Reviewerly/exHarmony huggingface dataset:
A Majorization-Minimization Algorithm for Neuroimage Registration

Authors:
raw_author_name: Daniel Tward
raw_affiliation_string: Computational Medicine, UCLA, Los Angeles, CA 90024 USA.
raw_author_name: Kenneth Lange
raw_affiliation_string: Computational Medicine, UCLA, Los Angeles, CA 90024 USA.

Abstract: .Intensity-based image registration is critical for neuroimaging tasks, such as 3D reconstruction, times-series alignment, and common coordinate mapping. The gradient-based optimization methods commonly used to solve this problem require a careful selection of step-length. This limitation imposes substantial time and computational costs. Here we propose a gradient-independent rigid-motion registration algorithm based on the majorization-minimization (MM) principle. Each iteration of our intensity-based MM algorithm reduces to a simple point-set rigid registration problem with a closed form solution that avoids the step-length issue altogether. The details of the algorithm are presented, and an error bound for its more practical truncated form is derived. The performance of the MM algorithm is shown to be more effective than gradient descent on simulated images and Nissl stained coronal slices of mouse brain. We also compare and contrast the similarities and differences between the MM algorithm and another gradient-free registration algorithm called the block-matching method. Finally, extensions of this algorithm to more complex problems are discussed.KeywordsMM algorithmimage registrationneuroimagingMSC codes92C5565K10
DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing

Authors:
raw_author_name: Qi Liu
raw_affiliation_string: Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services, Nanjing University of Information Science and Technology, Nanjing, 210044, China
raw_author_name: Jiawei Sun
raw_affiliation_string: School of Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China
raw_author_name: Yonghong Zhang
raw_affiliation_string: School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China

Abstract: Abstract In the field of meteorology, the global radar network is indispensable for detecting weather phenomena and offering early warning services. Nevertheless, radar data frequently exhibit anomalies, including gaps and clutter, arising from atmospheric refraction, equipment malfunctions, and other factors, resulting in diminished data quality. Traditional radar blockage correction methods, such as employing approximate radial information interpolation and supplementing missing data, often fail to effectively exploit potential patterns in massive radar data, for the large volume of data precludes a thorough analysis and understanding of the inherent complex patterns and dependencies through simple interpolation or supplementation techniques. Fortunately, edge computing possesses certain data processing capabilities and cloud center boasts substantial computational power, which together can collaboratively offer timely computation and storage for the correction of radar beam blockage. To this end, an edge-cloud collaborative driven deep learning model named DenMerD is proposed in this paper, which includes dense connection module and merge distribution (MD) unit. Compared to existing models such as RC-FCN, DenseNet, and VGG, this model greatly improves key performance metrics, with 30.7 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> improvement in Critical Success Index (CSI), 30.1 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> improvement in Probability of Detection (POD), and 3.1 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> improvement in False Alarm Rate (FAR). It also performs well in the Structure Similarity Index Measure (SSIM) metrics compared to its counterparts. These findings underscore the efficacy of the design in improving feature propagation and beam blockage accuracy, and also highlights the potential and value of mobile edge computing in processing large-scale meteorological data.
The use of a large language model to create plain language summaries of evidence reviews in healthcare: A feasibility study

Authors:
raw_author_name: Gerald Gartlehner
raw_affiliation_string: Center for Public Health Methods RTI International Research Triangle Park North Carolina USA; Department for Evidence-based Medicine and Evaluation University of Krems Krems Austria
raw_author_name: Meera Viswanathan
raw_affiliation_string: Center for Public Health Methods RTI International Research Triangle Park North Carolina USA

Abstract: Abstract Introduction Plain language summaries (PLSs) make complex healthcare evidence accessible to patients and the public. Large language models (LLMs) may assist in generating accurate, readable PLSs. This study explored using the LLM Claude 2 to create PLSs of evidence reviews from the Agency for Healthcare Research and Quality (AHRQ) Effective Health Care Program. Methods We selected 10 evidence reviews published from 2021 to 2023, representing a range of methods and topics. We iteratively developed a prompt to guide Claude 2 in creating PLSs which included specifications for plain language, reading level, length, organizational structure, active voice, and inclusive language. PLSs were assessed for adherence to prompt specifications, comprehensiveness, accuracy, readability, and cultural sensitivity. Results All PLSs met the word count. We judged one PLS as fully comprehensive; seven mostly comprehensive. We judged two PLSs as fully capturing the PICO elements; five with minor PICO errors. We judged three PLSs as accurately reporting the results; and four with minor result errors. We judged three PLSs as having major result errors for incorrectly reporting total participants. Five PLSs met the target 6th to 8th grade reading level. Passive voice use averaged 16%. All PLSs used inclusive language. Conclusions LLMs show promise for assisting in PLS creation but likely require human input to ensure accuracy, comprehensiveness, and the appropriate nuances of interpretation. Iterative prompt refinement may improve results and address the needs of specific reviews and audiences. As text‐only summaries, the AI‐generated PLSs could not meet all consumer communication criteria, such as textual design and visual representations. Further testing should include consumer reviewers and explore how to best leverage LLM support in drafting PLS text for complex evidence reviews.
Quran reciter identification using NASNetLarge

Authors:
raw_author_name: Ahmed Younes
raw_affiliation_string: Department of Mathematics and Computer Science, Faculty of Science, Alexandria University, Alexandria, 21511, Egypt
raw_author_name: Mohamed Osman
raw_affiliation_string: Department of Mathematics and Computer Science, Faculty of Science, Damanhour University, Damanhour, 22511, Egypt
raw_author_name: Islam Elkabani
raw_affiliation_string: Department of Mathematics and Computer Science, Faculty of Science, Alexandria University, Alexandria, 21511, Egypt

Abstract: Abstract Speaker identification has significant advantages for the field of human–computer interaction. Recently, many scholars have made contributions in this field and successfully created deep learning models for automatic speaker identification systems. However, most of the speech signal processing work is limited to English-only applications, despite numerous challenges with Arabic speech, particularly with the recitation of the Holy Quran, which is the Islamic holy book. In the light of these considerations, this study proposes a model for identifying the reciter of the Holy Quran using a dataset of 11,000 audio samples extracted from 20 Quran reciters. To enable feeding the audio samples' visual representation to the pre-trained models, the audio samples are converted from their original audio representation to visual representation using the Mel-Frequency Cepstrum Coefficients. Six pre-trained deep learning models are evaluated separately in the proposed model. The results from the test dataset reveal that the NASNetLarge model achieved the highest accuracy rate of 98.50% among the pre-trained models used in this study.
The role of hyperparameters in machine learning models and how to tune them

Authors:
raw_author_name: Luka Biedebach
raw_affiliation_string: Department of Computer Science, Reykjavik University, Reykjavik, Iceland
raw_author_name: Marcel Neunhoeffer
raw_affiliation_string: Department of Statistics, LMU Munich, Munich, Germany; Rafik B. Hariri Institute for Computing and Computational Science & Engineering, Boston University, Boston, MA, USA

Abstract: Abstract Hyperparameters critically influence how well machine learning models perform on unseen, out-of-sample data. Systematically comparing the performance of different hyperparameter settings will often go a long way in building confidence about a model's performance. However, analyzing 64 machine learning related manuscripts published in three leading political science journals (APSR, PA, and PSRM) between 2016 and 2021, we find that only 13 publications (20.31 percent) report the hyperparameters and also how they tuned them in either the paper or the appendix. We illustrate the dangers of cursory attention to model and tuning transparency in comparing machine learning models’ capability to predict electoral violence from tweets. The tuning of hyperparameters and their documentation should become a standard component of robustness checks for machine learning models.
An elitist whale optimization algorithm with the nonlinear parameter: Algorithm and application

Authors:
raw_author_name: Guoxu Zhang
raw_affiliation_string: Key Lab of Intelligent Data Information Processing and Control of Hebei Province, Tangshan College, Tangshan, China; Key Lab of Intelligent Motion Control System of Tangshan City, Tangshan College, Tangshan, China

Abstract: Abstract To address the problem that the whale optimization algorithm tends to fall into the local optimum and fails to maintain a balance between exploration and exploitation, an elitist whale optimization algorithm with the nonlinear parameter (EWOANP) is proposed in this paper. An elitist strategy based on the random Cauchy mutation is used in the shrinking encircling mechanism to increase the chance of escaping the local optimum. Cleverly, the strategy is to generate mutation solutions based on the random Cauchy mutation, after which the better population is selected to proceed to the next iteration. Then, a nonlinear parameter is used in the logarithmic spiral mechanism to balance exploration and exploitation. Various numerical optimization experiments are performed based on the IEEE CEC2020 benchmark suite and compared with eleven other algorithms. The results show that EWOANP outperforms most competitors in numerical optimization. Finally, the backpropagation neural network is optimized by EWOANP to build a prediction model for the sulfur content in the molten iron. The experimental results based on production data indicate that the proposed prediction model has a relatively small fluctuation in errors. Compared to the other seven competitors, the proposed model has a better prediction performance with and =0.916619.
Applicability of deep neural networks for lithofacies classification from conventional well logs: An integrated approach

Authors:
raw_author_name: Saud Qadir Khan
raw_affiliation_string: Mari Petroleum Company Limited, Islamabad, Pakistan

Abstract: Parametric understanding for specifying formation characteristics can be perceived through conventional approaches. Significantly, attributes of reservoir lithology are practiced for hydrocarbon exploration. Well logging is conventional approach which is applicable to predict lithology efficiently as compared to geophysical modeling and petrophysical analysis due to cost effectiveness and suitable interpretation time. However, manual interpretation of lithology identification through well logging data requires domain expertise with an extended length of time for measurement. Therefore, in this study, Deep Neural Network (DNN) has been deployed to automate the lithology identification process from well logging data which would provide support by increasing time-effective for monitoring lithology. DNN model has been developed for forecasting formation lithology leading to the optimization of the model through the thorough evaluation of the best parameters and hyperparameters including the number of neurons, number of layers, optimizer, learning rate, dropout values, and activation functions. Accuracy of the model is examined by utilizing different evaluation metrics through the division of the dataset into the subdomains of training, validation and testing. Additionally, an attempt is contributed to remove interception for formation lithology prediction while addressing the imbalanced nature of the associated dataset as well in the training process using class weight. It is assessed that accuracy is not a true and only reliable metric to evaluate the lithology classification model. The model with class weight recognizes all the classes but has low accuracy as well as a low F1-score while LSTM based model has high accuracy as well as a high F1-score.
Planning Purposeful Activities Autonomous Intelligent Robot with Knowledge Update in Short-Term Memory

Authors:
raw_author_name: V. B. Melekhin
raw_affiliation_string: Dagestan State Technical University
raw_author_name: M. V. Khachumov
raw_affiliation_string: Federal Research Center "Computer Science and Control"; Program Systems Institute of the Russian Academy Sciences

Abstract: The main problems associated with the creation of autonomous intelligent robots capable of performing various complex tasks in a priori undescribed unstable problematic environments, based on the processing of knowledge presented in an abstract way, are outlined. To store typical elements of an abstract knowledge representation model, the article recommends using long-term and short-term memory. Long-term memory with associative search and data retrieval is designed to permanently store information necessary for planning a variety of purposeful activities that provide the robot with the ability to solve various complex behavioral tasks. In short-term memory, submodels of knowledge representation are entered from long-term memory, which are necessary for solving the current task of a certain type in the short term, related to the fulfillment of the task formulated for the autonomous intelligent robot. At the same time, with each change in the type of the current task of behavior being solved by an autonomous intelligent robot, a corresponding update of knowledge stored in short-term memory is simultaneously carried out. Original constructions of typical elements of the model for representing abstract knowledge in the form of various behavioral skills, set regardless of a particular subject area, have been developed. This approach to building a knowledge representation model allows autonomous intelligent robots to adapt to the current operating conditions and, on this basis, organize purposeful activities in complex unstable problematic environments. Various tools and rules for processing abstract knowledge are proposed, which endow autonomous intelligent robots with the ability to eliminate the differences between the current and target situation of the problem environment both in terms of the values of structurally equivalent relations of the same name in them, and in the current states of objects in the environment. This, in turn, makes it possible to create intelligent problem solvers for autonomous intelligent robots for various purposes, capable of performing complex tasks in unstable a priori uncertain conditions of a problematic environment.
An efficient multimodal sentiment analysis in social media using hybrid optimal multi-scale residual attention network

Authors:
raw_author_name: Kanipriya Murugesan
raw_affiliation_string: Department of Computational Intelligence, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India
raw_author_name: Prabakeran Saravanan
raw_affiliation_string: Department of Networking and Communications, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India
raw_author_name: Krishnamurthy Marudhamuthu
raw_affiliation_string: Department of Computer Science and Engineering, KCG College of Technology, Karapakkam, Tamilnadu, India

Abstract: Abstract Sentiment analysis is a key component of many social media analysis projects. Additionally, prior research has concentrated on a single modality in particular, such as text descriptions for visual information. In contrast to standard image databases, social images frequently connect to one another, making sentiment analysis challenging. The majority of methods now in use consider different images individually, rendering them useless for interrelated images. We proposed a hybrid Arithmetic Optimization Algorithm- Hunger Games Search (AOA-HGS)-optimized Ensemble Multi-scale Residual Attention Network (EMRA-Net) technique in this paper to explore the modal correlations including texts, audio, social links, and video for more effective multimodal sentiment analysis. The hybrid AOA-HGS technique learns complementary and comprehensive features. The EMRA-Net uses two segments, including Ensemble Attention CNN (EA-CNN) and Three-scale Residual Attention Convolutional Neural Network (TRA-CNN), to analyze the multimodal sentiments. The loss of spatial domain image texture features can be reduced by adding the Wavelet transform to TRA-CNN. The feature-level fusion technique known as EA-CNN is used to combine visual, audio, and textual information. The proposed method performs significantly better than the existing multimodel sentimental analysis techniques of HALCB, HDF, and MMLatch when evaluated using the Multimodal Emotion Lines Dataset (MELD) and EmoryNLP datasets. Also, even though the size of the training set varies, the proposed method outperformed other techniques in terms of recall, accuracy, F score, and precision and takes less time to compute in both datasets.
Control of a Wheeled Robot on a Plane with Obstacles

Authors:
raw_author_name: E. A. Mikishanina
raw_affiliation_string: Chuvash State University

Abstract: The work proposes an algorithm for controlling a wheeled robot in an environment with static and dynamic obstacles. A wheeled robot (WR) consists of a platform, two wheels with a differential drive and one roller, which is used solely for the stability of the structure and does not affect the dynamics of the system. The robot’s motion algorithm assumes its movement from the starting point to the final point in an environment with obstacles. The robot’s motion program is specified through servo-constraints, and the algorithm that implements the motion program is based on the potential field method. In the case of a dynamic obstacle, a repulsive field of a shape elongated in the direction of movement of the obstacle is constructed, allowing the robot to safely go around it. It is possible to change the geometric dimensions of the field using the entered numerical parameters. An algorithm for overcoming a potential hole by a robot is presented, according to which the robot is taken out of the potential hole and directed to a global goal by an introduced fictitious point located outside the critical region (local minimum region) and having its own attractive field. The paper presents the results of numerical simulation of the robot’s movement both in an environment with static and dynamic obstacles, as well as the results of a numerical experiment with overcoming the region of a potential well. Graphs of the required mechanical parameters are presented. The results of numerical simulation confirm the effectiveness of the proposed algorithms.
Optimizing AI Social Chatbots for Relational Outcomes: The Effects of Profile Design, Communication Strategies, and Message Framing

Authors:
raw_author_name: Alvin Zhou
raw_affiliation_string: Hubbard School of Journalism & Mass Communication, University of Minnesota, Minneapolis, MN, USA
raw_author_name: Wan-Hsiu Sunny Tsai
raw_affiliation_string: Department of Strategic Communication, University of Miami, Coral Gables, FL, USA
raw_author_name: Linjuan Rita Men
raw_affiliation_string: Department of Public Relations, University of Florida, Gainesville, FL, USA

Abstract: With more corporations incorporating artificial intelligence (AI) tools like social chatbots into their public communication practices, we explore optimal chatbot designs for organization-public relational outcomes. Our 2 × 2 × 2 factorial web experiment with eight custom-designed chatbots showed that communication strategies (verbal and non-verbal social cues such as emoji, memes, filler words, and response delay) had a significant impact (Cohen’s d = 0.536, standardized coefficient β = 0.438) on chatbot social conversation, the central antecedent to the investigated relational outcomes such as trust in business. Furthermore, the effect of chatbot social conversation is partially mediated by perceived organizational listening, highlighting the importance of listening and its related theories and practices in automated business communication.