Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncovering Pattern Formation of Information Flow

Pattern formation is a ubiquitous phenomenon that describes the generation of orderly outcomes by self-organization. In both physical society and online social media, patterns formed by social interactions are mainly driven by information flow. Despite an increasing number of studies aiming to understand the spreads of information flow, little is known about the geometry of these spreading patterns and how they were formed during the spreading. In this paper, by exploring 432 million information flow patterns extracted from a large-scale online social media dataset, we uncover a wide range of complex geometric patterns characterized by a three-dimensional metric space. In contrast, the existing understanding of spreading patterns are limited to fanning-out or narrow tree-like geometries. We discover three key ingredients that govern the formation of complex geometric patterns of information flow. As a result, we propose a stochastic process model incorporating these ingredients, demonstrating that it successfully reproduces the diverse geometries discovered from the empirical spreading patterns. Our discoveries provide a theoretical foundation for the microscopic mechanisms of information flow, potentially leading to wide implications for prediction, control and policy decisions in social media.
Empirical study on multiclass classification‐based network intrusion detection

Early and effective network intrusion detection is deemed to be a critical basis for cybersecurity domain. In the past decade, although a significant amount of work has focused on network intrusion detection, it is still a challenge to establish an intrusion detection system with a high detection rate and a relatively low false alarm rate. In this paper, we have performed a comprehensive empirical study on network intrusion detection as a multiclass classification task, not just to detect a suspicious connection but also to assign the correct type as well. To surpass the previous studies, we have utilized four deep learning models, namely, deep neural networks, long short‐term memory recurrent neural networks, gated recurrent unit recurrent neural networks, and deep belief networks. Our approach relies on the pretraining of the models by exploiting a particle swarm optimization–based algorithm for their hyperparameters selection. In order to investigate the performance differences, we also included two well‐known shallow learning methods, namely, decision forest and decision jungle. Furthermore, we used in our experiments four datasets, which are dedicated to intrusion detection systems to explore various environments. These datasets are KDD CUP 99, NSL‐KDD, CIDDS, and CICIDS2017. Moreover, 22 evaluation metrics are used to assess the model's performance in each of the datasets. Finally, intensive quantitative, Friedman test, and ranking methods analyses of our results are provided at the end of this paper. The results show a significant improvement in the detection of network attacks with our recommended approach.
Hierarchical Deep Stereo Matching on High-Resolution Images

We explore the problem of real-time stereo matching on high-res imagery. Many state-of-the-art (SOTA) methods struggle to process high-res imagery because of memory constraints or speed limitations. To address this issue, we propose an end-to-end framework that searches for correspondences incrementally over a coarse-to-fine hierarchy. Because high-res stereo datasets are relatively rare, we introduce a dataset with high-res stereo pairs for both training and evaluation. Our approach achieved SOTA performance on Middlebury-v3 and KITTI-15 while running significantly faster than its competitors. The hierarchical design also naturally allows for anytime on-demand reports of disparity by capping intermediate coarse results, allowing us to accurately predict disparity for near-range structures with low latency (30ms). We demonstrate that the performance-vs-speed tradeoff afforded by on-demand hierarchies may address sensing needs for time-critical applications such as autonomous driving.
Analysis of students error in mathematical problem solving based on Newman’s error analysis

After conducting literature studies, the researchers found that more studies on problem solving focused on the application of learning methods and the development of teaching materials to improve students problem-solving ability rather than identification and discovery of student errors. This study aims to describe students error according to Newman's Error Analysis in solving derivative of algebraic function problem. This research used a qualitative approach with case study design and purposive sampling technique. Data were collected through problem-solving test on derivative of algebraic function and interview of senior high school students grade 11 in Bandung. The results of this study showed that students did five types of errors in solving the problem of derivative of algebraic function that were comprehension error, transformation error, process skill error, encoding error and careless. Analysis of student error was expected to help to reflect on solving mathematical problems and became a reference for teachers in choosing strategy, model or learning media to reduce errors made by students.
Graph-based Dependency Parsing with Graph Neural Networks

We investigate the problem of efficiently incorporating high-order features into neural graph-based dependency parsing. Instead of explicitly extracting high-order features from intermediate parse trees, we develop a more powerful dependency tree node representation which captures high-order information concisely and efficiently. We use graph neural networks (GNNs) to learn the representations and discuss several new configurations of GNN’s updating and aggregation functions. Experiments on PTB show that our parser achieves the best UAS and LAS on PTB (96.0%, 94.3%) among systems without using any external resources.
Macro Neural Architecture Search Revisited

Neural architecture search (NAS) has recently shown promising results for automatically ﬁnding cost-efﬁcient and accurate predictors. However, most recent work exclusively study the small search space of repeatable network modules (cells), instead of the more general overall network (macro), partially because the models found by macro-search typically require an order of magnitude more parameters compared to cell-search to have the same accuracy. In this work, we show through ablation study that this gap mainly exists due to the difference in initial models. In fact, by starting with the same condition as cell-search, the proposed macro-search can ﬁnd a CIFAR-10 model that has 2.93% test error rate and uses 3.1 million parameters. Our macro-search algorithm has the advantage of being simple and fast. The search procedure randomly and incrementally grows the most cost-efﬁcient models on the Pareto frontier. The proposed search takes only 6 GPU-days, which is much smaller than those of many existing methods, while achieving comparable or better results.
Characterization of thermographic images of skin cancer lesions using digital image processing

Skin cancer is the type of cancer with the highest incidence in the world. The alterations of temperature in the skin are organic indicators of the presence of several types of skin cancer, mainly squamous cell carcinomas and melanomas. In this work, thermographic images of lesions of this type were analyzed in order to find indicators of the presence of this neoplasm that allow the future development of a detection algorithm. The digital processing used consists of identification of areas of interest, color segmentation, quantitative discrimination according to color tonality and analysis by histograms. The results showed that lesions with skin cancer have values in the red component above 100 under the RGB color space on a scale of 0 to 255. Also, when segmenting, by the k-means algorithm, a thermography containing a melanoma, the area that contains the lesion has a higher average value in the red component with respect to the other areas. Taking these results into account, a non-invasive tool for prediagnosing skin cancer can be developed that reduces unnecessary clinical procedures and simplifies the diagnosis.
Exploring Numeracy in Word Embeddings

Word embeddings are now pervasive across NLP subfields as the de-facto method of forming text representataions. In this work, we show that existing embedding models are inadequate at constructing representations that capture salient aspects of mathematical meaning for numbers, which is important for language understanding. Numbers are ubiquitous and frequently appear in text. Inspired by cognitive studies on how humans perceive numbers, we develop an analysis framework to test how well word embeddings capture two essential properties of numbers: magnitude (e.g. 3<4) and numeration (e.g. 3=three). Our experiments reveal that most models capture an approximate notion of magnitude, but are inadequate at capturing numeration. We hope that our observations provide a starting point for the development of methods which better capture numeracy in NLP systems.
A Bayes Inference for Ordinal Response with Latent Variable Approach

In this paper, we propose a Bayesian model for the analysis of categorical data with an ordered outcome. The method provides a latent variable approach with an informative prior transformed from a Dirichlet distribution for the boundary parameters. A simulation study is carried out to assess the performance of the methods under various settings of the data structure. Our method produces predictive accuracy over the conventional classification procedures. Real data are analyzed to demonstrate the efficiency of the proposed method.
A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives

List of Tables and Figures. Preface. Foreword. SECTION I: THE TAXONOMY, EDUCATIONAL OBJECTIVES AND STUDENT LEARNING. 1. Introduction. 2. The Structure, Specificity, and Problems of Objectives. SECTION II: THE REVISED TAXONOMY STRUCTURE. 3. The Taxonomy Table. 4. The Knowledge Dimension. 5. The Cognitive Process Dimension. SECTION III: THE TAXONOMY IN USE. 6. Using the Taxonomy Table. 7. Introduction to the Vignettes. 8. Nutrition Vignette. 9. Macbeth Vignette. 10. Addition Facts Vignette. 11. Parliamentary Acts Vignette. 12. Volcanoes? Here? Vignette. 13. Report Writing Vignette. 14. Addressing Long-standing Problems in Classroom Instruction. APPENDICES. Appendix A: Summary of the Changes from the Original Framework. Appendix B: Condensed Version of the Original Taxonomy of Educational Objectives: Cognitive Domain. References. Credits. Index.
Optimal Resource and Power Allocation With Relay Selection for RF/RE Energy Harvesting Relay-Aided D2D Communication

Device-to-device (D2D) communication is considered as a promising technology for improving both the spectral and energy efficiencies of cellular networks by reusing the resources of conventional cellular users (CUs) for direct communication of two nearby devices in a spatial manner. When the channel between the two D2D devices is highly attenuated, it is necessary to use an intermediate relay to achieve reliable and flexible relay-aided D2D communication. In order to motivate the cooperative relays to participate, it is assumed that they can harvest energy from radio frequency (RF) signals based on the power splitting (PS) protocol as well as renewable energy (RE) sources. However, resource sharing between the cellular and relay-aided D2D links leads to mutual interference that degrades their sum rate. Considering the energy-harvesting relays (EHRs) and downlink (DL) resource sharing, this paper aims to maximize the sum rate of both the links without degrading the quality of service (QoS) requirements of all users. Our maximization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem that cannot be solved in a straightforward manner. Therefore, we propose a low complexity algorithm, namely the resource and power allocation with relay selection EH-aided algorithm (RPRS-EH), which determines the reuse partners, the PS factor sub-optimal value with optimal links power allocation, and provides two different strategies for optimal relay selection. The numerical results show the behavior of the proposed algorithm under various parameters as well as its considerable performance when compared to one of the most recent algorithms in terms of the links sum rate and relay energy efficiency.
DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks

Online prediction has become one of the most essential tasks in many real-world applications. Two main characteristics of typical online prediction tasks include tabular input space and online data generation. Specifically, tabular input space indicates the existence of both sparse categorical features and dense numerical ones, while online data generation implies continuous task-generated data with potentially dynamic distribution. Consequently, effective learning with tabular input space as well as fast adaption to online data generation become two vital challenges for obtaining the online prediction model. Although Gradient Boosting Decision Tree (GBDT) and Neural Network (NN) have been widely used in practice, either of them yields their own weaknesses. Particularly, GBDT can hardly be adapted to dynamic online data generation, and it tends to be ineffective when facing sparse categorical features; NN, on the other hand, is quite difficult to achieve satisfactory performance when facing dense numerical features. In this paper, we propose a new learning framework, DeepGBM, which integrates the advantages of the both NN and GBDT by using two corresponding NN components: (1) CatNN, focusing on handling sparse categorical features. (2) GBDT2NN, focusing on dense numerical features with distilled knowledge from GBDT. Powered by these two components, DeepGBM can leverage both categorical and numerical features while retaining the ability of efficient online update. Comprehensive experiments on a variety of publicly available datasets have demonstrated that DeepGBM can outperform other well-recognized baselines in various online prediction tasks.
Self-Supervised Neural Machine Translation

We present a simple new method where an emergent NMT system is used for simultaneously selecting training data and learning internal NMT representations. This is done in a self-supervised way without parallel data, in such a way that both tasks enhance each other during training. The method is language independent, introduces no additional hyper-parameters, and achieves BLEU scores of 29.21 (en2fr) and 27.36 (fr2en) on newstest2014 using English and French Wikipedia data for training.
Yes, we can! Mining Arguments in 50 Years of US Presidential Campaign Debates

Political debates offer a rare opportunity for citizens to compare the candidates’ positions on the most controversial topics of the campaign. Thus they represent a natural application scenario for Argument Mining. As existing research lacks solid empirical investigation of the typology of argument components in political debates, we fill this gap by proposing an Argument Mining approach to political debates. We address this task in an empirical manner by annotating 39 political debates from the last 50 years of US presidential campaigns, creating a new corpus of 29k argument components, labeled as premises and claims. We then propose two tasks: (1) identifying the argumentative components in such debates, and (2) classifying them as premises and claims. We show that feature-rich SVM learners and Neural Network architectures outperform standard baselines in Argument Mining over such complex data. We release the new corpus USElecDeb60To16 and the accompanying software under free licenses to the research community.
Large-Scale Transfer Learning for Natural Language Generation

Large-scale pretrained language models define state of the art in natural language processing, achieving outstanding performance on a variety of tasks. We study how these architectures can be applied and adapted for natural language generation, comparing a number of architectural and training schemes. We focus in particular on open-domain dialog as a typical high entropy generation task, presenting and comparing different architectures for adapting pretrained models with state of the art results.
Fuzzy Region-Based Active Contours Driven by Weighting Global and Local Fitting Energy

contour model (ACM) has been a successful method for image segmentation. The existing ACMs poorly segment the images with intensity inhomogeneity or non-homogeneity, and the results highly depend on the initial position of the contour. To overcome these disadvantages, we proposed a fuzzy region-based active contour driven by weighting global and local fitting energy, wherein we propose a fuzzy region energy with local spatial image information, which has been proved convex and ensures the segmentation results independent of initialization, to motivate an initial evolving curve of pseudo level set function (LSF), followed by the pseudo LSF and further smoothed by an edge energy to accurately extract the object boundaries and maintain its distance feature. In addition, in the fuzzy region energy, instead of using the Euler-Lagrange equation to minimize the energy functional, we develop a more direct method to calculate the change of the fuzzy region energy. The experimental results on synthetic and real images with high noise and intensity inhomogeneity show that the proposed model can obtain better performance than the state-of-the-art active contour models, and takes less running time. The code is available at: https://github.com/fangchj2002/FRAGL.
Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation

We present a simple yet powerful data augmentation method for boosting Neural Machine Translation (NMT) performance by leveraging information retrieved from a Translation Memory (TM). We propose and test two methods for augmenting NMT training data with fuzzy TM matches. Tests on the DGT-TM data set for two language pairs show consistent and substantial improvements over a range of baseline systems. The results suggest that this method is promising for any translation environment in which a sizeable TM is available and a certain amount of repetition across translations is to be expected, especially considering its ease of implementation.
A Novel Streaming Data Clustering Algorithm Based on Fitness Proportionate Sharing

As an unsupervised learning technique, clustering can effectively capture the patterns in a data stream based on similarities among the data. Traditional data stream clustering algorithms either heavily depend on some prior knowledge or predefined parameters while the characteristics of real-time data are considered unknown. Besides, the user-specified threshold is used to overcome the effect of outliers and noises, which significantly affects the clustering performance. The overlap among clusters is another major challenge for the existing stream clustering methods. These constraints strongly limit their real-time applications. In this paper, a two-phase stream clustering algorithm based on fitness proportionate sharing is proposed. It handles streaming data when prior knowledge is not available and maps the clustering problem into a multimodal optimization problem. It introduces a density-based objective function and adopts the fitness proportionate sharing strategy to perform a more effective search for the cluster centers. To capture the dynamic characteristics of streaming data, a recursive formula for the lower bound of the density function is derived, and a summary of historical data is established for the proposed algorithm. The proposed algorithm is applied to different data sets, and a comprehensive comparison between the proposed algorithm and five well-known data stream clustering algorithms in the literature is provided. Results show comparable or better performance relative to five popular data stream clustering algorithms. A scalability analysis of the proposed streaming clustering method is presented in this paper as well.
Power System Transient Diagnostics Based on Novel Traveling Wave Detection

Modern electrical power systems demand novel diagnostic approaches to enhancing the system resiliency by developing the state-of-the-art algorithms. The proliferation of highvoltage optical transducers and high time-resolution measurements provide opportunities to develop novel diagnostic methods of very fast transients in power systems. At the same time, emerging complex configurations, such as multi-terminal hybrid transmission systems, limit the applications of the traditional diagnostic methods, especially in fault location and health monitoring. The impedance-based fault-location methods are inefficient for cross-bounded cables, which are widely used for connection of offshore wind farms to the main grid. Thus, this dissertation first presents a novel traveling wave-based fault-location method for hybrid multi-terminal transmission systems. The proposed method utilizes time-synchronized high-sampling voltage measurements. The traveling wave arrival times (ATs) are detected by observing the squares of wavelet transformation coefficients. Using the ATs, an overdetermined set of linear equations are developed for noise reduction, and consequently, the faulty segment is determined based on the characteristics of the provided equation set. Then, the fault location is estimated. The accuracy and capabilities of the proposed fault location method is evaluated and also compared to the existing traveling-wave-based method for a wide range of fault parameters. In order to improve power systems stability, auto-reclosing (AR), single-phase autoreclosing (SPAR), and adaptive single-phase auto-reclosing (ASPAR) methods have been developed with the final objectives of distinguishing between the transient and permanent faults to clear the transient faults without de-energization of the solid phases. However, the i
Some New Findings on the Self-similarity Property in Communications Networks and on Statistical End-to-end Delay Guarantee

Real-time communication requires performance guarantee from the underlying network. In order to analyse the network performance, we must find the traffic characterization in every server of the network. Due to the strong experimental evidence that network traffic is self-similar in nature, it is important to study the problems to see whether the superposition of two self- similar processes retains the property of self-similarity and whether the service of a server changes the self-similarity property of the input traffic. In this paper, we first discusses some definitions and superposition properties of self-similar processes. Then we gives a model of a single server with infinite buffer and prove that when the queue length has finite second-order moment, the input process being strong asymptotically second-order self-similar(sas-s) is equivalent to the output process also bearing the sas-s property. Given the method for determinating the worst case cell delay for an ATM switch with self- similar input traffic, we can determine the end-to-end delay for such real-time communications in an ATM network by summing the cell delay experienced by each of the ATM switch along each connection.