计算机 | CCF推荐期刊专刊信息5条

全文截稿: 2020-11-21

影响因子: 0.0

CCF分类: C类

中科院JCR分区:

• 大类 : 工程技术 - 3区

• 小类 : 计算机：信息系统 - 3区

• 小类 : 电信学 - 3区

网址: http://www.journals.elsevier.com/pervasive-and-mobile-computing/

Throughout history, pandemics have ravaged humanity with plagues and infections that created humanitarian crises, severed social interactions, hindered economic growth, and caused human lives loss. With the most recent COVID-19 outbreak, researchers and practitioners across various domains such as medical and life sciences, economics, and engineering are coming together to put forward solutions to counter such a threat and aid the society in coping with the fallbacks. In the same context, the computing community in general and IoT researchers and practitioners in particular face a challenge about how IoT-based systems can be exploited to fight the COVID-19 pandemic. This special issue aims to find answers to some fundamental questions such as what IoT systems, technologies, and infrastructures can be exploited for data and knowledge-driven management of the pandemics, how IoT can enable innovative and unconventional solutions for mitigating outbreaks (through mechanisms such as context-sensitive contact tracing and symptomatic detection, smart lockdowns, crowd-sensed discovery of the emerging clusters), and how IoT can contribute to increased public awareness and safety, and counter the negative emotional and social impact.

This special issue invites technical papers that focus on theoretical and applied research contributions that present original ideas, modeling and simulation results, prototypes, and real-world experiences in the context of IoT for countering pandemics. Interdisciplinary works are most welcome.

This special issue will focus on (but will not be limited to) the following topics:

● Engineering of IoT Systems to Counter the COVID-19 Pandemic: IoT-driven smart lockdown; context-sensitive contact tracing and symptomatic detection; crowd-sensed identification of the pandemic hotspots; IoT-driven smart health in the time of pandemics; IoT-driven detection of transmission pathways and dose-response effect; data engineering for pandemic IoT systems.

● Algorithms for IoT Systems to Counter the COVID-19 Pandemic: Context modeling and reasoning applied to pandemics; activity and well-being recognition for early detection of symptoms and monitoring of disease progression; data mining, machine learning and causal reasoning applied to IoT systems to fight pandemics; social and complex networks of IoT devices during pandemics.

● Empirical Research on IoTs to Counter the COVID-19 Pandemic: Industrial findings and experience reports; validation and evaluation research; measurement studies; systematic mapping studies, or systematic literature reviews.

● Reference Architectures, Infrastructures, and Tools for IoT Systems to Counter the COVID-19 Pandemic: IoT-driven pandemic management; architectural patterns and styles for pandemic tracing; prototypes and tool support; mobile cloud computing, fog and edge computing; development environments, frameworks, and tools; technological IoT innovations; trust, security, and privacy.

● Analytical models of IoT Systems to Counter the COVID-19 Pandemic: performance models of IoT-driven containment and mitigation strategies; analytical studies of required IoT penetration to achieve control of the epidemic; data-driven IoT models and estimation of key parameters to feed into theoretical models.

● Application of IoT Systems to Counter the COVID-19 Pandemic: smart healthcare; smart emergency response systems; smart community and crowd management; food security; smart lockers and innovative choice, pack and delivery methods; unconnected infrastructure and IoT systems.

● IoT Systems beyond COVID-19: experience reports, applied solutions, frameworks, prototypes, simulations, and validation research to detect, manage, and counter epidemics like Dengue, Ebola, SARS, Zika, etc.

全文截稿: 2020-11-30

影响因子: 1.159

CCF分类: B类

中科院JCR分区:

• 大类 : 工程技术 - 4区

• 小类 : 计算机：硬件 - 4区

• 小类 : 计算机：软件工程 - 4区

网址: http://www.journals.elsevier.com/journal-of-systems-architecture/

Edge computing has been pushing computing services from clouds toward ends in recent years. Meanwhile, big data on edges are growing with privacy awareness, energy and efficiency concern to compose an urgent need to push the artificial intelligence (AI) applications towards network edges. These two emerging trends led to an interdisciplinary area, i.e. Edge Intelligence (EI), which starts to draw tremendous amounts of academic and industrial attention, even if the research on Edge Intelligence is in its early stage. One category of Edge Intelligence approaches is to make the traditional cloud-based AI models tailored for edges by model compression, model partition, edge caching etc. The other category is to implement the procedure of building AI models entirely on the edge by federated learning, gradient compression, gossip learning etc. Nevertheless, designing EI solutions has to make trade-offs between various aspects of the hardware capability, performance, cost, privacy, efficiency, reliability and be optimized with respect to its specific objectives. This brings many challenging research issues and exchanging latest advances in Edge Intelligence is desired by researchers in all related domains, e.g. computer architectures, computer systems, artificial intelligence, computer network, AI circuits and systems. This special issue aims at the latest and novel contributions from both academic researchers and industry practitioner in the emerging area of Edge Intelligence.

Topics of interest to the Special Issue include but not limited to:

Edge intelligence paradigm for manufacturer pipelines

Edge intelligence paradigm for smart communities

Edge intelligence paradigm for scientific apparatus

Edge intelligence architecture, circuits and systems

Federated learning algorithms and training schemes for edge intelligence systems

Privacy and safety protection for edge intelligence systems

Collaborative management for edge intelligence systems and clouds

Energy management of edge intelligence systems

Sensing and communication technologies for edge intelligence systems

全文截稿: 2020-12-20

影响因子: 2.559

CCF分类: B类

中科院JCR分区:

• 大类 : 工程技术 - 3区

• 小类 : 计算机：软件工程 - 2区

• 小类 : 计算机：理论方法 - 3区

网址: http://www.journals.elsevier.com/journal-of-systems-and-software/

The development of software-intensive systems is continuously evolving and faces many new challenges. New technologies, such as cloud computing, Internet of Things (IoT), Artificial Intelligence (AI), etc. as well as continuous engineering processes, such as DevOps, are being employed in new open contexts with more pervasive software, e.g., in Smart Cities, Smart Manufacturing, Smart Mobility, etc. This special issue will focus on collecting the latest research results on such emerging topics.

The topics relevant to this special issue include, but are not restricted to, the following:

- Software management & processes

- Software quality & technical debt

- Cloud-native computing & DevOps

- Embedded systems, edge computing & IoT

- Model-driven software engineering

- AI-supported software engineering

- Data and AI-driven software engineering

- Mining open data and software repositories

全文截稿: 2020-12-25

影响因子: 5.101

CCF分类: C类

中科院JCR分区:

• 大类 : 工程技术 - 2区

• 小类 : 计算机：人工智能 - 2区

网址: http://www.journals.elsevier.com/knowledge-based-systems/

Social media analytics have proven valuable in numerous research areas as a pragmatic tool for public opinion mining and analysis. Sentiment analysis addresses the dynamics of complex socio-affective applications that permeate intelligence and decision making in the sentient and solution-savvy Social Web.

Having started as simple polarity detection, contemporary sentiment analysis has advanced to a more nuanced analysis of affect and emotion sensing. Detecting finegrained sentiment in natural language, however, is tricky even for humans, making its automated detection very complicated. Moreover, online opinions can be put forth in the form of text reviews or ratings, for a product as a whole, or each of its individual aspects. Multiple and lengthy reviews, usage of casual dialect with microtext (wordplay, neologism and slang), use of figurative language (sarcasm, irony), multilingual content (code-mixed and code-switched) and opinion spamming add challenges to the task of extracting opinions.

Recently memes, GIFs, typo-graphic (artistic way of text representation), info-graphic (text embedded along with an image) visual content and edited videos also dominate social feeds. Consequently, the intra-modal modeling and inter-modal interactions between the textual, visual and acoustic components add to the linguistic challenges. Therefore, conceptualization and development of multi-faceted sentiment analysis models to adequately capture observed opinion-sensitive information are imperative.

Artificial intelligence (AI) driven models, especially deep learning models, have achieved state-of-the-art results for various natural language processing tasks including sentiment analysis. We get highly accurate predictions using these in conjunction with large datasets, but with little understanding of the internal features and representations of the data that a model uses to classify into sentiment categories. Most techniques do not disclose how and why decisions are taken. In other words, these black-box algorithms lack transparency and explainability.

Explainable AI (XAI) is an emerging field in machine learning that aims to address how AI systems make decisions. It refers to AI methods and techniques that produce humancomprehensible solutions. XAI solutions will enable enhanced prediction accuracy with decision understanding and traceability of actions taken. XAI aims to improve human understanding, determine the justifiability of decisions made by the machine, introduce trust and reduce bias.

This special issue aims to stimulate discussion on the design, use and evaluation of XAI models as the key knowledge-discovery drivers to recognize, interpret, process and simulate human emotion for various sentiment analysis tasks. We invite theoretical work and review articles on practical use-cases of XAI that discuss adding a layer of interpretability and trust to powerful algorithms such as neural networks, ensemble methods including random forests for delivering near real-time intelligence.

Concurrently, works on social computing, emotion recognition and affective computing research methods which help mediate, understand and analyze aspects of social behaviors, interactions, and affective states based on observable actions are also encouraged. Full length, original and unpublished research papers based on theoretical or experimental contributions related to understanding, visualizing and interpreting deep learning models for sentiment analysis and interpretable machine learning for sentiment analysis are also welcome.