Journal Information
Future Generation Computer Systems (FGCS)
http://www.journals.elsevier.com/future-generation-computer-systems/
Impact Factor:
5.768
Publisher:
Elsevier
ISSN:
0167-739X
Viewed:
37650
Tracked:
141

Call For Papers
The Grid is a rapidly developing computing structure that allows components of our information technology infrastructure, computational capabilities, databases, sensors, and people to be shared flexibly as true collaborative tools. Over the last 3 years there has been a real explosion of new theory and technological progress supporting a better understanding of these wide-area, fully distributed computing systems. After the advances made in distributed system design, collaborative environments, high performance computing and high throughput computing, the Grid is the logical next step.

The new Aims and Scope of FGCS will cover new developments in:

[1] Grid Applications and application support:

    Novel applications
    eScience and eBusiness applications
    Problem solving environments and virtual laboratories
    Grid economy
    Semantic and knowledge based grids
    Collaborative Grids and virtual organizations
    High Performance and high throughput computing on grids
    Complex application workflows
    Scientific, industrial and social implications
    Grids in education

[2] Grid methods and middleware:

    Tools for grid development: monitoring and scheduling
    Distributed dynamic resource management
    Grid- and web-services
    Information management
    Protocols and emerging standards
    Peer to peer and internet computing
    Pervasive computing
    Grid Security

[3] Grid Theory:

    Process specification; program and algorithm design
    Theoretical aspects of wide area communication and computation
    Scaling and performance theory
    Protocol verification


Last updated by Dou Sun in 2019-11-24
Special Issues
Special Issue on Future-Generation Personality Prediction From Digital Footprints
Submission Date: 2021-02-15

Personality traits are generally referred to as relatively stable patterns of thoughts, feelings, and behaviours that have been associated with a wide range of important life outcomes and choices. Specifically, personality traits have repeatedly been related to the individual (e.g., well-being, psychopathology), interpersonal (e.g., relationship satisfaction), and social-institutional outcomes (e.g., occupational choices, job success). Hence, in the recent years, there has been a massive increase in the interest to develop models which use online data on human behaviour and preferences (i.e., digital footprints) to automatically predict an individuals’ personality traits. Advances in consumer electronics (e.g., smartphones, wearables) and the subsequent development of mobile sensing methods have facilitated the collection of highly detailed, multi-dimensional data on behaviours and situations. Social media gives users the opportunity to build an online persona through posting of content such as text, images, links or through interaction with others. The way in which users present themselves is a type of behaviour usually determined by differences in demographic or psychological traits. The behavioural residue harvested from websites and online social media platforms is also another valuable source of data on behaviour linked to personality traits. Hence, automated personality prediction has important practical applications in diverse areas ranging from recommendation systems, computational advertising, marketing science, job screening to aiding in psychological counselling, intervention and therapy, enhanced human-computer interaction, etc. It is also interesting to see the benchmarking studies with regard to the sensitivity of the data, prediction performance of the models, and cost for businesses to securely store the data, models, react to GDPR (General Data Protection Regulation) requests, etc. However, it should be noted that automated personality prediction is a controversial topic and serious concerns have been raised with regard to implications for individual privacy and the conception of informed consent. While the performance of these models is not high enough to allow for the precise distinction of people based on their traits, predictions can still be "right" on average and be utilized for digital mass persuasion and for personalization efforts. Focusing research on explainable models, rather than just using them as black-box personality predictors can help to bridge the seemingly distant fields of computational personality detection and personality research in psychological science. The primary objective of this special issue is to bring together diverse, novel and impactful work on personality prediction in one place, thereby accelerating research in this field. The topics of interest for this special issue include, but are not limited to: Personality prediction from multimodal and diverse input modalities (e.g., audio, video, text) along with new approaches effectively fusing features extracted from multiple sources (for e.g., using heterogeneous data collected from different devices) Deep learning-based approaches (e.g., CNNs, GANs for data augmentation, deep RL, etc.) Machine learning for automated personality prediction from user behaviour. For example: ○ Social media interaction ○ Author profiling based on writing ○ Consumer device usage patterns (e.g., wearable devices, smartphones, etc.)
Last updated by Dou Sun in 2020-11-02
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