This study proposes a novel machine learning architecture and various learning algorithms to build-in anti-phishing services for avoiding cyber-phishing attack. For the rapid develop of information technology, hackers engage in cyber-phishing attack to steal important personal information, which draws information security concerns. The prevention of phishing website involves in various aspect, for example, user training, public awareness, fraudulent phishing, etc. However, recent phishing research has mainly focused on preventing fraudulent phishing and relied on manual identification that is inefficient for real-time detection systems. In this study, we used methods such as ANOVA, X2, and information gain to evaluate features. Then, we filtered out the unrelated features and obtained the top 28 most related features as the features to use for the training and evaluation of traditional machine learning algorithms, such as Support Vector Machine (SVM) with linear or rbf kernels, Logistic Regression (LR), Decision tree, and K-Nearest Neighbor (KNN). This research also evaluated the above algorithms with the ensemble learning concept by combining multiple classifiers, such as Adaboost, bagging, and voting. Finally, the eXtreme Gradient Boosting (XGBoost) model exhibited the best performance of 99.2%, among the algorithms considered in this study.

The-state-of-the-art neural quality estimation (QE) of machine translation model consists of two sub-networks that are tuned separately, a bidirectional recurrent neural network (RNN) encoder-decoder trained for neural machine translation, called the predictor, and an RNN trained for sentence-level QE tasks, called the estimator. We propose to combine the two sub-networks into a whole neural network, called the unified neural network. When training, the bidirectional RNN encoder-decoder are initialized and pre-trained with the bilingual parallel corpus, and then, the networks are trained jointly to minimize the mean absolute error over the QE training samples. Compared with the predictor and estimator approach, the use of a unified neural network helps to train the parameters of the neural networks that are more suitable for the QE task. Experimental results on the benchmark data set of the WMT17 sentence-level QE shared task show that the proposed unified neural network approach consistently outperforms the predictor and estimator approach and significantly outperforms the other baseline QE approaches.

In this letter, we explored the usage of spatio-temporal information in one unified framework to improve the performance of multichannel speech recognition. Generalized cross correlation (GCC) is served as spatial feature compensation, and an attention mechanism across time is embedded within long short-term memory (LSTM) neural networks. Experiments on the AMI meeting corpus show that the proposed method provides a 8.2% relative improvement in word error rate (WER) over the model trained directly on the concatenation of multiple microphone outputs.

Sponsored search is a mechanism that shows the appropriate advertisements (ads) according to search queries. The orders and payments of ads are determined by the auction. However, the externalities which give effects to CTR and haven't been considered in some existing works because the mechanism with externalities has high computational cost. In addition, some algorithms which can calculate the approximated solution considering the externalities within the polynomial-time are proposed, however, it assumed that one bidder can propose only a single ad. In this paper, we propose the approximation allocation algorithm that one bidder can offer many ads considering externalities. The proposed algorithm employs the concept of the combinatorial auction in order to consider the combinational bids. In addition, the proposed algorithm can find the approximated allocation by the dynamic programming. Moreover, we prove the computational complexity and the monotonicity of the proposed mechanism, and demonstrate computational costs and efficiency ratios by changing the number of ads, slots and maximum bids. The experimental results show that the proposed algorithm can calculate 0.7-approximation solution even though the full search can't find solutions in the limited times.

Authors previously studied the degradation of electrical contacts under the condition of various external micro-oscillations. They also developed a micro-sliding mechanism (MSM2), which causes micro-sliding and is driven by a piezoelectric actuator and elastic hinges. Using the mechanism, experimental results were obtained on the minimal sliding amplitude (MSA) required to make the electrical resistance fluctuate under various conditions. In this paper, to develop a more realistic model of input waveform than the previous one, Ts/2 is set as the rising or falling time, Tc as the flat time, and τ/2 as the duration in a sliding period T (0.25 s) of the input waveform. Using the Duhamel's integral method and an optimization method, the physical parameters of natural angular frequency ω0 (12000 s-1), damping ratio ζ (0.05), and rising and falling time Ts (1.3 or 1.2 ms) are obtained. Using the parameters and the MSA, the total acceleration of the input TA (=f(t)) and the displacement of the output x(t) are also obtained using the Fourier series expansion method. The waveforms x(t) and the experimental results are similar to each other. If the effective mass m, which is defined as that of the movable parts in the MSM2, is 0.1 kg, each total force TF (=2mTA) is estimated from TA and m. By the TF, the cases for 0.3 N/pin as frictional force or in impulsive as input waveform are more serious than the others. It is essential for the safety and the confidence of electrical contacts to evaluate the input waveform and the frictional force. The ringing waveforms of the output displacements x(t) are calculated at smaller values of Ts (1.0, 0.5, and 0.0 ms) than the above values (1.3 or 1.2 ms). When Ts is slightly changed from 1.3 or 1.2 ms to 1.0 ms, the ringing amplitude is doubled. For the degradation of electrical contacts, it is essential that Ts is reduced in a rectangular and impulsive input. Finally, a very simple wear model comprising three stages (I, II, and III) is introduced in this paper. Because Ts is much shorter in a rectangular or impulsive input than in a sinusoidal input, it is considered that the former more easily causes wear than the latter owing to a larger frictional force. Taking the adhesive wear in Stages I and III into consideration, the wear is expected to be more severe in the case of small damped oscillations owing to the ringing phenomenon.

To use millimeter wave bands in future cellular and outdoor wireless networks, understanding the multipath cluster characteristics such as delay and angular spread for different polarization is very important besides knowing the path loss and other large scale propagation parameters. This paper presents result from analysis of wide-band full polarimetric double directional channel measurement at the millimeter wave band in a typical urban pico-cell environment. Only limited number of multipath clusters with gains ranging from -8dB to -26.8dB below the free space path loss and mainly due to single reflection, double reflection and diffraction, under both line of sight (LOS) and obstructed LOS conditions are seen. The cluster gain and scattering intensity showed strong dependence on polarization. The scattering intensities for ϑ-ϑ polarization were seen to be stronger compared to ϕ-ϕ polarization and on average 6.1dB, 5.6dB and 4.5dB higher for clusters due to single reflection, double reflection and scattering respectively. In each cluster, the paths are highly concentrated in the delay domain with delay spread comparable to the delay resolution of 2.5ns irrespective of polarization. Unlike the scattering intensity, the angular spread of paths in each cluster did not show dependence on polarization. On the base station side, average angular spread in azimuth and in elevation were almost similar with ≤3.3° spread in azimuth and ≤3.2° spread in elevation for ϑ-ϑ polarization. These spreads were slightly smaller than those observed for ϕ-ϕ polarization. On the mobile station side the angular spread in azimuth was much higher compared to the base station side. On average, azimuth angular spread of ≤11.4° and elevation angular spread of ≤5° are observed for ϑ-ϑ polarization. These spreads were slightly larger than in ϕ-ϕ polarization. Knowing these characteristics will be vital for more accurate modeling of the channel, and in system and antenna design.

Authors have studied degradation phenomenon on electrical contacts under the influences of an external micro-oscillation. A new micro-sliding mechanism 2 (MSM2) has developed, which provides micro-sliding driven by a piezo-electric actuator and elastic hinges. The experimental results are obtained on “minimal sliding amplitudes” to make resistances fluctuate on electrical contacts under some conditions which are three types of inputwaveform (sinusoidal, rectangular, and impulsive) and three levels of frictional force (1.6, 1.0, and 0.3 N/pin) by using the MSM2. The dynamical characteristics are discussed under the conditions. The simple theoretical model on the input signal and the output of the mechanism is built and the theoretical expressions from the model are obtained. A natural angular frequency (ω0=12600[s-1]) and a damping ratio (ζ=0.03[-]) are evaluated using experimental dynamical responses. The waveforms of inputs and outputs are obtained and the characteristics between inputs and outputs are also obtained on the theoretical model using the above. The maximal gain between the input and the output in rectangular or impulsive (24.4) is much larger than that (0.0) in sinusoidal. The difference on the output-accelerations between in sinusoidal and in rectangular (impulsive) is discussed. It is shown that it is possible to cause the degradation phenomenon in sinusoidal only when the output displacement are enlarged. It is also shown that it is possible to cause the phenomenon in rectangular or in impulsive, in addition to the above, when the external force has sharper rising and falling waveforms even if the displacement and the frequency of the force is small. The difference on the output-amplitudes between in rectangular and in impulsive is discussed. It is not clear that there is the difference between the effect in rectangular and that in impulsive. It is indicated that it is necessary to discuss the other causes, for instance, another dynamical, thermal, and chemical process.

This article describes an approximate model of a group of cells in the wireless 4G network with implemented load balancing mechanism. An appropriately modified model of Erlang's Ideal Grading is used to model this group of cells. The model makes it possible to take into account limited availability of resources of individual cells to multi-rate elastic and adaptive traffic streams generated by Erlang and Engset sources. The developed solution allows the basic traffic characteristics in the considered system to be determined, i.e. the occupancy distribution and the blocking probability. Because of the approximate nature of the proposed model, the results obtained based on the model were compared with the results of a digital simulation. The present study validates the adopted assumptions of the proposed model.

Personal Wi-Fi Hotspot, the Wi-Fi tethering function, is widely deployed on mobile devices to allow other wireless clients to share Internet access via a broadband connection. Its advantages include no connection fee and support of non-3G/LTE devices. However, utilizing this function can rapidly deplete the battery power of the tethering device because both interface connections (3G/LTE and Wi-Fi) are always on. To address this problem, this paper proposes the Energy Management Mechanism for Wi-Fi Tethering Mode on Mobile Devices (EMWT). The mechanism is designed to effectively manage both interfaces by adjusting certain sleep durations according to the incoming traffic. Short, Long, and Deep sleep durations are introduced for saving energy. EMWT can also guarantee the packet delay bound by limiting the maximum sleep period. Five traffic rates, composed of very low, low, medium, high, and very high, are evaluated. NS-3 simulation results reveal that energy savings of up to 52.52% can be achieved with only a slight impact on system performance, in terms of end-to-end delay, throughput, and packet loss.

In the obnoxious facility game with a set of agents in a space, we wish to design a mechanism, a decision-making procedure that determines a location of an undesirable facility based on locations reported by the agents, where we do not know whether the location reported by an agent is where exactly the agent exists in the space. For a location of the facility, the benefit of each agent is defined to be the distance from the location of the facility to where the agent exists. Given a mechanism, all agents are informed of how the mechanism utilizes locations reported by the agents to determine a location of the facility before they report their locations. Some agent may try to manipulate the decision of the facility location by strategically misreporting her location. As a fair decision-making, mechanisms should be designed so that no particular group of agents can get a larger benefit by misreporting their locations. A mechanism is called group strategy-proof if no subset of agents can form a group such that every member of the group can increase her benefit by misreporting her location jointly with the rest of the group. For a given mechanism, a point in the space is called a candidate if it can be output as the location of the facility by the mechanism for some set of locations reported by agents. In this paper, we consider the case where a given space is a tree metric, and characterize the group strategy-proof mechanisms in terms of distribution of all candidates in the tree metric. We prove that there exists a group strategy-proof mechanism in the tree metric if and only if the tree has a point to which every candidate has the same distance.

In this paper, we propose a novel Autonomous Decentralized Control (ADC) scheme for indirectly controlling a system performance variable of large-scale and wide-area networks. In a large-scale and wide-area network, since it is impractical for any one node to gather full information of the entire network, network control must be realized by inter-node collaboration using information local to each node. Several critical network problems (e.g., resource allocation) are often formulated by a system performance variable that is an amount to quantify system state. We solve such problems by designing an autonomous node action that indirectly controls, via the Markov Chain Monte Carlo method, the probability distribution of a system performance variable by using only local information. Analyses based on statistical mechanics confirm the effectiveness of the proposed node action. Moreover, the proposal is used to implement traffic-aware virtual machine placement control with load balancing in a data center network. Simulations confirm that it can control the system performance variable and is robust against system fluctuations. A comparison against a centralized control scheme verifies the superiority of the proposal.

A gold coated carbon nanotubes composite was used as a contact material in Micro-Electrical-Mechanical-System (MEMS) switches. The switching contact was tested under typical conditions of MEMS relay applications: load voltage of 4 V, contact force of 1 mN, and load current varied between 20-200 mA. This paper focuses on the wear process over switching lifetime, and the dependence of the wear area on the current is discussed. It was shown that the contact was going to fail when the wear area approached the whole contact area, at which point the contact resistance increased sharply to three times the nominal resistance.

For the electric demand prediction problem, a modification mechanism of predicted demand data has been proposed in the previous work. In this paper, we analyze the performance of the modification mechanism in power balancing control. Then, we analytically derive an upper bound of the performance, which is characterized by system parameters and prediction precision.

Breast cancer is a serious disease across the world, and it is one of the largest causes of cancer death for women. The traditional diagnosis is not only time consuming but also easily affected. Hence, artificial intelligence (AI), especially neural networks, has been widely used to assist to detect cancer. However, in recent years, the computational ability of a neuron has attracted more and more attention. The main computational capacity of a neuron is located in the dendrites. In this paper, a novel neuron model with dendritic nonlinearity (NMDN) is proposed to classify breast cancer in the Wisconsin Breast Cancer Database (WBCD). In NMDN, the dendrites possess nonlinearity when realizing the excitatory synapses, inhibitory synapses, constant-1 synapses and constant-0 synapses instead of being simply weighted. Furthermore, the nonlinear interaction among the synapses on a dendrite is defined as a product of the synaptic inputs. The soma adds all of the products of the branches to produce an output. A back-propagation-based learning algorithm is introduced to train the NMDN. The performance of the NMDN is compared with classic back propagation neural networks (BPNNs). Simulation results indicate that NMDN possesses superior capability in terms of the accuracy, convergence rate, stability and area under the ROC curve (AUC). Moreover, regarding ROC, for continuum values, the existing 0-connections branches after evolving can be eliminated from the dendrite morphology to release computational load, but with no influence on the performance of classification. The results disclose that the computational ability of the neuron has been undervalued, and the proposed NMDN can be an interesting choice for medical researchers in further research.

In this work, resistive switching random-access memory (RRAM) devices having a structure of metal/Si$_{3}$N$_{4}$/Si with different top electrode metals were fabricated to investigate the changes in switching and conduction mechanisms depending on electrode metals. It is shown that the metal workfunction is not strongly related with either high-resistance state (HRS) and forming voltage. Top electrodes (TEs) of Al, Cu, and Ni show both bipolar and unipolar switching characteristics. The changes of resistances in these devices can be explained by the different defect arrangements in the switching layer (SL). Among the devices with different TE metals, one with Ag electrode does not show unipolar switching unlike the others. The conducting filaments of Ag-electrode device in the low-resistance state (LRS) demonstrated metallic behaviors in the temperature-controlled experiments, which supports that Ag substantially participates in the conduction as a filament source. Moreover, the difference in switching speed is identified depending on TE metals.

How to reduce communication complexity is a common important issue to design cryptographic protocols. This paper focuses on authenticated key exchange (AKE). Several AKE schemes have been studied, which satisfy strong security such as exposure-resilience in the standard model (StdM). However, there is a large gap on communication costs between schemes in the StdM and in the random oracle model. In this paper, we show a generic construction that is significantly compact (i.e., small communication cost) and secure in the StdM. We follow an existing generic construction from key encapsulated mechanism (KEM). Our main technique is to use a bounded chosen-ciphertext secure KEM instead of an ordinary chosen-ciphertext secure KEM. The communication cost can be reduced to half by this technique, and we achieve the most compact AKE scheme in the StdM. Moreover, our construction has instantiations under wider classes of hardness assumptions (e.g., subset-sum problems and multi-variate quadratic systems) than existing constructions. This work pioneers the first meaningful application of bounded chosen-ciphertext secure KEM.

We propose a method for finding an appropriate setting of a pay-per-performance payment system to prevent participation of insincere workers in crowdsourcing. Crowdsourcing enables fast and low-cost accomplishment of tasks; however, insincere workers prevent the task requester from obtaining high-quality results. Instead of a fixed payment system, the pay-per-performance payment system is promising for excluding insincere workers. However, it is difficult to learn what settings are better, and a naive payment setting may cause unsatisfactory outcomes. To overcome these drawbacks, we propose a method for calculating the expected payments for sincere and insincere workers, and then clarifying the conditions in the payment setting in which sincere workers are willing to choose a task, while insincere workers are not willing to choose the task. We evaluated the proposed method by conducting several experiments on tweet labeling tasks in Amazon Mechanical Turk. The results suggest that the pay-per-performance system is useful for preventing participation of insincere workers.

Wireless body area networks (WBANs) have to work with low power and long lifetime to satisfy human biological safety requirements in e-health; therefore extremely low energy consumption is significant for WBANs. IEEE 802.15.6 standard has been published for wearable and implanted applications which provide communication technology requirements in WBANs. In this paper, the cross-layering optimization methodology is used to minimize the network energy consumption. Both the priority strategy and sleep mechanism in IEEE802.15.6 are considered. Macroscopic sleep model based on WBAN traffic priority and microscopic sleep model based on MAC structure are proposed. Then the network energy consumption optimization problem is solved by Lagrange dual method, the master problem are vertically decomposed into two sub problems in MAC and transport layers which are dealt with gradient method. Finally, a solution including self-adaption sleep mechanism and node rate controlling is proposed. The results of this paper indicate that the algorithm converges quickly and reduces the network energy consumption remarkably.

The rendering mechanism plays an indispensable role in browser-based Web application. It generates active webpages dynamically and provides human-readable layout through template engines, which are used as a standard programming model to separate the business logic and data computations from the webpage presentation. The client-side rendering mechanism, owing to the advances of rich application technologies, has been widely adopted. The adoption of client side rendering brings not only various merits but also new problems. In this paper, we propose and construct “pagelet”, a segment-based template engine for developing flexible and extensible Web applications. By presenting principles, practice and usage experience of pagelet, we conduct a comprehensive analysis of possible advantages and disadvantages brought by client-side rendering mechanism from the viewpoints of both developers and end-users.

We propose a power-saving mechanism (PSM) specific to request-and-response-based applications, which simply changes the order of the operating procedure of the legacy PSM by considering the attributes of the request-and-response delay. We numerically analyze the PSM with respect to the energy consumption and buffering delay and characterize this performance by employing a simple energy-delay trade-off (EDT) curve that is determined by the operating parameters. The resulting EDT curve clearly shows that the proposed PSM outperforms the legacy PSM.