Reviewerly/exHarmony huggingface dataset:
Research Audit for the Transparent Psi Project (TPP)

The Transparent Psi Project (TPP) was intended as a potential model and learning experience for optimal research practices. The TPP is, to my knowledge, the only research study in psychology that was designed to use methods that are comparable to my experience in regulated clinical research. These methods include formal software validation, measures to prevent experimenter fraud, developing the operating characteristics for Bayesian analyses, and research audits. TPP will hopefully become a useful model for high quality research methodology.The purpose of this audit is to apply to the TPP my experience with research audits in regulated clinical research. I initiated the audit after reviewing the final reports of previous audits of TPP by an IT auditor and two research auditors, all with academic backgrounds. In reviewing their final reports, it was clear that their expectations were very different than mine. The previous audits did not have the range of topics, details, emphasis on protocol deviations, and edginess consistent with my expectations for an audit. My hope is to provide an audit that demonstrates useful expectations based on experience outside of academic research. The goals of a good academic research audit have two components: (a) verify that recognized good research practices were used, and (b) verify that the actions and practices specified in the preregistration and/or protocol were properly implemented.
Development of E-module Using Bengkulu Contexts to Improve Literacy Skills of Junior High School Students

This research aims to produce a valid and practical e-module using the Bengkulu context that potentially affects students' literacy skills to understand the materials of patterns and number sequences. This research employed a research and development method. The development stages refer to Plomp’s five developments: (1) preliminary research, (2) the prototype-making stage, and (3) the assessment stage. The subjects of this research were 24 eighth-graders of SMP Negeri 6 Bengkulu City. The data collection techniques of this research were observation and test. Meanwhile, the research instruments were validation instruments, questionnaires, and literacy test questions. Validity analysis was conducted descriptively and consisted of an analysis of validity, practicality, and literacy test results. The average score of the expert validity is 0.64 and meets the valid criterion. The e-module has a practical category with a score of 3.55, and the requirements are very practical. The e-module has impacted students' literacy skills with an average score of 74.25 or good criteria. Moreover, e-module using Bengkulu context problems can encourage students to improve their mathematical literacy skills.
Reviewer Assignment Problem: A Systematic Review of the Literature

Appropriate reviewer assignment significantly impacts the quality of proposal evaluation, as accurate and fair reviews are contingent on their assignment to relevant reviewers. The crucial task of assigning reviewers to submitted proposals is the starting point of the review process and is also known as the reviewer assignment problem (RAP). Due to the obvious restrictions of manual assignment, journal editors, conference organizers, and grant managers demand automatic reviewer assignment approaches. Many studies have proposed assignment solutions in response to the demand for automated procedures since 1992. The primary objective of this survey paper is to provide scholars and practitioners with a comprehensive overview of available research on the RAP. To achieve this goal, this article presents an in-depth systematic review of 103 publications in the field of reviewer assignment published in the past three decades and available in the Web of Science, Scopus, ScienceDirect, Google Scholar, and Semantic Scholar databases. This review paper classified and discussed the RAP approaches into two broad categories and numerous subcategories based on their underlying techniques. Furthermore, potential future research directions for each category are presented. This survey shows that the research on the RAP is becoming more significant and that more effort is required to develop new approaches and a framework.
Improving Continuous Hopfield Network Stability Using Runge-Kutta Method

Continuous Hopfield network is a recurring network that has shown its ability to solve important optimization problems. Continuous Hopfield network dynamic system is characterized by a differential equation. This equation is difficult to solve, especially for large problems. This led researchers to discretize the differential equation using Euler’s method. However, this method generally does not converge to a good solution because it is sensitive to the step size decision and initial conditions. In this work, we discretize the dynamic system of continuous Hopfield network by a new method of Runge-Kutta. This method is strong in terms of stability and performance in order to converge to a better solution. This new method introduces two phases for better network stability. The first phase targets to solve the dynamic equation by the Euler method, while the second phase allows refining the solution found in the first phase. Experimental results on benchmarks show that the proposed approach can effectively improve Hopfield neural network performance.
Developing landslide susceptibility map using Artificial Neural Network (ANN) method for mitigation of land degradation

&lt;p&gt;Landslides are one of the crucial problems that have an impact on land degradation and human life. This study aimed to develop vulnerability maps using ANN to mitigate land degradation in the Bromo Tengger Semeru with the extending area of Universal Transverse Mercator (UTM) Coordinate System Top 91277639, Bottom 911569, Left 692860, and Right 706860. The method applied the Artificial Neural Network (ANN) model using RStudio machine learning. Landslides were mapped using Sentinel Image and Orthomozaic photo interpretation from data acquisition using Unmanned Aerial Vehicle (UAV). The landslide control factor data was obtained through DEMNAS (National Digital Elevation Model) with a spatial resolution of 8 meters. Data normalisation was conducted using the Mix-Max method before it was processed using RStudio. The landslide existing for ANN workflow was processed using the Bioclim model. The results showed landslide susceptibility was categorised into four classes i.e., low susceptibility (29.83%), which was spatially spread on most in the lower slopes, moderate susceptibility (3.11%), high susceptibility (2.99%), and very high susceptibility (15.94) which is scattered on the upper slope to the middle slope of the watershed. The most significant factor influencing the landslide is the topography factor, with a Relative Importance (RI) value of 0.86; the hydrological factor, with an RI of 0.833 and the surface feature, with an RI of 0.355. The results of the landslide susceptibility model are very proper for land degradation mitigation strategies. It has high accuracy through an Area Under Curve (AUC) of 0.965 and a Precision Recall Curve (PRC) of 0.976.&lt;/p&gt;
Super-Resolving Face Image by Facial Parsing Information

Face super-resolution is a technology that transforms a low-resolution face image into the corresponding high-resolution one. In this paper, we build a novel parsing map guided face super-resolution network which extracts the face prior (i.e., parsing map) directly from low-resolution face image for the following utilization. To exploit the extracted prior fully, a parsing map attention fusion block is carefully designed, which can not only effectively explore the information of parsing map, but also combines powerful attention mechanism. Moreover, in light of that high-resolution features contain more precise spatial information while low-resolution features provide strong contextual information, we hope to maintain and utilize these complementary information. To achieve this goal, we develop a multi-scale refine block to maintain spatial and contextual information and take advantage of multi-scale features to refine the feature representations. Experimental results demonstrate that our method outperforms the state-of-the-arts in terms of quantitative metrics and visual quality. The source codes will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wcy-cs/FishFSRNet</uri> .
Tutorial: Metamodelling Power, Pitfalls and Model-Free Interpretation

Simulation metamodels have been a valuable tool since before the term was coined.They can provide insight on system behaviour that is not obvious from the simulation model and are a computationally inexpensive proxy for the simulation model that they approximate.But fitting and using metamodels requires care.This tutorial explains the power of metamodeling, while highlighting potential pitfalls in fitting and using various metamodel technologies.Finally, a particular advantage of metamodeling is illustrated: the counterintuitive ability to provide model-free interpretation of the simulation inputoutput behaviour.
CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society

The rapid advancement of chat-based language models has led to remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conversation, which can be challenging and time-consuming. This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents, and provides insight into their "cognitive" processes. To address the challenges of achieving autonomous cooperation, we propose a novel communicative agent framework named role-playing. Our approach involves using inception prompting to guide chat agents toward task completion while maintaining consistency with human intentions. We showcase how role-playing can be used to generate conversational data for studying the behaviors and capabilities of a society of agents, providing a valuable resource for investigating conversational language models. In particular, we conduct comprehensive studies on instruction-following cooperation in multi-agent settings. Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems, and open-sourcing our library to support research on communicative agents and beyond: https://github.com/camel-ai/camel.
Emotions recognition from human facial images based on fast learning network

Systems of facial emotion recognition have witnessed a high significance in the research field. The face emotions are based on human facial expressions which play a crucial role in silent communication. Machine learning algorithms have widely used in systems of human facial emotion detection from images. However, many systems suffer from low accuracy rate. In this paper, we present a system of facial emotion recognition by using images. In this proposed system, the samples of facial emotions have taken from Yale Face database. In addition, the histograms of oriented gradients (HOG) is used to extract features from the images. The extracted features will feed the fast learning network (FLN) algorithm for the classification part to identify the images of facial emotions with respect to their subjects. Many evaluation measurements have used to evaluate the performance of the proposed system. Based on the results of the experiment, the proposed system achieves 95.04% for the highest accuracy, 72.73% precision. Also, the results of the proposed system in terms of recall, f-measure, and G-main are all equal to 72.73%, respectively.
Hope or hype? Blockchain and accounting

Gartner’s hype cycle of technology famously progresses from the “peak of inflated expectations” to the “plateau of productivity” via the “trough of disillusionment”. Accounting researchers and practitioners—like researchers and practitioners in many other fields—have jumped onto the blockchain bandwagon for fear of missing out on what has been hailed as a world changing technology. Unfortunately, there is a pervasive lack of understanding of what blockchain is, and misconceptions about what it can do. A fundamental problem is that blockchain was derived from bitcoin and there is a great deal of difficulty in defining what blockchain is, and how suitable the methodology for a trustless, public cybercurrency application is to a public blockchain between trusted partners. It is time, we believe, to look at blockchain in accounting with more objectivity. We undertake a detailed exploration of blockchain and identify several key factors that will defines the uses of this technology, namely, the distinction between public and private blockchains and the importance of processing costs as a validating mechanism.
A Comparative Study between Haploid Genetic Algorithms and Diploid Genetic Algorithms

In this paper, we make a comprehensive comparison in terms of the quality of the achieved solutions, the corresponding execution time and impact of the genetic operators on the quality of the results between the Haploid Genetic Algorithms (HGAs) and Diploid Genetic Algorithms (DGAs). The standard genetic algorithms, referred to in our paper as HGAs are characterized by the fact that they are using a haploid representation relating an individual with a chromosome, while the DGAs are using diploid individuals which are made of two chromosomes corresponding to the dominant and recessive genes. Even though the general opinion is that DGAs do not provide much benefit as compared to classical GAs, based on extensive computational experiments, we do show that the DGAs are robust, have a high degree of consistency and perform better, sometimes almost twice as well, than the HGAs, but are slower due to the high number of operations to be performed, caused by the duplication of the genetic information. However, the quality of the solutions achieved by the DGAs compensate their relative high execution time. The better quality of the DGAs, proving the efficiency of using diploid genes, is given by the homogeneity of the population which covers the search space thoroughly and in this way being capable of avoiding the local optima.
Comparative Analysis of CHATGPT and the evolution of language models

Interest in Large Language Models (LLMs) have increased drastically since the emergence of ChatGPT and the outstanding positive societal response to the ease at which it performs tasks in Natural Language Processing (NLP).The triumph of ChatGPT, however, is how it seamlessly bridges the divide between language generation and knowledge models and in some cases provides anecdotal evidence of a framework for replicating human intuition over a knowledge domain.This paper highlights the prevailing ideas in NLP, including machine translation, machine summarization, question-answering, and language generation, and compares the performance of ChatGPT with the major algorithms in each of these categories using the Spontaneous Quality (SQ) score.A strategy for validating the arguments and results of ChatGPT is presented summarily as an example of safe large scale adoption of LLMs.
Tomographic completeness and robustness of quantum reservoir networks

Quantum reservoir processing offers an option to perform quantum tomography of input objects by postprocessing quantities, obtained from local measurements, from a quantum reservoir network that has interacted with the former. We develop a method to assess a tomographic completeness criterion for arbitrary quantum reservoir architectures. Furthermore, we propose a figure of merit that quantifies their robustness against imperfections. Measured quantities from the reservoir nodes correspond to effective observables acting on the input objects, and we provide a way to retrieve them. Finally, we present examples of quantum tomography for demonstration. Our general method offers guidance in optimizing implementations of quantum reservoir processing.
Cloud-Empowered Data-Centric Paradigm for Smart Manufacturing

In the manufacturing industry, there are claims about a novel system or paradigm to overcome current data interpretation challenges. Anecdotally, these studies have not been completely practical in real-world applications (e.g., data analytics). This article focuses on smart manufacturing (SM), proposed to address the inconsistencies within manufacturing that are often caused by reasons such as: (i) data realization using a general algorithm, (ii) no accurate methods to overcome the actual inconsistencies using anomaly detection modules, or (iii) real-time availability of insights of the data to change or adapt to the new challenges. A real-world case study on mattress protector manufacturing is used to prove the methods of data mining with the deployment of the isolation forest (IF)-based machine learning (ML) algorithm on a cloud scenario to address the inconsistencies stated above. The novel outcome of these studies was establishing efficient methods to enable efficient data analysis.
Security, Sustainability, and Legal Issues of Blockchain Technology Implementation: A Short Literature Review

Blockchain, discreetly introduced by Satoshi Nakamoto in 2008, has become one of the top emerging technologies in 2016 and was first used in financial sector as the basis for Bitcoin, a crypto-currency which allows peer to peer money transaction with smart contracts without any third party in between which is different from other payment method such as Paypal or other methods to send money. Many bank institutions have adopted blockchain technology to mainstream day to day operation. Based on the review of related articles, there are three major concerns behind blockchain technology; security, sustainability, and legal. These issues have become a step-back for some companies and banks to adopt blockchain into their daily operations. Hence, this paper posits that more research is required in this area.
Deep Learning Algorithm based Wearable Device for Basketball Stance Recognition in Basketball

With the continuous improvement of technology, modern sports training is gradually developing towards precision and efficiency, which requires more accurate identification of athletes' sports stances.The study first establishes a classification structure of basketball stance, then designs a hardware module to collect different stance data by using inertial sensors, thus extracting multidimensional motion stance features.Then the traditional convolutional neural network (CNN) is improved by principal component analysis (PCA) to form the PCA+CNN algorithm.Finally, the algorithm is simulated and tested.The outcomes demonstrated that the average discrimination error rate of the improved PCA+CNN algorithm in the Human 3.6M dataset was 3.15%, which was a low error rate.In recognition of basketball sports pose, the wearable based on the improved algorithm had the highest accuracy of 99.4% and took the quietest time of 18s, which was better than the other three methods.It demonstrated that the method had high discrimination precision and recognition efficiency, which could provide a reliable technical means to improve the science of basketball sports training plan and training effect.
A Novel Method of Invisible Video Watermarking Based on Index Mapping and Hybrid DWT-DCT

Watermarking is widely used in multimedia preservation and communication, which comprises, and is therefore not limited to, data security and validation. Security, readability, imperceptibility, and resilience are some of the key advantages of this technology. However, there are still certain issues that need to be addressed, such as the ability to withstand a variety of assaults without substantially affecting the quality and value of embedded data. Based on its operational domain, the watermarking technology may be divided into two groups: spatial and frequency watermarking. An index mapping-based watermarking approach for copyright protection of multi-media color videos is presented in this paper. We offer a hybrid discrete wavelet transform and discrete cosine transform (DCT) watermarking algorithm for digital video watermarking of a color video watermark. Peak signal to noise ratio (PSNR), similarity structure index measure (SSIM), have been used to analyze the distortion produced by watermarking. It is suggested that the proposed video watermarking method provides greater imperceptibility in conjunction with each other with the human visual system and it offers higher robustness against different attacks.
Characterizing quantum circuits with qubit functional configurations

Abstract We develop a systematic framework for characterizing all quantum circuits with qubit functional configurations. The qubit functional configuration is a mathematical structure that can classify the properties and behaviors of quantum circuits collectively. Major benefits of classifying quantum circuits in this way include: 1. All quantum circuits can be classified into corresponding types; 2. Each type characterizes important properties (such as circuit complexity) of the quantum circuits belonging to it; 3. Each type contains a huge collection of possible quantum circuits allowing systematic investigation of their common properties. We demonstrate the theory’s application to analyzing the hardware-efficient ansatzes of variational quantum algorithms. For potential applications, the functional configuration theory may allow systematic understanding and development of quantum algorithms based on their functional configuration types.
Quantifying non-stabilizerness through entanglement spectrum flatness

Non-stabilizerness - also colloquially referred to as magic - is the a resource for advantage in quantum computing and lies in the access to non-Clifford operations. Developing a comprehensive understanding of how non-stabilizerness can be quantified and how it relates other quantum resources is crucial for studying and characterizing the origin of quantum complexity. In this work, we establish a direct connection between non-stabilizerness and entanglement spectrum flatness for a pure quantum state. We show that this connection can be exploited to efficiently probe non-stabilizerness even in presence of noise. Our results reveal a direct connection between non-stabilizerness and entanglement response, and define a clear experimental protocol to probe non-stabilizerness in cold atom and solid-state platforms.
Online Stochastic Variational Gaussian Process Mapping for Large-Scale Bathymetric SLAM in Real Time

Rao-Blackwellized particle filter (RBPF) SLAM solutions with Gaussian Process (GP) maps can both maintain multiple hypotheses of a vehicle pose estimate and perform implicit data association for loop closure detection in continuous terrain representations. Both qualities are of particular interest for SLAM with autonomous underwater vehicles (AUVs) in the open sea, where distinguishable features are scarce. However, the applicability of GP regression to parallel, real-time mapping in an RBPF framework remains limited by the size of the area to survey and the computational cost of the GP training. To overcome these constraints, in this letter we propose the adaption of Stochastic Variational GP (SVGP) regression to online mapping in combination with a novel, efficient particle trajectory storing in the RBPF. We show how the resulting RBPF-SVGP framework can achieve real-time performance in an embedded platform on two AUV surveys containing millions of points. We further test the framework on a live mission on an AUV and we make the implementation publicly available.