Information Networking Architecture Research Articles

Common functional mechanisms underlying dynamic brain network changes across five general anesthetics: A rat fMRI study.

Reversible loss of consciousness is the primary therapeutic endpoint of general anesthesia; however, the drug-invariant mechanisms underlying anesthetic-induced unconsciousness are still unclear. This study aimed to investigate the static, dynamic, topological and organizational changes in functional brain network induced by five clinically-used general anesthetics in the rat brain. Male Sprague-Dawley rats (n = 57) were randomly allocated to received propofol, isoflurane, ketamine, dexmedetomidine, or combined isoflurane plus dexmedetomidine anesthesia. Resting-state functional magnetic resonance images were acquired under general anesthesia and analyzed for changes in dynamic functional brain networks compared to the awake state. Different general anesthetics induced distinct patterns of functional connectivity inhibition within brain-wide networks, resulting in multi-level network reorganization primarily by impairing the functional connectivity of cortico-subcortical networks as well as by reducing information transmission capacity, intrinsic connectivity, and network architecture stability of subcortical regions. Conversely, functional connectivity and topological properties were preserved within cortico-cortical networks, albeit with fewer dynamic fluctuations under general anesthesia. Our findings highlighted the effects of different general anesthetics on functional brain network reorganization, which might shed light on the drug-invariant mechanism of anesthetic-induced unconsciousness.

Empirical Efficiency Modeling of Online + Offline Teaching Evaluation Mode of Courses Based on 6G Real-Time Image Transmission Architecture

Putting forward the requirements of the 6G-oriented space-earth integrated information network architecture, and put forward a flexible and reconfigurable space-earth integrated information network architecture on this basis. On the basis of the research, it puts forward the countermeasures and suggestions to improve the evaluation mode of college English online teaching. From the aspects of curriculum structure, curriculum implementation, evaluation feedback, teacher team construction and talent training, the innovative teaching mode of "online micro-course" integrated into mathematical modeling courses is studied. A set of experimental teaching plans based on small modeling projects and corresponding teaching methods are proposed. The system of learning evaluation, and the effect of curriculum reform was analyzed through empirical teaching.

User Demographic Information and Deep Neural Network in Film Recommendation System based on Collaborative Filtering

Research about implementation of deep neural network in recommender system based on collaborative filtering received many attentions recently. One of the major problems in deep neural network based collaborative filtering recommendation system was coldstart problem. Some recent work tried to improve model performance by modifying how the model modelled the interaction between user and item features to generate TOP-N recommendations. This work proposed DNCF (Demographic Neural Collaborative Filtering) model that utilized user demographic information and deep neural network architecture to generate film recommendation system based on collaborative filtering in cold-start problem. NCF model was used as baseline model for model performance comparison. Hit Ratio and Normalized Discounted Cumulative Gain for TOP-10 recommendations were used as evaluation metrics for model performance. Experiment results showed that the proposed DNCF model outperform baseline NCF model by 23,61% in HR@10 and 22,40% in NDCG@10 evaluation metrics. Keywords — collaborative filtering, deep neural network, demographic information, neural collaborative filtering, recommendation system

TSAL: Two steps Adversarial learning based domain adaptation

Since labeled data availability differs greatly across domains, Domain Adaptation focuses on learning in new and unfamiliar domains by reducing distribution divergence. Recent research suggests that the adversarial learning approach could be a promising way to achieve the domain adaptation objective. Adversarial learning is a strategy for learning domain-transferable features in robust deep networks. This paper introduces the TSAL paradigm, a two-step adversarial learning framework. It addresses the real-world problem of text classification, where source domain(s) has labeled data but target domain (s) has only unlabeled data. TSAL utilizes joint adversarial learning with class information and domain alignment deep network architecture to learn both domain-invariant and domain-specific features extractors. It consists of two training steps that are similar to the paradigm, in which pre-trained model weights are used as initialization for training with new data. TSAL’s two training phases, however, are based on the same data, not different data, as is the case with fine-tuning. Furthermore, TSAL only uses the learned domain-invariant feature extractor from the first training as an initialization for its peer in subsequent training. By doubling the training, TSAL can emphasize the leverage of the small unlabeled target domain and learn effectively what to share between various domains. A detailed analysis of many benchmark datasets reveals that our model consistently outperforms the prior art across a wide range of dataset distributions.

Hybrid adversarial network for unsupervised domain adaptation

Recent advances suggest that adversarial domain adaptation has been embedding into deep neural networks to learn domain-transferable representations, which reduces distribution divergence in both the training and test samples. However, previous adversarial learning algorithms only resort to learn domain-transferable feature representation by bounding the feature distribution discrepancy cross-domain. These approaches, however, may lead to misalignment and poor generalization results due to without further exploiting class information and task-special adaptation. To cope with this issue, a joint adversarial learning with class information and domain alignment deep network architecture, is proposed which is named Hybrid Adversarial Network (HAN). Specifically, it incorporates a classification loss to learn a discriminative classifier, and a domain adversarial network learns a domain-transferable representation to reduce domain shift. Meanwhile, a CORAL loss is used to minimize the discrepancy between the covariance matrices in the two domains. Additionally, we introduce an adaptation layer for further boosting the performance of HAN model. Comprehensive cross-domain visual recognition experiments validate that our method exceeds the state-of-the-art methods on three real-world benchmark including Office-31, Office-Home, and ImageCLEF-DA datasets.

Research on a High Reliability Multi-Controller Deployment Method

In the software-defined aeronautical information network architecture, a multi-controller deployment scheme was proposed for the control plane scalability problem, which includes two phases: cluster domain partitioning and intra-domain deployment controller. An improved k-means algorithm was proposed in the cluster domain segmentation phase, which can effectively avoid discrete points from becoming cluster center. When the controller was deployed in the cluster domain, the minimum failure rate of the control path in the network was targeted, and an improved discrete particle swarm optimization algorithm was used to solve the problem to obtain effective control of the network. The simulation results show that the algorithm can effectively reduce the failure rate of the control path based on the reasonable division of the cluster domain. For example, when the number of controllers was increased from 3 to 15, the total failure rate was correspondingly reduced from 0.242 to 0.092, and good results are obtained. This solution provides a new way to solve the problem of multi-controller deployment under dynamic and large-scale networks.

Multipurpose IP-Based Space Air-Ground Information Network

Integration of space resources (e.g., satellites) in different orbits with the terrestrial/ground network (e.g., internet) is the new evolving era of information technology. This kind of information network can expand the ground services such as internet to the whole world with the help of satellites’ broader coverage. On the other hand, it can also provide realtime access to the satellite’s useful data (e.g. images of earth), for nearly everyone on the globe. For these reasons, there has been enormous amount of research on this topic, and different network architectures are proposed for the integration of space and ground networks for different applications. However, most of them are either general abstract architecture or for the single dedicated application. Also for such a network, it is desirable that it should be based on the TCP/IP (Transmission control protocol/internet protocol) stack for interoperability with terrestrial networks. However, the biggest challenge for this is the dynamic network topologies due to orbital dynamics of space nodes. In this paper, we have addressed both of these issues by proposing a Space-Air-Ground Information Network (SAGIN) Architecture, which can provide almost all of the services of space and ground networks for all the users around the world according to varying needs of users. Our proposed SAGIN architecture is IP-based to provide interoperability and compatibility with the already deployed ground network. We have also addressed the challenge of dynamic nature of the network topology by designing a simple mechanism which automatically maintains a relatively static IPs of nodes in space (satellites), despite their rapid physical movement in orbits. This SAGIN architecture could be among optimized candidate architecture for future to have for many countries (either individually or by the cooperation of different regional countries). Because, it can provide all the services to nearly everyone in the world, in contrast to traditional individual satellites providing limited services to a limited number of users.

Resting-state brain information flow predicts cognitive flexibility in humans

The human brain is a dynamic system, where communication between spatially distinct areas facilitates complex cognitive functions and behaviors. How information transfers between brain regions and how it gives rise to human cognition, however, are unclear. In this article, using resting-state functional magnetic resonance imaging (fMRI) data from 783 healthy adults in the Human Connectome Project (HCP) dataset, we map the brain’s directed information flow architecture through a Granger-Geweke causality prism. We demonstrate that the information flow profiles in the general population primarily involve local exchanges within specialized functional systems, long-distance exchanges from the dorsal brain to the ventral brain, and top-down exchanges from the higher-order systems to the primary systems. Using an information flow map discovered from 550 subjects, the individual directed information flow profiles can significantly predict cognitive flexibility scores in 233 novel individuals. Our results provide evidence for directed information network architecture in the cerebral cortex, and suggest that features of the information flow configuration during rest underpin cognitive ability in humans.

Creating a High-Frequency Electronic Database in the PICU: The Perpetual Patient.

Our objective was to construct a prospective high-quality and high-frequency database combining patient therapeutics and clinical variables in real time, automatically fed by the information system and network architecture available through fully electronic charting in our PICU. The purpose of this article is to describe the data acquisition process from bedside to the research electronic database. Descriptive report and analysis of a prospective database. A 24-bed PICU, medical ICU, surgical ICU, and cardiac ICU in a tertiary care free-standing maternal child health center in Canada. All patients less than 18 years old were included at admission to the PICU. None. Between May 21, 2015, and December 31, 2016, 1,386 consecutive PICU stays from 1,194 patients were recorded in the database. Data were prospectively collected from admission to discharge, every 5 seconds from monitors and every 30 seconds from mechanical ventilators and infusion pumps. These data were linked to the patient's electronic medical record. The database total volume was 241 GB. The patients' median age was 2.0 years (interquartile range, 0.0-9.0). Data were available for all mechanically ventilated patients (n = 511; recorded duration, 77,678 hr), and respiratory failure was the most frequent reason for admission (n = 360). The complete pharmacologic profile was synched to database for all PICU stays. Following this implementation, a validation phase is in process and several research projects are ongoing using this high-fidelity database. Using the existing bedside information system and network architecture of our PICU, we implemented an ongoing high-fidelity prospectively collected electronic database, preventing the continuous loss of scientific information. This offers the opportunity to develop research on clinical decision support systems and computational models of cardiorespiratory physiology for example.

Peer discovery for D2D communications based on social attribute and service attribute

As one of the essential components of future information network architecture, D2D communication can effectively improve the spectral efficiency and also alleviate the bottleneck of limited radio resources. In particular, peer discovery is the key technique affecting the D2D functioning. However, most of the conventional researches on peer discovery of D2D communications are mainly service requirements-oriented. In fact, communications between users are often affected by trust degree and other social attributes. Therefore, it can not guarantee an effective establishment of D2D communication link to perform peer discovery only according to the service attribute. Aiming at the deficiency of conventional peer discovery methods, this paper proposes a novel peer discovery scheme with assist of social network, which introduces a two-dimensional abstract model by analyzing the social attribute and service attribute. Based on the above model, the trust degree is quantified by user service attributes, and the communication willingness is evaluated combined with demand of user for the social attribute. Finally, the trust degree and communication willingness are utilized to form neighbor cluster recommendation.

A hierarchical autonomous system based space information network architecture and topology control

A hierarchical autonomous system based space information network architecture and topology control

A Study of Vehicular Information Network Architecture based Named Data Networking (NDN)

Vehicular Ad hoc Network (VANETs) technologies have recently been getting plenty of attention in these years. The VANETs goals to put into practice an limitless application connected to cars, site visitors information, motorists, guests, along with pedestrians. But, inter-vehicular connection (IVC) in the vehicular facts network remains in line with the TCP/IP venture stack which might be perhaps not really efficient together with scalable. To deal with the accomplishment and scalability issues in the IVC, we use name facts networking (NDN) Throughout NDN the conclusion user only concern about the necessary stuff and gives no targeted the number of location in the content. The NDN style and design is more suited to the IVC scenario having its hierarchical stuff labeling prepare and changing material access and caching assistance. The whole objective in the report would be to gauge the benefits of the NDN process within the accessible approaches. Also this specific report ultimately winds up with the several limits of earlier approaches.

101 Online: American Minitel Network and Lessons from Its Failure

In 1981, videotex and virtual circuits were the hot computer network technologies and promised to bring the world to the masses. Amid a worldwide battle over standards, France started Minitel, which quickly became the first successful mass-market digital information-distribution ecosystem. In 1991, France Telecom launched the American version of Minitel, 101 Online, in San Francisco. 101 Online was as massive a failure as Minitel had been a success. This article reveals the previously undocumented history of the 101 Online ecosystem, suggests reasons why it failed where Minitel had succeeded, and draws lessons for the current policy debate on what information-network architecture and implementation best fosters digital innovation.

Infrastructural temporalities: Facebook and the differential time of data management

Facebook presents itself as a tool in the service of humanism: it connects people through the sharing of information and experience. This article contests these assumptions about the innate humanness of Facebook’s connections through an examination of its information management and network architecture. It argues that Facebook depends on a number of radically different milieus expressed by way of different, competing conceptualizations of time that it does not or cannot negotiate. Consequentially, Facebook should not be imagined as a single network of human connectivity that will somehow realize newly identified human rights through technology. Facebook should be thought of as a multiplicity of incommensurate networks, not all of which can be brought into human experience. The time of infrastructure directs us to an uneven ‘social’ that emerges from the negotiation of multiple, often obscured forms of temporal difference, managed through multiple, often obscured systems of hardware and software that forever remain beyond the conscious experience of most Facebook users.

A Novel Vehicular Information Network Architecture Based on Named Data Networking (NDN)

Vehicular information network and Internet of Things (IoT) technologies have been receiving a lot of attention in recent years. As one of the most important and promising IoT areas, a vehicular information network aims to implement a myriad of applications related to vehicles, traffic information, drivers, passengers, and pedestrians. However, intervehicular communication (IVC) in a vehicular information network is still based on the TCP/IP protocol stack which is not efficient and scalable. To address the efficiency and scalability issues of the IVC, we leverage the named data networking (NDN) paradigm where the end user only cares about the needed content and pays no attention to the actual location of the content. The NDN model is highly suitable for the IVC scenario with its hierarchical content naming scheme and flexible content retrieval and caching support. We design a novel vehicular information network architecture based on the basic communication principle of NDN. Our proposed architecture aims to improve content naming, addressing, data aggregation, and mobility for IVC in the vehicular information network. In addition, the key parameter settings of the proposed schemes are analyzed in order to help guide their actual deployment.

A Proposed National Health Information Network Architecture and Complementary Federal Preemption of State Health Information Privacy Laws

The U.S. is currently experiencing a healthcare crisis including problems of quality and affordability. A national health information network (NHIN) holds great potential for reducing these problems by linking healthcare-related entities and patients to provide real-time information flow. The current evolutionary strategy for NHIN development is resulting in an unaffordable delay. NHIN architecture and patient privacy laws are interdependent, and information flow is impeded by disparate state privacy laws layered onto federal law. We propose a revolutionary NHIN architecture and a complementary framework for preemptive federal law that will allow a true NHIN to develop much faster.

A Novel Strategy for Load Balancing of Distributed Medical Applications

Current trends in medicine, specifically in the electronic handling of medical applications, ranging from digital imaging, paperless hospital administration and electronic medical records, telemedicine, to computer-aided diagnosis, creates a burden on the network. Distributed Service Architectures, such as Intelligent Network (IN), Telecommunication Information Networking Architecture (TINA) and Open Service Access (OSA), are able to meet this new challenge. Distribution enables computational tasks to be spread among multiple processors; hence, performance is an important issue. This paper proposes a novel approach in load balancing, the Random Sender Initiated Algorithm, for distribution of tasks among several nodes sharing the same computational object (CO) instances in Distributed Service Architectures. Simulations illustrate that the proposed algorithm produces better network performance than the benchmark load balancing algorithms-the Random Node Selection Algorithm and the Shortest Queue Algorithm, especially under medium and heavily loaded conditions.

Extending TINA with Secure On-Line Accounting Services

Concepts and principles of TINA (Telecommunications Information Networking Architecture) are introduced with the objective of correcting problems of the current centralized service control and service data model in an IN (Intelligent Network). It is becoming increasingly clear that the future sophisticated telecommunication services, e.g., multimedia, and multi-party conferencing, breaking away from the traditional telephony call model will need the solutions for rapid and efficient introduction, deployment, operations, and management. In this paper, we discuss accounting features and requirements, as well as security services in the TINA management context. We will introduce and present an implementation of a model for a security management, based on secure objects, cryptography and certificate distribution. In order to provide secure services, secure objects that have security functionality, such as authentication and access control, have been defined. Secure objects in our model are CORBA objects. The security domain is also called SBS (Security Base Server), provides security services and has an SMIB (Security Management Information Base) that contains security policies, cryptographic algorithms, and other relevant information. A prototype has been implemented and some experimental results are presented.

Service-centric networks

At present, competition and the latest network technologies threaten the profit margins of telecommunications providers. New services, however, represent an important prospect for increased revenue. To capitalize on this opportunity, new services must appeal to users and be capable of rapid introduction and scaling to meet demand. Moreover, cost-effective service introduction requires independence from the underlying access technology. This paper discusses today's service delivery methodology and introduces the theme of service-centric networks — the uncoupling of services from access networks. It also delineates an architecture approach for implementing service networks and describes our experiences with the Telecommunications Information Networking Architecture (TINA), which employs this approach.

A generic architecture for management and control of end-to-end quality of service over multiple domains

Currently, service providers and network operators are exploring new business opportunities that can be found in offering packages of advanced highly customized services to both residential and corporate subscribers. The semantics of these services will be more sophisticated, their access will be better controlled, their usage will be more flexible in time (scheduled) and space (mobile access), and their delivery will be possible with different grades of Quality of Service (QoS). Multimedia services such as video conferencing, distance learning, tele surgery, or dynamic and scheduled trunk provisioning, subject to certain Subject Level Agreements (e.g. corporate VPNs), are just a few examples of services with higher semantics. Although these services could have a high market potential, their implementation and delivery are hampered by the lack of an efficient, flexible and integrated network and service management system. Particularly, in the network management domain we encounter the following three problems. First, the setup of multimedia streams with end-to-end QoS over multiple administrative business domains is impossible without adequate federating mechanisms between network management systems. Secondly, dynamic and flexible interworking mechanisms between different technology domains is ubiquitous for the automatic provisioning of trails over a set of heterogeneous networked systems (e.g. from ATM in the access to IP in the core, from MPLS to DWDM, or from Frame Relay to ATM). Third, management of end-to-end QoS is not only a matter of network QoS. Management of QoS in end terminals and end devices or at a web server is an integral part of the global QoS delivery process. The Telecommunications Information Networking Architecture (TINA) has defined a distributed and integrated computational platform for the management and delivery of QoS based services. This paper starts from a state of the art TINA system and elaborates potential enhancements and extensions in order to bring a solution for each of the three previous issues.