Internal user threats such as information leakage or system destruction can cause significant damage to the organization, however it is very difficult to prevent or detect this attack in advance. In this paper, we propose an anomaly-based insider threat detection method with local features and global statistics over the assumption that a user shows different patterns from regular behaviors during harmful actions. We experimentally show that our detection mechanism can achieve superior performance compared to the state of the art approaches for CMU CERT dataset.

This letter reveals that an edge-triggered master-slave flip-flop (FF) using well-known soft error tolerant DICE (dual interlocked storage cell) is vulnerable to soft errors occurring around clock edge. This letter presents a design of a soft error tolerant FF based on the master-slave FF using DICE. The proposed design modifies the connection between the master and slave latches to make the FF not vulnerable to these errors. The hardware overhead is almost the same as that for the original edge-triggered FF using the DICE.

In this paper, we propose a salient region detection method with multi-feature fusion and edge constraint. First, an image feature extraction and fusion network based on dense connection structure and multi-channel convolution channel is designed. Then, a multi-scale atrous convolution block is applied to enlarge reception field. Finally, to increase accuracy, a combined loss function including classified loss and edge loss is built for multi-task training. Experimental results verify the effectiveness of the proposed method.

End-to-end delay, aiming to realize how much time it will take for a traffic load generated by a Mobile Node (MN) to reach Sink Node (SN), is a principal objective of most new trends in a Wireless Sensor Network (WSN). It has a direct link towards understanding the minimum time delay expected where the packet sent by MN can take to be received by SN. Most importantly, knowing the average minimum transmission time limit is a crucial piece of information in determining the future output of the network and the kind of technologies implemented. In this paper, we take network load and transmission delay issues into account in estimating the Average Minimum Time Limit (AMTL) needed for a health operating cognitive WSN. To further estimate the AMTL based on network load, an end-to-end delay analysis mechanism is presented and considers the total delay (service, queue, ACK, and MAC). This work is proposed to answer the AMTL needed before implementing any cognitive based WSN algorithms. Various time intervals and cogitative channel usage with different application payload are used for the result analysis. Through extensive simulations, our mechanism is able to identify the average time intervals needed depending on the load and MN broadcast interval in any cognitive WSN.

Depression is a major mental health problem in Thailand. The depression rates have been rapidly increasing. Over 1.17 million Thai people suffer from this mental illness. It is important that a reliable depression screening tool is made available so that depression could be early detected. Given Facebook is the most popular social network platform in Thailand, it could be a large-scale resource to develop a depression detection tool. This research employs techniques to develop a depression detection algorithm for the Thai language on Facebook where people use it as a tool for sharing opinions, feelings, and life events. To establish the reliable result, Thai Mental Health Questionnaire (TMHQ), a standardized psychological inventory that measures major mental health problems including depression. Depression scale of the TMHQ comprises of 20 items, is used as the baseline for concluding the result. Furthermore, this study also aims to do factor analysis and reduce the number of depression items. Data was collected from over 600 Facebook users. Descriptive statistics, Exploratory Factor Analysis, and Internal consistency were conducted. Results provide the optimized version of the TMHQ-depression that contain 9 items. The 9 items are categorized into four factors which are suicidal ideation, sleep problems, anhedonic, and guilty feelings. Internal consistency analysis shows that this short version of the TMHQ-depression has good to excellent reliability (Cronbach's alpha >.80). The findings suggest that this optimized TMHQ-depression questionnaire holds a good psychometric property and can be used for depression detection.

Handwritten numeral recognition is a classical and important task in the computer vision area. We propose two novel deep learning models for this task, which combine the edge extraction method and Siamese/Triple network structures. We evaluate the models on seven handwritten numeral datasets and the results demonstrate both the simplicity and effectiveness of our models, comparing to baseline methods.

The decentralized energy network is one of the promising solutions as a next-generation power grid. In this system, each house has a photovoltaic (PV) panel as a renewable energy source and a battery which is an essential component to balance between generation and demand. The common objective of the battery management on such systems is to minimize only the purchased energy from a power company, but battery degradation caused by charge/discharge cycles is also a serious problem. This paper proposes a State-of-Health (SOH) aware system-level battery management methodology for the decentralized energy network. The power distribution problem is often solved with mixed integer programming (MIP), and the proposed MIP formulation takes into account the SOH model. In order to minimize the purchased energy and reduce the battery degradation simultaneously, the optimization problem is divided into two stages: 1) the purchased energy minimization, and 2) the battery aging factor reducing, and the trade-off exploration between the purchased energy and the battery degradation is available. Experimental results show that the proposed method achieves the better trade-off and reduces the battery aging cost by 14% over the baseline method while keeping the purchased energy minimum.

Wireless power transfer (WPT) can be classified into magnetic induction, magnetic resonance, and RF radiation types, of which the magnetic resonance WPT system is especially attracting attention due to its high potential for development. The magnetic resonance system using a specific resonance frequency is applicable to small mobile devices and in-body wireless charging modules because it enables the implementation of single-input multiple-output (SIMO), where the transmitter transmits power to multiple receivers, and the miniaturization of receiving coil. The most important consideration of the magnetic resonance WPT is the optimization of the power transfer distance and efficiency, which requires a precise design and the analysis of the transmission coil. Ferrite-embedded LTCC inductors are more advantageous for WPT applications than coil inductors because of their low cost, batch manufacturing and durability. A coil with the substate size of 10.0×12.0×0.7mm3 was manufactured using the ferrite-embedded LTCC technology to miniaturize the receiver coil. The sum of power transferred from transmitter sized of 80×60mm2 to two receivers is approximately 32%, which indicates a high potential for use in small terminals or in-body modules.

Currently, mobile terminals face serious security threats. A Trusted Execution Environment (TEE) which can provide an isolated execution environment for sensitive workloads, is seen as a trusted relay for providing security services for any mobile application. However, mobile TEE's architecture design and implementation strategy are not unbreakable at present. The existing researches lack of detect mechanisms for attack behaviour and malicious software. This paper proposes a Malicious code Detection scheme for Trusted Execution Environment based on Homomorphic Encryption (HE-TEEMD), which is a novel detection mechanism for data and code in the trusted execution environment. HE-TEEMD uses the Paillier additive homomorphic algorithm to implement the signature matching and transmits the ciphertext information generated in the TEE to the normal world for detection by the homomorphism and randomness of the homomorphic encryption ciphertext. An experiment and security analysis proves that our scheme can achieve malicious code detection in the secure world with minimal cost. Furthermore, evaluation parameters are introduced to address the known plaintext attack problem of privileged users.

Kurotto and Juosan are Nikoli's pencil puzzles. We study the computational complexity of Kurotto and Juosan puzzles. It is shown that deciding whether a given instance of each puzzle has a solution is NP-complete.

The modern radar signals are in a wide frequency space. The receiving bandwidth of the radar reconnaissance receiver should be wide enough to intercept the modern radar signals. The Nyquist folding receiver (NYFR) is a novel wideband receiving architecture and it has a high intercept probability. Chirp signals are widely used in modern radar system. Because of the wideband receiving ability, the NYFR will receive the concurrent multiple chirp signals. In this letter, we propose a novel parameter estimation algorithm for the multiple chirp signals intercepted by single channel NYFR. Compared with the composite NYFR, the proposed method can save receiving resources. In addition, the proposed approach can estimate the parameters of the chirp signals even the NYFR outputs are under frequency aliasing circumstance. Simulation results show the efficacy of the proposed method.

This paper proposes an adaptive call-occurrence probability (COP) setting method for a slotted-ALOHA based consensus problem. Individual agents in the focused consensus problem control themselves in a distributed manner based on the partial information of overall control system which can be received only from the neighbor agents. In order to realize a reliable consensus problem based on wireless communications, we have to consider several constraints caused by the natures of wireless communications such as communication error, coverage, capacity, multi-user interference, half-duplex and so on. This work first investigates the impacts of wireless communication constraints, especially communication coverage, half-duplex, and multiple-access interference constraints, on the quality of control. To mitigate the impact of multiple-access constraint, we propose an adaptive COP setting method that changes the COP corresponding to the states of communication and control. The proposed adaptive COP based slotted-ALOHA needs the information about the number of neighbor agents at its own and neighbor agents, but can still work in a distributed manner. Computer simulations show that the proposed system can achieve better convergence performance compared to the case with the fixed COP based system.

In small cell deployments, the combined usage of user association and inter-cell interference coordination (ICIC) is inevitable. This paper investigates the joint optimization of user association and ICIC in the downlink. We first formulate the joint optimization problem as a utility maximization problem. We then employ the logarithmic utility function known as the proportional fair criteria. The optimum user association and the ICIC are derived by solving a convex optimization problem based on the average spectral efficiencies of all users. We propose an iterative algorithm to obtain the optimum solution to this problem. We evaluate the performance of the proposed algorithm for the small cell deployments and shows that the proposed algorithm works well. We also compare the performance of the proposed algorithm based on utility maximization user association with the CRE, and show the superiority of the utility maximization. Furthermore, we show that intra-tier ICIC and inter-tier ICIC can effectively improve the throughput performance according to the conditions. It is also shown that the combined usage of inter-tier ICIC and intra-tier ICIC enhances the throughput performance compared to schemes employing either the inter- or intra-tier ICIC scheme.

Register context-free grammars (RCFG) is an extension of context-free grammars to handle data values in a restricted way. In RCFG, a certain number of data values in registers are associated with each nonterminal symbol and a production rule has the guard condition, which checks the equality between the content of a register and an input data value. This paper starts with RCFG and introduces register type, which is a finite representation of a relation among the contents of registers. By using register type, the paper provides a translation of RCFG to a normal form and ϵ-removal from a given RCFG. We then define a generalized RCFG (GRCFG) where an arbitrary binary relation can be specified in the guard condition. Since the membership and emptiness problems are shown to be undecidable in general, we extend register type for GRCFG and introduce two properties of GRCFG, simulation and progress, which guarantee the decidability of these problems. As a corollary, these problems are shown to be EXPTIME-complete for GRCFG with a total order over a dense set.

This paper discusses the parallelization of boost and buck converters. Passivity-based control is applied to each converter to achieve the asymptotic stability of the system. The ripple characteristics, error characteristics, and time constants of the parallelized converters are discussed with considering the dependency on the feedback gains. The numerical results are confirmed to coincide with the results in the experiment for certain feedback gains. The stability of the system is also discussed in simulation and experiment. The results will be a step to achieve the design of parallel converters.

Feature learning based on deep network has been verified as beneficial for person re-identification (Re-ID) in recent years. However, most researches use a single network as the baseline, without considering the fusion of different deep features. By analyzing the attention maps of different networks, we find that the information learned by different networks can complement each other. Therefore, a novel Dual Network Fusion (DNF) framework is proposed. DNF is designed with a trunk branch and two auxiliary branches. In the trunk branch, deep features are cascaded directly along the channel direction. One of the auxiliary branch is channel attention branch, which is used to allocate weight for different deep features. Another one is multi-loss training branch. To verify the performance of DNF, we test it on three benchmark datasets, including CUHK03NP, Market-1501 and DukeMTMC-reID. The results show that the effect of using DNF is significantly better than a single network and is comparable to most state-of-the-art methods.

This paper proposes a defragmentation scheme using reroutable backup paths in toggled-based quasi 1+1 path protected elastic optical networks (EONs) to improve the efficiency of defragmentation and suppress the fragmentation effect. The proposed scheme can reallocate spectrum slots of backup paths and reroute of backup paths. The path exchange function of the proposed scheme makes the primary paths become the backup state while the backup paths become the primary. This allows utilization of the advantages of defragmentation in both primary and backup paths. We formulate a static spectrum reallocation problem with rerouting (SSRR) in the toggled-based quasi 1+1 path protected EON as an integer linear programming (ILP) problem. The decision version of SSRR is proven to be an NP-complete problem. A heuristic algorithm is introduced to solve the problem for large networks networks where the ILP problem is not tractable. For a dynamic traffic scenario, an approach that suppresses the fragmentation considering rerouting and path exchanging operations is presented. We evaluate the performances of the proposed scheme by comparing it to the conventional scheme in terms of dependencies on node degree, processing time of network operations and interval time between scheduled defragmentations. The numerical results obtained from the performance evaluation indicate that the proposed scheme increases the traffic admissibility compared to the conventional scheme.

Distributing codes to specific target sensors in order to fix bugs and/or install a new application is an important management task in WSNs (Wireless Sensor Networks). For the energy efficient dissemination of such codes to specific target sensors, it is required to select the minimum required number of forwarders with the fewest control messages. In this paper, we propose a novel RPL (Routing Protocol for Low-power and lossy networks)-based tree construction scheme for target-specific code dissemination, which is called R-TCS. The main idea of R-TCS is that by leveraging the data collection tree created by a standard routing protocol RPL, it is possible to construct the code dissemination tree with the minimum numbers of non-target sensors and control messages. Since by creating a data collection tree each sensor exchanges RPL messages with the root of the tree, every sensor knows which sensors compose its upwards route, i.e. the route towards the root, and downwards route, i.e. the route towards the leaves. Because of these properties, a target sensor can select the upward route that contains the minimum number of non-target sensors. In addition, a sensor whose downward routes do not contain a target sensor is not required to transmit redundant control messages which are related to the code dissemination operation. In this way, R-TCS can reduce the energy consumption which typically happens in other target-specific code dissemination schemes by the transmission of control messages. In fact, various performance evaluation results obtained by means of computer simulations show that R-TCS reduces by at least 50% energy consumption as compared to the other previous known target-specific code dissemination scheme under the condition where ratio of target sensors is 10% of all sensors.

When considering the deployment of a radio communications network, the study of multipath interference and its impact on the quality of signal reception is of the outmost importance in order to meet the necessary performance requirements. This work considers specifically the case of the lunar surface as the mission scenario for a community of autonomous mobile exploration robots, which communicate through a radiofrequency network to accomplish their mission. In this application, the low height of the mobile robots makes the influence of multipath interference effects on the performance of the radio communication channel relevant. However, no specific information about lunar soil reflection coefficients characteristics is available for radiofrequency communication bands. This work reviews the literature on the electrical parameter of Lunar soil. From this base, the reflection coefficients are estimated for the assumed radio profile in different communications frequency bands. Finally, the results obtained are discussed.

This paper presents a two-dimensional (2D) DOA algorithm for double L-shaped arrays. The algorithm is applied to the underwater environment for eliminating the performance error caused by the sound speed uncertainty factor. By introducing the third dimensional array, the algorithm eliminates the sound velocity variable in the depression angle expression, so that the DOA estimation no longer considering the true value of unknown sound velocity. In order to determine the parameters of a three-dimensional array, a parameter matching method with the double L-shaped array is also proposed. Simulations show that the proposed algorithm outperforms the conventional 2D-DOA estimation algorithm in unknown sound velocity environment.