Reviewerly/exHarmony huggingface dataset:
Impact of the form of weighted networks on the quantum extreme reservoir computation

The quantum extreme reservoir computation (QERC) is a versatile quantum neural network model that combines the concepts of extreme machine learning with quantum reservoir computation. Key to QERC is the generation of a complex quantum reservoir (feature space) that does not need to be optimized for different problem instances. Originally, a periodically driven system Hamiltonian dynamics was employed as the quantum feature map. In this work we capture how the quantum feature map is generated as the number of time-steps of the dynamics increases by a method to characterize unitary matrices in the form of weighted networks. Furthermore, to identify the key properties of the feature map that has sufficiently grown, we evaluate it with various weighted network models that could be used for the quantum reservoir in image classification situations. At last, we show how a simple Hamiltonian model based on a disordered discrete time crystal with its simple implementation route provides nearly optimal performance while removing the necessity of programming of the quantum processor gate by gate.
Coherence manipulation with stochastic incoherent operation

An important problem in the coherent resource theory is the convertibility of coherent states by free operations. In this paper we consider such a problem from a mixed coherent state into a pure one by using both stochastic incoherent operations and incoherent operations. We prove that stochastic incoherent operations can transform a mixed coherent state into a pure coherent state if and only if the density matrix of the initial state contains a singular principal submatrix. Then we provide two sufficient conditions with explicit expressions for the probabilistic transformation and the deterministic transformation. These two sufficient conditions facilitate the construction of concrete operators of stochastic incoherent operations and incoherent operations. These results demonstrate that incoherent operations are strictly more powerful than strictly incoherent operations within the probabilistic transformation as well as the deterministic transformation.
Performance, Emotion, Presence: Investigation of an Augmented Reality-Supported Concept for Flight Training

Augmented reality (AR) could be a means for a more sustainable education of the next generation of pilots. This study aims to assess an AR-supported training concept for approach to landing, which is the riskiest phase of flying an aircraft and the most difficult to learn. The evaluation was conducted with 59 participants (28 women and 31 men) in a pretest–post-test control group design. No significant effect of the AR-supported training was observed when comparing the experimental and the control groups. However, the results show that for the experimental group that trained with AR, higher performance in post-test was associated with higher AR presence and comfort with AR during training. Although both gender groups improved their approach quality after training, the improvement was larger in women as compared to men. Trainees’ workload, fear of failure, and negative emotions decreased in post-test as compared to pre-test, but the decrease was significantly larger in women than in men. The experimental group who used AR support during training showed improved performance despite the absence of AR support in post-test. However, the AR-based training concept had a similar effect to conventional simulator training. Although more research is necessary to explore the training opportunities in AR and mixed reality, the results of this study indicate that such an application would be beneficial to bridge the gap between theoretical and practical instruction.
Loneliness as a phenomenon of being Homo sapiens / Homo cosmicus

The article is devoted to the disclosure of the fundamental characteristics of the loneliness of Homo sapiens, acting as Homo cosmicus. In their research, the authors proceed from the assumption that a person for whom the development of outer space will become an everyday life reality will remain, in principle, the same natural, socio-spiritual being as it is now, despite all those external and internal changes, the undergoing of which is an inevitable attribute of his future. Philosophical search has shown that in relation to the existence of Homo sapiens/Homo cosmicus, loneliness is a phenomenon, the essence of which is socio-historical, and existence is eventual.
Long-Term Solar Power Time-Series Data Generation Method Based on Generative Adversarial Networks and Sunrise–Sunset Time Correction

Constructing long-term solar power time-series data is a challenging task for power system planners. This paper proposes a novel approach to generate long-term solar power time-series data through leveraging Time-series Generative Adversarial Networks (TimeGANs) in conjunction with adjustments based on sunrise–sunset times. A TimeGAN model including three key components, an autoencoder network, an adversarial network, and a supervised network, is proposed for data generation. In order to effectively capture autocorrelation and enhance the fidelity of the generated data, a Recurrent Neural Network (RNN) is proposed to construct each component of TimeGAN. The sunrise and sunset time calculated based on astronomical theory is proposed for adjusting the start and end time of solar power time-series, which are generated by the TimeGAN model. This case study, using real datasets of solar power stations at two different geographic locations, indicates that the proposed method is superior to previous methods in terms of four aspects: annual power generation, probability distribution, fluctuation, and periodicity features. A comparison of production cost simulation results between using the solar power data generated via the proposed method and using the actual data affirms the feasibility of the proposed method.
Being-in-touch: The (im)possibilities of communicative and tactile connection in online experiences

Amid social distancing and the sole reliance on communication technologies to “keep in touch” precipitated by COVID-19, touch, both communicatively and tactically, emerged in complex ways. This study examines Airbnb Online Experiences as a new media offering possibilities for people to be “in touch” with others around the world through short-lived interactive activities on Zoom. While new media technologies traditionally expand the potential for sustained connection and increasingly replicate elements of “in-person” communication, this emerging digital phenomenon, although enabling wider communicative reach and enhanced somatic experience, exists in fleeting, physically distanced connections. Through ethnography, the article examines how participants navigate circumstances of touch in online experiences, both by ways of communicative contact and physical proximity. The term being-in-touch is proposed, inspired by works on touch from relational ontological perspectives, to capture the multiple meanings and many ways of being in touch, exploring the relational nature and (im)possibilities of connection.
Fraud analytics practices in public-sector transactions: a systematic review

Purpose Increased fraudulent practices have heightened the need for innovation in anti-fraud programs, necessitating the development of analytics techniques for detecting and preventing fraud. The subject of fraud analytics will continue to expand in the future for public-sector organizations; therefore, this research examined the progress of fraud analytics in public-sector transactions and offers suggestions for its future development. Design/methodology/approach This study systematically reviewed research on fraud analytics development in public-sector transactions. The review was conducted from June 2021 to June 2023 by identifying research objectives and questions, performing literature quality assessment and extraction, data synthesis and research reporting. The research mainly identified 43 relevant articles that were used as references. Findings This research examined fraud analytics development related to public-sector financial transactions. The results revealed that fraud analytics expansion has not spread equally, as most programs have been implemented by governments and healthcare organizations in developed countries. This research also exposed that the analytics optimization in fraud prevention is higher than for fraud detection. Such analytics help organizations detect fraud, improve business effectiveness and efficiency, and refine administrative systems and work standards. Research limitations/implications This research offers comprehensive insights for researchers and public-sector professionals regarding current fraud analytics development in public-sector financial transactions and future trends. Originality/value This study presents the first systematic literature review to investigate the development of fraud analytics in public-sector transactions. The findings can aid scholars' and practitioners' future fraud analytics development.
YOLO-GCRS: A Remote Sensing Image Object Detection Algorithm Incorporating a Global Contextual Attention Mechanism

With the significant advancements in deep learning technology, the domain of remote sensing image processing has witnessed a surge in attention, particularly in the field of object detection. The detection of targets in remotely sensed images is a challenging task, primarily due to the abundance of small-sized targets and their multi-scale distribution. These challenges often result in inaccurate object detection, leading to both missed detections and false positives. To overcome these issues, this paper presents a novel algorithm called YOLO-GCRS. This algorithm builds upon the original YOLOv5s algorithm by enhancing the feature capture capability of the backbone network. This enhancement is achieved by integrating a new module, the Global Context Block (GC-C3), with the C3 backbone network. Additionally, the algorithm incorporates a convoluted block known as CBM (Convolution + BatchNormalization + Mish) to enhance the network model’s capability of extracting depth features. Moreover, a detection head, ECAHead, is proposed, which integrates an efficient attention channel (ECA) for extracting high-dimensional features from images. It achieves higher precision, recall, and mAP@0.5 values (98.3%, 94.7%, and 97.7%, respectively) on the publicly available RSOD dataset compared to the original YOLOv5s algorithm (improving by 5.3%, 0.8%, and 2.7%, respectively). Furthermore, when compared to mainstream detection algorithms like YOLOv7-tiny and YOLOv8s, the proposed algorithm exhibits improvements of 2.0% and 7.5%, respectively, in mAP@0.5. These results provide validation for the effectiveness of our YOLO-GCRS algorithm in addressing the challenges of missed and false detections in remote sensing object detection.
Assessment of solar photovoltaic potential of selected site locations in cities across sub-Saharan Africa

Abstract Despite the successes recorded over the years, photovoltaic (PV) cells’ power conversion efficiency (PCE) of commercially available crystalline silicon (c-Si) PV panels still hovers between 10 and 21%. For optimal performance at 17–21% PCE, certain factors need to be understood and addressed. This study estimates the solar PV potential of selected cities across Africa, using computational modelling. The selected sites’ cities are Abuja, Addis Ababa, Kinshasa, Pretoria, and Tripoli. Sites’ coordinate systems will be exploited to generate data from meteorological databases of the selected locations needed for the PV potential assessment. This information coupled with PV system configuration will be used as inputs for PV design and simulation. The PV potential of the selected location will be extracted from the resulting simulation reports in terms of irradiance, possible power output generation, performance ratio (PR) and capacity factor (CF). The study results and analysis as extracted from the reports of the modelled hypothetical 10-kWhp c-Si rooftop PV systems at the selected sited locations, show that—Pretoria possesses the highest GTI (2234.4 kWh/m 2 ) and the lowest GTI (1766.7 kWh/m 2 ) was observed in Kinshasa; Pretoria has the highest PV power output (PVOUT) (17.292 MWh/), and the least (13.678 MWh) in Kinshasa; the highest PR (77.4%) was observed in Kinshasa and Pretoria and the lowest PR (76.4%) in Tripoli; Pretoria and Kinshasa recorded the highest CF (19.7%) and lowest CF (15.6%), respectively. The results indicate that the examined locations are technically viable for the PV system schemes, and therefore, massive deployment of this technology in these areas is advised.
Pengelompokan Daerah di Jawa Timur Berbasis Indikator Kesejahteraan Masyarakat dengan Pendekatan Analisis Cluster Hierarki

Based on Central Statistics Agency (BPS) data in September 2021, East Java is a province with the largest number of poor people in Indonesia with a total of 26,503 million people. Poverty is one of the factors that affect people's welfare in East Java. Therefore, this research was conducted to classify regencies and cities in East Java based on indicators of community welfare through a hierarchical cluster analysis approach using the single linkage, complete linkage, average linkage, and ward methods, determine the optimum cluster for each method using Pseudo – F, then compare the four methods and determine the best method using the rated value, as well as identify the characteristics of each cluster group based on the best method. There are six variables that will be used in this study. All variable data is secondary data obtained from the official website of the Central Statistics Agency (BPS) of East Java Province. This study produced four clusters using the average linkage method as the best method. This research is expected to be useful as a consideration for evaluating the government and related agencies to overcome the main problems that still occur in each regency and city. Thus, the welfare of the people of East Java can be realized and the SDGs targets in Indonesia can be achieved.
Aplikasi Pengelompokan Data Runtun Waktu dengan Algoritma K-Medoids

The development of information technology will always be accompanied by the storage and accumulation of massive quantities of digital information. Cluster analysis is one of many data processing problems that require the selection of an appropriate algorithm when dealing with large data sets. Cluster analysis is a collection of techniques for dividing a set of observation objects into clusters. Cluster analysis is applicable to time series data, the processing of which differs slightly from that of cross-section data. Clustering time series is a technique for processing multivariable time series data. K-Medoids is the clustering algorithm used for time series clustering. The objective of this study is to obtain optimal K-values in determining the number of clusters based on silhouette coefficients and grouping outcomes using the K-Medoids algorithm. In this study, the dynamic time-warping distance is utilized as the similarity metric. This study provides cooking oil price data for 34 Indonesian provinces from October 2017 to October 2022. The optimal K value is determined for two clusters based on the results of the analysis, with 19 provinces joining cluster 1, where the cluster with cooking oil prices was below cluster 2 and 15 provinces joining cluster 2 which is the cluster with the highest cooking oil prices.
How May I Interrupt? Linguistic-Driven Design Guidelines for Proactive in-Car Voice Assistants

AbstractVoice assistants (VAs) are predicted to develop from merely reactive to increasingly proactive agents in the future. While proactivity allows a leap towards more intelligent conversations with VAs, designing proactive agents is not straightforward, as benefit and acceptance of proactive behavior is dependent on a plethora of factors. For instance, proactive agents run the risk of disrupting users who are already engaged in ongoing primary tasks. While a large body of research is therefore concerned with when to proactively interrupt users, how to interrupt them has received less attention. To close this gap, a driving simulator study was conducted to find linguistic best practices for designing proactive prompts in German. Low linguistic complexity as well as suggestive rather than imposing language were found to influence study participants’ preferences for proactive prompts. These findings underline that the existing framework for designing proactive interactions needs to be enhanced by nuanced linguistic considerations.Keywords: Conversational user interfacesproactivityin-car voice assistants Disclosure statementThe authors report there are no competing interests to declare.Notes1 LIX is the acronym for Swedish Läsbarhetsindex, i.e., readability index in English.2 The study was conducted in German. Prompts were translated by the author.Additional informationNotes on contributorsAnna-Maria MeckAnna-Maria Meck received her B.A. and M.A. degree in Phonetics and Speech Processing at Ludwig Maximilians University in Munich, Germany. She is currently pursuing her Ph.D. degree in collaboration with the BMW Group at the same institute, focusing her research on linguistic aspects and context-sensitivity in Human-Computer Interaction.Christoph DraxlerChristoph Draxler received his Ph.D. in computer science from TU Munich and Zurich University. He is a researcher at the Institute of Phonetics and Speech Processing and the co-manager of the Bavarian Archive for Speech Signals at LMU Munich. His current research interests include web-based transcription and spoken language processing.Thurid VogtThurid Vogt received her Diploma and Ph.D. in Computer Science with focus on language processing and emotion recognition from the University of Bielefeld, Germany. She is currently working at BMW Group AG on the in-car BMW Intelligent Personal Assistant.
Software techniques for training restricted Boltzmann machines on size-constrained quantum annealing hardware

Restricted Boltzmann machines are common machine learning models that can utilize quantum annealing devices in their training processes as quantum samplers. While this approach has shown promise as an alternative to classical sampling methods, the limitations of quantum annealing hardware, such as the number of qubits and the lack of connectivity between the qubits, still pose a barrier to wide-scale adoption. We propose the use of multiple software techniques such as dropout method, passive labeling, and parallelization techniques for addressing these hardware limitations. The study found that using these techniques along with quantum sampling showed comparable results to its classical counterparts in certain contexts, while in others the increased complexity of the sampling process hindered the performance of the trained models. This means that further research into the behavior of quantum sampling needs to be done to apply quantum annealing to training tasks of more complicated RBM models.
Quantum Combinational Logics and their Realizations with Circuits

Abstract Classical combinational logic circuits (CCLCs) are widely used in various fields. Corresponding to the CCLCs, here schemes are given for some quantum combinational logic circuits (QCLCs) based on the quantum NAND tree. Three typical circuits, adder, comparator, and seven‐segment display decoder, are discussed in detail as examples. All the designs of the schemes are based on the quantum random walk theory. Furthermore, these QCLCs are mapped onto the classical circuit networks and design new types of CCLCs, and take advantage of the fact that there is a good correspondence between the voltage in the circuit satisfying Kirchhoff's law and the system wave function satisfying the Schrodinger equation. These CCLCs that are designed have exponential speedup functions compared with conventional ones, which have been demonstrated experimentally. Because classical circuit networks possess good scalability and stability, the realization of QCLCs on classical circuits is expected to have potential applications for information processing in the era of big data.
Learning Media for Counting Operations Based on Pictoblox Gamification: Development Research in Class 1 Elementary School

This study aims to describe the development of pictoblox gamification learning media for mathematics learning in grade 1 elementary school, describe the design of pictoblox gamification learning media as a problem solution, describe the feasibility and testing of products that have been made, and describe the requirements for the final product of pictoblox gamification-based learning media. . The research used the Design Based Research (DBR) research and development method using the Reeves model research stages, namely: 1) assistance and problem analysis; 2) develop solutions; 3) do the process iteratively 4) reflection. The data collection technique used is through data collection using preliminary studies and field studies. This data collection technique uses several research instruments, in the form of (1) interview instruments conducted by researchers by asking several questions to the teacher; (2) expert judgment, conducted based on the results of validation by experts, where expert validation in this study was carried out to media expert lecturers and elementary school teachers; (3) questionnaires, conducted to teachers and grade I students at the elementary school under study; and (4) documentation in the form of writings, pictures, or monumental works of a person. This documentation is intended to support data regarding the teacher's experience using learning media in the classroom and students' enthusiasm in learning using the media. Based on the results of research at Rajamandala Elementary School, Tasikmalaya Regency, research data was obtained which showed that the pictoblox gamification-based arithmetic operations learning media developed was feasible to use in learning. Feasibility is shown from the results of validation by expert validators, and the usability of the results of trials that have been carried out. So based on the results of this study, the development of pictoblox-based learning media can be used as an alternative media in learning.
C3PO: a crop planning and production process ontology and knowledge graph

Vegetable crop farmers diversify their production by growing a range of crops during the season on the same plot. Crop diversification and rotation enables farmers to increase their income and crop yields while enhancing their farm sustainability against climatic events and pest attacks. Farmers must plan their agricultural work per year and over successive years. Planning decisions are made on the basis of their experience regarding previous plans. For the purpose of assisting farmers in planning decisions and monitoring, we developed the Crop Planning and Production Process Ontology (C3PO), i.e., a representation of agricultural knowledge and data for diversified crop production. C3PO is composed of eight modules to capture all crop production dimensions and complexity for representing farming practices and constraints. It encodes agricultural processes and farm plot organization and captures common agricultural knowledge. C3PO introduces a representation of technical itineraries, i.e., sequences of technical farming tasks to grow vegetables, from soil identification and seed selection to harvest and storage. C3PO is the backbone of a knowledge graph which aggregates data from heterogeneous related semantic resources, e.g., organism taxonomies, chemicals, reference crop listings, or development stages. C3PO and its knowledge graph are used by the Elzeard enterprise to develop knowledge-based decision support systems for farmers. This article describes how we built C3PO and its knowledge graph-which are both publicly available-and briefly outlines their applications.