Reviewerly/exHarmony huggingface dataset:
Calculating the ground-state energy of benzene under spatial deformations with noisy quantum computing

In this manuscript, we calculate the ground state energy of benzene under spatial deformations by using the variational quantum eigensolver (VQE). The primary goal of the study is estimating the feasibility of using quantum computing ansatze on near-term devices for solving problems with large number of orbitals in regions where classical methods are known to fail. Furthermore, by combining our advanced simulation platform with real quantum computers, we provided an analysis of how the noise, inherent to quantum computers, affects the results. The centers of our study are the hardware efficient and quantum unitary coupled cluster ansatze (qUCC). First, we find that the hardware efficient ansatz has the potential to outperform mean-field methods for extreme deformations of benzene. However, key problems remain at equilibrium, preventing real chemical application. Moreover, the hardware efficient ansatz yields results that strongly depend on the initial guess of parameters - both in the noisy and noiseless cases - and optimization issues have a higher impact on their convergence than noise. This is confirmed by comparison with real quantum computing experiments. On the other hand, the qUCC ansatz alternative exhibits deeper circuits. Therefore, noise effects increase and are so extreme that the method never outperform mean-field theories. Our dual simulator/8-16 qubits QPU computations of qUCC appears to be a lot more sensitive to hardware noise than shot noise, which give further indications about where the noise-reduction efforts should be directed towards. Finally, the study shows that qUCC method better captures the physics of the system as the qUCC method can be utilized together with the Huckel approximation. We discussed how going beyond this approximation sharply increases the optimization complexity of such a difficult problem.
Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

The detection performance of small objects in remote sensing images is not satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) shows remarkable image enhancement performance, but reconstructed images miss high-frequency edge information. Therefore, object detection performance degrades for the small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and used different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance. We propose an architecture with three components: ESRGAN, Edge Enhancement Network (EEN), and Detection network. We use residual-in-residual dense blocks (RRDB) for both the GAN and EEN, and for the detector network, we use the faster region-based convolutional network (FRCNN) (two-stage detector) and single-shot multi-box detector (SSD) (one stage detector). Extensive experiments on car overhead with context and oil and gas storage tank (created by us) data sets show superior performance of our method compared to the standalone state-of-the-art object detectors.
AI in Search of Unfairness in Consumer Contracts: The Terms of Service Landscape

Abstract This article explores the potential of artificial intelligence for identifying cases where digital vendors fail to comply with legal obligations, an endeavour that can generate insights about business practices. While heated regulatory debates about online platforms and AI are currently ongoing, we can look to existing horizontal norms, especially concerning the fairness of standard terms, which can serve as a benchmark against which to assess business-to-consumer practices in light of European Union law. We argue that such an assessment can to a certain extent be automated; we thus present an AI system for the automatic detection of unfair terms in business-to-consumer contracts, a system developed as part of the CLAUDETTE project. On the basis of the dataset prepared in this project, we lay out the landscape of contract terms used in different digital consumer markets and theorize their categories, with a focus on five categories of clauses concerning (i) the limitation of liability, (ii) unilateral changes to the contract and/or service, (iii) unilateral termination of the contract, (iv) content removal, and (v) arbitration. In so doing, the paper provides empirical support for the broader claim that AI systems for the automated analysis of textual documents can offer valuable insights into the practices of online vendors and can also provide valuable help in their legal qualification. We argue that the role of technology in protecting consumers in the digital economy is critical and not sufficiently reflected in EU legislative debates.
An Efficient Deadlock Handling Model Based on Neutrosophic Logic: Case Study on Real Time Healthcare Database Systems

The adoption of electronic patient records in hospitals will provide numerous benefits, but it will also present new issues.One of these is the creation of deadlocks, which causes delays in conducting tasks such as obtaining patient information.If the underlying system is distributed, the deadlock situation gets even more problematic.The strategy for preventing deadlock requires basic information from the transaction structure and requested resources.However, in most circumstances, this information is either unavailable or ambiguous.Because the cost of processing and storing sophisticated structures (e.g., wait-for graphs) is so high for a system in comparison to an array of structures, most discovery and resolved-deadlock protocols use them.Due to imprecise knowledge in specifying the transactions' attributes, distributed deadlock resolution methods present a new problem in dealing with uncertainty.To address this problem, a variety of fuzzy logic-based techniques have been proposed.However, very little attention has been paid to dealing with ambiguous, vague, incomplete, and inconsistent information about transaction attributes in a single framework.In this paper, we used neutrosophic logic, a generalization of fuzzy logic, to solve the problem of uncertainty in distributed real-time deadlock-resolving systems.The proposed method is structured to reflect multiple types of knowledge and relations among all features and tripartitions the transactions' features include validation factor degree, slackness degree, degree of deadline-missed transaction based on the degree of membership of truthiness, degree of membership of indeterminacy, and degree of membership of falsity.We developed a tool set and conducted experiments using benchmark datasets.There is an increase in detection rate and a large drop in rollback rate when this new strategy is used.Our technology resolved all database deadlocks and significantly increased database performance by up to three orders of magnitude.
Application of Chaos Mutation Adaptive Sparrow Search Algorithm in Edge Data Compression

In view of the large amount of data collected by an edge server, when compression technology is used for data compression, data classification accuracy is reduced and data loss is large. This paper proposes a data compression algorithm based on the chaotic mutation adaptive sparrow search algorithm (CMASSA). Constructing a new fitness function, CMASSA optimizes the hyperparameters of the Convolutional Auto-Encoder Network (CAEN) on the cloud service center, aiming to obtain the optimal CAEN model. The model is sent to the edge server to compress the data at the lower level of edge computing. The effectiveness of CMASSA performance is tested on ten high-dimensional benchmark functions, and the results show that CMASSA outperforms other comparison algorithms. Subsequently, experiments are compared with other literature on the Multi-class Weather Dataset (MWD). Experiments show that under the premise of ensuring a certain compression ratio, the proposed algorithm not only has better accuracy in classification tasks than other algorithms but also maintains a high degree of data reconstruction.
HERMES: Using Commit-Issue Linking to Detect Vulnerability-Fixing Commits

Software projects today rely on many third-party libraries, and therefore, are exposed to vulnerabilities in these libraries. When a library vulnerability is fixed, users are notified and advised to upgrade to a new version of the library. However, not all vulnerabilities are publicly disclosed, and users may not be aware of vulnerabilities that may affect their applications. Due to the above challenges, there is a need for techniques which can identify and alert users to silent fixes in libraries; commits that fix bugs with security implications that are not officially disclosed. We propose a machine learning approach to automatically identify vulnerability-fixing commits. Existing techniques consider only data within a commit, such as its commit message, which does not always have sufficiently discriminative information. To address this limitation, our approach incorporates the rich source of information from issue trackers. When a commit does not link to an issue, we use a commit-issue link recovery technique to infer the potential missing link. Our experiments are promising; incorporating information from issue trackers boosts the performance of a vulnerability-fixing commit classifier, improving over the strongest baseline by 11.1&#x0025; on the entire dataset, which includes commits that do not link to an issue. On a subset of the data in which all commits explicitly link to an issue, our approach improves over the baseline by 12.5&#x0025;.
Augmented Reality Media Development in STEAM Learning in Elementary Schools

The need for technology-based media is vital in the era of 21st-century education. Here, augmented reality media is a medium predicted to provide a more realistic experience successfully and in line with the developmental phase of elementary school students. For this reason, this study aims to analyze the need for augmented reality media development and develop an initial design for augmented reality media development in STEAM learning in elementary schools. This research used the R&amp;D method with two of the four stages of R&amp;D research. These stages are preliminary studies and initial design development. The subjects of this study were seven teachers and 129 students of a public elementary school in East Java, Indonesia. The instruments of this research were questionnaires and interviews. Data analysis used descriptive statistics and interpretive analysis. The results of this study revealed that teachers and students needed media that can represent material in-depth, increase interest and motivation, and provide experiences, such as STEAM-based augmented reality learning media. Meanwhile, the initial design development results of STEAM-based augmented reality learning media were in the form of six types of designs, containing Batik, Wayang (puppet), Gamelan, Borobudur Temple, Kereta Kencana (golden chariot), and Keris.
Financial Contract Administration in Construction via Cryptocurrency Blockchain and Smart Contract: A Proof of Concept

The blockchain that uses cryptocurrency is a paradigm shift in the way of data storage, retrieval, and verification due to the concept of decentralization. This paradigm is essential to ensure the security of crucial data in any project. Adding a smart contract to the blockchain would facilitate the automation of various processes. Thus, the cryptocurrency blockchain that uses the smart contract can be considered a suitable platform for an ecosystem of many industries. The construction industry needs a highly secure automated management system due to its complex contractual relationships and transactions between parties. Therefore, integrating the blockchain with the smart contract creates the most appropriate ecosystem to be developed. This study introduces an ecosystemic prototype using a programmable smart contract within a novel cryptocurrency blockchain for construction. The purpose of the prototype is to guarantee a decentralized system as an independent economic environment for the construction industry. The system guarantees the security of financial transactions and focuses on the payment clauses in the construction contract as well. The results depended on three well-known hypothetical case scenarios from the construction site and were displayed in the form of extracted access data tables. The prototype proved the efficiency of the decentralized system for the construction industry by minimizing human-factor interference in the transaction process and thus reducing time waste and cost.
Robust Interior Point Method for Quantum Key Distribution Rate Computation

Security proof methods for quantum key distribution, QKD, that are based on the numerical key rate calculation problem, are powerful in principle. However, the practicality of the methods are limited by computational resources and the efficiency and accuracy of the underlying algorithms for convex optimization. We derive a stable reformulation of the convex nonlinear semidefinite programming, SDP, model for the key rate calculation problems. We use this to develop an efficient, accurate algorithm. The stable reformulation is based on novel forms of facial reduction, FR, for both the linear constraints and nonlinear quantum relative entropy objective function. This allows for a Gauss-Newton type interior-point approach that avoids the need for perturbations to obtain strict feasibility, a technique currently used in the literature. The result is high accuracy solutions with theoretically proven lower bounds for the original QKD from the FR stable reformulation. This provides novel contributions for FR for general SDP. We report on empirical results that dramatically improve on speed and accuracy, as well as solving previously intractable problems.
Extracting Training Data from Large Language Models

It has become common to publish large (billion parameter) language models that have been trained on private datasets. This paper demonstrates that in such settings, an adversary can perform a training data extraction attack to recover individual training examples by querying the language model. We demonstrate our attack on GPT-2, a language model trained on scrapes of the public Internet, and are able to extract hundreds of verbatim text sequences from the model's training data. These extracted examples include (public) personally identifiable information (names, phone numbers, and email addresses), IRC conversations, code, and 128-bit UUIDs. Our attack is possible even though each of the above sequences are included in just one document in the training data. We comprehensively evaluate our extraction attack to understand the factors that contribute to its success. Worryingly, we find that larger models are more vulnerable than smaller models. We conclude by drawing lessons and discussing possible safeguards for training large language models.
What is the State of Neural Network Pruning?

Neural network pruning---the task of reducing the size of a network by removing parameters---has been the subject of a great deal of work in recent years. We provide a meta-analysis of the literature, including an overview of approaches to pruning and consistent findings in the literature. After aggregating results across 81 papers and pruning hundreds of models in controlled conditions, our clearest finding is that the community suffers from a lack of standardized benchmarks and metrics. This deficiency is substantial enough that it is hard to compare pruning techniques to one another or determine how much progress the field has made over the past three decades. To address this situation, we identify issues with current practices, suggest concrete remedies, and introduce ShrinkBench, an open-source framework to facilitate standardized evaluations of pruning methods. We use ShrinkBench to compare various pruning techniques and show that its comprehensive evaluation can prevent common pitfalls when comparing pruning methods.
TRUE: Re-evaluating Factual Consistency Evaluation

Grounded text generation systems often generate text that contains factual inconsistencies, hindering their real-world applicability.Automatic factual consistency evaluation may help alleviate this limitation by accelerating evaluation cycles, filtering inconsistent outputs and augmenting training data.While attracting increasing attention, such evaluation metrics are usually developed and evaluated in silo for a single task or dataset, slowing their adoption.Moreover, previous meta-evaluation protocols focused on system-level correlations with human annotations, which leave the examplelevel accuracy of such metrics unclear.In this work, we introduce TRUE: a comprehensive survey and assessment of factual consistency metrics on a standardized collection of existing texts from diverse tasks, manually annotated for factual consistency.Our standardization enables an example-level metaevaluation protocol that is more actionable and interpretable than previously reported correlations, yielding clearer quality measures.Across diverse state-of-the-art metrics and 11 datasets we find that large-scale NLI and question generation-and-answering-based approaches achieve strong and complementary results.We recommend those methods as a starting point for model and metric developers, and hope TRUE will foster progress towards even better evaluation methods.
Efficient Hierarchical Domain Adaptation for Pretrained Language Models

The remarkable success of large language models has been driven by dense models trained on massive unlabeled, unstructured corpora.These corpora typically contain text from diverse, heterogeneous sources, but information about the source of the text is rarely used during training.Transferring their knowledge to a target domain is typically done by continuing training in-domain.In this paper, we introduce a method to permit domain adaptation to many diverse domains using a computationally efficient adapter approach.Our method is based on the observation that textual domains are partially overlapping, and we represent domains as a hierarchical tree structure where each node in the tree is associated with a set of adapter weights.When combined with a frozen pretrained language model, this approach enables parameter sharing among related domains, while avoiding negative interference between unrelated ones.Experimental results with GPT-2 and a large fraction of the 100 most represented websites in C4 show across-the-board improvements indomain.We additionally provide an inference time algorithm for a held-out domain and show that averaging over multiple paths through the tree enables further gains in generalization, while adding only a marginal cost to inference.
How Gender Debiasing Affects Internal Model Representations, and Why It Matters

Common studies of gender bias in NLP focus either on extrinsic bias measured by model performance on a downstream task or on intrinsic bias found in models' internal representations.However, the relationship between extrinsic and intrinsic bias is relatively unknown.In this work, we illuminate this relationship by measuring both quantities together: we debias a model during downstream fine-tuning, which reduces extrinsic bias, and measure the effect on intrinsic bias, which is operationalized as bias extractability with information-theoretic probing.Through experiments on two tasks and multiple bias metrics, we show that our intrinsic bias metric is a better indicator of debiasing than (a contextual adaptation of) the standard WEAT metric, and can also expose cases of superficial debiasing.Our framework provides a comprehensive perspective on bias in NLP models, which can be applied to deploy NLP systems in a more informed manner. 1
A multi-objective agile project planning model and a comparative meta-heuristic approach

Agile software development methodologies are used to meet the changing needs in the market. The most popular framework among these methodologies is the Scrum framework. In Scrum planning, the assignment of user stories to sprints requires the consideration of multiple objectives to use the limited resources more effectively. In this paper, a multi-objective mixed-integer programming model is developed which considers three objectives: maximizing the sprint capacity usage, maximizing the assignment of user stories with high priority to primary sprints, and maximizing the assignment of affine user stories to the same sprint. The aim is to contribute to both theory and practice of Scrum planning considering multiple objectives. Additionally, different from the existing literature of Scrum planning, alternative user stories are also taken into account. The proposed model is applied to the small, medium, and big-sized instances of the problem taken from a real-life system. Non-dominated Sorting Genetic Algorithm (NSGA-II) and Strong Pareto Evolutionary Algorithm (SPEA2) are used as heuristic approaches since big-sized instances of the problem could not be solved using optimization approaches. To analyze the performances of these algorithms, Hypervolume (HV), Epsilon (ϵ), Generational Distance (GD), Inverted Generational Distance (IGD), Inverted Generational Distance Plus (IGD+), and Spread (Δ) indicators are used. Results showed that NSGA-II performs better than SPEA2 according to ϵ indicator for big-sized instance. On the other hand, SPEA2 performs better than NSGA-II according to HV, GD, IGD, IGD+, and Δ indicators. However, the results are very close to each other for HV, ϵ, IGD, and IGD+ indicators. In conclusion, both algorithms can be used to deal with the multi-objective Scrum planning problem.
“They’re not that hard to mitigate”: What Cryptographic Library Developers Think About Timing Attacks

Timing attacks are among the most devastating side-channel attacks, allowing remote attackers to retrieve secret material, including cryptographic keys, with relative ease. In principle, “these attacks are not that hard to mitigate the basic intuition, captured by the constant-time criterion, is that control-flow and memory accesses should be independent from secrets. Furthermore, there is a broad range of tools for automatically checking adherence to this intuition. Yet, these attacks still plague popular cryptographic libraries twenty-five years after their discovery, reflecting a dangerous gap between academic research and cryptographic engineering. This gap can potentially undermine the emerging shift towards high-assurance, formally verified cryptographic libraries. However, the causes for this gap remain uninvestigated. To understand the causes of this gap, we conducted a survey with 44 developers of 27 prominent open-source cryptographic libraries. The goal of the survey was to analyze if and how the developers ensure that their code executes in constant time. Our main findings are that developers are aware of timing attacks and of their potentially dramatic consequences and yet often prioritize other issues over the perceived huge investment of time and resources currently needed to make their code resistant to timing attacks. Based on the survey, we identify several shortcomings in existing analysis tools for constant-time, and issue recommendations that can make writing constant-time libraries less difficult. Our recommendations can inform future development of analysis tools, security-aware compilers, and cryptographic libraries, not only for constant-timeness, but in the broader context of side-channel attacks, in particular for micro-architectural side-channel attacks, which are a younger topic and too recent as focus for this survey.