Reviewerly/exHarmony huggingface dataset:
Federated stochastic configuration networks for distributed data analytics

Stochastic configuration networks (SCNs), as a class of randomized learning models, are incrementally built under a supervisory mechanism, and theoretically ensure error-free learning for training sets. This paper proposes a federated version of SCNs (FSCNs) for large-scale data, which are geographically distributed among different end-user clients with non-shareable data due to privacy and security concerns. Unlike centralized learning that needs to collect data from clients and store them collectively on a cloud server, FSCNs enable distributed analytics in a collaborative learning paradigm without centrally aggregating new data, thereby preventing the leakage of private information. Considering different supervisory and aggregate schemes of model parameters, two FSC algorithms with two aggregate strategies are presented. The experiment results on both data regression and classification show the effectiveness and feasibility of our proposed federated learning scheme.
GeoBERTSegmenter: Word Segmentation of Chinese Texts in the Geoscience Domain Using the Improved BERT Model

Abstract Unlike English, there is no natural separator‐like gap between words in Chinese, which makes Chinese word segmentation (CWS) a difficult information processing problem. At present, geological texts contain a large number of unregistered geological terms, and the existing rule‐based methods and machine‐learning and deep learning algorithms still cannot be used to solve the problem of word segmentation in geosciences, especially for the large number of unregistered words. In this study, we propose GeoBERTSegmenter, which is a GeoBERT‐based (Geoscience‐Bidirectional Encoder Representation from Transformers) CWS model that is specifically designed with various linguistic irregularities in mind. In this method, a general model is extended to a BERT bidirectional recurrent neural network (BiLSTM) and conditional random field (GeoBERT + BiLSTM + CRF) model with a number of features designed to address the CWS task in geological text. We also train a pretrained language model named GeoBERT on a geological domain that is based on a large amount of Chinese geological text. In open testing, a precision of 94.77%, recall of 96.31% and F1 of 95.44%, are obtained, indicating that the proposed strategy performs much better than alternative methods in our study. In this study, unregistered geological terms can be effectively identified, and the recognition rate of common words is ensured, which lays the foundation for natural language processing in the domain of geoscience through Chinese text word segmentation.
Data Hiding of Multicompressed Images Based on Shamir Threshold Sharing

Image-based data hiding methods have been used in the development of various applications in computer vision. At present, there are two main types of data hiding based on secret sharing, namely dual-image data hiding and multi-image data hiding. Dual-image data hiding is a kind of secret sharing-based data hiding in the extreme case. During the image transmission and storage process, the two shadow images are visually highly similar. Multi-image data hiding disassembles the cover image into multiple meaningless secret images through secret sharing. Both of the above two methods can easily attract attackers’ attention and cannot effectively guarantee the security of the secret message. In this paper, through the Shamir threshold scheme for secret sharing, the secret message is disassembled into multiple subsecrets that are embedded in the smooth blocks of multiple different images, by substituting the bitmap of block truncation coding. Thus, the shortcomings of the above two data hiding methods are effectively avoided. The proposed method embeds the secret messages in the compressed images, so it satisfactorily balances the visual quality and the embedding capacity. In our method, the shadow images make sense while they are not visually similar. The compression ratio is four, so the embedding capacity of our method has an obvious advantage under the same storage space.
Bilateral association tracking with parzen window density estimation

Multi-object tracking is an important branch of computer vision, which is mostly used for behavior recognition and event analysis. At present, most of the research focuses on the accuracy of tracking. However, the real-time performance is also urgently desired but there is lack of research. As a result, Deepsort, proposed 5 years ago, is still the most widely used tracker in real applications. In this paper, a Bilateral Association Tracking (BAT) framework is proposed. It uses tracklet as the basic node instead of discrete detection for tracking. Meanwhile, a Parzen density based Hierarchical Agglomerative Clustering (P-HAC) algorithm is introduced to describe the density distribution of targets and generate tracklets with high confidence. In addition, Dual Appearance Features (DAF) is proposed which considers both spatial and temporal features of tracklets and promotes the accuracy of tracklet association. Experiments are conducted on popular benchmarks such as MOT2017, Visdrone and KITTI. BAT outperforms Deepsort on both association accuracy and trajectory integrity without obvious efficiency decline. Compared with other state-of-the-art trackers, BAT shows significant advantage on computational cost while performing competitive tracking accuracy as well. It is hoped that the research can promote the applications on real-time tracking in the near future.
A General-Purpose Method for Applying Explainable AI for Anomaly Detection

The need for explainable AI (XAI) is well established but relatively little has been published outside of the supervised learning paradigm. This paper focuses on a principled approach to applying explainability and interpretability to the task of unsupervised anomaly detection. We argue that explainability is principally an algorithmic task and interpretability is principally a cognitive task, and draw on insights from the cognitive sciences to propose a general-purpose method for practical diagnosis using explained anomalies. We define Attribution Error, and demonstrate, using real-world labeled datasets, that our method based on Integrated Gradients (IG) yields significantly lower attribution errors than alternative methods.
Formal and Informal Infrastructures of Collaboration in the Human Brain Project

This article draws on long-term engagement with the Human Brain Project (HBP), one of the Future and Emerging Technology Flagship Initiatives funded by the European Commission to address EU “grand challenges” of understanding the human brain and applying these insights to brain-inspired technology development. Based on participant observation and interviews with researchers and project administrators, our findings suggest that the formal infrastructure built to facilitate and structure collaboration within large-scale interdisciplinary research projects can be in tension with the ways researchers collaborate. While much of the literature on infrastructure focuses on top-down, formal infrastructural design, we also pay attention to the informal, bottom-up infrastructural assemblage involved in large-scale interdisciplinary collaborations. This brings into question how scientists and science funders navigate the tensions and interactions between formal and informal infrastructure, rendering certain kinds of collaboration and knowledge (in)visible.
Unpacking the Hidden Curricula in Educational Automation: A Methodology for Ethical Praxis

Abstract The future of educational automation in higher education is commonly seen as an inevitable trajectory and beyond the control of individual institutions or communities. Much research has focused on how such technologies can remove agency and reproduce inequalities through encoded biases. Indeed, many conceptualisations of educational automation are problematic, but less is known about what can be done to take more control over them. By moving away from critique alone, this paper seeks to demystify educational automation and develop a methodology that enables both institutions and staff to take greater control over the technologies in their institutional work. This methodology emerges from multiple research projects exploring digital education, automation, and educational futures and brings together the findings from these to find ways to establish ethical praxis in future forms of educational automation. This methodology and its attendant ethical praxis posit that critique must be used in tandem with creativity and activism to fully realise new and just educational futures.
Examining Pre-Service and In-Service Teachers’ Perceptions of Their Readiness to Use Digital Technologies for Teaching and Learning

This study examines 105 pre-service and in-service teachers' perceptions of the importance and helpfulness of digital technologies for their learning and their teaching as well as their perceived competence and level of interest in digital technologies. The descriptive statistics from an online survey show that all respondents (n = 105) found collaboration tools, learning management systems, and supplemental video the most helpful for both teaching and learning, and found podcasts, social media, and mobile apps to be the least helpful and important for both teaching and learning. The findings of this study have implications for in-service teachers, pre-service teachers, school administrators, and teacher educators. This study indicates a need to further examine how pre-service and in-service teachers' beliefs about digital technologies influence their process of designing instruction and choosing whether to or how to use these technologies in their classrooms.
Testing software’s changing features with environment-driven abstraction identification

ions are significant domain terms that have assisted in requirements elicitation and modeling. To extend the assistance toward requirements validation, we present in this paper an automated approach to identifying the abstractions for supporting requirements-based testing. We select relevant Wikipedia pages to serve as a domain corpus that is independent from any specific software system. We further define five novel patterns based on part-of-speech tagging and dependency parsing, and frame our candidate abstractions in the form of <key, value> pairs for better testability, where the "key" helps locate "what to test", and the "value" helps guide "how to test it" by feeding in concrete data. We evaluate our approach with six software systems in two application domains: Electronic health records and Web conferencing. The results show that our abstractions are more accurate than those generated by a state-of-the-art technique. While the initial findings indicate our abstractions' capabilities of revealing bugs and matching the environmental assumptions created manually, we articulate a new way to perform requirements-based testing by focusing on a software system's changing features. Specifically, we hypothesize that the same feature would behave differently under a pair of opposing environmental conditions and assess our abstractions' applicability to this new form of feature testing.
NAPS Fusion: A framework to overcome experimental data limitations to predict human performance and cognitive task outcomes

In the area of human performance and cognitive research, machine learning (ML) problems become increasingly complex due to limitations in the experimental design, resulting in the development of poor predictive models. More specifically, experimental study designs produce very few data instances, have large class imbalances and conflicting ground truth labels, and generate wide data sets due to the diverse amount of sensors. From an ML perspective these problems are further exacerbated in anomaly detection cases where class imbalances occur and there are almost always more features than samples. Typically, dimensionality reduction methods (e.g., PCA, autoencoders) are utilized to handle these issues from wide data sets. However, these dimensionality reduction methods do not always map to a lower dimensional space appropriately, and they capture noise or irrelevant information. In addition, when new sensor modalities are incorporated, the entire ML paradigm has to be remodeled because of new dependencies introduced by the new information. Remodeling these ML paradigms is time-consuming and costly due to lack of modularity in the paradigm design, which is not ideal. Furthermore, human performance research experiments, at times, creates ambiguous class labels because the ground truth data cannot be agreed upon by subject-matter experts annotations, making ML paradigm nearly impossible to model. This work pulls insights from Dempster–Shafer theory (DST), stacking of ML models, and bagging to address uncertainty and ignorance for multi-classification ML problems caused by ambiguous ground truth, low samples, subject-to-subject variability, class imbalances, and wide data sets. Based on these insights, we propose a probabilistic model fusion approach, Naive Adaptive Probabilistic Sensor (NAPS), which combines ML paradigms built around bagging algorithms to overcome these experimental data concerns while maintaining a modular design for future sensor (new feature integration) and conflicting ground truth data. We demonstrate significant overall performance improvements using NAPS (an accuracy of 95.29%) in detecting human task errors (a four class problem) caused by impaired cognitive states and a negligible drop in performance with the case of ambiguous ground truth labels (an accuracy of 93.93%), when compared to other methodologies (an accuracy of 64.91%). This work potentially sets the foundation for other human-centric modeling systems that rely on human state prediction modeling.
Motivating members’ involvement to effectually conduct collaborative software process tailoring

Contemporary business and software environments are highly competitive and rapidly evolving, resulting in software projects that are highly customized and changeable during development. Therefore, software process tailoring (SPT) is important as software teams conduct SPT to adjust shared development processes and evolve the project to better meet unique and dynamic needs. SPT is a special type of teamwork in which members' active participation and critical input are necessary for understanding and synthesizing various business and technical concerns that may be divergent and conflictual and then jointly identifying an integrated tailoring solution. In this context, this study examines members' decisive and critical involvement in SPT and adopts a motivational perspective to explore how motivation can facilitate SPT performance. Specifically, we use empowerment theory to develop a model to theorize and examine how psychological empowerment (PE) in terms of meaningfulness, autonomy, potency, and impact motivates software teams to efficiently and effectively conduct SPT. The model also considers the power distance (PD) to understand how it functions in team-based critical thinking and decisional processes to energize team members' participative effort. The investigation surveyed 102 software development teams and used partial least squares (PLS) to analyze the data. The results show that PE in terms of the four components has various influences on SPT performance and that PD has nonsignificant moderating effects. This study contributes to the software engineering literature by uncovering the contextual mechanism underlying the relationship between PE and PD in SPT. The limitations and possible extensions of this study are also outlined for future research.
Secured control systems through integrated IoT devices and control systems

The Internet of Things (IoT) provides customers with seamless integration and interoperability between gadgets, technologies, or products, and across multiple networks focus on control schemas. Regardless of the integration node, whether it is a smartphone, tablet, laptop, TV, automobile, or other Internet-compatible gadget, end-users want a fast and clear connection. Intelligent IoT devices can act as flexible connectors with variable structures, allowing rapid responses and possibly universal access. Consequently, gadgets could pose serious security issues. When they are used to manage accounts receivable at IoT terminals, these methods are likely to replay, provoke human attacks in the middle, or by denial of service. Responding to the real-time challenge for safe control operations, this study provides a methodology that blends the characteristics of intelligent IoT systems with power system interfaces. A proposed method combines computation, cryptography, signal/image processing, and communication networks of authentication and authorization tasks, using both terminals and gateways. Unlike conventional methods, they provide a safer, more adaptable, and resilient setting for real-time performance. The researchers also describe Intel's IoT framework for power system inputs that are used in management, industrial, transport, and detailed monitoring contexts.
The influence of active projection speckle patterns on underwater binocular stereo vision 3D imaging

Due to the phenomena of refraction, absorption and scattering, underwater stereo vision faces more difficulties than in air. These phenomena will bring various problems such as blurred details, reduced contrast and brightness to underwater images, which will affect the accuracy and reliability of stereo matching and increase the difficulty of stereo matching. The method based on active speckle pattern is a more simple and reliable method to enhance the surface texture of underwater objects. A good-quality speckle pattern can provide more robust and accurate results for matching the corresponding points of the left and right images. This is a more important research problem in water than in air. In this paper, the row entropy of speckle pattern is adopted to quantify the quality of the projection speckle pattern suitable for underwater stereo vision, and the relevant experiment and analysis are carried out to prove the correctness and effectiveness of new parameters. Further, the influence of different projection speckle patterns on underwater 3D imaging is researched. The actual underwater experiment indicates that the speckle pattern exerts a great influence on the accuracy of underwater 3D imaging based on the binocular stereo vision, and the row entropy of higher-quality projection speckle pattern should be as small as possible. This work can give a reference for the selection of projection speckle patterns in underwater binocular stereo vision. An appropriate speckle pattern can effectively improve the accuracy of underwater 3D measurement.
Multiqubit Toffoli Gates and Optimal Geometry with Rydberg Atoms

Due to its potential for implementing a scalable quantum computer, the multiqubit Toffoli gate lies in the heart of quantum information processing. In this paper, we demonstrate a multiqubit blockade gate with atoms arranged in a three-dimensional spheroidal array. The gate performance is greatly improved by the method of optimizing control-qubit distributions on the spherical surface via evolutionary algorithm, which leads to an enhanced asymmetric Rydberg blockade. This spheroidal configuration, not only preserves the dipole blockade energy well between arbitrary control-target pairs, which keeps the asymmetric blockade error at a very low level, but also manifests an unprecedented robustness to the spatial position variations, leading to a negligible position error. Taking account of intrinsic errors and using typical experimental parameters, we numerically show that a c${}_{6}$not Rydberg gate can be created with a fidelity of 0.992, which is only limited by the Rydberg state decays. Our protocol opens up a platform of higher-dimensional atomic arrays for achieving multiqubit neutral-atom quantum computation.
Clustering Algorithm Based on Feature Space Partitioning

In clustering problems, there are problems associated, in particular, with outliers and linear inseparability of data. A new approach to robust clustering is proposed based on recursive division of the feature space into hypercubes (cells). In the process of analyzing cell densities and their homogeneities, cells merge into clusters. The algorithm is quite resistant to outliers and is capable of separating linearly inseparable points. An algorithm for robust clustering of linearly inseparable points, its software implementation, as well as test results on classical datasets are presented. The advantages and disadvantages of the proposed method, as well as possible ways of its development are discussed.
Using the Polar Transform for Efficient Deep Learning-Based Aorta Segmentation in CTA Images

Medical image segmentation often requires segmenting multiple elliptical objects on a single image. This includes, among other tasks, segmenting vessels such as the aorta in axial CTA slices. In this paper, we present a general approach to improving the semantic segmentation performance of neural networks in these tasks and validate our approach on the task of aorta segmentation. We use a cascade of two neural networks, where one performs a rough segmentation based on the U-Net architecture and the other performs the final segmentation on polar image transformations of the input. Connected component analysis of the rough segmentation is used to construct the polar transformations, and predictions on multiple transformations of the same image are fused using hysteresis thresholding. We show that this method improves aorta segmentation performance without requiring complex neural network architectures. In addition, we show that our approach improves robustness and pixel-level recall while achieving segmentation performance in line with the state of the art.
Trip Reinforcement Recommendation with Graph-based Representation Learning

Tourism is an important industry and a popular leisure activity involving billions of tourists per annum. One challenging problem tourists face is identifying attractive Places-of-Interest (POIs) and planning the personalized trip with time constraints. Most of the existing trip recommendation methods mainly consider POI popularity and user preferences, and focus on the last visited POI when choosing the next POI. However, the visit patterns and their asymmetry property have not been fully exploited. To this end, in this article, we present a GRM-RTrip (short for G raph-based R epresentation M ethod for R einforce Trip Recommendation) framework. GRM-RTrip learns POI representations from incoming and outgoing views to obtain asymmetric POI-POI transition probability via POI-POI graph networks, and then fuses the trained POI representation into a user-POI graph network to estimate user preferences. Finally, after formulating the personalized trip recommendation as a Markov Decision Process (MDP), we utilize a reinforcement learning algorithm for generating a personalized trip with maximal user travel experience. Extensive experiments are performed on the public datasets and the results demonstrate the superiority of GRM-RTrip compared with the state-of-the-art trip recommendation methods.
HAPGNN: Hop-wise attentive PageRank-Based graph neural network

With the emergence of graph neural networks (GNNs), deep learning techniques for non-Euclidean data have become the go-to method for various graph processing tasks. However, many GNNs suffer from over-smoothing, in which node features become indistinguishable with the use of multiple message passing layers and do not generalize real-world non-homogenous graph data well. To address these issues, we propose an enhanced adjustable method that attends to important hops via independent learnable weights and includes an initial connection method to further stabilize the larger model. We conducted extensive experiments on 12 real-world graph benchmark datasets of various sizes and network homophily levels to show our approach outperforms several recently proposed adaptive methods for node classification tasks .