Humans typically avert their gaze when faced with situations involving another person’s privacy, and humanoid robots should exhibit similar behaviors. Various approaches exist for privacy recognition, including an image privacy recognition model and a Large Vision-Language Model (LVLM). The former relies on datasets of labeled images, which raise ethical concerns, while the latter requires more time to recognize images accurately, making real-time responses difficult. To this end, we propose a method of automatically constructing the LLM Privacy Text Dataset (LPT Dataset), a privacy-related text dataset with privacy indicators, and a method of recognizing whether observing a scene violates privacy without ethically sensitive training images. In constructing the LPT Dataset, which consists of both private and public scenes, we use an LLM to define privacy indicators and generate texts scored for each indicator. Our model recognizes whether a given image is private or public by retrieving texts with privacy scores similar to the image in a multi-modal feature space. In our experiments, we evaluated the performance of our model on three image privacy datasets and a realistic experiment with a humanoid robot in terms of accuracy and responsibility. The experiments show that our approach identifies the private image as accurately as the highly tuned LVLM without delay.

We challenge the problem of efficiently identifying critical links that substantially degrade network performance if they do not function under a realistic situation where each link is probabilistically disconnected, e.g., unexpected traffic accident in a road network and unexpected server down in a communication network. To solve this problem, we utilize the bridge detection technique in graph theory and efficiently identify critical links in case the node reachability is taken as the performance measure.To be more precise, we define a set of target nodes and a new measure associated with it, Target-oriented latent link Criticalness Centrality (TCC), which is defined as the marginal loss of the expected number of nodes in the network that can reach, or equivalently can be reached from, one of the target nodes, and compute TCC for each link by use of detected bridges. We apply the proposed method to two real-world networks, one from social network and the other from spatial network, and empirically show that the proposed method has a good scalability with respect to the network size and the links our method identified possess unique properties. They are substantially more critical than those obtained by the others, and no known measures can replace the TCC measure.

We address the problem of opening and closing shops in group competitive environment, i.e., shops in the same group work cooperatively and those in different groups competitively, and analyze how the market share and location changes over time. We formulate a stochastic utility of each shop as a function of shop distance and attractiveness from which a market share is computed by weighting consumers buying power. We further place a constraint on the traveling time, which is crucial to reduce the computation time, and use a marginal gain of the market share as a measure to rank the candidate location. Using the real dataset of three convenience stores in four cities in Japan, we confirm that, despite the simplification we made in the model, rankings of the existing shops are shown to be high which implies that our model is reasonable. Further, comparison with the baseline gravity model shows that our model gives much more realistic results. Analyses of the dynamics of opening and closing shops indicate that the reasonable time-bound for walking is about 10 min., the market share of each group, thus total share, eventually increases although small, and the difference of the share within each group gradually becomes smaller, revealing that the spatial distribution of the shops in each group becomes more uniform.

Ranking nodes in uncertain graph is computationally expensive when the graph is huge due to the extremely large number of possible worlds. Some approximation is needed in general. We focus on PageRank centrality measure to rank and propose a method that does not use any approximation for uncertain graph in which all the links can be uncertain. We first compute the expected transition matrix over all the possible graphs accurately and then run PageRank algorithm only once to rank the nodes (p-avg approach). This is not the same as computing the scores for each individual graph first and then rank the nodes by taking their average (s-avg approach). Exact computation of the latter is not possible because of the heavy computational load and only the approximate scores are obtained by limiting the number of graphs by sampling. We have tested the performance from various angles using three real world networks. We show that the proposed method (p-avg approach) gives very high precision to the s-avg approach for highly ranked nodes and can be a good alternative to it. Pactically, the p-avg approach runs orders of magnitude, i.e., sample size, faster than the s-avg approach.

At its heart the act of reviewing is very subjective, but in reality many factors would influence user’s decision. This can be called social influence bias. We pick two factors, “Who” and “When” and discuss which factor is more influential when a user posts his/her own rate in an online review syst em. We consider two kinds of users: real and virtual. In the former each user has its own metric, but in the latter the metric is assigned to the order of review posting actions (rating). We propose a weighted multinomial generative model that can learn the factor metric quite efficiently from a vast amount of data already available in many online review systems. If the model can explain the data well enough, this implies that such a social bias does exist. We evaluate the proposed method and confirm its effectiveness by five review datasets, and empirically clarify that there is no universal solution, but the social bias does exist. In reality the influential factor depends on each dataset, the majority of users is normal (average), and there are two small groups of users, each with high metric value and low metric value.

Group centrality is an extension of the conventional node centrality in which the importance of a group of nodes together is the target for performance evaluation. We used this notion and formulated a link creation problem to a spatial network. The kind of problem we want to solve is to find the best places to construct k new roads to maximize the evacuation performance. The problem is formulated as an optimization problem to maximize the gain of the group closeness centrality by creating a set of new roads (k links) with the constraints on the road range distance (link length limit Δ). The problem is NP-hard. We show that this problem is reduced to a node selection problem and the objective function is submodular when there is no constraint on the link length limit. We devised an efficient greedy algorithm to search for the best k new roads under the constraint of Δ that comprises three steps: centrality calculation, parents selection and nodes selection. We applied this algorithm to three real road networks of different cities and evaluated its performance with respect to k and Δ. The algorithm is quite efficient and obtains the efficiency gain of 10^3 to 10^6 compared with a naive method in which every single node is blindly tested as a candidate from which to create a new road.

Acquiring a network of trust relations among users in social media sites, e.g., item-review sites, is important for analyzing users' behavior and efficiently finding reliable information on the Web. We address the problem of predicting trustlinks among users for an item-review site. Non-negative matrix factorization (NMF) methods have recently been shown useful for trust-link prediction in such a site where both link and activity information is available. Here, a user activity in an item-review site means posting a review and giving a rating for an item. In this paper, for better trust-link prediction, we propose a new NMF method that incorporates people's evaluation of users' activities as well as trust-links and users' activities themselves. We further apply it to an analysis of users' behavior. Using two real world item-review sites, we experimentally demonstrate the effectiveness of the proposed method.

Recent studies reveal that the covariance matrix adaptation evolution strategy (CMA-ES) updates the parameters based on the natural gradient. The rank-based weight is considered the result of the quantile-based transformation of the objective value and the parameters are adjusted in the direction of the natural gradient estimated by Monte-Carlo with the samples drawn from the current distribution. In this paper, we propose a sample reuse mechanism for the CMA-ES. On the basis of the importance sampling, the past samples are reused to reduce the estimation variance of the quantile and the natural gradient. We derive the formula for the rank-¥mu update of the covariance matrix and the mean vector update using the past samples, then incorporate it into the CMA-ES without the step-size adaptation. From the numerical experiments, we observe that the proposed approach helps to reduce the number of function evaluations on many benchmark functions, especially when the number of samples at each iteration is relatively small.

Node influence is yet another useful concept to quantify how important each node is over a network and can share the same role that other centrality measures have. It can provide new insight into the information diffusion phenomena such as existence of epidemic threshold which the other topology-based centralities cannot do. We focus on information diffusion process based on the SIR model, and address the problem of efficiently estimating the influence degree for all the nodes in the network. The proposed approach is a further improvement over the existing work of the bond percolation process [1], [2] which was demonstrated to be very effective, i.e., three orders of magnitude faster than direct Monte Carlo simulation, in approximately solving the influence maximization problem under a greedy search strategy. We introduce two pruning techniques which improve computational efficiency by an order of magnitude. This is a generic approach for the SIR model setting and can be instantiated to any specific diffusion model. It does not require any approximations or assumptions to the model, e.g., small diffusion probability, shortest path, maximum influence path, etc., that were needed in the existing approaches. We demonstrate its effectiveness by extensive experiments on two large real social networks. Main finding includes that different network structures have different epidemic thresholds and the node influence can identify influential nodes that the other centrality measures cannot.

We address a problem of efficiently estimating the influence of a node in information diffusion over a social network. Since the information diffusion is a stochastic process, the influence degree of a node is quantified by the expectation, which is usually obtained by very time consuming many runs of simulation. Our contribution is that we proposed a framework for predictive simulation based on the leave-N-out cross validation technique that well approximates the error from the unknown ground truth for two target problems: one to estimate the influence degree of each node, and the other to identify top-K influential nodes. The method we proposed for the first problem estimates the approximation error of the influence degree of each node, and the method for the second problem estimates the precision of the derived top-K nodes, both without knowing the true influence degree. We experimentally evaluate the proposed methods using the three real world networks, and show that they can serve as a good measure to solve the target problems with far fewer runs of simulation ensuring the accuracy if N is appropriately chosen, and that estimating the top-K nodes is easier than estimating the influence degree, which means one can identify the influential nodes without knowing exactly their influence degree.

We address a problem of detecting changes in information posted to social media taking both content and posting time distributions into account. To this end, we introduce a generative model consisting of two components, one for a content distribution and the other for a timing distribution, approximating the shape of the parameter change by a series of step functions. We then propose an efficient algorithm to detect change points by maximizing the likelihood of generating the observed sequence data, which has time complexity almost proportional to the length of observed sequence (possible change points). We experimentally evaluate the method on synthetic data streams and demonstrate the importance of considering both distributions to improve the accuracy. We, further, apply our method to real scoring stream data extracted from a Japanese word-of-mouth communication site for cosmetics and show that it can detect change points and the detected parameter change patterns are interpretable through an in-depth investigation of actual reviews.

We addressed the problem of detecting the change in behavior of information diffusion over a social network which is caused by an unknown external situation change using a small amount of observation data in a retrospective setting. The unknown change is assumed effectively reflected in changes in the parameter values in the probabilistic information diffusion model, and the problem is reduced to detecting where in time and how long this change persisted and how big this change is. We solved this problem by searching the change pattern that maximizes the likelihood of generating the observed information diffusion sequences, and in doing so we devised a very efficient general iterative search algorithm using the derivative of the likelihood which avoids parameter value optimization during each search step. This is in contrast to the naive learning algorithm in that it has to iteratively update the patten boundaries, each requiring the parameter value optimization and thus is very inefficient. We tested this algorithm for two instances of the probabilistic information diffusion model which has different characteristics. One is of information push style and the other is of information pull style. We chose Asynchronous Independent Cascade (AsIC) model as the former and Value-weighted Voter (VwV) model as the latter. The AsIC is the model for general information diffusion with binary states and the parameter to detect its change is diffusion probability and the VwV is the model for opinion formation with multiple states and the parameter to detect its change is opinion value. The results tested on these two models using four real-world network structures confirm that the algorithm is robust enough and can efficiently identify the correct change pattern of the parameter values. Comparison with the naive method that finds the best combination of change boundaries by an exhaustive search through a set of randomly selected boundary candidates shows that the proposed algorithm far outperforms the native method both in terms of accuracy and computation time.

We address the problem of estimating the parameters, from observed data in a complex social network, for an information diffusion model that takes time-delay into account, based on the popular independent cascade (IC) model. For this purpose we formu

We address the problem of predicting the expected opinion share over a social network at a target time from the opinion diffusion data under the value-weighted voter model with multiple opinions. The value update algorithm ensures that it converges to a correct solution and the share prediction results outperform a simple linear extrapolation approximation when the available data is limited. We further show in an extreme case of complete network that the opinion with the highest value eventually takes over, and the expected share prediction problem with uniform opinion value is not well-defined and any opinion can win.

In this paper, we present a new tree mining algorithm, DryadeParent, based on the hooking principle first introduced in DRYADE. In the experiments, we demonstrate that the branching factor and depth of the frequent patterns to find are key factors of complexity for tree mining algorithms, even if often overlooked in previous work. We show that DryadeParent outperforms the current fastest algorithm, CMTreeMiner, by orders of magnitude on data sets where the frequent tree patterns have a high branching factor.

Personalization is one of the most important mechanisms to make multimedia systems easy to use. In video applications, its embodiment is to tailor video contents for a particular viewer. For this purpose, we are now developing a system of retrieving and browsing video segments, called video portal with personalization (VIPP). VIPP is characterized by 1) supporting the viewer's access to video contents and making a summarized video clip by taking his/her preference into account and 2) acquiring the viewer's profile from his/her operations automatically. In this paper, we propose a method for learning to personalize from the viewer's operations such as retrieval and browsing, as well as describe how the personalized retrieval and summarization of videos can be realized. From the experiments, we clarify the effect of personalization on retrieval and summarization of baseball videos on VIPP.

In this paper, we present a new tree mining algorithm, DryadeParent, based on the hooking principle first introduced in Dryade (Termier et al, 2004). In the experiments, we demonstrate that the branching factor and depth of the frequent patterns to find are key factor of complexity for tree mining algorithms. We show that DryadeParent outperforms the current fastest algorithm, CMTreeMiner, by orders of magnitude on datasets where the frequent patterns have a high branching factor.

We propose a non-interactive method for solving the contradictions caused by exceptions to ordinary rules. It is realized by: detecting the ordinary rules which have the instances in their bodies; and converting them into the default rules. To reduce the default rules which need much reasoning time, we convert the minimal sets of ordinary rules to solve contradictions. Since the proposed method is executed without human interaction, it contributes to automatic rule-base maintenance.

We propose a nonmonotonic formalism for knowledge handling allowing exceptions, the Exc-Representation (ER), and its goal-directed proof procedure, the SLD-EXC resolution, for the nonmonotonic inference system, NISE. ER always provides a unique extension, which is a set of conclusions, and makes tractable the membership problem in nonmonotonic reasoning by introducing the conditional facts, and classifying exceptions into two types based on the relationships among rules.