For serious poker players, the quality of the chips used during a game can significantly influence the overall experience. A casino clay poker chip set is widely considered the best option for those who want an authentic and high-quality gaming experience. In this article, we’ll explore why a casino clay poker chip set is essential for elevating your poker nights.
The Benefits of a Casino Clay Poker Chip Set
A casino clay poker chip set is known for its superior qualities, making it the preferred option for poker enthusiasts. Here’s why it stands out:
Authentic Casino Feel
One of the most compelling reasons to choose a casino clay poker chip set is its ability to replicate the feel of chips used in professional casinos. Made from a high-quality clay composite, these chips offer a substantial weight and smooth texture that closely mimics the chips used in top casinos. This authentic feel enhances the gaming experience, making every hand more engaging and enjoyable.
Exceptional Durability
A casino clay poker chip set is built to last. Unlike plastic chips, which can easily chip or wear down over time, clay chips maintain their quality and appearance even after years of play. This durability ensures that your casino clay poker chip set will remain in excellent condition, making it a wise investment for any poker enthusiast.
Customization Options
A casino clay poker chip set also offers extensive customization possibilities. Whether you want to add a logo, unique designs, or specific text, these chips can be tailored to reflect your style or branding. This level of personalization adds a special touch to your poker games, making them more memorable and enjoyable for your guests.
Why Choose a Casino Clay Poker Chip Set?
When comparing a casino clay poker chip set to other types of poker chips, several factors make it the superior choice:
Consistent Weight and Feel
A casino clay poker chip set provides a consistent weight and feel, which is crucial for a smooth gaming experience. Unlike lighter plastic chips, which can feel insubstantial, clay chips offer a solid heft that enhances gameplay. This consistency is important for both casual games and competitive settings, ensuring that each hand feels the same.
Aesthetic Appeal
The smooth surface of a casino clay poker chip set allows for vibrant and detailed designs, making the chips visually appealing. Whether you prefer a classic design or something more modern, clay chips can be customized to match your preferences, adding to the overall ambiance of your poker table.
Long-Term Value
While a casino clay poker chip set may come with a higher upfront cost compared to other materials, its durability and customization options make it a valuable long-term investment. These chips are built to last, meaning you won’t need to replace them frequently. Their professional appearance also adds value, making them well worth the initial cost.
Conclusion: Elevate Your Poker Nights with a Casino Clay Poker Chip Set
Investing in a casino clay poker chip set is a smart decision for anyone serious about their poker games. With their authentic feel, superior durability, and customization options, casino clay poker chip sets can elevate your poker nights to a whole new level. Whether you’re a casual player or a dedicated enthusiast, these chips will enhance your gaming experience and add a touch of sophistication to your games.
If you’re looking to purchase a high-quality casino clay poker chip set, consider exploring the options available at Macaumr. With a strong reputation for excellence and a wide range of customization possibilities, Macaumr can help you find the perfect set of chips to suit your needs.
| Special Session & Workshop Proposals | May 30, 2019 |
| Paper Submission Deadline | August 15, 2019 August 30, 2019 |
| Author Notification | September 5, 2019 September 15, 2019 |
| Last version of accepted papers | Octorber 02, 2019 |
| Author registration | Octorber 05, 2019 |
| Conference Dates | December 19-20, 2019 |
| Early registration | Octorber 05, 2019 |
- Michele Della Ventura, Speech Assessment Based on Entropy and Similarity Measures
- Satyam Paul and Magnus Lofstrand, Discrete Time Sliding Mode Control of Milling Chatter
- Vinh Truong Hoang and Mai Bui Thuy Huynh, Fusing of Deep Learning, Transfer Learning and GAN for Breast Cancer Histopathological Image Classification
- Jiancheng Lyu, Jack Xin and Yifeng Yu, Computing Residual Diffusivity by Adaptive Basis Learning via Super-Resolution Deep Neural Networks
- Jiancheng Lyu and Spencer Sheen, A Channel-Pruned and Weight-Binarized Convolutional Neural Network for Keyword Spotting
- Thanh-Nghi Do, The-Phi Pham, Nguyen-Khang Pham, Huu-Hoa Nguyen, Karim Tabia and Salem Benferhat, Stacking of SVMs for classifying intangible cultural heritage images
- Orchida Dianita, Thomas Djorgie and Muhammad Kusumawan Herliansyah, Improvement of Production Layout in the Furniture Industry in Indonesia with the Concept of Group Technology
- Chien Pham Van and Giang Nguyen-Van, Assessment of the water area in the lowland region of the Mekong River using MODIS EVI time series
- Phuong Nguyen-Thanh, Duc Van-Tien, Tan Le-Nhat, Thanh-Tan Mai and Khuong Nguyen-An, An Intensive Empirical Study of Machine Learning Algorithms for Predicting Vietnamese Stock Prices
- Oanh Tran, Attention-based biLSTMs for Understanding Students Learning Experiences on Social Media
- Nga Ly-Tu, Qui Vo-Phu and Thuong Le-Tien, Using Support Vector Machine to Monitor Behaviour of an Object based WSN System
- Thi Thu Hong Phan, Emilie Poisson Caillault and Andre Bigand, eDTWBI: effective imputation method for univariate time series
- Thi Thanh Luu Le and Trong Hieu Tran, Belief merging for Possibilistic belief bases
- Chu Anh My, Duong Xuan Bien, Nguyen Van Cong and Le Chi Hieu, New Feed Rate Optimization Formulation in a Parametric Domain for 5-Axis Milling Robots
- Ha Duyen Trung and Nguyen Tai Hung, Opensource Based IoT Platform and LoRa Communications with Edge Device Calibration for Real-time Monitoring Systems
- Duc Manh Nguyen, A Combination of CMAES-APOP algorithm and quasi-Newton method
- Quang-Vinh Dang, Reinforcement Learning in Stock Trading
- Duc Quynh Tran, A new efficient algorithm for maximizing the pro?t and the compactness in land use planning problem
- Hue Chi Lam, Hanyu Gu and Yakov Zinder, A Genetic Algorithm Approach for Scheduling Trains Maintenance under Uncertainty
- Phuoc-Hung Vo, Thai-Son Nguyen, Van-Thanh Huynh, Thanh-C Vo and Thanh-Nghi Do, Secure and Robust Watermarking Scheme in Frequency Domain Using Chaotic Logistic Map Encoding
- Thi-Lich Nghiem and Thi-Toan Nghiem, Applying MASI algorithm to improve the classification performance of imbalanced data in fraud detection
- Tatiana Zarodnyuk, Aleksander Gornov, Anton Anikin and Pavel Sorokovikov, Numerical technologies for investigating optimal control problems with free right-hand end of trajectories
- Bach Tran and Hoai An Le Thi, Deep Clustering with Spherical Distance in Latent Space
- Dinh Tuyen Hoang, Botambu Collins, Hyojeon Yoon, Ngoc-Thanh Nguyen and Dosam Hwang, A Survey on forecasting models for preventing terrorism
- Van Thien Hoang, Dang Hung Kiet, Vu Van Giang and Le Hoang Thai, Palmprint Recognition Using Discriminant Local Line Directional Representation
- Thi Thoi Tran, Delphine Sinoquet, Sebastien Da Veiga and Marcel Mongeau, An adapted derivative-free optimization method for an optimal design application with mixed binary and continuous variables
- Thuong-Cang Phan, Anh-Cang Phan, Thi-To-Quyen Tran and Ngoan-Thanh Trieu, Efficient Processing of Recursive Joins on Large-Scale Datasets in Spark
- Vinh Thanh Ho, Hoai An Le Thi and Tao Pham Dinh, DCA with successive DC decomposition for convex piecewise-linear fitting
- Quang-Vu Nguyen and Hai-Bang Truong, An improvement of applying multi-objective optimization algorithm into higher order mutation testing
- Hung Son Nguyen and Sinh Hoa Nguyen, Learning Rough Set based Classifiers using Boolean Kernels
- Vuong Le Luong, Thuan Nguyen Quang and Quynh Tran Duc, A New Solution Method for a Mean-Risk Mixed Integer Nonlinear Program in Transportation Network Protection
- Quang Thuan Nguyen and Duc Anh Nguyen, A Novel Approach for Travel Time Optimization in Single-track Railway Networks
- Thi-Ngan Pham, Quang-Thuy Ha, Minh-Chau Nguyen and Tri-Thanh Nguyen, A probability-based close domain metric in lifelong learning for multi-label classification
- Thanh Hai Nguyen, Sergiu Carpov and Vincent Herbert, Homomorphic Encryption-based Privacy – Aware Design for Collaborative Information Sharing in Cybersecurity Cloud Platform
- Anh Son Ta, Hoai An Le Thi and Dinh Tao Pham, Solving Efficient Target-Oriented Scheduling in Directional Sensor Networks by DCA
- Andre Dembele, Babacar Mbaye Ndiaye, Guy Degla and Adam Ouorou, A triple stabilized bundle method for constrained nonconvex nonsmooth optimization
Prospective authors of papers are invited to submit contributions for presentations at ICCSAMA 2019. The submissions should present the results of original research or innovative practical applications relevant to conference topics.
The conference language is English.
The conference proceedings will be published in the series Advances in Intelligent Systems and Computing of Springer-Verlag and indexed by indexed by ISI Proceedings, DBLP, Ulrich’s, EI-Compendex, SCOPUS, Zentralblatt Math, MetaPress, Springerlink. All submissions should follow the LNCS/LNAI style and not exceed 12 pages.
| PLENARY LECTURE
Professor Aharon Ben-Tal |
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Title: Robust Optimization: the need, the challenge, the success
Abstract:
The Need: Optimization problems are affected by uncertainties, either due to inaccuracy in measurements, lack of timely information on values of parameters (which incurs estimation errors), and limitation on executing accurately a computed solution (causing implementation errors). Due to these uncertainties a solution may divert from the expected optimality, and even worse; can lead to infeasibility. Consequently, there is a great need to come up with a method that can address the above difficulties.
The challenge is to introduce a methodology which on one hand will not impose on the user of the optimization the need to provide full information on the uncertainties, which he cannot deliver, and on the other hand will not impose on the optimizer the need to solve intractable optimization problems, in particular dynamic (multi-stage) ones and Chance Constrained problems. Robust Optimization (RO) is an attempt to meet these challenges. The talks will explain and demonstrate how this is achieved. We will present in particular examples in Antenna design and in Signal processing.
The success: RO created a new branch of optimization theory; this is evidence by huge number of publications addressing topics in RO. What is even more impressive is the large number of diverse applications of RO (As of November 2019, (Google Scholar lists over 144,000,000 items when one clicks “application of robust optimization”!).
Brief bio:
Aharon Ben-Tal is a Professor of Operations Research Management at the Technion � Israel Institute of Technology. He received his Ph.D. in Applied Mathematics from Northwestern University in 1973. He has been a Visiting Professor at the University of Michigan, University of Copenhagen, Delft University of Technology, MIT and CWI Amsterdam, Columbia and NYU. His interests are in Continuous Optimization, particularly nonsmooth and large-scale problems, conic and robust optimization, as well as convex and nonsmooth analysis. In recent years the focus of his research is on optimization problems affected by uncertainty. In the last 20 years he has devoted much effort to engineering applications of optimization methodology and computational schemes. Some of the algorithms developed in the MINERVA Optimization Center are in use by Industry (Medical Imaging, Aerospace). He has published more than 135 papers in professional journals and co-authored three books. Prof. Ben-Tal was Dean of the Faculty of Industrial Engineering and Management at the Technion (1989-1992) and (2011-2014). He served in the editorial board of all major OR/Optimization journals. He gave numerous plenary and keynote lectures in international conferences.
In 2007 Professor Ben-Tal was awarded the EURO Gold Medal – the highest distinction of Operations Research within Europe.
In 2009 he was named Fellow of INFORMS.
In 2015 he was named Fellow of SIAM.
In 2016 he was awarded by INFORMS the Khchiyan prize for Lifetime Achievement in the area of Optimization.
In 2017, the Operation Research Society of Israel (ORSIS) awarded him the Lifetime Achievement Prize.
As of September 2018 his work has over 22,900 citations (Google scholar).
| PLENARY LECTURE
Professor Melvyn Sim |
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Title: Robust Stochastic Optimization
Abstract: We present a new distributionally robust optimization model called the robust stochastic optimization (RSO), which unifies both scenario-tree based stochastic linear optimization and distributionally robust optimization in a practicable framework that can be solved using the state-of-the-art commercial optimization solvers. The model of uncertainty incorporates both discrete and continuous random variables, typically assumed in scenario-tree based stochastic linear optimization and distributionally robust optimization respectively. To address the non-anticipativity of recourse decisions, we introduce the event-wise recourse adaptations, which integrate the scenario-tree adaptation originating from stochastic linear optimization and the affine adaptation popularized in distributionally robust optimization. Our proposed event-wise ambiguity set is rich enough to capture traditional statistic-based ambiguity sets with convex generalized moments, mixture distribution, f-divergence, Wasserstein (Kantorovich-Rubinstein) metric, and also inspire machine-learning- based ones using techniques such as K-means clustering, and classification and regression trees. Several interesting RSO models, including optimizing over the Hurwicz criterion and two-stage problems over Wasser- stein ambiguity sets, are provided. We develop a new algebraic modeling package, RSOME to facilitate the implementation of RSO models. This is a joint work with Zhi Chen and Peng Xiong.
Brief bio:
Dr. Melvyn Sim is Professor and Provost’s Chair at the Department of Analytics & Operations, NUS (National University of Singapore) Business School. He holds PhD in Operations Research, June 2004 Massachusetts Institute of Technology, Cambridge MA Thesis: Robust Optimization Advisor: Dimitris J. Bertsimas, MIT His research interests fall broadly under the categories of decision making and optimization under uncertainty with applications ranging from finance, supply chain management, healthcare to engineered systems. He is one of the active proponents of Robust Optimization and has given invited talks in this field at international conferences. Dr. Sim won second places in the 2002 and 2004 George Nicholson best student paper competition and first place in the 2007 Junior Faculty Interest Group (JFIG) best paper competition. He is also the recipient of the 2009 NUS outstanding young researcher award. Dr. Sim serves as an associate editor for Operations Research, Management Science and Mathematical Programming Computations.
Academic and Professional Experience
1. Head, Department of Analytics & Operations, 2017 – Present
2. Professor and Provost’s Chair, April 2016 – Present
3. Courtesy appointment at Industrial and Systems Engineering, Jan 2016 – Present
4. Deputy Director, NUS Global Asian Institute, Aug 2012 – Present
5. Professor, Jan 2012 – Present
6. Dean’s Chair, July 2009 – 2012
7. Deputy Head, Decision Sciences, May 2009 – July 2011.
8. Associate Professor (with tenure), Decision Sciences, July 2008 – Dec 2011
9. NUS Risk Management Institute Affiliated Researcher , 2007 – 2016
10. Fellow, Singapore-MIT-Alliance, 2004 – 2008
11. Assistant Professor , Decision Sciences, NUS, 2004 – 2008
12. Senior Tutor , Decision Sciences, NUS, 2000 – 2004
13. Research Engineer , Singapore Ministry of Defense, 1997 – 1999
Area of Expertise
1. Optimization and under Uncertainty
2. Modeling and Optimization of Operations/Supply chains/Healthcare systems
| PLENARY LECTURE
Professor Martin J. Wainwright |
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Title: Randomized algorithms for big data: From optmization to machine learning
Abstract: Large-scale data sets are now ubiquitous throughout engineering, science and technology, and they present a number of interesting challenges at the interface between machine learning and optimization. In this talk, we discuss the use of randomized dimensionality reduction techniques, also known as sketching, for quickly obtaining approximate solutions to large-scale optimization problems that arise in machine learning and statistics. We first show how sketching allows for much faster solution of constrained quadratic problems, and how the sketch dimension can be adapted to the intrinsic dimension of the solution space. We then show how these ideas lead to a faster, randomized version of the Newton algorithm with provable guarantees.
Based on joint work with Mert Pilanci, Stanford University
Brief bio: Martin Wainwright joined the faculty at University of California at Berkeley in Fall 2004, and is currently a Chancellor’s Professor with a joint appointment between the Department of Statistics and the Department of Electrical Engineering and Computer Sciences. He received his Bachelor’s degree in Mathematics from University of Waterloo, Canada, and his Ph.D. degree in Electrical Engineering and Computer Science (EECS) from Massachusetts Institute of Technology (MIT), for which he was awarded the George M. Sprowls Prize from the MIT EECS department in 2002. He is interested in high-dimensional statistics, information theory and statistics, and statistical machine learning. He has received an Alfred P. Sloan Foundation Research Fellowship (2005), IEEE Best Paper Awards from the Signal Processing Society (2008) and Communications Society (2010); the Joint Paper Award from IEEE Information Theory and Communication Societies (2012); a Medallion Lecturer (2013) of the Institute for Mathematical Statistics; a Section Lecturer at the International Congress of Mathematicians (2014); and the COPSS Presidents’ Award in Statistics (2014). He is currently serving as an Associate Editor for the Annals of Statistics, Journal of Machine Learning Research, Journal of the American Statistical Association, and Journal of Information and Inference.
| PLENARY LECTURE
Professor Jack Xin |
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Title: Nonconvex non-smooth optimization methods for reducing complexity of deep neural networks
Abstract: We discuss nonconvex optimization problems arising in training quantized and sparsified deep neural networks. Such networks have much lower memory and inference costs than their full precision counterparts. The training aims to maintain full precision network performance. Quantization restricts the network weights to discrete values such as {1,-1} up to a scalar multiplier or piecewise constant activation functions. Sparsification refers to network weights being sparse or groupwise sparse. The mathematical and algorithmic challenge is to reconcile the continuous nature of stochastic gradient descent and the discreteness in quantization or sparsification so that the training process is convergent and efficient. We show computational results on large image data sets as well as theoretical analysis on model problems.
Brief bio: Jack Xin is Chancellor’s Professor of Mathematics at UC Irvine. He received his Ph.D in applied mathematics at Courant Institute, New York University in 1990. He was a postdoctoral fellow at Berkeley and Princeton in 1991 and 1992. He was assistant and associate professor of mathematics at the University of Arizona from 1991 to 1999. He has been professor of mathematics at the University of Texas at Austin (1999-2005), and UC Irvine since 2005. His research interests include applied analysis, computational methods and their applications in multi-scale problems, nonconvex optimization, and data science. He authored over a hundred twenty journal papers and two Springer books. He is a fellow of the Guggenheim Foundation, and the American Mathematical Society. He is Editor-in-Chief of Society of Industrial and Applied Mathematics Interdisciplinary Journal Multi-scale Modeling and Simulation (MMS).
| SEMI-PLENARY LECTURE
Professor Takahito Kuno |
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Title: Global optimization of a class of DC functions over a polytope
Abstract:
It is known that every twice continuously differentiable function can be represented as the sum of a convex function and a separable concave function. To find the global minimum of such a class of DC functions over a polytope, we extend the rectangular branch-and- bound algorithm for separable concave minimization and try to improve the bounding process. We also report some numerical results, which indicate our algorithm is rather promising.
Brief bio: From 1988 to 1991, Takahito Kuno worked at TIT as an assistant professor, and then moved to University of Tsukuba in 1991, and now He is now professor in Faculty of Engineering, Information and Systems, University of Tsukuba. His research interest is in Global optimization of multiextremal nonconvex functions, and he is a coeditor of Journal of Global Optimization, Optimization Letters, and SN Operations Research forum.
Program Co-chairs:
- Le Thi Hoai An, University of Lorraine, France.
- Pham Dinh Tao, National Institute for Applied Sciences, Rouen, France
- Nguyen Ngoc Thanh, Wroclaw University of Technology, Poland.
Members
- Bui Alain, University of Versailles Saint-Quentin-en-Yvelines, France.
- Bui Minh Phong, Eotvos Lorand University, Hungary
- Do Nam Hoai, Budapest University of Technology and Economics, Hungary.
- Do Van Tien, Budapest University of Technology and Economics, Hungary.
- Do Thanh Nghi, Can Tho University, Vietnam.
- Ha Quang Thuy, Vietnam National University, Vietnam.
- Hain Ferenc, Budapest College of Communications and Business, Hungary.
- Ho Vinh Thanh, University of Lorraine, France.
- Le Chi Hieu, University of Greenwich, UK.
- Le Hoai Minh, University of Lorraine, France.
- Le Nguyen Thinh, Humboldt Universitat zu Berlin, Germany.
- Nguyen Anh Linh, Warsaw University, Poland.
- Nguyen Canh Nam, Hanoi University of Science and Technology, Vietnam.
- Nguyen Benjamin, University of Versailles Saint-Quentin-en-Yvelines, France.
- Nguyen Duc Cuong, School of Computer Science & Engineering of International University, Vietnam.
- Nguyen Duc Khuong, IPAG Business School, Paris, France
- Nguyen Duc Manh, Hanoi National University of Education, Vietnam.
- Nguyen Giang, Slovak Academy of Sciences, Slovakia.
- Nguyen Hung Son, Warsaw University, Poland.
- Nguyen Luu Lan Anh, Eotvos Lorand University, Hungary.
- Nguyen Manh Cuong, Hanoi University of Industry, Vietnam.
- Nguyen Quang Thuan, International School, Vietnam National University, Hanoi, Vietnam.
- Nguyen Thanh Binh, International Institute for Applied Systems Analysis (IIASA), Austria.
- Nguyen Thanh Thuy, National University of Hanoi, Vietnam.
- Nguyen Van Sinh, International University, Vietnam National University HCM, Vietnam
- Nguyen Viet Hung, Laboratory of Computer Sciences, Paris 6, France.
- Pham Cong Duc, University of Pau and Pays de l’Adour, France.
- Pham Dinh Tao, National Institute for Applied Sciences, Rouen, France.
- Phan Duong Hieu, Université Paris 8, France.
- Pham Duc Truong, University of Birmingham, UK.
- Pham Hoang, Rutgers, The State University of New Jersey, United States
- Pham Ngoc Anh, Posts and Telecommunications Institute of Technology, Vietnam.
- Pham Viet Nga, Vietnam National University of Agriculture, Vietnam.
- Phan Duy Nhat, University of Lorraine, France.
- Ta Thong Vinh, Budapest University of Technology and Economics, Hungary.
- Ta Anh Son, Hanoi University of Science and Technology, Vietnam.
- Tran Duc Quynh, Vietnam National University of Agriculture, Vietnam.
- Tran Dinh Viet, Slovak Academy of Sciences, Slovakia.
- Tran Gia Phuoc, University of Wuerzburg Am Hubland, Germany
- Tran Hoai Linh, Hanoi University of Science and Technology, Vietnam.
- Tran Thi Thuy, FPT University, Vietnam.
- Trinh Anh Tuan, Budapest University of Technology and Economics, Hungary.
- Truong Trong Tuong, Cergy-Pontoise University, France.
- Yi-Shuai Niu, Shanghai Jiao Tong University, China.
- Prof. Le Thi Hoai An, Lorraine University, France (Co-chair)
- Prof. Nguyen Ngoc Thanh, Wroclaw University of Technology, Poland (Co-chair)
- Prof. Bui Alain, Université de Versailles-St-Quentin-en-Yvelines, France
- Prof. Bui Minh Phong, Eotvos Lorand University, Hungary
- Prof. Do Van Tien, Budapest University of Technology and Economics, Hungary
- Prof. Dosam Hwang, Yeungnam University, Korea
- Prof. Pham Dinh Tao, National Institute for Applied Sciences, Rouen, France
- Prof. Pham Duc Truong, University of Birmingham, UK
- Prof. Pham Hoang, Rutgers, The State University of New Jersey, United States
- Assoc. Prof. Nguyen Anh Linh, Warsaw University, Poland
- Prof. Nguyen Dinh Chau, AGH University of Science and Technology, Poland
- Prof. Nguyen Hung Son, Warsaw University, Poland
- Dr. Tran Dinh Viet, Slovak Academy of Sciences, Slovakia
- Prof. Tran Gia Phuoc, University of Wuerzburg Am Hubland, Germany
The ICCSAMA 2019 invites proposals for Workshops & Special Sessions to be held during the conference. They intend to provide researchers in focused areas the opportunity to present and discuss their work, as well as to offer a forum for interaction among a broader community of researchers. A Special Session or Workshop will consist of a group of papers in a sub-discipline of Computer Science, Applied Mathematics and Applications related to the main topics of ICCSAMA 2019. The papers will be required to meet the same standards as ICCSAMA 2019 papers and will be published in the conference proceedings, in a bound volume by Springer-Verlag in their Advances in Inttelligent Systems and Computing series. All the Special Sessions will be centralized as tracks in the same conference submission and reviewing system as the regular papers.
Please send the Workshop / Special Session proposals to Prof. Le Thi Hoai An with the following information:
1. Title & acronym of the special session or workshop
2. Brief profiles of special session or workshop organizers
3. General description of the special session or workshop scope
4. List of topics
5. Proposed Session Program Committee (to be invited)
The organizers will be responsible for the advertisement and promotion of the special session and the conference including the Special Session & Workshop webpage preparation. The management of papers review will be achieved by Special Session & Workshop Committees, using the Conference System (a separate EasyChair track will be provided for each Special Session & Workshop). The organizers are responsible for managing the review process. All the reviews should be submitted through the ICCSAMA 2019 conference system. Each paper should obtain at least two reviews. The final decision as for the acceptance will be taken by the ICCSAMA 2019 Program Committee based on recommendations provided by Special Session & Workshop organizers.
The ICCSAMA 2019 invites proposals for Workshops & Special Sessions to be held during the conference. They intend to provide researchers in focused areas the opportunity to present and discuss their work, as well as to offer a forum for interaction among a broader community of researchers. A Special Session or Workshop will consist of a group of papers in a sub-discipline of Computer Science, Applied Mathematics and Applications related to the main topics of ICCSAMA 2019. The papers will be required to meet the same standards as ICCSAMA 2019 papers and will be published in the conference proceedings, in a bound volume by Springer-Verlag in their Advances in Inttelligent Systems and Computing series. All the Special Sessions will be centralized as tracks in the same conference submission and reviewing system as the regular papers.
Please send the Workshop / Special Session proposals to Prof. Le Thi Hoai An with the following information:
1. Title & acronym of the special session or workshop
2. Brief profiles of special session or workshop organizers
3. General description of the special session or workshop scope
4. List of topics
5. Proposed Session Program Committee (to be invited)
The organizers will be responsible for the advertisement and promotion of the special session and the conference including the Special Session & Workshop webpage preparation. The management of papers review will be achieved by Special Session & Workshop Committees, using the Conference System (a separate EasyChair track will be provided for each Special Session & Workshop). The organizers are responsible for managing the review process. All the reviews should be submitted through the ICCSAMA 2019 conference system. Each paper should obtain at least two reviews. The final decision as for the acceptance will be taken by the ICCSAMA 2019 Program Committee based on recommendations provided by Special Session & Workshop organizers.