Reviewerly/exHarmony huggingface dataset:
NMF with feature relationship preservation penalty term for clustering problems

The method proposed in this paper belongs to the family of orthogonal non-negative matrix factorization (ONMF) methods designed to solve clustering problems. Unlike some existing ONMF methods that explicitly constrain the orthogonality of the coefficient matrix in the cost function to derive their clustering models, the proposed method integrates it implicitly, so that it results in a new optimization model with a penalty term. The latter is added to impose a scale relationship between the scatter of the cluster centroids and that of the data points. The solution of the new model involves deriving a new parametrized update scheme for the basis matrix, which makes it possible to improve the performance of the clustering by adjusting a parameter. The proposed clustering algorithm, which we call "pairwise Feature Relationship preservation-based NMF" (FR-NMF), is evaluated on several real-life and synthetic datasets and compared to eight existing NMF-based clustering models. The results obtained show the effectiveness of the proposed algorithm.
AutoScaleSim: A simulation toolkit for auto-scaling Web applications in clouds

Auto-scaling of Web applications is an extensively investigated issue in cloud computing. To evaluate auto-scaling mechanisms, the cloud community is facing considerable challenges on either real cloud platforms or custom test-beds. Challenges include – but not limited to – deployment impediments, the complexity of setting parameters, and most importantly, the cost of hosting and testing Web applications on a massive scale. Hence, simulation is presently one of the most popular evaluation solutions to overcome these obstacles. Existing simulators, however, fail to provide support for hosting, deploying and subsequently auto-scaling of Web applications. In this paper, we introduce AutoScaleSim, which extends the existing CloudSim simulator, to support auto-scaling of Web applications in cloud environments in a customizable, extendable and scalable manner. Using AutoScaleSim, the cloud community can freely implement/evaluate policies for all four phases of auto-scaling mechanisms, that is, Monitoring, Analysis, Planning and Execution. AutoScaleSim can also be used for evaluating load balancing algorithms similarly. We conducted a set of experiments to validate and carefully evaluate the performance of AutoScaleSim in a real cloud platform, with a wide range of performance metrics.
Continual Representation Learning for Biometric Identification

With the explosion of digital data in recent years, continuously learning new tasks from a stream of data without forgetting previously acquired knowledge has become increasingly important. In this paper, we propose a new continual learning (CL) setting, namely "continual representation learning", which focuses on learning better representation in a continuous way. We also provide two large-scale multi-step benchmarks for biometric identification, where the visual appearance of different classes are highly relevant. In contrast to requiring the model to recognize more learned classes, we aim to learn feature representation that can be better generalized to not only previously unseen images but also unseen classes/identities. For the new setting, we propose a novel approach that performs the knowledge distillation over a large number of identities by applying the neighbourhood selection and consistency relaxation strategies to improve scalability and flexibility of the continual learning model. We demonstrate that existing CL methods can improve the representation in the new setting, and our method achieves better results than the competitors.
A study of the performance of classical minimizers in the Quantum Approximate Optimization Algorithm

The Quantum Approximate Optimization Algorithm (QAOA) was proposed as a way of finding good, approximate solutions to hard combinatorial optimization problems. QAOA uses a hybrid approach. A parametrized quantum state is repeatedly prepared and measured on a quantum computer to estimate its average energy. Then, a classical optimizer, running in a classical computer, uses such information to decide on the new parameters that are then provided to the quantum computer. This process is iterated until some convergence criteria are met. Theoretically, almost all classical minimizers can be used in the hybrid scheme. However, their behaviour can vary greatly in both the quality of the final solution and the time they take to find it. In this work, we study the performance of twelve different classical optimizers when used with QAOA to solve the maximum cut problem in graphs. We conduct a thorough set of tests on a quantum simulator both, with and without noise, and present results that show that some optimizers can be hundreds of times more efficient than others in some cases.
Detection of malicious javascript on an imbalanced dataset

In order to be able to detect new malicious JavaScript with low cost, methods with machine learning techniques have been proposed and gave positive results. These methods focus on achieving a light-weight filtering model that can quickly and precisely filter out malicious data for dynamic analysis. A method constructs a language model using Natural Language Processing techniques to represent the data in vector form from the source code for machine learning. This method has high score with the balanced dataset, however the experiment with an imbalanced dataset has not been done. Previous studies mainly focus on a balanced dataset, however the dataset is not representative of real-world data, and it rises questions in practical uses of the model. A good model that can have a high recall score with imbalanced dataset is needed for a good filter. To construct an efficient language model, and to deal with the data imbalance problem, we focus on oversampling techniques. In our research, our method is the first to use oversampling and machine learning to detect malicious JavaScript. The experimental result shows that our method can detect new malicious JavaScript more accurately and efficiently. Our model can quickly filter out malicious data for dynamic analysis. The best recall score achieves 0.72 with the Doc2Vec model. Our proposed method is shown to outperform the baseline method by 210% in terms of recal score with the same training time and test time per sample.
Dynamic Routing Networks

The deployment of deep neural networks in real-world applications is mostly restricted by their high inference costs. Extensive efforts have been made to improve the accuracy with expert-designed or algorithm-searched architectures. However, the incremental improvement is typically achieved with increasingly more expensive models that only a small portion of input instances really need. Inference with a static architecture that processes all input instances via the same transformation would thus incur unnecessary computational costs. Therefore, customizing the model capacity in an instance-aware manner is much needed for higher inference efficiency. In this paper, we propose Dynamic Routing Networks (DRNets), which support efficient instance-aware inference by routing the input instance to only necessary transformation branches selected from a candidate set of branches for each connection between transformation nodes. The branch selection is dynamically determined via the corresponding branch importance weights, which are first generated from lightweight hyper-networks (RouterNets) and then recalibrated with Gumbel-Softmax before the selection. Extensive experiments show that DRNets can reduce a substantial amount of parameter size and FLOPs during inference with prediction performance comparable to state-of-the-art architectures.
Edge Learning with Timeliness Constraints: Challenges and Solutions

Future machine learning (ML) powered applications, such as autonomous driving and augmented reality, involve training and inference tasks with timeliness requirements and are communication- and computation-intensive, which demands the edge learning framework. The real-time requirements drive us to go beyond accuracy for ML. In this article, we introduce the concept of timely edge learning, aiming to achieve accurate training and inference while minimizing the communication and computation delay. We discuss key challenges and propose corresponding solutions from data, model, and resource management perspectives to meet the timeliness requirements. In particular, for edge training, we argue that the total training delay rather than rounds should be considered, and propose data or model compression, and joint device scheduling and resource management schemes for both centralized training and federated learning systems. For edge inference, we explore the dependency between accuracy and delay for communication and computation, and propose dynamic data compression and flexible pruning schemes. Two case studies show that the timeliness performance, including the training accuracy under a given delay budget and the completion ratio of inference tasks within deadline, are highly improved with the proposed solutions.
Classification techniques in breast cancer diagnosis: A systematic literature review

Data mining (DM) consists in analysing a set of observations to find unsuspected relationships and then summarising the data in new ways that are both understandable and useful. It has become widely used in various medical fields including breast cancer (BC), which is the most common cancer and the leading cause of death among women worldwide. BC diagnosis is a challenging medical task and many studies have attempted to apply classification techniques to it.The objective of the present study is to identify studies on classification techniques in BC diagnosis and to analyse them from three perspectives: classification techniques used, accuracy of the classifiers, and comparison of performance.We performed a systematic literature review (SLR) of 176 selected studies published between January 2000 and November 2018. The results show that, of the nine classification techniques investigated, artificial neural networks, support vector machines and decision trees were the most frequently used. Moreover, artificial neural networks, support vector machines and ensemble classifiers performed better than the other techniques, with median accuracy values of 95%, 95% and 96% respectively. Most of the selected studies (57.4%) used datasets containing different types of images such as mammographic, ultrasound, and microarray images.
Selective spatiotemporal features learning for dynamic gesture recognition

Gesture recognition, which aims to understand meaningful movements of human bodies, plays an essential role in human–computer interaction. The key to gesture recognition is to learn compact and effective spatiotemporal information. However, it remains a challenging task due to the barriers of gesture-irrelevant factors. A number of attempts have been taken to address this problem by cascading deep heterogeneous architectures. However, this cascading strategy cannot capture both local and global spatiotemporal features at each stage of feature learning. In this paper, we propose a novel refined fusion model architecture combining the ResC3D network and Convolutional LSTM (ConvLSTM) with a dynamic select mechanism called Selective Spatiotemporal features learning (SeST). Such a heterogeneous network system is able to simultaneously learn short-term and long-term spatiotemporal features, and they are complementary to each other. The SeST block enables the ResC3D network and ConvLSTM to adaptively adjust their contributions to classification during feature learning with soft-attention. The method has been evaluated on the three publicly available datasets: the Sheffield Kinect Gesture (SKIG) dataset, the ChaLearn LAP large scale isolated gesture dataset (IsoGD), and the EgoGesture dataset. Experiment results show that the proposed method outperforms other state-of-the-art methods. Besides, our model is an end-to-end model, which can be embedded in many intelligent systems applications.
High Dimensional Frustum PointNet for 3D Object Detection from Camera, LiDAR, and Radar

Fusing the raw data from different automotive sensors for real-world environment perception is still challenging due to their different representations and data formats. In this work, we propose a novel method termed High Dimensional Frustum PointNet for 3D object detection in the context of autonomous driving. Motivated by the goals data diversity and lossless processing of the data, our deep learning approach directly and jointly uses the raw data from the camera, LiDAR, and radar. In more detail, given 2D region proposals and classification from camera images, a high dimensional convolution operator captures local features from a point cloud enhanced with color and temporal information. Radars are used as adaptive plug-in sensors to refine object detection performance. As shown by an extensive evaluation on the nuScenes 3D detection benchmark, our network outperforms most of the previous methods.
SilkViser: A Visual Explorer of Blockchain-based Cryptocurrency Transaction Data

Many blockchain-based cryptocurrencies provide users with online blockchain explorers for viewing online transaction data. However, traditional blockchain explorers mostly present transaction information in textual and tabular forms. Such forms make understanding cryptocurrency transaction mechanisms difficult for novice users (NUsers). They are also insufficiently informative for experienced users (EUsers) to recognize advanced transaction information. This study introduces a new online cryptocurrency transaction data viewing tool called SilkViser. Guided by detailed scenario and requirement analyses, we create a series of appreciating visualization designs, such as paper ledger-inspired block and blockchain visualizations and ancient copper coin-inspired transaction visualizations, to help users understand cryptocurrency transaction mechanisms and recognize advanced transaction information. We also provide a set of lightweight interactions to facilitate easy and free data exploration. Moreover, a controlled user study is conducted to quantitatively evaluate the usability and effectiveness of SilkViser. Results indicate that SilkViser can satisfy the requirements of NUsers and EUsers. Our visualization designs can compensate for the inexperience of NUsers in data viewing and attract potential users to participate in cryptocurrency transactions.
Bayesian parameter estimation using Gaussian states and measurements

Bayesian analysis is a framework for parameter estimation that applies even in uncertainty regimes where the commonly used local (frequentist) analysis based on the Cram\'er-Rao bound is not well defined. In particular, it applies when no initial information about the parameter value is available, e.g., when few measurements are performed. Here, we consider three paradigmatic estimation schemes in continuous-variable quantum metrology (estimation of displacements, phases, and squeezing strengths) and analyse them from the Bayesian perspective. For each of these scenarios, we investigate the precision achievable with single-mode Gaussian states under homodyne and heterodyne detection. This allows us to identify Bayesian estimation strategies that combine good performance with the potential for straightforward experimental realization in terms of Gaussian states and measurements. Our results provide practical solutions for reaching uncertainties where local estimation techniques apply, thus bridging the gap to regimes where asymptotically optimal strategies can be employed.
Intra-class Part Swapping for Fine-Grained Image Classification

Recent works such as Mixup and CutMix have demonstrated the effectiveness of augmenting training data for deep models. These methods generate new data by generally blending random image contents and mixing their labels proportionally. However, this strategy tends to produce unreasonable training samples for fine-grained recognition, leading to limited improvement. This is because mixing random image contents may potentially produce images containing destructed object structures. Further, as the category differences mainly reside in small part regions, mixing labels proportionally to the number of mixed pixels might result in label noisy problem. To augment more reasonable training data, we propose Intra-class Part Swapping (InPS) that produces new data by performing attention-guided content swapping on input pairs from the same class. Compared with previous approaches, InPS avoids introducing noisy labels and ensures a likely holistic structure of objects in generated images. We demonstrate InPS outperforms the most recent augmentation approaches in both fine-grained recognition and weakly object localization. Further, by simply incorporating the mid-level feature learning, our proposed method achieves state-of-the-art performance in the literature while maintaining the simplicity and inference efficiency. Our code is publicly available.
A Unified Collaborative Representation Learning for Neural-Network Based Recommender Systems

With the boosting of neural networks, recommendation methods become significantly improved by their powerful ability of prediction and inference. Existing neural-network based recommender systems (NN-RSs) usually first employ matrix embedding (ME) as a pre-process to learn users’ and items’ representations (latent vectors), then input these representations to a specific modified neural network framework to make accurate Top-k recommendations. Obviously, the performance of ME has a significant effect on RS models. However, most NN-RSs focus on accuracy by building representations from the direct user-item interactions (e.g., user-item rating matrix), while ignoring the underlying relatedness between users and items (e.g., users who rate the same ratings for the same items should be embedded into similar representations), which is an ideological disadvantage. On the other hand, ME models directly employ inner products as a default loss function metric that cannot project users and items into a proper latent space, which is a methodological disadvantage. In this paper, we propose a supervised collaborative representation learning model - Magnetic Metric Learning (MML) - to map users and items into a unified latent vector space, enhancing the representation learning for NN-RSs. First, MML utilizes dual triplets to model not only the observed relationships between users and items, but also the underlying relationships between users as well as items to overcome the ideological disadvantage. Specifically, a modified metric-based dual loss function is proposed in MML to gather similar entities and disperse the dissimilar ones. With MML, we can easily compare all the relationships (user to user, item to item, user to item) according to the weighted metric, which overcomes the methodological disadvantage. We conduct extensive experiments on four real-world datasets with large item space. The results demonstrate that MML can learn a proper unified latent space for representations from the user-item matrix with high accuracy and effectiveness, and lead to a performance gain over the state-of-the-art RS models by an average of 17 percent.
An improved approach for mining association rules in parallel using Spark Streaming

Summary Parallel computing is an effective method to solve computationally large and data‐intensive problems. The traditional data mining algorithm cannot mining association rules for large amounts of streaming data in a timely and effectively. In order to improve the speed and accuracy of association rules mining, distributed and parallel algorithms have become a research focus. This paper proposes a parallel FP‐growth approach using Spark Streaming, called SSPFP, which can parallel mining frequent itemsets and association rules in real‐time streaming data. In this paper, the proposed SSPFP algorithm is applied to mining the association rules between temperature and salinity in marine Argo data. The experimental results indicate that SSPFP algorithm is efficient for association rules mining.
Learning spatio-temporal correlation filter for visual tracking

Correlation filter (CF) trackers have performed impressive performance with high frame rates. However, the limited information in both spatial and temporal domains is only used in the learning of correlation filters, which might limit the tracking performance. To handle this problem, we propose a novel spatio-temporal correlation filter approach, which employs both spatial and temporal cues in the learning, for visual tracking. In particular, we explore the spatial contexts from background whose contents are ambiguous to the target and integrate them into the correlation filter model for more discriminative learning. Moreover, to capture the appearance variations in temporal domain, we also compute a set of target templates and incorporate them into our model. At the same time, the solution of the proposed spatio-temporal correlation filter is closed-form and the tracking efficiency is thus guaranteed. Experimental experiments on benchmark datasets demonstrate the effectiveness of the proposed tracker against several CF ones.
Improved Crow Search Algorithm Optimized Extreme Learning Machine Based on Classification Algorithm and Application

In view of the problems of the connection weights and thresholds of the extreme learning machine are randomly generated before training and remain unchanged during the training process, the number of hidden layer nodes is pre-allocated, and the hidden layer parameters are randomly selected. Too many hidden layer nodes not only make the network more complex but also reduce the generalization ability of the algorithm. Aiming at this problem, an improved crow search algorithm is proposed to optimize the extreme learning machine. Based on the analysis of the limitations of the original crow search algorithm, a particle swarm algorithm search strategy is proposed to enhance the global search capability. In the latter part of the algorithm iteration, Gaussian function is added, and the penalty coefficient of the function is used for local disturbance, gradually reducing the amplitude of the search trajectory, and then adaptively adjusting the parameters to avoid being attracted by local extremum. Finally, the improved crow search algorithm is used to optimize the hidden layer neurons and connection weights of the extreme learning machine neural network, so as to obtain accurate prediction results. Through function fitting, regression data set fitting and classification data set for classification experiment verification, the proposed algorithm has higher training speed and efficiency. At the same time, this method is not only significantly higher than the traditional ELM method, but also obtains a more compact network structure, which is an effective neural network optimization algorithm.