Humanlike robotic hands, such as prosthetic hands, become more advanced as technology develops, giving us more lightweight, sophisticated solutions with multiple degrees of freedom. Alongside the hardware improvements, control systems and human machine interfaces are also important areas of research to ensure that the operation of robotic hands is intuitive and easy to master. In particular, amputees are frequently disappointed with the difficulty in controlling their prostheses, which can lead to prostheses rejection. One method that has been explored to reduce the effort and cognitive load on the user is to implement semi-autonomy via appropriate control schemes. In this paper, a semi-autonomous control framework is proposed employing lightmyography based decoding of grasping motions. The proposed framework makes use of video and IMU data so as to reduce the number of possible grasps (grasp affordances) based on the object detected and the hand orientation. The efficiency of the proposed framework has been experimentally validated in comparison to a manual control framework. Using the semi-autonomous framework, misclassifications decreased, leading to 17/20 successful reach to grasps motions executed compared to 7/20 for the manual control case for a single subject. The automatically positioned thumb functionality has also robustified grasping, allowing certain objects to be more dexterously interacted with.

The human hand is an extraordinary example of evolution, capable of performing a wide range of tasks from intricate manipulation tasks to powerful grasps. Replicating such versatility and dexterity in both robotic and prosthetic hands is a longstanding engineering challenge. Despite paramount efforts from academia and industry, robotic and prosthetic hands still fall behind their human counterparts in many aspects, including dexterity. Closing this gap is essential for robotic and prosthetic hand applications such as general humanoids, prostheses, service robotics, and human-robot interaction, in which the need for human-like capabilities is of high significance. This paper presents the design of a highly actuated, 24-DoF, tendon-driven anthropomorphic robotic hand testbed for dexterous manipulation tasks. The hand is designed to be lightweight, affordable, and accessible with readily available components. A series of experiments is conducted to evaluate the hand's design and performance. Results show that the hand possesses a comparable workspace to the Shadow Hand and high repeatability in finger movements, essential for accurate sim2real transfer.

Network slices (NSs) are managed through a hierarchical architecture in recent 5G standards. Each NS is formed by connecting autonomously managed network slice subnets (NSSs) across the 5G network domains. To provision a new NS, users specify NS requirements as a network slice request (NSR). The NSR is decomposed into requirements for each NSS, and resources in each domain are allocated. This NSR decomposition is crucial as the selected decomposition affects resource usage and, ultimately, the total number of successfully provisioned NSs. Although several methods address NSR decomposition, most rely either on (i) detailed knowledge of domain-specific resource allocation mechanisms or (ii) extensive historical NS operational data. In practice, however, each domain’s internal processes act as "black boxes" in the hierarchical NS management architecture, making it infeasible to acquire detailed resource allocation algorithms. Additionally, historical data may be insufficient because network slicing remains an emerging technology. These limitations hinder the direct application of existing methods to real operational 5G networks. In this paper, we propose a multi-armed bandits (MABs)-based optimization method that formulates the NSR decomposition as a linear contextual bandits with knapsacks (linCBwK) problem and sequentially acquires the optimal decomposition policy. A MABs-based optimization approach enables us to avoid the need for domain-internal knowledge or extensive pre-collected training data. Simulations demonstrate that our method increases the total number of successfully provisioned NSs by 16.0% compared to the baseline method.

Cloud computing (CC) and edge computing (EC) enhance the performance of end devices (EDs) with limited computational power by offloading tasks to cloud and edge servers, respectively. Multi-access edge computing (MEC) further advances EC by integrating wireless network resources, thus improving mobile service efficiency. While CC is well-suited for intensive computational tasks, it may face latency issues due to geographical distances. EC and MEC aim to minimize this latency by deploying server resources closer to EDs, but they encounter challenges due to the limited resources of edge servers. Cooperative task offloading emerges as a solution to address the above challenges of optimizing resource allocation across cloud and edge based on task characteristics. Despite numerous research, existing methods often cover only a portion of the networks and servers, leading to sub-optimal task allocation. Therefore, we propose a cooperative task-offloading method for multi-access edge-cloud networks, simultaneously considering server and link resources, base station (BS), and wireless channel allocation. This method improves task-offloading efficiency by utilizing cooperative multi-group multi-agent deep reinforcement learning (CMG-MADRL) with different agent groups for BS and server allocation. Simulations have demonstrated that our method effectively reduces resource utilization and task latency while minimizing constraint violations.

This paper focuses on the Bregman divergence defined by the reciprocal function, called the inverse divergence. For the loss function defined by the monotonically increasing function$f$and inverse divergence, the conditions for the statistical model and function$f$under which the estimating equation is unbiased are clarified. Specifically, we characterize two types of statistical models, an inverse Gaussian type and a mixture of generalized inverse Gaussian type distributions, to show that the conditions for the function$f$are different for each model. We also define Bregman divergence as a linear sum over the dimensions of the inverse divergence and extend the results to the multi-dimensional case.

We discuss unbiased estimating equations in a class of objective functions using a monotonically increasing function f and Bregman divergence. The choice of the function f gives desirable properties, such as robustness against outliers. To obtain unbiased estimating equations, analytically intractable integrals are generally required as bias correction terms. In this study, we clarify the combination of Bregman divergence, statistical model, and function f in which the bias correction term vanishes. Focusing on Mahalanobis and Itakura-Saito distances, we generalize fundamental existing results and characterize a class of distributions of positive reals with a scale parameter, including the gamma distribution as a special case. We also generalized these results to general model classes characterized by one-dimensional Bregman divergence. Furthermore, we discuss the possibility of latent bias minimization when the proportion of outliers is large, which is induced by the extinction of the bias correction term. We conducted numerical experiments to show that the latent bias can approach zero under heavy contamination of outliers or very small inliers.

Network services provisioning with Service Function Chaining (SFC) consists of various controls such as Virtual Network Function (VNF) placement and traffic flow routing, executed dynamically. These controls take time to execute from start to completion (reconfiguration delay), and the reconfiguration delay varies depending on the type of control. When control is executed with a long reconfiguration delay to eliminate Quality of Service (QoS) degradation, it will take longer to complete, resulting in continued degradation. To optimize the network performance, a control method needs to consider the difference in the reconfiguration delay. However, most of existing works do not assume the difference, and aim to optimize QoS only at the completion of controls. In this paper, we assume different reconfiguration delays for each control in SFC provisioning, and propose a control scheduling that optimizes QoS during control execution on the basis of them. We first propose a network model in which the reconfiguration delay of each type of control is different and formulate the scheduling problem of network controls that minimizes the duration of QoS degradation. Then we propose a control scheduling method to solve the problem. Our method develops a stochastic search on the basis of the load degree of VNF instances to obtain a sub-optimal solution with low computational complexity. We also provide a packet-level simulation to verify the performance of our method when QoS degrades due to traffic demand rapidly increasing. Simulation results show that our method reduces the delay degradation and its duration compared with the control scheduling method that optimizes performance at the completion of controls.

Mobile networks require high availability to provide reliable connectivity for various mobile services. Therefore, when a service outage occurs due to mobile base station (BS) failures, mobile network operators need to immediately resolve the effects of the outage. Cell Outage Compensation (COC) is the critical technology that resolves the outage. The COC method is composed of two steps: antenna selection from antennas of neighboring BSs and optimization of the tilts of selected antennas to provide coverage in the outage area. Although most existing works on COC methods focus on the tilt optimization algorithm, the antenna selection algorithm has not been fully discussed. Since the COC method obtains a solution by optimizing the tilts of selected antennas, a poor antenna selection causes performance degradation of the COC solution. This paper proposes an antenna selection algorithm considering the positional relationship between the outage area and neighboring antennas. Moreover, we use deep reinforcement learning (DRL) in our algorithm to find the optimal antenna selection policy. The simulation results show that the COC method with our algorithm finds a practical solution within one minute and outperforms existing selection algorithms in terms of coverage in the outage area and overlap of coverage areas.

Owing to the critical role of the domain name system (DNS), its query log data are utilized for various network monitoring purposes. With the diversification of network services, these data have become increasingly complex, making mining useful information challenging. DNS query log data can be considered as the superposition of two types of communication patterns: groups of domains accessed simultaneously (e.g., ad servers and content delivery network (CDN) servers) and time-series access patterns based on user behavior characteristics (e.g., access trends during the night). However, previous studies have not focused on extracting both access patterns hidden in the data. This study proposes a method that extracts both patterns of accessed domains and temporal access patterns as user communication behaviors from DNS query log data and predicts future accesses based on these patterns. The proposed method first aggregates similar fully qualified domain names (FQDNs) associated with the same service. We then present temporal regularized nonnegative tensor factorization (TR-NTF) that extracts both access patterns from a third-order tensor expressing DNS query log data and enables prediction. We evaluate the proposed method using synthetic and actual data and demonstrate that it successfully extracts hidden communication patterns and achieves sufficient prediction accuracy.

Edge computing is a new paradigm to provide computing capability at the edge servers close to end devices. A significant research challenge in edge computing is finding efficient task offloading to edge and cloud servers considering various task characteristics and limited network and server resources. Several reinforcement learning (RL)-based task-offloading methods have been developed, because RL can immediately output efficient offloading by pre-learning. However, these methods do not take into account clouds or focus only on a single cloud. They also do not take into account the bandwidth and topology of the backbone network. Such shortcomings strongly limit the range of applicable networks and degrade task-offloading performance. Therefore, we formulate a task-offloading problem for multi-cloud and multi-edge networks considering network topology and bandwidth constraints. We also propose a task-offloading method that is based on cooperative multi-agent deep RL (Coop-MADRL). This method introduces a cooperative multi-agent technique through centralized training and decentralized execution, improving task-offloading efficiency. Simulations revealed that the proposed method can minimize network utilization and task latency while minimizing constraint violations in less than one millisecond in various network topologies. It also shows that cooperative learning improves the efficiency of task offloading. We demonstrated that the proposed method has generalization performance for various task types by pre-training with many resource-consuming tasks.

Edge computing is a new paradigm to provide computing capability at the edges close to end devices. A significant research challenge in edge computing is finding an efficient task offloading to edge and cloud servers, considering various task characteristics and limited network and server resources. Several studies have proposed the reinforcement learning (RL) based task offloading method, because RL can immediately output the efficient offloading by pre-learning. However, due to the performance problem of RL, these previous studies do not consider clouds or focus only on a single cloud. They also do not consider the bandwidth and topology of the backbone network. Such shortcomings could lead to degrading the performance of task offloading. Therefore, we formulated a task offloading problem for multi-cloud and multi-edge networks, considering network topology and bandwidth constraints. Moreover, we proposed a task offloading method based on cooperative multi-agent deep reinforcement learning (Coop-MADRL) to solve the performance problem of RL. This method introduces a cooperative multi-agent technique through centralized training and decentralized execution, improving the efficiency of task offloading. Simulations revealed that the proposed method drastically reduces the average latency while satisfying all constraints, compared with the greedy approach. It also revealed that the proposed cooperative learning method improves the efficiency of task offloading.

DP-means clustering was obtained as an extension of K-means clustering. While it is implemented with a simple and efficient algorithm, it can estimate the number of clusters simultaneously. However, DP-means is specifically designed for the average distortion measure. Therefore, it is vulnerable to outliers in data, and can cause large maximum distortion in clusters. In this work, we extend the objective function of the DP-means to f-separable distortion measures and propose a unified learning algorithm to overcome the above problems by selecting the function f. Further, the influence function of the estimated cluster center is analyzed to evaluate the robustness against outliers. We demonstrate the performance of the generalized method by numerical experiments using real datasets.

Clustering algorithms have wide applications and play an important role in data analysis fields including time series data analysis. However, in time series analysis, most of the algorithms used signal shape features or the initial value of hidden variable of a neural network. Little has been discussed on the methods based on the generative model of the time series. In this paper, we propose a new clustering algorithm focusing on the generative process of the signal with a recurrent neural network and the variational Bayes method. Our experiments show that the proposed algorithm not only has a robustness against for phase shift, amplitude and signal length variations but also provide a flexible clustering based on the property of the variational Bayes method.

We discuss unbiased estimation equations in a class of objective function using a monotonically increasing function f and Bregman divergence. The choice of the function f gives desirable properties such as robustness against outliers. In order to obtain unbiased estimation equations, analytically intractable integrals are generally required as bias correction terms. In this study, we clarify the combination of Bregman divergence, statistical model, and function f in which the bias correction term vanishes. Focusing on Mahalanobis and Itakura-Saito distances, we provide a generalization of fundamental existing results and characterize a class of distributions of positive reals with a scale parameter, which includes the gamma distribution as a special case. We discuss the possibility of latent bias minimization when the proportion of outliers is large, which is induced by the extinction of the bias correction term.

Network functions virtualization (NFV) enables telecommunications service providers to provide various network services by flexibly combining multiple virtual network functions (VNFs). To provide such services with carrier-grade quality, an NFV controller must optimally allocate such VNFs into physical networks and servers, taking into account combination(s) of objective functions and constraints for each metric defined for each VNF type. The NFV controller should also be extendable, i.e., new metrics should be able to be added. One approach for NFV control to optimize allocations is to construct an algorithm that simultaneously solves the combined optimization problem. However, this algorithm is not extendable because the problem formulation needs to be rebuilt every time, e.g., a new metric is added. Another approach involves using an extendable network-control architecture that coordinates multiple control algorithms specified for individual metrics. However, to the best of our knowledge, no method has been developed to optimize allocations through this kind of coordination. In this paper, we propose an extendable NFV-integrated control method by coordinating multiple control algorithms. We also propose an efficient coordination algorithm based on reinforcement learning. Finally, we evaluate the effectiveness of the proposed method through simulations.

DP-means clustering was obtained as an extension of K-means clustering. While it is implemented with a simple and efficient algorithm, it can estimate the number of clusters simultaneously. However, DP-means is specifically designed for the average distortion criterion. Therefore, it is vulnerable to outliers in data, and can cause large maximum distortion in clusters. This study introduces a new parameter to the objective function of DP-means to provide an extension of DP-means, which bridges robust estimation of cluster centers and minimization of the maximum distortion criterion.

Application layer multicast (ALM) is highly expected to replace IP multicasting as the new technological choice for content delivery. Depending on the streaming application, ALM nodes will construct a multicast tree and deliver the stream through this tree. However, if a node resides in the tree leaves, it cannot deliver the stream to its descendant nodes. In this case, quality of service (QoS) will be compromised dramatically. To overcome this problem, topology-aware hierarchical arrangement graph (THAG) was proposed. By employing multiple description coding (MDC), THAG first splits the stream into a number of descriptions, and then uses arrangement graph (AG) to construct node-disjoint multicast trees for each description. However, using a constant AG size in THAG creates difficulty in delivering descriptions appropriately across a heterogeneous network. In this paper, we propose a method, referred to as network-aware hierarchical arrangement graph (NHAG), to change the AG size dynamically to enhance THAG performance, even in heterogeneous networks. Finally, we evaluate the proposed scheme by experiments using the network simulator ns-2. By comparing our proposed method to THAG and SplitStream, we show that our method provides better performance in terms of throughput and QoS. The results indicate that our approach is more reliable than other methods in heterogeneous networks.

During the last several years, the Internet has evolved from a wired infrastructure to a hybrid of wired and wireless domains by spreading worldwide interoperability for microwave access (WiMAX), Wi-Fi, and cellular networks. Therefore, there is a growing need to facilitate reliable content delivery over such heterogeneous networks. On the other hand, application layer multicast (ALM) has become a promising approach for streaming media content from a server to a large number of interested nodes. ALM nodes construct a multicast tree and deliver the stream through this tree. However, if a node leaves, it cannot deliver the stream to its descendant nodes. In this case, quality-of-service (QoS) is compromised dramatically. Especially, this problem is exacerbated in wireless networks because of packet errors and handovers. In order to cope with this problem, multiple-tree multicasts have been proposed. However, existing methods fail to deliver contents reliably in combined wired and wireless networks. In this paper, we propose a method to ensure the robustness of node departure, while meeting various bandwidth constraints by using layered multiple description coding (LMDC). Finally, we evaluate the proposed method via extensive simulations by using the network simulator (ns-2). By comparing our proposed method with the existing ones, we demonstrate that our method provides better performance in terms of total throughput, relative delay penalty (RDP), and relative delay variation (RDV). The results indicate that our approach is a more reliable content delivery system when compared with contemporary methods in the context of heterogeneous networks containing wired and wireless environments.

Application Layer Multicast (ALM) is highly expected to be the new technological choice contents delivery in lieu of IP multicast. Depending on each node's streaming application, ALM constructs multicast trees and delivers the stream through those trees. The problem of ALM is that when a node resides in tree leaves, the stream cannot be delivered to descendant nodes. To overcome this problem, Topology-aware Hierarchical Arrangement Graph (THAG) was proposed. By employing Multiple Description Coding (MDC), THAG first splits the stream into a number of sub-streams, and then uses Arrangement Graph (AG) to construct an independent tree for each sub-stream. However, using the same size of AG in THAG has a difficulty delivering a stream appropriately across a heterogeneous network. In this paper, we propose a method to change the size of AG dynamically in enhancing THAG performance well even in a heterogeneous network. Finally, we evaluate the proposed scheme by experiments in ns -2. By comparing with THAG, we show that our proposal scheme provides a better performance in throughput and Bandwidth Satisfaction Rate (BSR).