Reviewerly/exHarmony huggingface dataset:
Multi-label Classification of Short Texts with Label Correlated Recurrent Neural Networks

Short texts are commonly seen nowadays on the Internet in various forms such as tweets, queries, comments, status updates, snippets of search results, and reviews from social platforms. Accurate categorization of these short texts is critical for enhancing information services as it provides the foundation for better search and recommendation. In many real-world applications, a short text is often associated with multiple categories. Due to the sparsity of context information, traditional multi-label classification methods do not perform well on short texts. In this paper, we propose a novel Label Correlated Recurrent Neural Network (LC-RNN) for multi-label classification of short texts by exploiting correlations between categories. We utilize a tree structure to represent the relationships among labels and consequently an efficient max-product algorithm can be developed for exact inference of label prediction. We conduct experiments on four testbeds and the results demonstrate the effectiveness of the proposed model.
An efficient anomaly detection method for uncertain data based on minimal rare patterns with the consideration of anti-monotonic constraints

The pattern-based anomaly detection method has obtained more attention since it was proposed. This is due to its ability to fully identify anomalies by considering two key features, including appearing rarely and deviating from most data elements. Although most pattern-based anomaly detection methods identify the anomalies from full data space, the process involved in performing this functionality is very time-consuming. Therefore, to solve this problem, this paper introduces a novel method, namely minimal rare pattern-based anomaly detection method through considering anti-monotonic constraints (MRPAC), for identifying anomalies in uncertain data. MRPAC detects the anomalies from a small scale of uncertain data that satisfy preset anti-monotonic constraints by mining constrained minimal rare patterns, thus, the time efficiency is increased. In addition, MRPAC also defines multiple deviation factors to compute the anomaly score for all transactions, to accurately discover potential anomalies through sorting their anomaly scores. Extensive experimental outcomes indicate that the MRPAC significantly outperforms five state-of-the-art pattern-based anomaly methods in terms of detection accuracy and time efficiency, and obtains good scalability.
Vine copula mixture models and clustering for non-Gaussian data

The majority of finite mixture models suffer from not allowing asymmetric tail dependencies within components and not capturing non-elliptical clusters in clustering applications. Since vine copulas are very flexible in capturing these types of dependencies, we propose a novel vine copula mixture model for continuous data. We discuss the model selection and parameter estimation problems and further formulate a new model-based clustering algorithm. The use of vine copulas in clustering allows for a range of shapes and dependency structures for the clusters. Our simulation experiments illustrate a significant gain in clustering accuracy when notably asymmetric tail dependencies or/and non-Gaussian margins within the components exist. The analysis of real data sets accompanies the proposed method. We show that the model-based clustering algorithm with vine copula mixture models outperforms the other model-based clustering techniques, especially for the non-Gaussian multivariate data.
QFlow: A Learning Approach to High QoE Video Streaming at the Wireless Edge

The predominant use of wireless access networks is for media streaming applications. However, current access networks treat all packets identically, and lack the agility to determine which clients are most in need of service at a given time. Software reconfigurability of networking devices has seen wide adoption, and this in turn implies that agile control policies can be now instantiated on access networks. Exploiting such reconfigurability requires the design of a system that can enable a configuration, measure the impact on the application performance (Quality of Experience), and adaptively select a new configuration. Effectively, this feedback loop is a Markov Decision Process whose parameters are unknown. The goal of this work is to develop QFlow, a platform that instantiates this feedback loop, and instantiate a variety of control policies over it. We use the popular application of video streaming over YouTube as our use case. Our context is priority queueing, with the action space being that of determining which clients should be assigned to each queue at each decision period. We first develop policies based on model-based and model-free reinforcement learning. We then design an auction-based system under which clients place bids for priority service, as well as a more structured index-based policy. Through experiments, we show how these learning-based policies on QFlow are able to select the right clients for prioritization in a high-load scenario to outperform the best known solutions with over 25&#x0025; improvement in QoE, and a perfect QoE score of 5 over 85&#x0025; of the time.
Full encryption

In this paper, we present a novel mechanism called Full Encryption (FE) in GaussDB. FE-in-GaussDB provides column-level encryption for sensitive data, and secures the asset from any malicious cloud administrator or information leakage attack. It ensures not only the security of operations on ciphertext data, but also the efficiency of query execution, by combining the advantages of cryptography algorithms (i.e. software mode) and Trusted Execution Environment (i.e. hardware mode). With this, FE-in-GaussDB supports full-scene query processing including the matching, the comparison and other rich computing functionalities. We demonstrate the prototype of FE-in-GaussDB and an experimental performance evaluation to prove its availability and effectiveness.
Symmetric Private Information Retrieval with User-Side Common Randomness

We consider the problem of symmetric private information retrieval (SPIR) with user-side common randomness. In SPIR, a user retrieves a message out of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> messages from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> non-colluding and replicated databases in such a way that no single database knows the retrieved message index (user privacy), and the user gets to know nothing further than the retrieved message (database privacy). SPIR has a capacity smaller than the PIR capacity which requires only user privacy, is infeasible in the case of a single database, and requires shared common randomness among the databases. We introduce a new variant of SPIR where the user is provided with a random subset of the shared database common randomness, which is unknown to the databases. We determine the exact capacity region of the triple ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d, \rho S, \rho U$</tex> ), where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> is the download cost, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\rho S$</tex> is the amount of shared database (server) common randomness, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\rho U$</tex> is the amount of available user-side common randomness. We show that with a suitable amount of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\rho U$</tex> , this new SPIR achieves the capacity of conventional PIR. As a corollary, single-database SPIR becomes feasible. Further, the presence of user-side <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\rho U$</tex> reduces the amount of required server-side <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\rho S$</tex> .
Sketch-based Anime Hairstyle Editing with Generative Inpainting

In this work, we propose an interactive sketch-based design interface for anime hairstyle editing. The proposed system adopts the gated convolutional layer in the Generative Adversarial Networks model to achieve the generative editing of anime images. The generator network is based on an encoder-decoder framework with gated convolution to ensure the network can learn the random mask feature in image editing. The discriminator network uses the spectral-normalized architecture to preserve the stability of learning. In this study, we first collect and preprocess the dataset for data training. After that, we construct the user interface based on the pretrained model. Finally, we evaluate the system usage and user experience of the proposed editing interface.
Attention-based Multi-Reference Learning for Image Super-Resolution

This paper proposes a novel Attention-based Multi-Reference Super-resolution network (AMRSR) that, given a low-resolution image, learns to adaptively transfer the most similar texture from multiple reference images to the super-resolution output whilst maintaining spatial coherence. The use of multiple reference images together with attention-based sampling is demonstrated to achieve significantly improved performance over state-of-the-art reference super-resolution approaches on multiple benchmark datasets. Reference super-resolution approaches have recently been proposed to overcome the ill-posed problem of image super-resolution by providing additional information from a high-resolution reference image. Multi-reference super-resolution extends this approach by providing a more diverse pool of image features to overcome the inherent information deficit whilst maintaining memory efficiency. A novel hierarchical attention-based sampling approach is introduced to learn the similarity between low-resolution image features and multiple reference images based on a perceptual loss. Ablation demonstrates the contribution of both multi-reference and hierarchical attention-based sampling to overall performance. Perceptual and quantitative ground-truth evaluation demonstrates significant improvement in performance even when the reference images deviate significantly from the target image. The project website can be found at https://marcopesavento.github.io/AMRSR/
A Promotive Particle Swarm Optimizer With Double Hierarchical Structures

In this study, a novel promotive particle swarm optimizer with double hierarchical structures is proposed. It is inspired by successful mechanisms present in social and biological systems to make particles compete fairly. In the proposed method, the swarm is first divided into multiple independent subpopulations organized in a hierarchical promotion structure, which protects subpopulation at each hierarchy to search for the optima in parallel. A unidirectional communication strategy and a promotion operator are further implemented to allow excellent particles to be promoted from low-hierarchy subpopulations to high-hierarchy subpopulations. Furthermore, for the internal competition within each subpopulation of the hierarchical promotion structure, a hierarchical multiscale optimum controlled by a tiered architecture of particles is constructed for particles, in which each particle can synthesize a set of optima of its different scales. The hierarchical promotion structure can protect particles that just fly to promising regions and have low fitness from competing with the entire swarm. Also, the double hierarchical structures increase the diversity of searching. Numerical experiments and statistical analysis of results reported on 30 benchmark problems show that the proposed method improves the accuracy and convergence speed especially in solving complex problems when compared with several variations of particle swarm optimization.
Fast CU Partition Decision Strategy Based on Human Visual System Perceptual Quality

A fast Coding Unit (CU) partition decision strategy based on Human Visual System (HVS) perception quality is proposed in this paper.Considering that it is difficult for existing fast algorithms to further improve compression efficiency, perceptual coding technology has been tried to remove visual redundancy for achieving the purpose of reducing the bit rate on the basis of maintaining subjective visual quality.However, the existing perceptual coding model is still insufficient to reflect the characteristics of the HVS, which has limited improvement in coding efficiency, especially in fast algorithms.In this method, the characteristics of the limited human capacity for spatial-temporal resolution and the visual sensory memory are used to improve coding performance.First, the color complexity is used as a control factor to optimize the Just Noticeable Difference (JND) model to remove visual redundancy in a way that is more in line with the characteristics of visual perception.Second, a classification model of motion patterns based on human visual saliency is designed to provide a basis for CU classification, which effectively improves the coding accuracy.Finally, an offline Decision Tree (DT) classifier is designed based on the above model, and texture features are incorporated into the classifier as another key attribute to further reduce the computational complexity.The results of performance evaluation confirm that the proposed method achieves significantly improved coding performance compared with original Versatile Test Model (VTM).Compared with existing algorithms, our method not only improves coding efficiency, but also improves subjective visual quality.
FairLOF: Fairness in Outlier Detection

Abstract An outlier detection method may be considered fair over specified sensitive attributes if the results of outlier detection are not skewed toward particular groups defined on such sensitive attributes. In this paper, we consider the task of fair outlier detection. Our focus is on the task of fair outlier detection over multiple multi-valued sensitive attributes (e.g., gender, race, religion, nationality and marital status, among others), one that has broad applications across modern data scenarios. We propose a fair outlier detection method, FairLOF , that is inspired by the popular LOF formulation for neighborhood-based outlier detection. We outline ways in which unfairness could be induced within LOF and develop three heuristic principles to enhance fairness, which form the basis of the FairLOF method. Being a novel task, we develop an evaluation framework for fair outlier detection, and use that to benchmark FairLOF on quality and fairness of results. Through an extensive empirical evaluation over real-world datasets, we illustrate that FairLOF is able to achieve significant improvements in fairness at sometimes marginal degradations on result quality as measured against the fairness-agnostic LOF method. We also show that a generalization of our method, named FairLOF-Flex , is able to open possibilities of further deepening fairness in outlier detection beyond what is offered by FairLOF .
PortraitNET: Photo-realistic portrait cartoon style transfer with self-supervised semantic supervision

We propose a novel framework to transfer the portrait image into its correspondence with photo-realistic and cartoon style. The existing work on neural style transfer conducts impressive results on artistic style transfer; however, the lack of semantic clues will lead to the color artifacts in photo-realistic style transfer because of the complex background and noise issues. In this work, we re-define the semantics as the pixel motion field according to the color displacement between adjacent animation frames along the optical direction and initiatively propose the self-supervised semantic network (SSNet) to learn semantic maps without human inference or any priories. The SSNet shares parameters with the style transfer network; thus, the superior alternatives can preserve the semantic completeness in the styled image. To solve the content missing and blur problems common in NST, we propose the bilateral convolution block (B-block) and feature fusion strategy (F-block) for visual smoothness to meet the perceptive satisfaction. The ablation studies are provided to validate the effectiveness, and comparative experiments with the state-of-the-art baselines demonstrate the advantages of the proposed method.
HCRNN: A Novel Architecture for Fast Online Handwritten Stroke Classification

Stroke classification is an essential task for applications with free-form handwriting input. Implementation of this type of application for mobile devices places stringent requirements on different aspects of embedded machine learning models, which results in finding a trade-off between model performance and model complexity. In this work, a novel hierarchical deep neural network (HDNN) architecture with high computational efficiency is proposed. It is adopted for handwritten document processing and particularly for multi-class stroke classification. The architecture uses a stack of 1D convolutional neural networks (CNN) on the lower (point) hierarchical level and a stack of recurrent neural networks (RNN) on the upper (stroke) level. The novel fragment pooling techniques for feature transition between hierarchical levels are presented. On-device implementation of the proposed architecture establishes new state-of-the-art results in the multi-class handwritten document processing with a classification accuracy of 97.58% on the IAMonDo dataset. Our method is also more efficient in both processing time and memory consumption than the previous state-of-the-art RNN-based stroke classifier.
Learning From Network Data Changes for Unsupervised Botnet Detection

The networks of infected devices (a.k.a., botnets) threaten network security due to their dynamic nature and support to different attacks (e.g., Distributed Denial of Services and personal data theft). Detecting botnets is a challenging task because the infected devices (bots) are numerous, widely and geographically spread. Significant attention has been given to improve the efficiency, robustness and adaptability of network security approaches. However, in the literature, botnet detection techniques usually ignore fast changes in statistical data distribution, performing over static windows, i.e., fixed intervals of time or fixed quantity of flows. Changes in statistical data distribution are known as concept drifts and they make the classification models obsolete. Furthermore, those works employing approaches aware of concept drift use supervised machine learning, which is slow, costly, and prone to error. Therefore, this article presents TRUSTED, a system for online and unsupervised botnet detection aware of concept drifts. Unlike other works, the TRUSTED system improves the learning process for botnet detection, applying concept drift in an online and unsupervised classification. Evaluations comprise offline and online scenarios. Results show that the TRUSTED system detects botnets using concept drift identification, reaching 87% to 95% accuracy, precision, recall, and F1-scores.
Stochastic Subgradient for Large-Scale Support Vector Machine Using the Generalized Pinball Loss Function

In this paper, we propose a stochastic gradient descent algorithm, called stochastic gradient descent method-based generalized pinball support vector machine (SG-GPSVM), to solve data classification problems. This approach was developed by replacing the hinge loss function in the conventional support vector machine (SVM) with a generalized pinball loss function. We show that SG-GPSVM is convergent and that it approximates the conventional generalized pinball support vector machine (GPSVM). Further, the symmetric kernel method was adopted to evaluate the performance of SG-GPSVM as a nonlinear classifier. Our suggested algorithm surpasses existing methods in terms of noise insensitivity, resampling stability, and accuracy for large-scale data scenarios, according to the experimental results.
Chaos and wavelet packets based cryptography targeting practical applications

The steps of the new enciphering algorithm are concisely given. Its performance is validated through MATLAB simulations, using well-known metrics as PSNR, NPCR or UACI. The method presents five levels of security, each element of the key being transmitted in different moments of the algorithm, when they are needed. Thus, a hacker should intercept them all in order to break the entire cipher. Evaluation is performed by enciphering images from a widely known database. The results are compared to those obtained with similar algorithms. Concluded as an algorithm with good cryptographic qualities, the method can be further added the targeted steps. This way, parallelization and increased randomness can be achieved, the proposed enciphering being robust and suited for practical applications. The new algorithm benefits from the process of trial-and-error in the authors’ previous research.
Evaluating tool point dynamics using smartphone-based visual vibrometry

Modern smartphones can record high megapixel videos at high frame rates. This paper leverages these capabilities and presents a new method to evaluate dynamics of a representative grooving tool using a smartphone’s video camera. Pixels within images are treated as vibration sensors measuring response to impact-based excitation. Motion is detected and tracked using image processing techniques to give pixel-displacement time series data. Spectra of these data give natural frequencies, and damping is estimated using the logarithmic decrement method. These parameters compare well with those extracted using experimental modal analysis procedures. Unscaled tool vibration shapes are visualized using motion magnification techniques.
A Focus System for the Alternation-Free $$\mu $$-Calculus

We introduce a cut-free sequent calculus for the alternation-free fragment of the modal \(\mu \)-calculus. This system allows both for infinite and for finite, circular proofs and uses a simple focus mechanism to control the unravelling of fixpoints along infinite branches. We show that the proof system is sound and complete for the set of guarded valid formulas of the alternation-free \(\mu \)-calculus.
20 years of industrial experience at SPLC

Software Product Lines (SPLs) have been around since the late 1970s and have established themselves as a way to deal with product variability. Tens of companies around the globe can pay testament to their advantages. Practitioners, however, have lamented the lack of data on other practitioners' experiences that would help them in the SPL journey. This work intends to analyze the application of SPLs in industry in the last 20 years. We departed from 194 industrial studies that were published at the Software Product Line Conference, the premier venue for SPL research. After the filtering process we selected 66 primary studies, from 43 different companies and 15 countries. The studies were classified to answer three research questions: (i) which contexts have SPLs been applied in?, (ii) what phenomena have been reported? and, (iii) what evidences have been collected in terms of obtained benefits, encountered issues and lessons learned? Regarding the context, SPLs have mainly been reported in USA and Germany (50%) and are used to develop embedded systems (76%). The most cited reason to adopt SPLs is the need to increase product variants (42.42%). As for the phenomena, the most reported problem area is adoption (39.39%). Last, as for evidences the most cited benefit is a cost reduction (53.03%), the issue is evolution (13.13%) and the learned lesson is that architecture is essential (24.24%). We believe the findings will be of interest to the community as a whole in quest to bridge the gap between industry and academia while balancing rigor, authenticity and relevance.