A labeled tree (or a trie) is a natural generalization of a string which can also be seen as a compact representation of a set of strings. This paper considers the labeled tree indexing problem, and provides a number of new results on space bound analysis and on algorithms for efficient construction and pattern matching queries. Kosaraju [FOCS 1989] was the first to consider the labeled tree indexing problem and he proposed the suffix tree for a backward trie, where the strings in the trie are read in the leaf-to-root direction. In contrast to a backward trie, we call an ordinary trie as a forward trie. Despite a few follow-up works after Kosaraju's paper, indexing forward/backward tries is not well understood yet. In this paper, we show a full perspective on the sizes of indexing structures such as suffix trees, DAWGs, CDAWGs, suffix arrays, affix trees, affix arrays for forward and backward tries. Some of them take O(n) space in the size n of the input trie, while the others can occupy O(n²) space in the worst case. In particular, we show that the size of the DAWG for a forward trie with n nodes is Ω(σn), where σ is the number of distinct characters in the trie. This becomes Ω(n²) for an alphabet of size σ =Θ(n). Still, we show that there is a compact O(n)-space implicit representation of the DAWG for a forward trie, whose space requirement is independent of the alphabet size. This compact representation allows for simulating each DAWG edge traversal in O(log σ) time, and can be constructed in O(n) time and space over any integer alphabet of size O(n). In addition, this readily extends to the first indexing structure that permits bidirectional pattern searches over a trie within linear space in the input trie size. We also discuss the size of the DAWG built on a labeled DAG or on an acyclic DFA, and present a quadratic lower bound for its size.

During emergencies and disaster situations, microblogging sites, especially twitter, can be used as a source of providing situational information needs. Monitoring and identifying informative tweets from tweet streams provide enormous opportunities for public safety personnel in coordinating aid operations as well as conducting the post-incident analysis. However, the brevity of tweets and noisy tweet contents makes it challenging to extract the situational information effectively and identify the tweets based on different information types. In this paper, we propose a neural network model with a naive rule-based classifier for actionable informative tweets classification. In our proposed neural architecture, we exploit the transfer learning features from a pre-trained sentence embeddings model along with a rich set of hand-crafted features to train a multilayer perceptron (MLP) network. In addition, we employ the state-of-the-art LSTM variants, nested LSTMs (NLSTMs) to capture the long-term dependency effectively. On top of nested LSTMs, we perform the convolution using multiple kernels (CMK) to obtain the higher-level representation of tweets. Experiments on the 2018 TREC incident streams (TREC-IS) dataset show that our proposed neural model learns the contextual information effectively and achieves the overall best result compared to the state-of-the-art methods.

Starting from their early years, many persons dream of being able to chat with their favorite anime characters. To make such a dream possible, we propose an efficient method for constructing a system that enables users to text chat with existing anime characters. We tackled two research problems to generate verbal and nonverbal behaviors for a text-chat agent system utilizing an existing character. A major issue in creating verbal behavior is generating utterance text that reflects the personality of existing characters in response to any user questions. To cope with this problem we propose use of role play-based question-answering to efficiently collect high-quality paired data of user questions and system answers reflecting the personality of an anime character. We also propose a new utterance generation method that uses a neural translation model with the collected data. Rich and natural expressions of nonverbal behavior greatly enhance the appeal of agent systems. However, not all existing anime characters move as naturally and as diversely as humans. Therefore, we propose a method that can automatically generate whole-body motion from spoken text in order to give the anime characters natural, human-like movements. In addition to these movements, we try to add a small amount of characteristic movement on a rule basis to reflect personality. We created a text-dialogue agent system of a popular existing anime character using our proposed generation methods. As a result of a subjective evaluation of the implemented system, our methods for generating verbal and nonverbal behavior improved the impression of the agent's responsiveness and reflected the personality of the character. Since generating characteristic motions with a small amount of characteristic movement on the basis of heuristic rules was not effective, our proposed motion generation method which can generate the average motion of many people, is useful for generating motion for existing anime characters. Therefore, our proposed methods for generating verbal and nonverbal behaviors and the system-construction method are likely to prove a powerful tool for achieving text-dialogue agent systems for existing characters.

Context recognition is a topic that has garnered considerable interest in the ubiquitous and pervasive computing research community. A wide variety of Internet-of-things devices with micro-electromechanical system (MEMS) sensors are used to obtain sensor data (e.g., acceleration, vibration, and sound) related to target contexts. However, devices for context recognition also have limitations such as deployment cost, battery maintenance cost, and the requirement for wearing/carrying the devices. To solve this problem, wireless sensing has attracted the attention of many researchers because it enables device-free and/or maintenance-free context recognition. In this study, we will comprehensively review studies on context recognition by wireless sensing, focusing on WiFi channel state information (CSI), radio-frequency identification (RFID), and backscatter. We will also discuss the design choices of wireless sensing with their pros and cons through a review of the state-of-the-art.

This paper proposes an indoor localization method for Bluetooth Low Energy (BLE) devices using an end-to-end LSTM neural network. We focus on a large-scale indoor space where there is a tough environment for wireless indoor localization due to signal instability. Our proposed method adopts end-to-end localization, which means input is a time-series of signal strength and output is the estimated location at the latest time in the input. The neural network in our proposed method consists of fully-connected and LSTM layers. We use a custom-made loss function with 3 error components: MSE, the direction of travel, and the leap of the estimated location. Considering the difficulty of data collection in a short preparation term, the data generated by a simple signal simulation is used in the training phase, before training with a small amount of real data. As a result, the estimation accuracy achieves an average of 1.92m, using the data collected in GEXPO exhibition in Miraikan, Tokyo. This paper also evaluates the estimation accuracy assuming the troubles in a real operation.

In wheelchair basketball (WB), players are constantly trying to improve their wheelchair maneuvering techniques since these are the most basic and important actions in all situations. However, assessing maneuvering quality is difficult due to the lack of quantitative metrics. In this paper, we propose two classification methods for maneuvering actions and turns by focusing on the specific wheelchair movement. For this purpose, inertial sensors are fixed to the left and right wheels of the wheelchair. In maneuver classification, the occurrence of maneuvers is detected using the angular velocity. Major maneuver activities in WB are classified into 2 types: PUSH and PULL. First, our method segments candidates of maneuver periods by the local maximum/minimum of the angular velocity since the rotation of the wheel generated by maneuvering that leads to sharp changes in the angular velocity. We then classify maneuvering actions based on thresholds. As for the turn classification, we first detect turns by calculating the amount of wheelchair rotation from the angular velocities of both wheels. We then classify the detected turns into PIVOT and TURN by using thresholds based on the typical movement of both wheels during each turn. To evaluate the performance of the proposed maneuver classification method, we collected real data from 6 players. From the result, we confirmed our method achieves an average recall and precision of 91.9% and 84.6% for maneuver classification, respectively. The results also show that our turn classification achieves an average recall and precision of 99.7% and 99.7%, respectively. Furthermore, we confirmed the effectiveness of the classification results for the assessment of maneuver quality.

As smartphones and tablets are widely spread and used, route recommendation and guidance services have become commonplace. Conventional services in route recommendation and guidance try to give best routes in terms of route length, time required, and train/bus fares, whereas even different users are given the same route when inputting the same parameters. However, each user has various preferences from the aspect of safety and comfort. It is strongly desirable to reflect the user's preferences in route recommendation and recommend the most preferable route to every user. Since user's preferences are extremely vague and complicated, how to evaluate them in route recommendation is one of the key problems there. In this paper, we propose a route recommendation method, called P-UCT method, considering individual user's preferences utilizing Monte-Carlo tree search. In the proposed method, we firstly extract route features based on the route recommendation history of every user and construct a route evaluator based on Support Vector Machine (SVM). After that, the method generates a random route from a start point to an end point by Monte-Carlo tree search. The route evaluator determines how well every generated route matches the user's preferences. By repeating the evaluation, the method obtains the route, which must be closest to the user's preferences. Experimental results demonstrate that the proposed method outperforms the existing method from the viewpoint of the average evaluation scores. They also demonstrate that the proposed method provides the recommended route reflecting the user's individual preferences even if it learns the recommended route history of areas in different situations.

Cloud services are maliciously used as an infrastructure for cyber-attacks. In a cloud service, the assigned Internet Protocol (IP) address for a server is owned by the cloud service provider. When the server is shut down, the assigned IP address is freed for reuse and assigned to another server in the same cloud service. Cyber-attackers abusing cloud services in this way therefore pose a serious risk since legitimate service providers, developers, and end users may be mistakenly blacklisted which lowers the image and hurts the reputation of the service. In this study, we conducted a large-scale measurement of cloud service abuse by using blacklisted IP addresses. Our analysis of four cloud services over 154 days using 39 blacklists revealed that a total of 61,060 IP addresses from these cloud service providers were blacklisted, approximately 14,000 IP addresses continue to be blacklisted, and approximately 5% are replaced daily. Moreover, our study revealed trends in attacks that abuse cloud services with respect to attack type, region, duration, and anti-abuse countermeasures. Finally, we discuss recommendations for cloud service users, cloud service providers, and blacklist providers.

Wireless LAN Roaming enables users to be authenticated and authorized for access to networks at various places they visit. The largest roaming federation today is probably eduroam developed for research and education institutions. There have been some projects for developing roaming federations for public wireless LAN services as well. Since the introduction of the Next Generation Hotspot concept, the need to interconnect these roaming federations has emerged. However, some technical problems exist and these need to be addressed simultaneously. In this paper, we analyze existing roaming systems and develop an inter-federation roaming architecture with the aim of realizing a worldwide Wireless LAN roaming system that would accommodate eduroam. To realize realm-based routing of authentication requests across different federations and to achieve system scalability, the roaming architecture is designed to use regional hubs instead of a single exchange hub. We also aim to develop an actual roaming platform for secure public wireless LAN services based on the developed architecture. The developed platform was implemented and tested in the City Wi-Fi Roaming trials conducted by the Wireless Broadband Alliance.

The prevalence of cloud computing and contents delivery networking has led to demand for OpenFlow-based centrally-managed networks with dynamic and flexible traffic engineering. Maintaining a high level of network service quality requires detecting and locating high-loss links. Therefore, in this paper, a measurement framework is proposed to promptly locate all high-loss links with a minimized load on both data-plane and control-plane incurred by the measurement, which assumes only standard OpenFlow functions. It combines an active measurement by probing multicast packets along a designed route and a passive measurement by collecting flow-stats of the probing flow at selected switch ports in an appropriate sequential order to access switches. In particular, by designing the measurement route based on the backbone-and-branch tree (BBT) route scheme, the measurement accuracy and the measurement overhead (the number of accesses to switches until locating all high-loss links) can be balanced. The numerical simulation demonstrates the effectiveness of our proposal.

Nowadays traffic congestion has increasingly been a significant problem, which results in a longer travel time and aggravates air pollution. Available works showed that back-pressure based traffic control algorithms can effectively reduce traffic congestion. However, those works control traffic based on either inaccurate traffic information or local traffic information, which causes inefficient traffic scheduling. In this paper, we propose an adaptive traffic control algorithm based on back-pressure and Q-learning, which can efficiently reduce congestion. Our algorithm controls traffic based on accurate real-time traffic information and global traffic information learned by Q-learning. As verified by simulation, our algorithm significantly decreases average vehicle traveling time from 17% to 38% when compared with a state-of-the-art algorithm under tested scenarios.

Many multichannel MAC protocols for wireless ad-hoc networks have been proposed to enhance the network performance by avoiding contention among adjacent nodes which want to transmit data frames. However, multichannel MAC protocols need channel switching behavior with some time period, therefore frequent channel switching may deteriorate the network performance. In this paper, we propose a new multi-channel MAC protocol called RPNT-MMAC (Receiver-Prioritized Next Transmission in Multichannel MAC) protocol. In RPNT-MMAC protocol, the transmitter and the receiver nodes negotiate the data channel for data frame transmission by exchanging RTS/CTS frame, and if the receiver node already has another data frame to be forwarded to its neighbor node, then it can acquire the right of the next transmission on the same data channel of ongoing transmission procedure and notifies the next transmission's receiver before switching channel. When the node received the notification for the next transmission, it switches to the same data channel at the end of first data frame transmission and receives the data frame. Our proposed method can decrease the frequency of switching channels and improve the network performance such as the packet arrival rate and the end-to-end delay. Moreover, the fairness among data flows can be improved. Network simulation results showed a better performance of our proposed method than the traditional methods.

Autonomous mobile devices, such as robots and unmanned aerial vehicles, as alternatives to humans, are expected to be applied to searching for and manipulating a variety of emergent events of which the location and number of occurrences are unknown. When an autonomous mobile device searches for an event, it needs to sense a physical signal emitted by an event, such as radio waves, smell or temperature. After a device finds an event, it must manipulate the event. We previously proposed Mobile Sensing Cluster (MSC), which applies swarm intelligence to multiple autonomous mobile devices to quickly search for and manipulate multiple events using dynamically formed multiple swarms of mobile devices. However, in an environment that the physical signal emitted by an event and sensed by a device includes some random noises, the behavior of swarms in MSC becomes unstable. As a result, MSC requires a long time to search and manipulate. In this paper, we propose a dynamic swarm spatial scaling MSC for improving the tolerance of MSC against such random noises, and show its effectiveness.

For IoT applications LPWA is a useful communication choice that enables us to connect tiny devices spread over the land to the Internet. Since many low-price IoT devices usually need to work with limited power budget, this kind of low-power long-range communication technique is a strong tool to populate IoT deployment. Since LPWA devices are less functional, localization of devices are addressed as one of the important practical problems. UNB (Ultra Narrow Band)-based LPWA networks such as Sigfox are one of the major LPWA services for IoT applications, which have a long communication range more than 10km. However, due to the long-range communications and the property of UNB-based modulation, it is not possible to use state-of-the-art localization techniques with high-accuracy; UNB-based LPWA should use simple methods based on RSSI (Radio Signal Strength Indicator) that involves large position estimation errors. In this paper, we propose a method to improve accuracy of device localization in UNB-based LPWA networks by utilizing portable Access Points (APs). By introducing a distance-based weighting technique, we improve the localization accuracy in combination with stationary and portable APs. We demonstrated that the portable AP and the new weighting technique effectively works in UNB-based LPWA networks.

The quantity of data available for analysis, including data collected by sensors and wearable devices, has been increasing hugely. However, to obtain accurate analysis results, data pre-processing such as outlier detection, handling of missing data, and preparing data recorded by different measuring instruments in different units, is essential. Considering that the pre-processing task consumes 80% of analyst resources, we previously proposed a method to address this problem. The method integrates machine learning based on Bayesian inference with human knowledge by using programming by example approach. However, in situations in which the process of generating the model and the process of updating the model are executed at different sites, the previous method is problematic in two ways: 1) all sites have to use the same features defined when the model is generated, and 2) a helpful process to generate new training data from features without using inference data when updating the model, is not available. This prompted us to propose APREP-S, which has flexible feature processes and a process for updating the model using a clustering method. We evaluate the accuracy of the imputation and the similarity of the trends by comparing APREP-S with the original data and other existing methods. The results show that APREP-S can return the most optimal methods with both accuracy and similarity.

In a variant of the stable marriage problem where ties and incomplete lists are allowed, finding a stable matching of maximum cardinality is known to be NP-hard. There are a lot of experimental studies for evaluating the performance of approximation algorithms or heuristics, using randomly generated or artificial instances. One of standard evaluation methods is to compare an algorithm's solution with an optimal solution, but finding an optimal solution itself is already hard. In this paper, we investigate the possibility of generating instances with known optimal solutions. We propose three instance generators based on a known random generation algorithm, but unfortunately show that none of them meet our requirements, implying a difficulty of instance generation in this approach.

One of the common problems with the recursive descent parsing method is that when parsing with a left-recursive grammar, the parsing does not terminate because the same parsing function is recursively invoked indefinitely without consuming the input string. Packrat parsing, which is a variant of recursive descent parsing method that handles grammars described in parsing expression grammars (PEGs) by backtracking, is also affected by the above problem. Although naive backtracking parsers may exhibit an exponential execution time, packrat parsers achieve a linear time complexity (for grammars that are not left-recursive) by memoizing the result of each call to the parsing functions. Some methods have been proposed to solve the problem of left recursion in packrat parsers. In these methods, memoization tables in packrat parsers are modified to limit the depth of the recursive calls. By calling the same parsing function repeatedly while increasing the limit, the parsed range in the input string is expanded gradually. These methods have problems in that multiple occurences of left-recursive calls at the same input position cannot be handled correctly, and some of the grammars that does not include left recursion cannot be handled. In this research, we propose and implement a new packrat parser to address these problems. This packrat parser can handle multiple occurences of left-recursive calls at the same position in the input by giving priority to the most recently used rule when gradually increasing the parsed range of the recursion. In the evaluation of the proposed method, in addition to the grammars including left recursion manageable by the methods proposed in existing studies, we confirmed that our approach supports the grammars that cannot be handled by those existing methods.

Searching communities on attributed graphs has attracted much attention in recent years. The community search algorithm is currently an essential graph data management tool to find a community suited to a user-specified query node. Although community search algorithms are useful in various web-based applications and services, they have trouble handling attributed graphs due to the strict topological constraints of traditional algorithms. In this paper, we propose an accurate community search algorithm for attributed graphs. To relax the topological constraints, we proposed a new model of the community. And we defined the problem of finding them in an attributed graph class called the Flexible Attributed Truss Community (F-ATC). The F-ATC problem has the advantage of being applicable in many situations because it can explore diverse communities. Consequently, the community search accuracy is enhanced compared to traditional community search algorithms. Additionally, we present a novel heuristic algorithm to solve the F-ATC problem. This effective algorithm detects more accurate communities from attributed graphs than the traditional algorithms. For further optimization, we pre-processed the query response to make it faster. Finally, we conducted extensive experiments with real-world attributed graphs to demonstrate that our approach outperforms state-of-the-art methods.

In order to handle a computer security incident or network failure, it is important to grasp a list of pairs of IP and MAC addresses of the hosts. A traditional method based upon ARP table polling, however, has two major drawbacks that 1) some pairs of IP and MAC addresses may not be obtained and 2) it incurs a heavy load on a core switch. In order to overcome these drawbacks, this paper proposes AXARPSC that is the novel scalable ARP snooping to build a list of pairs of IP and MAC addresses. AXARPSC can avoid missing pairs of IP and MAC addresses by monitoring all ARP traffic. AXARPSC also can reduce a CPU load on a recent high-end core switch by approximately 20%. AXARPSC is scalable because AXARPSC incurs no additional CPU load even though the number of hosts increases. AXARPSC employs a policy-based mirroring of a switch that mirrors traffic that matches a specified filter. The policy-based mirroring can mirror ARP traffic only, and reduce the load on an ARP parsing server. AXARPSC can also contract multiple contiguous ARP messages that have the same pair of an IP address and MAC address, as if one ARP message is observed.

In this paper, we propose a switching support system for live broadcast of oral presentation. Analysis of the switching of professional switchers revealed that, in 57 oral presentations, professional switchers carry out switching based on events such as the presenter moving onto a new slide or pointing to the screen. In this paper, with the goal of even switching beginners being able to do switching intuitively, we proposed an event-based switching interface reflecting special characteristics of switching. We implemented a prototype of the proposed system and used it to conduct an evaluation experiment. The results of the experiment suggested that the proposed method can be operated as easily as the previous method and that the proposed method conforms to professional switching more than the previous method.

Embedded systems such as self-driving systems require a computing platform with high computing power and low power consumption. Multi-/many-core platforms definitely meet these requirements. However, for hard real-time applications, multiple demands on shared resources can hinder real-time performance. Memory is one of the resources that can most dramatically impair desired performance. Therefore, we addressed contentions induced by shared memory. The ability to predict contentions that may occur during memory access helps to reduce them. We improved the predictability of contentions by dividing tasks into the memory access phase and the execution phase using a Directed Acyclic Graph (DAG). Existing methods can make accurate contention estimations for one Compute Cluster (CC) of a clustered many-core processor. Our method is able to perform accurate contention estimations for multiple CCs, thereby doubling the scalability when contentions are taken into account. Using an Integer Linear Programming (ILP) formulation, we produced a static, non-preemptive, partitioned, and time-triggered schedule. We also conducted an experiment in order to minimize the makespan. The evaluation confirmed that our new method reduced the makespan by increasing the number of CCs.

In recent year, autonomous vehicles have been developed worldwide. ROS, which is a middleware suitable for the development of a self-driving system, is rarely used in the automotive industry. MATLAB/Simulink, which is a development software suitable for Model-based development, is usually utilized. To integrate a program created with MATLAB/Simulink into a ROS-based self-driving system, it is necessary to convert the program into C++ code and adapt to the network of the ROS-based self-driving system, which makes development inefficient. We used Autoware as ROS-based self-driving system and provided a framework which realizes co-simulation between Autoware and MATLAB/Simulink (CoSAM). CoSAM enables developers to integrate the program created with MATLAB/Simulink into the ROS-based self-driving system without converting into C++ code. Therefore, CoSAM makes the development of the self-driving system easy and efficient. Furthermore, our evaluations of the proposed framework demonstrated its practical potential.

Recently, the great demand for integrated circuits (ICs) drives third parties to be involved in IC design and manufacturing steps. At the same time, the threat of injecting a malicious circuit, called a hardware Trojan, by third parties has been increasing. Machine learning is one of the powerful solutions for detecting hardware Trojans. However, a weakness of such a machine-learning-based classification method against adversarial examples (AEs) has been reported, which causes misclassification by adding perturbation in input samples. This paper firstly proposes a framework generating adversarial examples for hardware-Trojan detection at gate-level netlists utilizing neural networks. The proposed framework replaces hardware Trojan circuits with logically equivalent ones, and makes it difficult to detect them. Secondly, we propose a Trojan-net concealment degree (TCD) and a modification evaluating value (MEV) as measures of the amount of modifications. Finally, based on the MEV, we pick up adversarial modification patterns to apply to the circuits against hardware-Trojan detection. The experimental results using benchmarks demonstrate that the proposed framework successfully decreases the true positive rate (TPR) by a maximum of 30.15 points.

We consider the 1-maximal independent set (1-MIS) problem: given a graph G = (V, E), our goal is to find a 1-maximal independent set (1-MIS) of a given network G, that is, a maximal independent set (MIS) S ⊂ V of G such that S ∪ {v, w} ∖ {u} is not an independent set for any nodes u ∈ S, and v, w ∉ S (v ≠ w). We give a silent, self-stabilizing, and asynchronous distributed algorithm to construct a 1-MIS on a network of any topology. We assume the processes have unique identifiers and the scheduler is weakly-fair and distributed. The time complexity, i.e., the number of rounds to reach a legitimate configuration in the worst case of the proposed algorithm is O(nD), where n is the number of processes in the network and D is the diameter of the network. We use a composition technique called loop composition [Datta et al., 2017] to iterate the same procedure consistently, which results in a small space complexity, O(log n) bits per process.

Flow-based network traffic information has been recently used to detect malicious intrusion. However, several available public flow-based datasets are unidirectional, and bidirectional flow-based datasets are rarely available. In this paper, a novel framework to generate bidirectional flow-based datasets for IDS evaluation is proposed. The generated dataset has the mixed combination of normal background traffic and attack traffic. The background traffic is based on the key traffic feature of the MAWI network traffic traces, and five popular attack traffics are generated based on their statistical traffic features. The generated dataset is characterized using the PCA approach, and we found out that benign and malicious traffic are distinct. With the proposed framework, a dataset of bi-directional flow-based traffic is generated and it would be used for evaluating an effective intrusion detection engine.

Several secret sharing schemes with low computational costs have been proposed. XOR-based secret sharing schemes have been reported to be a part of such low-cost schemes. However, no discussion has been provided on the connection between them and the properties of circulant matrices. In this paper, we propose several theorems of circulant matrices to discuss the rank of a matrix and then show that we can discuss XOR-based secret sharing schemes using the properties of circulant matrices. We also present an evaluation of our software implementation.

In this paper, we propose a replay attack detection (RAD) method that uses spatial and spectral features of a stereo signal. To distinguish genuine and replayed utterance, we focus on non-speech segments, in which a human does not emit sound, but a loudspeaker for replay attack might emit some recorded noise or its electromagnetic noise. The generalized cross-correlation (GCC) based spatial features capture this difference. To improve the robustness against the variety of recording environments, we combine the spatial features with spectral features. In particular, we fuse the output scores of GCC-based and spectral feature-based methods. In experiments, we confirm the effectiveness of the combination of spatial and spectral features.

Exascale computers consume huge amounts of power and their variation over time makes system energy management important. Because of time lag in cooling-units operation, predictive control is desirable for effective power control. In this work, we report a state-of-the-art power prediction model. Conventional methods with topic model use the power of past job as a prediction based on the similarity of job information. The prediction, however, fails, if there is no correct data before. To resolve this, we developed a recurrent neural network model with variable network size, which detects features of power shape from its power history and enables precise prediction during job execution. By integrating these models into a single algorithm, the optimal model is automatically adopted for prediction according to the job status. We demonstrated high-precision prediction with an average relative error of 5.7% in K computer as compared to that of 20.1% by the conventional method.

Open source software (OSS) is software whose source code can be reused under some particular terms and conditions. These terms and conditions are usually described by one or more software licenses written in the header part of the source files. A license may violate another one according to the terms and conditions. Making software by reusing OSS as dependency may cause dependency-related license violation if the developers overlook the license of the dependency. In this paper, we first conduct an empirical study on npm - a JavaScript-based software ecosystem - to study the prevalence of dependency-related license violation. The result suggests that only a few packages (0.644%) in npm have dependency-related license violations. However, we also observe that including the packages licensed under copyleft licenses in the dependency network potentially causes a high dependency-related license violation. We then conduct a preliminary questionnaire on the authors of packages detected as having dependency-related license violations to study the developers' attitudes. The results reveal: 1) the developers' overlooking and misunderstanding of the dependency-related license violations; 2) the difficulties in managing dependency-related license violations and the developers' demands for help.

Comprehending the behavior of an object-oriented system solely from its source code is troublesome owing to its dynamism. To aid comprehension, visualizing program behavior through reverse-engineered sequence diagrams from execution traces is a promising approach. However, because of the massiveness of traces, recovered diagrams tend to become very large causing scalability issues. To address these issues, we propose an object grouping technique that horizontally summarizes a reverse-engineered sequence diagram. Our technique constructs object groups based on Pree's meta patterns in which each group corresponds to a concept in the domain of a subject system. By visualizing interactions only among important groups, we generate a summarized sequence diagram depicting a behavioral overview of the system. Our experiment showed that our technique outperformed the state-of-the-art trace summarization technique in terms of reducing the horizontal size of reverse-engineered sequence diagrams. Regarding the quality of object grouping, our technique achieved an F-score of 0.670 and a Recall of 0.793 on average under the condition of #lifelines (i.e., the horizontal size of a sequence diagram) < 30, whereas those of the state-of-the-art technique were 0.421 and 0.670, respectively. The runtime overhead imposed by our technique was 129.2% on average, which is relatively smaller than other figures found in the reference literature.

B-cells inducing antigen-specific immune responses in vivo produce large amounts of antigen-specific antibodies by recognizing the subregions (epitope regions) of antigen proteins. These antibodies can inhibit the functioning of antigen proteins. Predicting epitope regions is beneficial for the design and development of vaccines aimed to induce antigen-specific antibody production. However, prediction accuracy requires improvement. The conventional epitope region prediction methods have focused only on the target sequence in the amino acid sequences of an entire antigen protein and have not thoroughly considered its sequence and features as a whole. In the present paper, we propose a deep learning method based on long short-term memory with an attention mechanism to consider the characteristics of a whole antigen protein in addition to the target sequence. The proposed method achieves better accuracy compared with the conventional method in the experimental prediction of epitope regions using the data from the immune epitope database.

In this paper we propose a novel approach to build a single shot regressor, called SFLNet, that directly predicts a parameter set relating a sports field seen in an input frame to its metric model. This problem is challenging due to the huge intra-class variance of sports fields and the large number of free parameters to be predicted. To address these issues, we propose to train our regressor in combination with semantic segmentation in a multi-task learning framework. We also introduce an additional module to exploit the spacial consistency of sports fields, which boosts both regression and segmentation performances. SFLNet can be trained with a dataset that can be semi-automatically built from human annotated point-to-point correspondences. To our knowledge, this work is the first attempt to solve this sports field localization problem relying only on an end-to-end deep learning framework. Experiments on our new dataset based on basketball games validate our approach over baseline methods.

The game 2048 is a stochastic single-player game and several computer players have been developed in not only research work but also student projects. Among them, the most successful approach is based on N-tuple networks trained by reinforcement learning methods. Though there have been several works on computer players with deep neural networks, their performance were not as good in most cases. In our previous work, we designed policy networks and applied supervised learning, which resulted in an average score of 215,802. In this study, we tackle the problem with value networks and reinforcement learning methods, since value networks are important to combine with game-tree search methods. We investigate the training methods in several aspects, including batches of training, use of symmetry, network structures, and use of game-specific tricks. We then conduct a training for 240 hours with the best configuration. With the best value network obtained, we achieved an average score of 228,100 with the greedy (1-ply search) play, and furthermore an average score of 406,927 by combining it with the 3-ply expectimax search.

Deep learning for the game of Go achieved considerable success with the victory of AlphaGo against Ke Jie in May 2017. Thus far, there is no clear understanding of why deep learning performs so well in the game of Go. In this paper, we introduce visualization techniques used in image recognition that provide insights into the function of intermediate layers and the operation of the Go policy network. When used as a diagnostic tool, these visualizations enable us to understand what occurs during the training process of policy networks. Further, we introduce a visualization technique that performs a sensitivity analysis of the classifier output by occluding portions of the input Go board, and revealing parts that important for predicting the next move. Further, we attempt to identify important areas through Grad-CAM and combine it with the Go board to provide explanations for next move decisions.

This paper presents hierarchical latent words language models (h-LWLMs) for improving automatic speech recognition (ASR) performance in out-of-domain tasks. Language models called h-LWLM are an advanced form of LWLM that are one one hopeful approach to domain robust language modeling. The key strength of the LWLMs is having a latent word space that helps to efficiently capture linguistic phenomena not present in a training data set. However, standard LWLMs cannot consider that the function and meaning of words are essentially hierarchical. Therefore, h-LWLMs employ a multiple latent word space with hierarchical structure by estimating a latent word of a latent word recursively. The hierarchical latent word space helps us to flexibly calculate generative probability for unseen words. This paper provides a definition of h-LWLM as well as a training method. In addition, we present two implementation methods that enable us to introduce the h-LWLMs into ASR tasks. Our experiments on a perplexity evaluation and an ASR evaluation show the effectiveness of h-LWLMs in out-of-domain tasks.

Personal authentication of the wearable device is important because it has become personalized. Personal identification number (PIN) is generally used to identify the user. However, the user has to remember the PIN and it is easily attacked by shoulder hacking. Therefore, in this study, we propose an authentication method using active acoustic sensing to the user's body. Concretely, the speaker transmits the ultrasonic signal to the user's body and the microphone receives the reflected signal from the body. Each user has a different body composition, and it causes a different frequency response. The system authenticates the user utilizing the difference. Moreover, to improve authentication performance, we add four hand poses to authenticate. Since the proposed method requires one hand for authentication, it is suitable for the wearable computing environment. We implemented the prototype and evaluated the proposed method with nine participants. The evaluation results confirmed that the accuracy of user identification was 91.2% and the equal error rate of user authentication was 2.94% using four hand poses.

In this paper, a generic framework for IoT device registration is proposed. Unlike existing approaches, the proposed method is designed to provide a high level of compatibility, allowing it to work well with devices from different manufacturers. Furthermore, this framework requires only some commonly available technologies (like Bluetooth or BLE) to perform, making it highly applicable to most of the current generation of smart devices available in the market. With security and user-friendliness in mind, the developed registration protocol requires less user interaction while maintaining a considerable high level of security against various types of attacks, i.e., eavesdropping, replay attacks, modification, and man-in-the-middle attack.

Fake antivirus (AV) software is a type of malware that disguises as legitimate antivirus software and causes harm to users and their devices. Fake removal information advertisement (FRAD) sites, which introduce fake removal information for cyber threats, have emerged as platforms for distributing fake AV software. Although FRAD sites seriously threaten users who have been suffering from cyber threats and need information for removing them, little attention has been given to revealing these sites. In this paper, we propose a system to automatically crawl the web and identify FRAD sites. To shed light on the pervasiveness of this type of attack, we performed a comprehensive analysis of both passively and actively collected data. Our system collected 2, 913 FRAD sites in 31 languages, which have 73.5 million visits per month in total. We show that FRAD sites occupy search results when users search for cyber threats, thus preventing the users from obtaining the correct information.

This paper proposes a statistical approach to 2D pose estimation from human images. The main problems with the standard supervised approach, which is based on a deep recognition (image-to-pose) model, are that it often yields anatomically implausible poses, and its performance is limited by the amount of paired data. To solve these problems, we propose a semi-supervised method that can make effective use of images with and without pose annotations. Specifically, we formulate a hierarchical generative model of poses and images by integrating a deep generative model of poses from pose features with that of images from poses and image features. We then introduce a deep recognition model that infers poses from images. Given images as observed data, these models can be trained jointly in a hierarchical variational autoencoding (image-to-pose-to-feature-to-pose-to-image) manner. The results of experiments show that the proposed reflective architecture makes estimated poses anatomically plausible, and the pose estimation performance is improved by integrating the recognition and generative models and also by feeding non-annotated images.

The Likert Scale (LS) is more commonly used in the psychological and affective engineering field. However, LS has some problems, such as the fact that it can only be used for non-parametric analysis if it cannot be treated as an interval measure and is susceptible to biases such as the central tendency and the halo effect. In this study, we propose an analysis method using the Visual Analog Scale (VAS) in which a point marked on a straight line is the evaluation value instead of a five or seven-point scale. The VAS allows us to identify trends in the distribution of data, even in small samples. We visualize the VAS experimental results by overlaying box plots and beeswarm plots to visually grasp the data's distributional trends, even for small samples. We experimented with 30 subjects on conversations with a talking toy robot. We investigated the user's emotions from the conversation with a robot and whether it relates to the conversation's smoothness. The number of questions was 10, and two cases of smooth and non-smooth conversations with the talking robot were evaluated using the VAS method, respectively. The hierarchical clustering results showed that a group of questions expected to show a similar trend was classified into the same cluster. Parametric tests were also performed on data groups following a normal distribution.

Backtracking-based load balancing is a promising method for task parallel processing with work stealing. Tascell is a framework for developing applications with backtracking-based load balancing. Users are responsible for ensuring the consistent behavior of Tascell programs when backtracking is triggered in the Tascell runtimes. Nevertheless, the operational semantics for Tascell programs have not been formally studied. Moreover, no extensional equivalence between Tascell programs is provided. In this paper, we formally specify operational semantics for Tascell programs and define extensional equivalence between Tascell programs using the Church-Rosser modulo equivalence notion in abstract rewriting theory. We propose left invertibility and well-formedness properties for Tascell programs, which ensure extensional equivalence between sequential and concurrent behaviors of Tascell programs. We also propose a domain-specific language based on reversible computation, which allows only symmetric pre/post-processing to update states. Tascell programs written in our language have left invertibility and well-formedness properties by construction. Finally, we confirm that Tascell programs to solve typical search problems such as pentomino puzzles, N-queens, and traveling salesman problems can be written in our language.

Obstacle detection is an essential process in consumer's autonomous mobility systems such as autonomous vehicles inside the dedicated lane to acquire the location of obstacles, and it has become a popular topic in this decade with the blooming of various object detection algorithms and the enhancement of sensor quality. To maintain high accuracy of obstacles' detection in mobility systems outdoor, a sensor fusion system is required to essentially support environmental influence such as lousy weather as well as high moving speeds and adaptably deal with clutter and miss detection based on the incoming measurements from heterogenous sensors with Camera, LiDAR and Radar. Since no current literature about Gaussian mixture probability hypothesis density (GMPHD) handles the above low accuracy fusion problem due to environmental influence for heterogeneous sensors, we propose the concept of integrating GMPHD to heterogeneous sensor fusion with three architectures, Track-to-Track-Fusion (T2TF), Measurement-to-Track-Fusion (M2TF) and Track-to-Association-Fusion (T2AF) and further evaluate their performances respectively in terms of their fusion improvement abilities to determine their practicalities for mobility systems by using the simulation datasets which reproduce ordinary and poorer conditions with the degradation of sensors' performance in the assumption of environmental influences.