Improvements in information technology have made it easier for industry to communicate with their customers, raising hopes for a scheme that can estimate when customers will want to make purchases. Although a number of models have been developed to estimate the time-varying purchase probability, they are based on very restrictive assumptions such as preceding purchase-event dependence and discrete-time effect of covariates. Our preliminary analysis of real-world data finds that these assumptions are invalid: self-exciting behavior, as well as marketing stimulus and preceding purchase dependence, should be examined as possible factors influencing purchase probability. In this paper, by employing the novel idea of hierarchical time rescaling, we propose a tractable but highly flexible model that can meld various types of intrinsic history dependency and marketing stimuli in a continuous-time setting. By employing the proposed model, which incorporates the three factors, we analyze actual data, and show that our model has the ability to precisely track the temporal dynamics of purchase probability at the level of individuals. It enables us to take effective marketing actions such as advertising and recommendations on timely and individual bases, leading to the construction of a profitable relationship with each customer.

In Twitter-like services, countless messages are being posted in real-time every second all around the world. Timely knowledge about what kinds of information are diffusing in social media is quite important. For example, in emergency situations such as earthquakes, users provide instant information on their situation through social media. The collective intelligence of social media is useful as a means of information detection complementary to conventional observation. We have developed a system for monitoring and analyzing information diffusion data in real-time by tracking retweeted tweets. A tweet retweeted by many users indicates that they find the content interesting and impactful. Analysts who use this system can find tweets retweeted by many users and identify the key people who are retweeted frequently by many users or who have retweeted tweets about particular topics. However, bursting situations occur when thousands of social media messages are suddenly posted simultaneously, and the lack of machine resources to handle such situations lowers the system's query performance. Since our system is designed to be used interactively in real-time by many analysts, waiting more than one second for a query results is simply not acceptable. To maintain an acceptable query performance, we propose a capacity control method for filtering incoming tweets using extra attribute information from tweets themselves. Conventionally, there is a trade-off between the query performance and the accuracy of the analysis results. We show that the query performance is improved by our proposed method and that our method is better than the existing methods in terms of maintaining query accuracy.

We propose a Simulink model of a ring oscillator using saturating integrators. The oscillator's period is tuned via the saturation time of the integrators. Thus, timing jitters due to white and flicker noises are easily introduced into the model, enabling an efficient phase noise evaluation before transistor-level circuit design.

To identify problems in a software development process, we have been developing an automated measurement tool called TaskPit, which monitors software development tasks such as programming, testing and documentation based on the execution history of software applications. This paper introduces the system requirements, design and implementation of TaskPit; then, presents two real-world case studies applying TaskPit to actual software development. In the first case study, we applied TaskPit to 12 software developers in a certain software development division. As a result, several concerns (to be improved) have been revealed such as (a) a project leader spent too much time on development tasks while he was supposed to be a manager rather than a developer, (b) several developers rarely used e-mails despite the company's instruction to use e-mail as much as possible to leave communication records during development, and (c) several developers wrote too long e-mails to their customers. In the second case study, we have recorded the planned, actual, and self reported time of development tasks. As a result, we found that (d) there were unplanned tasks in more than half of days, and (e) the declared time became closer day by day to the actual time measured by TaskPit. These findings suggest that TaskPit is useful not only for a project manager who is responsible for process monitoring and improvement but also for a developer who wants to improve by him/herself.

The compressive sensing (CS) theory has been widely used in synthetic aperture radar (SAR) imaging for its ability to reconstruct image from an extremely small set of measurements than what is generally considered necessary. Because block-based CS approaches in SAR imaging always cause block boundaries between two adjacent blocks, resulting in namely the block artefacts. In this paper, we propose a weighted overlapped block-based compressive sensing (WOBCS) method to reduce the block artefacts and accomplish SAR imaging. It has two main characteristics: 1) the strategy of sensing small and recovering big and 2) adaptive weighting technique among overlapped blocks. This proposed method is implemented by the well-known CS recovery schemes like orthogonal matching pursuit (OMP) and BCS-SPL. Promising results are demonstrated through several experiments.

Image-to-sound mapping is a technique that transforms an image to a sound signal, which is subsequently treated as a sound spectrogram. In general, the transformed sound differs from a human speech signal. Herein an efficient image-to-sound mapping method, which provides an understandable speech signal without any training, is proposed. To synthesize such a speech signal, the proposed method utilizes a multi-column image and a speech spectral phase that is obtained from a long-time observation of the speech. The original image can be retrieved from the sound spectrogram of the synthesized speech signal. The synthesized speech and the reconstructed image qualities are evaluated using objective tests.

RC4 is a well-known stream cipher designed by Rivest. Due to considerable cryptanalysis efforts over past 20 years, several kinds of statistic biases in a key stream of RC4 have been observed so far. Finally, practical full plaintext recovery attacks on RC4 in SSL/TLS were independently proposed by AlFardan et al. and Isobe et al. in 2013. Responded to these attacks, usage of RC4 has drastically decreased in SSL/TLS. However, according to the research by Trustworthy Internet Movement, RC4 is still used by some websites for the encryption on SSL/TLS. In this paper, we shows a new plaintext recovery attack for RC4 under the assumption of HTTPS. We develop a method for exploiting single-byte and double-byte biases together to efficiently guess the target bytes, while previous attacks use either single-byte biases or double-byte biases. As a result, target plaintext bytes can be extracted with higher probability than previous best attacks given 229 ciphertexts encrypted by randomly-chosen keys. In the most efficient case, the success probability of our attack are more than twice compared to previous best attacks.

An all-digital fully-synthesizable PVT-tolerant clock data recovery (CDR) architecture for wireline chip-to-chip interconnects is presented. The proposed architecture enables the co-synthesis of the CDR with the digital core. By eliminating the resource hungry manual layout and interfacing steps, which are necessary for conventional CDR topologies, the design process and the time-to-market can be drastically improved. Besides, the proposed CDR architecture enables the re-usability of majority of the sub-systems which enables easy migration to different process nodes. The proposed CDR is also equipped with a self-calibration scheme for ensuring tolerence over PVT. The proposed fully-syntehsizable CDR was implemented in 28nm FDSOI. The system achieves a maximum data rate of 10.06Gbps while consuming a power of 16.1mW from a 1V power supply.

The differential fault analysis of SOSEMNAUK was presented in Africacrypt in 2011. In this paper, we improve previous work with algebraic techniques which can result in a considerable reduction not only in the number of fault injections but also in time complexity. First, we propose an enhanced method to determine the fault position with a success rate up to 99% based on the single-word fault model. Then, instead of following the design of SOSEMANUK at word levels, we view SOSEMANUK at bit levels during the fault analysis and calculate most components of SOSEMANUK as bit-oriented. We show how to build algebraic equations for SOSEMANUK and how to represent the injected faults in bit-level. Finally, an SAT solver is exploited to solve the combined equations to recover the secret inner state. The results of simulations on a PC show that the full 384 bits initial inner state of SOSEMANUK can be recovered with only 15 fault injections in 3.97h.

Similarity search is an important and fundamental problem, and thus widely used in various fields of computer science including multimedia, computer vision, database, information retrieval, etc. Recently, since loitering behavior often leads to abnormal situations, such as pickpocketing and terrorist attacks, its analysis attracts increasing attention from research communities. In this paper, we present AntiLoiter, a loitering discovery system adopting efficient similarity search on surveillance videos. As we know, most of existing systems for loitering analysis, mainly focus on how to detect or identify loiterers by behavior tracking techniques. However, the difficulties of tracking-based methods are known as that their analysis results are heavily influenced by occlusions, overlaps, and shadows. Moreover, tracking-based methods need to track the human appearance continuously. Therefore, existing methods are not readily applied to real-world surveillance cameras due to the appearance discontinuity of criminal loiterers. To solve this problem, we abandon the tracking method, instead, propose AntiLoiter to efficiently discover loiterers based on their frequent appearance patterns in longtime multiple surveillance videos. In AntiLoiter, we propose a novel data structure Luigi that indexes data using only similarity value returned by a corresponding function (e.g., face matching). Luigi is adopted to perform efficient similarity search to realize loitering discovery. We conducted extensive experiments on both synthetic and real surveillance videos to evaluate the efficiency and efficacy of our approach. The experimental results show that our system can find out loitering candidates correctly and outperforms existing method by 100 times in terms of runtime.

This paper introduces a base station antenna system as a future cellular technology. The base station antenna system is the key to achieving high-speed data transmission. It is particularly important to improve the frequency reuse factor as one of the roles of a base station. Furthermore, in order to solve the interference problem due to the same frequency being used by the macro cell and the small cell, the author focuses on beam and null control using an AAS (Active Antenna System) and elucidates their effects through area simulations and field tests. The results showed that AAS can improve the SINR (signal to interference-plus-noise ratio) of the small cell area inside macro cells. The paper shows that cell quality performance can be improved by incorporating the AAS into a cellular base station as its antenna system for beyond 4G radio access technology including the 5G cellular system.

We have developed membrane distributed Bragg reflector (DBR) lasers on thermally oxidized Si substrate (SiO2/Si substrate) to evaluate the parameters of the on-Si lasers we have been developing. The lasers have InGaAsP-based multi-quantum wells (MQWs) grown on InP substrate. We used direct bonding to transfer this active epitaxial layer to SiO2/Si substrate, followed by epitaxial growth of InP to fabricate a buried-heterostructure (BH) on Si. The lateral p-i-n structure was formed by thermal diffusion of Zn and ion implantation of Si. For the purpose of evaluating laser parameters such as internal quantum efficiency and internal loss, we fabricated long-cavity lasers that have 200- to 600-µm-long active regions. The fabricated DBR lasers exhibit threshold currents of 1.7, 2.1, 2.8, and 3.7mA for active-region lengths of 200, 300, 400, and 600µm, respectively. The differential quantum efficiency also depends on active-region length. In addition, the laser characteristics depend on the distance between active region and p-doped region. We evaluated the internal loss to be 10.2cm-1 and internal quantum efficiency to be 32.4% with appropriate doping profile.

Massive MIMO transmission, whose number of antennas is much larger than the number of user terminals, has been attracted much attention as one of key technologies in next-generation mobile communication system because it enables improvement in service area and interference mitigation by simple signal processing. Multi-beam massive MIMO has proposed that utilizes the beam selection with high power in analog part and blind algorithm such as constant modulus algorithm (CMA) which does not need channel state information (CSI) is applied in digital part. Proposed configuration obtains high transmission efficiency. We have evaluated QPSK signals because the CMA basically focuses on the constant amplitude modulation. In this paper, in order to achieve the further higher transmission rate, the amplitude and phase compensation scheme is proposed when using the CMA with amplitude and phase modulation scheme such as QAM. The effectiveness of proposed method is verified by the computer simulation.

The write operations on emerging Non-Volatile Memory (NVM), such as NAND Flash and Phase Change Memory (PCM), usually incur high access latency, and are required to be optimized. In this paper, we propose Asymmetric Read-Write (ARW) policies to minimize the write traffic sent to NVM. ARW policies exploit the asymmetry costs of read and write operations, and make adjustments on the insertion policy and hit-promotion policy of the replacement algorithm. ARW can reduce the write traffic to NVM by preventing dirty data blocks from frequent evictions. We evaluate ARW policies on systems with PCM as main memory and NAND Flash as disk. Simulation results on an 8-core multicore show that ARW adopted on the last-level cache (LLC) can reduce write traffic by more than 15% on average compared to LRU baseline. When used on both LLC and DRAM cache, ARW policies achieve an impressive reduction of 40% in write traffic without system performance degradation. When employed on the on-disk buffer of the Solid State Drive (SSD), ARW demonstrates significant reductions in both write traffic and overall access latency. Moreover, ARW policies are lightweight, easy to implement, and incur negligible storage and runtime overhead.

Card-based protocols enable us to easily perform cryptographic tasks such as secure multiparty computation using a deck of physical cards. Since the first card-based protocol appeared in 1989, many protocols have been designed. A protocol is usually described with a series of somewhat intuitive and verbal descriptions, such as “turn over this card,” “shuffle these two cards,” “apply a random cut to these five cards,” and so on. On the other hand, a formal computational model of card-based protocols via abstract machine was constructed in 2014. By virtue of the formalization, card-based protocols can be treated more rigorously; for example, it enables one to discuss the lower bounds on the number of cards required for secure computations. In this paper, an overview of the computational model with its applications to designing protocols and a survey of the recent progress in card-based protocols are presented.

Compressed sensing (CS)-based wideband spectrum sensing approaches have attracted much attention because they release the burden of high signal acquisition costs. However, in CS-based sensing approaches, highly non-linear reconstruction methods are used for spectrum recovery, which require high computational complexity. This letter proposes a two-step compressive wideband sensing algorithm. This algorithm introduces a coarse sensing step to further compress the sub-Nyquist measurements before spectrum recovery in the following compressive fine sensing step, as a result of the significant reduction in computational complexity. Its enabled sufficient condition and computational complexity are analyzed. Even when the sufficient condition is just satisfied, the average reduced ratio of computational complexity can reach 50% compared with directly performing compressive sensing with the excellent algorithm that is used in our fine sensing step.

We propose a new configuration for phased array antennas. The proposal uses radiation pattern reconfigurable antennas as the antenna element to improve the gain on the scanning angle and to suppress the grating lobes of sparse phased array antennas. This configuration can reduce the element number because the desired gain of the total array can be achieved by using fewer elements. We demonstrate the concept by designing a radiation pattern reconfigurable Yagi-Uda antenna. PIN diode switches are added to the parasitic elements to change director and reflector. The switches of multiple array elements are concurrently controlled by just a single one-pair line. This control structure is simple and can be applied to large-scale arrays. The proposed antenna yields an element gain that almost matches the theoretical limit across about half the coverage, even if the element spacing is enlarged to 1λ. If the switch states are interchanged, the gain in the mirror direction can be increased. We design a 48-element array and compare its gain against those of normal dipole antennas. We also fabricate the proposed antenna and demonstrate radiation pattern switching.

With the fast growth of the international tourism industry, it has been a challenge to forecast the tourism demand in the international tourism market. Traditional forecasting methods usually suffer from the prediction accuracy problem due to the high volatility, irregular movements and non-stationarity of the tourist time series. In this study, a novel single dendritic neuron model (SDNM) is proposed to perform the tourism demand forecasting. First, we use a phase space reconstruction to analyze the characteristics of the tourism and reconstruct the time series into proper phase space points. Then, the maximum Lyapunov exponent is employed to identify the chaotic properties of time series which is used to determine the limit of prediction. Finally, we use SDNM to make a short-term prediction. Experimental results of the forecasting of the monthly foreign tourist arrivals to Japan indicate that the proposed SDNM is more efficient and accurate than other neural networks including the multi-layered perceptron, the neuro-fuzzy inference system, the Elman network, and the single multiplicative neuron model.

All the existing sender-based message logging (SBML) protocols share a well-known limitation that they cannot tolerate concurrent failures. In this paper, we analyze the cause for this limitation in a unicast network environment, and present an enhanced SBML protocol to overcome this shortcoming while preserving the strengths of SBML. When the processes on different nodes execute a distributed application together in a broadcast network, this new protocol replicates the log information of each message to volatile storages of other processes within the same broadcast network. It may reduce the communication overhead for the log replication by taking advantage of the broadcast nature of the network. Simulation results show our protocol performs better than the traditional one modified to tolerate concurrent failures in terms of failure-free execution time regardless of distributed application communication pattern.

A multi-phase crystal-less clock generator (MPCLCG) with a process-voltage-temperature (PVT) calibration circuit is proposed. It operates at 192 MHz with 8 phases outputs, and is implemented as a 0.18µm CMOS process for digital power management systems. A temperature calibrated circuit is proposed to align operational frequency under process and supply voltage variations. It occupies an area of 65µm × 75µm and consumes 1.1mW with the power supply of 1.8V. Temperature coefficient (TC) is 69.5ppm/°C from 0 to 100°C, and 2-point calibration is applied to calibrate PVT variation. The measured period jitter is a 4.58-ps RMS jitter and a 34.55-ps peak-to-peak jitter (P2P jitter) at 192MHz within 12.67k-hits. At 192MHz, it shows a 1-MHz-offset phase noise of -102dBc/Hz. Phase to phase errors and duty cycle errors are less than 5.5% and 4.3%, respectively.