Reviewerly/exHarmony huggingface dataset:
A new bio-inspired metaheuristic algorithm for solving optimization problems based on walruses behavior

This paper introduces a new bio-inspired metaheuristic algorithm called Walrus Optimization Algorithm (WaOA), which mimics walrus behaviors in nature. The fundamental inspirations employed in WaOA design are the process of feeding, migrating, escaping, and fighting predators. The WaOA implementation steps are mathematically modeled in three phases exploration, migration, and exploitation. Sixty-eight standard benchmark functions consisting of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, CEC 2015 test suite, and CEC 2017 test suite are employed to evaluate WaOA performance in optimization applications. The optimization results of unimodal functions indicate the exploitation ability of WaOA, the optimization results of multimodal functions indicate the exploration ability of WaOA, and the optimization results of CEC 2015 and CEC 2017 test suites indicate the high ability of WaOA in balancing exploration and exploitation during the search process. The performance of WaOA is compared with the results of ten well-known metaheuristic algorithms. The results of the simulations demonstrate that WaOA, due to its excellent ability to balance exploration and exploitation, and its capacity to deliver superior results for most of the benchmark functions, has exhibited a remarkably competitive and superior performance in contrast to other comparable algorithms. In addition, the use of WaOA in addressing four design engineering issues and twenty-two real-world optimization problems from the CEC 2011 test suite demonstrates the apparent effectiveness of WaOA in real-world applications. The MATLAB codes of WaOA are available in https://uk.mathworks.com/matlabcentral/profile/authors/13903104 .
Public perceptions towards ChatGPT​ a​s the​ Robo​-Assistant

The widespread adoption of digital technologies in various economic activities paves the way for the establishment of a unified digital space. ChatGPT, an artificial intelligence language model, can generate increasingly realistic text, with no information on the accuracy and integrity of using these models in scientific writing. This study aims to investigate factors influencing public perceptions toward the acceptance of ChatGPT as the Robo-Assistant, using a mixed method. The quantitative approach in this study employed convenience sampling to collect data through closed-ended questionnaires from a sample size of 1,880 respondents. Statistical analysis software was used for data analysis. The researchers used binary regression to examine the relationship between various independent variables (such as score, gender, education, social media usage) and the acceptance of ChatGPT, as dependent variable. As part of the qualitative approach, in-depth interviews were conducted with a purposive sample of six participants. The qualitative data was analyzed using the content analysis method and the NVivo software program. Findings show that ChatGPT awareness and usage are influenced by variables like score, gender, education, and social media usage. Occupation and monthly income were not significant factors. The model with all independent variables was able to predict the use of ChatGPT as the Robo-Assistant in Thailand with an accuracy rate of 96.3%. The study also confirms acceptance of ChatGPT among Thai people and emphasizes the importance of developing sociable robots that consider human interaction factors. This study significantly enhances our comprehension of public perceptions, acceptance, and the prospective ramifications associated with the adoption of ChatGPT as the Robo-Assistant. The acquired findings offer indispensable guidance for the effective utilization of AI models and the advancement of sociable robots within the domain of human-robot interaction.
Hybrid Pixel-Unshuffled Network for Lightweight Image Super-resolution

Convolutional neural network (CNN) has achieved great success on image super-resolution (SR). However, most deep CNN-based SR models take massive computations to obtain high performance. Downsampling features for multi-resolution fusion is an efficient and effective way to improve the performance of visual recognition. Still, it is counter-intuitive in the SR task, which needs to project a low-resolution input to high-resolution. In this paper, we propose a novel Hybrid Pixel-Unshuffled Network (HPUN) by introducing an efficient and effective downsampling module into the SR task. The network contains pixel-unshuffled downsampling and Self-Residual Depthwise Separable Convolutions. Specifically, we utilize pixel-unshuffle operation to downsample the input features and use grouped convolution to reduce the channels. Besides, we enhance the depthwise convolution's performance by adding the input feature to its output. The comparison findings demonstrate that, with fewer parameters and computational costs, our HPUN achieves and surpasses the state-of-the-art performance on SISR. All results are provided in the github https://github.com/Sun1992/HPUN.
Learning Progressive Modality-Shared Transformers for Effective Visible-Infrared Person Re-identification

Visible-Infrared Person Re-Identification (VI-ReID) is a challenging retrieval task under complex modality changes. Existing methods usually focus on extracting discriminative visual features while ignoring the reliability and commonality of visual features between different modalities. In this paper, we propose a novel deep learning framework named Progressive Modality-shared Transformer (PMT) for effective VI-ReID. To reduce the negative effect of modality gaps, we first take the gray-scale images as an auxiliary modality and propose a progressive learning strategy. Then, we propose a Modality-Shared Enhancement Loss (MSEL) to guide the model to explore more reliable identity information from modality-shared features. Finally, to cope with the problem of large intra-class differences and small inter-class differences, we propose a Discriminative Center Loss (DCL) combined with the MSEL to further improve the discrimination of reliable features. Extensive experiments on SYSU-MM01 and RegDB datasets show that our proposed framework performs better than most state-of-the-art methods. For model reproduction, we release the source code at https://github.com/hulu88/PMT.
Guided Emotional State Regulation: Understanding and Shaping Players’ Affective Experiences in Digital Games

Designing adaptive games for individual emotional experiences is a tricky task, especially when detecting a player’s emotional state in real time requires physiological sensing hardware and signal processing software. There is currently a lack of software that can identify and learn how emotional states in games are triggered. To address this problem, we developed a system capable of understanding the fundamental relations between emotional responses and their eliciting events. We propose time-evolving Affective Reaction Models (ARM), which learn new affective reactions and manage conflicting ones. These models are then meant to provide information on how a set of predetermined game parameters (e.g., enemy and item spawning, music and lighting effects) should be adapted, to modulate the player’s emotional state. In this paper, we propose and describe a framework for modulating player emotions and the main components involved in regulating players’ affective experience. We expect our technique will allow game designers to focus on defining high-level rules for generating gameplay experiences instead of having to create and test different content for each player type.
Evaluation of a foreign speaker in forensic phonetics: a report

Expert witnesses in phonetics find themselves more and more often in forensic situations in which they have to identify the voice of a speaker who does not speak their native language. Until recently, little has been known about the role which the native language background of the listener plays in such speaker identification tasks. In this report, several aspects of an experimental investigation on the influence of native-language background on speaker identification are reviewed. Results of a first experiment are reported and some follow-up experiments currently being carried our are described within that context.
Cost-Based Heuristic Search Is Sensitive to the Ratio of Operator Costs

In many domains, different actions have different costs. In this paper, we show that various kinds of best-first search algorithms are sensitive to the ratio between the lowest and highest operator costs. First, we take common benchmark domains and show that when we increase the ratio of operator costs, the number of node expansions required to find a solution increases. Second, we provide a theoretical analysis showing one reason this phenomenon occurs. We also discuss additional domain features that can cause this increased difficulty. Third, we show that searching using distance-to-go estimates can significantly ameliorate this problem. Our analysis takes an important step toward understanding algorithm performance in the presence of differing costs. This research direction will likely only grow in importance as heuristic search is deployed to solve real-world problems.
Microlocal analysis of a spindle transform

An analysis of the stability of the spindle transform, introduced in [16], is presented. We do this via a microlocal approach and show that the normal operator for the spindle transform is a type of paired Lagrangian operator with 'blowdown–blowdown' singularities analogous to that of a limited data synthetic aperture radar (SAR) problem studied by Felea et. al. [4]. We find that the normal operator for the spindle transform belongs to a class of distibutions $ I^{p, l}(\Delta, \Lambda)+I^{p, l}(\widetilde{\Delta}, \Lambda) $ studied by Felea and Marhuenda in [4,10], where $ \widetilde{\Delta} $ is reflection through the origin, and $ \Lambda $ is associated to a rotation artefact. Later, we derive a filter to reduce the strength of the image artefact and show that it is of convolution type. We also provide simulated reconstructions to show the artefacts produced by $ \Lambda $ and show how the filter we derived can be applied to reduce the strength of the artefact.
Embedding deep networks into visual explanations

In this paper, we propose a novel Explanation Neural Network (XNN) to explain the predictions made by a deep network. The XNN works by learning a nonlinear embedding of a high-dimensional activation vector of a deep network layer into a low-dimensional explanation space while retaining faithfulness i.e., the original deep learning predictions can be constructed from the few concepts extracted by our explanation network. We then visualize such concepts for human to learn about the high-level concepts that the deep network is using to make decisions. We propose an algorithm called Sparse Reconstruction Autoencoder (SRAE) for learning the embedding to the explanation space. SRAE aims to reconstruct part of the original feature space while retaining faithfulness. A pull-away term is applied to SRAE to make the bases of the explanation space more orthogonal to each other. A visualization system is then introduced for human understanding of the features in the explanation space. The proposed method is applied to explain CNN models in image classification tasks. We conducted a human study, which shows that the proposed approach outperforms single saliency map baselines, and improves human performance on a difficult classification tasks. Also, several novel metrics are introduced to evaluate the performance of explanations quantitatively without human involvement.
Neural Network-Based Architecture for Sentiment Analysis in Indian Languages

Abstract Sentiment analysis refers to determining the polarity of the opinions represented by text. The paper proposes an approach to determine the sentiments of tweets in one of the Indian languages (Hindi, Bengali, and Tamil). Thirty-nine sequential models have been created using three different neural network layers [recurrent neural networks (RNNs), long short-term memory (LSTM), convolutional neural network (CNN)] with optimum parameter settings (to avoid over-fitting and error accumulation). These sequential models have been investigated for each of the three languages. The proposed sequential models are experimented to identify how the hidden layers affect the overall performance of the approach. A comparison has also been performed with existing approaches to find out if neural networks have an added advantage over traditional machine learning techniques.
An improved Richardson-Lucy iterative algorithm for C-scan image restoration and inclusion size measurement

The accuracy of measuring inclusion size in direct C-scan image of immersion ultrasonic testing is restricted by the lateral resolution of the focused transducer, even if a high frequency is used, and the blurred edge due to scattering of sound waves at inclusions. In this work, an improved image restoration method that is based on the Richardson-Lucy (RL) iterative algorithm is proposed, which is used to restore the C-scan image and improve the accuracy of inclusion size measurement in immersion ultrasonic testing. For the improved RL iterative algorithm, the point spread function (PSF) is derived based on the multi-Gaussian beam model and Kirchhoff approximation, which considers the propagation properties of sound waves at water-steel interface and the spectral characteristics of the transducer with high frequency. In order to determine the final iteration number, the relationship between final iteration number and size of the inclusion in the image is established by restoring the simulated C-scan image and further calibrated with size correction factor. The size correction factor considers the effect of sound attenuation and electro-mechanical transformation encountered in practical testing equipment. Experimental results show that the inclusion sizes measured in restored C-scan images agree well with the optical micrograph results, which prove the effectiveness of the proposed method.
Analysis and simplification of lens distortion model for the scheimpflug imaging system calibration

The Scheimpflug imaging system has been widely applied in many fields, such as 3D reconstruction and tomographic PIV. Modeling and calibration are essential steps for further analysis of the Scheimpflug imaging system. In this paper, lens distortion model of the Scheimpflug imaging system is established and simplified for calibration. Firstly, lens distortion model of the Scheimpflug imaging system is analyzed and established, which is proved to be different from the conventional imaging system. Secondly, the established lens distortion model is simplified to reduce the complexity of the calibration for the infinite parameters. The application condition of the simplified lens distortion model is also analyzed. Finally, calibration experiment is implemented for the Scheimpflug imaging system with the simplified lens distortion model. The high calibration accuracy verifies that the simplified lens distortion model is feasible.
Rectification Using Different Types of Cameras Attached to a Vehicle

The rectification process is a compulsory step in stereo matching computation. To obtain depth information, stereo camera systems are often installed in vehicles for outdoor and street-related applications, including vehicle and pedestrian detection, lane detection, and traffic sign recognition. In this paper, we propose a rectification method that uses currently available front- and rear-view vehicle cameras to produce rectified stereo images. The proposed method can be employed with different types of cameras that have varying focal lengths. In addition, this method tolerates the problem of camera alignment variation from normal stereo camera systems. To achieve this, a compensation method for different focal lengths and the estimation of image relationships are introduced. The experimental results demonstrate that the proposed method can operate robustly and accurately with different kinds of stereo images and significantly outperforms a state-of-the-art rectification method.
Image Manipulation Specifications on Social Networking Services for Encryption-then-Compression Systems

Encryption-then-Compression (EtC) systems have been proposed to securely transmit images through an untrusted channel provider. In this study, EtC systems were applied to social media like Twitter that carry out image manipulations. The block scrambling-based encryption schemes used in EtC systems were evaluated in terms of their robustness against image manipulation on social media. The aim was to investigate how five social networking service (SNS) providers, Facebook, Twitter, Google+, Tumblr and Flickr, manipulate images and to determine whether the encrypted images uploaded to SNS providers can avoid being distorted by such manipulations. In an experiment, encrypted and non-encrypted JPEG images were uploaded to various SNS providers. The results show that EtC systems are applicable to the five SNS providers.
Language independent sequence labelling for Opinion Target Extraction

In this research note we present a language independent system to model Opinion Target Extraction (OTE) as a sequence labelling task. The system consists of a combination of clustering features implemented on top of a simple set of shallow local features. Experiments on the well known Aspect Based Sentiment Analysis (ABSA) benchmarks show that our approach is very competitive across languages, obtaining best results for six languages in seven different datasets. Furthermore, the results provide further insights into the behaviour of clustering features for sequence labelling tasks. The system and models generated in this work are available for public use and to facilitate reproducibility of results.
Some two-weight and three-weight linear codes

Let $\Bbb F_q$ be the finite field with $q = p^m$ elements, where $p$ is an odd prime and $m$ is a positive integer. For a positive integer $t$, let $D \subset \Bbb F_q^t$ and let $\mbox{Tr}_m$ be the trace function from $\Bbb F_q$ onto $\Bbb F_p$. We define a $p$-ary linear code $\mathcal C_D$ by $ \mathcal C_D = \{\textbf{c}(a_1,a_2, ..., a_t): a_1, a_2, ..., a_t ∈ \Bbb F_{p^m}\}, $ where $\textbf{c}(a_1,a_2, ..., a_t) = \big(\mbox{Tr}_m(a_1x_1+a_2x_2+···+a_tx_t)\big)_{(x_1,x_2, ..., x_t)∈ D}.$ In this paper, we will present the weight enumerators of the linear codes $\mathcal C_D$ in the following two cases:1. $D = \{(x_1,x_2, ..., x_t) ∈ \Bbb F_q^t \setminus \{(0,0, ..., 0)\}: \mbox{Tr}_m(x_1^2+x_2^2+···+x_t^2) = 0\}$;2. $D = \{(x_1,x_2, ..., x_t) ∈ \Bbb F_q^t: \mbox{Tr}_m(x_1^2+x_2^2+···+x_t^2) = 1\}$.It is shown that $\mathcal C_D$ is a two-weight code if $tm$ is even and three-weight code if $tm$ is odd in both cases. The weight enumerators of $\mathcal C_D$ in the first case generalize the results in [17] and [18]. The complete weight enumerators of $\mathcal C_D$ are also investigated.
OLOA: Based Task Scheduling in Heterogeneous Clouds

Cloud computing provides increased performance, scalability and is both cost efficient as well as low accounts for maintenance, which makes it a preferred choice when the dynamic allocation of resources is considered.Of the various advantages that cloud computing provides, task scheduling is an essential feature that helps to boost performance and reduce operation cost.In the proposed OLOA, a solution is provided for optimization, taking the makespan and cost as major constraints.This is accomplished using the two algorithms, Lion optimization algorithm (LOA) and the Opposition Based Learning (OBL) algorithm; and creating a hybrid Oppositional Lion optimization algorithm (OLOA).The given solution is simulated and demonstrated in the cloudsim programming environment, where the obtained results show drastic improvement in performance, in comparison to that of the previously used other existing algorithms such as Particle Swarm Optimization (PSO) algorithm, oppositional learning based grey wolf optimizer (OGWO) and the Genetic algorithm (GA), all of which do not match the performance rates of the proposed hybrid algorithm.
BlinkML

The rising volume of datasets has made training machine learning (ML) models a major computational cost in the enterprise. Given the iterative nature of model and parameter tuning, many analysts use a small sample of their entire data during their initial stage of analysis to make quick decisions (e.g., what features or hyperparameters to use) and use the entire dataset only in later stages (i.e., when they have converged to a specific model). This sampling, however, is performed in an ad-hoc fashion. Most practitioners cannot precisely capture the effect of sampling on the quality of their model, and eventually on their decision-making process during the tuning phase. Moreover, without systematic support for sampling operators, many optimizations and reuse opportunities are lost. In this paper, we introduce BlinkML, a system for fast, quality-guaranteed ML training. BlinkML allows users to make error-computation tradeoffs: instead of training a model on their full data (i.e., full model), BlinkML can quickly train an approximate model with quality guarantees using a sample. The quality guarantees ensure that, with high probability, the approximate model makes the same predictions as the full model. BlinkML currently supports any ML model that relies on maximum likelihood estimation (MLE), which includes Generalized Linear Models (e.g., linear regression, logistic regression, max entropy classifier, Poisson regression) as well as PPCA (Probabilistic Principal Component Analysis). Our experiments show that BlinkML can speed up the training of large-scale ML tasks by 6.26×?629× while guaranteeing the same predictions, with 95% probability, as the full model.
Infrared Multi-Pedestrian Tracking in Vertical View via Siamese Convolution Network

Target tracking has become one of the research hotspots in the field of computer vision in recent years. In this paper, a new intelligent algorithm of infrared multi-pedestrian tracking in vertical view is proposed. In the algorithm, the pedestrians in the infrared image can be quickly detected and located with the method of the Faster Regions with CNN features (RCNN) and then are tracked with the improved Siamese network. The tracking method based on Siamese network transforms the tracking problem into a similarity verification problem and evaluates the similarity score between new frame feature and target frame feature by convolution network. The candidate region with the highest score is considered as the current position of the target. In this paper, the Siamese network is combined with Faster RCNN for multi-pedestrian tracking. In addition, the tracking results of adjacent frames are introduced into the similarity evaluation of current frames to improve the tracking accuracy when the pedestrian posture changes. The experimental results show that the algorithm has good robustness and tracking result and achieves competitive performance.