Reviewerly/exHarmony huggingface dataset:
Abnormal Entity-Aware Knowledge Graph Completion

In real-world scenarios, knowledge graphs remain incomplete and contain abnormal information, such as redundan-cies, contradictions, inconsistencies, misspellings, and abnormal values. These shortcomings in the knowledge graphs potentially affect service quality in many applications. Although many approaches are proposed to perform knowledge graph completion, they are incapable of handling the abnormal information of knowledge graphs. Therefore, to address the abnormal information issue for the knowledge graph completion task, we design a novel knowledge graph completion framework called ABET, which specially focuses on abnormal entities. ABET consists of two components: a) abnormal entity prediction and b) knowledge graph completion. Firstly, the prediction component automati-cally predicts the abnormal entities in knowledge graphs. Then, the completion component effectively captures the heterogeneous structural information and the high-order features of neighbours based on different relations. Experiments demonstrate that ABET is an effective knowledge graph completion framework, which has made significant improvements over baselines. We further verify that ABET is robust for knowledge graph completion task with abnormal entities.
Comparison of Ensemble Models as Solutions for Imbalanced Class Classification of Datasets

A phenomenon known as “class imbalance” occurs when an excessive number of classes are evaluated in relation to other classes. This circumstance is quite common in the challenges that classification modeling is used to in the actual world. Because of the influence of class imbalance on the dataset, the classification model's performance is not at its highest possible level. In addition, the presence of these factors might make the possibility of incorrect categorization greater. Utilizing an ensemble model is one approach that may be used to resolve this issue. The originality of the dataset is preserved, which is one of the many benefits of this method. In this work, three different types of ensemble models-XGBoost, Stacking, and Bagging-were examined and contrasted. All three were put through their paces using five distinct unbalanced multiclass datasets, each with a different value for the imbalanced ratio. The results of the three experiments that used five different assessment indicators reveal that the XGBoost model performs much better than the Bagging and Stacking models when it comes to overall performance. The XGBoost model performs exceptionally well in all of the indicators that were evaluated, including Balanced Accuracy, True Positive Rate, True Negative Rate, Geometric Mean, and Multiclass Area Under Curve. These findings provide more evidence that XGBoost is a viable option for addressing multiclass unbalanced issues in datasets.
LA-Net: LSTM and attention based point cloud down-sampling and its application

At present, the learning-based down-sampling method has achieved good result by using the loss of subsequent tasks to optimize the process of sampling points selection. However, for the current methods there still has two problems, on the one hand, the sampled points may not be in the original point cloud, and the sampled generated points of the sample need to be matched with the original point cloud (re-matching problem), on the other hand, the number of points obtained after matching may be insufficient for the sampling demand, which need to be supplemented by Farthest Point Sampling, nearest neighbor point, or other methods (re-supplement problem). In order to solve the above problems of re-matching and re-supplement in the learning-based sampling method, we propose a new sampling network which based on high score selection strategy. In our method, LSTM (Long Short Term Memory) is used as the feature extraction method to obtain the relational weight which relative to the subsequent tasks. The attention mechanism is used to select the high score points. The experimental results show that the performances of our network (We dubbed it as LA-Net) are better than other sampling methods and achieve the state-of-the-art in the unified benchmark. Detailed ablation studies have also been carried out, which provably affirm the advantage of each component we proposed. Finally, the methods in this paper are applied to a real backbone network to optimize its performance, and real robot grasping experiments are designed to realize automatic robot grasping of multi-target objects in real scenes.
A network‐theoretical perspective on oscillator‐based Ising machines

Summary The standard van Neumann computer excels at many things. However, it can be very inefficient in solving optimization problems with a large solution space. For that reason, a novel analog approach, the oscillator‐based Ising machine, has been proposed as a better alternative for dealing with such problems. In this work, we review the concept of oscillator‐based Ising machine and address how optimization problems can be mapped onto such machines when the quadratic unconstrained binary optimization (QUBO) formulation is given. Furthermore, we provide an ideal circuit that can be used in combination with the wave digital concept for real‐time simulated annealing. The functionality of this circuit is explained on the basis of a Lyapunov stability analysis. The latter also provides an answer for the decision‐making question: When has the Ising machine solved a mapped problem? At the end, we provide emulation results demonstrating the correlation between functionality and stability. Our results are based on an eigenvalue analysis of the linearized system and demonstrate that all eigenvalues converge to the left complex half plane, once an optimization problem has been optimally solved. This implies that the system enters an attractor region and is hence forced to eventually converge to the optimal solution.
Machine learning based Breast Cancer screening: trends, challenges, and opportunities

Although breast cancer (BC) deaths have decreased over time, it remains the second leading cause of cancer-related deaths among women. With the technical advancement of artificial intelligence (AI) and availability of healthcare data, many researchers have attempted to employ machine learning (ML) techniques to gain a better understanding of this disease. The present study was a systematic literature review (SLR) of the use of machine learning techniques in breast cancer screening (BCS) between 2011 and 2021. A total of 66 papers were selected and analysed to address nine criteria: year of publication, publication venue, paper type, BCS modality, empirical type, ML technique, performance, advantages and disadvantages, and dataset used. The results showed that mammography was the most frequently used BCS modality, and that classification was the most used ML objective. Moreover, of the six investigated ML techniques, convolutional neural network models scored the highest median accuracy with 96.67%, followed by adaptive boosting (88.9%).
Feeling our way: methodological explorations on researching touch through uncertainty

This paper argues that methodological uncertainty, such as that experienced by the social research community through the COVID-19 pandemic (2020-2022) is, and has always been, a vital part of the research landscape. Whilst recognising the many damaging effects of the uncertainties of the pandemic on research and researchers, we home in on the potential of the challenges raised by uncertainty as a force for methodological innovation. We introduce three InTouch project research studies conducted during Lockdown and reflect on the methodological challenges raised by the change and uncertainty of the pandemic. We describe our use, adaptation and reorientation of creative, sensory, and speculative methods to overcome these challenges. We reflect on how we mobilised the uncertain methodological terrain of digital touch and social research in the pandemic as a resource for methodological innovation.
Machine Learning Literacy for Measurement Professionals: A Practical Tutorial

Abstract The COVID‐19 pandemic has accelerated the digitalization of assessment, creating new challenges for measurement professionals, including big data management, test security, and analyzing new validity evidence. In response to these challenges, Machine Learning (ML) emerges as an increasingly important skill in the toolbox of measurement professionals in this new era. However, most ML tutorials are technical and conceptual‐focused. Therefore, this tutorial aims to provide a practical introduction to ML in the context of educational measurement. We also supplement our tutorial with several examples of supervised and unsupervised ML techniques applied to marking a short‐answer question. Python codes are available on GitHub. In the end, common misconceptions about ML are discussed.
A multiobjective path-smoothing algorithm based on node adjustment and turn-smoothing

Presently, mobile robots experience problems of time consumption, poor security, and high computational complexity in global path-smoothing algorithms. This study presents a multiobjective path-smoothing algorithm, including a path point adjustment algorithm and a turn-smoothing method called the point adjustment algorithm and smoothing. First, the proposed path point adjustment algorithm filters and moves the nodes in an original path to decrease the length of the path and increase the angle of the turns in the path. Second, the proposed turn-smoothing method inserts the B-spline curve into the processed path to smooth the discontinuous turns in the path. Subsequently, the positions of the control points are adjusted based on the property of the B-spline to ensure that the smoothed path can avoid obstacles and satisfy the maximum curvature constraint. Simulation results show that the proposed algorithm can quickly calculate the path satisfying the robot dynamic constraints in various environments combined with different global path planning algorithms. Compared with other mainstream path-smoothing algorithms, this algorithm makes a substantial improvement in path length.
Day‐ahead scheduling of a hybrid renewable energy system based on generation forecasting using a deep‐learning approach

Abstract A significant amount of electricity in numerous regions worldwide is used for lighting roads, squares, and other public spaces. Renewable energy can contribute notably to electricity usage for public lighting. This paper focuses on the day‐ahead scheduling of a hybrid renewable energy system (HRES) exploiting solar–wind energy potential to meet the electrical energy needs of public lighting. The studied HRES provides electricity for a Wi‐Fi hotspot and a charging hotspot for the end users and has an energy storage system that ensures a reliable electricity supply without interruptions. The day‐ahead scheduling of the studied HRES is based on electricity generation forecasting by using a deep‐learning approach. Particularly, the long short‐term memory model is utilized, considering the fact that it is able to perceive long‐term dependencies among the time series. Moreover, the model's performance is investigated through the determination of diverse inputs: (a) historical data, (b) weather predictions, and (c) historical data with weather predictions. Multiple scenarios of energy consumption are assumed and applied to optimize the day‐ahead scheduling. A new recommendation method is proposed and applied for day‐ahead scheduling, utilizing the power forecasts to achieve optimum operation and energy savings. The results point out that the utilization of the proposed recommendation method controls loads when a shortage in power generation and battery capacity is forecasted for the day ahead, leading to significant energy savings and minimizing the power demand from the grid.
Information seeking by experimentation: Trying something out to discover what happens

Abstract Experimentation is the process of trying something out to discover what happens. It is a widespread information practice, yet often bypassed in information‐behavior research. This article argues that experimentation complements prior knowledge, documents, and people as an important fourth class of information sources. Relative to the other classes, the distinguishing characteristics of experimentation are that it is a personal—as opposed to interpersonal—source and that it provides “backtalk.” When the information seeker tries something out and then attends to the resulting situation, it is as though the materials of the situation talk back: They provide the information seeker with a situated and direct experience of the consequences of the tried‐out options. In this way, experimentation involves obtaining information by creating it. It also involves turning material and behavioral processes into information interactions. Thereby, information seeking by experimentation is important to practical information literacy and extends information‐behavior research with new insights on the interrelations between creating and seeking information.
Downscaling of Hourly Climate Data for the Assessment of Building Energy Performance

In Italy, the calculation of the energy needs of buildings has been mainly based on quasi-steady state calculation procedures. Nowadays, the increasing interest in more detailed energy analysis for high-efficiency buildings requires more accurate calculation methods. In this work, starting from the hourly data of UNI 10349, the downscaling of a typical meteorological year was carried out by applying different mathematical and physical models for the main climate variables considered in the energy balance of a building to be used in dynamic simulation tools. All results were validated with one-minute measurements observed at the ENEA Research Centre in Rome, Italy. The results showed an MBE% of 0.008% and RMSE% of 0.114% using the interpolation spline method for the temperature, while, for the global horizontal irradiance, applying the novel sinusoidal–physical interpolation method showed an MBE% of −0.4% and an RMSE% of 31.8% for the 1 min observation data. In this paper, an easily implemented novel model for downscaling solar irradiance for all sky conditions that takes into account the physical aspects of atmospheric phenomena is presented.
Machine Learning for Early Outcome Prediction in Septic Patients in the Emergency Department

Background: Sepsis is one of the major causes of in-hospital death, and is frequent in patients presenting to the emergency department (ED). Early identification of high-risk septic patients is critical. Machine learning (ML) techniques have been proposed for identification and prognostication of ED septic patients, but these models often lack pre-hospital data and lack validation against early sepsis identification scores (such as qSOFA) and scores for critically ill patients (SOFA, APACHE II). Methods We conducted an electronic health record (EHR) study to test whether interpretable and scalable ML models predict mortality in septic ED patients and compared their performance with clinical scores. Consecutive adult septic patients admitted to ED over 18 months were included. We built ML models, ranging from a simple-classifier model, to unbalanced and balanced logistic regression, and random forest, and compared their performance to qSOFA, SOFA, and APACHE II scores. Results: We included 425 sepsis patients after screening 38,500 EHR for sepsis criteria. Overall mortality was 15.2% and peaked in patients coming from retirement homes (38%). Random forest, like balanced (0.811) and unbalanced logistic regression (0.863), identified patients at risk of mortality (0.813). All ML models outperformed qSOFA, APACHE II, and SOFA scores. Age, mean arterial pressure, and serum sodium were major mortality predictors. Conclusions: We confirmed that random forest models outperform previous models, including qSOFA, SOFA, and APACHE II, in identifying septic patients at higher mortality risk, while maintaining good interpretability. Machine learning models may gain further adoption in the future with increasing diffusion and granularity of EHR data, yielding the advantage of increased scalability compared to standard statistical techniques.
Enhancing Recommendation with Search Data in a Causal Learning Manner

Recommender systems are currently widely used in various applications helping people filter information. Existing models always embed the rich information for recommendation, such as items, users, and contexts in real-value vectors, and make predictions based on these vectors. In the view of causal inference, the associations between representation vectors and user feedback are inevitably a mixture of the causal part that describes why a user prefers an item, and the non-causal part that merely reflects the statistical dependencies, for example, the display ranking position and sales promotion. However, most recommender systems assume the user-item interactions are only affected by user preferences, neglecting the striking differences between these two associations. To address this problem, we propose a model-agnostic causal learning framework called IV4Rec+ that can effectively decompose the embedding vectors into these two parts. Moreover, two strategies are proposed to utilize search queries as instrumental variables: IV4Rec+(I) only decomposes the item embeddings, while IV4Rec+(UI) decomposes both user and item embeddings. IV4Rec+ is a model-agnostic design that can be applied to many existing recommender systems, e.g., DIN, NRHUB, and SRGNN. Extensive experiments on three datasets show that IV4Rec+ significantly facilitates the performance of recommender systems and outperforms state-of-the-art frameworks.
Non-Pattern-Based Anomaly Detection in Time-Series

Anomaly detection across critical infrastructures is not only a key step towards detecting threats but also gives early warnings of the likelihood of potential cyber-attacks, faults, or infrastructure failures. Owing to the heterogeneity and complexity of the cybersecurity field, several anomaly detection algorithms have been suggested in the recent past based on the literature; however, there still exists little or no research that points or focuses on Non-Pattern Anomaly Detection (NP-AD) in Time-Series at the time of writing this paper. Most of the existing anomaly detection approaches refer to the initial profiling, i.e., defining which behavior represented by time series is “normal”, whereas everything that does not meet the criteria of “normality” is set as “abnormal” or anomalous. Such a definition does not reflect the complexity and sophistication of anomaly nature. Under different conditions, the same behavior may or may not be anomalous. Therefore, the authors of this paper posit the need for NP-AD in Time-Series as a step toward showing the relevance of deviating or not conforming to expected behaviors. Non-Pattern (NP), in the context of this paper, illustrates non-conforming patterns or a technique of deviating with respect to some characteristics while dynamically adapting to changes. Based on the experiments that have been conducted in this paper, it has been observed that the likelihood of NP-AD in Time-Series is a significant approach based on the margins of data streams that have been used from the perspective of non-seasonal time series with outliers, the Numenta Anomaly Benchmark (NAB) dataset and the SIEM SPLUNK machine learning toolkit. It is the authors’ opinion that this approach provides a significant step toward predicting futuristic anomalies across diverse cyber, critical infrastructures, and other complex settings.
Continuous variable quantum conference network with a Greenberger–Horne–Zeilinger entangled state

Quantum conference (QC) is a cryptographic task in secure communications that involves more than two users wishing to establish identical secret keys among N users. The Greenberger–Horne–Zeilinger (GHZ) entangled state is the basic resource for quantum cryptographic communication due to the existence of multipartite quantum correlations. An unconditional and efficient quantum network can be established with a continuous variable (CV) GHZ entangled state because of its deterministic entanglement. Here, we report an implementation of QC scheme using a CV multipartite GHZ entangled state. The submodes of a quadripartite GHZ entangled state are distributed to four spatially separated users. The proposed QC scheme is proved to be secure even when the entanglement is distributed through lossy quantum channels and the collective Gaussian attacks are in the all lossy channels. The presented QC scheme has the capability to be directly extended to a larger scale quantum network by using entangled states with more submodes.
Applications of machine learning in tabular document digitisation

Data acquisition forms the primary step in all empirical research. The availability of data directly impacts the quality and extent of conclusions and insights. In particular, larger and more detailed datasets provide convincing answers even to complex research questions. The main problem is that large and detailed usually imply costly and difficult, especially when the data medium is paper and books. Human operators and manual transcription has been the traditional approach for collecting historical data. We instead advocate the use of modern machine learning techniques to automate the digitization and transcription process. We propose a customizable end-to-end transcription pipeline to perform layout classification, table segmentation, and transcribe handwritten text that is suitable for tabular data, as is common in, e.g., census lists and birth and death records. We showcase our pipeline through two applications: The first demonstrates that unsupervised layout classification applied to raw scans of nurse journals can be used to obtain valuable insights into an extended nurse home visiting program. The second application uses attention-based neural networks for handwritten text recognition to transcribe age and birth and death dates and includes a comparison to automated transcription using Transkribus in the regime of tabular data. We describe each step in our pipeline and provide implementation insights.
How we see time – the evolution and current state of visualizations of temporal data

Time, much like space, has always influenced the human experience due to its ubiquity. Yet, how we have communicated temporal information graphically throughout our history, is still inadequately studied. How does our image of time and temporal events evolve as the human world continuously transforms into a globally more and more synchronized community? Within this overview paper, we elaborate on these questions, we analyze visualizations of time and temporal data from a variety of sources connected to exploratory data analysis. We assign codes and cluster the visualizations based on their graphical properties. The result gives an overview of different visual structures apparent in graphic representations of time.