Geometric dilution of precision (GDOP) is a measure showing the positioning accuracy at different spatial locations in location systems. Although expressions of GDOP for the time of arrival (TOA), time difference of arrival (TDOA), and angle of arrival (AOA) systems have been developed, no closed form expression of GDOP are available for the received signal strength (RSS) system. This letter derives an explicit GDOP expression utilizing the RSS measurement in the wireless sensor networks.

In this paper, we study the approximate point set matching (APSM) problem with minimum RMSD score under translation, rotation, and one-to-one correspondence in d-dimension. Since most of the previous works about APSM problems use similality scores that do not especially care about one-to-one correspondence between points, such as Hausdorff distance, we cannot easily apply previously proposed methods to our APSM problem. So, we focus on speed-up of exhaustive search algorithms that can find all approximate matches. First, we present an efficient branch-and-bound algorithm using a novel lower bound function of the minimum RMSD score for the enumeration version of APSM problem. Then, we modify this algorithm for the optimization version. Next, we present another algorithm that runs fast with high probability when a set of parameters are fixed. Experimental results on both synthetic datasets and real 3-D molecular datasets showed that our branch-and-bound algorithm achieved significant speed-up over the naive algorithm still keeping the advantage of generating all answers.

In this paper, we consider the problem of enumerating spanning subgraphs with high edge-connectivity of an input graph. Such subgraphs ensure multiple routes between two vertices. We first present an algorithm that enumerates all the 2-edge-connected spanning subgraphs of a given plane graph with n vertices. The algorithm generates each 2-edge-connected spanning subgraph of the input graph in O(n) time. We next present an algorithm that enumerates all the k-edge-connected spanning subgraphs of a given general graph with m edges. The algorithm generates each k-edge-connected spanning subgraph of the input graph in O(mT) time, where T is the running time to check the k-edge-connectivity of a graph.

Revisiting the Sekine-Imai-Tani top-down algorithm to compute the BDD of all spanning trees and the Tutte polynomial of a given graph, we explicitly analyze the Fixed-Parameter Tractable (FPT) time complexity with respect to its (proper) pathwidth, pw (ppw), and obtain a bound of O*(Bellmin{pw}+1,ppw}), where Belln denotes the n-th Bell number, defined as the number of partitions of a set of n elements. We further investigate the case of complete graphs in terms of Bell numbers and related combinatorics, obtaining a time complexity bound of Belln-O(n/log n).

At PQCrypto 2016, Szepieniec et al. proposed a new type of trapdoor called Extension Field Cancellation (EFC) for constructing secure multivariate encryption cryptosystems. They also specifically suggested two schemes EFCp- and EFCpt2- that apply this trapdoor and some modifiers. Although both of them seem to avoid all attacks used for cryptanalysis on multivariate cryptography, their decryption efficiency has room for improvement. On the other hand, their security was analyzed mainly through an algebraic attack of computing the Gröbner basis of the public key, and there possibly exists more effective attacks. In this paper, we introduce a more efficient decryption approach for EFCp- and EFCpt2-, which manages to avoid all redundant computation involved in the original decryption algorithms without altering their public key. In addition, we estimate the secure parameters for EFCp- and EFCpt2- through a hybrid attack of algebraic attack and exhaustive search.

Along with the proliferation of IoT (Internet of Things) devices, cyberattacks towards them are on the rise. In this paper, aiming at efficient precaution and mitigation of emerging IoT cyberthreats, we present a multimodal study on applying machine learning methods to characterize malicious programs which target multiple IoT platforms. Experiments show that opcode sequences obtained from static analysis and API sequences obtained by dynamic analysis provide sufficient discriminant information such that IoT malware can be classified with near optimal accuracy. Automated and accelerated identification and mitigation of new IoT cyberthreats can be enabled based on the findings reported in this study.

In this paper, we focus on a large-scale ICN (Information-Centric Networking), and reveal the scaling property of ICN. Because of in-network content caching, ICN is a sort of cache networks and expected to be a promising architecture for replacing future Internet. To realize a global-scale (e.g., Internet-scale) ICN, it is crucial to understand the fundamental properties of such large-scale cache networks. However, the scaling property of ICN has not been well understood due to the lack of theoretical foundations and analysis methodologies. For answering research questions regarding the scaling property of ICN, we derive the cache hit probability at each router, the average content delivery delay of each entity, and the average content delivery delay of all entities over a content distribution tree comprised of a single repository (i.e., content provider), multiple routers, and multiple entities (i.e., content consumers). Through several numerical examples, we investigate the effect of the topology and the size of the content distribution tree and the cache size at routers on the average content delivery delay of all entities. Our findings include that the average content delivery delay of ICNs converges to a constant value if the cache size of routers are not small, which implies high scalability of ICNs, and that even when the network size would grow indefinitely, the average content delivery delay is upper-bounded by a constant value if routers in the network are provided with a fair amount of content caches.

Information-Centric Networking (ICN) can offer rich functionalities to the network, e.g, in-network caching, and name-based forwarding. Incremental deployment of ICN is a key challenge that enable smooth migration from current IP network to ICN. We can say that Network Function Virtualization (NFV) must be one of the key technologies to achieve this deployment because of its flexibility to support new network functions. However, when we consider the ICN deployment with NFV, there exist two performance issues, processing delay of name-based forwarding and computational overhead of virtual machine. In this paper we proposed a NFV infrastructure-assisted ICN packet forwarding by integrating the name look-up to the Open vSwitch. The contributions are twofold: 1) First, we provide the novel name look-up scheme that can forward ICN packets without costly longest prefix match searching. 2) Second, we design the ICN packet forwarding scheme that integrates the partial name look-up into the virtualization infrastructure to mitigate computation overhead.

Social networks often demonstrate hierarchical community structure with communities embedded in other ones. Most existing hierarchical community detection methods need one or more tunable parameters to control the resolution levels, and the obtained dendrograms, a tree describing the hierarchical community structure, are extremely complex to understand and analyze. In the paper, we propose a parameter-free hierarchical community detection method based on micro-community and minimum spanning tree. The proposed method first identifies micro-communities based on link strength between adjacent vertices, and then, it constructs minimum spanning tree by successively linking these micro-communities one by one. The hierarchical community structure of social networks can be intuitively revealed from the merging order of these micro-communities. Experimental results on synthetic and real-world networks show that our proposed method exhibits good accuracy and efficiency performance and outperforms other state-of-the-art methods. In addition, our proposed method does not require any pre-defined parameters, and the output dendrogram is simple and meaningful for understanding and analyzing the hierarchical community structure of social networks.

The sparse unmixing of hyperspectral data has attracted much attention in recent years because it does not need to estimate the number of endmembers nor consider the lack of pure pixels in a given hyperspectral scene. However, the high mutual coherence of spectral libraries strongly affects the practicality of sparse unmixing. The collaborative sparse unmixing via variable splitting and augmented Lagrangian (CLSUnSAL) algorithm is a classic sparse unmixing algorithm that performs better than other sparse unmixing methods. In this paper, we propose a CLSUnSAL-based hyperspectral unmixing method based on dictionary pruning and reweighted sparse regression. First, the algorithm identifies a subset of the original library elements using a dictionary pruning strategy. Second, we present a weighted sparse regression algorithm based on CLSUnSAL to further enhance the sparsity of endmember spectra in a given library. Third, we apply the weighted sparse regression algorithm on the pruned spectral library. The effectiveness of the proposed algorithm is demonstrated on both simulated and real hyperspectral datasets. For simulated data cubes (DC1, DC2 and DC3), the number of the pruned spectral library elements is reduced by at least 94% and the runtime of the proposed algorithm is less than 10% of that of CLSUnSAL. For simulated DC4 and DC5, the runtime of the proposed algorithm is less than 15% of that of CLSUnSAL. For the real hyperspectral datasets, the pruned spectral library successfully reduces the original dictionary size by 76% and the runtime of the proposed algorithm is 11.21% of that of CLSUnSAL. These experimental results show that our proposed algorithm not only substantially improves the accuracy of unmixing solutions but is also much faster than some other state-of-the-art sparse unmixing algorithms.

The security of elliptic curve cryptography is closely related to the computational complexity of the elliptic curve discrete logarithm problem (ECDLP). Today, the best practical attacks against ECDLP are exponential-time generic discrete logarithm algorithms such as Pollard's rho method. A recent line of inquiry in index calculus for ECDLP started by Semaev, Gaudry, and Diem has shown that, under certain heuristic assumptions, such algorithms could lead to subexponential attacks to ECDLP. In this study, we investigate the computational complexity of ECDLP for elliptic curves in various forms — including Hessian, Montgomery, (twisted) Edwards, and Weierstrass representations — using index calculus. Using index calculus, we aim to determine whether there is any significant difference in the computational complexity of ECDLP for elliptic curves in various forms. We provide empirical evidence and insight showing an affirmative answer in this paper.

We present constant-time testing algorithms for generalized shogi (Japanese chess), chess, and xiangqi (Chinese chess). These problems are known or believed to be EXPTIME-complete. A testing algorithm (or a tester) for a property accepts an input if it has the property, and rejects it with high probability if it is far from having the property (e.g., at least 2/3) by reading only a constant part of the input. A property is said to be testable if a tester exists. Given any position on a ⌊√n⌋×⌊√n⌋ board with O(n) pieces, the generalized shogi, chess, and xiangqi problem are problems determining the property that “the player who moves first has a winning strategy.” We propose that this property is testable for shogi, chess, and xiangqi. The shogi tester and xiangqi tester have a one-sided-error, but surprisingly, the chess tester has no-error. Over the last decade, many problems have been revealed to be testable, but most of such problems belong to NP. This is the first result on the constant-time testability of EXPTIME-complete problems.

In this paper, we investigate computational complexity of pipe puzzles. A pipe puzzle is a kind of tiling puzzle; the input is a set of cards, and a part of a pipe is drawn on each card. For a given set of cards, we arrange them and connect the pipes. We have to connect all pipes without creating any local loop. While ordinary tiling puzzles, like jigsaw puzzles, ask to arrange the tiles with local consistency, pipe puzzles ask to join all pipes. We first show that the pipe puzzle is NP-complete in general even if the goal shape is quite restricted. We also investigate restricted cases and show some polynomial-time algorithms.

In this paper, we propose a Pyramid Predictive Attention Network (PPAN) for medical image segmentation. In the medical field, the size of dataset generally restricts the performance of deep CNN and deploying the trained network with gross parameters into the terminal device with limited memory is an expectation. Our team aims to the future home medical diagnosis and search for lightweight medical image segmentation network. Therefore, we designed PPAN mainly made of Xception blocks which are modified from DeepLab v3+ and consist of separable depthwise convolutions to speed up the computation and reduce the parameters. Meanwhile, by utilizing pyramid predictions from each dimension stage will guide the network more accessible to optimize the training process towards the final segmentation target without degrading the performance. IoU metric is used for the evaluation on the test dataset. We compared our designed network performance with the current state of the art segmentation networks on our RGB tongue dataset which was captured by the developed TIAS system for tongue diagnosis. Our designed network reduced 80 percentage parameters compared to the most widely used U-Net in medical image segmentation and achieved similar or better performance. Any terminal with limited storage which is needed a segment of RGB image can refer to our designed PPAN.

HIGHT is a 64-bit block lightweight cipher, which adopts the ARX-based generalized Feistel network, and it accepts a 128-bit key. It is a standard encryption algorithm in South Korea and also is internationally standardized by ISO/IEC 18033-3. Therefore, many third-party cryptanalyses have been proposed against HIGHT. Impossible differential and integral attacks are applied to reduced-round HIGHT, and especially, the impossible differential attack causes the 27-round attack, which is the current best attack under the single-key setting. In this paper, we propose some improved integral attacks against HIGHT. We first apply the division property to HIGHT and find new 19-round integral characteristics, which are improved by two rounds compared with the previous best ones. We append 9-round key recovery to these characteristics and it enables us to attack 28-round HIGHT. Its time complexity is 2127.02 where 263 chosen plaintexts and 2117 memory are required. Moreover, we can attack 29-round HIGHT if the full codebook is used, where its time and memory complexities are 2126.07 and 2118, respectively. It improves by two rounds compared with the previous best attack.

FPGA-based switches are appealing nowadays due to the balance between hardware performance and software flexibility. Packet parser, as the foundational component of FPGA-based switches, is to identify and extract specific fields used in forwarding decisions, e.g., destination IP address. However, traditional parsers are too rigid to accommodate new protocols. In addition, FPGAs usually have a much lower clock frequency and fewer hardware resources, compared to ASICs. In this paper, we present PLANET, a programmable packet-level parallel parsing architecture for FPGA-based switches, to overcome these two limitations. First, PLANET has flexible programmability of updating parsing algorithms at run-time. Second, PLANET highly exploits parallelism inside packet parsing to compensate FPGA's low clock frequency and reduces resource consumption with one-block recycling design. We implemented PLANET on an FPGA-based switch prototype with well-integrated datacenter protocols. Evaluation results show that our design can parse packets at up to 100 Gbps, as well as maintain a relative low parsing latency and fewer hardware resources than existing proposals.

This paper analyzes the performance of various decoders in a two-user interference channel, and some improved decoders based on enhanced utilization of channel state information at the receiver side are presented. Further, new decoders, namely hierarchical constellation based decoders, are proposed. Simulations show that the improved decoders and the proposed decoders have much better performance than existing decoders. Moreover, the proposed decoders have lower decoding complexity than the traditional maximum likelihood decoder.

A Multipath TCP (MPTCP) connection uses multiple subflows (i.e., TCP flows), each of which traverses over a wireless link, enabling throughput and resilience enhancements in mobile wireless networks. However, to achieve the benefits, the subflows are necessarily initialized (i.e., must complete TCP handshakes) and sequentially attached to the MPTCP connection. In the standard (MPTCPST), MPTCP initialization raises several problems. First, the TCP handshake of opening subflow is generally associated with a predetermined network. That leads to degraded MPTCP performance when the network does not have the lowest latency among available ones. Second, the first subflow's initialization needs to be successful before the next subflow can commence its attempt to achieve initialization. Therefore, the resilience of multiple paths fails when the first initialization fails. This paper proposes a novel method for MPTCP initialization, namely MPTCPSD (i.e., MPTCP with SYN duplication), which can solve the problems. MPTCPSD duplicates the first SYN and attempts to establish TCP handshakes for all subflows simultaneously, hence inherently improves the loss-resiliency. The subflow that achieves initialization first, is selected as the first subflow, consequently solving the first problem. We have implemented and extensively evaluated MPTCPSD in comparison to MPTCPST. In an emulated network, the evaluation results show that MPTCPSD has better performance that MPTCPST with the scenarios of medium and short flows. Moreover, MPTCPSD outperforms MPTCPST in the case that the opening subflow fails. Moreover, a real network evaluation proves that MPTCPSD efficiently selects the lowest delay network among three ones for the first subflow regardless of the preconfigured default network. Additionally, we propose and implement a security feature for MPTCPSD, that prevents the malicious subflow from being established by a third party.

This paper investigates data uploading in cellular networks with the consideration of device-to-device (D2D) communications. A generalized data uploading scheme is proposed by leveraging D2D cooperation among the devices to reduce the data uploading time. In this scheme, we extend the conventional schemes on cooperative D2D data uploading for cellular networks to a more general case, which considers D2D cooperation among both the devices with or without uploading data. To motivate D2D cooperation among all available devices, we organize the devices within communication range by offering them rewards to construct multi-hop D2D chains for data uploading. Specifically, we formulate the problem of chain formation among the devices for data uploading as a coalitional game. Based on merge-and-split rules, we develop a coalition formation algorithm to obtain the solution for the formulated coalitional game with convergence on a stable coalitional structure. Finally, extensive numerical results show the effectiveness of our proposed scheme in reducing the average data uploading time.

With the advantages of higher blocking voltage, higher operation temperature, fast-switching characteristics, and lower switching losses, the silicon carbide (SiC) MOSFET has attracted more attentions and become an available replacement of traditional silicon (Si) power semiconductor in applications. Despite of all the merits above, electromagnetic interference (EMI) issues will be induced consequently by the ultra-fast switching transitions of the SiC MOSFET. To quickly and precisely assess the switching behaviors of the SiC MOSFET for EMI investigation, an analytical model is proposed. This model has comprehensively considered most of the key factors, including parasitic inductances, non-linearity of the junction capacitors, negative feedback effect of Ls and Cgd shared by the power and the gate stage loops, non-linearity of the trans-conductance, and skin effect during voltage and current ringing stages, which will considerably affect the switching performance of the SiC MOSFET. Additionally, a finite-state machine (FSM) is especially utilized so as to analytically and intuitively describe the switching behaviors of the SiC MOSFET via Stateflow. Based on double pulse test (DPT), the effectiveness and correctness of the proposed model are validated through the comparison between the calculated and the measured waveforms during switching transitions. Besides, the model can appropriately depict the spectrum of the drain-source voltage of the MOSFET and is suitable for EMI investigation in applying of SiC devices.