Reviewerly/exHarmony huggingface dataset:
A deep clustering-based state-space model for improved disease risk prediction in personalized healthcare

Authors:
raw_author_name: Shuai Niu
raw_affiliation_string: Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, 000000, Hong Kong, China
raw_author_name: Jing Ma
raw_affiliation_string: Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, 000000, Hong Kong, China
raw_author_name: Qing Yin
raw_affiliation_string: Alliance Manchester Business School, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK
raw_author_name: Liang Bai
raw_affiliation_string: Computer and Information Technology School, Shanxi University, Wu Cheng Road, Taiyuan, 030006, Shan Xi, China
raw_author_name: Chen Li
raw_affiliation_string: School of Physical Education, Huazhong University of Science and Technology, Luo Yu Road, Wu Han, 430074, Hu Bei, China
raw_author_name: Xian Yang
raw_affiliation_string: Alliance Manchester Business School, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK

Abstract: Abstract Decision support systems are being developed to assist clinicians in complex decision-making processes by leveraging information from clinical knowledge and electronic health records (EHRs). One typical application is disease risk prediction, which can be challenging due to the complexity of modelling longitudinal EHR data, including unstructured medical notes. To address this challenge, we propose a deep state-space model (DSSM) that simulates the patient’s state transition process and formally integrates latent states with risk observations. A typical DSSM consists of three parts: a prior module that generates the distribution of the current latent state based on previous states; a posterior module that approximates the latent states using up-to-date medical notes; and a likelihood module that predicts disease risks using latent states. To efficiently and effectively encode raw medical notes, our posterior module uses an attentive encoder to better extract information from unstructured high-dimensional medical notes. Additionally, we couple a predictive clustering algorithm into our DSSM to learn clinically useful representations of patients’ latent states. The latent states are clustered into multiple groups, and the weighted average of the cluster centres is used for prediction. We demonstrate the effectiveness of our deep clustering-based state-space model using two real-world EHR datasets, showing that it not only generates better risk prediction results than other baseline methods but also clusters similar patient health states into groups.
A Novel Dual-Channel Temporal Convolutional Network for Photovoltaic Power Forecasting

Authors:
raw_author_name: Xiaoying Ren
raw_affiliation_string: College of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China; School of Renewable Energy, North China Electric Power University, Beijing 100000, China
raw_author_name: Fei Zhang
raw_affiliation_string: College of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China; School of Renewable Energy, North China Electric Power University, Beijing 100000, China
raw_author_name: Yongqian Liu
raw_affiliation_string: School of Renewable Energy, North China Electric Power University, Beijing 100000, China

Abstract: A large proportion of photovoltaic (PV) power generation is connected to the power grid, and its volatility and stochasticity have significant impacts on the power system. Accurate PV power forecasting is of great significance in optimizing the safe operation of the power grid and power market transactions. In this paper, a novel dual-channel PV power forecasting method based on a temporal convolutional network (TCN) is proposed. The method deeply integrates the PV station feature data with the model computing mechanism through the dual-channel model architecture; utilizes the combination of multihead attention (MHA) and TCN to extract the multidimensional spatio-temporal features between other meteorological variables and the PV power; and utilizes a single TCN to fully extract the temporal constraints of the power sequence elements. The weighted fusion of the dual-channel feature data ultimately yields the ideal forecasting results. The experimental data in this study are from a 26.52 kW PV power plant in central Australia. The experiments were carried out over seven different input window widths, and the two models that currently show superior performance within the field of PV power forecasting: the convolutional neural network (CNN), and the convolutional neural network combined with a long and short-term memory network (CNN_LSTM), are used as the baseline models. The experimental results show that the proposed model and the baseline models both obtained the best forecasting performance over a 1-day input window width, while the proposed model exhibited superior forecasting performance compared to the baseline model. It also shows that designing model architectures that deeply integrate the data input method with the model mechanism has research potential in the field of PV power forecasting.
Cultivating Ensemble Diversity through Targeted Injection of Synthetic Data: Path Loss Prediction Examples

Authors:
raw_author_name: Sotirios P. Sotiroudis
raw_affiliation_string: ELEDIA@AUTH, School of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Abstract: Machine Learning (ML)-based models are steadily gaining popularity. Their performance is determined from the amount and the quality of data used at their inputs, as well as from the competence and proper tuning of the ML algorithm used. However, collecting high-quality real data is time-consuming and expensive. Synthetic Data Generation (SDG) is therefore employed in order to augment the limited real data. Moreover, Ensemble Learning (EL) provides the framework to optimally combine a set of standalone ML algorithms (base learners), capitalizing on their individual strengths. Base learner diversity is essential to build a strong ensemble. The proposed method of Targeted Injection of Synthetic Data (TIoSD) combines the EL and SDG concepts in order to further diversify the base learners’ predictions, thus giving rise to an even stronger ensemble model. We have applied TIoSD in two different Path Loss (PL) datasets, using two well-established SDG methods (namely SMOGN and CTGAN). While the conventional ensemble model reached a Minimum Absolute Error (MAE) value of 3.25 dB, the TIoSD-triggered ensemble provided a MAE value of 3.16 dB. It is therefore concluded that targeted synthetic data injection, due to its diversity-triggering characteristics, enhances the ensemble’s performance. Moreover, the ratio between synthetic and real data has been investigated. The results showed that a proportion of 0.1 is optimal.
Cooperative Vehicle Localization in Multi-Sensor Multi-Vehicle Systems Based on an Interval Split Covariance Intersection Filter with Fault Detection and Exclusion

Authors:
raw_author_name: Adnane Cabani
raw_affiliation_string: ESIGELEC, IRSEEM, Université de Rouen Normandie, 76000 Rouen, France

Abstract: In the cooperative multi-sensor multi-vehicle (MSMV) localization domain, the data incest problem yields inconsistent data fusion results, thereby reducing the accuracy of vehicle localization. In order to address this problem, we propose the interval split covariance intersection filter (ISCIF). At first, the proposed ISCIF method is applied to the absolute positioning step. Then, we combine the interval constraint propagation (ICP) method and the proposed ISCIF method to realize relative positioning. Additionally, in order to enhance the robustness of the MSMV localization system, a Kullback–Leibler divergence (KLD)-based fault detection and exclusion (FDE) method is implemented in our system. Three simulations were carried out: Simulation scenarios 1 and 2 aimed to assess the accuracy of the proposed ISCIF with various capabilities of absolute vehicle positioning, while simulation scenario 3 was designed to evaluate the localization performance when faults were present. The simulation results of scenarios 1 and 2 demonstrated that our proposed vehicle localization method reduced the root mean square error (RMSE) by 8.9% and 15.5%, respectively, compared to the conventional split covariance intersection filter (SCIF) method. The simulation results of scenario 3 indicated that the implemented FDE method could effectively reduce the RMSE of vehicles (by about 55%) when faults were present in the system.
EEG Opto-Processor: Epileptic Seizure Detection Using Diffractive Photonic Computing Units

Authors:
raw_author_name: Tao Yan
raw_affiliation_string: Department of Automation, Tsinghua University, Beijing 100084, China
raw_author_name: Maoqi Zhang
raw_affiliation_string: Department of Electronic Engineering, Tsinghua University, Beijing 100084, China; School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China
raw_author_name: Hang Chen
raw_affiliation_string: Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
raw_author_name: Sen Wan
raw_affiliation_string: Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
raw_author_name: Haiou Zhang
raw_affiliation_string: Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
raw_author_name: Xun Cao
raw_affiliation_string: School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China
raw_author_name: Xing Lin
raw_affiliation_string: Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China; Department of Electronic Engineering, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China
raw_author_name: Qionghai Dai
raw_affiliation_string: Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China

Abstract: Electroencephalography (EEG) analysis extracts critical information from brain signals, enabling brain disease diagnosis and providing fundamental support for brain–computer interfaces. However, performing an artificial intelligence analysis of EEG signals with high energy efficiency poses significant challenges for electronic processors on edge computing devices, especially with large neural network models. Herein, we propose an EEG opto-processor based on diffractive photonic computing units (DPUs) to process extracranial and intracranial EEG signals effectively and to detect epileptic seizures. The signals of the EEG channels within a second-time window are optically encoded as inputs to the constructed diffractive neural networks for classification, which monitors the brain state to identify symptoms of an epileptic seizure. We developed both free-space and integrated DPUs as edge computing systems and demonstrated their applications for real-time epileptic seizure detection using benchmark datasets, that is, the Children’s Hospital Boston (CHB)–Massachusetts Institute of Technology (MIT) extracranial and Epilepsy-iEEG-Multicenter intracranial EEG datasets, with excellent computing performance results. Along with the channel selection mechanism, both numerical evaluations and experimental results validated the sufficiently high classification accuracies of the proposed opto-processors for supervising clinical diagnosis. Our study opens a new research direction for utilizing photonic computing techniques to process large-scale EEG signals and promote broader applications.
Particle guided metaheuristic algorithm for global optimization and feature selection problems

Authors:
raw_author_name: Evans Baidoo
raw_affiliation_string: Institute of High Frequency Technik, Technische Universitat Braunschweig, Universitatsplatz 2, Braunschweig, 38106, Germany
raw_author_name: Theophilus Quachie Asenso
raw_affiliation_string: Faculty of Medicine, University of Oslo, Domus Medica, Postbokx1122 Blindern, Oslo, 0317, Norway

Abstract: Optimization problems can be seen in numerous fields of practical studies. One area making waves in the application of optimization methods is data mining in machine learning. An important preprocessing technique of data mining where irrelevant variables are discarded from the datasets and holding onto variables with important information is referred to as feature selection (FS). FS is critical to tackling the ‘curse of dimensionality’ by reducing the number of features, minimizing computational expensiveness and maximizing the accuracy of the machine learning models. Swarm Intelligence (SI)-based meta-heuristic algorithms (MAs) have been widely employed to solve several optimization problems like FS. However, common drawbacks identified with these algorithms include getting trapped in local optima, especially in situations where the search space is large (high dimensional space). This study proposes a new hybrid SI-based MA called Particle Swarm-guided Bald Eagle Search (PS-BES). The algorithm utilizes the speed of Particle Swarm to guide Bald Eagles in their search to ensure a smooth transition of the algorithm from exploration to exploitation. Additionally, we introduce the Attack-Retreat-Surrender technique, a new local-optima escape technique to enhance the balance between diversification and intensification of PS-BES. To establish the outstanding performance of the proposed algorithm, PS-BES is comprehensively analyzed utilizing 26 Benchmark functions. Further, the practicality of PS-BES is highlighted by its binary version for feature selection and evaluated using 27 classification datasets from the UCI repository. The results prove the overall superiority of PS-BES and bPS-BES as opposed the 10 state-of-the-art algorithms employed in the study.
A multi-scale information fusion-based multiple correlations for unsupervised attribute selection

Authors:
raw_author_name: Pengfei Zhang
raw_affiliation_string: School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, PR China
raw_author_name: Dexian Wang
raw_affiliation_string: School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, PR China
raw_author_name: Zheng Yu
raw_affiliation_string: School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, PR China
raw_author_name: Yujie Zhang
raw_affiliation_string: School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, PR China
raw_author_name: Tao Jiang
raw_affiliation_string: School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, PR China
raw_author_name: Tianrui Li
raw_affiliation_string: School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, PR China

Abstract: With the continuous evolution of artificial intelligence and sensor technology, there is a growing accumulation of unlabeled data. Uncovering valuable insights from this data has become a crucial task, propelling the ongoing development of unsupervised knowledge discovery techniques. Unsupervised attribute selection plays a pivotal role in data preprocessing for knowledge discovery, aiming to extract relevant attributes from the dataset without relying on prior labeling or category information. This article introduces an unsupervised attribute selection method that integrates multi-scale information while considering multiple correlations. Initially, a multi-scale fuzzy similarity relation information system (MsFSRIS) is established by integrating multi-scale information with fuzzy relations. This system unveils fuzzy similarity relations stemming from both numerical and categorical attributes, while also integrating information across multiple scales. Within this MsFSRIS, information measures of fuzzy multi-scale are defined, encompassing multiple correlations related to multi-scale fuzzy relevance, multi-scale fuzzy redundancy, multi-scale fuzzy complementarity, and multi-scale fuzzy interaction during the attribute selection process. Furthermore, we propose an unsupervised attribute selection algorithm (UAS_MsIFMC) that fuses information from multiple scales and considers multiple interactions among selected, unselected, and candidate attributes, enhancing the identification of valuable attributes. Finally, the experimental results demonstrate the superiority of the proposed UAS_MsIFMC algorithm, presenting a promising solution for unsupervised attribute selection methods.
The inadvertently revealing statistic: A systemic gap in statistical training?

Authors:
raw_author_name: Elizabeth Green
raw_affiliation_string: Elizabeth Green is senior lecturer in economics at the University of the West of England (UWE Bristol). Her research interest is in developing data governance training and practice which is psychologically and pedagogically informed
raw_author_name: Felix Ritchie
raw_affiliation_string: Felix Ritchie is professor of economics at the University of the West of England (UWE Bristol). With Elizabeth Green, he developed the national Safe Researcher Training (SRT) for researchers and output checking courses for data service staff
raw_author_name: Jim Smith
raw_affiliation_string: Jim Smith is a professor of interactive AI at the University of the West of England (UWE Bristol). His research encompasses both using AI for statistical disclosure control (SDC) and the SDC of AI models trained on sensitive data

Abstract: Abstract While concerns around data privacy are well-known, there's a lack of awareness and training when it comes to the confidentiality risk of published statistics, argue Ben Derrick, Elizabeth Green, Felix Ritchie, Jim Smith, Paul White
Development of central bank digital currencies: a bibliometric analysis

Authors:
raw_author_name: Alfreda Šapkauskienė
raw_affiliation_string: Faculty of Economics and Business Administration, Vilnius University, Vilnius, Lithuania

Abstract: Purpose We aim to conduct a bibliometric analysis that explores and maps quantitative data of the emerging field of central bank digital currencies in science and its implications in practice. We seek to clarify the underlying research structures and streams of the new phenomena, and our motivation is the rising number of pilots between governments seeking to implement different types of central bank digital currency. Design/methodology/approach We designed the unique set of keywords to explore ongoing projects on central bank digital currencies and the evolution of scientific thought on the topic. We conducted a descriptive analysis and an evaluating bibliometric analysis on the timeline from 2018 to April 18, 2023 and investigated 76 articles in the Web of Science database and 152 articles in the Scopus database using VOSviewer. Findings We highlight three main directions of discourse on central bank digital currencies in economics using authors keyword analysis, that are: (1) cash, (2) monetary policy and (3) financial stability. We conducted a map-based text analysis of the abstracts and identified the following main streams of discussion in the field: (1) policy-related research on financial systems, (2) a comprehensive review of the design and features of central bank digital currencies and (3) research on the impact of central bank digital currencies on the banking system. Originality/value The unique set of keywords allows us to continue the discourse on central bank digital currencies including implications of ongoing governmental projects on the topic and provide directions for future research. We brought the focus on the impact of central bank digital currencies on the banking sector and the new possible order for cash, deposits and payments.
Linguistic annotation of cuneiform texts using treebanks and deep learning

Authors:
raw_author_name: Shai Gordin
raw_affiliation_string: Digital Humanities and Social Sciences Hub, Open University of Israel , Ra'anana, Israel; Digital Pasts Lab, Department of Land of Israel Studies and Archaeology, Ariel University , Ariel, Israel

Abstract: Abstract We describe an efficient pipeline for morpho-syntactically annotating an ancient language corpus which takes advantage of bootstrapping techniques. This pipeline is designed for ancient language scholars looking to jump-start their own treebank projects, which can in turn serve further pedagogical research projects in the target language. We situate our work in the field of similar ancient language treebank projects, arguing that our approach shows that individual humanities scholars can leverage current machine-learning tools to produce their own richly annotated corpora. We illustrate this pipeline by producing a new Akkadian-language treebank based on two volumes from the online editions of the State Archives of Assyria project hosted on Oracc, as well as a spaCy language model named AkkParser trained on that treebank. Both of these are made publicly available for annotating other Akkadian corpora. In addition, we discuss linguistic issues particular to the Neo-Assyrian letter corpus and data-encoding complications of cuneiform texts in Oracc. The strategies, language models, and processing scripts we developed to handle both linguistic and data-encoding issues in this project will be of special interest to scholars seeking to develop their own cuneiform treebanks.