Reviewerly/exHarmony huggingface dataset:
On the Applicability of Annotation-Based Source Code Modification in Kotlin (Work in Progress)

Annotations add metadata to source code entities such as classes or functions, which later can be processed by so-called annotation processors to, for example, modify the annotated program’s source code. While annotation processing has been well-explored in Java, the Kotlin community still lacks a comprehensive summary. Thus, in this paper, we summarize the main approaches available in Kotlin: (1) ‍Compile-time annotation processing using (a) ‍Kotlin Annotation Processing Tool (KAPT), (b) ‍Kotlin Symbolic Processing (KSP), or (c) ‍writing a custom Kotlin Compiler plugin; as well as (2) ‍load-time code modification using an agent or a custom class loader. We provide proof-of-concept implementations, discuss advantages and disadvantages, and specifically focus on how well each approach supports modifying the annotated source code. This should help developers and researchers to better decide when to use which approach.
Capable VMs Project Overview (Poster Abstract)

In this poster, we will outline the scope and contributions of the Capable VMs project, in the framework of the UKRI Digital Security by Design programme.
Self-Directed Mobile Robot Navigation Based on Functional Firefly Algorithm (FFA)

This paper proposes an optimized mobile robot navigation strategy using a functional firefly algorithm (FFA) and choice function. This approach has two key advantages: first, the linear objective function performs efficiently with the single degree and finite-order polynomial time operation, and second, the cartesian constraint performs compactly with the chosen degree of freedom on the finite interval. This functional approach optimizes the size of operational parameters in context with key size, operation time, and a finite range of verification. The choice function achieves parameter order (size) reduction. The attraction characteristic of fireflies is represented by the choice function for optimizing the choice between low and high intensities of fireflies. In 2D and 3D environments, the proposed robot navigation performs well in an uncertain environment with static and dynamic obstacles. This efficiency includes the robot’s speed as determined by the choice function’s minimum path lengths. The collision-free path is achieved by the non-void family of non-void sets. The obtained results are optimal in terms of path length and navigational time. The proposed controller is also compared with the other existing controllers, and it is observed that the FFA gives the shortest path in less time for the same environmental condition.
Deep Learning Performance Characterization on GPUs for Various Quantization Frameworks

Deep learning is employed in many applications, such as computer vision, natural language processing, robotics, and recommender systems. Large and complex neural networks lead to high accuracy; however, they adversely affect many aspects of deep learning performance, such as training time, latency, throughput, energy consumption, and memory usage in the training and inference stages. To solve these challenges, various optimization techniques and frameworks have been developed for the efficient performance of deep learning models in the training and inference stages. Although optimization techniques such as quantization have been studied thoroughly in the past, less work has been done to study the performance of frameworks that provide quantization techniques. In this paper, we have used different performance metrics to study the performance of various quantization frameworks, including TensorFlow automatic mixed precision and TensorRT. These performance metrics include training time and memory utilization in the training stage along with latency and throughput for graphics processing units (GPUs) in the inference stage. We have applied the automatic mixed precision (AMP) technique during the training stage using the TensorFlow framework, while for inference we have utilized the TensorRT framework for the post-training quantization technique using the TensorFlow TensorRT (TF-TRT) application programming interface (API).We performed model profiling for different deep learning models, datasets, image sizes, and batch sizes for both the training and inference stages, the results of which can help developers and researchers to devise and deploy efficient deep learning models for GPUs.
AI-enabled human capital management (HCM) software adoption using full consistency method (FUCOM): evidence from banking industry

Purpose Artificial intelligence (AI)-based human capital management (HCM) software solutions represent a potentially effective way to leverage and streamline a bank’s human resources. However, despite the attractiveness of AI-based HCM solutions to improve banks’ effectiveness, to the best of the authors’ knowledge, there are no current studies that identify critical success factors (CSFs) for adopting AI-based HCM in the banking sector. This study aims to fill this gap by investigating CSFs for adopting AI-based HCM software solutions in the banking sector. Design/methodology/approach Full consistency method methodology and technology–organization–environment, economic and human framework are used for categorizing and ranking CSFs. Findings The study identifies the technological and environmental dimensions as the most and least important dimensions for AI-based HCM adoption in banks. Among specific CSFs, compatible technology facilities, sufficient privacy and security and relative advantages of technology over competing technologies were identified as the most important. Implementation of AI-based HCM solutions requires significant outlays of resources, both human and financial, for banks. Originality/value The study provides bank administrators a set of objective parameters and criterion to evaluate the feasibility of adopting a particular AI-based HCM solution in banks.
Density operators

Abstract The first part of the chapter is devoted to the discussion of density operators. We first introduce the definition of a density operator and the unitary evolution of a quantum state. We then examine the measurement of a quantum system using the density operator approach. We present the density operator associated with a qubit and a harmonic oscillator at thermal equilibrium. The second part of the chapter focuses on bipartite systems. We show that the quantum state of a subsystem is given by the partial trace of the density operator of the global system. Finally, we explain the concept of purification of a density operator
Microwave resonators for superconducting devices

Abstract In this chapter, we present microwave resonators, one of the two main elements composing a quantum computer based on superconducting devices. We describe planar microwave resonators and introduce their classical modeling as RLC circuits. We introduce transmission lines coupled to microwave resonators and explore the dependence of the transmission coefficient on temperature and power. We then describe how to drive a microwave resonator with an electromagnetic pulse. In the last part of the chapter, we model a microwave resonator as a quantum harmonic oscillation and discuss photon losses with a master equation
Quantum mechanics

Abstract In this chapter, we present the theoretical foundations of quantum mechanics. Our presentation follows the von Neumann postulates. We discuss them stepping through the parts of a typical quantum experiment: the preparation of the system, the subsequent time evolution, and the final measurement. Specifically, we present normalized vectors in a Hilbert space, that describe preparations; projection-valued measures and Hermitian operators, that describe measurable observables; and the Schrodinger equation governing time evolution. In the last part of the chapter, we present two examples of quantum systems: the qubit and the quantum harmonic oscillator. We study the interaction between them in two different regimes
Quantum maps

Abstract This chapter is devoted to the discussion of quantum maps. We present three equivalent definitions of a quantum map and important examples, including the depolarizing channel, amplitude damping, and dephasing. We briefly describe the inversion recovery and the Ramsey experiment, two experimental techniques designed to measure the relaxation and coherence time of a qubit. We present the Kraus representation of a quantum map, and we show that quantum maps arise when the dynamics of an additional system are averaged out, a result known as Stinespring’s dilation theorem. Lastly, we present the Choi-Jamiolkowki isomorphism
Computational models

Abstract In this chapter, we introduce the definitions of automata Turing machines. We present different flavors of Turing machines including deterministic, probabilistic, and multi-tape machines. We present several complexity classes, i.e. collections of problems that can be solved by a machine with similar resources, including the P and NP classes. We present the Church-Turing thesis, which states that if a problem can be solved by a physical machine, then it can also be solved by any deterministic Turing machine. In the last part of the chapter, we introduce quantum Turing machines and the quantum circuit model. We study the complexity class BQP. This is the class of problems that can be solved by a quantum computer with polynomial resources and a small error probability. Finally, we introduce QMA, the quantum version of the complexity class NP. We present the k-LOCAL problem, and we show that this problem is in QMA
Quantum simulation of Hamiltonian dynamics

Abstract This chapter presents some quantum algorithms to simulate the dynamics of a quantum system, one of the most important applications of a quantum computer. We introduce the conditions for efficient simulation of Hamiltonian dynamics. We then present some Trotterization methods, in which the time evolution operator is approximated as a product of terms, each of which can be implemented by a quantum circuit at polynomial cost. We show high-order decompositions and illustrate the use of a Trotterization method to simulate sparse Hamiltonians. We discuss the Taylor series method and the qubitization method. The latter achieves optimal cost in simulation time and target accuracy. We conclude the chapter with a proof that simulating Hamiltonian dynamics is BQP-complete
Entanglement

Abstract This chapter describes entanglement in bipartite systems. We define entanglement for pure states introducing the concept of Schmidt decomposition, and we show that entanglement can be quantified through the von Neumann entropy. We then present dense coding and teleportation, two quantum information protocols that use entanglement as a resource. We present the notions of locality and realism and derive the Clauser-Horne-Shimony-Holt inequality, an extended version of Bell’s inequalities. We then focus on mixed states and we present different techniques to solve the separability problem
The molecular Hamiltonian in second quantization

Subject Astronomy and Astrophysics Computational Physics Collection: Oxford Scholarship Online
Quantum circuits

Abstract In this chapter, we describe the circuit model of quantum computation, focusing on the initialization of a set of qubits, their unitary evolution, and final projective measurement. We outline Di Vincenzo’s criteria for constructing a functional quantum computer. We describe single-qubit gates and measurements, and we present two-qubit gates. We prove that single-qubit gates and a particular two-qubit gate, the CNOT gate, are universal for quantum computation. In the last part of the chapter, we present the Clifford group of unitary transformations that stabilize the Pauli group, and the Gottesman-Knill theorem.
Decoherence

Abstract This chapter is dedicated to open quantum systems. We explain the origin of non-unitary time evolution by studying different interactions between a qubit and the environment. The first part of the chapter introduces the time evolution equation of an open quantum system, also called the Born-Markov master equation and the Redfield equation. We then study the interaction between a qubit and a thermal bath of harmonic oscillators and introduce the qubit relaxation and the dephasing times. After discussing the relationship between relaxation and dephasing times of a qubit and the power spectral density of the noise source, we present the Hahn echo and the Carr-Purcell-Meiboom-Gill experiments, two protocols that provide information about the noise spectrum. The chapter ends with a discussion of the driven evolution of a qubit subject to spontaneous emission and dephasing
The quantum Fourier transform

Subject Astronomy and Astrophysics Computational Physics Collection: Oxford Scholarship Online
Oblivious amplitude amplification lemma

Subject Astronomy and Astrophysics Computational Physics Collection: Oxford Scholarship Online
Quantum signal processing

Subject Astronomy and Astrophysics Computational Physics Collection: Oxford Scholarship Online
A Lightweight Recurrent Grouping Attention Network for Video Super-Resolution

Effective aggregation of temporal information of consecutive frames is the core of achieving video super-resolution. Many scholars have utilized structures such as sliding windows and recurrences to gather the spatio-temporal information of frames. However, although the performances of constructed video super-resolution models are improving, the sizes of the models are also increasing, exacerbating the demand on the equipment. Thus, to reduce the stress on the device, we propose a novel lightweight recurrent grouping attention network. The parameters of this model are only 0.878 M, which is much lower than the current mainstream model for studying video super-resolution. We have designed a forward feature extraction module and a backward feature extraction module to collect temporal information between consecutive frames from two directions. Moreover, a new grouping mechanism is proposed to efficiently collect spatio-temporal information of the reference frame and its neighboring frames. The attention supplementation module is presented to further enhance the information gathering range of the model. The feature reconstruction module aims to aggregate information from different directions to reconstruct high-resolution features. Experiments demonstrate that our model achieves state-of-the-art performance on multiple datasets.
Timeline Navigators: User Interface Design Patterns for Time

While a wide range of software applications allow users to manipulate time-based data, or time series, they all work with user interfaces designed with specific affordances related to time. This article investigates how interfaces help users capture, manipulate, and understand time series data. By exploring how user interface elements of time-based software applications address seven User Time Needs, the authors propose a set of nine different interface design patterns called timeline navigators that generalize across applications. While existing research evaluates specific time-based interface elements, the authors have not seen a framework for considering time-based interface elements and users’ corresponding underlying needs more generally across software domains. The analysis provided is intended to help designers become aware of the major practices and conventions found in timeline navigators as well as the user needs that they support.
Linear algebra

Abstract In this chapter, we present an overview of linear algebra with the purpose of outlining concepts relevant to quantum computing using the notation common to the field. The chapter consists of two parts. In the first part, we introduce vector spaces. We present the notions of linear dependence, dimensionality, inner product, and distance. We use these concepts to introduce finite-dimensional Hilbert spaces, a key ingredient for quantum information theory. In the second part of the chapter, we introduce linear operators. We describe the main classes of linear operators and show their mutual relations.
A Novel Approach of Adpative Window 2 Technique and Kalman Filter- “KalADWIN2” for Detection of Concept Drift

A recommendation engine (RE) is a machine learning technique that provides personalized recommendations and anticipates a user's future preference for a collection of goods or services. In Online Supervised Learning (OSL) settings like various REs, where data vary over time, Concept Drift (CD) issue usually occurs. There are many CD Detectors in the literature work but the most preferred choice for the non-stationary, dynamic and streaming data is the supervised technique- Adaptive Window (ADWIN) approach. The paper aims towards the limitations of the ADWIN approach, where ADWIN2 approach is more time &memory efficient than ADWIN. The paper also focusses on novel proposed technique of the combination of Kalman Filter and ADWIN2 approach, named-“KalADWIN2”, as it’s the best estimator for detection even in noisy environment. It ultimately helps in fast CD detection in REs.
Unleashing Trustworthy Cloud Storage: Harnessing Blockchain for Cloud Data Integrity Verification

This position paper explores the transformative potential of blockchain technology in ensuring data integrity within cloud storage systems. The increasing adoption of cloud storage services has raised concerns about the security and integrity of stored data. In this paper, we argue that integrating blockchain technology into cloud data integrity verification schemes offers a robust and decentralized solution. Through a comprehensive review of relevant literature, we examine the benefits and challenges associated with this approach. Our analysis reveals that blockchain-based data integrity verification schemes provide tamper-proof and transparent mechanisms, enhancing trust and security in cloud storage environments. We further discuss the potential impact of this integration on data privacy, scalability, and performance. While acknowledging the current limitations and ongoing research efforts, we propose that embracing blockchain for cloud data integrity verification can foster a new era of trustworthy and reliable cloud storage systems. This position paper aims to stimulate further discussion and research on the transformative role of blockchain in ensuring data integrity within the cloud.
Abstractive Summarization Evaluation for Prompt Engineering

The task of summarizing large documents for easier and faster readability is widely acknowledged and a standard task in the field of Natural Language Processing. The metrics that are used to measure the working of this task are based on statistical measures such as n-grams and Longest Common Subsequences. Abstractive summarization is a type of automatic text summarization which refers to creating the summary from the main document without entirely copying words from the original document. With the advent of Deep learning architectures, abstract summarization has increased in popularity. The Large language models which are used for abstractive text summarization need proper prompts to generate summary. Engineering proper prompts is important as the quality of the summary generated depends on the prompt. In this paper, an abstractive measure of similarity is proposed where the textual similarity is measured by using Euclidean distance to compare a Principal Component based transformed BERT Embedding vector of the document and the summary. The metric is used to create prompts for Generative Pretrained Transformer and Text to Text Transfer Transformer models which are standard state-of-the-art language models. The summary generated shows significant improvement and the prompts generated by using the abstractive comparison metric are seen to have the perplexity almost the same as the document thus promising better summarization results.
Feature Selection Techniques on Breast Cancer Classification Using Fine Needle Aspiration Features: A Comparative Study

Breast cancer remains a prevalent invasive cancer in women as it ranks as the second leading cause of cancer-related death among women. It poses a significant global medical challenge due to its substantial increase in cases over the last decade. Early detection of breast cancer is vital; hence the development of computer-aided diagnosis (CAD) systems is crucial in assisting pathologists to accurately interpret and diagnose the tumor. Feature selection plays a significant role in CAD as it involves choosing the most relevant and informative features from the original dataset to improve the performance of the system. Thus, this study focuses on evaluating various feature selection methods on fine needle aspiration (FNA) features which are adapted from Wisconsin Diagnostic Breast Cancer (WDBC) dataset from UCI Repository. The analysis involved five feature selection techniques; Information Gain (InfoGain), Correlation Feature Selection (CFS), Fast-Correlation Based Filter (FCBF), Consistency and Relief-F with three different machine learning classifiers including Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF) with 10-fold cross-validations. Based on the experimental outcomes, it was observed that FCBF with LR classifier surpassed other FS techniques (ACC = 0.9718 and AUC = 0.993) with 7 features. On the other hand, Relief-F outshined other FS with both classifiers of SVM (ACC = 0.9772 and AUC = 0.971) and RF (ACC = 0.9684 and AUC = 0.991). This study validated that the Relief-F technique exhibited supremacy over other FS techniques. However, the task of identifying important features from high-dimensional data remains a significant obstacle in intelligent diagnosis. Henceforth, it is essential to dedicate further efforts to the development of CAD systems using efficient feature selection techniques to maximize the performance and effectiveness of diagnostic models.
Harnessing Technology for Efficient Coagulation Profile E-Reporting: A Design Thinking Approach

The coagulation profile report contains international normalized ratio (INR) blood test, activated partial thromboplastin clotting time (APTT), platelets, and fibrinogen. The report is for identifying abnormal blood clotting tendencies by examining key factors associated with bleeding issues. Traditional paper-based reporting methods frequently encounter difficulties such as manual data entry errors, delayed result dissemination, and restricted accessibility. The incorporation of technology in coagulation profile e-reporting offers a promising solution for addressing these deficiencies and revolutionizing the documentation and communication of laboratory results. This exploratory study seeks to evaluate the practicability and prospective advantages of integrating technology into coagulation profile reporting processes. Utilizing a design-thinking methodology, this study investigates the stakeholder and users using persona, empathy, journey mapping, ideation and prototyping processes in order to determine the requirements for a proposed mobile application. The paper presents a solution to enhance existing operations by describing its components, prototype design, and validation procedure. The solution is driven by the imperative to provide timely notifications to healthcare providers regarding the availability of laboratory results in the electronic medical record (EMR) system.
Actuality, and the Necessity of Distinctness and Possibility

Abstract An actuality operator is introduced, governed by two principles according to which p if possibly actually p, and necessarily actually p if p. With actuality added to Classicism in this way, two new principles, namely the necessity of distinctness and the necessity of possibility, become derivable. Relative to Classicism, these two principles are equivalent. Setting aside the actuality operator, this motivates extending Classicism by the necessity of possibility; the resulting view is called C5. It is shown that this leads to the strong and well-known modal logic S5.