Automatic error correction of users' search terms for search engines is an important aspect of improving search engine retrieval efficiency, accuracy and user experience. In the era of big data, we can analyze and mine massive search engine logs to release the hidden mind with big data ideas. It can obtain better results through statistical modeling of query errors in search engine log data. But when we cannot find the error query in the log, we can't make good use of the information in the log to correct the query result. These undiscovered error queries are called Bad Case. This paper combines the error correction algorithm model and search engine query log mining analysis. First, we explored Bad Cases in the query error correction process through the search engine query logs. Then we quantified the characteristics of these Bad Cases and built a model to allow search engines to automatically mine Bad Cases with these features. Finally, we applied Bad Cases to the N-gram error correction algorithm model to check the impact of Bad Case mining on error correction. The experimental results show that the error correction based on Bad Case mining makes the precision rate and recall rate of the automatic error correction improved obviously. Users experience is improved and the interaction becomes more friendly.

We discuss our recent progress in photonic crystal nanocavity quantum dot lasers. We show how enhanced light matter interactions in the nanocavity lead to diverse and fascinating lasing phenomena that are in general inaccessible by conventional bulky semiconductor lasers. First, we demonstrate thresholdless lasing, in which any clear kink in the output laser curve does not appear. This is a result of near unity coupling of spontaneous emission into the lasing cavity mode, enabled by the strong Purcell effect supported in the nanocavity. Then, we discuss self-frequency conversion nanolasers, in which both near infrared lasing oscillation and nonlinear optical frequency conversion to visible light are simultaneously supported in the individual nanocavity. Owing to the tight optical confinement both in time and space, a high normalized conversion efficiency over a few hundred %/W is demonstrated. We also show that the intracavity nonlinear frequency conversion can be utilized to measure the statistics of the intracavity photons. These novel phenomena will be useful for developing various nano-optoelectronic devices with advanced functionalities.

Frequency chirp of a semiconductor laser is controlled by using hybrid modulation, which simultaneously modulates intra-cavity loss and injection current to the laser. The positive adiabatic chirp of injection-current modulation is compensated with the negative adiabatic chirp created by intra-cavity-loss modulation, which enhances the chromatic-dispersion tolerance of the laser. A proof-of-concept transmission experiment confirmed that the hybrid modulation laser has a larger dispersion tolerance than conventional directly modulated lasers due to the negative frequency chirp originating from intra-cavity-loss modulation.

A lens-integrated surface-emitting DFB laser and its application to low-cost single-mode optical sub-assemblies (OSAs) are discussed. By using the LISEL, high-efficient optical coupling with reduced number of optical components and non-hermetic packaging are demonstrated. Designing the integrated lens of LISELs makes it possible to achieve passive alignment optical coupling to an SMF without the need for an additional lens. For SiP coupling, the light-emission angle from the LISEL can be controlled by the mirror angle and by displacing the lens. The capability for a low coupling loss of 3.9 dB between the LISEL and a grating coupler on the SiP platform was demonstrated. The LISEL with facet-free structure, integrating DBR mirror, PD, and window structure on its end facet, showed the same lasing performance as the conventional laser with AR facet coating. A storage test (200-hour saturated pressure-cooker test (PCT) at 138°C and 85% RH.) showed that the lasing characteristics did not degrade with high-humidity, demonstrating the potential for applying non-hermetic packaging. Our results indicate that the LISEL is one of the promising light sources for creating cost-effective OSAs.

In this paper, we establish a novel bimodal emotion database from physiological signals and facial expression, which is named as PSFE. The physiological signals and facial expression of the PSFE database are respectively recorded by the equipment of the BIOPAC MP 150 and the Kinect for Windows in the meantime. The PSFE database altogether records 32 subjects which include 11 women and 21 man, and their age distribution is from 20 to 25. Moreover, the PSFE database records three basic emotion classes containing calmness, happiness and sadness, which respectively correspond to the neutral, positive and negative emotion state. The general sample number of the PSFE database is 288 and each emotion class contains 96 samples.

Geosocial networking allows users to interact with respect to their current locations, which enables a group of users to determine where to meet. This calls for techniques that support processing of Multiple-user Location-based Keyword (MULK) queries, which return a set of Point-of-Interests (POIs) that are 'close' to the locations of the users in a group and can provide them with potential options at the lowest expense (e.g., minimizing travel distance). In this paper, we formalize the MULK query and propose a dynamic programming-based algorithm to find the optimal result set. Further, we design two approximation algorithms to improve MULK query processing efficiency. The experimental evaluations show that our solutions are feasible and efficient under various parameter settings.

The traditional tests are planned and designed at the early stages, but it is possible to execute test cases after implementing source code. Since there is a time difference between design stage and testing stage, by the time a software design error is found it will be too late. To solve this problem, this paper suggests a virtual pre-testing process. While the virtual pre-testing process can find software and testing errors before the developing stage, it can automatically generate and execute test cases with modeling and simulation (M&S) in a virtual environment. The first part of this method is to create test cases with state transition tree based on state diagram, which include state, transition, instruction pair, and all path coverage. The second part is to model and simulate a virtual target, which then pre-test the target with test cases. In other words, these generated test cases are automatically transformed into the event list. This simultaneously executes test cases to the simulated target within a virtual environment. As a result, it is possible to find the design and test error at the early stages of the development cycle and in turn can reduce development time and cost as much as possible.

In this paper, we propose the first identity-based dynamic multi-cast key distribution (ID-DMKD) protocol which is secure against maximum exposure of secret information (e.g., secret keys and session-specific randomness). In DMKD protocols, users share a common session key without revealing any information of the session key to the semi-honest server, and can join/leave to/from the group at any time even after establishing the session key. Most of the known DMKD protocols are insecure if some secret information is exposed. Recently, an exposure resilient DMKD protocol was introduced, however, each user must manage his/her certificate by using the public-key infrastructure. We solve this problem by constructing the DMKD protocol authenticated by user's ID (i.e., without certificate). We introduce a formal security definition for ID-DMKD by extending the previous definition for DMKD. We must carefully consider exposure of the server's static secret key in the ID-DMKD setting because exposure of the server's static secret key causes exposure of all users' static secret keys. We prove that our protocol is secure in our security model in the standard model. Another advantage of our protocol is scalability: communication and computation costs of each user are independent from the number of users. Furthermore, we show how to extend our protocol to achieve non-interactive join by using certificateless encryption. Such an extension is useful in applications that the group members frequently change like group chat services.

This letter proposes performance evaluation of phase-only correlation (POC) functions using signal-to-noise ratio (SNR) and peak-to-correlation energy (PCE). We derive the general expressions of SNR and PCE of the POC functions as correlation performance measures. SNR is expressed by simple fractional function of circular variance. PCE is simply given by squared peak value of the POC functions, and its expectation can be expressed in terms of circular variance.

In this study, the effect of atomically flat Si(100) surface on Hf-based Metal-Oxide-Nitride-Oxide-Silicon (MONOS) structure was investigated. After the atomically flat Si(100) surface formation by annealing at 1050/60min in Ar/4%H2 ambient, HfO2(O)/HfN1.0(N)/HfO2(O) structure with thickness of 10/3/2nm, respectively, was in-situ deposited by electron cyclotron resonance (ECR) plasma sputtering. The memory window (MW) of Al/HfO2/HfN1.0/HfO2/p-Si(100) diodes was increased from 1.0V to 2.5V by flattening of Si(100) surface. The program and erase (P/E) voltage/time were set as 10V/5s and -8V/5s, respectively. Furthermore, it was found that the gate current density after the 103P/E cycles was decreased one order of magnitude by flattening of Si(100) surface in Ar/4.0%H2 ambient.

High frame rate and ultra-low delay matching system plays an increasingly important role in human-machine interactive applications which call for higher frame rate and lower delay for a better experience. The large amount of processing data and the complex computation in a local feature based matching system, make it difficult to achieve a high process speed and ultra-low delay matching with limited resource. Aiming at a matching system with the process speed of more than 1000 fps and with the delay of less than 1 ms/frame, this paper puts forward a local binary feature based matching system with field-programmable gate array (FPGA). Pixel selection based 4-1-4 parallel matching and intensity directed symmetry are proposed for the implementation of this system. To design a basic framework with the high process speed and ultra-low delay using limited resource, pixel selection based 4-1-4 parallel matching is proposed, which makes it possible to use only one-thread resource consumption to achieve a four-thread processing. Assumes that the orientation of the keypoint will bisect the patch best and will point to the region with high intensity, intensity directed symmetry is proposed to calculate the keypoint orientation in a hardware friendly way, which is an important part for a rotation-robust matching system. Software experiment result shows that the proposed keypoint orientation calculation method achieves almost the same performance with the state-of-art intensity centroid orientation calculation method in a matching system. Hardware experiment result shows that the designed image process core supports to process VGA (640×480) videos at a process speed of 1306 fps and with a delay of 0.8083 ms/frame.

The physics and applications of superconducting phase shifts and their control in superconducting systems are reviewed herein. The operation principle of almost all superconducting devices is related to the superconducting phase, and an efficient control of the phase is crucial for improving the performance and scalability. Furthermore, employing new methods to shift or control the phase may lead to the development of novel superconducting device applications, such as cryogenic memory and quantum computing devices. Recently, as a result of the progress in nanofabrication techniques, superconducting phase shifts utilizing π states have been realized. In this review, following a discussion of the basic physics of phase propagation and shifts in hybrid superconducting structures, interesting phenomena and device applications in phase-shifted superconducting systems are presented. In addition, various possibilities for developing electrically and magnetically controllable 0 and π junctions are presented; these possibilities are expected to be useful for future devices.

In the era of e-commerce, purchase behavior prediction is one of the most important issues to promote both online companies' sales and the consumers' experience. The previous researches usually use the feature engineering and ensemble machine learning algorithms for the prediction. The performance really depends on designed features and the scalability of algorithms because the large-scale data and a lot of categorical features lead to huge samples and the high-dimensional feature. In this study, we explore an alternative to use tree-based Feature Transformation (FT) and simple machine learning algorithms (e.g. Logistic Regression). Random Forest (RF) and Gradient Boosting decision tree (GB) are used for FT. Then, the simple algorithm, rather than ensemble algorithms, is used to predict purchase behavior based on transformed features. Tree-based FT regards the leaves of trees as transformed features, and can learn high-order interactions among original features. Compared with RF, if GB is used for FT, simple algorithms are enough to achieve better performance.

Recently, mobile applications for recording everyday meals draw much attention for self dietary. However, most of the applications return food calorie values simply associated with the estimated food categories, or need for users to indicate the rough amount of foods manually. In fact, it has not been achieved to estimate food calorie from a food photo with practical accuracy, and it remains an unsolved problem. Then, in this paper, we propose estimating food calorie from a food photo by simultaneous learning of food calories, categories, ingredients and cooking directions using deep learning. Since there exists a strong correlation between food calories and food categories, ingredients and cooking directions information in general, we expect that simultaneous training of them brings performance boosting compared to independent single training. To this end, we use a multi-task CNN. In addition, in this research, we construct two kinds of datasets that is a dataset of calorie-annotated recipe collected from Japanese recipe sites on the Web and a dataset collected from an American recipe site. In the experiments, we trained both multi-task and single-task CNNs, and compared them. As a result, a multi-task CNN achieved the better performance on both food category estimation and food calorie estimation than single-task CNNs. For the Japanese recipe dataset, by introducing a multi-task CNN, 0.039 were improved on the correlation coefficient, while for the American recipe dataset, 0.090 were raised compared to the result by the single-task CNN. In addition, we showed that the proposed multi-task CNN based method outperformed search-based methods proposed before.

The elastic optical network (EON) is a promising new optical technology that uses spectrum resources much more efficiently than does traditional wavelength division multiplexing (WDM). This paper focuses on the routing, modulation level, spectrum and transceiver allocation (RMSTA) problems of the EON. In contrast to previous works that consider only the routing and spectrum allocation (RSA) or routing, modulation level and spectrum allocation (RMSA) problems, we additionally consider the transceiver allocation problem. Because transceivers can be used to regenerate signals (by connecting two transceivers back-to-back) along a transmission path, different regeneration sites on a transmission path result in different spectrum and transceiver usage. Thus, the RMSTA problem is both more complex and more challenging than are the RSA and RMSA problems. To address this problem, we first propose an integer linear programming (ILP) model whose objective is to optimize the balance between spectrum usage and transceiver usage by tuning a weighting coefficient to minimize the cost of network operations. Then, we propose a novel virtual network-based heuristic algorithm to solve the problem and present the results of experiments on representative network topologies. The results verify that, compared to previous works, the proposed algorithm can significantly reduce both resource consumption and time complexity.

Stochastic resonance can improve the signal-to-noise ratio of digital baseband signals. However, the output of SR system needs some time for evolution to achieve global steady-state. This paper first analyzes the evolution time of SR systems, which is an important factor for digital baseband signal processing based on SR. This investigation shows that the sampling number per symbol should be rather large, and the minimum sampling number per symbol is deduced according to the evolution time of SR system.

Recently, the join processing of large-scale datasets in MapReduce environments has become an important issue. However, the existing MapReduce-based join algorithms suffer from too much overhead for constructing and updating the data index. Moreover, the similarity computation cost is high because the existing algorithms partition data without considering the data distribution. In this paper, we propose two grid-based join algorithms for MapReduce. First, we propose a similarity join algorithm that evenly distributes join candidates using a dynamic grid index, which partitions data considering data density and similarity threshold. We use a bottom-up approach by merging initial grid cells into partitions and assigning them to MapReduce jobs. Second, we propose a k-NN join query processing algorithm for MapReduce. To reduce the data transmission cost, we determine an optimal grid cell size by considering the data distribution of randomly selected samples. Then, we perform kNN join by assigning the only related join data to a reducer. From performance analysis, we show that our similarity join query processing algorithm and our k-NN join algorithm outperform existing algorithms by up to 10 times, in terms of query processing time.

This paper proposes the mesh-topology based wireless-powered communication network (MT-WPCN), which consists of a hybrid-access point (H-AP) and nodes. The H-AP broadcasts energy to all nodes by wireless, and the nodes harvest the energy and then communicate with other nodes including the H-AP. For the communication in the MT-WPCN, we propose the harvest-then-transceive protocol to ensure that the nodes can harvest energy from the H-AP and transmit information selectively to the H-AP or other nodes, which is not supported in most protocols proposed for the conventional WPCN. In the proposed protocol, we consider that the energy harvesting can be interrupted at nodes, since the nodes cannot harvest energy during transmission or reception. We also consider that the harvested energy is consumed by the reception of information from other nodes. In addition, the energy reservation model is required to guarantee the QoS, which reserves the infimum energy to receive information reliably by the transmission power control. Under these considerations, first, we design the half harvest-then-transceive protocol, which indicates that a node transmits information only to other nodes which do not transmit information yet, for investing the effect of the energy harvesting interruption. Secondly, we also design the full harvest-then-transceive protocol for the information exchange among nodes and compatibility with the conventional star-topology based WPCN, which indicates that a node can transmit information to any network unit, i.e., the H-AP and all nodes. We study the sum-throughput maximization in the MT-WPCN based on the half and full harvest-then-transceive protocols, respectively. Furthermore, the amount of harvested energy is analytically compared according to the energy harvesting interruption in the protocols. Simulation results show that the proposed MT-WPCN outperforms the conventional star-topology based WPCN in terms of the sum-throughput maximization, when wireless information transmission among nodes occurs frequently.

This paper presents a 27.5-29.6GHz fractional-N frequency synthesizer using reference and frequency doublers to achieve low in-band and out-of-band phase-noise for 5G mobile communications. A consideration of the baseband carrier recovery circuit helps estimate phase noise requirement for high modulation scheme. The push-push amplifier and 28GHz balun help achieving differential signals with low out-of-band phase noise while consuming low power. A charge pump with gated offset as well as reference doubler help reducing PD noise resulting in low in-band phase noise while sampling loop filter helps reduce spurs. The proposed synthesizer has been implemented in 65nm CMOS technology achieving an in-band and out-of-band phase noise of -78dBc/Hz and -126dBc/Hz, respectively. It consumes only a total power of 33mW. The jitter-power figure-of-merit (FOM) is -231dB which is the highest among the state of the art >20GHz fractional-N PLLs using a low reference clock (<200MHz). The measured reference spurs are less than -80dBc.

With the recent explosion of geographic data generated by smartphones, sensors, and satellites, a data storage that can handle the massive volume of data and support high-computational spatial queries is becoming essential. Although key-value stores efficiently handle large-scale data, they are not equipped with effective functions for supporting geographic data. To solve this problem, in this paper, we present G-HBase, a high-performance geographical database based on HBase, a standard key-value store. To index geographic data, we first use Geohash as the rowkey in HBase. Then, we present a novel partitioning method, namely binary Geohash rectangle partitioning, to support spatial queries. Our extensive experiments on real datasets have demonstrated an improved performance with k nearest neighbors and range query in G-HBase when compared with SpatialHadoop, a state-of-the-art framework with native support for spatial data. We also observed that performance of spatial join in G-HBase is on par with SpatialHadoop and outperforms SJMR algorithm in HBase.