The security of Unmanned Aerial Vehicle (UAV) swarms is threatened by the deployment of anti-UAV systems under complicated environments such as battlefield. Specifically, the faults caused by anti-UAV systems exhibit sparse and compressible characteristics. In this paper, in order to improve the survivability of UAV swarms under complicated environments, we propose a novel multi-abnormality self-detecting and faults location method, which is based on compressed sensing (CS) and takes account of the communication characteristics of UAV swarms. The method can locate the faults when UAV swarms are suffering physical damages or signal attacks. Simulations confirm that the proposed method performs well in terms of abnormalities detecting and faults location when the faults quantity is less than 17% of the quantity of UAVs.

Unmanned aerial vehicle (UAV) communication has drawn rising interest recently with the distinctive gains brought by its inherent mobility. In this paper, we investigate the throughput maximization problem in UAV-enabled uplink communication, where multiple ground nodes communicate with a UAV while a group of ground jammers send jamming signals to jam the communications between UAV and the ground nodes. In contrast to the previous works that only considering UAV's transmit power allocation and two-dimension (2D) trajectory design, the ground nodes' transmit power allocation and scheduling along with the UAV's three-dimensional (3D) trajectory design are jointly optimized. The formulated throughput maximization problem is a mixed-integer non-convex programme that hard to be solved in general. Thus, we propose an iterative algorithm to make the problem trackable by applying the block coordinate descent and successive convex optimization techniques. Simulation results show that our proposed algorithm outperforms the benchmark methods that improving the throughput of the system significantly.

The distinctive characteristics of unmanned aerial vehicle networks (UAVNs), including highly dynamic network topology, high mobility, and open-air wireless environments, may make UAVNs vulnerable to attacks and threats. Due to the special security requirements, researching in the high reliability of the power and communication network in drone monitoring system become special important. The reliability of the communication network and power in the drone monitoring system has been studied. In order to assess the reliability of the system power supply in the drone emergency monitoring system, the accelerated life tests under constant stress were presented based on the exponential distribution. Through a comparative analysis of lots of factors, the temperature was chosen as the constant accelerated stress parameter. With regard to the data statistical analysis, the type-I censoring sample method was put forward. The mathematical model of the drone monitoring power supply was established and the average life expectancy curve was obtained under different temperatures through the analysis of experimental data. The results demonstrated that the mathematical model and the average life expectancy curve were fit for the actual very well. With overall consideration of the communication network topology structure and network capacity the improved EED-SDP method was put forward in drone monitoring. It is concluded that reliability analysis of power and communication network in drone monitoring system is remarkably important to improve the reliability of drone monitoring system.

The Shortest Vector Problem (SVP) is one of the most important lattice problems in computer science and cryptography. The LLL lattice basis reduction algorithm runs in polynomial time and can compute an LLL-reduced basis that provably contains an approximate solution to the SVP. On the other hand, the LLL algorithm in practice tends to solve low-dimensional exact SVPs with high probability, i.e., >99.9%. Filling this theoretical-practical gap would lead to an understanding of the computational hardness of the SVP. In this paper, we try to fill the gap in 3,4 and 5 dimensions and obtain two results. First, we prove that given a 3,4 or 5-dimensional LLL-reduced basis, the shortest vector is one of the basis vectors or it is a limited integer linear combination of the basis vectors. In particular, we construct explicit representations of the shortest vector by using the LLL-reduced basis. Our analysis yields a necessary and sufficient condition for checking whether the output of the LLL algorithm contains the shortest vector or not. Second, we estimate the failure probability that a 3-dimensional random LLL-reduced basis does not contain the shortest vector. The upper bound seems rather tight by comparison with a Monte Carlo simulation.

In previous studies, we determined that workloads often contain many input-output (IO) concentrations. Such concentrations are aggregations of IO accesses. They appear in narrow regions of a storage volume and continue for durations of up to about an hour. These narrow regions occupy a small percentage of the logical unit number capacity, include most IO accesses, and appear at unpredictable logical block addresses. We investigated these workloads by focusing on page-level regularity and found that they often include few regularities. This means that simple caching may not reduce the response time for these workloads sufficiently because the cache migration algorithm uses page-level regularity. We previously developed an on-the-fly automated storage tiering (OTF-AST) system consisting of an SSD and an HDD. The migration algorithm identifies IO concentrations with moderately long durations and migrates them from the HDD to the SSD. This means that there is little or no reduction in the response time when the workload includes few such concentrations. We have now developed a hybrid storage system consisting of a cache drive with an SSD and HDD and a multi-tier SSD that uses OTF-AST, called “OTF-AST with caching.” The OTF-AST scheme handles the IO accesses that produce moderately long duration IO concentrations while the caching scheme handles the remaining IO accesses. Experiments showed that the average response time for our system was 45% that of Facebook FlashCache on a Microsoft Research Cambridge workload.

Secure two-party comparison plays a crucial role in many privacy-preserving applications, such as privacy-preserving data mining and machine learning. In particular, the available comparison protocols with the appropriate input/output configuration have a significant impact on the performance of these applications. In this paper, we firstly describe a taxonomy of secure two-party comparison protocols which allows us to describe the different configurations used for these protocols in a systematic manner. This taxonomy leads to a total of 216 types of comparison protocols. We then describe conversions among these types. While these conversions are based on known techniques and have explicitly or implicitly been considered previously, we show that a combination of these conversion techniques can be used to convert a perhaps less-known two-party comparison protocol by Nergiz et al. (IEEE SocialCom 2010) into a very efficient protocol in a configuration where the two parties hold shares of the values being compared, and obtain a share of the comparison result. This setting is often used in multi-party computation protocols, and hence in many privacy-preserving applications as well. We furthermore implement the protocol and measure its performance. Our measurement suggests that the protocol outperforms the previously proposed protocols for this input/output configuration, when off-line pre-computation is not permitted.

Boolean functions used in the filter model of stream ciphers should have balancedness, large nonlinearity, optimal algebraic immunity and high algebraic degree. Besides, one more criterion called strict avalanche criterion (SAC) can be also considered. During the last fifteen years, much work has been done to construct balanced Boolean functions with optimal algebraic immunity. However, none of them has the SAC property. In this paper, we first present a construction of balanced Boolean functions with SAC property by a slight modification of a known method for constructing Boolean functions with SAC property and consider the cryptographic properties of the constructed functions. Then we propose an infinite class of balanced functions with optimal algebraic immunity and SAC property in odd number of variables. This is the first time that such kind of functions have been constructed. The algebraic degree and nonlinearity of the functions in this class are also determined.

At EUROCRYPT 2012, Libert, Peters and Yung (LPY) proposed the first scalable revocable group signature (R-GS) scheme in the standard model which achieves constant signing/verification costs and other costs regarding signers are at most logarithmic in N, where N is the maximum number of group members. However, although the LPY R-GS scheme is asymptotically quite efficient, this scheme is not sufficiently efficient in practice. For example, the signature size of the LPY scheme is roughly 10 times larger than that of an RSA signature (for 160-bit security). In this paper, we propose a compact R-GS scheme secure in the random oracle model that is efficient not only in the asymptotic sense but also in practical parameter settings. We achieve the same efficiency as the LPY scheme in an asymptotic sense, and the signature size is nearly equal to that of an RSA signature (for 160-bit security). It is particularly worth noting that our R-GS scheme has the smallest signature size compared to those of previous R-GS schemes which enable constant signing/verification costs. Our technique, which we call parallel Boneh-Boyen-Shacham group signature technique, helps to construct an R-GS scheme without following the technique used in LPY, i.e., we directly apply the Naor-Naor-Lotspiech framework without using any identity-based encryption.

The packet classification problem to determine the behavior of incoming packets at the network devices. The processing latency of packet classification by linear search is proportional to the number of classification rules. To limit the latency caused by classification to a certain level, we should develop a classification algorithm that classifies packets in a time independent of the number of classification rules. Arbitrary (including noncontiguous) bitmask rules are efficiently expressive for controlling higher layer communication, achiving access control lists, Quality of Service and so on. In this paper, we propose a classification algorithm based on run-based trie [1] according to arbitrary bitmask rules. The space complexity of proposed algorithm is in linear in the size of a rule list. The time complexity except for construction of that can be regarded as constant which is independent the number of rules. Experimental results using a packet classification algorithm benchmark [2] show that our method classifies packets in constant time independent of the number of rules.

Upcoming mood prediction plays an important role in different topics such as bipolar depression disorder in psychology and quality-of-life and recommendations on health-related quality of life research. The mood in this study is defined as the general emotional state of a user. In contrast to emotions which is more specific and varying within a day, the mood is described as having either a positive or negative valence[1]. We propose an autonomous system that predicts the upcoming user mood based on their online activities over cyber, social and physical spaces without using extra-devices and sensors. Recently, many researchers have relied on online social networks (OSNs) to detect user mood. However, all the existing works focused on inferring the current mood and only few works have focused on predicting the upcoming mood. For this reason, we define a new goal of predicting the upcoming mood. We, first, collected ground truth data during two months from 383 subjects. Then, we studied the correlation between extracted features and user's mood. Finally, we used these features to train two predictive systems: generalized and personalized. The results suggest a statistically significant correlation between tomorrow's mood and today's activities on OSNs, which can be used to develop a decent predictive system with an average accuracy of 70% and a recall of 75% for the correlated users. This performance was increased to an average accuracy of 79% and a recall of 80% for active users who have more than 30 days of history data. Moreover, we showed that, for non-active users, referring to a generalized system can be a solution to compensate the lack of data at the early stage of the system, but when enough data for each user is available, a personalized system is used to individually predict the upcoming mood.

Deterioration of software quality developed by multiple organizations has become a serious problem. To predict software degradation after an organizational change, this paper investigates the influence of quality deterioration on software metrics by analyzing three software projects. To detect factors indicating a low evolvability, we focus on the relationships between the change in software metric values and refactoring tendencies. Refactoring after an organization change impacts the quality.

The Cyber-Physical Integrated Society (CPIS) is being formed with the fusion of cyber-space and the real-world. In this paper, we will discuss Data-Driven Decision-Making (DDDM) support systems to solve social problems in the CPIS. First, we introduce a Web of Resources (WoR) that uses Web booking log data for destination data management. Next, we introduce an Internet of Persons (IoP) system to visualize individual and group flows of people by analyzing collected Wi-Fi usage log data. Specifically, we present examples of how WoR and IoP visualize flows of groups of people that can be shared across different industries, including telecommunications carriers and railway operators, and policy decision support for local, short-term events. Finally, the importance of data-driven training of human resources to support DDDM in the future CPIS is discussed.

System logs are useful to understand the status of and detect faults in large scale networks. However, due to their diversity and volume of these logs, log analysis requires much time and effort. In this paper, we propose a log event anomaly detection method for large-scale networks without pre-processing and feature extraction. The key idea is to embed a large amount of diverse data into hidden states by using latent variables. We evaluate our method with 12 months of system logs obtained from a nation-wide academic network in Japan. Through comparisons with Kleinberg's univariate burst detection and a traditional multivariate analysis (i.e., PCA), we demonstrate that our proposed method achieves 14.5% higher recall and 3% higher precision than PCA. A case study shows detected anomalies are effective information for troubleshooting of network system faults.

We propose a method for preventing smartphone theft when the owner dozes off. The owner of the smartphone wears a wristwatch type device that has an acceleration sensor and a vibration mode. This device detects when the owner dozes off. When the acceleration sensor in the smartphone detects an accident while dozing, the device vibrates. We implemented this function and tested its usefulness.

With the acceptance of social sharing, public bike sharing services have become popular worldwide. One of the most important tasks in operating a bike sharing system is managing the bike supply at each station to avoid either running out of bicycles or docks to park them. This requires the system operator to redistribute bicycles from overcrowded stations to under-supplied ones. Trip demand prediction plays a crucial role in improving redistribution strategies. Predicting trip demand is a highly challenging problem because it is influenced by multiple levels of factors, both environmental and individual, e.g., weather and user characteristics. Although several existing studies successfully address either of them in isolation, no framework exists that can consider all factors simultaneously. This paper starts by analyzing trip data from real-world bike-sharing systems. The analysis reveals the interplay of the multiple levels of the factors. Based on the analysis results, we develop a novel form of the point process; it jointly incorporates multiple levels of factors to predict trip demand, i.e., predicting the pick-up and drop-off levels in the future and when over-demand is likely to occur. Our extensive experiments on real-world bike sharing systems demonstrate the superiority of our trip demand prediction method over five existing methods.

We propose a method that detects vehicles from in-vehicle monocular camera images captured during nighttime driving. Detecting vehicles from their shape is difficult at night; however, many vehicle detection methods focusing on light have been proposed. We detect bright spots by appropriate binarization based on the characteristics of vehicle lights such as brightness and color. Also, as the detected bright spots include lights other than vehicles, we need to distinguish the vehicle lights from other bright spots. Therefore, the bright spots were distinguished using Random Forest, a multiclass classification machine-learning algorithm. The features of bright spots not associated with vehicles were effectively utilized in the vehicle detection in our proposed method. More precisely vehicle detection is performed by giving weights to the results of the Random Forest based on the features of vehicle bright spots and the features of bright spots not related to the vehicle. Our proposed method was applied to nighttime images and confirmed effectiveness.

Existing fault localization based on neural networks utilize the information of whether a statement is executed or not executed to identify suspicious statements potentially responsible for a failure. However, the information just shows the binary execution states of a statement, and cannot show how important a statement is in executions. Consequently, it may degrade fault localization effectiveness. To address this issue, this paper proposes TFIDF-FL by using term frequency-inverse document frequency to identify a high or low degree of the influence of a statement in an execution. Our empirical results on 8 real-world programs show that TFIDF-FL significantly improves fault localization effectiveness.

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.