolf optimizer (GWO) and the whale optimization algorithm (WOA), with the purpose of conjoining the advantages of each algorithm for minimizing costs, energy consumption, and total execution time needed for task implementation, beside that improving the use of resources. Assessment of the aims of the proposed approach is carried out with the help of the tool known as CloudSim. As pointed out by the results of the experimental work undertaken, the proposed approach has the capability of performing at a superior level by comparison to the original algorithms GWO and WOA on their own with regard to costs, energy consumption, makespan, use of resources, and degree of imbalance.
Eigen-GNN: a Graph Structure Preserving Plug-in for GNNs

Graph Neural Networks (GNNs) are emerging machine learning models on graphs. Although sufficiently deep GNNs are shown theoretically capable of fully preserving graph structures, most existing GNN models in practice are shallow and essentially feature-centric. We show empirically and analytically that the existing shallow GNNs cannot preserve graph structures well. To overcome this fundamental challenge, we propose Eigen-GNN, a simple yet effective and general plug-in module to boost GNNs ability in preserving graph structures. Specifically, we integrate the eigenspace of graph structures with GNNs by treating GNNs as a type of dimensionality reduction and expanding the initial dimensionality reduction bases. Without needing to increase depths, Eigen-GNN possesses more flexibilities in handling both feature-driven and structure-driven tasks since the initial bases contain both node features and graph structures. We present extensive experimental results to demonstrate the effectiveness of Eigen-GNN for tasks including node classification, link prediction, and graph isomorphism tests.
Enriching and Controlling Global Semantics for Text Summarization

Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries.Nevertheless, these models still suffer from the short-range dependency problem, causing them to produce summaries that miss the key points of document.In this paper, we attempt to address this issue by introducing a neural topic model empowered with normalizing flow to capture the global semantics of the document, which are then integrated into the summarization model.In addition, to avoid the overwhelming effect of global semantics on contextualized representation, we introduce a mechanism to control the amount of global semantics supplied to the text generation module.Our method outperforms state-of-the-art summarization models on five common text summarization datasets, namely CNN/DailyMail, XSum, Reddit TIFU, arXiv, and PubMed.
Design and Implementation of An Intelligent Classroom Teaching System for Music Class Based on Internet of Things

The intelligent classroom teaching system can analyze the student features and teaching content, and then push the suitable content to students. With the aid of such a system, teachers can develop more flexible teaching strategies, and evaluate the student performance more accurately. Drawing on the theories of the Internet of things (IoT), this paper designed an intelligent classroom teaching system for music teachers, and applied the system to actual teaching practice. The results showed that the proposed system provided music teachers with an ideal tool of teaching design; the network structure of the system consists of collaborative, master, camera monitoring, and standby servers; the proposed system was found to satisfy all requirements through repeated tests. The research results provided a good theoretical support to the application of IoT in teaching design.