Reviewerly/exHarmony huggingface dataset:
XAI-based cross-ensemble feature ranking methodology for machine learning models

Abstract Artificial Intelligence (AI) as one robust technology has been used in various fields, making innovative society possible and changing our lifestyles. However, the black box problem is still one big problem for artificial intelligence. In this study, we first compared the results of kernel Shapley Additive exPlanations (SHAP) for various machine learning models and found that the single SHAP model cannot explain the models at the human knowledge level. Then the factors’ global ranking was calculated using our proposed ensemble methodology. Finally, the new factors’ ranking was compared with other factor ranking method. Our experimental results declare that the proposed cross-ensemble feature ranking methodology provides stable and comparatively reliable feature ranking in both the classification and regression models.
A Novel Differential Evolution Algorithm Based on Local Fitness Landscape Information for Optimization Problems

The selection of mutation strategy greatly affects the performance of differential evolution algorithm (DE). For different types of optimization problems, different mutation strategies should be selected. How to choose a suitable mutation strategy for different problems is a challenging task. To deal with this challenge, this paper proposes a novel DE algorithm based on local fitness landscape, called FLIDE. In the proposed method, fitness landscape information is obtained to guide the selection of mutation operators. In this way, different problems can be solved with proper evolutionary mechanisms. Moreover, a population adjustment method is used to balance the search ability and population diversity. On one hand, the diversity of the population in the early stage is enhanced with a relative large population. One the other hand, the computational cost is reduced in the later stage with a relative small population. The evolutionary information is utilized as much as possible to guide the search direction. The proposed method is compared with five popular algorithms on 30 test functions with different characteristics. Experimental results show that the proposed FLIDE is more effective on problems with high dimensions.
Overview of Metaheuristic Algorithms

Metaheuristic algorithms are optimization algorithms that are used to address complicated issues that cannot be solved using standard approaches. These algorithms are inspired by natural processes such as genetics, swarm behavior, and evolution, and they are used to explore a broad search space to identify the global optimum of a problem. Genetic algorithms, particle swarm optimization, ant colony optimization, simulated annealing, and tabu search are examples of popular metaheuristic algorithms. These algorithms have been widely utilized to address complicated issues in domains like as engineering, finance, and computer science. In general, the history of metaheuristic algorithms spans several decades and involves the development of various optimization algorithms that are inspired by natural systems. Metaheuristic algorithms have become a valuable tool in solving complex optimization problems in various fields, and they are likely to continue to play an important role in the development of new technologies and applications.
Are Emergent Abilities of Large Language Models a Mirage?

Recent work claims that large language models display emergent abilities, abilities not present in smaller-scale models that are present in larger-scale models. What makes emergent abilities intriguing is two-fold: their sharpness, transitioning seemingly instantaneously from not present to present, and their unpredictability, appearing at seemingly unforeseeable model scales. Here, we present an alternative explanation for emergent abilities: that for a particular task and model family, when analyzing fixed model outputs, emergent abilities appear due to the researcher's choice of metric rather than due to fundamental changes in model behavior with scale. Specifically, nonlinear or discontinuous metrics produce apparent emergent abilities, whereas linear or continuous metrics produce smooth, continuous predictable changes in model performance. We present our alternative explanation in a simple mathematical model, then test it in three complementary ways: we (1) make, test and confirm three predictions on the effect of metric choice using the InstructGPT/GPT-3 family on tasks with claimed emergent abilities; (2) make, test and confirm two predictions about metric choices in a meta-analysis of emergent abilities on BIG-Bench; and (3) show to choose metrics to produce never-before-seen seemingly emergent abilities in multiple vision tasks across diverse deep networks. Via all three analyses, we provide evidence that alleged emergent abilities evaporate with different metrics or with better statistics, and may not be a fundamental property of scaling AI models.
Experimental Realization and Characterization of Stabilized Pair-Coherent States

The tools to prepare and characterize multimode bosonic states is expanded, allowing the experimental demonstration of pair-coherent states on superconducting cavities---a step closer to the pair-cat code.
A method for extracting tumor events from clinical CT examination reports

Accurate and efficient extraction of key information related to diseases from medical examination reports, such as X-ray and ultrasound images, CT scans, and others, is crucial for accurate diagnosis and treatment. These reports provide a detailed record of a patient's health condition and are an important part of the clinical examination process. By organizing this information in a structured way, doctors can more easily review and analyze the data, leading to better patient care. In this paper, we introduce a new technique for extracting useful information from unstructured clinical text examination reports, which we refer to as a medical event extraction (EE) task. Our approach is based on Machine Reading Comprehension (MRC) and involves two sub-tasks: Question Answerability Judgment (QAJ) and Span Selection (SS). We use BERT to build a question answerability discriminator (Judger) that determines whether a reading comprehension question can be answered or not, thereby avoiding the extraction of arguments from unanswerable questions. The SS sub-task first obtains the encoding of each word in the medical text from the final layer of BERT's Transformer, then utilizes the attention mechanism to identify important information related to the answer from these word encodings. This information is then input into a bidirectional LSTM (BiLSTM) module to obtain a global representation of the text, which is used, along with the softmax function, to predict the span of the answer (i.e., the start and end positions of the answer in the text report). We use interpretable methods to calculate the Jensen-Shannon Divergence (JSD) score between various layers of the network and confirm that our model has strong word representation capabilities, enabling it to effectively extract contextual information from medical reports. Our experiments demonstrate that our method outperforms existing medical event extraction methods, achieving state-of-the-art results with a notable F1 score.
Iterative Oblique Decision Trees Deliver Explainable RL Models

The demand for explainable and transparent models increases with the continued success of reinforcement learning. In this article, we explore the potential of generating shallow decision trees (DT) as simple and transparent surrogate models for opaque deep reinforcement learning (DRL) agents. We investigate three algorithms for generating training data for axis-parallel and oblique DTs with the help of DRL agents ("oracles") and evaluate these methods on classic control problems from OpenAI Gym. The results show that one of our newly developed algorithms, the iterative training, outperforms traditional sampling algorithms, resulting in well-performing DTs that often even surpass the oracle from which they were trained. Even higher dimensional problems can be solved with surprisingly shallow DTs. We discuss the advantages and disadvantages of different sampling methods and insights into the decision-making process made possible by the transparent nature of DTs. Our work contributes to the development of not only powerful but also explainable RL agents and highlights the potential of DTs as a simple and effective alternative to complex DRL models.
BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images

The early detection of breast cancer using mammogram images is critical for lowering women’s mortality rates and allowing for proper treatment. Deep learning techniques are commonly used for feature extraction and have demonstrated significant performance in the literature. However, these features do not perform well in several cases due to redundant and irrelevant information. We created a new framework for diagnosing breast cancer using entropy-controlled deep learning and flower pollination optimization from the mammogram images. In the proposed framework, a filter fusion-based method for contrast enhancement is developed. The pre-trained ResNet-50 model is then improved and trained using transfer learning on both the original and enhanced datasets. Deep features are extracted and combined into a single vector in the following phase using a serial technique known as serial mid-value features. The top features are then classified using neural networks and machine learning classifiers in the following stage. To accomplish this, a technique for flower pollination optimization with entropy control has been developed. The exercise used three publicly available datasets: CBIS-DDSM, INbreast, and MIAS. On these selected datasets, the proposed framework achieved 93.8, 99.5, and 99.8% accuracy, respectively. Compared to the current methods, the increase in accuracy and decrease in computational time are explained.
The Influence of Traditional Exports on Economic Growth in Tanzania: The VECM Analysis

Background: Tanzania, like other developing countries, sees commerce as one of the most important tools for the country's growth and progress. Given that export is one of Tanzania's primary economic sectors, several empirical studies on the relationship between international commerce and the country's economic growth have been conducted. Objectives: The study looked primarily at the influence of traditional exports as the primary independent variable. Furthermore, the Natural Logarithm of Terms of Trade (TOT), the currency rate, and Foreign Direct Investment (FDI) are control variables, whereas GDP is the dependent variable. Methods: The research employed time-series data from the World Bank, the Bank of Tanzania (BOT), and the United Nations Conference on Trade and Development (UNCTAD) that spans 31 years from 1991 to 2021 based on the Vector Error Correlation Model (VECM). Results: The findings revealed a link between Tanzania's economic growth and traditional exports, trade terms and currency rate. Furthermore, studies have found a negative and substantial association between exchange rates and economic growth in both the short and long run. Conclusion: The findings indicated that trade terms and traditional exports had a favorable and considerable influence on economic growth in the short run. In the long run, traditional exports and FDI had a negative and positive influence on economic growth, but trade terms had a considerable and positive effect. As a result, the study argues that Tanzania's government should prioritize export promotion measures above traditional exports to accelerate Tanzania's economic growth. The government should prioritize the establishment of factories that will add value to traditional export items
An Analysis of Multi-stage Progressive Image Restoration Network (MPRNet)

Multi-stage progressive image restoration network (MPRNet) is a three-stage CNN (convolutional neural network) for image restoration. MPRNet has been shown to provide high performance gains on several datasets for a range of image restoration problems including image denoising, deblurring, and deraining. The network is interesting because it manages to remove the three kinds of artifacts with a single architecture. Here, we provide an overview of the network and study its performance and computational complexity in comparison with other state-of-the-art methods. **This is an MLBriefs article, the source code has not been reviewed!**<br> **The original source code is [[available here|https://github.com/swz30/MPRNet]] (last checked 2023/04/12).**<br>
Decoherence, entanglement negativity, and circuit complexity for an open quantum system

In this paper, we compare the saturation timescales for complexity, linear entropy, and entanglement negativity for two open quantum systems. Our first model is a coupled harmonic oscillator, where we treat one of the oscillators as the bath. The second one is a type of Caldeira-Leggett model, where we consider a one-dimensional free scalar field as the bath. Using these open quantum systems, we discovered that both the complexity of purification and the complexity from operator-state mapping is always saturated for a completely mixed state. More explicitly, the saturation timescale for both types of complexity is smaller than the saturation timescale for linear entropy. On top of this, we found that the saturation timescale for linear entropy and entanglement negativity is of the same order for the Caldeira-Leggett model.
An automatically discovered chain-of-thought prompt generalizes to novel models and datasets

Emergent chain-of-thought (CoT) reasoning capabilities promise to improve performance and explainability of large language models (LLMs). However, uncertainties remain about how reasoning strategies formulated for previous model generations generalize to new model generations and different datasets. In this small-scale study, we compare different reasoning strategies induced by zero-shot prompting across six recently released LLMs (davinci-002, davinci-003, GPT-3.5-turbo, GPT-4, Flan-T5-xxl and Cohere command-xlarge) on a mixture of six question-answering datasets, including datasets from scientific and medical domains. Our findings demonstrate that while some variations in effectiveness occur, gains from CoT reasoning strategies remain robust across different models and datasets. GPT-4 has the most benefit from current state-of-the-art reasoning strategies and exhibits the best performance by applying a prompt previously discovered through automated discovery.
Robot path planning by integrating improved A* algorithm and DWA algorithm

Abstract Traversing many nodes, low search efficiency, path slanting through obstacles, many path redundancy points, and the inability of dynamic real-time obstacle avoidance are all typical problems of A* algorithm. In this paper, A robot path planning method combining improved A* algorithm and DWA algorithm is proposed.The ratio of the distance between the current point and the starting point and the target point is introduced into the heuristic function, and the search efficiency is improved through the optimized heuristic function; the neighborhood search principle is simplified to 5-neighborhood search, and the nodes adjacent to obstacles are deleted to reduce the traversal nodes while avoiding diagonal penetration of obstacles; redundant node improvement method is used to reduce the degree of path transition; finally the path key points of the improved A* algorithm are extracted and DWA algorithm is fused to realize the robot to avoid unknown obstacles. Finally, experiments are carried out on Matlab to prove the feasibility of the new algorithm.
Advances in medical image watermarking: a state of the art review

Watermarking has been considered to be a potent and persuasive gizmo for its application in healthcare setups that work online, especially in the current COVID-19 scenario. The security and protection of medical image data from various manipulations that take place over the internet is a topic of concern that needs to be addressed. A detailed review of security and privacy protection using watermarking has been presented in this paper. Watermarking of medical images helps in the protection of image content, authentication of Electronic Patient Record (EPR), and integrity verification. At first, we discuss the various prerequisites of medical image watermarking systems, followed by the classification of Medical Image Watermarking Techniques (MIWT) that include state-of-the-art. We have classified MIWT's into four broader classes for providing better understanding of medical image watermarking. The existing schemes have been presented along with their cons so that the reader may be able to grasp the shortcomings of the technique in order to develop novel techniques proving the inevitability of the presented review. Further, various evaluation parameters along with potential challenges pertaining to medical image watermarking systems have been discussed to provide a deep insight into this research area.
Maximum Likelihood Estimation of the Weighted Mixture Generalized Gamma Distribution

The three-parameter weighted mixture generalized gamma (WMGG) distribution was developed from the four-parameter mixture generalized gamma (MGG) distribution since the parameter estimation of MGG distribution faced with the problem.The estimate of the weighted parameter p was out of the interval [0, 1].The previous study proposed the maximum likelihood estimators (MLEs) of the WMGG distribution.However, their MLEs were written in nonlinear equations, and certain iterative methods were necessarily needed to solve numerically.The three parameters λ, β, and α were estimated by the quasi-Newton method.Nevertheless, this method performed well only the parameter λ.This motivated the main objective of this work.Consequently, the parameter estimation of the WMGG was further improved.This article developed two maximum likelihood estimation methods: expectation-maximization (EM) algorithm and simulated annealing algorithm of the three parameters of the WMGG distribution.These two methods were compared to the previous study's quasi-Newton method.Monte Carlo simulation technique was employed to assess the algorithm's performance.Sample sizes ranged from small to large as 10, 30, 50, and 100.The simulation was repeated 10,000 rounds in each scenario.Assessment criteria were the mean square error (MSE) and bias.The results revealed that the EM algorithm outperformed the other methods.Furthermore, the quasi-Newton method had the lowest efficiency.
Conditionals, Infeasible Worlds, and Reasoning with System W

The recently introduced notion of an inductive inference operator captures the process of completing a given conditional belief base to an inference relation. System W is such an inductive inference operator exhibiting some notable properties like extending rational closure and satisfying syntax splitting for inference from conditional belief bases. However, the definition of system W and the shown results regarding its properties only take belief bases into account that satisfy a strong notion of consistency where no worlds may be completely infeasible. In this paper, we lift this limitation and extend the definition of system W to also cover belief bases that force some worlds to be infeasible. We establish the position of the extended system W within a map of other inductive inference operators being able to deal with the presence of infeasible worlds, including system Z and multipreference closure. For placing lexicographic inference in this map, we show that the definition of lexicographic inference must be slightly modified so that it is an inductive inference operator satisfying direct inference even when there are worlds that are infeasible. Furthermore, we show that, like its unextended version, the extended system W enjoys other desirable properties such as still fully complying with syntax splitting.
Change Resistance Management And The Transition To Distance Learning During COVID-19: Moderating Role Of Education Technology

Purpose: Current study aimed at examining the moderating role of education technology on the relationship between change resistance management (Efficient Managing, Participation, Communication, Organizational Culture, and Managerial Practices) and the transition to distance learning. Theoretical Framework: It's important to remember that resistance is a natural reaction caused by Fear of the unknown and uncertainty about the goals of change and, in some cases, a lack of sufficient information about reform, and that it shouldn't be viewed as a bad thing in and of itself. Design/Methodology/Approach: Quantitative methodology was adopted, and a questionnaire was distributed on a convenience sample of (391) lecturers of different tanks within private universities in Jordan. SPSS was employed in order to screen, process and analyze gathered primary data. Findings: Results of study indicated that education technology moderated the relationship between change resistance management and the transition to distance learning that was attributed to managerial practices as the highest influential approach with B= .585 while organizational culture scored an influence but it was the lowest with B= 0.82. Research , Practical &amp; Social implications: As for the main hypothesis of study, results indicated that education technology manifested the transition to distance learning through the approach which helped in managing the transition through educational technology, as it constituted a comprehensive reference guides for education and provided basic information and framework for distance education that included many channels such as television, digital technology, online platforms and mobile phone. Originality/ Value: The current study showed that most of the resistance during the transitional period of distance education due to the COVID 19 pandemic was due to a lack of knowledge, information, skills, and technological capabilities, as most of the resistance appeared after identifying the platforms they dealt with, the existing infrastructure, and weaknesses. How well university IT departments can handle the massive demand caused by this rapid and unanticipated transformation.
Corporate Social Responsibility and Brand Loyalty in Punjab's Banking Sector: Exploring the Mediating Role of Corporate Branding

The purpose of this research paper is to find out the impact of Corporate Social Responsibility (CSR) on brand loyalty with the mediating role of cooperate branding from the customers of the retail banking sector of Punjab, Pakistan. The current study uses the structured survey data collection technique consisting of 5- Likert scale points while collecting the data from 335 individual customers of retail banking sectors of Dera Ghazi Khan city, Punjab, Pakistan. Further, data was analyzed through SEM (structural equation modeling) using SmartPLS (Smart Partial Least Square) software. The study's results suggest a significant positive impact of corporate social responsibility (CSR) on brand loyalty; other developments suggest the positive mediating role between CSR and brand loyalty. Further, the study uses the auxiliary hypothesis, showing a significant positive impact on brand loyalty. Additionally, the study has substantial implications for the managers of banking sectors. This will help managers promote CSR in their banking sectors to increase sustainability and profitability and provide an edge in the market.