With the increase of the number of elderly people living alone around the world, there is a growing demand for sensor-based detection of anomalous behaviors. Although smart homes with ambient sensors could be useful for detecting such anomalies, there is a problem of lack of sufficient real data for developing detection algorithms. For coping with this problem, several sensor data simulators have been proposed, but they have not been able to model appropriately the long-term transitions and correlations between anomalies that exist in reality. In this article, therefore, we propose a novel sensor data simulator that can model these factors in generation of sensor data. Anomalies considered in this study were classified into three types of state anomalies, activity anomalies, and moving anomalies. The simulator produces ten years data in 100 min, including six anomalies, two for each type. Numerical evaluations based on edit distance between activity series show that this simulator is superior to the past simulators in the sense that it simulates well day-to-day variations of real data.

Multi-task learning (MTL) improves generalization by sharing information among related tasks. Structured sparsity-inducing regularization has been widely used in MTL to learn interpretable and compact models, especially in high-dimensional settings. These methods have achieved much success in practice, however, there are still some key limitations, such as limited generalization ability due to specific sparse constraints on parameters, usually restricted in matrix form that ignores high-order feature interactions among tasks, and formulated in various forms with different optimization algorithms. Inspired by Generalized Lasso, we propose the Generalized Group Lasso (GenGL) to overcome these limitations. In GenGL, a linear operator is introduced to make it adaptable to diverse sparsity settings, and helps it to handle hierarchical sparsity and multi-component decomposition in general tensor form, leading to enhanced flexibility and expressivity. Based on GenGL, we propose a novel framework for Structured Sparse MTL (SSMTL), that unifies a number of existing MTL methods, and implement its two new variants in shallow and deep architectures, respectively. An efficient optimization algorithm is developed to solve the unified problem, and its effectiveness is validated by synthetic and real-world experiments.

One blockchain technological update thus far includes smart contract functionality implemented in Ethereum. One of its use cases, the ERC721 Non-Fungible Token Standard (NFT), has gained attention from current industries. NFTs demonstrated digital content transactions with guaranteed uniqueness using unchangeable timestamp on the blockchain. However, the functional limitations of NFT assurance are rarely in focus, and contrary to excessive NFT user expectations, security risks that must be addressed include complex issues. One such risk is NFT fraudulent trading using forged content. Specifically, hard forks in blockchains are extremely important triggers for attacks, as they allow the exact same NFT to be operated on multiple chains. This could depreciate NFT value, and presents a risk that must be addressed to ensure sound NFT transactions. In this study, NFT operational issues in the case of a blockchain fork are summarized, and a cross-referencing scheme using network identities is proposed to prevent the impact of a hard fork.

Multi-Label Classification (MLC), which aims to assign multiple labels to each sample simultaneously, has achieved great success in a wide range of applications. MLC saves global label correlation by building a single model shared by all samples but ignores sample-specific local structures, while Personalized Learning (PL) is able to preserve sample-specific information by learning local models but ignores the global structure. Integrating PL with MLC is a straightforward way to overcome the limitations, but it still faces three key challenges. 1) capture both local and global structures in a unified model; 2) efficiently preserve high-order interactions among labels, features and samples; 3) learn a concise and interpretable model where only a fraction of interactions are associated with multiple labels. In this paper, we propose a novel Multi-Label Personalized Classification (MLPC) method to handle these challenges. For 1), it integrates local and global components to preserve sample-specific information and global structure shared across samples, respectively. For 2), a multilinear model is developed to capture high-order interactions, and over-parameterization is avoided by tensor factorization. For 3), exclusive sparsity regularization penalizes factorization by promoting intra-group competition, thereby eliminating irrelevant and redundant interactions during Exclusive Sparse Tensor Factorization (ESTF). Moreover, theoretical analysis reveals the equivalence between MLPC with a family of jointly regularized counterparts. We develop an alternating algorithm to solve the optimization problem, and extensive experiments on various datasets demonstrate its effectiveness.

The number of elderly people living alone has been increasing worldwide. The Internet of Things (IoT) technology has a high potential in protecting them from risks such as falls. This can be achieved by continuously monitoring the behavior of the residents in a smart home. To develop anomaly detection algorithms in such smart homes, we need a simulator to produce realistic sensor data over a long period of time. Unfortunately, however, existing simulators lack a high degree of reproducibility.In this study, we developed a novel behavioral simulator centered on wandering behavior. Users can easily and intuitively embed their knowledge in the simulator, keeping the exact definitions of target anomalies. Sensor data can be obtained from several types of sensors attached to the simulated home. We focus on simulating wandering behavior as an important anomaly, considering the degree of progress of dementia as a latent variable. We show that the proposed simulator is more usable than previous simulators from the view point of reproducibility.

We had previously proposed a supervised Laplacian eigenmap for visualization (SLE-ML) that can handle multi-label data. In addition, SLE-ML can control the trade-off between the class separability and local structure by a single trade-off parameter. However, SLE-ML cannot transform new data, that is, it has the “out-of-sample” problem. In this paper, we show that this problem is solvable, that is, it is possible to simulate the same transformation perfectly using a set of linear sums of reproducing kernels (KSLE-ML) with a nonsingular Gram matrix. We experimentally showed that the difference between training and testing is not large; thus, a high separability of classes in a low-dimensional space is realizable with KSLE-ML by assigning an appropriate value to the trade-off parameter. This offers the possibility of separability-guided feature extraction for classification. In addition, to optimize the performance of KSLE-ML, we conducted both kernel selection and parameter selection. As a result, it is shown that parameter selection is more important than kernel selection. We experimentally demonstrated the advantage of using KSLE-ML for visualization and for feature extraction compared with a few typical algorithms.

Multi-label classification aims to assign multiple labels to a single test instance. Recently, more and more multi-label classification applications arise as large-scale problems, where the numbers of instances, features and labels are either or all large. To tackle such problems, in this paper we develop a clustering-based local multi-label classification method, attempting to reduce the problem size in instances, features and labels. Our method consists of low-dimensional data clustering and local model learning. Specifically, the original dataset is firstly decomposed into several regular-scale parts by applying clustering analysis on the feature subspace, which is induced by a supervised multi-label dimension reduction technique; then, an efficient local multi-label model, meta-label classifier chains, is trained on each data cluster. Given a test instance, only the local model belonging to the nearest cluster to it is activated to make the prediction. Extensive experiments performed on eighteen benchmark datasets demonstrated the efficiency of the proposed method compared with the state-of-the-art algorithms.

Multi-label classification (MLC), allowing instances to have multiple labels, has been received a surge of interests in recent years due to its wide range of applications such as image annotation and document tagging. One of simplest ways to solve MLC problems is label-power set method (LP) that regards all possible label subsets as classes. LP validates traditional multi-classification classifiers such as multi-class SVM but it suffers from the increased number of classes. Therefore, several improvements have been made for LP to be scaled for large problems with many labels. Random k labELsets (RAkEL) proposed by Tsoumakas et al. solves this problem by randomly sampling a small number of labels and taking ensemble of them. However, RAkEL needs all instances for constructing each model and thus suffers from high computational complexity. In this paper, we propose a new fast algorithm for RAkEL. First, we assign each training instance to a small number of models. Then LP is applied for each model with only the assigned instances. Experiments on twelve benchmark datasets demonstrated that the proposed algorithm works faster than the conventional methods while keeping accuracy. In the best case, it was 100 times faster than baseline method (LP) and 30 times faster than the original RAkEL.

Visualization helps us to understand single-label and multi-label classification problems. In this paper, we show several standard techniques for simultaneous visualization of samples, features and multi-classes on the basis of linear regression and matrix factorization. The experiment with two real-life multi-label datasets showed that such techniques are effective to know how labels are correlated to each other and how features are related to labels in a given multi-label classification problem.

Lately, multi-label classification (MLC) problems have drawn a lot of attention in a wide range of fields including medical, web, and entertainment. The scale and the diversity of MLC problems is much larger than single-label classification problems. Especially we have to face all possible combinations of labels. To solve MLC problems more efficiently, we focus on three kinds of locality hidden in a given MLC problem. In this paper, first we show how large degree of locality exists in nine datasets, then examine how closely they are related to labels, and last propose a method of reducing the problem size using one kind of locality.

Multi-label classification has attracted many attentions in various fields, such as text categorization and semantic image annotation. Aiming to classify an instance into multiple labels, various multi-label classification methods have been proposed. However, the existing methods typically build models in the identical feature (sub)space for all labels, possibly inconsistent with real-world problems. In this paper, we develop a novel method based on the assumption that meta-labels with specific features exist in the scenario of multi-label classification. The proposed method consists of meta-label learning and specific feature selection. Experiments on twelve benchmark multi-label datasets show the efficiency of the proposed method compared with several state-of-the-art methods.

Nonnegative Tensor Factorization (NTF) has become a popular tool for extracting informative patterns from tensor data. However, NTF has high computational cost both in space and in time, mostly in iterative calculation of the gradient. In this paper, we consider variable selection to reduce the cost, assuming sparsity of the factor matrices. In fact, it is known that the factor matrices are often very sparse in many applications such as network analysis, text analysis and image analysis. We update only a small subset of important variables in each iterative step. We show the effectiveness of the algorithm analytically and experimentally in comparison with conventional NTF algorithms. The algorithm was five times faster than the naive algorithm in the best case and required one to five hundred times less memory while keeping the approximation accuracy as the same.