Reviewerly/exHarmony huggingface dataset:
RSLBO: Random Selected Leader Based Optimizer

Designed optimization problems in different disciplines of science should be solved using appropriate techniques.Optimization algorithms are among the most effective and widely used methods in solving optimization problems that are able to provide suitable solutions for these problems.Innovation and scientific contribution of this article in designing a new optimizer called Random Selected Leader Based Optimizer (RSLBO) in order to be used in optimizing the objective functions of optimization problems and achieving the desired solutions.The main idea of the proposed RSLBO is to increase the search power using a random leader.In fact, instead of the algorithm population update relying on several specific members, such as the best member or the worst member, any ordinary member of the population can be a leader in guiding and updating the algorithm population.RSLBO is described, then mathematically modelled to be used in solving optimization problems.The main feature and advantage of the proposed RSLBO is that its implementation and relationships are very simple and understandable, and it also lacks control parameters.The performance of the proposed RSLBO is evaluated on a set of twenty-three standard functions.Also, in order to analyse the quality of the proposed RSLBO in providing appropriate solutions to optimization problems, the results obtained from the proposed algorithm are compared with eight well-known algorithms including Flow Direction Algorithm (FDA), Hide Object Game Optimizer (HOGO), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Teaching-Learning-Based Optimization (TLBO), Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).The results of optimization of objective functions indicate the high ability of the RSLBO to solve optimization problems and provide appropriate and acceptable solutions.Also, the analysis and comparison of the performance of the eight well-known optimization algorithms against the proposed RSLBO shows that the RSLBO has provided more appropriate solutions and is superior and much more competitive than the eight compared algorithms.
A Time Sequence Images Matching Method Based on the Siamese Network

The similar analysis of time sequence images to achieve image matching is a foundation of tasks in dynamic environments, such as multi-object tracking and dynamic gesture recognition. Therefore, we propose a matching method of time sequence images based on the Siamese network. Inspired by comparative learning, two different comparative parts are designed and embedded in the network. The first part makes a comparison between the input image pairs to generate the correlation matrix. The second part compares the correlation matrix, which is the output of the first comparison part, with a template, in order to calculate the similarity. The improved loss function is used to constrain the image matching and similarity calculation. After experimental verification, we found that it not only performs better, but also has some ability to estimate the camera pose.
Translating C to safer Rust

Rust is a relatively new programming language that targets efficient and safe systems-level applications. It includes a sophisticated type system that allows for provable memory- and thread-safety, and is explicitly designed to take the place of unsafe languages such as C and C++ in the coding ecosystem. There is a large existing C and C++ codebase (many of which have been affected by bugs and security vulnerabilities due to unsafety) that would benefit from being rewritten in Rust to remove an entire class of potential bugs. However, porting these applications to Rust manually is a daunting task. In this paper we investigate the problem of automatically translating C programs into safer Rust programs--that is, Rust programs that improve on the safety guarantees of the original C programs. We conduct an in-depth study into the underlying causes of unsafety in translated programs and the relative impact of fixing each cause. We also describe a novel technique for automatically removing a particular cause of unsafety and evaluate its effectiveness and impact. This paper presents the first empirical study of unsafety in translated Rust programs (as opposed to programs originally written in Rust) and also the first technique for automatically removing causes of unsafety in translated Rust programs.
Analytical Energy Gradient for State-Averaged Orbital-Optimized Variational Quantum Eigensolvers and Its Application to a Photochemical Reaction

Elucidating photochemical reactions is vital to understanding various biochemical phenomena and developing functional materials such as artificial photosynthesis and organic solar cells, albeit with notorious difficulty in both experiments and theories. The best theoretical way so far to analyze photochemical reactions at the level of ab initio electronic structure is the state-averaged multiconfigurational self-consistent field (SA-MCSCF) method. However, the exponential computational cost of classical computers with the increasing number of molecular orbitals hinders applications of SA-MCSCF for large systems we are interested in. Utilizing quantum computers was recently proposed as a promising approach to overcome such computational cost, dubbed as state-averaged orbital-optimized variational quantum eigensolver (SA-OO-VQE). Here, we extend a theory of SA-OO-VQE so that analytical gradients of energy can be evaluated by standard techniques that are feasible with near-term quantum computers. The analytical gradients, known only for the state-specific OO-VQE in previous studies, allow us to determine various characteristics of photochemical reactions such as the conical intersection (CI) points. We perform a proof-of-principle calculation of our methods by applying it to the photochemical cis-trans isomerization of 1,3,3,3-tetrafluoropropene. Numerical simulations of quantum circuits and measurements can correctly capture the photochemical reaction pathway of this model system, including the CI points. Our results illustrate the possibility of leveraging quantum computers for studying photochemical reactions.
L-Boost: Identifying Offensive Texts From Social Media Post in Bengali

Due to the significant increase in Internet activity since the COVID-19 epidemic, many informal, unstructured, offensive, and even misspelled textual content has been used for online communication through various social media. The Bengali and Banglish(Bengali words written in English format) offensive texts have recently been widely used to harass and criticize people on various social media. Our deep excavation reveals that limited work has been done to identify offensive Bengali texts. In this study, we have engineered a detection mechanism using natural language processing to identify Bengali and Banglish offensive messages in social media that could abuse other people. First, different classifiers have been employed to classify the offensive text as baseline classifiers from real-life datasets. Then, we applied boosting algorithms based on baseline classifiers. AdaBoost is the most effective ensemble method called adaptive boosting, which enhances the outcomes of the classifiers. The long short-term memory (LSTM) model is used to eliminate long-term dependency problems when classifying text, but overfitting problems occur. AdaBoost has strong forecasting ability and overfitting problem does not occur easily. By considering these two powerful and diverse models, we propose L-Boost, the modified AdaBoost algorithm using bidirectional encoder representations from transformers (BERT) with LSTM models. We tested the L-Boost model on three separate datasets, including the BERT pre-trained word-embedding vector model. We find our proposed L-Boost’s efficacy better than all the baseline classification algorithms reaching an accuracy of 95.11%.
Identifying Machine-Paraphrased Plagiarism

Employing paraphrasing tools to conceal plagiarized text is a severe threat to academic integrity. To enable the detection of machine-paraphrased text, we evaluate the effectiveness of five pre-trained word embedding models combined with machine-learning classifiers and eight state-of-the-art neural language models. We analyzed preprints of research papers, graduation theses, and Wikipedia articles, which we paraphrased using different configurations of the tools SpinBot and SpinnerChief. The best-performing technique, Longformer, achieved an average F1 score of 81.0% (F1=99.7% for SpinBot and F1=71.6% for SpinnerChief cases), while human evaluators achieved F1=78.4% for SpinBot and F1=65.6% for SpinnerChief cases. We show that the automated classification alleviates shortcomings of widely-used text-matching systems, such as Turnitin and PlagScan. To facilitate future research, all data, code, and two web applications showcasing our contributions are openly available at https://github.com/jpwahle/iconf22-paraphrase.
Modified Artificial Potential Field for the Path Planning of Aircraft Swarms in Three-Dimensional Environments

Path planning techniques are of major importance for the motion of autonomous systems. In addition, the chosen path, safety, and computational burden are essential for ensuring the successful application of such strategies in the presence of obstacles. In this context, this work introduces a modified potential field method that is capable of providing obstacle avoidance, as well as eliminating local minima problems and oscillations in the influence threshold of repulsive fields. A three-dimensional (3D) vortex field is introduced for this purpose so that each robot can choose the best direction of the vortex field rotation automatically and independently according to its position with respect to each object in the workspace. A scenario that addresses swarm flight with sequential cooperation and the pursuit of moving targets in dynamic environments is proposed. Experimental results are presented and thoroughly discussed using a Crazyflie 2.0 aircraft associated with the loco positioning system for state estimation. It is effectively demonstrated that the proposed algorithm can generate feasible paths while taking into account the aforementioned problems in real-time applications.
High-Resolution Hyperspectral Imaging Using Low-Cost Components: Application within Environmental Monitoring Scenarios

High-resolution hyperspectral imaging is becoming indispensable, enabling the precise detection of spectral variations across complex, spatially intricate targets. However, despite these significant benefits, currently available high-resolution set-ups are typically prohibitively expensive, significantly limiting their user base and accessibility. These limitations can have wider implications, limiting data collection opportunities, and therefore our knowledge, across a wide range of environments. In this article we introduce a low-cost alternative to the currently available instrumentation. This instrument provides hyperspectral datasets capable of resolving spectral variations in mm-scale targets, that cannot typically be resolved with many existing low-cost hyperspectral imaging alternatives. Instrument metrology is provided, and its efficacy is demonstrated within a mineralogy-based environmental monitoring application highlighting it as a valuable addition to the field of low-cost hyperspectral imaging.
A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring System

The emerging field of eXplainable AI (XAI) in the medical domain is considered to be of utmost importance. Meanwhile, incorporating explanations in the medical domain with respect to legal and ethical AI is necessary to understand detailed decisions, results, and current status of the patient's conditions. Successively, we will be presenting a detailed survey for the medical XAI with the model enhancements, evaluation methods, significant overview of case studies with open box architecture, medical open datasets, and future improvements. Potential differences in AI and XAI methods are provided with the recent XAI methods stated as (i) local and global methods for preprocessing, (ii) knowledge base and distillation algorithms, and (iii) interpretable machine learning. XAI characteristics details with future healthcare explainability is included prominently, whereas the pre-requisite provides insights for the brainstorming sessions before beginning a medical XAI project. Practical case study determines the recent XAI progress leading to the advance developments within the medical field. Ultimately, this survey proposes critical ideas surrounding a user-in-the-loop approach, with an emphasis on human-machine collaboration, to better produce explainable solutions. The surrounding details of the XAI feedback system for human rating-based evaluation provides intelligible insights into a constructive method to produce human enforced explanation feedback. For a long time, XAI limitations of the ratings, scores and grading are present. Therefore, a novel XAI recommendation system and XAI scoring system are designed and approached from this work. Additionally, this paper encourages the importance of implementing explainable solutions into the high impact medical field.
Nanowire-based integrated photonics for quantum information and quantum sensing

Abstract At the core of quantum photonic information processing and sensing, two major building pillars are single-photon emitters and single-photon detectors. In this review, we systematically summarize the working theory, material platform, fabrication process, and game-changing applications enabled by state-of-the-art quantum dots in nanowire emitters and superconducting nanowire single-photon detectors. Such nanowire-based quantum hardware offers promising properties for modern quantum optics experiments. We highlight several burgeoning quantum photonics applications using nanowires and discuss development trends of integrated quantum photonics. Also, we propose quantum information processing and sensing experiments for the quantum optics community, and future interdisciplinary applications.
TensorCircuit: a Quantum Software Framework for the NISQ Era

TensorCircuit is an open source quantum circuit simulator based on tensor network contraction, designed for speed, flexibility and code efficiency. Written purely in Python, and built on top of industry-standard machine learning frameworks, TensorCircuit supports automatic differentiation, just-in-time compilation, vectorized parallelism and hardware acceleration. These features allow TensorCircuit to simulate larger and more complex quantum circuits than existing simulators, and are especially suited to variational algorithms based on parameterized quantum circuits. TensorCircuit enables orders of magnitude speedup for various quantum simulation tasks compared to other common quantum software, and can simulate up to 600 qubits with moderate circuit depth and low-dimensional connectivity. With its time and space efficiency, flexible and extensible architecture and compact, user-friendly API, TensorCircuit has been built to facilitate the design, simulation and analysis of quantum algorithms in the Noisy Intermediate-Scale Quantum (NISQ) era.
Better Parameter-Free Anytime Search by Minimizing Time Between Solutions

This paper presents a new anytime search algorithm, anytime explicitestimation search (AEES). AEES is an anytime search algorithm which attempts to minimize the time between improvements to its incumbent solution by taking advantage of the differences between solution cost and length. We provide an argument that minimizing the time between solutions is ideal behavior for an anytime search algorithm and show that when actions have differing costs, many state-of-the-art search algorithms, including the search strategy of LAMA11 and anytime nonparametric A*, do not minimize the time between solutions. An empirical evaluation on seven domains shows that AEES often has boththe shortest time between incumbent solutions and the best solution in hand for a wide variety of cutoffs.
Ergodicity in Stationary Graph Processes: A Weak Law of Large Numbers

For stationary signals in time the weak law of large numbers (WLLN) states that ensemble and realization averages are within e of each other with a probability of order O(1/Ne^2) when considering N signal components. The graph WLLN introduced in this paper shows that the same is essentially true for signals supported on graphs. However, the notions of stationarity, ensemble mean, and realization mean are different. Recent papers have defined graph stationary signals as those that satisfy a form of invariance with respect to graph diffusion. The ensemble mean of a graph stationary signal is not a constant but a node-varying signal whose structure depends on the spectral properties of the graph. The realization average of a graph signal is defined here as an average of successive weighted averages of local signal values with signal values of neighboring nodes. The graph WLLN shows that this two node-varying signals are within e of each other with probability of order O(1/Ne^2) in at least some nodes. In stationary time signals, the realization average is not only a consistent estimator of the ensemble mean but also optimal in terms of mean squared error (MSE). This is not true of graph signals. Optimal MSE graph filter designs are also presented. An example problem concerning the estimation of the mean of a Gaussian random field is presented.
Testing, testing: a usability case study at University of Toronto Scarborough Library

Purpose With the rise of virtual library users and a steady increase in digital content, it is imperative that libraries build websites that provide seamless access to key resources and services. The paper aims to discuss these issues. Design/methodology/approach Usability testing is a valuable method for measuring user habits and expectations, as well as identifying problematic areas for improvement within a website. Findings In this paper, the authors provide an overview of user experience research carried out on the University of Toronto Scarborough Library website using a mixture of qualitative and quantitative research methods and detail insights gained from subsequent data analysis. Originality/value In particular, the authors discuss methods used for task-oriented usability testing and card sorting procedures using pages from the library website. Widely applicable results from this study include key findings and lessons learned from conducting usability testing in order to improve library websites.
Gradual type theory

Gradually typed languages are designed to support both dynamically typed and statically typed programming styles while preserving the benefits of each. While existing gradual type soundness theorems for these languages aim to show that type-based reasoning is preserved when moving from the fully static setting to a gradual one, these theorems do not imply that correctness of type-based refactorings and optimizations is preserved. Establishing correctness of program transformations is technically difficult, because it requires reasoning about program equivalence, and is often neglected in the metatheory of gradual languages. In this paper, we propose an axiomatic account of program equivalence in a gradual cast calculus, which we formalize in a logic we call gradual type theory (GTT). Based on Levy’s call-by-push-value, GTT gives an axiomatic account of both call-by-value and call-by-name gradual languages. Based on our axiomatic account we prove many theorems that justify optimizations and refactorings in gradually typed languages. For example, uniqueness principles for gradual type connectives show that if the βη laws hold for a connective, then casts between that connective must be equivalent to the so-called “lazy” cast semantics. Contrapositively, this shows that “eager” cast semantics violates the extensionality of function types. As another example, we show that gradual upcasts are pure functions and, dually, gradual downcasts are strict functions. We show the consistency and applicability of our axiomatic theory by proving that a contract-based implementation using the lazy cast semantics gives a logical relations model of our type theory, where equivalence in GTT implies contextual equivalence of the programs. Since GTT also axiomatizes the dynamic gradual guarantee, our model also establishes this central theorem of gradual typing. The model is parametrized by the implementation of the dynamic types, and so gives a family of implementations that validate type-based optimization and the gradual guarantee.
Game semantics for quantum programming

Quantum programming languages permit a hardware independent, high-level description of quantum algo rithms. In particular, the quantum lambda-calculus is a higher-order programming language with quantum primitives, mixing quantum data and classical control. Giving satisfactory denotational semantics to the quantum lambda-calculus is a challenging problem that has attracted significant interest in the past few years. Several models have been proposed but for those that address the whole quantum λ-calculus, they either do not represent the dynamics of computation, or they lack the compositionality one often expects from denotational models. In this paper, we give the first compositional and interactive model of the full quantum lambda-calculus, based on game semantics. To achieve this we introduce a model of quantum games and strategies, combining quantum data with a representation of the dynamics of computation inspired from causal models of concurrent systems. In this model we first give a computationally adequate interpretation of the affine fragment. Then, we extend the model with a notion of symmetry, allowing us to deal with replication. In this refined setting, we interpret and prove adequacy for the full quantum lambda-calculus. We do this both from a sequential and a parallel interpretation, the latter representing faithfully the causal independence between sub-computations.
Multi-objective training of Generative Adversarial Networks with multiple discriminators

Recent literature has demonstrated promising results for training Generative Adversarial Networks by employing a set of discriminators, in contrast to the traditional game involving one generator against a single adversary. Such methods perform single-objective optimization on some simple consolidation of the losses, e.g. an arithmetic average. In this work, we revisit the multiple-discriminator setting by framing the simultaneous minimization of losses provided by different models as a multi-objective optimization problem. Specifically, we evaluate the performance of multiple gradient descent and the hypervolume maximization algorithm on a number of different datasets. Moreover, we argue that the previously proposed methods and hypervolume maximization can all be seen as variations of multiple gradient descent in which the update direction can be computed efficiently. Our results indicate that hypervolume maximization presents a better compromise between sample quality and computational cost than previous methods.