Reviewerly/exHarmony huggingface dataset:
Parallel Hierarchical Transformer with Attention Alignment for Abstractive Multi-Document Summarization

In comparison to single-document summarization, abstractive Multi-Document Summarization (MDS) brings challenges on the representation and coverage of its lengthy and linked sources. This study develops a Parallel Hierarchical Transformer (PHT) with attention alignment for MDS. By incorporating word- and paragraph-level multi-head attentions, the hierarchical architecture of PHT allows better processing of dependencies at both token and document levels. To guide the decoding towards a better coverage of the source documents, the attention-alignment mechanism is then introduced to calibrate beam search with predicted optimal attention distributions. Based on the WikiSum data, a comprehensive evaluation is conducted to test improvements on MDS by the proposed architecture. By better handling the inner- and cross-document information, results in both ROUGE and human evaluation suggest that our hierarchical model generates summaries of higher quality relative to other Transformer-based baselines at relatively low computational cost.
OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement

Morphing attacks are one of the many threats that are constantly affecting deep face recognition systems. It consists of selecting two faces from different individuals and fusing them into a final image that contains the identity information of both. In this work, we propose a novel regularisation term that takes into account the existent identity information in both and promotes the creation of two orthogonal latent vectors. We evaluate our proposed method (OrthoMAD) in five different types of morphing in the FRLL dataset and evaluate the performance of our model when trained on five distinct datasets. With a small ResNet-18 as the backbone, we achieve state-of-the-art results in the majority of the experiments, and competitive results in the others. The code of this paper will be publicly available.
BERT(s) to Detect Multiword Expressions

Multiword expressions (MWEs) present groups of words in which the meaning of the whole is not derived from the meaning of its parts. The task of processing MWEs is crucial in many natural language processing (NLP) applications, including machine translation and terminology extraction. Therefore, detecting MWEs is a popular research theme. In this paper, we explore state-of-the-art neural transformers in the task of detecting MWEs.We empirically evaluate several transformer models in the dataset for SemEval-2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM). We show that transformer models outperform the previous neural models based on long short-term memory (LSTM). The code and pre-trained model will be made freely available to the community.
Temporal Concept Drift and Alignment: An empirical approach to comparing Knowledge Organization Systems over time

This research explores temporal concept drift and temporal alignment in knowledge organization systems (KOS). A comparative analysis is pursued using the 1910 Library of Congress Subject Headings, 2020 FAST Topical, and automatic indexing. The use case involves a sample of 90 nineteenth-century Encyclopedia Britannica entries. The entries were indexed using two approaches: 1) full-text indexing; 2) Named Entity Recognition was performed upon the entries with Stanza, Stanford's NLP toolkit, and entities were automatically indexed with the Helping Interdisciplinary Vocabulary application (HIVE), using both 1910 LCSH and FAST Topical. The analysis focused on three goals: 1) identifying results that were exclusive to the 1910 LCSH output; 2) identifying terms in the exclusive set that have been deprecated from the contemporary LCSH, demonstrating temporal concept drift; and 3) exploring the historical significance of these deprecated terms. Results confirm that historical vocabularies can be used to generate anachronistic subject headings representing conceptual drift across time in KOS and historical resources. A methodological contribution is made demonstrating how to study changes in KOS over time and improve the contextualization of historical humanities resources.
Non-Gaussian entanglement via splitting of a few thermal quanta

Quanta splitting is an essential generator of Gaussian entanglement, apparently the most commonly occurring form of entanglement in linearised systems. In general, such Gaussian entanglement results from the strong pumping of a nonlinear process with the highly coherent and low-noise external drive necessary for the linearisation. In contrast, recent experiments involving efficient trilinear processes in trapped ions and superconducting circuits have opened the complementary possibility to test the splitting of a few thermal quanta without a linearisation process. Stimulated by such small thermal energy, the strong degenerate trilinear coupling generates large amounts of nonclassicality, detectable by more than 3 dB of distillable quadrature squeezing, in contrast with the nondegenerate trilinear coupling. Substantial entanglement can be generated in tandem with the nonclassicality generation via linear coupling to a third mode, achieving the entanglement potential of the nonclassical states. Such entanglement, although not captured by the covariance matrix, grows with the mean number of split thermal quanta; something which does not happen with Gaussian entanglement. We extend our analysis to higher-order degenerate quanta splitting processes to demonstrate that this non-Gaussian entanglement is already common for strongly nonlinear systems at the low-energy thermal level. We thus shed light on the generic entanglement mechanism for nonlinear bosonic systems operating in such low-energy regimes.
An Adaptive Image Encryption Scheme Guided by Fuzzy Models

A new image encryption scheme using the advanced encryption standard (AES), a chaotic map, a genetic operator, and a fuzzy inference system is proposed in this paper. In this work, plain images were used as input, and the required security level was achieved. Security criteria were computed after running a proposed encryption process. Then an adaptive fuzzy system decided whether to repeat the encryption process, terminate it, or run the next stage based on the achieved results and user demand. The SHA-512 hash function was employed to increase key sensitivity. Security analysis was conducted to evaluate the security of the proposed scheme, which showed it had high security and all the criteria necessary for a good and efficient encryption algorithm were met. Simulation results and the comparison of similar works showed the proposed encryptor had a pseudo-noise output and was strongly dependent upon the changing key and plain image.
Counterfactual Supervision-based Information Bottleneck for Out-of-Distribution Generalization

Learning invariant (causal) features for out-of-distribution (OOD) generalization has attracted extensive attention recently, and among the proposals invariant risk minimization (IRM) is a notable solution. In spite of its theoretical promise for linear regression, the challenges of using IRM in linear classification problems remain. By introducing the information bottleneck (IB) principle into the learning of IRM, IB-IRM approach has demonstrated its power to solve these challenges. In this paper, we further improve IB-IRM from two aspects. First, we show that the key assumption of support overlap of invariant features used in IB-IRM is strong for the guarantee of OOD generalization and it is still possible to achieve the optimal solution without this assumption. Second, we illustrate two failure modes that IB-IRM (and IRM) could fail for learning the invariant features, and to address such failures, we propose a \textit{Counterfactual Supervision-based Information Bottleneck (CSIB)} learning algorithm that provably recovers the invariant features. By requiring counterfactual inference, CSIB works even when accessing data from a single environment. Empirical experiments on several datasets verify our theoretical results.
Achieving Heisenberg limit in the phase measurement through three-qubit graph states

We study the reciprocal of the mean quantum Fisher information (RMQFI), $\chi^2$ for general three qubit states, having graph and hypergraph states as special cases, for identifying genuine multi party entanglement characterized by $\chi^2 <1$. We demonstrate that the most symmetric graph state and the GHZ state have the lowest RMQFI values leading to the highest statistical speed showing that both these states attain the Heisenberg limit in phase sensitivity. Unlike the GHZ state, graph states have the same RMQFI values for measurement through different parameters, a property shared by the hypergraph states. Three qubit graph and hypergraph states can violate Bell's inequality as $F_Q > N$. Both the GHZ state and the most symmetric graph state have the highest concurrence equalling 3 and the maximum QFI values.
Efficient parameterised compilation for hybrid quantum programming

Near term quantum devices have the potential to outperform classical computing through the use of hybrid classical-quantum algorithms such as Variational Quantum Eigensolvers. These iterative algorithms use a classical optimiser to update a parameterised quantum circuit. Each iteration, the circuit is executed on a physical quantum processor or quantum computing simulator, and the average measurement result is passed back to the classical optimiser. When many iterations are required, the whole quantum program is also recompiled many times. We have implemented explicit parameters that prevent recompilation of the whole program in quantum programming framework OpenQL, called OpenQL_PC, to improve the compilation and therefore total run-time of hybrid algorithms. We compare the time required for compilation and simulation of the MAXCUT algorithm in OpenQL to the same algorithm in both PyQuil and Qiskit. With the new parameters, compilation time in OpenQL is reduced considerably for the MAXCUT benchmark. When using OpenQL_PC, compilation of hybrid algorithms is up to two times faster than when using PyQuil or Qiskit.
Online Learning for Non-monotone Submodular Maximization: From Full Information to Bandit Feedback

In this paper, we revisit the online non-monotone continuous DR-submodular maximization problem over a down-closed convex set, which finds wide real-world applications in the domain of machine learning, economics, and operations research. At first, we present the Meta-MFW algorithm achieving a $1/e$-regret of $O(\sqrt{T})$ at the cost of $T^{3/2}$ stochastic gradient evaluations per round. As far as we know, Meta-MFW is the first algorithm to obtain $1/e$-regret of $O(\sqrt{T})$ for the online non-monotone continuous DR-submodular maximization problem over a down-closed convex set. Furthermore, in sharp contrast with ODC algorithm \citep{thang2021online}, Meta-MFW relies on the simple online linear oracle without discretization, lifting, or rounding operations. Considering the practical restrictions, we then propose the Mono-MFW algorithm, which reduces the per-function stochastic gradient evaluations from $T^{3/2}$ to 1 and achieves a $1/e$-regret bound of $O(T^{4/5})$. Next, we extend Mono-MFW to the bandit setting and propose the Bandit-MFW algorithm which attains a $1/e$-regret bound of $O(T^{8/9})$. To the best of our knowledge, Mono-MFW and Bandit-MFW are the first sublinear-regret algorithms to explore the one-shot and bandit setting for online non-monotone continuous DR-submodular maximization problem over a down-closed convex set, respectively. Finally, we conduct numerical experiments on both synthetic and real-world datasets to verify the effectiveness of our methods.
Educating Reflective Systems Developers at Scale: Towards productive feedback in a semi-capstone large-scale software engineering course

Feedback is critical in education. This Innovative Practice Full Paper reports lessons learned from improving the quality of feedback in a semi-capstone software engineering course, with particular focus on how to deliver productive feedback in large scale during project work. The bachelor-level introduction to software engineering course is taken by about 500 students from eight study programs, organised into 72 project teams. The course aims to educate reflective systems developers. The teaching staff includes 29 teaching assistants as supervisor and product owners for teams. Project teams get feedback on seven deliverables as part of formative portfolio assessment. Students expressed frustration on feedback not being aligned, that they got critique on topics not stated in assignments and that teaching assistants were reluctant to discuss the feedback. This article provides a description of the course design, an assessment of the quality of feedback and lessons learned from three main changes: Revising assignments and rubrics, reorganising the teaching staff and increasing training of teaching assistants. In discussing the changes, we draw on a survey to students with 142 respondents, a survey to teaching assistants with 18 respondents, meeting minutes from a student reference group and experience reports from teaching assistants as well as literature and own experience. The article concludes with three actionable lessons learned for large-scale semi-capstone courses.
Don't Reinvent the Wheel: Towards Automatic Replacement of Custom Implementations with APIs

Reusing code is a common practice in software development: It helps developers speedup the implementation task while also reducing the chances of introducing bugs, given the assumption that the reused code has been tested, possibly in production. Despite these benefits, opportunities for reuse are not always in plain sight and, thus, developers may miss them. We present our preliminary steps in building RETIWA, a recommender able to automatically identify custom implementations in a given project that are good candidates to be replaced by open source APIs. RETIWA relies on a ``knowledge base'' consisting of real examples of custom implementation-to-API replacements. In this work, we present the mining strategy we tailored to automatically and reliably extract replacements of custom implementations with APIs from open source projects. This is the first step towards building the envisioned recommender.
Extending the Variational Quantum Eigensolver to Finite Temperatures

We present a variational quantum thermalizer (VQT), called quantum-VQT (qVQT), which extends the variational quantum eigensolver (VQE) to finite temperatures. The qVQT makes use of an intermediate measurement between two variational circuits to encode a density matrix on a quantum device. A classical optimization provides the thermal state and, simultaneously, all associated excited states of a quantum mechanical system. We demonstrate the capabilities of the qVQT for two different spin systems. First, we analyze the performance of qVQT as a function of the circuit depth and the temperature for a 1-dimensional Heisenberg chain. Second, we use the excited states to map the complete, temperature dependent phase diagram of a 2-dimensional J1-J2 Heisenberg model. The numerical experiments demonstrate the efficiency of our approach, which can be readily applied to study various quantum many-body systems at finite temperatures on currently available NISQ devices.
QPQ 1DLT: A system for the rapid deployment of secure and efficient EVM-based blockchains

Limited scalability and transaction costs are, among others, some of the critical issues that hamper a wider adoption of distributed ledger technologies (DLT). That is particularly true for the Ethereum blockchain, which, so far, has been the ecosystem with the highest adoption rate. Quite a few solutions, especially on the Ethereum side of things, have been attempted in the last few years. Most of them adopt the approach to offload transactions from the blockchain mainnet, a.k.a. Level 1 (L1), to a separate network. Such systems are collectively known as Level 2 (L2) systems. While mitigating the scalability issue, the adoption of L2 introduces additional drawbacks: users have to trust that the L2 system has correctly performed transactions or, conversely, high computational power is required to prove transactions correctness. In addition, significant technical knowledge is needed to set up and manage such an L2 system. To tackle such limitations, we propose 1DLT: a novel system that enables rapid and trustless deployment of an Ethereum Virtual Machine based blockchain that overcomes those drawbacks.
Texture and semantic integrated small objects detection in foggy scenes

In recent years, small objects detection has received extensive attention from scholars for its important value in application. Some effective methods for small objects detection have been proposed. However, the data collected in real scenes are often foggy images, so the models trained with these methods are difficult to extract discriminative object features from such images. In addition, the existing small objects detection algorithms ignore the texture information and high-level semantic information of tiny objects, which limits the improvement of detection performance. Aiming at the above problems, this paper proposes a texture and semantic integrated small objects detection in foggy scenes. The algorithm focuses on extracting discriminative features unaffected by the environment, and obtaining texture information and high-level semantic information of small objects. Specifically, considering the adverse impact of foggy images on recognition performance, a knowledge guidance module is designed, and the discriminative features extracted from clear images by the model are used to guide the network to learn foggy images. Second, the features of high-resolution images and low-resolution images are extracted, and the adversarial learning method is adopted to train the model to give the network the ability to obtain the texture information of tiny objects from low-resolution images. Finally, an attention mechanism is constructed between feature maps of the same scale and different scales to further enrich the high-level semantic information of small objects. A large number of experiments have been conducted on data sets such as "Cityscape to Foggy" and "CoCo". The mean prediction accuracy (mAP) has reached 46.2% on "Cityscape to Fogg", and 33.3% on "CoCo", which fully proves the effectiveness and superiority of the proposed method.
Anomaly detection at the subnetwork level: traffic classification for machine learning algorithms

Abstract In this paper we propose a traffic classification methodology for subnetwork level anomaly detection using machine learning algorithms. Our proposed techniques allow researchers and practitioners in the field to classify the corresponding traffic multiplex constituents (subnetworks) according to their predictability level beforehand. Subnetworks deemed as predictable yield smaller prediction error than the rest, under well-known machine learning prediction algorithms, such as ANN, GRU and LSTM. Thus, such predictability features makes the former better suited for anomaly detection purposes, that are based on deviations from predicted values, while the rest are prone to produce false positives.
Machine Learning-Based Test Smell Detection

Context: Test smells are symptoms of sub-optimal design choices adopted when developing test cases. Previous studies have proved their harmfulness for test code maintainability and effectiveness. Therefore, researchers have been proposing automated, heuristic-based techniques to detect them. However, the performance of such detectors is still limited and dependent on thresholds to be tuned. Objective: We propose the design and experimentation of a novel test smell detection approach based on machine learning to detect four test smells. Method: We plan to develop the largest dataset of manually-validated test smells. This dataset will be leveraged to train six machine learners and assess their capabilities in within- and cross-project scenarios. Finally, we plan to compare our approach with state-of-the-art heuristic-based techniques.
On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models

In this paper we study the computation of the nonparametric maximum likelihood estimator (NPMLE) in multivariate mixture models. Our first approach discretizes this infinite dimensional convex optimization problem by fixing the support points of the NPMLE and optimizing over the mixture proportions. In this context we propose, leveraging the sparsity of the solution, an efficient and scalable semismooth Newton based augmented Lagrangian method (ALM). Our algorithm beats the state-of-the-art methods~\cite{koenker2017rebayes, kim2020fast} and can handle $n \approx 10^6$ data points with $m \approx 10^4$ support points. Our second procedure, which combines the expectation-maximization (EM) algorithm with the ALM approach above, allows for joint optimization of both the support points and the probability weights. For both our algorithms we provide formal results on their (superlinear) convergence properties. The computed NPMLE can be immediately used for denoising the observations in the framework of empirical Bayes. We propose new denoising estimands in this context along with their consistent estimates. Extensive numerical experiments are conducted to illustrate the effectiveness of our methods. In particular, we employ our procedures to analyze two astronomy data sets: (i) Gaia-TGAS Catalog~\cite{anderson2018improving} containing $n \approx 1.4 \times 10^6$ data points in two dimensions, and (ii) the $d=19$ dimensional data set from the APOGEE survey~\cite{majewski2017apache} with $n \approx 2.7 \times 10^4$.
A Machine Learning Model Selection Considering Tradeoffs between Accuracy and Interpretability

Applying black-box ML models in high-stakes fields like criminology, healthcare and realtime operating systems might create issues because of poor interpretability and complexity.Also, model building methods that include interpretability is now one of the growing research topics due to the absence of interpretability metrics that are both model-agnostic and quantitative.This paper introduces model selection methods with trade off between interpretability and accuracy of a model.Our results show 97% improvement in interpretability with 2.5% drop in accuracy in AutoMPG dataset using MLP model (65% improvement in interpretability with 1.5% drop in accuracy in MNIST dataset).
A Review: Insider Attack in New Normal

Technology brings comfort and makes our work easy, but it may come with a cost of your data breach. The Covid-19 crises have fundamentally changed ways of working as many companies are extending the remote working policies that become necessary during the pandemic. Static, network-based security parameters will no longer be sufficient. The security dynamic among users, assets and resources must be protected. Keywords:- Insider attack, new normal, cyber security