This work is based on cooperative multiagent systems in which each agent models a connected object. We will leverage interaction protocols in multiagent systems in order to design a platform capable of generating an adaptive multiagent systems automatically. The adaptation takes into consideration the context of the person as well as the existing interaction protocols.
New partnership to drive industrialisation of laser functional texturing
This paper presents an infrastructure which allows the use of ontologies for agent-oriented software engineering. The first results are very promising since they show several advantages of the approach in terms of adaptability of the generated multiagent system, as indicated by the experiments we have carried out.
This paper aims to put a new approach in the picture to the payment scheduling problem, which looks for a schedule that maximizes the benefit of all parties in a project. In a project, both sponsor and contractor seek to have a good payment strategy on their own.
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- Hydrology: A Science of Nature?
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The timing of payments and the completion times of activities in projects are determined simultaneously in order to achieve an equitable schedule among the sponsor and the development team. In previous research, we developed a Unified Game-Based Model for conflicts in project management. In this paper, we applied this model to this problem, implemented in an open sourced evolutionary computation library named MOEA framework.
The use of a Unified Game-Based Model enables us to figure out a suitable schedule for the problem, and in the tool, we conducted an experimental test of the model by the used of several multi-objective optimization algorithms. The experimental results demonstrated that the presented approach is effective and promising so that both parties could use this model to choose the proper tactics for each of them in scheduling payment.
One way to recognize human emotions is to use physiological signals. In particular, EEG is noticed because it is non-invasive and inexpensive. However, it is difficult to perform recognition with high accuracy because there are a number of problems such as EEG signals have a lot of noise. The high accuracy analysis of EEG is the subject of research by many researchers. The EEG signal is divided into short segments based on a predetermined time window and plotted in time series data format to generate images.
About the data plotting method, the image is generated by the method of making 32 classes and the method of making 4 classes. The generated images are classified into each emotions using a convolutional neural network. The classification use two axes, arousal and valence.
The best results differ by gender. Men are able to get the best results when the time window is 1. The accuracy at these results is The time window is 1. On the other hand, valence is Also, it is found that arousal tends to be higher for women and valence tends to be higher for men. The experimental results show that the proposed method outperforms some related work. The proposed method is not dependent on the dataset, so it can be applied to research using various data.
Whilst, R and A parameters for BatNN will dynamically self-adapt in searching the optimal configuration during the training process. Hidden node HN , iteration number IN and learning rate LR for both models are predetermined to be , and 1 respectively for comparison. Data from to are used for training, whilst validation data are from to Model output is the forecasted precipitation.
The higher accuracy of BatNN can be attributed to the modifications done where dynamical parameters R and A are used in place of static parameter to allow BatNN to self-adapt during the training process. Systems operated complex engineering structure ES constituting smart industrial or commercial product, prototype, or experimental configuration requires new way in model-based engineering.
New style of engineering model system EMS is developed and applied in the course of continuous engineering to achieve system level, generic, and contextual model object structure which is in possession of self-modification capability to change itself for changed inside and outside context. Comprehensive software platform provides modeling capabilities during the whole innovation and life cycle of ES in industries engaged with smart products and production.
Introducing work in engineering modeling for smart ES this paper focuses on the problem of multiple context driving of model object. The purpose of the reported work is to develop concept and methodology in intellectual driving structure for objects included in EMS and in cyber units of cyber physical systems. Paper starts with a scenario of multiple outside and inside context driving of object parameters and an outline of multiple context driven EMS.
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Following this, general process for managing contextual object structure of EMS which represents an ES is introduced. Imbalanced data classification is an important task in data mining and machine learning. Imbalanced data consists of majority class and minority class, where the majority class leads to miss-classification of minority samples. Various approaches have been proposed in recent years to address this problem. Sampling, which focuses on balancing between classes, is one of the methods to solve the class imbalance problem.
In previous our research, we have proposed Multivariate Normal Distribution based Over-Sampling MNDO , which uses correlations between attributes and statistical methods, and have tackled this problem. Next, calculate the distance between the generated data and the original data, and identify 5 nearest neighbors. The categorical data is sampled by applying a voting strategy for the neighborhood sample. Some existing methods generate new samples using distance function, but our method uses positive class statistics. Therefore, it can be applied even if the number of training samples is very small.
In addition, outliers can be reproduced stochastically, so more realistic samples can be generated. In the experiment, we used 17 imbalanced datasets, which consist of numerical data and categorical data. To compare with the existing method, 6 sampling methods, 2 scaling and 3 learning methods were used. As a result of the experiment, the proposed method showed the same result as other methods.
Debugging is a laborious part of the software development process as well as of programming education. Although existing editors and IDEs support the identification of syntax errors, their functions for detecting logical errors in compilable program code are very limited. Algorithms have been developed taking either a static code analysis approach or a deep learning approach. However, although overall experimental results are positive in terms of the detection of logic errors, the results have limitations.
We should take advantage of algorithm capacity as well as avoidance of mismatches caused by weakness in implementing the corresponding intelligent coding editors. In the present paper, we analyze the two different approaches through accumulated source codes for solving a programming task in an online judge system. Experimental results reveal the strengths and weaknesses of these approaches, and we conclude that these approaches are an appropriate basis for developing a hybrid algorithm to enhance the accuracy of logic error detection.
Handling the missing values play important step in the preprocessing phase of hydrological modeling analysis.
One of the challenges in preprocessing phase is to deal with the problems of missing data with good consideration on the pattern and approaches of the missing data. Hence, this paper presents a study on Feedforward neural network algorithm FFNN and Elman neural network ENN imputation algorithm in estimating missing rainfall data at different percentages of missingness. Reliable rainfall data series from nearest neighbor gauging stations were used as inputs to predict the missing rainfall data for an output station.
The selected study area is Sungai Merang, East Malaysia. It is also observed that the ENN model-infilling method could be highly beneficial in reducing the data gaps for continuous hydrological modelling analysis. Data augmentation is being widely used to enrich the datasets and enhance the performance of neural network for classification and detection. However most of the recent works focus only on the augmentation for classification.
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A technique for detection augmentation by template blending has been introduced in the literature. The limitation of blending technique is an extra polygon shape of each object needed to blend with the scene. In this paper we investigate the effect of the geometric transformations for detection augmentation on the Malaysian Traffic Sign Detection MTSD datasets.
We propose and investigate a new augmentation framework for object detection datasets and train using faster-rcnn with ZF network as a backbone. Our findings show that data augmentation improving the performance for true positive. However, many false positive also occur but decreased by The unmanned surface vehicle USV has been widely used to accomplish tasks that cannot be completed by ships with human drivers on certain sea areas. It is not only necessary but essential to obtain a robust strategy in order to ensure multiple USVs accomplish collaborative tasks successfully and efficiently.
To meet the challenge, a deep reinforcement learning method is proposed, which is combined with an improved A star algorithm. A statistically promising collaborative strategy is achieved by the proposed method under the guidance from the unmanned aerial vehicles UAVs. After the collaborative strategy is generated, the improved A star algorithm is used to navigate the USVs.
To verify the proposed algorithm, several tasks are tested on a simulation platform. Experimental results demonstrate that the proposed method outperforms state-of-the-art reinforcement learning methods such as DQN and DeepSarsa. Recently, another cloud taxonomy service in addition to IaaS, PaaS and Saas services has been added: this is the business process as a service in the cloud BPaaS.
A BPaaS is any business process delivered through a cloud service model via internet with access through web interfaces. Therefore, process models will be developed by providers for discovery and use by tenants. In a previous work, we perform the design of an e-learning process as a business process in cloud. This paper examines the problem of discovering the similarity between e-learning processes. Given a pair of e-learning process models, the consumer lunches a request and the provider present a target process. These technology platforms, rendered easy to use by the smartphone, convene people, assets, and data—thus creating entirely new ways of consuming goods and services in the process.
In addition, they lower the barriers for businesses and individuals to create wealth, altering the personal and professional environments of workers. These new platform businesses are rapidly multiplying into many new services, ranging from laundry to shopping, from chores to parking, from massages to travel. On the whole, there are four main effects that the Fourth Industrial Revolution has on business—on customer expectations, on product enhancement, on collaborative innovation, and on organizational forms. Whether consumers or businesses, customers are increasingly at the epicenter of the economy, which is all about improving how customers are served.
Physical products and services, moreover, can now be enhanced with digital capabilities that increase their value. New technologies make assets more durable and resilient, while data and analytics are transforming how they are maintained. A world of customer experiences, data-based services, and asset performance through analytics, meanwhile, requires new forms of collaboration, particularly given the speed at which innovation and disruption are taking place.
And the emergence of global platforms and other new business models, finally, means that talent, culture, and organizational forms will have to be rethought. Overall, the inexorable shift from simple digitization the Third Industrial Revolution to innovation based on combinations of technologies the Fourth Industrial Revolution is forcing companies to reexamine the way they do business.
The bottom line, however, is the same: business leaders and senior executives need to understand their changing environment, challenge the assumptions of their operating teams, and relentlessly and continuously innovate. As the physical, digital, and biological worlds continue to converge, new technologies and platforms will increasingly enable citizens to engage with governments, voice their opinions, coordinate their efforts, and even circumvent the supervision of public authorities.
Simultaneously, governments will gain new technological powers to increase their control over populations, based on pervasive surveillance systems and the ability to control digital infrastructure.
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On the whole, however, governments will increasingly face pressure to change their current approach to public engagement and policymaking, as their central role of conducting policy diminishes owing to new sources of competition and the redistribution and decentralization of power that new technologies make possible. Ultimately, the ability of government systems and public authorities to adapt will determine their survival. If they prove capable of embracing a world of disruptive change, subjecting their structures to the levels of transparency and efficiency that will enable them to maintain their competitive edge, they will endure.
If they cannot evolve, they will face increasing trouble. This will be particularly true in the realm of regulation. Current systems of public policy and decision-making evolved alongside the Second Industrial Revolution, when decision-makers had time to study a specific issue and develop the necessary response or appropriate regulatory framework. But such an approach is no longer feasible. How, then, can they preserve the interest of the consumers and the public at large while continuing to support innovation and technological development? This means regulators must continuously adapt to a new, fast-changing environment, reinventing themselves so they can truly understand what it is they are regulating.
To do so, governments and regulatory agencies will need to collaborate closely with business and civil society. The Fourth Industrial Revolution will also profoundly impact the nature of national and international security, affecting both the probability and the nature of conflict. The history of warfare and international security is the history of technological innovation, and today is no exception.
The distinction between war and peace, combatant and noncombatant, and even violence and nonviolence think cyberwarfare is becoming uncomfortably blurry. As this process takes place and new technologies such as autonomous or biological weapons become easier to use, individuals and small groups will increasingly join states in being capable of causing mass harm. This new vulnerability will lead to new fears. But at the same time, advances in technology will create the potential to reduce the scale or impact of violence, through the development of new modes of protection, for example, or greater precision in targeting.
The Fourth Industrial Revolution, finally, will change not only what we do but also who we are. It will affect our identity and all the issues associated with it: our sense of privacy, our notions of ownership, our consumption patterns, the time we devote to work and leisure, and how we develop our careers, cultivate our skills, meet people, and nurture relationships. The list is endless because it is bound only by our imagination. I am a great enthusiast and early adopter of technology, but sometimes I wonder whether the inexorable integration of technology in our lives could diminish some of our quintessential human capacities, such as compassion and cooperation.
Our relationship with our smartphones is a case in point. One of the greatest individual challenges posed by new information technologies is privacy. We instinctively understand why it is so essential, yet the tracking and sharing of information about us is a crucial part of the new connectivity. Debates about fundamental issues such as the impact on our inner lives of the loss of control over our data will only intensify in the years ahead. The project will also reduce the amount of component testing required through the establishment of a knowledge data management system and modelling software, and will provide a cost model analysis for implementing laser surface texturing in specific application areas.
The outcomes of the project will be a game-changer, not just for the partners involved, but to the wider manufacturing community in Europe and globally. Laser texturing has a wide range of applications in sectors as diverse as aerospace, medical, food and drink, power generation and tooling. The SHARK project is focusing on specific end use applications including anti-icing for ice cream production lines and controlled friction for high-friction discs.
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