The dynamic job-shop scheduling problem (DJSSP) is an advanced form of the classical job-shop scheduling problem (JSSP), incorporating dynamic events that make it even more challenging. This article proposes a novel approach involving deep reinforcement learning and graph neural networks to solve this optimization problem. To effectively model DJSSP, we use a disjunctive graph, designing specific node features that reflect the unique characteristics of JSSP with machine breakdowns and stochastic job arrivals. Our proposed method can dynamically adapt to the occurrence of disruptions, ensuring that it accurately reflects the current state of the environment. Furthermore, we use the attention mechanism to prioritize crucial nodes while discarding irrelevant ones. This study proposes a model that applies graph attention networks to learn node embeddings, serving as input for the actor–critic model. The proximal policy optimization is then utilized to train the actor–critic model, which assists the model in learning the scheduling of job operations for machines. We conducted extensive experiments in static and public environments. Experimental results indicate that our method is superior to current state-of-the-art methods.

The job-shop scheduling problem (JSSP) is one of the best-known combinatorial optimization problems and is also an essential task in various sectors. In most real-world environments, scheduling is complex, stochastic, and dynamic, with inevitable uncertainties. Therefore, this article proposes a novel framework based on graph neural networks (GNNs) and deep reinforcement learning (DRL) to deal with the dynamic JSSP (DJSSP) with stochastic job arrivals and random machine breakdowns by minimizing the makespan. In the proposed framework, JSSP is formulated as a Markov decision process (MDP) and is associated with a disjunctive graph to encode the information of jobs and machines as nodes and arcs. We propose a GNN architecture to perform representation learning by transforming graph states into node embeddings. Then, the agent takes actions using a parameterized policy in terms of policy learning. Operations are used as actions, and an effective reward is well designed to guide the agent. We train our proposed method using proximal policy optimization (PPO), which helps minimize the loss function while ensuring that the deviation is relatively small. Extensive experiments show that the proposed method can achieve excellent results considering different criteria: efficiency, effectiveness, robustness, and generalizability. Once the proposed method is trained, it can directly schedule new JSSPs of different sizes and distributions in static benchmark tests, showing its excellent generalizability and effectiveness compared to another DRL-based method. Furthermore, the proposed method simultaneously maintains the win rate (a quantitative metric) and the scheduling score (a qualitative metric) when scheduling in dynamic environments.

Parallel machine scheduling (PMS) is a common setting in many manufacturing facilities, in which each job is allowed to be processed on one of the machines of the same type. It involves scheduling$n$jobs on$m$machines to minimize certain objective functions. For preemptive scheduling, most problems are not only NP-hard but also difficult in practice. Moreover, many unexpected events, such as machine failure and requirement change, are inevitable in the practical production process, meaning that rescheduling is required for static scheduling methods. Deep reinforcement learning (DRL), which combines deep learning and reinforcement learning, has achieved promising results in several domains and has shown the potential to solve large Markov decision process (MDP) optimization tasks. Moreover, PMS problems can be formulated as an MDP problem, inspiring us to devise a DRL method to deal with PMS problems in a dynamic environment. We develop a novel DRL-based PMS method, called DPMS, in which the developed model considers the characteristics of PMS to design states and the reward. The actions involve dispatching rules, so DPMS can be considered a meta-dispatching-rule system that can efficiently select a sequence of dispatching rules based on the current environment or unexpected events. The experimental results demonstrate that DPMS can yield promising results in a dynamic environment by learning from the interactions between the agent and the environment. Furthermore, we conduct extensive experiments to analyze DPMS in the context of developing a DRL to deal with dynamic PMS problems.

The non-invasive and easily accessible characteristics of electrocardiogram (ECG) attract many studies targeting AI-enabled cardiovascular-related disease screening tools based on ECG. However, the high cost of manual labels makes high-performance deep learning models challenging to obtain. Hence, we propose a new self-supervised representation learning framework, contrastive heartbeats (CT-HB), which learns general and robust electrocardiogram representations for efficient training on various downstream tasks. We employ a novel heartbeat sampling method to define positive and negative pairs of heartbeats for contrastive learning by utilizing the periodic and meaningful patterns of electrocardiogram signals. Using the CT-HB framework, the self-supervised learning model learns personalized heartbeat representations representing the specific cardiology context of a patient. Evaluations on public benchmark datasets and a private large-scale real-world dataset with multiple tasks demonstrate that the learned semantic representations result in better performance on downstream tasks and retain high performance while supervised learning suffers performance degradation with fewer supervised labels in downstream tasks.

Learning from imbalanced data is an important and challenging topic in machine learning. Many works have devised methods to cope with imbalanced data, but most methods only consider minority or majority classes without considering the relationship between the two classes. In addition, many synthetic minority oversampling technique-based methods generate synthetic samples from the original feature space and use the Euclidean distance to search for the nearest neighbors. However, the Euclidean distance is not a precise distance metric in a high-dimensional space. This article proposes a novel method, called deep density hybrid sampling (DDHS), to address imbalanced data problems. The proposed method learns an embedding network to project the data samples into a low-dimensional separable latent space. The goal is to preserve class proximity during data projection, and we use within-class and between-class concepts to devise loss functions. We propose to use density as a criterion to select minority and majority samples. Subsequently, we apply a feature-level approach to the selected minority samples and generate diverse and valid synthetic samples for the minority class. This work conducts extensive experiments to assess our proposed method and compare it with several methods. The experimental results show that the proposed method can yield promising and stable results. The proposed method is a data-level algorithm, and we combine the proposed method with the boosting technique to develop a method called DDHS-boosting. We compare DDHS-boosting with several ensemble methods, and DDHS-boosting shows promising results.

The use of a few examples for each class to train a predictive model that can be generalizedto novel classes is a crucial and valuable research direction in artificial intelligence. Thiswork addresses this problem by proposing a few-shot learning (FSL) algorithm called proxynetwork under the architecture of meta-learning. Metric-learning based approaches assumethat the data points within the same class should be close, whereas the data points inthe different classes should be separated as far as possible in the embedding space. Weconclude that the success of metric-learning based approaches lies in the data embedding,the representative of each class, and the distance metric. In this work, we propose asimple but effective end-to-end model that directly learns proxies for class representativeand distance metric from data simultaneously. We conduct experiments on CUB andmini-ImageNet datasets in 1-shot-5-way and 5-shot-5-way scenarios, and the experimentalresults demonstrate the superiority of our proposed method over state-of-the-art methods.Besides, we provide a detailed analysis of our proposed method.

Imbalanced data is characterized by the severe difference in observation frequency between classes and has received a lot of attention in data mining research. The prediction performances usually deteriorate as classifiers learn from imbalanced data, as most classifiers assume the class distribution is balanced or the costs for different types of classification errors are equal. Although several methods have been devised to deal with imbalance problems, it is still difficult to generalize those methods to achieve stable improvement in most cases. In this study, we propose a novel framework called model-based synthetic sampling (MBS) to cope with imbalance problems, in which we integrate modeling and sampling techniques to generate synthetic data. The key idea behind the proposed method is to use regression models to capture the relationship between features and to consider data diversity in the process of data generation. We conduct experiments on 13 datasets and compare the proposed method with 10 methods. The experimental results indicate that the proposed method is not only comparative but also stable. We also provide detailed investigations and visualizations of the proposed method to empirically demonstrate why it could generate good data samples.

Early classification on multivariate time series is an important research topic in data mining with wide applications to various domains like medical diagnosis, motion detection and financial prediction, etc. Shapelet is probably one of the most commonly used approaches to tackle early classification problem, but one drawback of shaplet is its inefficiency. More importantly, the extracted shapelets may not be applicable to every test case at any time point. This work focuses on early classification of multivariate time series and proposes a novel framework named Multi-Domain Deep Neural Network (MDDNN), in which convolutional neural network (CNN) and long-short term memory (LSTM) are incorporated to learn feature representation and relationship embedding in the long sequences with long time lags. The proposed model can make predictions at any time point of a multivariate time series with the help of a truncation process. We conducted experiments on four real datasets and compared with state-of-the-art algorithms. The experimental results indicate that the proposed method outperforms the alternatives significantly on both of earliness and accuracy. Detailed analysis about the proposed model is also provided in this work. To the best of our knowledge, this is the first work that incorporates deep neural network methods (CNN and LSTM) and multi-domain approach to boost the problem of early classification on multivariate time series.

The imbalanced data problem occurs in many application domains and is considered to be a challenging problem in machine learning and data mining. Most resampling methods for synthetic data focus on minority class without considering the data distribution of major classes. In contrast to previous works, the proposed method considers both majority classes and minority classes to learn feature embeddings and utilizes appropriate loss functions to make feature embedding as discriminative as possible. The proposed method is a comprehensive framework and different deep learning feature extractors can be utilized for different domains. We conduct experiments utilizing seven numerical datasets and one image dataset based on multiclass classification tasks. The experimental results indicate that the proposed method provides accurate and stable results.

Data exploration is essential to data analytics, especially when one is confronted with massive datasets. Clustering is a commonly used technique in data exploration, since it can automatically group data instances into a list of meaningful categories, and capture the natural structure of data. Tra ditional finite mixture model requires the number of clusters to be specified in advance of analyzing the data, and this parameter is crucial to the clustering performance. Chinese restaurant process (CRP) mixture model provides an alternative to this problem, allowing the model complexity to grow as more data instances are observed. Although CRP provides the flexibility to create a new cluster for subsequent data instances, one still has to determine the hyperparameter of the prior and the parameters for the base distribution in the likelihood part. This work proposes a non-parametric clustering algorithm based on CRP with two main differences. First, we propose to create a new cluster based on entropy of the posterior, whereas the CRP uses a hyperparameter to control the probability of creating a new cluster. Second, we propose to dynamically adjust the parameters of the base distribution according to the mean of the observed data owing to Chebyshev’s inequality. Additionally, detailed derivation and update rules are provided to perform posterior inference with the proposed collapsed Gibbs sampling algorithm. The experimental results indicate that the proposed algorithm avoids to specify the number of clusters and works well on several datasets.

Multi-label learning has attracted significant attention from machine learning and data mining over the last decade. Although many multi-label classification algorithms have been devised, few research studies focus on multi-assignment clustering (MAC), in which a data instance can be assigned to mu ltiple clusters. The MAC problem is practical in many application domains, such as document clustering, customer segmentation and image clustering. Additionally, specifying the number of clusters is always a difficult but critical problem for a certain class of clustering algorithms. Hence, this work proposes a nonparametric multi-assignment clustering algorithm called multi-assignment Chinese restaurant process (MACRP), which allows the model complexity to grow as more data instances are observed. The proposed algorithm determines the number of clusters from data, so it provides a practical model to process massive data sets. In the proposed algorithm, we devise a novel prior distribution based on the similarity graph to achieve the goal of multi-assignment, and propose a Gibbs sampling algorithm to carry out posterior inference. The implementation in this work uses collapsed Gibbs sampling and compares with several methods. Additionally, previous evaluation metrics used by multi-label classification are inappropriate for MAC, since label information is unavailable. This work further devises an evaluation metric for MAC based on the characteristics of clustering and multi-assignment problems. We conduct experiments on two real data sets, and the experimental results indicate that the proposed method is competitive and outperforms the alternatives on most data sets.

Universum, a collection of nonexamples that do not belong to any class of interest, has become a new research topic in machine learning. This paper devises a semi-supervised learning with Universum algorithm based on boosting technique, and focuses on situations where only a few labeled examples are available. We also show that the training error of AdaBoost with Universum is bounded by the product of normalization factor, and the training error drops exponentially fast when each weak classifier is slightly better than random guessing. Finally, the experiments use four data sets with several combinations. Experimental results indicate that the proposed algorithm can benefit from Universum examples and outperform several alternative methods, particularly when insufficient labeled examples are available. When the number of labeled examples is insufficient to estimate the parameters of classification functions, the Universum can be used to approximate the prior distribution of the classification functions. The experimental results can be explained using the concept of Universum introduced by Vapnik, that is, Universum examples implicitly specify a prior distribution on the set of classification functions.

Processing large volumes of streaming data in near-real-time is becoming increasingly important as the Internet, sensor networks and network traffic grow. Online machine learning is a typical means of dealing with streaming data, since it allows the classification model to learn one instance of data at a time. Although many online learning methods have been developed since the development of the Perceptron algorithm, existing online methods assume that the number of classes is available in advance of classification process. However, this assumption is unrealistic for large scale or streaming data sets. This work proposes an online Chinese restaurant process (CRP) algorithm, which is an online and nonparametric algorithm, to tackle this problem. This work proposes a relaxing function as part of the prior and updates the parameters with the likelihood function in terms of the consistency between the true label information and predicted result. This work presents two Gibbs sampling algorithms to perform posterior inference. In the experiments, the online CRP is applied to three massive data sets, and compared with several online learning and batch learning algorithms. One of the data sets is obtained from Wikipedia, which comprises approximately two million documents. The experimental results reveal that the proposed online CRP performs well and efficiently on massive data sets. Finally, this work proposes two methods to update the hyperparameter $\alpha$ of the online CRP. The first method is based on the posterior distribution of $\alpha$, and the second exploits the property of online learning, namely adapting to change, to adjust $\alpha$ dynamically.

This paper devises a semi-supervised learning method called semi-supervised linear discriminant clustering (Semi-LDC). The proposed algorithm considers clustering and dimensionality reduction simultaneously by connecting K -means and linear discriminant analysis (LDA). The goal is to find a feature space where the K -means can perform well in the new space. To exploit the information brought by unlabeled examples, this paper proposes to use soft labels to denote the labels of unlabeled examples. The Semi-LDC uses the proposed algorithm, called constrained-PLSA, to estimate the soft labels of unlabeled examples. We use soft LDA with hard labels of labeled examples and soft labels of unlabeled examples to find a projection matrix. The clustering is then performed in the new feature space. We conduct experiments on three data sets. The experimental results indicate that the proposed method can generally outperform other semi-supervised methods. We further discuss and analyze the influence of soft labels on classification performance by conducting experiments with different percentages of labeled examples. The finding shows that using soft labels can improve performance particularly when the number of available labeled examples is insufficient to train a robust and accurate model. Additionally, the proposed method can be viewed as a framework, since different soft label estimation methods can be used in the proposed method according to application requirements.

In this paper, we propose an algorithm called coherence hidden Markov model (HMM) to extract coherence features and rank content. Coherence HMM is a variant of HMM and is used to model the stochastic process of essay writing and identify topics as hidden states, given sequenced clauses as observations. This study uses probabilistic latent semantic analysis for parameter estimation of coherence HMM. In coherence-feature extraction, support vector regression (SVR) with surface features and coherence features is used for essay grading. The experimental results indicate that SVR can benefit from coherence features. The adjacent agreement rate and the exact agreement rate are 95.24% and 59.80%, respectively. Moreover, this study submits high-scoring essays to the same experiment and finds that the adjacent agreement rate and exact agreement rate are 98.33% and 64.50%, respectively. In content ranking, we design and implement an intelligent assisted blog writing system based on the coherence-HMM ranking model. Several corpora are employed to help users efficiently compose blog articles. When users finish composing a clause or sentence, the system provides candidate texts for their reference based on current clause or sentence content. The experimental results demonstrate that all participants can benefit from the system and save considerable time on writing articles.

In this paper, we design and develop a movie-rating and review-summarization system in a mobile environment. The movie-rating information is based on the sentiment-classification result. The condensed descriptions of movie reviews are generated from the feature-based summarization. We propose a novel approach based on latent semantic analysis (LSA) to identify product features. Furthermore, we find a way to reduce the size of summary based on the product features obtained from LSA. We consider both sentiment-classification accuracy and system response time to design the system. The rating and review-summarization system can be extended to other product-review domains easily.

To acquire the largest possible coverage for environmental monitoring, it is important in most situations that the overlapping areas and the convergent angles of respective satellite images be small. The traditional bundle adjustment method used in aerial photogrammetry may not be the most suitable for direct orientation modeling in situations characterized by weak convergence geometry. We propose and compare three block adjustment methods for the processing of satellite images using the digital elevation model (DEM) as the elevation control. The first of these methods is a revised traditional bundle adjustment approach. The second is based on the direct georeferencing approach. The third is a rational function model with sensor-oriented rational polynomial coefficients. A collocation technique is integrated into all three methods to improve the positioning accuracy. Experimental results indicate that using the DEM as an elevation control can significantly improve the geometric accuracy as well as the geometric discrepancies between images. This is the case for all three methods. Moreover, the geometric performance of the three methods is similar. There is a significant improvement in geometric consistency between overlapping SPOT images with respect to single image adjustment for steep areas.

A mobile ad hoc network (MANET) is vulnerable to malicious attacks although it is suitable for various environments because of its rapid establishment. In order to set up a secured MANET, we should not only adopt encryption and authentication, but also equip each node with an intrusion detection system to detect malicious attackers. Focusing on intrusion detection, we propose an intrusion detection system that integrates a finite state machine (FSM) and a support vector machine (SVM) to analyze traffic patterns of MANETs. Shown by numerical examples, such an intrusion detection system is able to amend drawbacks of single-technique systems and enhance usage/right of normal users as well as security of MANETs.

The main purpose of this investigation is to build up a rigorous procedure to perform georeferencing for Formosat-2 satellite. The proposed scheme comprises two major components: (1) orbit modeling and (2) image orthorectification. In the orbit modeling, instead of bundle adjustment, we propose a collocation procedure to determine the precision orbits. The field-of-view (FOV) of Formosat-2 satellite is so small, i.e. 1.5 degree, that produces extremely high correlation between orbital parameters and attitude data. Thus, using on-board ephemeris data, we iteratively adjust the orbital parameters and attitude data. Then, a least squares collocation technique is applied to collocate the orbit. In the image back projection, we use patch-based approach to accelerate the computation without losing accuracy. A standard scene of 24km by 24km and a strip with some 200km long are tested in the validation.

In this paper, we study a new problem of statically estimating the power sensitivity of a given logic circuit with respect to the primary inputs. The power sensitivity defines the characteristics of power dissipation due to changes in state of primary inputs, Consequently, estimating the power sensitivity among the inputs is essential not only to measure the power consumption of the circuit efficiently but also to provide potential opportunities of redesigning the circuit for low power, In this context, we propose a fast and reliable static estimation technique for power sensitivity based on a new concept called power equations, which are then collectively transformed into a table called power table. Experimental data on MCNC benchmark examples show that the proposed technique is useful and effective in estimating power consumption. In summary, the relative error for the estimation of maximum power consumption is 9.4% with a huge speed-up in simulation.

We propose an effective algorithm for power optimization in behavioral synthesis. In previous work, it has been shown that several hardware allocation/binding problems for power optimization can be formulated as network flow problems and be solved optimally. However, in these formulations, a fixed schedule was assumed. In such context, one key problem is: given an optimal network flow solution to a hardware allocation/binding problem for a schedule, how to generate a new optimal network flow solution rapidly for a local change of the schedule. To this end, from a comprehensive analysis of the relation between network structure and flow computation, we devise a two-step procedure: (Step 1) max-flow computation step which finds a valid (maximum) flow solution while retaining the previous (maximum flow of minimum cost) solution as much as possible; (Step 2) min-cost computation step which incrementally refines the flow solution obtained in Step 1, using the concept of finding a negative cost cycle in the residual graph for the flow. The proposed algorithm can be applied effectively to several important high-level data path optimization problems (e.g., allocations/bindings of functional units, registers, buses, and memory ports) when we have the freedom to choose a schedule that will minimize power consumption. Experimental results (for bus synthesis) on benchmark problems show that our designs are 5.2% more power-efficient over the best known results, which is due to (a) exploitation of the effect of scheduling and (b) optimal binding for every schedule instance. Furthermore, our algorithm is about 2.8 times faster in run time over the full network flow based (optimal) bus synthesis algorithm, which is due to (c) our novel (two-step) mechanism which utilize the previous flow solution to reduce redundant flow computations.

In this paper, we study the problem of behavioral-level partitioning for low power design. By behavioral-level partitioning, we mean a partitioning which is done at the behavioral description where scheduling and allocation have not been carried out. The motivation is that turning on/off individual operations cycle-by-cycle is very expensive, thereby we provide a partitioning solution so that all operations in the same partition can be controlled by the same gated clock signal. Our partitioning algorithm is specifically focused on the applications which contain many nested conditional branches and loops.

The advent of poTtable digital devices such as laptop peTsona1 computers has made low poweT ciTcuit design an incTeasingly impoTtant TeseaTch area.Recently, low power decomposition foT simple logic gates such as AND and OR has been extensively Teseazhed.HoweveT, the pToblem of MUX decomposition to minimize poweT dissipation has not been addTessed.In this papeT, we study the pToblem of low power multiplexer (MUX) decomposition.MUX decomposition is the procedure of tTansfo?ming an n-to-one MUX into an equivalent tTee of two-to-one MUXes.We propose a formulation for the minimum power MUX decomposition problem based on the common CMOS pass tTan-sistoT implementation of a MUX.Given the occuTTence pTobabilities of the data signals and theiT on probabilities, we analyze the poweT dissipation of OUT MUX implementation and give a geneTa1 method for computing the poweT dissipation of a MUX tTee decomposition.We then present seveTa1 algorithms which eficientiy generate minimum power MUX decompositiotas.We demon&ate the effectiveness of OUT algoTi&ns wi& experimental Tesults.