Leakage resilient cryptography is often considered in the presence of a very strong leakage oracle: An adversary may submit arbitrary efficiently computable function f to the leakage oracle to receive f(x), where x denotes the entire secret that a party possesses. This model is somewhat too strong in the setting of public-key encryption (PKE). It is known that no secret-key leakage resilient PKE scheme exists if the adversary may have access to the secret-key leakage oracle to receive only one bit after it was given the challenge ciphertext. Similarly, there exists no sender-randomness leakage resilient PKE scheme if one-bit leakage occurs after the target public key was given to the adversary. At TCC 2011, Halevi and Lin have broken the barrier of after-the-fact leakage, by proposing the so-called split state model, where a secret key of a party is explicitly divided into at least two pieces, and the adversary may have not access to the entire secret at once, but each divided pieces, one by one. In the split-state model, they have constructed post-challenge secret-key leakage resilient CPA secure PKEs from hash proof systems, but the construction of CCA secure post-challenge secret-key leakage PKE has remained open. They have also remained open to construct sender-randomness leakage PKE in the split state model. This paper provides a solution to the open issues. We also note that the proposal of Halevi and Lin is post-challenge secret-key leakage CPA secure against a single challenge ciphertext; not against multiple challenges. We present an efficient generic construction that converts any CCA secure PKE scheme into a multiple-challenge CCA secure PKE that simultaneously tolerates post-challenge secret-key and sender-randomness leakage in the split state model, without any additional assumption. In addition, our leakage amount of the resulting schemes is the same as that of Halevi and Lin CPA PKE, i.e., (1/2+γ)l/2 where l denotes the length of the entire secret (key or randomness) and γ denotes a universal (possitive) constant less than 1/2. Our conversion is generic and available for many other public-key primitives. For instance, it can convert any identity-based encryption (IBE) scheme to a post-challenge master-key leakage and sender-randomness leakage secure IBE.

Displacement current measurement (DCM) is widely used as a method for analyzing carrier behaviors of organic devices. Carrier behaviors are analyzed using transient currents. On the other hand, electric-field-induced optical second-harmonic generation (EFISHG) measurement is capable of directly probing carrier motions in organic devices, where the migration of electric field stemmed from carriers is measured. In this study, we employed the DCM and EFISHG measurements for analyzing interfacial carrier behaviors in Au/pentacene/polyimide (PI)/indium-tin-oxide (ITO) double layer organic devices, where interfacial accumulated charges and electric fields formed in the pentacene layer were explored.

This paper proposes a disaster recovery method for transport networks. In a scenario of recovery from a disaster, a network is repaired through multiple restoration stages because repair resources are limited. In a practical case, a network should provide the reachability of important traffic in transient stages, even as service interruption risks and/or operational overheads caused by transport paths switching are suppressed. Then, we define the multi-objective optimization problem: maximizing the traffic recovery ratio and minimizing the number of switched transport paths at each stage. We formulate our problem as linear programming, and show that it yields pareto-optimal solutions of traffic recovery versus the number of switched paths. We also propose a heuristic algorithm for applying to networks consisting of a few hundred nodes, and show that it can produce sub-optimal solutions that differ only slightly from optimal solutions.

Support of incoming traffic differentiation and Quality of Service (QoS) assurance is very important for the development of high performance packet switches, capable of separating traffic flows. In our previous paper, we proposed the implementation of two buffers at each crosspoint of a crossbar fabric that leads to the Dual Crosspoint Queued (DCQ) switch. Inside DCQ switch, one buffer is used to store the real-time traffic and the other for the non-real-time traffic. We also showed that the static priority algorithms can provide the QoS only for the real-time traffic due to their greedy nature that gives the absolute priority to that type of traffic. In order to overcome this problem, in our paper we propose the DCQ switch with the Largest Weighted Occupancy First scheduling algorithm that provides the desired QoS support for both traffic flows. Detailed analysis of the simulation results confirms the validity of proposed solution.

In this paper, a compression-friendly copyright- and privacy-protected image trading system is proposed. In the image trading system, the copyright of the image and the consumer's privacy is important. In addition, it should preserve existing image compression standards. In the proposed method, for privacy protection, the content provider (CP) multiplies random signs to the discrete wavelet transformed (DWTed) coefficients of an image to generate the visually encrypted image. The proposed visually protected images can be efficiently compressed by using JPEG 2000 which compresses the image in the DWTed domain as well. For copyright protection, the trusted third party (TTP) applies digital fingerprinting to the image in the encrypted domain. While in the conventional system, the amplitude-only image (AOI) which is the inversely transformed amplitude spectra of an image is used for privacy protection. Since, the AOI consists of real numbers, to store and transmit the AOI, it has to be quantized before compression. Therefore, quantization errors cannot be avoided in the conventional system. On the other hand, the proposed method applies the digital fingerprint in the DWTed domain, so clipping errors in decoding the image by the TTP is avoided. In addition, only a seed number which is input to a pseudo random number generator is shared between the CP and the consumer, whereas an extra image is shared in the conventional systems. Experimental results show that the proposed system is efficient in terms of privacy protection, compression performance, quality of fingerprinted images, and correct fingerprint extracting performance.

In this paper, we investigate the problems of the established congestion solution and then introduce a self-adjustable rate control that supports quality of service assurances over multi-hop wireless mesh networks. This scheme eliminates two phases of the established congestion solution and works on the MAC layer for congestion control. Each node performs rate control by itself so network congestion is eliminated after it independently collects its vector parameters and network status parameters for rate control. It decides its transmission rate based on a predication model which uses a rate function including a congestion risk level and a passing function. We prove that our scheme works efficiently without any negative effects between the network layer and the data link layer. Simulation results show that the proposed scheme is more effective and has better performance than the existing method.

In the optimization of decision diagrams, variable reordering approaches are often used to minimize the number of nodes. However, such approaches are less effective for analysis of multi-state systems given by monotone structure functions. Thus, in this paper, we propose algorithms to minimize the number of edges in an edge-valued multi-valued decision diagram (EVMDD) for fast analysis of multi-state systems. The proposed algorithms minimize the number of edges by grouping multi-valued variables into larger-valued variables. By grouping multi-valued variables, we can reduce the number of nodes as well. To show the effectiveness of the proposed algorithms, we compare the proposed algorithms with conventional optimization algorithms based on a variable reordering approach. Experimental results show that the proposed algorithms reduce the number of edges by up to 15% and the number of nodes by up to 47%, compared to the conventional ones. This results in a speed-up of the analysis of multi-state systems by about three times.

High-speed wireless access technologies have evolved over the last years setting new challenges for TCP. That is, to effectively utilize the available network resources and to minimize the effects of wireless channel errors on TCP performance. This paper introduces a new TCP variant, called TCP-BIAD aiming at enhancing TCP performance in broadband wireless access networks. We provide analytical expressions for evaluating the stability, throughput, fairness and friendliness properties of our proposal, and we validate our results by means of computer simulations. Initial results presented in this paper show that this approach achieves high network utilization levels in a wide range of network conditions, while maintaining an adequately fair and friendly behavior with respect to coexisting TCP flows.

A novel fusion method for semantic concept detection in images, called tree fusion, is proposed. Various kinds of features are given to different classifiers. Then, according to the importance of features and effectiveness of classifiers, the results of feature-classifier pairs are ranked and fused using C4.5 algorithm. Experimental results conducted on the MSRC and PASCAL VOC 2007 datasets have demonstrated the effectiveness of the proposed method over the traditional fusion methods.

We analyze the defects of window-based TCP algorithm in datacenter networks and propose Rate-based Datacenter TCP (RDT) algorithm in this paper. The RDT algorithm combines rate-based congestion control technology with ECN (Explicit Congestion Notification) mechanism of DCTCP. The experiments in NS2 show that RDT has a potential to completely avoid TCP incast collapse in datacenters and inherit the low latency advantages of DCTCP.

In data center networks, group communication is currently playing an important role and multicast communications is an effective way to support group communication for large numbers of virtual machines. Layer-2 congestion control named QCN (Quantized Congestion Notification) has been proposed to realize the high reliability required by LAN/SAN integration in data center networking. Our preliminary evaluation in this paper shows that a multicast flow suffers lower throughput than unicast flows when conventional QCN is applied in a naive manner. This is because a sending device receives congestion feedback from multiple locations on a multicast tree and decreases transmission rate accordingly. To counter this throughput degradation of multicast flows, we propose a new Layer 2 congestion control algorithm in multicast environment, Quantized Congestion Notification with Bottleneck Selection (QCN/BS). In QCN/BS, the switch in the worst congestion level is selected and the transmission rate of the sending device is calculated exclusively according to feedback from the selected switch. Simulation results show that when conventional QCN is used, a multicast flow experiences lower and more severely unfair throughput than a unicast flow. The proposed QCN/BS resolves this problem.

This letter proposes an adaptive base plane filtering algorithm for the inter-plane estimation of RGB images in HEVC RExt. Because most high-frequency components of RGB images have low inter-plane correlation, our proposed scheme adaptively removes the high-frequency components of the base plane in order to enhance the inter-plane estimation accuracy. The experimental results show that the proposed scheme provides average BD rate gains of 0.6%, 1.0%, and 1.2% in the G, B, and R planes, respectively, with slightly decreased complexity, as compared to the previous inter-plane filtering method.

Synthetic aperture radar (SAR) is an indispensable tool for low visibility ground surface measurement owing to its robustness against optically harsh environments such as adverse weather or darkness. As a leading-edge approach for SAR image processing, the coherent change detection (CCD) technique has been recently established; it detects a temporal change in the same region according to the phase interferometry of two complex SAR images. However, in the case of general damage assessment following an earthquake or mudslide, the technique requires not only the detection of surface change but also an assessment for height change quantity, such as occurs with a building collapse or road subsidence. While the interferometric SAR (InSAR) approach is suitable for height assessment, it is basically unable to detect change if only a single observation is made. To address this issue, we previously proposed a method of estimating height change according to phase interferometry of the coherence function obtained by dual band-divided SAR images. However, the accuracy of this method significantly degrades in noisy situations owing to the use of the phase difference. To resolve this problem, this paper proposes a novel height estimation method by exploiting the frequency characteristic of coherence phases obtained by each SAR image multiply band-divided. The results obtained from numerical simulations and experimental data demonstrate that our proposed method offers accurate height change estimation while avoiding degradation in the spatial resolution.

A lot of work has been conducted on time series classification and similarity search over the past decades. However, the classification of a time series with high accuracy is still insufficient in applications such as ubiquitous or sensor systems. In this paper, a novel textual approximation of a time series, called TAX, is proposed to achieve high accuracy time series classification. l-TAX, an extended version of TAX that shows promising classification accuracy over TAX and other existing methods, is also proposed. We also provide a comprehensive comparison between TAX and l-TAX, and discuss the benefits of both methods. Both TAX and l-TAX transform a time series into a textual structure using existing document retrieval methods and bioinformatics algorithms. In TAX, a time series is represented as a document like structure, whereas l-TAX used a sequence of textual symbols. This paper provides a comprehensive overview of the textual approximation and techniques used by TAX and l-TAX

This paper proposes a vanishing point-based road detection method. Firstly, a vanishing point is detected using a texture-based method proposed in a recent study. After that, a histogram is generated for detecting two road borders. The road area is defined as the region between the two road borders and below the vanishing point. The experimental results demonstrate that our method performs well in general road images.

In P2P applications, networks are formed by devices belonging to independent users. Therefore, routing hotspots or routing congestions are typically created by an unanticipated new event that triggers an unanticipated surge of users to request streaming service from some particular nodes; and a challenging problem is how to provide incentive mechanisms to allocation bandwidth more fairly in order to avoid congestion and other short backs for P2P QoS. In this paper, we study P2P bandwidth game — the bandwidth allocation in P2P networks. Unlike previous works which focus either on routing or on forwarding, this paper investigates the game theoretic mechanism to incentivize node's real bandwidth demands and propose novel method that avoid congestion proactively, that is, prior to a congestion event. More specifically, we define an incentive-compatible pricing vector explicitly and give theoretical proofs to demonstrate that our mechanism can provide incentives for nodes to tell the true bandwidth demand. In order to apply this mechanism to the P2P distribution applications, we evaluate our mechanism by NS-2 simulations. The simulation results show that the incentive pricing mechanism can distribute the bandwidth fairly and effectively and can also avoid the routing hotspot and congestion effectively.

In this paper, we propose an adaptive voltage huddle-based distributed-register architecture (AVHDR architecture), which integrates dynamic multiple supply voltages and interconnection delay into high-level synthesis. In AVHDR architecture, voltages can be dynamically assigned for energy reduction. In other words, low supply voltages are assigned to non-critical operations, and leakage power is cut off by turning off the power supply to the sleeping functional units. Next, an AVHDR-based high-level synthesis algorithm is proposed. Our algorithm is based on iterative improvement of scheduling/binding and floorplanning. In the iteration process, the modules in each huddle can be placed close to each other and the corresponding AVHDR architecture can be generated and optimized with floorplanning information. Experimental results show that on average our algorithm achieves 43.9% energy-saving compared with conventional algorithms.

In general, the sensor network has a many-to-one communication architecture wherein each node transmits its data to a sink. This leads the congested nodes to die early and nodes nears the sink suffer from significant traffic concentrations. In this paper, we propose a cross-layer based routing and MAC protocol which is compatible with the IEEE 802.15.4 standard without additional overhead. The key mechanism is to provide dynamic route discovery and route maintenance operations to avoid and mitigate the most congested nodes by monitoring link status such as link delay, buffer occupancy and residential energy. In addition, the proposed protocol also provides a dynamic tuning of BE (Binary Exponent) and frame retransmission opportunities according to the hop distance to the sink node to mitigate funnel effects. We conducted simulations, verifying the performance over existing protocols.

Content-Centric Networking (CCN) employs a hierarchical but location independent content naming scheme. While such a location independent naming brings various benefits including efficient content delivery, mobility, and multihoming, location independent name prefixes are hard to aggregate. This poses a serious scaling issue on the efficiency of looking up content names in a huge Forwarding Information Base (FIB) by longest prefix matching, which requires seeking the longest matching prefix through all candidate prefix lengths. We propose a new scheme for efficiently looking up non-aggregatable name prefixes in a large FIB. The proposed scheme is based on the observation that the bottleneck of FIB lookup is the random accesses to the high-latency off-chip DRAM for prefix seeking and this can be reduced by exploiting the information on the longest matching prefix length in the previous hop. Our evaluation results show that the proposed scheme significantly improves FIB lookup latency with a reasonable traffic parameters observed in today's Internet.

In communication networks, congestion control, routing, and multiple access schemes for scheduling transmissions are typically regulated by distributed algorithms. Engineers designed these algorithms using clever heuristics that they refined in the light of simulation results and experiments. Over the last two decades, a deeper understanding of these algorithms emerged through the work of researchers. This understanding has a real potential for improving the design of protocols for data centers, cloud computing, and even wireless networks. Since protocols tend to be standardized by engineers, it is important that they become familiar with the insights that emerged in research. We hope that this paper might appeal to practitioners and make the research results intuitive and useful. The methods that the paper describes may be useful for many other resource allocation problems such as in call centers, manufacturing lines, hospitals and the service industry.