Tokyo police have arrested three teenage boys on suspicion of fraudulently subscribing to Rakuten Mobile's phone service via a self-made program using artificial intelligence. The Metropolitan Police Department's cybercrime unit believes that the boys obtained at least about 2,500 mobile phone subscriptions in about six months from December 2023 and sold them for a total of about 7.5 million in crypto assets. The arrests were made for allegedly obtaining 105 mobile phone subscriptions between May and August last year by logging into the Rakuten Mobile system with other people's IDs and passwords. The boys -- a 14-year-old third-year junior high school student in Tokyo, a 16-year-old first-year high school student in Gifu Prefecture and a 15-year-old third-year junior high school student in Shiga Prefecture -- have admitted to the allegations, according to police sources. One of the three was quoted as saying that he wanted to attract attention on social media by devising and carrying out a sophisticated criminal scheme.

We empirically demonstrate that a transformer pre-trained on country-scale unlabeled human mobility data learns embeddings capable, through fine-tuning, of developing a deep understanding of the target geography and its corresponding mobility patterns. Utilizing an adaptation framework, we evaluate the performance of our pre-trained embeddings in encapsulating a broad spectrum of concepts directly and indirectly related to human mobility. This includes basic notions, such as geographic location and distance, and extends to more complex constructs, such as administrative divisions and land cover. Our extensive empirical analysis reveals a substantial performance boost gained from pre-training, reaching up to 38% in tasks such as tree-cover regression. We attribute this result to the ability of the pre-training to uncover meaningful patterns hidden in the raw data, beneficial for modeling relevant Figure 1: A transformer pre-trained from scratch on countryscale high-level concepts. The pre-trained embeddings emerge as robust unlabeled human mobility data is adapted to model a representations of regions and trajectories, potentially valuable for variety of high-level concepts manifesting at different levels a wide range of downstream applications.

Developing software projects allows students to put knowledge into practice and gain teamwork skills. However, assessing student performance in project-oriented courses poses significant challenges, particularly as the size of classes increases. The current paper introduces a fuzzy intelligent system designed to evaluate academic software projects using object-oriented programming and design course as an example. To establish evaluation criteria, we first conducted a survey of student project teams (n=31) and faculty (n=3) to identify key parameters and their applicable ranges. The selected criteria - clean code, use of inheritance, and functionality - were selected as essential for assessing the quality of academic software projects. These criteria were then represented as fuzzy variables with corresponding fuzzy sets. Collaborating with three experts, including one professor and two course instructors, we defined a set of fuzzy rules for a fuzzy inference system. This system processes the input criteria to produce a quantifiable measure of project success. The system demonstrated promising results in automating the evaluation of projects. Our approach standardizes project evaluations and helps to reduce the subjective bias in manual grading.

Watching movies is one of the social activities typically done in groups. Emotion is the most vital factor that affects movie viewers' preferences. So, the emotional aspect of the movie needs to be determined and analyzed for further recommendations. It can be challenging to choose a movie that appeals to the emotions of a diverse group. Reaching an agreement for a group can be difficult due to the various genres and choices. This paper proposes a novel approach to group movie suggestions by examining emotions from three different channels: movie descriptions (text), soundtracks (audio), and posters (image). We employ the Jaccard similarity index to match each participant's emotional preferences to prospective movie choices, followed by a fuzzy inference technique to determine group consensus. We use a weighted integration process for the fusion of emotion scores from diverse data types. Then, group movie recommendation is based on prevailing emotions and viewers' best-loved movies. After determining the recommendations, the group's consensus level is calculated using a fuzzy inference system, taking participants' feedback as input. Participants (n=130) in the survey were provided with different emotion categories and asked to select the emotions best suited for particular movies (n=12). Comparison results between predicted and actual scores demonstrate the efficiency of using emotion detection for this problem (Jaccard similarity index = 0.76). We explored the relationship between induced emotions and movie popularity as an additional experiment, analyzing emotion distribution in 100 popular movies from the TMDB database. Such systems can potentially improve the accuracy of movie recommendation systems and achieve a high level of consensus among participants with diverse preferences.

Explainable artificial intelligence (XAI) has helped elucidate the internal mechanisms of machine learning algorithms, bolstering their reliability by demonstrating the basis of their predictions. Several XAI models consider causal relationships to explain models by examining the input-output relationships of prediction models and the dependencies between features. The majority of these models have been based their explanations on counterfactual probabilities, assuming that the causal graph is known. However, this assumption complicates the application of such models to real data, given that the causal relationships between features are unknown in most cases. Thus, this study proposed a novel XAI framework that relaxed the constraint that the causal graph is known. This framework leveraged counterfactual probabilities and additional prior information on causal structure, facilitating the integration of a causal graph estimated through causal discovery methods and a black-box classification model. Furthermore, explanatory scores were estimated based on counterfactual probabilities. Numerical experiments conducted employing artificial data confirmed the possibility of estimating the explanatory score more accurately than in the absence of a causal graph. Finally, as an application to real data, we constructed a classification model of credit ratings assigned by Shiga Bank, Shiga prefecture, Japan. We demonstrated the effectiveness of the proposed method in cases where the causal graph is unknown.

To effectively optimize Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a mini-batch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA) algorithm was recently proposed. This paper further proposes FCM-RDpA, which improves MBGD-RDA by replacing the grid partition approach in rule initialization by fuzzy c-means clustering, and AdaBound by Powerball AdaBelief, which integrates recently proposed Powerball gradient and AdaBelief to further expedite and stabilize parameter optimization. Extensive experiments on 22 regression datasets with various sizes and dimensionalities validated the superiority of FCM-RDpA over MBGD-RDA, especially when the feature dimensionality is higher. We also propose an additional approach, FCM-RDpAx, that further improves FCM-RDpA by using augmented features in both the antecedents and consequents of the rules.

Contactless technologies have come into the spotlight amid the spread of the new coronavirus as people have become more conscious of the risks of infection from touching doorknobs and buttons. While wearing face masks has become commonplace, Glory Ltd., a money-changer manufacturer, has developed an advanced facial recognition technology that is capable of distinguishing a face even when covered by a mask. Glory, based in Hyogo Prefecture, said it envisions using the new technology for walk-through entry control at offices, for example. The technology detects the shape of each person's eyes, forehead and nose -- the area that is not usually covered by a mask -- with the support of artificial intelligence and then confirms their identity, it said. The company said it will put the new product on sale in June at a suggested retail price of ¥2.2 million ($20,600).

"Through an AI theoretical analysis of past data, we will be able to properly respond to cases without just relying on teachers' past experiences," Otsu Mayor Naomi Koshi said of the planned analysis, set to begin from the next fiscal year. AI will be used to analyze 9,000 suspected bullying cases reported by elementary and junior high schools in the city over the six years through fiscal 2018. It will examine the school grade and gender of the suspected victims and perpetrators as well as when and where the incidents occurred. Statistical analysis of the data is expected to help local authorities and teachers identify forms of bullying that tend to escalate in seriousness and which therefore require extra attention, the Otsu board of education said. The AI analysis will also look at other factors, such as school absenteeism and academic achievement, and the findings will be compiled into a report for use by teachers and in training seminars.

The ¥300,000 ($2,650) masks, made of resin and plastic by five employees at REAL-f Co., attempt to accurately duplicate an individual's face down to fine wrinkles and skin texture. Company founder Osamu Kitagawa came up with the idea while working at a printing machine manufacturer. But it took him two years of experimentation before he found a way to use three-dimensional facial data from high-quality photographs to make the masks, and started selling them in 2011. The company, based in Shiga Prefecture, receives about 100 orders every year from entertainment, automobile, technology and security companies, mainly in Japan. For example, a Japanese car company ordered a mask of a sleeping face to improve its facial recognition technology to detect if a driver had dozed off, Kitagawa said.

Superrealistic plastic face masks produced by a firm in Otsu, Shiga Prefecture, have recently attracted attention at home and abroad, from facial-recognition system developers to a Saudi Arabian royal family member. "Look, it makes your heart pound, doesn't it?" The masks -- named Real Face -- are made of plastic resin roughly 1 to 2 millimeters thick. Kitagawa says he came up with the idea of making realistic masks more than a decade ago, when he was developing copy machines at a major printing device manufacturer. "I wanted to make copies of human beings," he said.