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Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account. Successfully preventing this requires the detection of as many fraudsters as possible, without producing too many false alarms. This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud. In addition, classical machine learning methods must be extended, minimizing expected financial losses. Finally, fraud can only be combated systematically and economically if the risks and costs in payment channels are known. We define three models that overcome these challenges: machine learning-based fraud detection, economic optimization of machine learning results, and a risk model to predict the risk of fraud while considering countermeasures. The models were tested utilizing real data. Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15% compared to a benchmark consisting of static if-then rules. Optimizing the machine-learning model further reduces the expected losses by 52%. These results hold with a low false positive rate of 0.4%. Thus, the risk framework of the three models is viable from a business and risk perspective.

The discussion about how to present instructors in instructional videos has become a hot topic in recent years. This systematic review explores how the instructors’ presence affects affective, cognitive, and social aspects of learning in different conditions and with different video types. The review analyses 41 empirical studies indexed in Web of Science, ERIC, Scopus, and Education Source research databases from 2014 to 2022. The results indicated that (i) many instructor-present videos were in picture-in-picture format and included direct gaze as a social cue, (ii) learners had positive feelings for instructor-present videos, (iii) the on-screen instructor could not be beneficial for gathering positive learning outcomes, but social and attentional cues provided by the on-screen instructor could foster learning, and (iv) findings regarding the social aspect of learning were inconclusive. This study also emphasizes the need for further studies to clearly explore the role of the instructor in different learning conditions.

The need for early detection of temporal events from sequential data arises in a wide spectrum of applications ranging from human-robot interaction to video security. While temporal event detection has been extensively studied, early detection is a relatively unexplored problem. This paper proposes a maximum-margin framework for training temporal event detectors to recognize partial events, enabling early detection. Our method is based on Structured Output SVM, but extends it to accommodate sequential data. Experiments on datasets of varying complexity, for detecting facial expressions, hand gestures, and human activities, demonstrate the benefits of our approach.

During the COVID-19 pandemic, there was an increase in the use of online courses, which required improvements in their effectiveness. To address this, the online flipped model was suggested as a solution, and this study aimed to assess the efficacy of using interactive instructional videos within an online flipped course design. The study employed a quasi-experimental method and involved fifty-five voluntary students. The experimental group watched interactive educational videos with pop-up questions for six weeks before live courses; meanwhile, the control group watched linear instructional versions of the same videos. The same online learning activities were carried out in both groups during the online live classes for each week. Independent sample t-tests were performed to assess whether there were any differences in the learning performance, lecture engagement, sustained attention, mental effort, positive emotions, and satisfaction scores between the interactive group and the control group. The results indicated that the use of interactive videos in the online flipped model improved learning performance and reduced cognitive load, but did not significantly affect lecture engagement, sustained attention, positive emotion, and satisfaction.

Interaction data are characterized by two sets of objects, each described by their own set of features. They are often modeled as networks and the values of interest are the possible interactions between two instances, represented usually as a matrix. Here, a novel global decision tree learning method is proposed, where multi-output decision trees are constructed over the global interaction setting, addressing the problem of interaction prediction as a multi-label classification task. More specifically, the tree is constructed by splitting the interaction matrix both row-wise and column-wise, incorporating this way both interaction dataset features in the learning procedure. Experiments are conducted across several heterogeneous interaction datasets from the biomedical domain. The experimental results indicate the superiority of the proposed method against other decision tree approaches in terms of predictive accuracy, model size and computational efficiency. The performance is boosted by fully exploiting the multi-output structure of the model. We conclude that the proposed method should be considered in interaction prediction tasks, especially where interpretable models are desired.

In this work, the short-term electric load demand forecasting problem is addressed, proposing a method inspired by the use of anchors in object detection methods. Specifically, a method named Anchored Input–Output Learning (AnIO) is proposed. AnIO proposes to define and use an anchor, reformulating the problem into offset prediction instead of actual load value prediction. Additionally, the use of anchor-encoded input features to match the encoded output is proposed. Extensive experiments were conducted, considering different anchors and model architectures on different datasets. Considering the Greek energy market, AnIO improves the performance from 2.914 to 2.251% in terms of MAPE. In conclusion, AnIO method achieves to improve the performance, considering time-series forecasting tasks.

Decoding models based on pattern recognition (PR) are becoming increasingly important tools for neuroimaging data analysis. In contrast to alternative (mass-univariate) encoding approaches that use hierarchical models to capture inter-subject variability, inter-subject differences are not typically handled efficiently in PR. In this work, we propose to overcome this problem by recasting the decoding problem in a multi-task learning (MTL) framework. In MTL, a single PR model is used to learn different but related “tasks” simultaneously. The primary advantage of MTL is that it makes more efficient use of the data available and leads to more accurate models by making use of the relationships between tasks. In this work, we construct MTL models where each subject is modelled by a separate task. We use a flexible covariance structure to model the relationships between tasks and induce coupling between them using Gaussian process priors. We present an MTL method for classification problems and demonstrate a novel mapping method suitable for PR models. We apply these MTL approaches to classifying many different contrasts in a publicly available fMRI dataset and show that the proposed MTL methods produce higher decoding accuracy and more consistent discriminative activity patterns than currently used techniques. Our results demonstrate that MTL provides a promising method for multi-subject decoding studies by focusing on the commonalities between a group of subjects rather than the idiosyncratic properties of different subjects. •In mass-univariate analysis, mixed effects models can capture subject variability.•In pattern recognition (PR), subject variability is usually not modelled explicitly.•Multi-task learning (MTL) is proposed to accommodate subject variability in PR.•The proposed approach improves predictive accuracy and pattern reproducibility.•A novel brain mapping approach is also proposed for MTL and existing PR models.

Purpose>This study aims to explore the influence of topic familiarity on the four stages of college students' learning search process.Design/methodology/approach>This study clarified the effects of topic familiarity on students' learning search process by conducting a simulation experiment based on query formulation, information item selection, information sources and learning output.Findings>The results characterized users' interaction behaviors in increasing topic familiarity through their use of more task descriptions as queries, increased reformulation of queries, construction of more purposeful query formulation, reduced attention to a topic's basic concept content and increased exploration of academic platform contents.Originality/value>This study proposed three innovative indicators which were proposed to evaluate the effects of topic familiarity on college students' learning search process, and the adopted metrics were useful for observing differences in college students' learning output as their topic familiarity increased. It contributes to the understanding of a user's search process and learning output to support the optimization function of learning-related information search systems and improve their effect on the user's search process for learning.

Recently, with the discovery of various security threats, diversification of hacking attacks, and changes in the network environment such as the Internet of Things, security threats on the network are increasing. Attack graph is being actively studied to cope with the recent increase in cyber threats. However, the conventional attack graph generation method is costly and time-consuming. In this paper, we propose a cheap and simple method for generating the attack graph. The proposed approach consists of learning and generating stages. First, it learns how to generate an attack path from the attack graph, which is created based on the vulnerability database, using machine learning and deep learning. Second, it generates the attack graph using network topology and system information with a machine learning model that is trained with the attack graph generated from the vulnerability database. We construct the dataset for attack graph generation with topological and system information. The attack graph generation problem is recast as a multi-output learning and binary classification problem. It shows attack path detection accuracy of 89.52% in the multi-output learning approach and 80.68% in the binary classification approach using the in-house dataset, respectively.

Network intrusion detection systems (NIDS) based on deep learning have continued to make significant advances. However, the following challenges remain: on the one hand, simply applying only Temporal Convolutional Networks (TCNs) can lead to models that ignore the impact of network traffic features at different scales on the detection performance. On the other hand, some intrusion detection methods consider multi-scale information of traffic data, but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features. To address both of these issues, we propose a hybrid Convolutional Neural Network that supports a multi-output strategy (BONUS) for industrial internet intrusion detection. First, we create a multiscale Temporal Convolutional Network by stacking TCN of different scales to capture the multiscale information of network traffic. Meanwhile, we propose a bi-directional structure and dynamically set the weights to fuse the forward and backward contextual information of network traffic at each scale to enhance the model’s performance in capturing the multi-scale temporal features of network traffic. In addition, we introduce a gated network for each of the two branches in the proposed method to assist the model in learning the feature representation of each branch. Extensive experiments reveal the effectiveness of the proposed approach on two publicly available traffic intrusion detection datasets named UNSW-NB15 and NSL-KDD with F1 score of 85.03% and 99.31%, respectively, which also validates the effectiveness of enhancing the model’s ability to capture multi-scale temporal features of traffic data on detection performance.