Reviewerly/exHarmony huggingface dataset:
Enhancing conceptual models with computational capabilities: A methodical approach to executable integrative modeling

Authors:
raw_author_name: Ahmad Jbara
raw_affiliation_string: Faculty of Data and Decision Sciences Technion Israel Institute of Technology Haifa Israel; School of Computer Science Netanya Academic College Netanya Israel
raw_author_name: Dov Dori
raw_affiliation_string: Faculty of Data and Decision Sciences Technion Israel Institute of Technology Haifa Israel

Abstract: Abstract The lack of a common executable modeling framework that integrates systems engineering, software design, and other engineering domains is a major impediment to seamless product development processes. Our research aims to overcome this system‐software modeling gap by integrating computational, software‐related, and model execution capabilities into OPM‐based conceptual modeling, resulting in a holistic unified executable quantitative‐qualitative modeling framework. The gap is overcome via a Methodical Approach to Executable Integrative Modeling—MAXIM, an extension of OPM ISO 19450:2015, a standardization approvement given on 2015. We present the principles of MAXIM and demonstrate its operation within OPCloud—a web‐based collaborative conceptual OPM modeling framework. As a proof‐of‐concept, a model of an Airbus civil aircraft landing gear braking system is constructed and executed. Using MAXIM, engineers from five domains can collaborate at the very early phase of the system development and jointly construct a unified model that fuses qualitative and quantitative aspects of the various disciplines. This case study illustrates an important first step towards satisfying the critical and growing need to integrate systems engineering with software computations into a unified framework that enables a smooth transition from high‐level architecting to detailed, discipline‐oriented design. Such a framework is a key to agile yet robust future development of software‐intensive systems.
Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks

Authors:
raw_author_name: Chunxiao Lin
raw_affiliation_string: Bradley Department of Electrical and Computing Engineering, Virginia Tech, Blacksburg, VA, United States.
raw_author_name: Muhammad Farhan Azmine
raw_affiliation_string: Bradley Department of Electrical and Computing Engineering, Virginia Tech, Blacksburg, VA, United States.
raw_author_name: Yibin Liang
raw_affiliation_string: Bradley Department of Electrical and Computing Engineering, Virginia Tech, Blacksburg, VA, United States.
raw_author_name: Yang Yi
raw_affiliation_string: Bradley Department of Electrical and Computing Engineering, Virginia Tech, Blacksburg, VA, United States.

Abstract: The field of wireless communication is currently being pushed to new boundaries with the emergence of 6G technology. This advanced technology requires substantially increased data rates and processing speeds while simultaneously requiring energy-efficient solutions for real-world practicality. In this work, we apply a neuroscience-inspired machine learning model called echo state network (ESN) to the critical task of symbol detection in massive MIMO-OFDM systems, a key technology for 6G networks. Our work encompasses the design of a hardware-accelerated reservoir neuron architecture to speed up the ESN-based symbol detector. The design is then validated through a proof of concept on the Xilinx Virtex-7 FPGA board in real-world scenarios. The experiment results show the great performance and scalability of our symbol detector design across a range of MIMO configurations, compared with traditional MIMO symbol detection methods like linear minimum mean square error. Our findings also confirm the performance and feasibility of our entire system, reflected in low bit error rates, low resource utilization, and high throughput.
Integrating Blockchain in Smart Grids for Enhanced Demand Response: Challenges, Strategies, and Future Directions

Authors:
raw_author_name: Paraskevas Koukaras
raw_affiliation_string: Information Technologies Institute, Centre for Research & Technology, 57001 Thessaloniki, Greece; School of Science and Technology, International Hellenic University, 14th km Thessaloniki-Moudania, 57001 Thessaloniki, Greece
raw_author_name: Konstantinos D. Afentoulis
raw_affiliation_string: Department of Energy Systems, University of Thessaly, Gaiopolis Campus, Ring Road of Larissa-Trikala, 41500 Larissa, Greece
raw_author_name: Dimosthenis Ioannidis
raw_affiliation_string: Information Technologies Institute, Centre for Research & Technology, 57001 Thessaloniki, Greece
raw_author_name: Stylianos I. Vagropoulos
raw_affiliation_string: Department of Energy Systems, University of Thessaly, Gaiopolis Campus, Ring Road of Larissa-Trikala, 41500 Larissa, Greece
raw_author_name: Christos Tjortjis
raw_affiliation_string: School of Science and Technology, International Hellenic University, 14th km Thessaloniki-Moudania, 57001 Thessaloniki, Greece

Abstract: This research, conducted throughout the years 2022 and 2023, examines the role of blockchain technology in optimizing Demand Response (DR) within Smart Grids (SGs). It critically assesses a range of blockchain architectures, evaluating their impact on enhancing DR’s efficiency, security, and consumer engagement. Concurrently, it addresses challenges like scalability, interoperability, and regulatory complexities inherent in merging blockchain with existing energy systems. By integrating theoretical and practical viewpoints, it reveals the potential of blockchain technology to revolutionize Demand Response (DR). Findings affirm that integrating blockchain technology into SGs effectively enhances the efficiency and security of DR, and empirical data illustrate substantial improvements in both cases. Furthermore, key challenges include scalability and interoperability, and also identifying opportunities to enhance consumer engagement and foster system transparency in the adoption of blockchain within DR and SGs. Finally, this work emphasizes the necessity for further investigation to address development hurdles and enhance the effectiveness of blockchain technology in sustainable energy management in SGs.
The Geochemical Characteristics of Trace Elements in the Magnetite and Fe Isotope Geochemistry of the Makeng Iron Deposit in Southwest Fujian and Their Significance in Ore Genesis

Authors:
raw_author_name: Lei Zhang
raw_affiliation_string: Cores and Samples Center of Natural Resources, China Geological Survey, Langfang 065201, China
raw_author_name: Sen Wang
raw_affiliation_string: Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China
raw_author_name: Huang Deng
raw_affiliation_string: Cores and Samples Center of Natural Resources, China Geological Survey, Langfang 065201, China

Abstract: The Makeng iron deposit in southwest Fujian is a significant iron polymetallic deposit with various types of iron ore, including garnet magnetite, diopside magnetite, and quartz magnetite. The metallogenetic type of the deposit has been a subject of debate, in particular the genesis of magnetite and source of iron. In situ microanalysis of trace elements in magnetite from different ores shows relatively low levels of V, Ti, Cu, and Zn, with higher concentrations of Ca and Si, indicating the characteristics of a skarn type deposit. The δ57Fe values of the magnetite range from −0.091‰ to 0.317‰. Combining these data, whole-rock iron isotope analyses, including Juzhou and Dayang granites, diabase, and the Lower Carboniferous Lindi Formation sandstone, suggest that Fe in the magnetite primarily originates from granitic pluton, with potential contributions from diabase and the Lower Carboniferous Lindi Formation sandstone. Combined with field work, it is indicated that Makeng iron deposit is a skarn-type magnetite deposit associated with Yanshanian granitic intrusions. Therefore, the initial ore-forming fluid is postulated to be a high-temperature magmatic hydrothermal fluid with high oxygen fugacity. This fluid infiltrated spaces such as interlayer fracture zones between the Upper Carboniferous Jingshe Formation–Middle Permian Qixia Formation carbonate rocks and the Lower Carboniferous Lindi Formation sandstone, resulting in diverse magnetite ores due to metasomatism. The mineralization process of the Makeng iron deposit is basically the same and composed of typical skarn deposits. Magnetite was mainly formed during calcic skarn formation and continued to the initial stages of the retrograde alteration of skarns. In contrast, sulfide minerals, including molybdenite, sphalerite, and galena, precipitated during the quartz–sulfide stage.
Electron Probe Micro-Analyzer Studies and Raman Spectroscopic Characterization of Plagioclase in Basaltic Rocks from the Asal–Ghoubbet Area, Republic of Djibouti

Authors:
raw_author_name: Yusuf Kağan Kadioğlu
raw_affiliation_string: Department of Geological Engineering, Ankara University, Ankara 06830, Turkey

Abstract: The Asal–Ghoubbet Rift area comprises basaltic rocks with similar compositions that were formed by volcanic eruptions. To gain insight into the magmatic processes of these volcanic formations, we investigated the mineral chemistry of plagioclase macrocrysts and microcrysts found in the basaltic rocks by using an electron probe micro-analyzer (EPMA). These basaltic rocks contain olivine, pyroxene, euhedral plagioclase macrocrysts, and euhedral to subhedral plagioclase microcrysts. These plagioclase macrocrysts reach up to 4 cm in length in the form of giant crystals as plagioclase ultraphyric basalts (PUBs). They have a mineral composition varying from bytownite to labradorite with anorthite content ranging from An53 to An86. Also, the microcrysts of all these volcanic rocks are characterized by labradorite and andesine compositions with An22–80. According to the calculated plagioclase thermobarometry, the crystallization temperature of the plagioclase macrocrysts and microcrysts is 1082 to 1216 °C and 1072 to 1203 °C, respectively, and the pressure is 3.92 to 14.51 kbar for the macrocrysts and 2.99 to 14.84 kbar for the microcrysts. Based on these thermobarometry results for the plagioclases, we conclude that the volcanic formations located in the Asal–Ghoubbet area would have come from different eruptions from a single magmatic chamber.
Concept logic trees: enabling user interaction for transparent image classification and human-in-the-loop learning

Authors:
raw_author_name: Manuel P. Cuéllar
raw_affiliation_string: University of Granada, 18071, Granada, Spain
raw_author_name: Diego P. Morales
raw_affiliation_string: University of Granada, 18071, Granada, Spain

Abstract: Abstract Interpretable deep learning models are increasingly important in domains where transparent decision-making is required. In this field, the interaction of the user with the model can contribute to the interpretability of the model. In this research work, we present an innovative approach that combines soft decision trees, neural symbolic learning, and concept learning to create an image classification model that enhances interpretability and user interaction, control, and intervention. The key novelty of our method relies on the fusion of an interpretable architecture with neural symbolic learning, allowing the incorporation of expert knowledge and user interaction. Furthermore, our solution facilitates the inspection of the model through queries in the form of first-order logic predicates. Our main contribution is a human-in-the-loop model as a result of the fusion of neural symbolic learning and an interpretable architecture. We validate the effectiveness of our approach through comprehensive experimental results, demonstrating competitive performance on challenging datasets when compared to state-of-the-art solutions.
Bitterling fish optimization (BFO) algorithm

Authors:
raw_author_name: Javad Rahebi
raw_author_name: Mohammad Javad Shayegan

Abstract: Abstract The bitterling fish is a prime example of intelligent behavior in nature for survival. The bitterling fish uses the oyster spawning strategy as their babysitter. The female bitterling fish looks for a male fish stronger than other fish to find the right pair. In order to solve optimization issues, the Bitterling Fish Optimization (BFO) algorithm is modeled in this manuscript based on the mating behavior of these fish. The bitterling fish optimization algorithm is more accurate than the gray wolf optimization algorithm, whale optimization algorithm, butterfly optimization algorithm, Harris Hawks optimization algorithm, and black widow optimization algorithm, according to experiments and implementations on various benchmark functions. Data mining and machine learning are two areas where meta-heuristic techniques are frequently used. In trials, the MLP artificial neural network and a binary version of the BFO algorithm are used to lower the detection error for intrusion traffic. The proposed method's accuracy, precision, and sensitivity index for detecting network intrusion are 99.14%, 98.87%, and 98.85%, respectively, according to experiments on the NSL KDD data set. Compared to machine learning approaches like NNIA, DT, RF, XGBoot, and CNN, the proposed method is more accurate at detecting intrusion. The BFO algorithm is used for feature selection in the UNSW-NB15 dataset, and the tests showed that the accuracy of the proposed method is 96.72% in this dataset. The proposed method of the BFO algorithm is also used to improve Kmeans clustering, and the tests performed on the dataset of covid 19, diabetes, and kidney disease show that the proposed method performs better than iECA*, ECA*, GENCLUST + + (G + +) methods. Deep has KNN, LVQ, SVM, ANN, and KNN.
TMNIO:Triplet merged network with involution operators for improved few‐shot image classification

Authors:
raw_author_name: Zhao Shaojie
raw_affiliation_string: School of Mathematics, Physics and Statistics Shanghai University of Engineering Science Shanghai China
raw_author_name: Zheng Mingming
raw_affiliation_string: School of Mathematics, Physics and Statistics Shanghai University of Engineering Science Shanghai China
raw_author_name: Yu Weiqin
raw_affiliation_string: School of Mathematics, Physics and Statistics Shanghai University of Engineering Science Shanghai China

Abstract: Abstract Few‐shot learning enables machines to learn efficiently from limited labelled data. However, existing few‐shot learning methods may perform poorly when there is a lack of sufficient samples, and may encounter problems such as domain shift or overfitting when applied to new domains or tasks. To address the issues of poor fitting and insufficient generalization ability in new domains, a new method called triplet merged network with involution operators (TMNIO) is proposed. This method employs dual encoders that extract common and distinctive features from the prototype network, thereby enhancing the model's feature extraction capability. To further improve this ability, the traditional convolutional kernels are replaced with involution operators, which not only reduce the parameter count but also enlarge the receptive field to better extract local feature information. Additionally, this method employs a two‐stage training strategy, where triplet loss is used in the first stage to train the model and enhance its robustness and generalization ability. Extensive experiments on the miniImageNet, Omniglot, and Caltech‐UCSD Birds‐200 (CUB) datasets have shown that our proposed method achieved significant improvements in both training speed and accuracy, particularly on the miniImageNet dataset, where it achieved an outstanding 10% performance improvement.
The role of Artificial Intelligence in digital forensics: Case studies and future directions

Authors:
raw_author_name: Simon Parkinson
raw_affiliation_string: Saad Khan is a Senior Lecturer in the School of Computing and Engineering at the University of Huddersfield and part of the Centre for Cyber Security. His research interest involves developing and utilising artificial intelligence and machine learning techniques for cyber security in various domains, such as digital forensics, vulnerability, and anomaly detection, learning domain knowledge, mitigation planning and access control.
raw_author_name: Saad Khan
raw_affiliation_string: Simon Parkinson is Professor in the Department of Computer Science at the University of Huddersfield and advisor to the UK Government’s Cyber Security Advisory Board (GCAB). His research interest is in the intersection between Computer Security and Artificial Intelligence. This includes undertaking research in biometric authentication systems and access control policy analysis. He has authored numerous papers on these topics as well as other cross-discipline applications of artificial intelligence, where he has a special interest in Automated Planning.

Abstract: Key digested message The increase in digital evidence, especially in cases involving Indecent Images of Children (IIOC), presents a pressing challenge for law enforcement agencies. In this article, we discuss two of the most prominent types of Artificial Intelligence (AI) and how they can be used in digital forensic processes, providing examples, and highlighting potential challenges that are likely to be experienced in developing and adopting AI. The two main types are of Data-Driven Model (DDM) age classification and ModelBased Reasoning (MBR), and in this article, examples for both are provided and discussed in the contents of IIOC investigations.
Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review

Authors:
raw_author_name: Nicolas P. Avdelidis
raw_affiliation_string: Integrated Vehicle Health Management Centre (IVHM), School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire MK43 0AL, UK
raw_author_name: Angelos Plastropoulos
raw_affiliation_string: Integrated Vehicle Health Management Centre (IVHM), School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire MK43 0AL, UK

Abstract: The field of learning-based navigation for mobile robots is experiencing a surge of interest from research and industry sectors. The application of this technology for visual aircraft inspection tasks within a maintenance, repair, and overhaul (MRO) hangar necessitates efficient perception and obstacle avoidance capabilities to ensure a reliable navigation experience. The present reliance on manual labour, static processes, and outdated technologies limits operation efficiency in the inherently dynamic and increasingly complex nature of the real-world hangar environment. The challenging environment limits the practical application of conventional methods and real-time adaptability to changes. In response to these challenges, recent years research efforts have witnessed advancement with machine learning integration aimed at enhancing navigational capability in both static and dynamic scenarios. However, most of these studies have not been specific to the MRO hangar environment, but related challenges have been addressed, and applicable solutions have been developed. This paper provides a comprehensive review of learning-based strategies with an emphasis on advancements in deep learning, object detection, and the integration of multiple approaches to create hybrid systems. The review delineates the application of learning-based methodologies to real-time navigational tasks, encompassing environment perception, obstacle detection, avoidance, and path planning through the use of vision-based sensors. The concluding section addresses the prevailing challenges and prospective development directions in this domain.
T‐Skeleton: Accurate scene text detection via instance‐aware skeleton embedding

Authors:
raw_author_name: Haiyan Li
raw_affiliation_string: Department of Computer Science and Engineering MOE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China; Department of Computer Science and Technology Kashi University Kashi China
raw_author_name: Xingfei Hu
raw_affiliation_string: Department of Computer Science and Engineering MOE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China
raw_author_name: Hongtao Lu
raw_affiliation_string: Department of Computer Science and Engineering MOE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

Abstract: Abstract Existing segmentation‐based methods have made considerable progress in arbitrarily shaped text detection due to the advantage of dealing with shape variation. However, there still exist challenges to detecting accurate text instances with dense layouts, inaccurate annotations, and complex backgrounds. Many recent works have focused on improving arbitrary boundary prediction, but it may be difficult to accurately distinguish each instance of dense layouts because their boundary pixels may be mistakenly classified to produce inaccurate results (i.e., adhesive texts) with inaccurate annotation and complex backgrounds. Considering the local and long‐range dependencies, this paper proposes an efficient text detector, namely T‐Skeleton, to obtain more reliable segmentation detections. In the spirit of object skeletonization, we introduce the text instance skeleton highlighting the semantically significant structure (similar to the skeleton of a fish) to explicitly capture the long‐range dependencies of text instances. The key idea of T‐Skeleton is to calibrate the coarse text proposals by embedding text instance skeletons to separate crowd texts accurately and robustly. We further design a channel attention module to enlarge the performance margin between T‐Skeleton and the segmentation baseline. Experimental results on four publicly available datasets show the superiority of T‐Skeleton in handling long and curved texts.
Statistical Perspective on the Petrological Utility of Polyphase Groundmass Compositions Inferred via Defocused Beam Electron Probe Microanalysis

Authors:
raw_author_name: Yoshiyuki Iizuka
raw_affiliation_string: Institute of Earth Sciences, Academia Sinica No. 128 Sec. 2, Academia Road, Nangang Taipei 11529 Taiwan

Abstract: Polyphase groundmasses (micro‐scale minerals with or without glass) are generated from silicate liquids during the cooling of natural lavas often alongside larger minerals formed long before eruption. Many researchers have posited that compositions gleaned from the analysis of groundmasses closely approximate the compositions of the melts they were derived from, and these have been used frequently to model pre‐eruptive magma conditions. However, it is difficult to confidently identify and sample these groundmasses once they are formed. Using a sample of lava that exhibits a wide degree of textural variation (ranging from holocrystalline to hypohyaline) we show that compositions of groundmasses sampled using defocused electron beams are significantly different from glass compositions in terms of mean composition and covariance. Despite this, several groundmass compositions qualify as ‘in equilibrium’ with matrix/rim olivine. When processed using available thermometers, however, modelled equilibrium temperatures are significantly higher than those produced using glass data, on average. Because of this, we prescribe caution in using polyphase groundmass data generated using defocused beam analysis even as a rudimentary approach.