Reviewerly/exHarmony huggingface dataset:
Gated recurrent unit decision model for device argumentation in ambient assisted living

Authors:
raw_author_name: G. S. Madhan Kumar
raw_affiliation_string: JSS Science and Technology University
raw_author_name: S. P. Shiva Prakash
raw_affiliation_string: JSS Science and Technology University
raw_author_name: Kirill Krinkin
raw_affiliation_string: Co-Evolutionary Artificial Intelligence

Abstract: The increasing elderly population worldwide is facing a variety of social, physical, and cognitive issues, such as walking problems, falls, and difficulties in performing daily activities. To support elderly people, continuous monitoring and supervision are needed. Due to the busy modern lifestyle of caretakers, taking care of elderly people is difficult. As a result, many elderly people prefer to live independently at home without any assistance. To help such people, an ambient assisted living (AAL) environment is provided that monitors and evaluates the daily activities of elderly individuals. An AAL environment has heterogeneous devices that interact, and exchange information of the activities performed by the users. The devices can be involve in an argumentation about the occurrence of an activity thus leading to generate conflicts. To address this issue, the paper proposes a gated recurrent unit (GRU) learning techniques to facilitate decision-making for device argumentation during activity occurrences. The proposed model is used to initially classify user activities and each sensor value status. Then a novel method is used to identify argumentation among devices for activity occurrences in the classified user activities. Later, the GRU decision making model is used to resolve the argumentation and to identify the target activity that occurred. The result of the proposed model is compared with other existing techniques. The proposed model outperformed the other existing methods with an accuracy of 85.45%, precision of 72.32%, recall of 65.83%, and F1-Score of 60.22%.
MMDataLoader: Reusing Preprocessed Data Among Concurrent Model Training Tasks

Authors:
raw_author_name: Hai Jin
raw_affiliation_string: National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Huazhong University of Science and Technology, Wuhan, China
raw_author_name: Ligang He
raw_affiliation_string: University of Warwick, Coventry, U.K.
raw_author_name: Yuhao Li
raw_affiliation_string: National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Huazhong University of Science and Technology, Wuhan, China
raw_author_name: Xuanhua Shi
raw_affiliation_string: National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Huazhong University of Science and Technology, Wuhan, China

Abstract: Data preprocessing plays an important role in deep learning, which directly affects the training efficiency. Data preprocessing is performed on the CPU. The preprocessed data are then fed to the models that are trained on the GPU. We observed that data preprocessing on the CPU can potentially create a bottleneck in the entire process of a model training task. In order to tackle this issue, we have developed MMDataLoader, which enables reusing preprocessed data among multiple model training tasks. MMDataLoader automatically constructs a data preprocessing pipeline based on each task’s specific preprocessing workflow, allowing for maximum data reuse and reduced computing workload on the CPU. Unlike conventional data loaders that operate at the task level and provide data provision services to specific training tasks, MMDataLoader operates at the server level and provides data for all concurrently running tasks. We have conducted extensive experiments. The results show that MMDataLoader can significantly increase preprocessing throughput without affecting model convergence when compared to conventional methods where model training tasks are executed concurrently. For instance, with three tasks running, the preprocessing throughput can increase by 1.6x to 3.15x, depending on the tasks being executed and the proportion of preprocessing operations that are shared among them.
An off-policy multi-agent stochastic policy gradient algorithm for cooperative continuous control

Authors:
raw_author_name: Lan Tang
raw_affiliation_string: School of Electronic Science and Engineering, Nanjing University, Nanjing, 210093, China
raw_author_name: Xinggan Zhang
raw_affiliation_string: School of Electronic Science and Engineering, Nanjing University, Nanjing, 210093, China
raw_author_name: Ying-chang Liang
raw_affiliation_string: Center for Intelligent Networking and Communications, University of Electronic Science and Technology of China, Chengdu, 611731, China

Abstract: Multi-agent reinforcement learning (MARL) algorithms based on trust regions (TR) have achieved significant success in numerous cooperative multi-agent tasks. These algorithms restrain the Kullback–Leibler (KL) divergence (i.e., TR constraint) between the current and new policies to avoid aggressive update steps and improve learning performance. However, the majority of existing TR-based MARL algorithms are on-policy, meaning that they require new data sampled by current policies for training and cannot utilize off-policy (or historical) data, leading to low sample efficiency. This study aims to enhance the data efficiency of TR-based learning methods. To achieve this, an approximation of the original objective function is designed. In addition, it is proven that as long as the update size of the policy (measured by the KL divergence) is restricted, optimizing the designed objective function using historical data can guarantee the monotonic improvement of the original target. Building on the designed objective, a practical off-policy multi-agent stochastic policy gradient algorithm is proposed within the framework of centralized training with decentralized execution (CTDE). Additionally, policy entropy is integrated into the reward to promote exploration, and consequently, improve stability. Comprehensive experiments are conducted on a representative benchmark for multi-agent MuJoCo (MAMuJoCo), which offers a range of challenging tasks in cooperative continuous multi-agent control. The results demonstrate that the proposed algorithm outperforms all other existing algorithms by a significant margin.
Real and Synthetic Punjabi Speech Datasets for Automatic Speech Recognition

Authors:
raw_author_name: Satwinder Singh
raw_affiliation_string: School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand
raw_author_name: Feng Hou
raw_affiliation_string: School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand
raw_author_name: Ruili Wang
raw_affiliation_string: School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand

Abstract: Automatic speech recognition (ASR) has been an active area of research. Training with large annotated datasets is the key to the development of robust ASR systems. However, most available datasets are focused on high-resource languages like English, leaving a significant gap for low-resource languages. Among these languages is Punjabi, despite its large number of speakers, Punjabi lacks high-quality annotated datasets for accurate speech recognition. To address this gap, we introduce three labeled Punjabi speech datasets: Punjabi Speech (real speech dataset) and Google-synth/CMU-synth (synthesized speech datasets). The Punjabi Speech dataset consists of read speech recordings captured in various environments, including both studio and open settings. In addition, the Google-synth dataset is synthesized using Google's Punjabi text-to-speech cloud services. Furthermore, the CMU-synth dataset is created using the Clustergen model available in the Festival speech synthesis system developed by CMU. These datasets aim to facilitate the development of accurate Punjabi speech recognition systems, bridging the resource gap for this important language.
Fractal modeling of oil and gas geochemical data in the Taiwan Strait Basin

Authors:
raw_author_name: Yan Zhang
raw_affiliation_string: Key Laboratory of Marine Mineral Resources, Ministry of Natural Resources, Guangzhou Marine Geological Survey, China Geological Survey, Guangzhou 511458, China; National Engineering Research Center for Gas Hydrate Exploration and Development, Guangzhou 511458, China; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
raw_author_name: Li Zhang
raw_affiliation_string: Key Laboratory of Marine Mineral Resources, Ministry of Natural Resources, Guangzhou Marine Geological Survey, China Geological Survey, Guangzhou 511458, China
raw_author_name: Fan Xiao
raw_affiliation_string: School of Earth Sciences and Engineering, Sun Yat-sen University, Zhuhai 519000, China
raw_author_name: Zhenyu Lei
raw_affiliation_string: Key Laboratory of Marine Mineral Resources, Ministry of Natural Resources, Guangzhou Marine Geological Survey, China Geological Survey, Guangzhou 511458, China
raw_author_name: Xing Qian
raw_affiliation_string: Key Laboratory of Marine Mineral Resources, Ministry of Natural Resources, Guangzhou Marine Geological Survey, China Geological Survey, Guangzhou 511458, China

Abstract: This study investigated the fractal characteristics of oil and gas indicators in the Taiwan Strait Basin using multifractal methods. The results revealed that these indicators can be categorized into five groups through cluster analysis, principal component analysis, and multifractal analysis. Acidolysis hydrocarbon indicators exhibited strong multifractal characteristics, while other indicators showed weak multifractal characteristics. The high values of Polycyclic aromatic hydrocarbons (F) indicators were mainly found in the southwest direction of the study area, whereas the high values of total aromatic hydrocarbons and their derivatives (UV) indicators were distributed in the eastern part. Erosion carbonate (ΔC) indicators were predominantly located in the western part, while the distribution of acid extractable hydrocarbons (A) and pyrolytic hydrocarbons (P) indicators was sporadic. The distribution of Erosion carbonate (ΔC) indicators in the Jinjiang Depression resembled that in the Jiulongjiang Depression, but the distribution of other indicators varied. The S-A method was employed to identify oil and gas anomaly target areas and predict and evaluate favorable prospect zones in the basin. This study provides a theoretical foundation for future oil and gas exploration and evaluation in the Taiwan Strait.
The nexus of supply chain performance and blockchain technology in the digitalization era: Insights from a fast-growing economy

Authors:
raw_author_name: Ana Beatriz Lopes de Sousa Jabbour
raw_affiliation_string: EM Normandie Business School, Metis Lab, 30-32 Rue Henri Barbusse, 92110 Clichy, France
raw_author_name: Joseph Sarkis
raw_affiliation_string: Foisie Business School, Worcester Polytechnic Institute, WPI Washburn Shops, 50 Prescott St, Worcester, MA 01609, USA; Humlog Institute, Hanken School of Economics, Arkadiankatu 22, 00100 Helsinki, Finland
raw_author_name: Charbel Jose Chiappetta Jabbour
raw_affiliation_string: NEOMA Business School, 1 rue du Maréchal Juin - BP 215, 76130 Mont-Saint-Aignan Cedex, France
raw_author_name: Murad Ali
raw_affiliation_string: Newcastle Business School, Northumbria University, City Campus East 1, NE1 8ST, Newcastle Upon Tyne, UK

Abstract: This article extends the coverage of previous studies and addresses persistent gaps in the literature by assessing the simultaneous effects of adopting blockchain technology (BCT) on supply chain performance. It empirically examines the indirect effect of BCT adoption on supply chain performance through the mediating variables of supply chain agility, adaptability, transparency and coordination, based on the expanded triple A supply chain concept. The study analyzes data from manufacturing firms listed on the Indonesian Stock Exchange. The findings align with propositions derived from the Resource Based View (RBV), in which capabilities viewed as mediators—rather than testing for direct effects of BCT on organizational performance—are the most likely scenario for multifaceted supply chain operations. This study contributes to the ongoing debate on the relationship between BCT and organizational performance. The research offers insights for policymakers and blockchain vendors into this relationship.
Performance evaluation of generative adversarial networks for generating mugshot images from text description

Authors:
raw_author_name: Nur Nabilah Bahrum
raw_affiliation_string: Universiti Teknologi MARA
raw_author_name: Samsul Setumin
raw_affiliation_string: Universiti Teknologi MARA
raw_author_name: Nor Azlan Othman
raw_affiliation_string: Universiti Teknologi MARA
raw_author_name: Mohd Ikmal Fitri Maruzuki
raw_affiliation_string: Universiti Teknologi MARA
raw_author_name: Mohd Firdaus Abdullah
raw_affiliation_string: Universiti Teknologi MARA
raw_author_name: Adi Izhar Che Ani
raw_affiliation_string: Universiti Teknologi MARA

Abstract: The process of identifying photos from a sketch has been explored by many researchers, and the performance of the identification process is almost perfect, particularly for viewed sketches. Suspect identification based on sketches is one of the applications in forensic science. To identify the suspect using these kinds of methods, a face sketch is required. Hence, the methods require skilled artists to sketch the suspect based on descriptions provided by eyewitnesses. However, the skills of these artists are different from one another, which results in different rendered sketches. Therefore, this work attempts to propose a new identification method based only on forensic face-written descriptions. To investigate the feasibility of the proposed method, this study has evaluated the performance of some text-to-photo generators on both viewed and forensic datasets using three different models of GAN which are SAGAN, DFGAN, and DCGAN. Then, the generated images are compared to the real photo contained within those datasets to evaluate how well the proposed method recognizes the faces. The results demonstrated that the recognition rate for the generated photos by the DCGAN models is better than the other two models which achieve a 38.3% recognition rate at rank-10 for mugshot identification.
An answer set programming-based implementation of epistemic probabilistic event calculus

Authors:
raw_author_name: Fabio Aurelio D'Asaro
raw_affiliation_string: Department of Human Sciences, University of Verona, Verona, Italy
raw_author_name: Antonis Bikakis
raw_affiliation_string: Department of Information Studies, University College London, London, UK
raw_author_name: Luke Dickens
raw_affiliation_string: Department of Information Studies, University College London, London, UK
raw_author_name: Rob Miller
raw_affiliation_string: Department of Information Studies, University College London, London, UK

Abstract: We describe a general procedure for translating Epistemic Probabilistic Event Calculus (EPEC) action language domains into Answer Set Programs (ASP), and show how the Python-driven features of the ASP solver Clingo can be used to provide efficient computation in this probabilistic setting. EPEC supports probabilistic, epistemic reasoning in domains containing narratives that include both an agent's own action executions and environmentally triggered events. Some of the agent's actions may be belief-conditioned, and some may be imperfect sensing actions that alter the strengths of previously held beliefs. We show that our ASP implementation can be used to provide query answers that fully correspond to EPEC's own declarative, Bayesian-inspired semantics.
Fractal modeling for geochemical data of deep-sea surface sediments: A case study from Zhongsha Island, Southern China Sea

Authors:
raw_author_name: Yan Zhang
raw_affiliation_string: Key Laboratory of Marine Mineral Resources, Ministry of Natural Resources, Guangzhou Marine Geological Survey, China Geological Survey, Guangzhou 511458, China; National Engineering Research Center for Gas Hydrate Exploration and Development, Guangzhou 511458, China; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
raw_author_name: Li Zhang
raw_affiliation_string: Key Laboratory of Marine Mineral Resources, Ministry of Natural Resources, Guangzhou Marine Geological Survey, China Geological Survey, Guangzhou 511458, China
raw_author_name: Fan Xiao
raw_affiliation_string: School of Earth Sciences and Engineering, Sun Yat-sen University, Zhuhai 519000, China
raw_author_name: Yongzhang Zhou
raw_affiliation_string: School of Earth Sciences and Engineering, Sun Yat-sen University, Zhuhai 519000, China
raw_author_name: Shiqiao Liu
raw_affiliation_string: Haikou Marine Geological Survey Center, China Geological Survey, Haikou 571127, China

Abstract: This study applied principal component analysis (PCA), concentration-area (C-A) and spectrum-area (S-A) fractal models to identify geochemical element combination anomalies related to heavy metals from the data for 43 geochemical elements in surface sediments of the Zhongsha Islands. The relationships between these 43 elements were first examined by applying correlation and cluster analysis to the surface sediment sample dataset. The multifractal spectral functions of the 43 elements were then calculated and all elements were classified using the curve shape of the spectral function. The results of classification were consistent with that of cluster analysis, thereby verifying the reliability of fractal analysis of the study area. PCA was then used to identify element combination anomalies and to assess high-value areas of element contents, and the S-A model was used to distinguish between the anomaly and background signal. The results of this multiple-analysis approach show that the element combination anomaly is mainly located in the deep-sea plain area, with a few elevated values in the Zhongsha South Basin. This study showed that quantitative evaluation of anomalies of multi-element associations can effectively delineate a target area and that anomalies of element associations extracted by the S-A fractal method can provide a more accurate outline areas containing high contents of combined elements. This study can act as a theoretical reference for the environmental assessment of the Zhongsha sea area.
Performance counter dataset for behavioural biometric purpose

Authors:
raw_author_name: Hendrio Bragança
raw_affiliation_string: Institute of Computing, Federal University of Amazonas, Amazonas, Brazil
raw_author_name: Eduardo Feitosa
raw_affiliation_string: Institute of Computing, Federal University of Amazonas, Amazonas, Brazil
raw_author_name: Eduardo Souto
raw_affiliation_string: Institute of Computing, Federal University of Amazonas, Amazonas, Brazil

Abstract: In the pursuit of advancing research in continuous user authentication, we introduce COUNT-OS-I and COUNT-OS-II, two distinct performance counter datasets from Windows operating systems, crafted to bolster research in continuous user authentication. Encompassing data from 63 computers and users, the datasets offer rich, real-world insights for developing and evaluating authentication models. COUNT-OS-I spans 26 users in an IT department, capturing 159 attributes across diverse hardware and software environments over 26 h on average per user. COUNT-OS-II, on the other hand, encompasses 37 users with identical system configurations, recording 218 attributes per sample over a 48-hour period. Both datasets utilize pseudonymization to safeguard user identities while maintaining data integrity and statistical accuracy. The well-balanced nature of the data, confirmed by comprehensive statistical analysis, positions these datasets as reliable benchmarks for the continuous user authentication domain. Through their release, we aim to empower the development of robust, real-world applicable authentication models, contributing to enhanced system security and user trust.
Dynamic adaptive control of boiler combustion based on improved GNG algorithm

Authors:
raw_author_name: Wei Wang
raw_affiliation_string: Inner Mongolia Jingneng Kangbashi Thermal Power Co.,Ltd., Ordos City, Inner Mongolia, 017000, China
raw_author_name: Quan Zhang
raw_affiliation_string: Inner Mongolia Jingneng Kangbashi Thermal Power Co.,Ltd., Ordos City, Inner Mongolia, 017000, China
raw_author_name: Xiaofeng Wang
raw_affiliation_string: Inner Mongolia Jingneng Kangbashi Thermal Power Co.,Ltd., Ordos City, Inner Mongolia, 017000, China
raw_author_name: Liping Wang
raw_affiliation_string: Inner Mongolia Jingneng Kangbashi Thermal Power Co.,Ltd., Ordos City, Inner Mongolia, 017000, China
raw_author_name: Yongxing Lv
raw_affiliation_string: Inner Mongolia Jingneng Kangbashi Thermal Power Co.,Ltd., Ordos City, Inner Mongolia, 017000, China
raw_author_name: Zhijun Jia
raw_affiliation_string: Inner Mongolia Jingneng Kangbashi Thermal Power Co.,Ltd., Ordos City, Inner Mongolia, 017000, China
raw_author_name: Bo Zhang
raw_affiliation_string: Inner Mongolia Jingneng Kangbashi Thermal Power Co.,Ltd., Ordos City, Inner Mongolia, 017000, China

Abstract: The boiler combustion process contains complex physicochemical changes, which is a nonlinear time-varying industrial process with strong interference and multivariate strong coupling. For this kind of boiler combustion process with typical nonlinear characteristics and complex mechanism, it is difficult to establish an accurate mechanism model by conventional modeling methods, so it is difficult to meet the new requirements for the optimal control of the boiler. The large amount of data accumulated in the operation process contains rich system information, and the data-driven modeling and control method provides an effective way for the operation optimization of the unit. Data dynamic characterization and control technology is an important means of data mining, coal-fired boiler data has obvious temporal and drift characteristics, for the current data tracking and supervision algorithms mostly lack of dynamics, real-time and stability issues, design an adaptive clustering model based on the improved growth of neural gas model (GNG), the establishment of nodes based on the probability, the range of the search, the average distance of node A node generation and deletion mechanism based on probability, range search and average distance of nodes is established to realize real-time monitoring of drift data. Finally, the experiments are carried out by analyzing the dynamic data of coal-fired boiler, and the experimental results show that the model and algorithm have stronger real-time tracking ability for dynamic drift data, and can accurately and effectively monitor and control the dynamic data of coal-fired boiler.