A travel route recommendation service that recommends a sequence of points of interest for tourists traveling in an unfamiliar city is a very useful tool in the field of location-based social networks. Although there are many web services and mobile applications that can help tourists to plan their trips by providing information about sightseeing attractions, travel route recommendation services are still not widely applied. One reason could be that most of the previous studies that addressed this task were based on the orienteering problem model, which mainly focuses on the estimation of a user-location relation (for example, a user preference). This assumes that a user receives a reward by visiting a point of interest and the travel route is recommended by maximizing the total rewards from visiting those locations. However, a location-location relation, which we introduce as a transition pattern in this paper, implies useful information such as visiting order and can help to improve the quality of travel route recommendations. To this end, we propose a travel route recommendation method by combining location and transition knowledge, which assigns rewards for both locations and transitions.

We propose a novel hue-preserving tone mapping scheme. Various tone mapping operations have been studied so far, but there are very few works on color distortion caused in image tone mapping. First, LDR images produced from HDR ones by using conventional tone mapping operators (TMOs) are pointed out to have some distortion in hue values due to clipping and rounding quantization processing. Next,we propose a novel method which allows LDR images to have the same maximally saturated color values as those of HDR ones. Generated LDR images by the proposed method have smaller hue degradation than LDR ones generated by conventional TMOs. Moreover, the proposed method is applicable to any TMOs. In an experiment, the proposed method is demonstrated not only to produce images with small hue degradation but also to maintain well-mapped luminance, in terms of three objective metrics: TMQI, hue value in CIEDE2000, and the maximally saturated color on the constant-hue plane in the RGB color space.

Convolutional Neural Networks (CNNs) usually have millions or even billions of parameters, which make them hard to be deployed into mobile devices. In this work, we present a novel filter-level pruning method to alleviate this issue. More concretely, we first construct an undirected fully connected graph to represent a pre-trained CNN model. Then, we employ the spectral clustering algorithm to divide the graph into some subgraphs, which is equivalent to clustering the similar filters of the CNN into the same groups. After gaining the grouping relationships among the filters, we finally keep one filter for one group and retrain the pruned model. Compared with previous pruning methods that identify the redundant filters by heuristic ways, the proposed method can select the pruning candidates more reasonably and precisely. Experimental results also show that our proposed pruning method has significant improvements over the state-of-the-arts.

Spoofing attacks are one of the biggest concerns for most biometric recognition systems. This will be also the case with silhouette-based gait recognition in the near future. So far, gait recognition has been fortunately out of the scope of spoofing attacks. However, it is becoming a real threat with the rapid growth and spread of deep neural network-based multimedia generation techniques, which will allow attackers to generate a fake video of gait silhouettes resembling a target person's walking motion. We refer to such computer-generated fake silhouettes as gait silhouette clones (GSCs). To deal with the future threat caused by GSCs, in this paper, we propose a supervised method for discriminating GSCs from genuine gait silhouettes (GGSs) that are observed from actual walking people. For training a good discriminator, it is important to collect training datasets of both GGSs and GSCs which do not differ from each other in any aspect other than genuineness. To this end, we propose to generate a training set of GSCs from GGSs by transforming them using multiple autoencoders. The generated GSCs are used together with their original GGSs for training the discriminator. In our experiments, the proposed method achieved the recognition accuracy of up to 94% for several test datasets, which demonstrates the effectiveness and the generality of the proposed method.

Free/libre open source software (FLOSS) are being rapidly employed in several companies and organizations, because it can be modified and used for free. Hence, the use of FLOSS could contribute to its originally intended benefits and to the competence of its users. In this study, we analyzed the effect of using FLOSS on related competences. We investigated the change in the competences through an empirical study before and after the use of FLOSS among project participants. Consequently, it was confirmed that the competences of the participants improved after utilizing FLOSS.

File systems based on persistent memory deploy Copy-on-Write (COW) or logging to guarantee data consistency. However, COW has a write amplification problem and logging has a double write problem. Both COW and logging increase write traffic on persistent memory. In this work, we present adaptive differential logging and zero-copy logging for persistent memory. Adaptive differential logging applies COW or logging selectively to each block. If the updated size of a block is smaller than or equal to half of the block size, we apply logging to the block. If the updated size of a block is larger than half of the block size, we apply COW to the block. Zero-copy logging treats an user buffer on persistent memory as a redo log. Zero-copy logging does not incur any additional data copy. We implement adaptive differential logging and zero-copy logging on both NOVA and PMFS file systems. Our measurement on real workloads shows that adaptive differential logging and zero-copy logging get 150.6% and 149.2% performance improvement over COW, respectively.

Detecting phishing websites is imperative. Among several detection schemes, the promising ones are the visual similarity-based approaches. In those, targeted legitimate website's visual features referred to as signatures are stored in SDB (Signature Database) by the system administrator. They can only detect phishing websites whose signatures are highly similar to SDB's one. Thus, the system administrator has to register multiple signatures to detect various phishing websites and that cost is very high. This incurs the vulnerability of zero-day phishing attack. In order to address this issue, an auto signature update mechanism is needed. The naive way of auto updating SDB is expanding the scope of detection by adding detected phishing website's signature to SDB. However, the previous approaches are not suitable for auto updating since their similarity can be highly different among targeted legitimate website and subspecies of phishing website targeting that legitimate website. Furthermore, the previous signatures can be easily manipulated by attackers. In order to overcome the problems mentioned above, in this paper, we propose a hue signature auto update system for visual similarity-based phishing detection with tolerance to zero-day attack. The phishing websites targeting certain legitimate website tend to use the targeted website's theme color to deceive users. In other words, the users can easily distinguish phishing website if it has highly different hue information from targeted legitimate one (e.g. red colored Facebook is suspicious). Thus, the hue signature has a common feature among the targeted legitimate website and subspecies of phishing websites, and it is difficult for attackers to change it. Based on this notion, we argue that the hue signature fulfills the requirements about auto updating SDB and robustness for attackers' manipulating. This commonness can effectively expand the scope of detection when auto updating is applied to the hue signature. By the computer simulation with a real dataset, we demonstrate that our system achieves high detection performance compared with the previous scheme.

This paper demonstrates latent word recurrent neural network language models (LW-RNN-LMs) for enhancing automatic speech recognition (ASR). LW-RNN-LMs are constructed so as to pick up advantages in both recurrent neural network language models (RNN-LMs) and latent word language models (LW-LMs). The RNN-LMs can capture long-range context information and offer strong performance, and the LW-LMs are robust for out-of-domain tasks based on the latent word space modeling. However, the RNN-LMs cannot explicitly capture hidden relationships behind observed words since a concept of a latent variable space is not present. In addition, the LW-LMs cannot take into account long-range relationships between latent words. Our idea is to combine RNN-LM and LW-LM so as to compensate individual disadvantages. The LW-RNN-LMs can support both a latent variable space modeling as well as LW-LMs and a long-range relationship modeling as well as RNN-LMs at the same time. From the viewpoint of RNN-LMs, LW-RNN-LM can be considered as a soft class RNN-LM with a vast latent variable space. In contrast, from the viewpoint of LW-LMs, LW-RNN-LM can be considered as an LW-LM that uses the RNN structure for latent variable modeling instead of an n-gram structure. This paper also details a parameter inference method and two kinds of implementation methods, an n-gram approximation and a Viterbi approximation, for introducing the LW-LM to ASR. Our experiments show effectiveness of LW-RNN-LMs on a perplexity evaluation for the Penn Treebank corpus and an ASR evaluation for Japanese spontaneous speech tasks.

Recently, social relation analysis receives an increasing amount of attention from text to image data. However, social relation analysis from video is an important problem, which is lacking in the current literature. There are still some challenges: 1) it is hard to learn a satisfactory mapping function from low-level pixels to high-level social relation space; 2) how to efficiently select the most relevant information from noisy and unsegmented video. In this paper, we present an Attentive Sequences Recurrent Network model, called ASRN, to deal with the above challenges. First, in order to explore multiple clues, we design a Multiple Feature Attention (MFA) mechanism to fuse multiple visual features (i.e. image, motion, body, and face). Through this manner, we can generate an appropriate mapping function from low-level video pixels to high-level social relation space. Second, we design a sequence recurrent network based on Global and Local Attention (GLA) mechanism. Specially, an attention mechanism is used in GLA to integrate global feature with local sequence feature to select more relevant sequences for the recognition task. Therefore, the GLA module can better deal with noisy and unsegmented video. At last, extensive experiments on the SRIV dataset demonstrate the performance of our ASRN model.

The aim of this paper is to propose effective attentional regions for fine-grained visual recognition. Based on the Spatial Transformers' capability of spatial manipulation within networks, we propose an extension model, the Attention-Guided Spatial Transformer Networks (AG-STNs). This model can guide the Spatial Transformers with hard-coded attentional regions at first. Then such guidance can be turned off, and the network model will adjust the region learning in terms of the location and scale. Such adjustment is conditioned to the classification loss so that it is actually optimized for better recognition results. With this model, we are able to successfully capture detailed attentional information. Also, the AG-STNs are able to capture attentional information in multiple levels, and different levels of attentional information are complementary to each other in our experiments. A fusion of them brings better results.

Detecting community structures and analyzing temporal evolution in dynamic networks are challenging tasks to explore the inherent characteristics of the complex networks. In this paper, we propose a semi-supervised evolutionary clustering model based on symmetric nonnegative matrix factorization to detect communities in dynamic networks, named sEC-SNMF. We use the results of community partition at the previous time step as the priori information to modify the current network topology, then smooth-out the evolution of the communities and reduce the impact of noise. Furthermore, we introduce a community transition probability matrix to track and analyze the temporal evolutions. Different from previous algorithms, our approach does not need to know the number of communities in advance and can deal with the situation in which the number of communities and nodes varies over time. Extensive experiments on synthetic datasets demonstrate that the proposed method is competitive and has a superior performance.

A non-photorealistic rendering method has been proposed for generating oil-film-like images from photographic images by bilateral infra-envelope filter. The conventional method has a disadvantage that it takes much time to process. We propose a method for generating oil-film-like images that can be processed faster than the conventional method. The proposed method uses an iterative process with upper and lower smoothing filters. To verify the effectiveness of the proposed method, we conduct experiments using Lenna image. As a result of the experiments, we show that the proposed method can process faster than the conventional method.

In-class species classification based on animal sounds is a highly challenging task even with the latest deep learning technique applied. The difficulty of distinguishing the species is further compounded when the number of species is large within the same class. This paper presents a novel approach for fine categorization of animal species based on their sounds by using pre-trained CNNs and a new self-attention module well-suited for acoustic signals The proposed method is shown effective as it achieves average species accuracy of 98.37% and the minimum species accuracy of 94.38%, the highest among the competing baselines, which include CNN's without self-attention and CNN's with CBAM, FAM, and CFAM but without pre-training.

Fountain codes are erasure correcting codes realizing reliable communication systems for the multicast on the Internet. The zigzag decodable fountain (ZDF) codes are one of generalization of the Raptor codes, i.e., applying shift operation to generate the output packets. The ZDF codes are decoded by a two-stage iterative decoding algorithm, which combines the packet-wise peeling algorithm and the bit-wise peeling algorithm. By the bit-wise peeling algorithm and shift operation, ZDF codes outperform Raptor codes under iterative decoding in terms of decoding erasure rates and overheads. However, the bit-wise peeling algorithm spends long decoding time. This paper proposes fast bit-wise decoding algorithms for the ZDF codes. Simulation results show that the proposed algorithm drastically reduces the decoding time compared with the previous algorithm.

This paper proposes a novel interaction technique to transfer data across various types of digital devices in uniform a manner and to allow specifying what kind of data should be sent. In our framework, when users tap multiple devices rhythmically, data corresponding to the rhythm (transfer type) are transferred from a device tapped in the first tap (source device) to the other (target device). It is easy to operate, applicable to a wide range of devices, and extensible in a sense that we can adopt new transfer types by adding new rhythms. Through a subjective evaluation and a simulation, we had a prospect that our approach would be feasible. We also discuss suggestions and limitation to implement the technique.

Hadamard matrix is defined as a square matrix where any components are -1 or +1, and where any pairs of rows are mutually orthogonal. In this work, we consider the similar matrix on finite field GF(p) where p is an odd prime. In such a matrix, every component is one of the integers on GF(p){0}, that is, {1,2,...,p-1}. Any additions and multiplications should be executed under modulo p. In this paper, a method to generate such matrices is proposed. In addition, the paper includes the applications to generate n-shift orthogonal sequences and complete complementary codes. The generated complete complementary code is a family of multi-valued sequences on GF(p){0}, where the number of sequence sets, the number of sequences in each sequence set and the sequence length depend on the various divisors of p-1. Such complete complementary codes with various parameters have not been proposed in previous studies.

A random access scheme is a fundamental scenario in which the users transmit through a shared channel and cannot coordinate with each other. Recently, successive interference cancellation (SIC) is introduced into the random access scheme. The SIC decodes the transmitted packets using collided packets. The coded slotted ALOHA (CSA) is a random access scheme using the SIC. The CSA encodes each packet by a local code prior to transmission. It is known that the CSA achieves excellent throughput. On the other hand, it is reported that shift operation improves the decoding performance for packet-oriented erasure correcting coding systems. In this paper, we propose a protocol which applies the shift operation to the CSA. Numerical simulations show that the proposed protocol achieves better throughput and packet loss rate than the CSA. Moreover, we analyze the asymptotic behavior of the throughput and the decoding erasure rate for the proposed protocol by the density evolution.

In this paper, we show that the orbital angular momentum (OAM) communication performance with a circular loop antenna array can be drastically improved by exploiting the port azimuth effect at the 5-GHz band. The received signal and interference powers are analytically derived with generalized Z-matrices and the perturbation method for short-range OAM communication. The resulting formulas show that the interference power can be drastically suppressed by selecting the proper combination of port azimuths. We also explain the mechanism behind the reduction in interference power. For the obtained port azimuth combination, the simulated and measured transmission isolations at 1cm are better than 24.0 and 23.6dB at 5.3GHz, respectively. Furthermore, to estimate performance in 2×2 MIMO communication, constellations for 64-QAM are estimated. Measured EVMs are less than 3% where signals are clearly discriminated without any signal processing. For long-range OAM communication using paraboloids, the optimum port azimuth combination is estimated by monitoring the current distribution. For the obtained combination of the port azimuths, simulated and measured transmission isolations at 125cm are better than 15.7 and 12.0dB at 5.3GHz, respectively. The measured isolation for short and long ranges are improved by 9.2 and 4.5dB, respectively, compared with the data for the combination of the identical port azimuth.

In this paper we design a fast fabric defect detection framework (Fast-DDF) based on gray histogram back-projection, which adopts end to end multi-convoluted network model to realize defect classification. First, the back-projection image is established through the gray histogram on fabric image, and the closing operation and adaptive threshold segmentation method are performed to screen the impurity information and extract the defect regions. Then, the defect images segmented by the Fast-DDF are marked and normalized into the multi-layer convolutional neural network for training. Finally, in order to solve the problem of difficult adjustment of network model parameters and long training time, some strategies such as batch normalization of samples and network fine tuning are proposed. The experimental results on the TILDA database show that our method can deal with various defect types of textile fabrics. The average detection accuracy with a higher rate of 96.12% in the database of five different defects, and the single image detection speed only needs 0.72s.

Non-volatile memory (NVM) is a promising technology for low-energy and high-capacity main memory of computers. The characteristics of NVM devices, however, tend to be fundamentally different from those of DRAM (i.e., the memory device currently used for main memory), because of differences in principles of memory cells. Typically, the write latency of an NVM device such as PCM and ReRAM is much higher than its read latency. The asymmetry in read/write latencies likely affects the performance of applications significantly. For analyzing behavior of applications running on NVM-based main memory, most researchers use software-based emulation tools due to the limited number of commercial NVM products. However, these existing emulation tools are too slow to emulate a large-scale, realistic workload or too simplistic to investigate the details of application behavior on NVM with asymmetric read/write latencies. This paper therefore proposes a new NVM emulation mechanism that is not only light-weight but also aware of a read/write latency gap in NVM-based main memory. We implemented the prototype of the proposed mechanism for the Intel CPU processors of the Haswell architecture. We also evaluated its accuracy and performed case studies for practical benchmarks. The results showed that our prototype accurately emulated write-latencies of NVM-based main memory: it emulated the NVM write latencies in a range from 200 ns to 1000 ns with negligible errors from 0.2% to 1.1%. We confirmed that the use of our emulator enabled us to successfully estimate performance of practical workloads for NVM-based main memory, while an existing light-weight emulation model misestimated.