Information Processing & Management is devoted to refereed reporting of:

1. Basic and applied research in information science, computer science, cognitive science and related areas that deals with: the generation, representation, organization, storage, retrieval, and use of information; the nature, manifestations, behavior, and effects of information and knowledge; communication and distribution of information and knowledge; and human information behavior.

2. Experimental and advanced processes, related to: information retrieval (IR); digital libraries; knowledge organization and distribution; digitized contents - text, image, sound and multimedia processing; and human-computer interfaces in information systems. Implementations in information retrieval systems and a variety of information systems, networks, and contexts. Related evaluation.

3. Management of information resources, services, systems and networks, and digital libraries. Related studies of the economics of information and the principles of information management.

The aim is to provide an international forum for advanced works and critical analysis in these interdependent and interdisciplinary areas. Invited are original papers and critical reviews of trends reporting on:
• Progress in the theory, principles, and procedures in information processing, particularly involving information retrieval; search engines; knowledge and distributed intelligence; information representation, classification, extraction, filtering and summarization; question answering; information navigation, browsing and visualization; and human-computer interaction in information systems.
• Research on the formal characteristics and properties of information and knowledge and the associated processes of communication among humans and between humans and machines. Includes studies of human information needs, seeking, searching, and use; and bibliometric and infometric studies of the structural and statistical properties of information artifacts.
• Modeling and evaluation of information systems performance, particularly of information retrieval systems, knowledge systems, and digital libraries. Studies of their effectiveness, efficiency, value, or impact.
• Studies in management and economics of information and information systems. Use of information for decision making and problem solving.
• Studies in information policies. Data and issues relevant to information policies on organizational, national, and international levels. Derivation and use of information indicators.

Special Issue on New Techniques in Media Quality Modeling
Submission Date: 2019-12-01

With the deployment of low-cost sensors, social media platforms, and cloud storage, the tremendous amount of image, video, and textual signals are cheaply available. As a standard tool to analyze these data, quality model has been pervasively used in domains like intelligent systems and 3D rendering. In the past decades, many shallow quality models have been released and commercialized. Despite their success, conventional quality models might be deficiently effective to handle the massive-scale data nowadays. 

Potential challenges include (not limited to): 

First, owing to the significant progress in deep feature engineering, deep quality models have been proposed and satisfactory performance was received. But deep model is conducted in a black-box manner, how to make it interpretable or transparent to quality modeling, and encoding human subjective wills and perception are still unsolved. 

Second, compared to the fully-annotated signals when modeling small-scale data, it is infeasible to label large-scale image/video at pixel-level due to the unaffordable human resources. In practice, only image/video-level labels or partial labels are available. Even worse, sometimes these weak labels are contaminated. Therefore, how to design a noiserobust weakly-supervised learning algorithm to exploring pixel-level quality-related elements is a tough problem. 

Third, conventional quality models typically leverage local/global features to evaluate each image/video, where human visual perception cannot be encoded explicitly. Apparently, human visual perception plays a significant role in quality modeling. In the literature, it is difficult to mimic human visual perception, i.e., predicting human gaze behavior and subsequently modeling the visual signal cognition in human brain. 

In this special issue, we will focus on the recent progress in image/video/text quality modeling and analytics. We aim to explore interpretable, noise-tolerant, and perceptionaware deep models to enhance quality models. Submissions related to new image/video/text benchmarks for testing the performance of quality models are also welcome. The primary objective for this special issue is to foster focused attention on the latest research progress in this cutting-edge area. We intend to attract researchers and practitioners from both industry and academia. 

Topics of interest include (but are not limited to): 

o New deep architectures for image/video quality evaluation; 
o Deep algorithms for enhancing the shallow-feature-based intelligent systems; o Quality-driven image/video processing techniques; 
o New Quality models in management applications; 
o Semantic models for deep image/video quality prediction; 
o New management tools based on deep quality models; 
o New machine learning algorithms for deep media quality modeling; 
o Visual quality prediction for photo and video management systems; 
o Leveraging human interactions to improve deep quality models; 
o Perception-aware quality models for Internet-scale media retrieval; 
o Novel deep quality features and their applications in pattern recognition. 
o Deep models trained using small samples for quality understanding; 
o Novel photo or video retargeting/cropping/re-composition using deep features; 
o New datasets, benchmarks, and validation of deep quality models; 
o Subjective methodologies to estimate the quality in real-world systems; 
o Novel visualization technologies for deep quality features;

Special Issue on Cross-Media Analysis and Understanding
Submission Date: 2020-01-01

The purpose of this special issue is to solicit the latest unpublished work from both academia and industry on cross-media analysis and understanding. The rationales to original contributions are four-fold: 1) showcasing new theories and new application on cross-media uniform representation; 2) cross-media correlation understanding and deep mining; 3) cross-media knowledge graph construction and learning methodologies; 4) surveying the recent advances in this area. The areas of interest include, but are not limited to, the following:

1. Cross-media Analysis and Understanding

Cross-Media Feature Learning and Fusion
Multi-Task Learning over Cross-Media Analysis
Domain Adaption Learning over Cross-Media Understanding
Novel Dataset and Benchmark for Cross-Media Analysis and Understanding
Deep Learning for Cross-Media Analysis, Knowledge Transfer and Information Sharing

2. Applications

Event Detection
Oject Tracking
Obect Recognition
Cross-Media Indexing & Retrieval
Question Answering System
Healthcare Applications
Human Computer Interaction

Special Issue on Dark Side of Online Information Behavior
Submission Date: 2020-01-31

The dark side of online information behavior represents the negative phenomena associated with the management of information in the online environment. With the widespread availability of Internet and the emerging technologies, cyberspace becomes one of the most important channels for people to generate, organize, store, retrieve, acquire, disseminate and utilize information. Recognizing that information can be easily managed online although it causes different types of negative consequences. For example, 87 million Facebook user profiles have been improperly shared and misused by Cambridge Analytica, and online information privacy becomes a worldwide concern in recent years. Online fake news also exerts profound influence on political, economic, and social well-being. With the increasing volume of available information, we also witnessed a society of information overload and information anxiety. At the same time, information violence and harassment foster a hostile online environment. The power of artificial intelligence makes it easier for people to access the information they need, but it also creates information cocoons.

Although there are many dark sides of online information behavior, current studies on this topic are still limited, leaving considerable gaps in the literature, particularly on how to conceptualize and operationalize the dark or unexpected negative sides of online information behaviors, how to theorize the underlying cognitive, psychological and social processes of such behaviors, and how to implement system design and information recognition to avoid negative information behaviors. The objective of this special issue thus is to push the boundaries of information behavior research, and draw the urgent attention of academics and practitioners to this important and fertile area.

We believe this is a topic of challenges faced by multidisciplinary fields such as information systems, library and information science, computer science, marketing, communication and cognitive sciences. This special issue seeks high-quality and original contributions that advance the concepts, methods and theories by exploring the dark side of online information behaviors, and address the mechanisms, strategies and techniques for behavioral interventions. All contributions should clearly address the knowledge gaps indicated in the literature and will be peer-reviewed by the panel of experts associated with relevant field. This special issue is open to submissions from all theoretical and methodological perspectives. We particularly welcome research that challenges the boundaries of traditional academic thinking, integrating and expanding the knowledge rooted in diverse disciplines and within diverse contexts, and comes up with innovative ideas in theorizing and resolving the negative issues related to online information behavior.

The topics of interest include, but are not limited to:

Misinformation, disinformation and online fake news
Information addiction, overload and underload
Information privacy and security concerns
Technophobia and information anxiety
Information violence and harassment
Illegal or unethical information searching, distribution and use
Deceptive online communication
Information cocoons and echo-chambers
Information distractions, disruptions and interruptions
Counterproductive online information behaviors
Data-driven negative information extraction, recognition and validation methods
System design that tracks and solves the above negative issues related to information behavior

Special Issue on Transformative computing approaches for advanced management solutions and cognitive processing
Submission Date: 2020-02-01

Transformative computing is a quite new branch of computer sciences, and define advanced computational paradigm, which allow to join wireless communication technologies, sensing devices and artificial intelligence technologies. It enhances computational possibilities, and increase efficiency of data fusion, exploration and analysis using edge sensors and augmented cognition. It can be applicable for advanced information processing and management areas, especially thanks to the development of new AI approaches and techniques, oriented on using of novel models for data processing, and cognitive computing. Artificial Intelligence and cognitive reasoning are based on human visual perception models and perceptual abilities. Such human-oriented information processing methods, allow to intelligently analyze a great amount of data (Cloud, Big, multimedia, etc.), and manage them in secure manner, and transmit over global communication networks.

In this SI we’ll try to focus on new possible applications of cognitive approaches and transformative computing for information processing, data fusion and analysis, knowledge extraction, and secure distributed information management. These subjects, as well as a number of others, connected with transformative computing, and advanced information processing will form the subject of this Special Issue on “Transformative computing approaches for advanced management solutions and cognitive processing” in the Information Processing & Management Journal.

Topics of interest include, but are not limited to, the following:

Transformative computing applications
Behavioral and perceptual models for data analysis and management
Cognitive approaches for information processing and understanding
New models for secure and hierarchical information management
Application of cognitive information systems
Innovative approaches for information acquisition and management
Human oriented protocols for services management
Information retrieval and decision support systems
Computational intelligence and ambient technologies for data evaluation
Cognitive security protocols