Reviewerly/exHarmony huggingface dataset:
DBMS-Benchmarker: Benchmark and Evaluate DBMS in Python

DBMS-Benchmarker is a Python-based application-level blackbox benchmark tool for Database Management Systems (DBMS).It is intended for reproducible measurement and easy evaluation of the performance the user receives, even in complex benchmark situations.It connects to a given list of DBMS (via JDBC) and runs a given list of (SQL) benchmark queries.Queries can be parametrized and randomized.Results and evaluations are available via a Python interface and can be inspected with standard Python tools like pandas DataFrames.An interactive visual dashboard assists in multi-dimensional analysis of the results.This module has been tested with Clickhouse, Exasol, Citus Data (Hyperscale), IBM DB2, MariaDB, MariaDB Columnstore, MemSQL (SingleStore), MonetDB, MySQL, OmniSci (HEAVY.AI), Oracle DB, PostgreSQL, SQL Server, SAP HANA, TimescaleDB, and Vertica.See the homepage and the documentation for more details.
Compositionality in Computational Linguistics

Neural models greatly outperform grammar-based models across many tasks in modern computational linguistics. This raises the question of whether linguistic principles, such as the Principle of Compositionality, still have value as modeling tools. We review the recent literature and find that while an overly strict interpretation of compositionality makes it hard to achieve broad coverage in semantic parsing tasks, compositionality is still necessary for a model to learn the correct linguistic generalizations from limited data. Reconciling both of these qualities requires the careful exploration of a novel design space; we also review some recent results that may help in this exploration.
Quantifying the effects of dissipation and temperature on dynamics of a superconducting qubit-cavity system

The superconducting circuits involving Josephson junction offer macroscopic quantum two-level system (qubit) which are coupled to cavity resonators and are operated via microwave signals. In this work, we study the dynamics of superconducting qubits coupled to a cavity with including dissipation in a subKelvin temperature domain. In the first step, a classical finite element method is used to simulate the cavities and basic circuit elements to model Josephson junctions. Then, the quantization of the circuit is done to obtain the full Hamiltonian of the system using energy participation ratios of the junctions. Once the parameters of Hamiltonian are obtained, the dynamics is studied via the Lindblad equation for an open quantum system using a realistic set of dissipative parameters and include temperature effects. Finally, we get frequency spectra and/or dynamics of the system with time which have quantum imprints. Such devices work at tens of milliKelvins and we search for a set of parameters which could enable to observe quantum behaviour at temperatures as high as 1 K.
Coot optimization based Enhanced Global Pyramid Network for 3D hand pose estimation

Abstract Due to its importance in various applications that need human-computer interaction (HCI), the field of 3D hand pose estimation (HPE) has recently got a lot of attention. The use of technological developments, such as deep learning networks has accelerated the development of reliable 3D HPE systems. Therefore, in this paper, a 3D HPE based on Enhanced Global Pyramid Network (EGPNet) is proposed. Initially, feature extraction is done by backbone model of DetNetwork with improved EGPNet. The EGPNet is enhanced by the Smish activation function. After the feature extraction, the HPE is performed based on 3D pose correction network. Additionally, to enhance the estimation performance, Coot optimization algorithm is used to optimize the error between estimated and ground truth hand pose. The effectiveness of the proposed method is experimented on Bharatanatyam, yoga, Kathakali and sign language datasets with different networks in terms of area under the curve, median end-point-error (EPE) and mean EPE. The Coot optimization is also compared with existing optimization algorithms.
Pedagogical agents in learning videos: Which one is best for children?

An increasing number of MOOCs use learning videos featuring an instructor and slides, but there are few specific guidelines for the design of instructor images for these videos. A pedagogical agent (PA) is a kind of virtual instructor image in a learning video, and its image design is mostly guided by anthropomorphism. Our research attempts to introduce zoomorphism, which is the opposite of traditional anthropomorphism, into the image design of PA. This study tested the effects of two different types of PA images (i.e. anthropomorphic and zoomorphic) as well as a control condition of no PA image on learning from a learning video in a sample of 94 s-grade children. The children were randomly assigned to watch one of three learning videos on pandas. The ANOVA and Kruskal–Wallis tests showed that children who watched the zoomorphic PA image video obtained significantly higher retention, transfer, learning interest, and experience than children who watched the other two types of videos. Children self-reported more enjoyment in the zoomorphic PA image condition than in the no PA condition. Furthermore, the results of multiple regression analysis showed that children’s learning interest and experience positively predicted retention and transfer scores; boredom negatively predicted transfer scores. These findings highlight the positive effect of zoomorphism on PA image design, suggesting that zoomorphism holds great prospects in PA image development.
Short-Term Load Forecasting Model of Ameliorated CNN Based on Adaptive Mutation Fruit Fly Optimization Algorithm

In order to improve the accuracy and calculating speed of load forecasting for the strong nonlinear problem of short-term load, this article proposes a Short-term Load Forecasting Model of Ameliorated CNN Based on Adaptive Mutation Fruit Fly Optimization Algorithm. This method integrates the Extreme Learning Machine (ELM) algorithm into the Convolutional Neural Network (CNN): replace the fully connected layer in the original CNN network with ELM to form a CNN-ELM network. The purpose is to improve the calculation accuracy. An Adaptive Mutation Fruit Fly Optimization Algorithm (AMFOA) was proposed to reduce the probability that the Fruit Fly Optimization Algorithm (FOA) would easily fall into a local optimal value. And then AMFOA is used to optimize the parameters in CNN-ELM network. The above model is used to predict the grid load of a certain area in northern China. Compared with other prediction algorithms, it is proved that the model proposed in this article has higher prediction accuracy and also proved that the model has higher calculation speed than other models.
General Vapnik–Chervonenkis dimension bounds for quantum circuit learning

Abstract Quantifying the model complexity of quantum circuits provides a guide to avoid overfitting in quantum machine learning. Previously we established a Vapnik–Chervonenkis (VC) dimension upper bound for ‘encoding-first’ quantum circuits, where the input layer is the first layer of the circuit. In this work, we prove a general VC dimension upper bound for quantum circuit learning including ‘data re-uploading’ circuits, where the input gates can be single qubit rotations anywhere in the circuit. A linear lower bound is also constructed. The properties of the bounds and approximation-estimation trade-off considerations are discussed.
Time Series-Based Photovoltaic Power Forecasting to Optimize Grid Stability

The increase in penetration of solar photovoltaics into the traditional grid and the accelerating growth of smart grids have introduced new challenges to grid stability. Forecasting the output power from solar PV systems and time-based analysis for the performance characteristics of solar PV under different weather conditions is essential to improve the grid stability. The generated PV power is intermittent in nature and is influenced by meteorological parameters such as pressure, temperature, relative humidity, and solar zenith angle. With the influence of the above parameters, a novel power forecasting model has been developed using Supervised Machine Learning Algorithm. The historical weather data of a given location have been fetched from National Solar Radiation Database (NSRDB) with the corresponding location coordinates. Multivariate data are used as inputs to train a Decision Tree Regression Model in order to predict the solar irradiance parameters such as Global Horizontal Irradiance, Direct Normal Irradiance, and Diffuse Horizontal Irradiance which are essential to calculate the output power harnessed from the grid-connected PV system. The results are favorable for the application and have depicted minimal deviation with an average accuracy of 86.02%. This technique also rules out the need of hardware power prediction modules, favoring a cost-efficient methodology.
The guts of assessment: a digital architecture for machine learning and analogue judgement

Educational assessment is inherently uncertain, where physiological, psychological and social factors play an important role in establishing judgements which are assumed to be “absolute”. AI and other algorithmic approaches to grading of student work strip-out uncertainty, leading to a lack of inspectability in machine judgement and consequent problems of trust and reliability. The technique of Adaptive Comparative Judgement (ACJ), in focusing on small-stakes binary comparisons provides an alternative approach to dealing with uncertainty in grading. Rankings can be produced which codify uncertainty, rendering machine judgements inspectable. However, ACJ demands resources in terms of time and the number of people making comparisons. Machine learning trained to make binary comparisons can help to make the process of human comparison more efficient. In combining ACJ and AI, we argue that the result is an analogue-digital system where the physiological/analogue processes of assessment can be coordinated with digital services which steer human assessment to those judgements which are most uncertain requiring most human deliberation. Drawing on our design of such a system developed for medical judgements, we describe a general architecture for human–machine assessment in education and discuss its potential in bridging the gap between analogue human cognition and digital machine learning.
On the performance analysis of solving the Rubik’s cube using swarm intelligence algorithms

Swarm intelligence algorithms are nature-inspired algorithms that mimic natural phenomena to solve optimization problems. These natural phenomena are intelligent animal behavior used by animals for survival from hunting prey, migration, escaping predators, and reproduction. Some examples are ant colonies, flocking of birds, tracking patterns of hawks, herding behaviour of animals, bacterial growth, fish schooling, and intelligent microbial organisms. The Rubik’s cube is a 3D combinatorial puzzle with six faces covered by nine stickers of colors: white, red, blue, orange, green, and yellow. The objective is to turn the scrambled cube, where each side will have more than one colour, into a solved cube having only one colour on each side. This study uses the following algorithms – particle swarm optimization, ant colony optimization, discrete krill herd optimization, and a greedy tree search algorithm – to investigate which of the four can solve the Rubik’s cube in the shortest time using the shortest possible move sequence and show that swarm intelligence algorithms are capable of solving the Rubik’s cube.
Multiview subspace clustering via low‐rank correlation analysis

Abstract In order to explore multi‐view data, existing low‐rank‐based multi‐view subspace clustering methods seek a common low‐rank structure from different views. However, in real‐world scenarios, each view will often hold complex structures resulting from noise or outliers, causing unreliable and imprecise graphs, which the previous methods cannot effectively ameliorate. This study proposes a new method based on low‐rank correlation analysis to overcome these limitations. Firstly, the canonical correlation analysis strategy is introduced to jointly find the low‐rank structures in different views. In order to facilitate a robust solution, a dual regularisation term is further introduced to find such low‐rank structures that maximise the correlation in respective views much better. Thus, a unifying clustering structure is then integrated into the model to characterise the connections between different views adaptively. In this way, noise suppression is achieved more effectively. Furthermore, we avoid the uncertainty of spectral post‐processing of the unifying clustering structure by imposing a rank constraint on its Laplacian matrix to obtain the clustering results explicitly, further enhancing computation efficiency. Experimental results obtained from several clustering and classification experiments performed using 3Sources, Caltech101‐20, 100leaves, WebKB, and Hdigit datasets reveal the proposed method's superiority over compared state‐of‐the‐art methods in Accuracy, Normalised Mutual Information, and F‐score evaluation metrics.
SubStop: An analysis on subscription email bombing attack and machine learning based mitigation

Email Bombing, a kind of denial-of-service (DoS) attack is crippling internet users and is on the rise recently. A particularly notorious type is the Subscription Bombing attack, where a victim user’s inbox is bombarded with a stream of subscription emails at a particular period. This kind of attack helps the perpetrator to hide their real motive in lieu of a barrage of legitimate-looking emails. The main challenge for detecting subscription bombing attacks is that most of the attacking email appears to be legitimate and benign and thus can bypass existing anti-spam filters. In order to shed some light on the direction of detecting the bombing attacks, in this paper we first conduct some reverse engineering study on the Gmail anti-spam mechanism (as the information is not publicly available) and in-depth feature analysis of real-life bombing attack emails. Leveraging the insights from our reverse engineering study and data analysis, we propose a novel layered detection architecture, termed as SubStop, to detect and mitigate subscription bombs. SubStop exploits the statistics of incoming volume, source domain distribution, the correlation among different features, and implements machine learning to achieve effective detection. In specific, we utilize the weighted support vector machine (WSVM) and properly tune the class weights to achieve high accuracy in detecting bombing attacks. Despite the scarcity of public email data sets, we conduct extensive experiments on a real-life subscription bomb attack and real-time attacks using our bombing simulation script (which is facilitated by our reverse engineering findings), on test email accounts. Detailed experimental results show that our proposed architecture is very robust and highly accurate in detecting and mitigating a subscription bombing attack.
Sign determination to measure non-monotonous displacements in temporal speckle pattern interferometry without a temporal carrier

In this paper, we present a generalized function to automatically detect the correct sign of non-monotonous displacements in temporal speckle pattern interferometry without the introduction of a temporal carrier in the optical setup. This approach can be used with any phase retrieval technique. Here, when the phase change is evaluated, the sign ambiguity is resolved due to a function that determines the correct sign of the displacement between two consecutive speckle interferograms. The errors introduced by the proposed function are determined using computer-simulated speckle interferograms, and its performance is analyzed using three phase recovery techniques and different phase distributions and average speckle sizes. A relation between the sliding window used to compute the correct sign and the speckle sizes is also presented. Finally, the presentation is illustrated with an experimental application of the proposed method to detect the correct sign of the out-of-plane displacement field of a metal plate.
Fundamentals of Metaheuristic Computation

This chapter presents the main concepts of metaheuristic schemes. The objective of this chapter is to introduce the characteristics and properties of these approaches. An important purpose of this chapter is also to recognize the importance of metaheuristic methods to solve optimization problems in cases in which traditional techniques are not suitable.
An Introduction to Fuzzy and Fuzzy Control Systems

In the early 1990s, successful applications of the fuzzy logic increased in the automated control.
Direct Adaptive Fuzzy Control

This chapter analyzes direct adaptive fuzzy control. In the designated systems, the inputs emerge as linear in equations (i.e., affine systems).
Method of Selecting the Optimal Location of Barrier-Free Bus Stops Using Clustering

Recently, there has been a greater need for barrier-free bus stops as the number of elderly people has rapidly grown, and social interest in the right to transport for the disabled has increased. Barrier-free bus stops refer to stops without obstacles, like flowerbeds and trash cans, to ensure the disabled, the elderly, etc., are comfortable getting on and off the bus. These stops may also have additional support facilities such as enhanced sidewalk block maintenance, braille block reinforcement, and low-floor bus location indications. However, the transportation environment for those in need is still not improving. Of the buses operating in Seoul, only 47.3% are low-floor buses designed for wheelchairs, and there are not enough barrier-free bus stops. In addition, notwithstanding the recent protests regarding the right to travel for the disabled. However, barrier-free bus stops have not increased, and the right to travel is not guaranteed. According to the Nodeul Independent Living Center for the Disabled, after monitoring mobility in Jongno-gu since 2019, it was found that the accessibility for wheelchairs and blind route information in Jongno-gu was minimal. Jongno-gu is an essential stopover for bus routes and is the core of Seoul’s transportation system, with a floating population of more than two million. Thus, the lack of accessible transportation is an issue. This study address these problems, by proposing installation locations of barrier-free bus stops by using cluster analysis. The analysis was conducted using information on ‘convenience facilities with provisions for the disabled’ (ease of use for the disabled), ‘institutional transportation facilities for the vulnerable’ (welfare facilities), and ‘the population getting on and off of transportation’ (accessibility of transportation). This study used convenience, welfare, and transportation accessibility to determine stops.
The Effect of Self-Determination and Quality of VR-Based Education in the Metaverse on Learner Satisfaction

While online learning had traditionally been implemented to aid in-person education, it has recently evolved into a critical tool for remote education. In particular, the advent of the COVID-19 pandemic has accelerated the spread of online learning and its significance. However, this wave of innovation in the education field has revealed the lack of research on how online education should be systematically provided and which educational aspects should be considered to enhance learner satisfaction in online settings. With the recent emergence of the Metaverse, VR education is once again proposed as a major tool for online learning despite existing limitations in research which put both systematic and educational aspects into consideration. Hence, this study presents and discusses a research model that converges technology acceptance model, information systems success model, and self-determination theory with the purpose of exploring variables that affect learner satisfaction, mediating the flow theory. Results showed that most variables of both self-determination theory and information system quality within VR education impact learner satisfaction. In particular, learners’ flow—complete immersion in learning—functions as an important mediating variable for learner satisfaction. These findings suggest designing and running a systematic platform that reflects self-directed learning is imperative to bring the best educational practices into the Metaverse.
Seismic P‐wave first‐arrival picking model based on <scp>EQK‐IncResNet</scp>

Summary Seismic P‐wave first arrival picking is one of the critical problems in seismic source research. At present, the picking of seismic P‐wave first arrival mainly relies on the combination of traditional methods and manual picking. However, traditional algorithms have poor picking performance and low manual picking efficiency, which cannot meet the needs of research and application. Based on the deep learning network InceptionResNet‐V2, this paper proposes an improved network (EQK‐IncResNet) that can be used for seismic P‐wave first arrival picking. Compared with traditional methods, this model does not need to set the threshold manually and only needs to input the three‐component data of the seismic waveform data to intelligently identify the first‐arrival of the seismic P‐wave. This model has a lightweight structure and excellent feature extraction capabilities, which can effectively identify low signal‐to‐noise ratio data. The experimental results show that within the error thresholds of 0.1, 0.3, and 0.5 s, the hit rate of this method is 74.45%, 96.79%, and 98.68%, respectively. The average picking error is 0.031 s, which is better than the traditional methods such as AR‐AIC + STA/LTA and the mainstream deep learning methods such as GRU.
Predicting User Confidence in Video Recordings with Spatio-Temporal Multimodal Analytics

A critical component of effective communication is the ability to project confidence. In video presentations (e.g., video interviews), there are many factors that influence perceived confidence by a listener. Advances in computer vision, speech processing, and natural language processing have enabled the automatic extraction of salient features that can be used to model a presenter’s perceived confidence. Moreover, these multimodal features can be used to automatically provide feedback to a user with ways they can improve their projected confidence. This paper introduces a multimodal approach to modeling user confidence in video presentations by leveraging features from visual cues (i.e., eye gaze) and speech patterns. We investigate the degree to which the extracted multimodal features were predictive of user confidence with a dataset of 48 2-minute videos, where the participants used a webcam and microphone to record themselves responding to a prompt. Comparative experimental results indicate that our modeling approach of using both visual and speech features are able to score 83% and 78% improvements over the random and majority label baselines, respectively. We discuss implications of using the multimodal features for modeling confidence as well as the potential for automated feedback to users who want to improve their confidence in video presentations.