Reviewerly/exHarmony huggingface dataset:
GPU accelerated scalable parallel coordinates plots

Parallel coordinates are a powerful technique to visually analyze multi-parameter data, i.e., sets of datapoints with potentially many associated parameter values per datapoint. When these sets are large, line rendering becomes a severe performance bottleneck, and since many lines fall into the same pixel the numerical precision of the color buffer is quickly reached. We propose a scalable GPU realization of parallel coordinates building upon 2D pairwise attribute bins, to significantly reduce the number of lines to be rendered. Our approach comprises a GPU compute pipeline that combines shader-based scattering with atomic increment operations to efficiently count how often a line is drawn. These counts are then used to draw all pairwise sub-plots in the parallel coordinates plot, by analytically calculating the opacity for each count and rendering a line with end points determined by the 2D coordinates of the bin. In this way, framebuffer precision issues that are paramount in classical approaches can be overcome. We demonstrate the efficiency of the proposed realization for visualizing a weather forecast ensemble comprising 2.7 billion datapoints, each carrying 7 prognostic floating-point variables like temperature, precipitation and pressure, plus spatial and simulation input variables. We compare our pipeline to a rasterization-based approach regarding performance, and demonstrate interactive brushing at 4 s per frame at full HD viewport resolution. • Parallel Coordinates Plots (PCP) useful for visual multiparameter data analysis • Limited scalability of PCPs for datasets > 100 million data points • Binned PCPs grow with viewport resolution instead of number of data points • Utilization of modern GPUs for efficient binned parallel coordinates computation • Combination with analytical blending to accurately handle transparency
Detection of anomalous modeling behavior

Precise and timely detection of anomalous modeling behaviors is critical for both teaching and application of modeling methods. Existing methods usually focus on evaluating the modeling results rather than mining the knowledge hidden in the modeling process. In this paper, we propose to monitor and analyze the modeling process in order to timely detect anomalous modeling behaviors, potentially contributing to a comprehensive assessment of the modeling practice. Specifically, we propose to systematically build a goal model for characterizing the normal modeling behaviors, which establishes the connections between modelers' high-level modeling behaviors and low-level modeling operations. On top of such a goal model, we propose a data mining-based approach to semi-automatically validate the design of the goal model and explore other normal modeling behaviors. Then, we propose to automatically detect anomalous modeling behaviors by capturing normal modeling behaviors obtained from the goal model and actual modeling sequences. We have developed and deployed a data-flow diagram modeling platform, which implemented our proposed approach. We have conducted an experiment with 57 participants, the preliminary results of which show that our approach can effectively detect modelers' anomalous behaviors. The experiment results are beneficial for not only assessing the modelers' performance but also identifying the usability issues of the modeling tool.
API recommendation for machine learning libraries: how far are we?

Application Programming Interfaces (APIs) are designed to help developers build software more effectively. Recommending the right APIs for specific tasks is gaining increasing attention among researchers and developers. However, most of the existing approaches are mainly evaluated for general programming tasks using statically typed programming languages such as Java. Little is known about their practical effectiveness and usefulness for machine learning (ML) programming tasks with dynamically typed programming languages such as Python, whose paradigms are fundamentally different from general programming tasks. This is of great value considering the increasing popularity of ML and the large number of new questions appearing on question answering websites. In this work, we set out to investigate the effectiveness of existing API recommendation approaches for Python-based ML programming tasks from Stack Overflow (SO). Specifically, we conducted an empirical study of six widely-used Python-based ML libraries using two state-of-the-art API recommendation approaches, i.e., BIKER and DeepAPI. We found that the existing approaches perform poorly for two main reasons: (1) Python-based ML tasks often require significant long API sequences; and (2) there are common API usage patterns in Python-based ML programming tasks that existing approaches cannot handle. Inspired by our findings, we proposed a simple but effective frequent itemset mining-based approach, i.e., FIMAX, to boost API recommendation approaches, i.e., enhance existing API recommendation approaches for Python-based ML programming tasks by leveraging the common API usage information from SO questions. Our evaluation shows that FIMAX improves existing state-of-the-art API recommendation approaches by up to 54.3% and 57.4% in MRR and MAP, respectively. Our user study with 14 developers further demonstrates the practicality of FIMAX for API recommendation.
Modeling and Predictive Analysis of the Validity of Children's Behavior Observation in Preschool Education based on Action Recognition Network Algorithm

In view of the high complexity of RGB image background and the lack of depth information, this paper proposes the M-CNN model, which fuses the RGB image information of static human actions and the skeleton point information, and uses the skeleton point information to effectively make up for the RGB image information in the image. For the lack of in-depth information, formulate a framework for the development of children's behavior observation ability for preschool education students; add a professional skills course of "children's behavior observation and evaluation"; add "children's behavior observation ability" task assessment in educational probation; strengthen the penetration of other related courses, based on the action recognition network to establish a prediction model of children's behavior observation, the efficiency is increased by 7.3%.
Self-paced uncertainty estimation for one-shot person re-identification

The one-shot person Re-ID scenario faces two kinds of uncertainties when constructing the prediction model from X to Y. The first is model uncertainty, which captures the noise of the parameters in DNNs due to a lack of training data. The second is data uncertainty, which can be divided into two subtypes: image noise, where severe occlusion and the complex background contain irrelevant information about the identity; and label noise, where mislabeling affects visual appearance learning. We find that the state-of-the-art one-shot person Re-ID addresses the first issue of model uncertainty via a dynamic sampling strategy, while the second issue of data uncertainty remains. In this paper, to simultaneously address both issues, we propose a novel SPUE-Net for one-shot person Re-ID. By introducing a self-paced sampling strategy, our method can estimate the pseudolabels of unlabeled samples iteratively to expand the labeled samples gradually and remove model uncertainty without extra supervision. We divide the pseudolabel samples into two subsets to make the use of training samples more reasonable and effective. In addition, we apply a co-operative learning method of local uncertainty estimation combined with determinacy estimation to achieve better-hidden space feature mining and to improve the precision of selected pseudolabeled samples, which reduces data uncertainty. Extensive comparative evaluation experiments on video-based and image-based datasets show that SPUE-Net has significant advantages over state-of-the-art methods.
Pure Transformer with Integrated Experts for Scene Text Recognition

Scene text recognition (STR) involves the task of reading text in cropped images of natural scenes. Conventional models in STR employ convolutional neural network (CNN) followed by recurrent neural network in an encoder-decoder framework. In recent times, the transformer architecture is being widely adopted in STR as it shows strong capability in capturing long-term dependency which appears to be prominent in scene text images. Many researchers utilized transformer as part of a hybrid CNN-transformer encoder, often followed by a transformer decoder. However, such methods only make use of the long-term dependency mid-way through the encoding process. Although the vision transformer (ViT) is able to capture such dependency at an early stage, its utilization remains largely unexploited in STR. This work proposes the use of a transformer-only model as a simple baseline which outperforms hybrid CNN-transformer models. Furthermore, two key areas for improvement were identified. Firstly, the first decoded character has the lowest prediction accuracy. Secondly, images of different original aspect ratios react differently to the patch resolutions while ViT only employ one fixed patch resolution. To explore these areas, Pure Transformer with Integrated Experts (PTIE) is proposed. PTIE is a transformer model that can process multiple patch resolutions and decode in both the original and reverse character orders. It is examined on 7 commonly used benchmarks and compared with over 20 state-of-the-art methods. The experimental results show that the proposed method outperforms them and obtains state-of-the-art results in most benchmarks.
Exploring Team-Sourced Hyperlinks to Address Navigation Challenges for Low-Vision Readers of Scientific Papers

Reading academic papers is a fundamental part of higher education and research, but navigating these information-dense texts can be challenging. In particular, low-vision readers using magnification encounter additional barriers to quickly skimming and visually locating information. In this work, we explored the design of interfaces to enable readers to: 1) navigate papers more easily, and 2) input the required navigation hooks that AI cannot currently automate. To explore this design space, we ran two exploratory studies. The first focused on current practices of low-vision paper readers, the challenges they encounter, and the interfaces they desire. During this study, low-vision participants were interviewed, and tried out four new paper navigation prototypes. Results from this study grounded the design of our end-to-end system prototype Ocean, which provides an accessible front-end for low-vision readers, and enables all readers to contribute to the backend by leaving traces of their reading paths for others to leverage. Our second study used this exploratory interface in a field study with groups of low-vision and sighted readers to probe the user experience of reading and creating traces. Our findings suggest that it may be possible for readers of all abilities to organically leave traces in papers, and that these traces can be used to facilitate navigation tasks, in particular for low-vision readers. Based on our findings, we present design considerations for creating future paper-reading tools that improve access, and organically source the required data from readers.
Optimization-based meta-learning approaches

This chapter examines the benefits of the state-of-the-art approaches to optimization-based meta-learning: classical long short-term memory (LSTM) meta-learner; model-agnostic meta-learning (MAML) and its variations in few-shot learning, reinforcement learning, and intimation learning; first-order model-agnostic meta-learning; and Reptile. It recaps the motivation behind LSTM meta-learner: covariate shift, batch normalization, LSTM cell, and classical gradient-based optimization. Then, it discusses the intuition of transfer learning and fine-tuning for MAML and MAML's key concepts: adaptation and MAML. Finally, this chapter focuses on several scenarios of Reptile: serial version, parallel, or batch version. It also explains several creative techniques in Reptile: the optimization assumption, minimization of the expected loss function, maximization of generalization within a task, and three modifications of Reptile algorithm. This chapter ends with a precise comparison of these three main methods of optimization-based meta-learning.
Constructive and mechanised meta-theory of IEL and similar modal logics

Abstract Artemov and Protopopescu proposed intuitionistic epistemic logic (IEL) to capture an intuitionistic conception of knowledge. By establishing completeness, they provided the base for a meta-theoretic investigation of IEL, which was continued by Krupski with a proof of cut-elimination, and Su and Sano establishing semantic cut-elimination and the finite model property. However, no analysis of these results in a constructive meta-logic has been conducted, arguably impeding the intuitionistic justification of IEL. We aim to close this gap and investigate IEL in the constructive-type theory of the Coq proof assistant. Concretely, we present a constructive and mechanised completeness proof for IEL, employing a syntactic decidability proof based on cut-elimination to constructivise the ideas from the literature. Following Su and Sano, we then also give constructive versions of semantic cut-elimination and the finite model property. Given our constructive and mechanised setting, all these results now bear executable algorithms. Our particular strategy to establish constructive completeness exploiting syntactic decidability can be used for similar modal logics, which we illustrate with the examples of the classical modal logics K, D and T. For modal logics including the four axioms, however, the method seems not to apply immediately.
DWDP-Stream: A Dynamic Weight and Density Peaks Clustering Algorithm for Data Stream

Abstract Identifying clusters of arbitrary shapes and constantly processing the newly arrived data points are two critical challenges in the study of clustering. This paper proposes a dynamic weight and density peaks clustering algorithm to simultaneously solve these two key issues. An online–offline framework is used, creating and maintaining micro-clusters in the online phase, and treating the micro-clusters as pseudo-points to form the final cluster in the offline phase. In the online phase, when a new data point is merged into the corresponding micro-cluster, a dynamic weight method is proposed to update the weight of the micro-cluster according to the distance between the point and the center of the micro-cluster, so as to more accurately describe the information of the micro-cluster. In the offline phase, the density peak clustering algorithm is improved, natural neighbors are introduced to adaptively obtain the local density of the data point, and the allocation process is improved to reduce the probability of allocation errors. The algorithm is evaluated on different synthetic and real-world datasets using different quality metrics. The experimental results show that the proposed algorithm improves the clustering quality in both static and streaming environments.
Dynamic Pinhole Paper: Interacting with Horizontal Displays through Perforated Paper

Ubiquitous nature, versatility, and durability enabled paper to maintain its importance in the digital age. It is not surprising there have been numerous attempts to combine paper with the digital content. One way to do so is to place paper on a horizontal interactive display (e.g. tabletop or tablet). The paper thus becomes "the screen" on which the digital content is viewed, yet it also acts as a barrier that degrades the quality of the perceived image. This research tries to address this problem by proposing and evaluating a novel paper display concept called Dynamic pinhole paper. The concept is based on perforating the paper (to decrease its opacity) and moving digital content beneath the perforated area (to increase the resolution). To evaluate this novel concept, we fabricated the pinhole paper and implemented the software in order to run multiple user studies exploring the concept’s viability, optimal movement trajectory (amount, direction and velocity), and the effect of perforation on printing, writing and reading. The results show that the movement of digital content is a highly effective strategy for improving the resolution of the digital content through perforation where the optimal velocity is independent from trajectory direction (e.g. horizontal or circular) and amount of movement. Results also show the concept is viable on of the shelf hardware and that it is possible to write and print on perforated paper.
Research on vision measurement method based on binocular structured light

Aiming at the problem that the phase jumping point of binocular structured light interferes with phase matching, an improved monotonic zero-setting method is proposed for phase correction. On the basis of the traditional monotonic zero-setting method, the ratio judgment of the absolute value difference between the front and rear phase points and the overall phase fitting slope is added, which effectively improves the phase matching rate. In order to further improve the accuracy of 3D reconstruction of binocular structured light, the influence of the relative distance between the intersection of binocular camera sight and the object to be measured on the same horizontal line below the camera on the accuracy is studied. After reconstruction, the average point cloud height of each position is analyzed, and it is concluded that the optimal distance range between the binocular camera and the object to be measured is near the intersection of the binocular line of sight. The data at different positions and heights of the test are averaged to perform quadratic polynomial fitting to obtain the error function. Through the calculation of the error function, the error is added to the average point cloud as compensation to obtain a new point cloud height. The result shows that in the distance The point cloud error of the 13.141mm reference plane is reduced by 0.026mm, and the point cloud error of 23.897mm from the reference plane is reduced by 0.1mm, which effectively improves the 3D reconstruction accuracy.
A Fuzzy approach for load balancing in Blockchain-based Software Defined Vehicular Networks

Software Defined Vehicular Networks (SDVN) are increasingly being considered for vehicular communication sys-tems as they could provide both flexibility, programmability and improved performance. The SDVN control plane should be distributed between a set of SDN controllers managing specific geographical areas and network functions (load balancing, fire-wall, etc.) to ensure a high level of scalability. In this environment, the Blockchain technology could be used to guarantee the coordination of the different SDVN controllers. Therefore, many works have demonstrated the benefits of Blockchain-based SDVN architectures. However, they did not investigate the optimization of the underlying Blockchain network. That is why, in this paper, we propose a high-performance multi-level Blockchain architecture which could enable SDVNs to scale up. We also introduce a Fuzzy logic-based mechanism allowing the real-time reconfiguration of the Blockchain network depending on the actual SDVN requirements: traffic increase, integration of a new controller, etc. We finally demonstrate, through experimentation, the benefits of our approach compared to existing solutions in terms of latency, CPU, throughput and scalability.
Over-the-Counter Breast Cancer Classification Using Machine Learning and Patient Registration Records

This study aims to determine the feasibility of machine learning (ML) and patient registration record to be utilised to develop an over-the-counter (OTC) screening model for breast cancer risk estimation. Data were retrospectively collected from women who came to the Hospital Universiti Sains Malaysia, Malaysia for breast-related problems. Eight ML models were used: k-nearest neighbour (kNN), elastic-net logistic regression, multivariate adaptive regression splines, artificial neural network, partial least square, random forest, support vector machine (SVM), and extreme gradient boosting. Features utilised for the development of the screening models were limited to information in the patient registration form. The final model was evaluated in terms of performance across a mammographic density. Additionally, the feature importance of the final model was assessed using the model agnostic approach. kNN had the highest Youden J index, precision, and PR-AUC, while SVM had the highest F2 score. The kNN model was selected as the final model. The model had a balanced performance in terms of sensitivity, specificity, and PR-AUC across the mammographic density groups. The most important feature was the age at examination. In conclusion, this study showed that ML and patient registration information are feasible to be used as the OTC screening model for breast cancer.
Brain-inspired Predictive Coding Improves the Performance of Machine Challenging Tasks

Backpropagation has been regarded as the most favorable algorithm for training artificial neural networks. However, it has been criticized for its biological implausibility because its learning mechanism contradicts the human brain. Although backpropagation has achieved super-human performance in various machine learning applications, it often shows limited performance in specific tasks. We collectively referred to such tasks as machine-challenging tasks (MCTs) and aimed to investigate methods to enhance machine learning for MCTs. Specifically, we start with a natural question: Can a learning mechanism that mimics the human brain lead to the improvement of MCT performances? We hypothesized that a learning mechanism replicating the human brain is effective for tasks where machine intelligence is difficult. Multiple experiments corresponding to specific types of MCTs where machine intelligence has room to improve performance were performed using predictive coding, a more biologically plausible learning algorithm than backpropagation. This study regarded incremental learning, long-tailed, and few-shot recognition as representative MCTs. With extensive experiments, we examined the effectiveness of predictive coding that robustly outperformed backpropagation-trained networks for the MCTs. We demonstrated that predictive coding-based incremental learning alleviates the effect of catastrophic forgetting. Next, predictive coding-based learning mitigates the classification bias in long-tailed recognition. Finally, we verified that the network trained with predictive coding could correctly predict corresponding targets with few samples. We analyzed the experimental result by drawing analogies between the properties of predictive coding networks and those of the human brain and discussing the potential of predictive coding networks in general machine learning.
Learning Temporal Context of Normality for Unsupervised Anomaly Detection in Videos

Incomplete reconstruction of abnormal samples using convolutional autoencoders trained only on normal samples has been the key principle of anomaly detection. Such detection mechanisms utilize reconstruction error differences between normal and abnormal frames. This is not consistent, however, causing the normal and abnormal samples undistin-guishable. To handle this problem, we propose a shuffle-and-sort strategy for learning the temporal context of normality. The purpose of the strategy is to reconstruct shuffled input frames into an output with the correct order using a self-attention mechanism. Consequently, the proposed method can model the temporal context of normal events, which prevents the successful completion of reconstructing anomalies by the convolutional layers. We demonstrated the detection efficiency of the proposed method using public benchmark datasets: UCSD Pedestrian 2, CUHK Avenue, and ShanghaiTech Campus Datasets.