Reviewerly/exHarmony huggingface dataset:
Aspect-Based Sentiment Analysis of Racial Issues in Singapore: Enhancing Model Performance Using ChatGPT

This study employs Aspect-Based Sentiment Analysis (ABSA) and advanced AI methodologies to analyze public sentiment on racial issues in Singapore from 2018–2023. By utilizing synthetic data generation and In-Context Learning with ChatGPT API, we enhanced the performance of our ABSA model. Our findings highlight the utility of these methods in overcoming data imbalance and providing a comprehensive understanding of sentiment polarity associated with racially related aspect terms. Despite the higher cost of using sophisticated language models, the study underscores the potential of these techniques in offering nuanced insights into complex societal dynamics, illuminating a promising path for future research in sentiment analysis.
Digitalisation, neo-Taylorism and translation in the 2020s

This paper aims to provide a cursory yet critical overview of digital transcultural communication in the third decade of the new millennium. Today, digital entrepreneurs create lucrative business models based on user data whilst simultaneously enticing individuals to get used to working with these digital models. We will relate these two commercial objectives to modern translation practices, interweaving our discussion with the growing debate on the computer-assisted exploitation of the workforce. Digital neo-Taylorism is especially rife in the global translation industry, which today is overwhelmingly located on globally interconnected virtual online platforms, where largely underpaid professionals tend to be exploited by means of non-creative yet laborious post editing tasks or crowdsourcing translation activities. By linking the three values speed, efficiency and quantity to neural machine translation, postediting translation and crowdsourcing translation, we embark on a cursory critical discourse analysis, partly to show digital neo-Taylorism at work in the language used to describe new digital translation processes. In doing so, we endeavour to account for the underlying sociotechnical relationships across wider systemic processes such as digitalisation, alienation and capitalist social relations, and their effects on language and translation professionals.
DNA image encryption algorithm based on serrated spiral scrambling and cross bit plane

In response to the problem of uneven information distribution in DNA encoding, in this article, a new DNA chaotic image encryption algorithm combining serrated spiral scrambling and cross bit plane using lookup table diffusion is proposed. First of all, a four-dimensional hyperchaotic system is proposed, which can produce four interrelated sequences. Next, perform serrated spiral scrambling and index scrambling on the plain image to change the position of pixels. Then DNA is encoded in combination with the crossed bit plane, and then the pixel size is changed by using the new look-up table diffusion. Lastly, DNA decoding is performed to obtain the encrypted image. The test of whether the feasibility and security of the algorithm have been improved can be obtained by analyzing the size of the experimental data and the degree of deviation from the ideal value. After comprehensive analysis, it is concluded that this paper has good encryption effect on the basis of encryption security has also been improved.
AI, Human–Robot Interaction, and Natural Language Processing

An AI-driven (or AI-assisted) speech or dialogue system, from an engineering perspective, can be decomposed into a pipeline with a subset of the following three distinct processing activities: (1) Speech processing that turns sampled acoustic sound waves into enriched phonetic information through automatic speech recognition (ASR), and vice versa via text-to-speech (TTS); (2) Natural Language Processing (NLP), which operates at both syntactic and semantic levels to get at the meanings of words as well as of the enriched phonetic information; (3) Dialogue processing which ties both together so that the system can function within the specified latency and semantic constraints. This perspective allows for at least three levels of context. The lowest level is phonetic, where the fundamental components of speech are built from a time-sequence string of acoustic symbols (analyzed in ASR or generated in TTS). The next higher level of context is word- or character-level, normally postulated as sequence-to-sequence modeling. The highest level of context typically used today keeps track of a conversation or topic. An even higher level of context, generally missing today, but which will be essential in future, is that of our beliefs, desires, and intentions.
A Vision on Intentions in Software Engineering

Intentions are fundamental in software engineering, but they are typically only implicitly considered through different abstractions, such as requirements, use cases, features, or issues. Specifically, software engineers develop and evolve (i.e., change) a software system based on such abstractions of a stakeholder’s intention—something a stakeholder wants the system to be able to do. Unfortunately, existing abstractions are (inherently) limited when it comes to representing stakeholder intentions and are mostly used for documenting only. So, whether a change in a system fulfills its underlying intention (and only this one) is an essential problem in practice that motivates many research areas (e.g., testing to ensure intended behavior, untangling intentions in commits). We argue that none of the existing abstractions is ideal for capturing intentions and controlling software evolution, which is why intentions are often vague and must be recovered, untangled, or understood in retrospect. In this paper, we reflect on the role of intentions (represented by changes) in software engineering and sketch how improving their management may support developers. Particularly, we argue that continuously managing and controlling intentions as well as their fulfillment has the potential to improve the reasoning about which stakeholder requests have been addressed, avoid misunderstandings, and prevent expensive retrospective analyses. To guide future research for achieving such benefits for researchers and practitioners, we discuss the relationships between different abstractions and intentions, and propose steps towards managing intentions.
Tritor: Detecting Semantic Code Clones by Building Social Network-Based Triads Model

Code clone detection refers to finding the functional similarities between two code fragments, which is becoming increasingly important with the evolution of software engineering. It is reasonable because code cloning can increase maintenance costs and even cause the propagation of vulnerabilities, which can have a negative impact on software security. Numbers of code clone detection methods have been proposed, including tree-based methods that are capable of detecting semantic code clones. However, since tree structure is complex, these methods are difficult to apply to large-scale clone detection. In this paper, we propose a scalable semantic code clone detector based on semantically enhanced abstract syntax tree. Specifically, we add the control flow and data flow details into the original tree and regard the enhanced tree as a social network. Then we build a social network-based triads model to collect the similarity features between the two methods by analyzing different types of triads within the network. After obtaining all features, we use them to train a machine learning-based code clone detector (i.e., Tritor). Our comparative experimental results show that Tritor is superior to SourcererCC, RtvNN, Deckard, ASTNN, TBCNN, CDLH, and SCDetector, are equally good with DeepSim and FCCA. As for scalability, Tritor is about 39 times faster than another current state-of-the-art tree-based code clone detector ASTNN.
Approximately Equivariant Quantum Neural Network for p4m Group Symmetries in Images

Quantum Neural Networks (QNNs) are suggested as one of the quantum algorithms which can be efficiently simulated with a low depth on near-term quantum hardware in the presence of noises. However, their performance highly relies on choosing the most suitable architecture of Variational Quantum Algorithms (VQAs), and the problem-agnostic models often suffer issues regarding trainability and generalization power. As a solution, the most recent works explore Geometric Quantum Machine Learning (GQML) using QNNs equivariant with respect to the underlying symmetry of the dataset. GQML adds an inductive bias to the model by incorporating the prior knowledge on the given dataset and leads to enhancing the optimization performance while constraining the search space. This work proposes equivariant Quantum Convolutional Neural Networks (EquivQCNNs) for image classification under planar p4m symmetry, including reflectional and 90° rotational symmetry. We present the results tested in different use cases, such as phase detection of the 2D Ising model and classification of the extended MNIST dataset, and compare them with those obtained with the non-equivariant model, proving that the equivariance fosters better generalization of the model.
Towards An End-To-End Approach For Quantum Principal Component Analysis

Quantum Machine Learning has gained significant attention in recent years as a way to leverage the relationship between quantum information and machine learning. Principal Component Analysis (PCA) is a fundamental technique in machine learning, and the potential for its quantum acceleration has been extensively studied. However, an algorithmic end-to-end implementation remains challenging. This paper covers quantum PCA implementation up to extracting the principal components. We extend existing processes for quantum state tomography to extract the eigenvectors from the output state, addressing the challenges of dealing with complex amplitudes in the case of non-integer eigenvalues. Finally, we apply our implementation to a practical quantum finance use case related to interest rate risk, and present the results of our experiments.
Optimal Qubit Reuse for Near-Term Quantum Computers

Near-term quantum computations are limited by high error rates, the scarcity of qubits and low qubit connectivity. Increasing support for mid-circuit measurements and qubit reset in near-term quantum computers enables qubit reuse that may yield quantum computations with fewer qubits and lower errors. In this work, we introduce a formal model for qubit reuse optimization that delivers provably optimal solutions with respect to quantum circuit depth, number of qubits, or number of swap gates for the first time. This is in contrast to related work where qubit reuse is used heuristically or optimally but without consideration of the mapping effort. We further investigate reset errors on near-term quantum computers by performing reset error characterization experiments. Using the hereby obtained reset error characterization and calibration data of a near-term quantum computer, we then determine a qubit assignment that is optimal with respect to a given cost function. We define this cost function to include gate errors and decoherence as well as the individual reset error of each qubit. We found the reset fidelity to be state-dependent and to range, depending on the reset qubit, from 67.5% to 100% in a near-term quantum computer. We demonstrate the applicability of the developed method to a number of quantum circuits and show improvements in the number of qubits and swap gate insertions, estimated success probability, and Hellinger fidelity of the investigated quantum circuits.
Towards Fidelity-Optimal Qubit Mapping on NISQ Computers

Quantum computing is gaining momentum in revolutionizing the way we approach complex problem-solving. However, the practical implementation of quantum algorithms remains a significant challenge due to the error-prone and hardware limits of near-term quantum devices. For instance, physical qubit connections are limited, which necessitates the use of quantum SWAP gates to dynamically transform the logical topology during execution. In addition, to optimize fidelity, it is essential to ensure that 1) the allocated hardware has a low error rate and 2) the number of SWAP gates injected into the circuit is minimized. To address these challenges, we propose a suite of algorithms: the Fidelity-aware Graph Extraction Algorithm (FGEA) is used to identify the hardware region with the lowest probability of error, the Frequency-based Mapping Algorithm (FMA) allocates logical-physical qubits that reduce the potential distance of topological transformation, and the Heuristic Routing Algorithm (HRA) searches for an optimal swapping injection strategy. We evaluate the proposed algorithms on the IBM-provided Noisy Intermediate-Scale Quantum (NISQ) computer, using a dataset consisting of 17 different quantum circuits of various sizes. The circuits are executed on the IBM Toronto Falcon processor. The three proposed algorithms outperform the existing SABRE algorithm in reducing the number of SWAP gates required. Therefore, our proposed algorithms hold significant promise in enhancing the fidelity and reducing the number of SWAP gates required in implementing Quantum algorithms.
An Automated Approach to Extracting Local Variables

Extract local variable is a well-known and widely used refactoring. It is frequently employed to replace one or more occurrences of a complex expression with simple accesses to a newly added variable. Although most IDEs provide tool support for extract local variables, such tools without deep analysis of the refactorings may result in semantic errors. To this end, in this paper, we propose a novel and more reliable approach, called ValExtractor, to conduct extract variable refactorings automatically. The major challenge of automated extract local variable refactorings is how to efficiently and accurately identify the side effect of the extracted expressions and the potential interaction between the extracted expressions and their contexts without time-consuming dynamic execution of the involved programs. To resolve this challenge, ValExtractor leverages a lightweight static source code analysis to validate the side effect of the selected expression, and to identify which occurrences of the selected expression could be extracted together without changing the semantics of the program or introducing potential new exceptions. Our evaluation results on open-source Java applications suggest that Eclipse and IntelliJ IDEA, the state-of-the-practice refactoring engines, resulted in a large number of faulty extract variable refactorings whereas ValExtractor successfully avoided all such errors. The proposed approach has been merged into (and distributed with) Eclipse to improve the safety of extract local variable refactoring.
qgym: A Gym for Training and Benchmarking RL-Based Quantum Compilation

Compiling a quantum circuit for specific quantum hardware is a challenging task. Moreover, current quantum computers have severe hardware limitations. To make the most use of the limited resources, the compilation process should be optimized. To improve currents methods, Reinforcement Learning (RL), a technique in which an agent interacts with an environment to learn complex policies to attain a specific goal, can be used. In this work, we present qgym, a software framework derived from the OpenAI gym, together with environments that are specifically tailored towards quantum compilation. The goal of qgym is to connect the research fields of Artificial Intelligence (AI) with quantum compilation by abstracting parts of the process that are irrelevant to either domain. It can be used to train and benchmark RL agents and algorithms in highly customizable environments.
Depth-Optimal Synthesis of Clifford Circuits with SAT Solvers

Circuit synthesis is the task of decomposing a given logical functionality into a sequence of elementary gates. It is (depth-)optimal if it is impossible to achieve the desired functionality with even shorter circuits. Optimal synthesis is a central problem in both quantum and classical hardware design, but also plagued by complexity-theoretic obstacles. Motivated by fault-tolerant quantum computation, we consider the special case of synthesizing blocks of Clifford unitaries. Leveraging entangling input stimuli and the stabilizer formalism allows us to reduce the Clifford synthesis problem to a family of poly-size satisfiability (SAT) problems - one for each target circuit depth. On a conceptual level, our result showcases that the Clifford synthesis problem is contained in the first level of the polynomial hierarchy (NP), while the classical synthesis problem for logical circuits is known to be complete for the second level of the polynomial hierarchy (Σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ). Based on this theoretical reduction, we formulate a SAT encoding for depth-optimal Clifford synthesis. We then employ SAT solvers to determine a satisfying assignment or to prove that no such assignment exists. From that, the shortest depth for which synthesis is still possible (optimality) as well as the actual circuit (synthesis) can be obtained. Empirical evaluations show that the optimal synthesis approach yields a substantial depth improvement for random Clifford circuits and Clifford+T circuits for Grover search.
Cyber-Resilience, Principles, and Practices

The increasing cyber-attack and threat landscape has raised security and privacy concerns in industrial and critical infrastructure environments. Data is the most valuable asset for malicious actors, and having intentions to steal, exploit, gain access to any form of encrypted or unencrypted data is their ultimate goal. To protect the data-driven, autonomous, agile, and intelligent smart manufacturing environment, it is essential to have a systematic process for developing a cybersecurity strategy and having an incident response for mitigating potential attacks it may be susceptible to. For developing a robust cyber-strategy, it is significant to understand the sophisticated technologies used within the environment, assess the risks it may be exposed to, implement standards and controls to mitigate those risks, have threat intelligence and an incident response in place, and continuously monitor the ever-changing attack surface. The existing standards, governance, risk, and controls (GRC) may not completely mitigate compliance-based risks in situations of lack of alignment. As one standard may not fit all, each industry needs to have a distinct cybersecurity strategy based on the levels of security (i.e., baseline/moderate/maximum) required, implementation of security standards, and GRC mapped with the industry's operational environment. Having resilience enables an industry to be proactive and mitigate operation disruption and cyber-risk/shock scenarios. With the help of a manufacturing use-case example, this chapter provides insights and understanding of various potential threat scenarios (i.e., espionage, loss of command and control, compromised data or device, etc.), high-risk concerns that need to be considered for building cyber-resiliency within an industry. Best practices, standards, risks, policies, and alignment with cyber-resilience act and law are also summarized.
Defining Best Practices for Quantum Benchmarks

As quantum computers grow in size and scope, a question of great importance is how best to benchmark performance. Here we define a set of characteristics that any bench-mark should follow-randomized, well-defined, holistic, device independent-and make a distinction between benchmarks and diagnostics. We use Quantum Volume (QV) [1] as an example case for clear rules in benchmarking, illustrating the implications for using different success statistics, as in Ref. [2]. We discuss the is-sue of benchmark optimizations, detail when those optimizations are appropriate, and how they should be reported. Reporting the use of quantum error mitigation techniques is especially critical for interpreting benchmarking results, as their ability to yield highly accurate observables comes with exponential overhead, which is often omitted in performance evaluations. Finally, we use application-oriented and mirror benchmarking techniques to demonstrate some of the highlighted optimization principles, and introduce a scalable mirror quantum volume benchmark. We elucidate the importance of simple optimizations for improving benchmarking results, and note that such omissions can make a critical difference in comparisons. For example, when running mirror randomized benchmarking, we observe a reduction in error per qubit from 2 % to 1 % on a 26-qubit circuit with the inclusion of dynamical decoupling.
Provably secure and lightweight three-factor authentication scheme for industrial medical CPS

Cyber-Physical System (CPS) is a multidimensional complex system that integrates computing, network, and physical environment, which is widely used to promote the upgrading of industrial production and technology. Recently, the application of industrial CPS in the medical field has attracted the attention of scholars and medical experts. Medical CPS can establish a perfect medical network to help doctors monitor patients’ conditions in real-time and make treatments. However, how to design a provably secure and lightweight authentication protocol for industrial medical CPS is a challenge. Very recently, Qi et al. proposed an authentication protocol for industrial medical CPS based on the chaotic map, the Artificial Intelligence (AI) biometric technique is used in the protocol to resist password guessing attack and smart card lost attack. However, we find that their protocol is still vulnerable to identity guessing attack, user impersonation attack, trace attack, desynchronization attack, and has no perfect forward secrecy. Therefore, we propose a security-enhanced and lightweight authentication protocol for industrial medical CPS. In the protocol, a dynamic temporary identity strategy is designed to protect anonymity and privacy, which enables the updating of temporary identities while resisting desynchronization attacks. The protocol is proved secure through formal security proof in random oracle model. Meanwhile, compared with the related protocols, our protocol is superior in security and cost to meet the lightweight requirements in medical scenarios.
Demystifying the Composition and Code Reuse in Solidity Smart Contracts

As the development of Solidity smart contracts has increased in popularity, the reliance on external sources such as third-party packages increases to reduce development costs. However, despite the use of external sources bringing flexibility and efficiency to the development, they could also complicate the process of assuring the security of downstream applications due to the lack of package managers for standardized ways and sources. While previous studies have only focused on code clones without considering how the external components are introduced, the compositions of a smart contract and their characteristics still remain puzzling.
Improving Rating Prediction in Multi-criteria Recommender Systems via a Collective Factor Model

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