【计算机类】国际会议截稿信息6条

计算机综合与前沿

InfQ 2017

New Frontiers in Quantitative Methods in Informatics

全文截稿: 2017-09-30

开会时间: 2017-12-04

会议难度: ★★

CCF分类: 无

会议地点: VENICE, ITALY

网址：http://valuetools.org/2017/show/cf-workshops

InfQ welcomes both contributions on methodology and application, and gives value to both the theoretical and practical perspectives. Conference topics include, but are not limited to:
- quantitative modelling formalisms, solution techniques, and tools for probabilistic or stochastic systems possibly combining non-determinism, queueing networks, analytical numerical or statistical solution, fluid and hybrid behavior, emergent behavior and collective systems, game theory, hierarchical or multi-formalism composition and model driven engineering, soft computing;
- applications of quantitative methods in a variety of contexts, notably including Industry 4.0, Internet of Things, Cyber-Physical Systems, smart cities, smart energy, e-healthcare and Ambient Assisted Living, social and urban computing, Bioinformatics, Software Defined Networks, quantum computing, cloud/edge/fog computing, capacity planning, load characterization, self adaptive systems.

人工智能

BDTL 2017

International Workshop on Big Data Transfer Learning

全文截稿: 2017-10-10

开会时间: 2017-12-11

会议难度: ★★

CCF分类: 无

会议地点: Boston MA, USA

网址：http://www.cis.umassd.edu/~mshao/BDTL2017/index.html

Although widely applied on lots of scientific research, conventional statistical machine learning revolves on a simplified assumption that the training data, from which the algorithms learn, are drawn i.i.d. from the same distribution as the test data, to which the learned models are applied. This assumption, being broken down by numerous real-world applications and practice, espe-cially with the emergence of large-scale healthcare data (e.g., electronic medical record, medical sensors, MRI/CT/X-Ray images) from both private Intranet and public Internet/databases1, has fundamentally restricted the development of practical learning algorithms. For example, intelli-gent recognition systems are trained to recognize malignant tumors or predict certain disease; however, when deployed in the new environment, these algorithms may confront tumors in different shapes, textures with different background, or patient with different demographics from different regions. Although both benign and malignant tumors and the disease being pre-dicted have been registered in system already, it may still fail due to enormous variations be-tween training data and test data in terms of appearance or feature space.

With AI and machine learning algorithms being increasingly popular towards knowledge mining for health informatics, there is an urgent need to ''smooth'' the transition and deployment work from intelligent system trained in manufacturer’s lab to that operated in hospitals. On the other hand, weakly labeled or unlabeled data in relevant fields may contribute generic features and representations for healthcare data in variety of formats, e.g., zero-shot learning, self-taught learning, which open up a new way for knowledge transfer in health informatics.

计算机体系结构,并行与分布式计算

DSSCB 2017

International Workshop on Distributed Storage Systems and Coding for Big Data

全文截稿: 2017-10-10

开会时间: 2017-12-11

会议难度: ★★★

CCF分类: 无

会议地点: Boston, MA, USA

网址：http://www.ece.pku.edu.cn/workshop/bigdata2017/

In recent years, data storage has emerged as an important research field driven by the demand for scalable structures and technologies to satisfy the growing needs of massive data management and processing. Big Data challenges storage systems with more capacity, scalability and efficient accessibility. Dispersing a huge data object in a large-scale distributed storage system is necessary to enhance data reliability and availability. By introducing redundancy in the system, we can protect data integrity from node failures. As node failures occur frequently in large-scale storage systems, a considerable volume of network traffic is dedicated to the repair of failed storage nodes. Several classes of distributed storage codes, such as regenerating codes, locally repairable codes, have been introduced recently to reduce this overhead and disk input/output cost. However, there still remains substantial research work for advancing distributed storage coding and systems in both theory and applications.

This workshop will provide an excellent platform for researchers and practitioners from academia and industry to exchange ideas and experiences that distributed storage systems can offer to Big Data applications, and to understand the challenges that we need tackle to realize the full potential.

人工智能

MPREF 2018

Multidisciplinary Workshop on Advances in Preference Handling

全文截稿: 2017-10-13

开会时间: 2018-02-02

会议难度: ★★

CCF分类: 无

会议地点: New Orleans, USA

网址：http://www.mpref-2018.preflib.org/

The workshop on Advances in Preference Handling addresses all computational aspects of preference handling. This includes methods for the elicitation, learning, modeling, representation, aggregation, and management of preferences and for reasoning about preferences. The workshop studies the usage of preferences in computational tasks from decision making, database querying, web search, personalized human-computer interaction, personalized recommender systems, e-commerce, multi-agent systems, game theory, social choice, combinatorial optimization, planning and robotics, automated problem solving, perception and natural language understanding and other computational tasks involving choices. The workshop seeks to improve the overall understanding of and best methodologies for preferences in order to realize their benefits in the multiplicity of tasks for which they are used. Another important goal is to provide cross-fertilization between the numerous sub-fields that work with preferences.
-Preference handling in artificial intelligence
-Preference handling in database systems
-Preference handling in multiagent systems
-Applications of preferences
-Preference elicitation
-Preference representation and modeling
-Properties and semantics of preferences
-Practical preferences

计算机体系结构,并行与分布式计算

EDIS 2017

International conference on Embedded & Distributed Systems

摘要截稿: 2017-10-01

全文截稿: 2017-10-15

开会时间: 2017-12-17

会议难度: ★★

CCF分类: 无

会议地点: Oran, Algeria

网址：https://sites.google.com/view/edis2017/

Under the patronage of the chancellor of the University of Oran 1, Pr. SENOUCI Mohamed, the laboratory of parallel, embedded architectures and high performance computing (LAPECI) of the Computer Science Department, Faculty of Exact and Applied Sciences at the University of Oran 1, Ahmed Ben Bella, is pleased to announce the organization of the 1st international conference on Embedded and Distributed Systems, EDiS '17.

EDiS aims to bring together researchers about distributed systems, embedded systems, optimization, high performance computing and model driven engineering.  Junior and senior researchers are invited to submit their original unpublished papers on the following topics, but not limited to :
-Embedded and Real-time systems:  Real-time systems, Mapping and routing, Signal processing
-Information retrieval Machine learning , Indexation, Data mining
-High performance computing, Parallel & distributed systems Big data processing
-Multi-objective optimization Heuristics, Metaheuristics, Hybridization
-Model-Driven Engineering  
-Cyber-physical systems
-Hardware design for low power

计算机体系结构,并行与分布式计算

MULTIPROG 2018

International Workshop on  Programmability and Architectures for Heterogeneous Multicores

全文截稿: 2017-10-22

开会时间: 2018-01-24

会议难度: ★★

CCF分类: 无

会议地点: Manchester, UK

网址：http://research.ac.upc.edu/multiprog/

Computer manufacturers have embarked on the many-core roadmap, promising to add more and more cores/hardware threads on their chips. The ever-increasing number of cores and heterogeneity in architectures has placed new burdens on the programming community. Software needs to be parallelized and optimized for accelerators such as GPUs in order to take advantage of the new breed of multi-/many-core computers. As a result, progress in how to easily harness the computing power of multi-core architectures is in great demand.

Papers are sought on topics including, but not limited to:
1.Multi-core architectures
-Architectural support for compilers/programming models
-Processor (core) architecture and accelerators, in particular GPUs
-Memory system architecture
-Performance, power, temperature, and reliability issues
2.Heterogeneous computing
-Algorithms and data structures for heterogeneous systems
-Applications for heterogeneous computing and real-time graphics
3.Programming models for multi-core architectures
-Language extensions
-Run-time systems
-Compiler optimizations and techniques
4.Benchmarking of multi-/many-core architectures
-Tools for discovering and understanding parallelism
-Tools for understanding performance and debugging
-Case studies and performance evaluation

会议推广

UPINLBS 2018 链接