A systematic model of stable multilateral automated negotiation in e-market environment
Abstracts:In e-market environment, the participants are usually bilateral such as in Consumer-to-Business or Customer-to-Customer business models. The participant on each side prefers the counterpart from which the concerned issues or profits can be pursued. Hence, the effective matching from a global point of view and the stable matching from an individual point of view become the critical function of the business models. In this paper, a systematic model of Stable Multilateral Automated Negotiation (SMAN) is proposed to facilitate the involved parties’ matching process in two-sided e-market, where confidential mediator agent as well as party agents communicate and make decisions on behalf of their principal parties. To make the matching effective and stable, two optimization models are designed. One is matching points model which makes an effective balance among the proposal value of issues for each possible pair of matching, such that the joint weighted profit measure is optimized with feature rescaling. The other one is matching scheme model which optimizes Social Welfare (SW) subject to the stable constraints, and ensures the engaged individual party satisfies the matching result from its viewpoint. And the optimality of stable matching is proved by mathematical deduction. Finally, numerical experiments are illustrated and show that the designed systematic models can generate effective matchings with individually stable advantages over the traditional Multilateral Automated Negotiation of Two Sides (MANTS).
CANDYMAN: Classifying Android malware families by modelling dynamic traces with Markov chains
Abstracts:Malware writers are usually focused on those platforms which are most used among common users, with the aim of attacking as many devices as possible. Due to this reason, Android has been heavily attacked for years. Efforts dedicated to combat Android malware are mainly concentrated on detection, in order to prevent malicious software to be installed in a target device. However, it is equally important to put effort into an automatic classification of the type, or family, of a malware sample, in order to establish which actions are necessary to mitigate the damage caused. In this paper, we present CANDYMAN, a tool that classifies Android malware families by combining dynamic analysis and Markov chains. A dynamic analysis process allows to extract representative information of a malware sample, in form of a sequence of states, while a Markov chain allows to model the transition probabilities between the states of the sequence, which will be used as features in the classification process. The space of features built is used to train classical Machine Learning, including methods for imbalanced learning, and Deep Learning algorithms, over a dataset of malware samples from different families, in order to evaluate the proposed method. Using a collection of 5,560 malware samples grouped into 179 different families (extracted from the Drebin dataset), and once made a selection based on a minimum number of relevant and valid samples, a final set of 4,442 samples grouped into 24 different malware families was used. The experimental results indicate a precision performance of 81.8% over this dataset.
A soft computing approach for inverse kinematics of robot manipulators
Abstracts:The solution of the inverse kinematics problem is an essential capability for robotic manipulators. This capability is used to solve tasks such as path planning, control of manipulators, object grasping, etc. In this paper, we present an approach for solving the inverse kinematics of robot arm manipulators using a soft computing approach. Given a desired end effector pose, the proposed approach is able to solve both the position and orientation for the inverse kinematic problem. In addition, the proposed approach avoids singularities configurations, since, it is based on the forward kinematics equations. We present simulations and experiments, where a comparative study among some selected soft computing algorithms is realized. The simulations and experiments illustrate the effectiveness of the proposed approach.
CNC internal data based incremental cost-sensitive support vector machine method for tool breakage monitoring in end milling
Abstracts:A tool breakage monitoring (TBM) system needs to detect tool breakage promptly in an unattended automation workshop. Traditional TBM systems that employ external sensors to acquire diagnostic signals such as spindle power for making judgments are inconvenient since extra sensors should be installed. Moreover, the signals from the external sensors are independent of the computer numerical control (CNC) system, and it is difficult to label them with the corresponding machining task information so that the target data can be segmented automatically. This paper proposes an incremental cost-sensitive support vector machine (ICSSVM) tool breakage monitoring method based on CNC internal data, which is inherently machining-task labeled and can be accessed directly from the CNC system without extra sensors. To satisfy the dataset’s integrity at the initial stage of model training, a simulation method that is based on the actual tool breakage characteristics is applied to generate simulated tool breakage data. The ICSSVM method combines cost-sensitive SVM (CSSVM) and modified incremental SVM (ISVM) to solve the imbalanced classification problem, which increases the misclassification probability of the minority class, train the model incrementally from the absence of samples, and guarantee high algorithm efficiency as the size of the dataset increases. It is proved that the ICSSVM algorithm has better algorithmic efficiency compared to the batch cost-sensitive SVM (BCSSVM). It is also proved that the ICSSVM algorithm has better imbalanced classification performance than batch SVM (BSVM), as assessed by the receiver operator characteristic (ROC) curves. The industrial practicability of the proposed method is verified by actual machining with a CNC system integrated with the TBM module.
A Dial-a-Ride evaluation for solving the job-shop with routing considerations
Abstracts:The Job-Shop scheduling Problem with Transport (JSPT) is a combinatorial optimization problem that combines both scheduling and routing problems. It has received attention for decades, resulting in numerous publications focused on the makespan minimization. The JSPT is commonly modeled by a disjunctive graph that encompasses both machine-operations and transport-operations. The transport-operations define a sub-problem which is close to the DARP where pickup and delivery operations have to be scheduled. The vast majority of the evaluation functions used into disjunctive graphs of JSPT, minimizes the makespan and there is no routing criteria in the objective function. Commonly used evaluation functions lead to left-shifted solutions for both machine-operations and transport-operations.
A survey of the literature on airline crew scheduling
Abstracts:As the airline industry is ever expanding, companies are increasing their fleet sizes to obtain greater market shares. Moreover, as the airlines seek more growth, the complexity and size of the airline crew scheduling problem, which is one of the major planning problems in the industry, is also increasing. For this reason, companies dedicate resources to solve this problem, and lease software at great expense from external sources. Rigorous mathematical models and algorithms are used in solving these problems. This paper presents a survey of airline crew scheduling problems, and their proposed solutions from the literature. As a conclusion, prospective studies will be proposed and discussed with the aim of developing better solutions for airline crew scheduling problems in the future.
Integration of an improved dynamic ensemble selection approach to enhance one-vs-one scheme
Abstracts:The One-vs-One (OVO) scheme that decomposes the original more complicated problem into as many as possible pairs of easier-to-solve binary sub-problems is one of the most popular techniques for handling multi-class classification problems. In this paper, we propose an improved Dynamic Ensemble Selection (DES) procedure, which aims to enhance the OVO scheme via dynamically selecting a group of appropriate heterogeneous classifiers in each sub-problem for each query example. To do so, twenty heterogeneous classification algorithms are selected to obtain a set of candidate classifiers for each sub-problem derived from the OVO decomposition. Then, a simple yet efficient DES procedure is developed to execute the dynamic selection for each query example in each sub-problem. Finally, all the selected binary heterogeneous ensembles are combined by using majority voting to obtain the final output class. To evaluate the proposed method, we carry out a series of experiments on twenty datasets selected from the KEEL repository. The results supported by proper statistical tests demonstrate the validity and effectiveness of our proposed method, compared with state-of-the-art methods for OVO-based multi-class classification.
Real-time Deep Neural Networks for internet-enabled arc-fault detection
Abstracts:We examine methods for detecting and disrupting electronic arc faults, proposing an approach leveraging Internet of Things connectivity, artificial intelligence, and adaptive learning. We develop Deep Neural Networks (DNNs) taking Fourier coefficients, Mel-Frequency Cepstrum data, and Wavelet features as input for differentiating normal from malignant current measurements. We further discuss how hardware-accelerated signal capture facilitates real-time classification, enabling our classifier to reach 99.95% accuracy for binary classification and 95.61% for multi-device classification, with trigger-to-trip latency under . Finally, we discuss how IoT supports aggregate and user-specific risk models and suggest how future versions of this system might effectively supervise multiple circuits.
New diversity measure for data stream classification ensembles
Abstracts:The diversity of a voting committee is one of the key characteristics of ensemble systems. It determines the benefits that can be obtained through classifier fusion. There are many measures of diversity that can be used in classical decision-making systems which operate in stationary environments. A plethora of algorithms have also been proposed to ensure ensemble diversity. Bagging and boosting are a few of the most popular examples. Unfortunately, these measures and algorithms cannot be applied in systems that process streaming data. Not only must a different implementation be designed for processing fast moving samples in a stream, but the notion of diversity must also be redefined. In this paper it is proposed to assess diversity based on analysis of classifier reactions to changes in data streams. Therefore, two novel error trend diversity measures are introduced that compare the error trends of classifiers while processing subsequent samples. A practical application of these measures is also proposed in the form of a novel error trend diversity driven ensemble algorithm, where our measures are incorporated into the training procedure. The performance of the proposed algorithm is evaluated through a series of experiments and compared to several competing methods. The results demonstrate that our measures accurately evaluate diversity and that their application facilitates the creation of small and effective ensemble classifier systems.
A hybrid algorithm of ABC variant and enhanced EGS local search technique for enhanced optimization performance
Abstracts:A hybrid algorithm for optimizing a complex power system-based problem, economic environmental dispatch (EED) is proposed. The algorithm hybridizes a recently proposed artificial bee colony (ABC) variant referred to as JA-ABC3 and a local search technique, evolutionary gradient search (EGS) which has been enhanced i.e., augmented. The enhanced EGS has been inserted into JA-ABC3’s framework and the resulting hybrid algorithm, known as EGSJAABC3 is expected to exhibit robust optimization performance by showing the capability to reach the global optimum in less number of generations. JA-ABC3 which is generated through few modifications towards the standard ABC algorithm is the best candidate as it has exhibited better performance than the standard ABC and other ABC variants. Since JA-ABC3 is a global search algorithm, a local search technique, EGS that has been augmented is selected to be its hybrid partner as it also exhibits better or same performance than its kind. The task of the augmented EGS is to enhance the exploitation capability of the algorithm and thus, guides the solution faster towards the global optimum. In other word, the enhanced EGS is taking part in the exploitation process while JA-ABC3 takes role in exploration and some parts of the exploitation processes. Then, a number of benchmark functions are used to evaluate the robustness of EGSJAABC3 in terms of convergence speed and global optimum achievement. Next, the main significant output of this research is a robust optimization algorithm (i.e., EGSJAABC3) later applied to solve complex real-world problems that is known for their uncertainty. In this paper, EGSJAABC3 is tested to minimize EED on three test generator systems; 6, 10 and 40 units. The acquired outcome on both benchmark functions and EED application demonstrate the robustness of EGSJAABC3 as an optimization algorithm and therefore, provide other researchers and engineers a tool for solving optimization problems.