Reviewerly/exHarmony huggingface dataset:
Does e-service for research and community service boost the performance of university lecturers?

Authors:
raw_author_name: Dwi Oktaria Sari
raw_affiliation_string: Universitas Sriwijaya
raw_author_name: Raniasa Putra
raw_affiliation_string: Universitas Sriwijaya
raw_author_name: Alamsyah Alamsyah
raw_affiliation_string: Universitas Sriwijaya

Abstract: Indonesian universities are implementing various strategies to improve the quality and quantity of scientific publications. Sriwijaya University has restructured its research and community service grant services by implementing e-services through the new generation management information system of the institute of research and community service (SIM LPPM NG). Numerous studies have been performed to determine the factors that influence lecturer performance, particularly in scientific journals. The purpose of this research is to utilize a model to investigate the impact of system quality, information quality, and service quality on user satisfaction and performance. For partial least squares structural equation modelling (PLS-SEM), 280 respondents comprised the dataset. The results demonstrated that system quality, information quality, and service quality influence user satisfaction. Individual performance is influenced by the level of user satisfaction. According to the findings of this study, the DeLone and McLean information system success model can be used to evaluate obligatory e-services that are integrated.
The digital-mediated extensive reading on English Language learning of agriculture students

Authors:
raw_author_name: Mushoffan Prasetianto
raw_affiliation_string: Universitas Brawijaya
raw_author_name: Rizkiana Maharddhika
raw_affiliation_string: Universitas Brawijaya

Abstract: Reading is a skill that is initially taught in schools. Students are introduced to the alphabet and later learn to read texts. Reading Foreign languages is also introduced when students learn a foreign language. The student requires reading activities that include vocabulary learning. Extensive reading is one of the practical activities of reading. Nowadays, extensive reading activities include reading printed and digital text in e-books, journals, and internet and social media articles. In addition, digital extensive reading research on university students of agriculture is still rare. Thus, research on digital-mediated extensive reading in agricultural students’ needs to be conducted to investigate whether it improves their reading comprehension. This study involved 84 agricultural students majoring Agroecotechnology study program. The participants take an English course. The data for this study was taken from the pre-test and post-test scores and questionnaire. The data will be analyzed using descriptive and inferential statistics. The results of the study shows that the digital-mediated extensive reading effectively enhances the student's reading comprehension in English as academic purposes (EAP) setting, and the students express that the digital-mediated extensive reading provides flexibility, technology utilization, and reading.
Fishing risky behavior recognition based on adaptive transformer, reinforcement learning and stochastic configuration networks

Authors:
raw_author_name: Shengshi Yang
raw_affiliation_string: Department of Electric Engineering and Automation, Hefei University of Technology, Hefei, 230009, China
raw_author_name: Lijian Ding
raw_affiliation_string: Department of Electric Engineering and Automation, Hefei University of Technology, Hefei, 230009, China
raw_author_name: Weitao Li
raw_affiliation_string: Department of Electric Engineering and Automation, Hefei University of Technology, Hefei, 230009, China
raw_author_name: Wei Sun
raw_affiliation_string: Department of Electric Engineering and Automation, Hefei University of Technology, Hefei, 230009, China
raw_author_name: Qiyue Li
raw_affiliation_string: Department of Electric Engineering and Automation, Hefei University of Technology, Hefei, 230009, China

Abstract: The fishing behavior in a high-voltage environment may lead to electric shock accidents. This paper proposes a fishing risky behavior recognition method based on adaptive Transformer, reinforcement learning and stochastic configuration networks (SCNs). Firstly, the fishing behavior image sample training set is evaluated using the actor network based on linear entropy in the reinforcement learning module to select suitable training samples that possess abundant straight-line elements. Subsequently, the selected training samples are fed into a multi-scale spatial deep convolution to extract continuous spatial features of elongated objects. The deformable Transformer network, enhanced with adaptive encoding and decoding layers through SCNs, is used to obtain the position and detection results of the fishing rod. The trained Transformer model is evaluated by a defined credibility evaluation metric for acquiring rewards to update the training sample set iteratively. Then, an adaptive adjustment mechanism is constructed for the encoding and decoding layers of the adaptive Transformer network to establish a library of adaptive Transformer models with different encoding and decoding layer levels to accommodate the feature requirements of training samples in various scenarios. Finally, the blending learning method for combining the detection results from the model library is integrated with human body object detection and pose estimation methods, and a predefined expert system is utilized for logic reasoning on fishing risky behavior. Experimental results demonstrate the effectiveness of the proposed method in this paper.
An Enhanced Group Abnormity Detection Model in Social Networks Through Multi-Scale Knowledge Graph-Based Deep Learning

Authors:
raw_author_name: Jierui Zhang
raw_affiliation_string: China University of Petroleum-Beijing at Karamay, Xinjiang 834000, P. R. China

Abstract: In area of group abnormity detection, deep learning-based methods have received more attention in recent years. But existing research works mostly failed to well-integrate complex graph-level characteristics of social network context into feature representation. To deal with this issue, this paper proposes an enhanced group abnormity detection model in social networks through multi-scale knowledge graph-based deep learning. First, multi-scale knowledge graph is formulated to extract and field knowledge as the fundamental support. Then, a recurrent neural network structure is utilized to express graph-level features, so as to aggregate the information of the surrounding nodes. For output of group abnormity detection results, the interaction information among users is further considered, and the correlation analysis of abnormal behaviors is conducted based on the probability expression. On this basis, probabilistic soft logic is used to express the relationship between abnormal association users and their behavior occurrence. Finally, a set of identification rules are developed to discover abnormal association users, and those who have the same association probability value are identified as abnormal user groups. Experiments are conducted on social network dataset to evaluate performance of the proposed model. The results reveal that it can make group abnormity identification with higher accuracy compared with baseline methods.
Composition of feature selection techniques for improving the global horizontal irradiance estimation via machine learning models

Authors:
raw_author_name: Rahul Gupta
raw_affiliation_string: Department of Electrical Engineering, Netaji Subhas University of Technology, Dwarka, New Delhi 110078, India
raw_author_name: Anil Kumar Yadav
raw_affiliation_string: Department of Instrumentation & Control Engineering, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Jalandhar, Punjab 144008, India
raw_author_name: Pawan Kumar Pathak
raw_affiliation_string: School of Automation, Banasthali Vidyapith, Rajasthan 304022, India

Abstract: The rise in photovoltaic capacity installed globally and the sporadic nature of solar resources underline the significance of solar radiation forecasting for grid integration. The choice of relevant climatic variables for estimating global horizontal irradiance (GHI) is crucial because it influences estimation accuracy. To keep this in mind, different feature selection techniques such as filter, wrapper, and embedded are computed and compared to identify a pertinent input variable for the prediction of GHI. In order to reduce the dimensionality of the input features, it is pertinent to remove unnecessary variables using feature selection techniques. This comprehensive study explores the use of tree-based ensemble machine learning (ML) models in the modeling of global solar radiation. Eighteen statistical and ML regressor algorithms, along with ten feature selection methods are evaluated based on statistical comparisons of their computational effectiveness and estimation errors. The lowest mean absolute error (MAE) and root mean squared error (RMSE) of 0.9938 and 2.5327 achieved by the extra-tree regressor algorithm reveals that it outperforms the other models on the test dataset. The integration of feature selection techniques improves predictive capabilities, reducing MAE and RMSE by 13.15% and 5.24%, respectively, for the Mutual Information method. This study also identifies influential features critical to accurate GHI estimation, such as solar zenith angle, diffuse horizontal irradiance, and direct normal irradiance, which provide valuable insights into the dynamics governing solar radiation estimation.
Asymmetric transition of electrical resistance in an all-solid-state redox device with Fe<sub>3</sub>O<sub>4</sub> and Li-ion electrolyte thin films for physical reservoir computing

Authors:
raw_author_name: Wataru NAMIKI
raw_affiliation_string: Research Center for Materials Nanoarchitectonics, National Institute for Materials Science, Tsukuba, Ibaraki 305-0044, Japan
raw_author_name: Takashi TSUCHIYA
raw_affiliation_string: Research Center for Materials Nanoarchitectonics, National Institute for Materials Science, Tsukuba, Ibaraki 305-0044, Japan
raw_author_name: Daiki Nishioka
raw_affiliation_string: Department of Applied Physics, Faculty of Science, Tokyo University of Science, Katsushika, Tokyo 125-8585, Japan; Research Center for Materials Nanoarchitectonics, National Institute for Materials Science, Tsukuba, Ibaraki 305-0044, Japan
raw_author_name: Tohru HIGUCHI
raw_affiliation_string: Department of Applied Physics, Faculty of Science, Tokyo University of Science, Katsushika, Tokyo 125-8585, Japan
raw_author_name: Kazuya TERABE
raw_affiliation_string: Research Center for Materials Nanoarchitectonics, National Institute for Materials Science, Tsukuba, Ibaraki 305-0044, Japan

Abstract: Abstract In recent years, ion-gating devices have been used in artificial neuromorphic computing and achieved high performance for time-series data processing. However, the origin of this performance still needs to be clarified. In this study, we fabricated an all-solid-state redox device with functional material Fe 3 O 4 and Li-ion conducting solid electrolytes, and the transient response of the electrical resistance of the Fe 3 O 4 thin film to time-series data input was investigated. The transition between high and low electrical resistance states was asymmetric, and residual Li-ion in the thin film led to a hysteresis effect. These unique features, which are induced by ion-electron dynamics coupling, contributes to the high performance of physical reservoir computing utilizing an ion-gating device.
A survey on deep learning-based spatio-temporal action detection

Authors:
raw_author_name: Peng Wang
raw_affiliation_string: College of Computer Science, Zhejiang University, Hangzhou, Zhejiang 310007, P. R. China
raw_author_name: Yuntao Qian
raw_affiliation_string: College of Computer Science, Zhejiang University, Hangzhou, Zhejiang 310007, P. R. China

Abstract: Spatio-temporal action detection (STAD) aims to classify the actions present in a video and localize them in space and time. It has become a particularly active area of research in computer vision because of its explosively emerging real-world applications, such as autonomous driving, visual surveillance and entertainment. Many efforts have been devoted in recent years to build a robust and effective framework for STAD. This paper provides a comprehensive review of the state-of-the-art deep learning-based methods for STAD. First, a taxonomy is developed to organize these methods. Next, the linking algorithms, which aim to associate the frame- or clip-level detection results together to form action tubes, are reviewed. Then, the commonly used benchmark datasets and evaluation metrics are introduced, and the performance of state-of-the-art models is compared. At last, this paper is concluded, and a set of potential research directions of STAD are discussed.
Ontology learning from object-relational mapping metadata and relational database

Authors:
raw_author_name: Rahmat Gernowo
raw_affiliation_string: Universitas Diponegoro
raw_author_name: Michael Andreas Purwoadi
raw_affiliation_string: National Research and Innovation Agency Republic of Indonesia (BRIN)

Abstract: Ontologies play an important role in representing the semantics of data sources. Building an ontology as a representation of domain knowledge from available data sources is not a simple process, particularly when dealing with relational data, which remains prevalent in existing knowledge systems. In this study, we create an ontology from a relational database using object-relational mapping (ORM) metadata as additional rules for mapping. Our method comprises two main phases: ontology schema construction using ORM metadata and the generation of ontology instances from the relational database. During the initial phase, we analyzed the ORM metadata to map it to an resource description framework schema (RDF(S))-OWL representation of the ontology. In the subsequent phase, we applied mapping rules to convert the relational database (RDB) data into ontological instances, which are then represented as RDF triples. Using ORM metadata, we enhance the accuracy of the resulting ontology, particularly in terms of extracting concepts and hierarchical relationships. This study contributes to the field of ontology learning by showcasing a novel approach that leverages ORM metadata to create ontologies from relational databases.
Benchmarking a neutral-atom quantum computer

Authors:
raw_author_name: N. Wagner
raw_affiliation_string: Department of Physics, University of Wisconsin-Madison, Madison, WI, USA; West High School, 30 Ash St. Madison, WI, 53726, USA
raw_author_name: C. Poole
raw_affiliation_string: Department of Physics, University of Wisconsin-Madison, Madison, WI, USA
raw_author_name: T. M. Graham
raw_affiliation_string: Department of Physics, University of Wisconsin-Madison, Madison, WI, USA
raw_author_name: M. Saffman
raw_affiliation_string: Department of Physics, University of Wisconsin-Madison, Madison, WI, USA; Infleqtion, Inc., Madison, WI, 53703, USA

Abstract: In this study, we simulated the algorithmic performance of a small neutral atom quantum computer and compared its performance when operating with all-to-all versus nearest-neighbor connectivity. This comparison was made using a suite of algorithmic benchmarks developed by the Quantum Economic Development Consortium. Circuits were simulated with a noise model consistent with experimental data from [Nature 604, 457 (2022)]. We find that all-to-all connectivity improves simulated circuit fidelity by [Formula: see text]–[Formula: see text], compared to nearest-neighbor connectivity.
Application of machine learning to experimental design in quantum mechanics

Authors:
raw_author_name: Federico Belliardo
raw_affiliation_string: NEST, Scuola Normale Superiore, I-56126 Pisa, Italy
raw_author_name: Fabio Zoratti
raw_affiliation_string: Scuola Normale Superiore, I-56126 Pisa, Italy
raw_author_name: Vittorio Giovannetti
raw_affiliation_string: NEST, Scuola Normale Superiore and Istituto Nanoscienze-CNR, I-56126 Pisa, Italy

Abstract: The recent advances in machine learning hold great promise for the fields of quantum sensing and metrology. With the help of reinforcement learning, we can tame the complexity of quantum systems and solve the problem of optimal experimental design. Reinforcement learning is a powerful model-free technique that allows an agent, typically a neural network, to learn the best strategy to reach a certain goal in a completely a priori unknown environment. However, in general, we know something about the quantum system with which the agent is interacting, at least that it follows the rules of quantum mechanics. In quantum metrology, we typically have a model for the system, and only some parameters of the evolution or the initial state are unknown. We present here a general machine learning technique that can optimize the precision of quantum sensors, exploiting the knowledge we have on the system through model-aware reinforcement learning. This framework has been implemented in the Python package qsensoropt, which is able to optimize a broad class of problems found in quantum metrology and quantum parameter estimation. The agent learns an optimal adaptive strategy that, based on previous outcomes, decides the next measurements to perform. We have explored some applications of this technique to NV centers and photonic circuits. So far, we have been able to certify better results than the current state-of-the-art controls for many cases. The machine learning technique developed here can be applied in all scenarios where the quantum system is well-characterized and relatively simple and small. In these cases, we can extract every last bit of information from a quantum sensor by appropriately controlling it with a trained neural network. The qsensoropt software is available on PyPI and can be installed with pip.
Interval Uncertainty Identification and Application of Strain Modes in Bridge Structures Based on Monitoring Big Data

Authors:
raw_author_name: Ruiyang Pan
raw_affiliation_string: School of Civil Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, P. R. China
raw_author_name: Xingfei Yan
raw_affiliation_string: Shanghai Engineering Research Center of Industrialized, and Prefabricated Municipal Civil Engineering, Shanghai 200240, P. R. China; Shanghai Urban Construction Design & Research, Institute (Group) Co., Ltd, Shanghai 200125, P. R. China

Abstract: In order to solve the problem of strain modal identification under strain monitoring signals with poor vibration modal information, this paper proposes a strain mode identification method with statistical stability significance. This method removes noise, vehicle-induced effects, and temperature effects from the original dynamic strain signal, retaining only vibration-related components, and obtaining a statistically stable high quality bridge strain power spectrum, thereby identifying high quality strain mode parameters. Furthermore, in order to verify the confidence level of the strain modes obtained by this method, this paper adopts the interval estimation method to estimate the power spectrum, natural frequency, damping ratio, and modal shape after statistical processing. The credibility of strain modes has been estimated by interval estimation. The confidence interval of 95% confidence for each modal parameter is obtained, achieving the confidence-level evaluation of corresponding variable modal parameter identification. In response to practical engineering problems, this paper evaluates the actual bridge data of Tongji Road Bridge in Shanghai, and explains the abnormal phenomena that occurred in the data evaluation based on the measured diseases, verifying the practicality of this method.
Research on tactile sensation by physical reservoir computing with a robot arm and a Ag<sub>2</sub>S reservoir

Authors:
raw_author_name: Tsuyoshi HASEGAWA
raw_affiliation_string: Department of Pure and Applied Physics, Graduate School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan

Abstract: Abstract In recent years, physical reservoir computing has attracted much attention because of its low computational cost and low power consumption. In terms of social implementation of artificial intelligence, physical reservoir has a potential to meet the request, such as the need for AI robots to process information related to tactile sensation. It has been reported that a Ag 2 S polycrystalline thin film retains short-term memory and non-linearity when used as a physical reservoir. In this study, we applied the technique to tactile sensation by combining with a pressure sensor attached to a robot arm. In the object grasping task, a Ag 2 S physical reservoir enabled the objective recognition with the accuracy of 81.3%, although the task failed with linear regression of the direct output from the pressure sensor. We also demonstrate the potential of the system to detect anomalies in object grabbing.
Generative AI And Software Variability - A Research Vision

Authors:
raw_author_name: Sandra Greiner
raw_affiliation_string: Institute of Computer Science, University of Bern, Switzerland
raw_author_name: Klaus Schmid
raw_affiliation_string: Institute of Computer Science, University of Hildesheim, Germany
raw_author_name: Thorsten Berger
raw_affiliation_string: Faculty of Computer Science, Ruhr University Bochum, Germany
raw_author_name: Sebastian Krieter
raw_affiliation_string: Institute of Software Engineering and Programming Languages, University of Ulm, Germany
raw_author_name: Kristof Meixner
raw_affiliation_string: Institute of Information Systems Engineering - Software Engineering, Vienna University of Technology, Austria

Abstract: Generative Artificial Intelligence (GAI) promises groundbreaking automation technology - a potential which may raise the management of variability-intensive software systems to a new level of automation. Several activities in maintaining variability-intensive software systems, such as extracting feature traces to updating features consistently, are repetitive and performed mainly manually or semi-automatically. Exploiting the potentials of GAI in maintaining variability-intensive software systems opens a fundamentally new research perspective, where GAI shall solve repetitive and hard-to-automate tasks. In this vision paper, we propose and discuss increasing levels of maintaining variability-intensive software systems automatically enabled through the support of GAI. We sketch actions necessary to reach the next levels of automation while discussing the current state-of-the-art.
Data Introspection: An Embroidered PhD Trajectory in Unconventional Personal Data

Authors:
raw_author_name: Renee Noortman
raw_affiliation_string: Industrial Design, Eindhoven University of Technology, Netherlands

Abstract: In an age of data, we are increasingly defined by the data sets collected about us. Expressing identity in these data sets can be difficult as they are aggregated and collected according to specific values and beliefs that might not match our own. Through an elaborate process that I refer to as data introspection, I explored three data sets about myself (books I read, board games I played and my yoga practice) to create a feminist, hand-embroidered physicalisation of those data sets. Building on the principles of Data Feminism, I reflect on the process of creating that artefact, including the selection and cleaning of the data, the materiality of the artefact and the crafting process. The artefact exemplifies new ways to use unconventional personal data as a creative, feminist material for design.