We propose a generalization of the rolling guidance filter (RGF) to a similarity-based clustering (SBC) algorithm which can handle general vector data. The proposed RGF-based SBC algorithm makes the similarities between data clearer than the original similarity values computed from the original data. On the basis of the similarity values, we assign cluster labels to data by an SBC algorithm. Experimental results show that the proposed algorithm achieves better clustering result than the result by the naive application of the SBC algorithm to the original similarity values. Additionally, we study the convergence of a unimodal vector dataset to its mean vector.

Autonomous vehicles and advanced driver assistant systems (ADAS) are receiving notable attention as research fields in both academia and private industry. Some decision-making systems use sets of logical rules to map knowledge of the ego-vehicle and its environment into actions the ego-vehicle should take. However, such rulesets can be difficult to create — for example by manually writing them — due to the complexity of traffic as an operating environment. Furthermore, the building blocks of the rules must be defined. One common solution to this is using an ontology specifically aimed at describing traffic concepts and their hierarchy. These ontologies must have a certain expressive power to enable construction of useful rules. We propose a process of generating sets of explanatory rules for ADAS applications from data using ontology as a base vocabulary and present a ruleset generated as a result of our experiments that is correct for the scope of the experiment.

Most aggregate signature schemes are relying on pairings, but high computational and storage costs of pairings limit the feasibility of those schemes in practice. Zhao proposed the first pairing-free aggregate signature scheme (AsiaCCS 2019). However, the security of Zhao's scheme is based on the hardness of a newly introduced non-standard computational problem. The recent impossibility results of Drijvers et al. (IEEE S&P 2019) on two-round pairing-free multi-signature schemes whose security based on the standard discrete logarithm (DL) problem have strengthened the view that constructing a pairing-free aggregate signature scheme which is proven secure based on standard problems such as DL problem is indeed a challenging open problem. In this paper, we offer a novel solution to this open problem. We introduce a new paradigm of aggregate signatures, i.e., aggregate signatures with an additional pre-communication stage. In the pre-communication stage, each signer interacts with the aggregator to agree on a specific random value before deciding messages to be signed. We also discover that the impossibility results of Drijvers et al. take effect if the adversary can decide the whole randomness part of any individual signature. Based on the new paradigm and our discovery of the applicability of the impossibility result, we propose a pairing-free aggregate signature scheme such that any individual signature includes a random nonce which can be freely generated by the signer. We prove the security of our scheme based on the hardness of the standard DL problem. As a trade-off, in contrast to the plain public-key model, which Zhao's scheme uses, we employ a more restricted key setup model, i.e., the knowledge of secret-key model.

There are many knowledge entities in sci-tech intelligence resources. Extracting these knowledge entities is of great importance for building knowledge networks, exploring the relationship between knowledge, and optimizing search engines. Many existing methods, which are mainly based on rules and traditional machine learning, require significant human involvement, but still suffer from unsatisfactory extraction accuracy. This paper proposes a novel approach for knowledge entity extraction based on BiLSTM and conditional random field (CRF).A BiLSTM neural network to obtain the context information of sentences, and CRF is then employed to integrate global label information to achieve optimal labels. This approach does not require the manual construction of features, and outperforms conventional methods. In the experiments presented in this paper, the titles and abstracts of 20,000 items in the existing sci-tech literature are processed, of which 50,243 items are used to build benchmark datasets. Based on these datasets, comparative experiments are conducted to evaluate the effectiveness of the proposed approach. Knowledge entities are extracted and corresponding knowledge networks are established with a further elaboration on the correlation of two different types of knowledge entities. The proposed research has the potential to improve the quality of sci-tech information services.

The spread of the Internet of Things (IoT) has led to the generation of large amounts of data, requiring massive communication, computing, and storage resources. Cloud computing plays an important role in realizing most IoT applications classified as massive machine type communication and cyber-physical control applications in vertical domains. To handle the increasing amount of IoT data, it is important to reduce the traffic concentrated in the cloud by distributing the computing and storage resources to the network edge side and to suppress the latency of the IoT applications. In this paper, we first present a recent literature review on fog/edge computing and data aggregation as representative traffic reduction technologies for efficiently utilizing communication, computing, and storage resources in IoT systems, and then focus on data aggregation control minimizing the latency in an IoT gateway. We then present a unified modeling for statistical and nonstatistical data aggregation and analyze its latency. We analytically derive the Laplace-Stieltjes transform and average of the stationary distribution of the latency and approximate the average latency; we subsequently apply it to an adaptive aggregation number control for the time-variant data arrival. The transient traffic characteristics, that is, the absorption of traffic fluctuations realizing a stable optimal latency, were clarified through a simulation with a time-variant Poisson input and non-Poisson inputs, such as a Beta input, which is a typical IoT traffic model.

Edge computing offers computing capability with ultra-low response times by leveraging servers close to end-user devices. Due to the mobility of end-user devices, the latency between the servers and the end-user devices can become long and the response time might become unacceptable for an application service. Service (container) migration that follows the handover of end-user devices retains the response time. Service migration following the mass movement of people in the same geographic area and at the same time due to an event (e.g., commuting) generates heavy bandwidth usage in the mobile backhaul network. Heavy usage by service migration reduces available bandwidth for ordinary application traffic in the network. Shaping the migration traffic limits the bandwidth usage while delaying service migration and increasing the response time of the container for the moving end-user device. Furthermore, targets of migration decisions increase (i.e., the system load) because delaying a migration process accumulates containers waiting for migration. In this paper, we propose a migration scheduling method to control bandwidth usage for migration in a network and ensure timely processing of service migration. Simulations that compare the proposal with state-of-the-art methods show that the proposal always suppresses the bandwidth usage under the predetermined threshold. The method reduced the number of containers exceeding the acceptable response time up to 40% of the compared state-of-the-art methods. Furthermore, the proposed method minimized the targets of migration decisions.

The Internet of Things (IoT) with its support for cyber-physical systems (CPS) will provide many latency-sensitive services that require very fast responses from network services. Mobile edge computing (MEC), one of the distributed computing models, is a promising component of the low-latency network architecture. In network architectures with MEC, mobile devices will offload heavy computing tasks to edge servers. There exist numbers of researches about low-latency network architecture with MEC. However, none of the existing researches simultaneously satisfy the followings: (1) guarantee the latency of computing tasks and (2) implement a real system. In this paper, we designed and implemented an MEC based network architecture that guarantees the latency of offloading tasks. More specifically, we first estimate the total latency including computing and communication ones at the centralized node called orchestrator. If the estimated value exceeds the latency requirement, the task will be rejected. We then evaluated its performance in terms of the blocking probability of the tasks. To analyze the results, we compared the performance between obtained from experiments and simulations. Based on the comparisons, we clarified that the computing latency estimation accuracy is a significant factor for this system.

This paper deals with the problem of enumerating 3-edge-connected spanning subgraphs of an input plane graph. In 2018, Yamanaka et al. proposed two enumeration algorithms for such a problem. Their algorithm generates each 2-edge-connected spanning subgraph of a given plane graph with n vertices in O(n) time, and another one generates each k-edge-connected spanning subgraph of a general graph with m edges in O(mT) time, where T is the running time to check the k-edge connectivity of a graph. This paper focuses on the case of the 3-edge-connectivity in a plane graph. We give an algorithm which generates each 3-edge-connected spanning subgraph of the input plane graph in O(n2) time. This time complexity is the same as the algorithm by Yamanaka et al., but our algorithm is simpler than theirs.

AI (artificial intelligence) has grown at an overwhelming speed for the last decade, to the extent that it has become one of the mainstream tools that drive the advancements in science and technology. Meanwhile, the paradigm of edge computing has emerged as one of the foremost areas in which applications using the AI technology are being most actively researched, due to its potential benefits and impact on today's widespread networked computing environments. In this paper, we evaluate two major entry-level offerings in the state-of-the-art edge device technology, which highlight increased computing power and specialized hardware support for AI applications. We perform a set of deep learning benchmarks on the devices to measure their performance. By comparing the performance with other GPU (graphics processing unit) accelerated systems in different platforms, we assess the computational capability of the modern edge devices featuring a significant amount of hardware parallelism.

This study compared two extended concept mapping approaches and investigated the distribution of students' understanding and knowledge structure. The students in the experimental group used Extended Kit-Build (EKB), where a learner extends a concept map built by kit-building, and those in the control group utilized the Extended Scratch-Build (ESB), where a learner extends a concept map made by scratch-building. The results suggested that the experimental group had better achievements in both the original material and the additional material.

Knowledge distillation approaches can transfer information from a large network (teacher network) to a small network (student network) to compress and accelerate deep neural networks. This paper proposes a novel knowledge distillation approach called multi-knowledge distillation (MKD). MKD consists of two stages. In the first stage, it employs autoencoders to learn compact and precise representations of the feature maps (FM) from the teacher network and the student network, these representations can be treated as the essential of the FM, i.e., EFM. In the second stage, MKD utilizes multiple kinds of knowledge, i.e., the magnitude of individual sample's EFM and the similarity relationships among several samples' EFM to enhance the generalization ability of the student network. Compared with previous approaches that employ FM or the handcrafted features from FM, the EFM learned from autoencoders can be transferred more efficiently and reliably. Furthermore, the rich information provided by the multiple kinds of knowledge guarantees the student network to mimic the teacher network as closely as possible. Experimental results also show that MKD is superior to the-state-of-arts.

Knowledge-of-exponent assumptions (KEAs) are a somewhat controversial but nevertheless commonly used type of cryptographic assumptions. While traditional cryptographic assumptions simply assert that certain tasks (like factoring integers or computing discrete logarithms) cannot be performed efficiently, KEAs assert that certain tasks can be performed efficiently, but only in certain ways. The controversy surrounding those assumptions is due to their non-falsifiability, which is due to the way this idea is formalised, and to the general idea that these assumptions are “strong”. Nevertheless, their relationship to existing assumptions has not received much attention thus far. In this paper, we show that the first KEA (KEA1), introduced by Damgård in 1991, implies that computing discrete logarithms is equivalent to solving the computational Diffie-Hellman (CDH) problem. Since showing this equivalence in the standard setting (i.e., without the assumption that KEA1 holds) is a longstanding open question, this indicates that KEA1 (and KEAs in general) are indeed quite strong assumptions.

With the development of cloud computing, the Mobile Edge Computing has emerged and attracted widespread attentions. In this paper, we focus on the load balancing in MEC with energy harvesting. We first introduce the load balancing in MEC as a problem of minimizing both the energy consumption and queue redundancy. Thereafter, we adapt such a optimization problem to the Lyapunov algorithm and solve this optimization problem. Finally, extensive simulation results validate that the obtained strategy improves the capabilities of MEC systems.

The minimum biclique edge cover problem (MBECP) is NP-hard for general graphs. It is known that if we restrict an input graph to the bipartite domino-free class, MBECP can be solved within polynomial-time of input graph size. We show a new polynomial-time solvable graph class for MBECP that is characterized by three forbidden graphs, a domino, a gem and K4. This graph class allows that an input graph is non-bipartite, and includes the bipartite domino-free graph class properly.

Optical navigation (OPNAV) is the use of the on-board imaging data to provide a direct measurement of the image coordinates of the target as navigation information. Among the optical observables in deep-space, the edge of the celestial body is an important feature that can be utilized for locating the planet centroid. However, traditional edge detection algorithms like Canny algorithm cannot be applied directly for OPNAV due to the noise edges caused by surface markings. Moreover, due to the constrained computation and energy capacity on-board, light-weight image-processing algorithms with less computational complexity are desirable for real-time processing. Thus, to fast and accurately extract the edge of the celestial body from high-resolution satellite imageries, this paper presents an algorithm-hardware co-design of real-time edge detection for OPNAV. First, a light-weight edge detection algorithm is proposed to efficiently detect the edge of the celestial body while suppressing the noise edges caused by surface markings. Then, we further present an FPGA implementation of the proposed algorithm with an optimized real-time performance and resource efficiency. Experimental results show that, compared with the traditional edge detection algorithms, our proposed one enables more accurate celestial body edge detection, while simplifying the hardware implementation.

After the work of Impagliazzo and Rudich (STOC, 1989), the black box framework has become one of the main research domain of cryptography. However black box techniques say nothing about non-black box techniques such as making use of zero-knowledge proofs. Brakerski et al. introduced a new black box framework named augmented black box framework, in which they gave a zero-knowledge proof oracle in addition to a base primitive oracle (TCC, 2011). They showed a construction of a non-interactive zero knowledge proof system based on a witness indistinguishable proof system oracle. They presented augmented black box construction of chosen ciphertext secure public key encryption scheme based on chosen plaintext secure public key encryption scheme and augmented black box separation between one-way function and key agreement. In this paper we simplify the work of Brakerski et al. by introducing a proof system oracle without witness indistinguishability, named coin-free proof system oracle, that aims to give the same construction and separation results of previous work. As a result, the augmented black box framework becomes easier to handle. Since our oracle is not witness indistinguishable, our result encompasses the result of previous work.

Advances in deep reinforcement learning have demonstrated its effectiveness in a wide variety of domains. Deep neural networks are capable of approximating value functions and policies in complex environments. However, deep neural networks inherit a number of drawbacks. Lack of interpretability limits their usability in many safety-critical real-world scenarios. Moreover, they rely on huge amounts of data to learn efficiently. This may be suitable in simulated tasks, but restricts their use to many real-world applications. Finally, their generalization capability is low, the ability to determine that a situation is similar to one encountered previously. We present a method to combine external knowledge and interpretable reinforcement learning. We derive a rule-based variant version of the Sarsa(λ) algorithm, which we call Sarsa-rb(λ), that augments data with prior knowledge and exploits similarities among states. We demonstrate that our approach leverages small amounts of prior knowledge to significantly accelerate the learning in multiple domains such as trading or visual navigation. The resulting agent provides substantial gains in training speed and performance over deep q-learning (DQN), deep deterministic policy gradients (DDPG), and improves stability over proximal policy optimization (PPO).

Modern deep learning has significantly improved performance and has been used in a wide variety of applications. Since the amount of computation required for the inference process of the neural network is large, it is processed not by the data acquisition location like a surveillance camera but by the server with abundant computing power installed in the data center. Edge computing is getting considerable attention to solve this problem. However, edge computing can provide limited computation resources. Therefore, we assumed a divided/distributed neural network model using both the edge device and the server. By processing part of the convolution layer on edge, the amount of communication becomes smaller than that of the sensor data. In this paper, we have evaluated AlexNet and the other eight models on the distributed environment and estimated FPS values with Wi-Fi, 3G, and 5G communication. To reduce communication costs, we also introduced the compression process before communication. This compression may degrade the object recognition accuracy. As necessary conditions, we set FPS to 30 or faster and object recognition accuracy to 69.7% or higher. This value is determined based on that of an approximation model that binarizes the activation of Neural Network. We constructed performance and energy models to find the optimal configuration that consumes minimum energy while satisfying the necessary conditions. Through the comprehensive evaluation, we found that the optimal configurations of all nine models. For small models, such as AlexNet, processing entire models in the edge was the best. On the other hand, for huge models, such as VGG16, processing entire models in the server was the best. For medium-size models, the distributed models were good candidates. We confirmed that our model found the most energy efficient configuration while satisfying FPS and accuracy requirements, and the distributed models successfully reduced the energy consumption up to 48.6%, and 6.6% on average. We also found that HEVC compression is important before transferring the input data or the feature data between the distributed inference processes.

While existing inference engines solved real world problems using probabilistic knowledge representation, one challenging task is to efficiently utilize the representation under a situation of uncertainty during conflict resolution. This paper presents a new approach to straightforwardly combine a rule-based system (RB) with a probabilistic graphical inference framework, i.e., naïve Bayesian network (BN), towards probabilistic argumentation via a so-called probabilistic assumption-based argumentation (PABA) framework. A rule-based system (RB) formalizes its rules into defeasible logic under the assumption-based argumentation (ABA) framework while the Bayesian network (BN) provides probabilistic reasoning. By knowledge integration, while the former provides a solid testbed for inference, the latter helps the former to solve persistent conflicts by setting an acceptance threshold. By experiments, effectiveness of this approach on conflict resolution is shown via an example of liver disorder diagnosis.

This paper proposes an efficient server selection scheme in successive participation scenario with participating-domain segmentation. The scheme is utilized by distributed processing systems for real-time interactive communication to suppress the communication latency of a wide-area network. In the proposed scheme, users participate for server selection one after another. The proposed scheme determines a recommended server, and a new user selects the recommended server first. Before each user participates, the recommended servers are determined assuming that users exist in the considered regions. A recommended server is determined for each divided region to minimize the latency. The new user selects the recommended available server, where the user is located. We formulate an integer linear programming problem to determine the recommended servers. Numerical results indicate that, at the cost additional computation, the proposed scheme offers smaller latency than the conventional scheme. We investigate different policies to divide the users' participation for the recommended server finding process in the proposed scheme.