Reviewerly/exHarmony huggingface dataset:
Flowcuits: Crafting Tangible and Interactive Electrical Components with Liquid Metal Circuits

We present Flowcuits, a DIY fabrication method to prototype tangible, interactive and functional electrical components by manipulating liquid metals. The prototypes afford both physical and visual interactions to demonstrate the inner working mechanics of fundamental electronic elements, which enables tangible and playful learning. The fabrication process follows simple imprinting and sealing of fluidic circuits with a 3D-printed stamp on an accessible moldable-substrates such as 'Blu Tack'. Utilizing conductive gallium indium liquid metal, we demonstrated interactive and re-configurable electronic components such as switches, variable resistors, variable capacitors, logic gates and pressure sensors. In this paper, we present the design analogy of Flowcuits, DIY fabrication approach including a parametric 3D stamp design toolkit and results from a technical evaluation. The stamps are printed with a low-cost 3D printer and all the materials are inexpensive and reusable, enabling Flowcuits to be easily used without any advanced lab facilities.
DOFM: Domain Feature Miner for robust extractive summarization

The domain feature retrieval has potential applications in text summarization. However, it is challenging to mine domain features from the user reviews. In this paper, a novel Domain Feature Miner (DOFM) is designed by (i) formulating the feature mining problem as a clustering problem and (ii) engaging three newly conceived empirical observations such as frequency count, grouping semantics, and distributional statistics of features. Later, Symmetric Cluster Extraction (SCE) and Asymmetric Cluster Extraction (ACE) algorithms are designed to identify domain features from clusters. The effectiveness of the DOFM is verified on benchmarks provided by the University of Illinois at Urbana–Champaign and compared with the four state-of-the-art (SOTA) approaches using Precision, Recall, and F-score. Moreover, ROUGE (Recall-Oriented Understudy for Gisting Evaluation), a well-known package for automatic evaluation of summaries is used to evaluate the DOFM generated summaries. The Error Analysis reveals that at least one of three annotators would prefer 84% sentences of all DOFM generated summaries, while 36% sentences are preferred by all three. This indicates the robustness of DOFM in domain feature retrieval and extractive summarization.
Multilayer Radial-basis Function Network and its Learning

The radial basis function (RBF) network may serve as a good alternative for multilayer perceptron (MLP), since RBFN structure is much easier, and learning speed is faster. This article aims to present the architecture of the multilayer radial basis function network and the usage of the backpropagation algorithm as one of the approaches for finetuning the weights of a neural network based on the error rate. Using Epanechnikov kernels as activations function allows to avoid the undesirable phenomena during the tuning like vanishing and exploiding gradient. In addition, because input signal of the last layer is linearly depends of the tuning synaptic weights, this allows optimize the speed of learning process. In result error backpropagation algorithm for multilayer network was proposed, whose layers in fact are radial-basis function neural network.
Self-Supervised Representation Learning from Flow Equivariance

Self-supervised representation learning is able to learn semantically meaningful features; however, much of its recent success relies on multiple crops of an image with very few objects. Instead of learning view-invariant representation from simple images, humans learn representations in a complex world with changing scenes by observing object movement, deformation, pose variation and ego motion. Motivated by this ability, we present a new self-supervised learning representation framework that can be directly deployed on a video stream of complex scenes with many moving objects. Our framework features a simple flow equivariance objective that encourages the network to predict the features of another frame by applying a flow transformation to the features of the current frame. Our representations, learned from high-resolution raw video, can be readily used for downstream tasks on static images. Readout experiments on challenging semantic segmentation, instance segmentation, and object detection benchmarks show that we are able to outperform representations obtained from previous state-of-the-art methods including SimCLR [6] and BYOL [18].
Application of GA Feature Selection on Naive Bayes, Random Forest and SVM for Credit Card Fraud Detection

Credit Card Fraud (CCF) is a serious challenge facing credit card holder and the credit card delivering companies in the past decades. There are two levels CCF are performed, the transaction level frauds and application level frauds. This paper focuses on the application level of CCF detection using Genetic Algorithm (GA) as a feature selection technique. The GA feature selection technique is in two phases, the first phase is designated as the first priority features where eight (8) attributes were selected as the fittest attributes. At second stage which is referred to as the second priority features where another set of eight (8) attributes were considered and selected. The Naïve Bayes (NB), Random Forest (RF) and Support Vector Machine (SVM) supervised machine learning techniques were used for the detection of CCF on German credit card dataset which is an imbalance dataset. The experimental findings of the proposed model revealed that the first priority features are the most important features. Also, the obtained results showed that the RF algorithm outperformed NB and SVM in terms of accuracy, fraud detection rate and precision.
TrackNetV2: Efficient Shuttlecock Tracking Network

TrackNet, a deep learning network, was proposed to track high-speed and tiny objects such as tennis balls and shuttlecocks from videos. To conquer low image quality issues such as blur, afterimage, and short-term occlusion, some number of consecutive images are input together to detect an flying object. In this work, TrackNetV2 is proposed to improve the performance of TrackNet from various aspects, especially processing speed, prediction accuracy, and GPU memory usage. First of all, the processing speed is improved from 2.6 FPS to 31.8 FPS. The performance boost is achieved by reducing the input image size and re-engineering the network from a Multiple-In Single-Out (MISO) design to a Multiple-In Multiple-Out (MIMO) design. Then, to improve the prediction accuracy, a comprehensive dataset from diverse badminton match videos is collected and labeled for training and testing. The dataset consists of 55563 frames from 18 badminton match videos. In addition, the network mechanisms are composed of not only VGG16 and upsampling layers but also U-net. Last, to reduce GPU memory usage, the data structure of the heatmap layer is remodeled from a pixel-wise one-hot encoding 3D array to a real-valued 2D array. To reflect the change of the heatmap representation, the loss function is redesigned from a RMSE-based function to a weighted cross-entropy based function. An overall validation shows that the accuracy, precision and recall of TrackNetV2 respectively reach 96.3%, 97.0% and 98.7% in the training phase and 85.2%, 97.2% and 85.4% in a test on a brand new match. The processing speed of the 3-in and 3-out version TrackNetV2 can reach 31.84 FPS. The dataset and source code of this work are available at https://nol.cs.nctu.edu.tw:234/open-source/TrackNetv2/.
Efficient global color correction for large-scale multiple-view images in three-dimensional reconstruction

Consistent global color correction across multiple-view images in three-dimensional (3D) reconstruction is an important and challenging problem. The present work addresses this issue by proposing a novel global color correction method for multi-view images based on a spline curve remapping function. In contrast to existing methods, we obtain a series of optimal functions by minimizing the variance in the color values of all observations of every sparse point generated by the Structure from Motion (SfM) technique. We also find that adding only simple constraints to the spline is required to prevent the loss of image contrast and gradient information. The robustness of the proposed method is ensured by the adoption of strong geometric constraints between multi-view images. Finally, the applicability of the method to large-scale multiple-view images is facilitated by proposing a parallelizable hierarchical image color correction strategy based on a tree structure. The performance of the proposed method is compared with the performances of existing state-of-the-art methods when applied to several challenging datasets. The results indicate that the notable flexibility of the spline curve, along with the proposed optimization process and hierarchical strategy, not only enable the proposed method to perform well with challenging datasets, but also provide high computational efficiency when working with large-scale image sets.
Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks

Online social networks, World Wide Web, media and technological networks, and other types of so-called information networks are ubiquitous nowadays. These information networks are inherently heterogeneous and dynamic. They are heterogeneous as they consist of multi-typed objects and relations, and they are dynamic as they are constantly evolving over time. One of the challenging issues in such heterogeneous and dynamic environments is to forecast those relationships in the network that will appear in the future. In this paper, we try to solve the problem of continuous-time relationship prediction in dynamic and heterogeneous information networks. This implies predicting the time it takes for a relationship to appear in the future, given its features that have been extracted by considering both heterogeneity and temporal dynamics of the underlying network. To this end, we first introduce a feature extraction framework that combines the power of meta-path-based modeling and recurrent neural networks to effectively extract features suitable for relationship prediction regarding heterogeneity and dynamicity of the networks. Next, we propose a supervised non-parametric approach, called Non-Parametric Generalized Linear Model (NP-GLM), which infers the hidden underlying probability distribution of the relationship building time given its features. We then present a learning algorithm to train NP-GLM and an inference method to answer time-related queries. Extensive experiments conducted on synthetic data and three real-world datasets, namely Delicious, MovieLens, and DBLP, demonstrate the effectiveness of NP-GLM in solving continuous-time relationship prediction problem vis-a-vis competitive baselines
Geometrically Editable Face Image Translation With Adversarial Networks

Recently, image-to-image translation has received increasing attention, which aims to map images in one domain to another specific one. Existing methods mainly solve this task via a deep generative model that they focus on exploring the bi-directional or multi-directional relationship between specific domains. Those domains are often categorized by attribute-level or class-level labels, which do not incorporate any geometric information in learning process. As a result, existing methods are incapable of editing geometric contents during translation. They also neglect to utilize higher-level and instance-specific information to further guide the training process, leading to a great deal of unrealistic synthesized images of low fidelity, especially for face images. To address these challenges, we formulate the general image translation problem as multi-domain mappings in both geometric and attribute directions within an image set that shares a same latent vector. Particularly, we propose a novel Geometrically Editable Generative Adversarial Networks (GEGAN) model to solve this problem for face images by leveraging facial semantic segmentation to explicitly guide its geometric editing. In details, input face images are encoded to their latent representations via a variational autoencoder, a segmentor network is designed to impose semantic information on the generated images, and multi-scale regional discriminators are employed to force the generator to pay attention to the details of key components. We provide both quantitative and qualitative evaluations on CelebA dataset to demonstrate our ability of the geometric modification and our improvement in image fidelity.
Modeling Energy Efficiency of Future Green Data centers

This paper focuses on creating power models for modern data centers. Due to massive energy consumption by an increasing number of data centers, the economic and environmental sustainability of the earth is severely affected. The power grids burn brown energy resources like coal and petroleum to meet the ever surging power demands of the data centers, thereby producing tons of CO2 and causing global pollution. To balance the global carbon footprint, we are motivated to construct power models dedicated to future green data centers. Our research aims towards building an energy model for data centers that make use of more green energy resources while eliminating brown energy resources. We validate the accuracy of this model and demonstrate the usage of this model during peak and non-peak hours in a day in terms of reducing the brown energy consumption.
Geophysical Contributions to Gold Exploration in Western Mali According to Airborne Electromagnetic Data Interpretations

The Birimian of West African Craton (WAC) is known for its gold potential. Among Birimian structures, N-S and NE-SW trends have been defined as controlling gold mineralizations in Kedougou-Kenieba Inlier (KKI), whereas some of these structures are not gold-bearing. To determine structures related to gold mineralization, airborne electromagnetic data collected during the “Système Minier” of Mali have been processed and interpreted. Identified lineaments have been followed in the ground to establish their link with gold showings in the Kenieba area. Geophysical interpretations show trends similarity for different orders of conductivity. They allowed for characterizing resistance and conductive structures with prevalent N-S and NE-SW directionalities. Conductive structures are qualified as good conductors and are strongly related to known Artisanal Mining Sites (AMS). They coincide with tourmaline sandstones and quartz-albite veins, which are both often artisanally recognized as indicators of gold mineralization in Western Mali. Field observations show that resistance structures correspond to felsic rocks. These structures can bear gold only when silicified and they have spatial relations with Artisanal Mining Sites (AMS) within the Kenieba area. This study shows the efficiency of electromagnetic methods to characterize Birimian structures in relation to the gold mineralization in Kedougou-Kenieba Inlier (KKI).
Integrating Stacked Sparse Auto-Encoder Into Matrix Factorization for Rating Prediction

Currently, collaborative filtering technology has been widely used in personalized recommender systems.The problem of data sparsity is a severe challenge faced by traditional collaborative filtering methods based on matrix factorization techniques.A lot of improved collaborative filtering methods have been proposed to alleviate the data sparsity problem; However, due to the sparsity of the user rating matrix, the latent factor learned by these improved methods may be not efficient.In this paper, we propose a novel recommendation algorithm named SSAERec by integrating stacked sparse auto-encoder into matrix factorization for rating prediction, which can learn effective representation from user-item rating matrix.Extensive experiments on three real-world datasets demonstrate the proposed method outperforms other baselines in the rating prediction task.
Practically Deploying Heavyweight Adaptive Bitrate Algorithms With Teacher-Student Learning

Major commercial client-side video players employ adaptive bitrate (ABR) algorithms to improve the user quality of experience (QoE). With the evolvement of ABR algorithms, increasingly complex methods such as neural networks have been adopted to pursue better performance. However, these complex methods are too heavyweight to be directly deployed in client devices with limited resources, such as mobile phones. Existing solutions suffer from a trade-off between algorithm performance and deployment overhead. To make the deployment of sophisticated ABR algorithms practical, we propose PiTree, a general, high-performance, and scalable framework that can faithfully convert sophisticated ABR algorithms into decision trees with teacher-student learning. In this way, network operators can train complex models offline and deploy converted lightweight decision trees online. We also present theoretical analysis on the conversion and provide two upper bounds of the prediction error during the conversion and the generalization loss after conversion. Evaluation on three representative ABR algorithms with both trace-driven emulation and real-world experiments demonstrates that PiTree could convert ABR algorithms into decision trees with <; 3% average performance degradation. Moreover, compared to original deployment solutions, PiTree could save considerable operating expenses for content providers.
Device-independent characterization of entanglement based on bell nonlocality

Quantum entanglement, has been acknowledged as a precious resource due to its inherent nonclassical correlations between subsystems. These quantum correlations have the potential for many quantum processes, including canonical ones: quantum cryptography, quantum teleportation, and dense coding. To exploit the advantages of quantum entanglement, two essential premises are required, i.e., to prepare high-quality entanglement and characterize quality level of prepared entanglement. Thus far, quantum entanglement can be produced in various quantum systems; however, it appears that this new resource is complex and difficult to characterize. The standard methods to characterize multipartite entanglement, e.g., entanglement witness, state tomography, or quantum state verification, require full knowledge of the Hilbert space dimension and precise calibration of measurement devices, which are usually difficult to acquire in experiment. The most radical way to overcome these problems is to detect entanglement solely based on the Bell-like correlations of measurement outcomes collected in the experiment, namely, device-independent characterization of entanglement. This article reviews the recently developed device-independent methods to characterize entanglement, including self-testing and device-independent certification of entanglement. These approaches can be widely applied in kinds of quantum information processing, especially for those with security demands.
Cost-sensitive positive and unlabeled learning

Positive and Unlabeled learning (PU learning) aims to train a binary classifier solely based on positively labeled and unlabeled data when negatively labeled data are absent or distributed too diversely. However, none of the existing PU learning methods takes the class imbalance problem into account, which significantly neglects the minority class and is likely to generate a biased classifier. Therefore, this paper proposes a novel algorithm termed "Cost-Sensitive Positive and Unlabeled learning" (CSPU) which imposes different misclassification costs on different classes when conducting PU classification. Specifically, we assign distinct weights to the losses caused by false negative and false positive examples, and employ double hinge loss to build our CSPU algorithm under the framework of empirical risk minimization. Theoretically, we analyze the computational complexity, and also derive a generalization error bound of CSPU which guarantees the good performance of our algorithm on test data. Empirically, we compare CSPU with the state-of-the-art PU learning methods on synthetic dataset, OpenML benchmark datasets, and real-world datasets. The results clearly demonstrate the superiority of the proposed CSPU to other comparators in dealing with class imbalanced tasks.
Bridge health anomaly detection using deep support vector data description

As an extremely important part of traffic arteries, bridge structure plays an essential role in national economic construction, social development and smart city. Thus the monitoring of the bridge structure health are increasingly concerned by the bridge industry scholars and engineering people at home and aboard. In this paper, we propose a deep learning framework to evaluate the safety of the bridge structural state. More specifically, the proposed system generates a learnable transformation which attempts to map most of the data network representations into a hypersphere characterized of minimum volume. During inference, mappings of normal examples fall within the learned hypersphere, whereas mappings of anomalies fall outside the hypersphere. The whole system is end-to-end trainable and outperforms other advanced methods in real-world dataset.
Auto-weighted Robust Federated Learning with Corrupted Data Sources

Federated learning provides a communication-efficient and privacy-preserving training process by enabling learning statistical models with massive participants without accessing their local data. Standard federated learning techniques that naively minimize an average loss function are vulnerable to data corruptions from outliers, systematic mislabeling, or even adversaries. In this article, we address this challenge by proposing Auto-weighted Robust Federated Learning ( ARFL ), a novel approach that jointly learns the global model and the weights of local updates to provide robustness against corrupted data sources. We prove a learning bound on the expected loss with respect to the predictor and the weights of clients, which guides the definition of the objective for robust federated learning. We present an objective that minimizes the weighted sum of empirical risk of clients with a regularization term, where the weights can be allocated by comparing the empirical risk of each client with the average empirical risk of the best \( p \) clients. This method can downweight the clients with significantly higher losses, thereby lowering their contributions to the global model. We show that this approach achieves robustness when the data of corrupted clients is distributed differently from the benign ones. To optimize the objective function, we propose a communication-efficient algorithm based on the blockwise minimization paradigm. We conduct extensive experiments on multiple benchmark datasets, including CIFAR-10, FEMNIST, and Shakespeare, considering different neural network models. The results show that our solution is robust against different scenarios, including label shuffling, label flipping, and noisy features, and outperforms the state-of-the-art methods in most scenarios.