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The purpose of this paper is to research the approach of memory institutions to collaboration by analysing collaboration patterns in the networks developed in digitisation initiatives.

Qualitative and quantitative content analysis of the comments about partners and contractors made by respondents of the NUMERIC survey on the progress of digitisation in European cultural institutions was performed. Several attributes of collaborative networks of memory institutions were analysed: their size, members by type of organisation, and visibility of collaborators of particular type. Additionally, comparative analysis of collaborative networks of archives, libraries and museums was carried out.

Memory institutions did not approach collaboration strategically. They exhibited a low engagement in collaboration and focused on establishing resource‐sharing networks. Many of them established networks with the institutions of the same type.

The number of archives which provided comments about their networks was much fewer than the number of libraries and museums. It suggests that additional research on archives and their networks is needed to obtain more reliable data.

The results of this research are useful for managers responsible for digitisation initiatives, evaluating and revising collaboration strategies; professional associations, governmental and advisory bodies working with memory institutions for planning, and providing support and advice in digitisation.

The results of this research would be relevant for professional associations, governmental and advisory bodies, working with memory institutions.

The purpose of this paper is to develop a methodology for the design of cellular neural networks with interconnection topologies optimized and suitable for spatially distributed implementation.

The authors perform combinatorial optimization on the neural network's topology to obtain a sparser network, in which the links between the components of the network that reside in different physical locations are minimized. The approach builds on existing computationally efficient tools for the design of cellular neural networks and uses the concept of the network's stability parameters to assess the performance of the network prior to testing.

It turns out that the sparser cellular neural networks thus produced exhibit performance that can be on par with that of networks with full connectivity, and that for implementations of modest size, communication delays are not that significant to affect the stability of the dynamical system.

The novelty of the proposed approach lies in the formulation of the combinatorial optimization problem in a way that trades‐off network performance for communication overhead, and the use of this method for the physical implementation of associative memories across different interconnected processors.

Balance Theory has accumulated an impressive record of empirical confirmation at both the micro- and macro-levels. Yet, it is unclear why humans consistently prefer balanced relations when imbalance offers the opportunity to reap material rewards. We argue that balance is preferred because it functions as a “compression heuristic,” allowing networks to be more easily encoded in, and recalled from, memory.

We present the results of a novel randomized laboratory experiment using nearly 300 subjects. We evaluate the independent and joint effects of degree of balance/imbalance and presence/absence of kin compression heuristics on network recall.

We find that memory for relationship valence is more accurate for balanced, rather than imbalanced, networks and that relationship existence and relationship valence are separable cognitive elements. We also use comparisons between kin and non-kin networks to suggest that humans are implicitly aware of the conditions under which imbalanced networks will be most durable.

We show that the tension/strain postulated to generate mental and behavioral responses to increase balance likely stems from cognitive limitations. More broadly, this connects balance theory to models of human cognition and evolution and suggests that human general processing ability may have evolved in response to social, rather than physical, challenges.

In Latin America, technology has been advancing faster than society is able to adjust to it on its own. Thus, information and communication professionals should merge their efforts to research and discuss the relevant role they play in the society transformed by technology.

This paper presents and discusses the initiatives that the Brazilian government and stakeholder institutions are developing in regards to ICT and memory and heritage preservation.

The international relevance of describing the Brazilian initiative of ruling the internet in a non-restrictive way shows the trend the country has adopted.

In the current context of development of the Information Society and expansion of cultural economy and digital culture in Brazil, it is imperative to define public policies for digitizing Brazilian memory and heritage collections. Such a national policy involving the three levels of the Federation as well as private institutions committed to the custody of cultural collections, should play an essential role in guiding the efforts to digital reproduction of collections and their publication on the internet.

Brazil is the first country in the world to rule the use of internet openly, in a non-restrictive way, through the Marco Civil da Internet. Since 2007, an academic initiative named Memory Network has been working to promote the digitization and access to Brazilian collections of memory and heritage.

Limited by the types of sensors, the state information available for musculoskeletal robots with highly redundant, nonlinear muscles is often incomplete, which makes the control face a bottleneck problem. The aim of this paper is to design a method to improve the motion performance of musculoskeletal robots in partially observable scenarios, and to leverage the ontology knowledge to enhance the algorithm’s adaptability to musculoskeletal robots that have undergone changes.

A memory and attention-based reinforcement learning method is proposed for musculoskeletal robots with prior knowledge of muscle synergies. First, to deal with partially observed states available to musculoskeletal robots, a memory and attention-based network architecture is proposed for inferring more sufficient and intrinsic states. Second, inspired by muscle synergy hypothesis in neuroscience, prior knowledge of a musculoskeletal robot’s muscle synergies is embedded in network structure and reward shaping.

Based on systematic validation, it is found that the proposed method demonstrates superiority over the traditional twin delayed deep deterministic policy gradients (TD3) algorithm. A musculoskeletal robot with highly redundant, nonlinear muscles is adopted to implement goal-directed tasks. In the case of 21-dimensional states, the learning efficiency and accuracy are significantly improved compared with the traditional TD3 algorithm; in the case of 13-dimensional states without velocities and information from the end effector, the traditional TD3 is unable to complete the reaching tasks, while the proposed method breaks through this bottleneck problem.

In this paper, a novel memory and attention-based reinforcement learning method with prior knowledge of muscle synergies is proposed for musculoskeletal robots to deal with partially observable scenarios. Compared with the existing methods, the proposed method effectively improves the performance. Furthermore, this paper promotes the fusion of neuroscience and robotics.

Rotating machinery is a crucial component of large equipment, and detecting faults in it accurately is critical for reliable operation. Although fault diagnosis methods based on deep learning have been significantly developed, the existing methods model spatial and temporal features separately and then weigh them, resulting in the decoupling of spatiotemporal features.

The authors propose a spatiotemporal long short-term memory (ST-LSTM) method for fault diagnosis of rotating machinery. The authors collected vibration signals from real rolling bearing and gearing test rigs for verification.

Through these two experiments, the authors demonstrate that machine learning methods still have advantages on small-scale data sets, but our proposed method exhibits a significant advantage due to the simultaneous modeling of the time domain and space domain. These results indicate the potential of the interactive spatiotemporal modeling method for fault diagnosis of rotating machinery.

The authors propose a ST-LSTM method for fault diagnosis of rotating machinery. The authors collected vibration signals from real rolling bearing and gearing test rigs for verification.

This paper articulates a multi-contextual and dynamic system for memory research in relation to multi-cultural organizations (MCOs) by a qualitative research method.

Face-to-face semi-structured interviews were conducted with representatives of 30 national officers in North Atlantic Treaty Organization (NATO) to illuminate how the construction of organizational memory (OM) can then be compared and contrasted across different cultures.

The findings show that OM still mostly resides in individuals with the social transfer. However, even if, cultural aspects define what should be stored, time and purpose, the static memory of individuals becomes dynamic OM that is represented and interpreted in an organization's practices, policies and learning.

The primary contribution is to attempt to dissolve the seeming assumption of dialectical metaphoric perspectives of OM between different but related sub-communities of practice and outcomes. Consequently, socially constructed and individual memory models are necessary to integrate different metaphors according to the multi-context theory, which extends the understanding of the diversity between the cultural backgrounds of individuals and groups.

The purpose of this paper is to solve the non-fragile consensus problem of networked robotic manipulators over communication networks by using information from topology-dependent memory.

This paper proposes a topology-dependent memory protocol with distributed consensus controllers for multiple networked robotic manipulators.

The distributed controller gain fluctuations are taken into account with sampled data information exchanges. By the derived results of model transformation, the topology-dependent memory protocol is investigated using sufficient consensus criteria in the form of linear matrix inequalities.

A novel consensus protocol with topology-dependent memory is designed, which can potentially improve consensus performance and deal with the controller gain fluctuations of the robotic manipulators in practical applications.

The Optical Character Recognition (OCR) system described in this article was developed for an application in a major high street bank requiring the high speed reading of cheque serial numbers.

The progress of parallel computing in Information Retrieval (IR) is reviewed. In particular we stress the importance of the motivation in using parallel computing for text retrieval. We analyse parallel IR systems using a classification defined by Rasmussen and describe some parallel IR systems. We give a description of the retrieval models used in parallel information processing. We describe areas of research which we believe are needed.