The long-term goal of artificial intelligence (AI) is to make machines learn and think like human beings. Due to the high levels of uncertainty and vulnerability in human life and the open-ended nature of problems that humans are facing, no matter how intelligent machines are, they are unable to completely replace humans. Therefore, it is necessary to introduce human cognitive capabilities or human-like cognitive models into AI systems to develop a new form of AI, that is, hybrid-augmented intelligence. This form of AI or machine intelligence is a feasible and important developing model. Hybrid-augmented intelligence can be divided into two basic models: one is human-in-the-loop augmented intelligence with human-computer collaboration, and the other is cognitive computing based augmented intelligence, in which a cognitive model is embedded in the machine learning system. This survey describes a basic framework for human-computer collaborative hybrid-augmented intelligence, and the basic elements of hybrid-augmented intelligence based on cognitive computing. These elements include intuitive reasoning, causal models, evolution of memory and knowledge, especially the role and basic principles of intuitive reasoning for complex problem solving, and the cognitive learning framework for visual scene understanding based on memory and reasoning. Several typical applications of hybrid-augmented intelligence in related fields are given.

Credit-assignment schemas are widely applied by providing fixed or flexible credit distribution formulas to evaluate the contributions of coauthors of a scientific publication. In this paper, we propose an approach named First and Others (F&O) counting. By introducing a tuning parameter α and a weight β, two new properties are obtained: (1) flexible assignment of credits by modifying the formula (with the change of α) and applying preference to the individual author by adjusting the weights (with the change of β), and (2) calculation of the credits by separating the formula for the first author from others. With formula separation, the credit of the second author shows an inflection point according to the change of α. The developed theorems and proofs concerning the modification of α and β reveal new properties and complement the base theory for informetrics. The F&O schema is also adapted when considering the policy of ‘first-corresponding-author-emphasis’. Through a comparative analysis using a set of empirical data from the fields of chemistry, medicine, psychology, and the Harvard survey data, the performance of the F&O approach is compared with those of other methods to demonstrate its benefits by the criteria of lack of fit and coefficient of determination.

Entity recognition and disambiguation (ERD) is a crucial technique for knowledge base population and information extraction. In recent years, numerous papers have been published on this subject, and various ERD systems have been developed. However, there are still some confusions over the ERD field for a fair and complete comparison of these systems. Therefore, it is of emerging interest to develop a unified evaluation framework. In this paper, we present an easy-to-use evaluation framework (EUEF), which aims at facilitating the evaluation process and giving a fair comparison of ERD systems. EUEF is well designed and released to the public as an open source, and thus could be easily extended with novel ERD systems, datasets, and evaluation metrics. It is easy to discover the advantages and disadvantages of a specific ERD system and its components based on EUEF. We perform a comparison of several popular and publicly available ERD systems by using EUEF, and draw some interesting conclusions after a detailed analysis.

In today’s networks, load balancing and priority queues in switches are used to support various quality-of-service (QoS) features and provide preferential treatment to certain types of traffic. Traditionally, network operators use ’traceroute’ and ‘ping’ to troubleshoot load balancing and QoS problems. However, these tools are not supported by the common OpenFlow-based switches in software-defined networking (SDN). In addition, traceroute and ping have potential problems. Because load balancing mechanisms balance flows to different paths, it is impossible for these tools to send a single type of probe packet to find the forwarding paths of flows and measure latencies. Therefore, tracing flows’ real forwarding paths is needed before measuring their latencies, and path tracing and latency measurement should be jointly considered. To this end, FlowTrace is proposed to find arbitrary flow paths and measure flow latencies in OpenFlow networks. FlowTrace collects all flow entries and calculates flow paths according to the collected flow entries. However, polling flow entries from switches will induce high overhead in the control plane of SDN. Therefore, a passive flow table collecting method with zero control plane overhead is proposed to address this problem. After finding flows’ real forwarding paths, FlowTrace uses a new measurement method to measure the latencies of different flows. Results of experiments conducted in Mininet indicate that FlowTrace can correctly find flow paths and accurately measure the latencies of flows in different priority classes.

New non-volatile memory (e.g., phase-change memory) provides fast access, large capacity, byte-addressability, and non-volatility features. These features, fast-byte-persistency, will bring new opportunities to fault tolerance. We propose a fine-grained checkpoint based on non-volatile memory. We extend the current virtual memory manager to manage non-volatile memory, and design a persistent heap with support for fast allocation and checkpointing of persistent objects. To achieve a fine-grained checkpoint, we scatter objects across virtual pages and rely on hardware page-protection to monitor the modifications. In our system, two objects in different virtual pages may reside on the same physical page. Modifying one object would not interfere with the other object. This allows us to monitor and checkpoint objects smaller than 4096 bytes in a fine-grained way. Compared with previous page-grained based checkpoint mechanisms, our new checkpoint method can greatly reduce the data copied at checkpoint time and better leverage the limited bandwidth of non-volatile memory.

The detection of fully and partially defective sensors in a linear array composed of N sensors is addressed. First, the symmetrical structure of a linear array is proposed. Second, a hybrid technique based on the cultural algorithm with differential evolution is developed. The symmetrical structure has two advantages: (1) Instead of finding all damaged patterns, only (N–1)/2 patterns are needed; (2) We are required to scan the region from 0 to 90° instead of from 0 to 180°. Obviously, the computational complexity can be reduced. Monte Carlo simulations were carried out to validate the performance of the proposed scheme, compared with existing methods in terms of computational time and mean square error.

We consider a cooperative system consisting of a source node, a destination node, N (N>1) wireless-powered relays, and an eavesdropper. Each relay is assumed to be with a nonlinear energy harvester, in which there exists a saturation threshold, limiting the level of the harvested power. For decode-and-forward and power splitting protocols, the Kth best relay is selected to assist the source-relay-destination transmission. An analytical expression for the secrecy outage probability is derived, and also verified by simulation.

The optimization of the network throughput and transmission range is one of the most important issues in cognitive relay networks (CRNs). Existing research has focused on the dual-hop network, which cannot be extended to a triple-hop network due to its shortcomings, including the limited transmission range and one-way communication. In this paper, a novel, triple-hop relay scheme is proposed to implement time-division duplex (TDD) transmission among secondary users (SUs) in a three-phase transmission. Moreover, a superposition coding (SC) method is adopted for handling two-receiver cases in triple-hop networks with a cognitive relay. We studied a joint optimization of time and power allocation in all three phases, which is formulated as a nonlinear and concave problem. Both analytical and numerical results show that the proposed scheme is able to improve the throughput of SUs, and enlarge the transmission range of primary users (PUs) without increasing the number of hops.

The single-walled carbon nanotube (SWCNT) is a promising nanostructure in the design of future high-frequency system-on-chip, especially in network-on-chip, where the quality of communication between intellectual property (IP) modules is a major concern. Shrinking dimensions of circuits and systems have restricted the use of high-frequency signal characteristics for frequencies up to 1000 GHz. Four key electrical parameters, impedance, propagation constant, current density, and signal delay time, which are crucial in the design of a high-quality interconnect, are derived for different structural configurations of SWCNT. Each of these parameters exhibits strong dependence on the frequency range over which the interconnect is designed to operate, as well as on the configuration of SWCNT. The novelty of the proposed model for solving next-generation high-speed integrated circuit (IC) interconnect challenges is illustrated, compared with existing theoretical and experimental results in the literature.

Targeting the mode-mixing problem of intrinsic time-scale decomposition (ITD) and the parameter optimization problem of least-square support vector machine (LSSVM), we propose a novel approach based on complete ensemble intrinsic time-scale decomposition (CEITD) and LSSVM optimized by the hybrid differential evolution and particle swarm optimization (HDEPSO) algorithm for the identification of the fault in a diesel engine. The approach consists mainly of three stages. First, to solve the mode-mixing problem of ITD, a novel CEITD method is proposed. Then the CEITD method is used to decompose the nonstationary vibration signal into a set of stationary proper rotation components (PRCs) and a residual signal. Second, three typical types of time-frequency features, namely singular values, PRCs energy and energy entropy, and AR model parameters, are extracted from the first several PRCs and used as the fault feature vectors. Finally, a HDEPSO algorithm is proposed for the parameter optimization of LSSVM, and the fault diagnosis results can be obtained by inputting the fault feature vectors into the HDEPSO-LSSVM classifier. Simulation and experimental results demonstrate that the proposed fault diagnosis approach can overcome the mode-mixing problem of ITD and accurately identify the fault patterns of diesel engines.

The realization of road traffic prediction not only provides real-time and effective information for travelers, but also helps them select the optimal route to reduce travel time. Road traffic prediction offers traffic guidance for travelers and relieves traffic jams. In this paper, a real-time road traffic state prediction based on autoregressive integrated moving average (ARIMA) and the Kalman filter is proposed. First, an ARIMA model of road traffic data in a time series is built on the basis of historical road traffic data. Second, this ARIMA model is combined with the Kalman filter to construct a road traffic state prediction algorithm, which can acquire the state, measurement, and updating equations of the Kalman filter. Third, the optimal parameters of the algorithm are discussed on the basis of historical road traffic data. Finally, four road segments in Beijing are adopted for case studies. Experimental results show that the real-time road traffic state prediction based on ARIMA and the Kalman filter is feasible and can achieve high accuracy.