Effective assessment of cyber risks in the increasingly dynamic threat landscape must be supported by artificial intelligence techniques due to their ability to dynamically scale and adapt. This article provides the state of the art of AI-supported security risk assessment approaches in terms of a systematic mapping study. The overall goal is to obtain an overview of security risk assessment approaches that use AI techniques to identify, estimate, and/or evaluate cyber risks. We carried out the systematic mapping study following standard processes and identified in total 33 relevant primary studies that we included in our mapping study. The results of our study show that on average, the number of papers about AI-supported security risk assessment has been increasing since 2010 with the growth rate of 133% between 2010 and 2020. The risk assessment approaches reported have mainly been used to assess cyber risks related to intrusion detection, malware detection, and industrial systems. The approaches focus mostly on identifying and/or estimating security risks, and primarily make use of Bayesian networks and neural networks as supporting AI methods/techniques.

Although numerous methods have been formalized for handling the technical aspects of developing domain-specific modeling languages (DSMLs), user needs and usability aspects are often addressed in ad hoc manners and late in the development process. Working in this context, this paper presents the development of the customer journey modeling language (CJML), a DSML for modeling service processes from the end-user's perspective. CJML targets a wide and heterogeneous group of users, making it especially challenging regarding usability. This paper describes how an industry-relevant DSML was systematically improved by using a variety of user-centered design techniques in close collaboration with the target group and how their feedback was used to refine and evolve the syntax and semantics of CJML. We also suggest how a service-providing organization may benefit from adopting CJML as a unifying language for documentation purposes, compliance analysis, and service innovation. Finally, we generalize the experience gained into lessons learned and methodological guidelines.

Using sensor data from devices such as smart-watches or mobile phones is very popular in both computer science and medical research. Such movement data can predict certain health states or performance outcomes. However, in order to increase reliability and replication of the research it is important to share data and results openly. In medicine, this is often difficult due to legal restrictions or to the fact that data collected from clinical trials is seen as very valuable and something that should be kept "in-house". In this paper, we therefore present PSYKOSE, a publicly shared dataset consisting of motor activity data collected from body sensors. The dataset contains data collected from patients with schizophrenia. Schizophrenia is a severe mental disorder characterized by psychotic symptoms like hallucinations and delusions, as well as symptoms of cognitive dysfunction and diminished motivation. In total, we have data from 22 patients with schizophrenia and 32 healthy control persons. For each person in the dataset, we provide sensor data collected over several days in a row. In addition to the sensor data, we also provide some demographic data and medical assessments during the observation period. The patients were assessed by medical experts from Haukeland University hospital. In addition to the data, we provide a baseline analysis and possible use-cases of the dataset.

Games are often defined as engines of experience, and they are heavily relying on emotions, they arouse in players. In this paper, we present a dataset called Toadstool as well as a reproducible methodology to extend on the dataset. The dataset consists of video, sensor, and demographic data collected from ten participants playing Super Mario Bros, an iconic and famous video game. The sensor data is collected through an Empatica E4 wristband, which provides high-quality measurements and is graded as a medical device. In addition to the dataset and the methodology for data collection, we present a set of baseline experiments which show that we can use video game frames together with the facial expressions to predict the blood volume pulse of the person playing Super Mario Bros. With the dataset and the collection methodology we aim to contribute to research on emotionally aware machine learning algorithms, focusing on reinforcement learning and multimodal data fusion. We believe that the presented dataset can be interesting for a manifold of researchers to explore exciting new interdisciplinary questions.

With the emerging ubiquitous sensing field, it has become possible to build assistive technologies for persons during their daily life activities to provide personalized feedback and services. For instance, it is possible to detect an individual's behavioral information (e.g. physical activity, location, and mood) by using sensors embedded in smartwatches and smartphones. To detect human's daily life activities, accelerometers have been widely used in wearable devices. In the current research, usually a single data representation is used, i.e., either image or feature vector representations. In this paper, a novel method is proposed to address two key aspects for the future development of robust deep learning methods for Human Activity Recognition (HAR): (1) multiple representations of a single sensor's data and (2) fusion of these multiple representations. The presented method utilizes Deep Convolutional Neural Networks (CNNs) and was evaluated using a publicly available HAR dataset. The proposed method showed promising performance, with the best result reaching an overall accuracy of 0.97, which outperforms current conventional approaches.

Modern software systems interact with multiple 3rd party dependencies such as the OS file system, libraries, databases or remote services. To verify these interactions, developers write so-called "integration tests" that exercise the software within a specific environment. These tests are not only difficult to write as their environment is complicated, but they are also brittle because changes outside the code (i.e., in the environment) might make them fail unexpectedly. Integration tests are thus underused whereas they could help find many more issues. We hence propose CAMP, a tool that amplifies an existing integration test by exploring variations of the given environment. The tests that CAMP generates alter the services orchestration, the software stacks, the individual components' configuration or any combination thereof. We used CAMP to amplify tests from the Sphinx and Atom open-source projects, and in both cases, we were able to spot undocumented issues related to incompatible environments.

Wearable sensors measuring different parts of people's activity are a common technology nowadays. Data created using these devices holds a lot of potential besides measuring the quantity of daily steps or calories burned, since continuous recordings of heart rate and activity levels usually are collected. Furthermore, there is an increasing awareness in the field of psychiatry on how these activity data relates to various mental health issues such as changes in mood, personality, inability to cope with daily problems or stress and withdrawal from friends and activities. In this paper we present the analysis of a unique dataset containing sensor data collected from patients suffering from depression. The dataset contains motor activity recordings of 23 unipolar and bipolar depressed patients and 32 healthy controls. We apply machine learning to classify patients into depressed and nondepressed. For evaluation of the algorithms, leave one patient out validation is performed. The best results achieved are an F1 score of 0.73 and a MCC of 0.44. The overall findings show that sensor data contains information that can be used to determine the depression status of a person.

Wearable sensors measuring different parts of people's activity are a common technology nowadays. In research, data collected using these devices also draws attention. Nevertheless, datasets containing sensor data in the field of medicine are rare. Often, data is non-public and only results are published. This makes it hard for other researchers to reproduce and compare results or even collaborate. In this paper we present a unique dataset containing sensor data collected from patients suffering from depression. The dataset contains motor activity recordings of 23 unipolar and bipolar depressed patients and 32 healthy controls. For each patient we provide sensor data over several days of continuous measuring and also some demographic data. The severity of the patients' depressive state was labeled using ratings done by medical experts on the Montgomery-Asberg Depression Rating Scale (MADRS). In this respect, the here presented dataset can be useful to explore and understand the association between depression and motor activity better. By making this dataset available, we invite and enable interested researchers the possibility to tackle this challenging and important societal problem.

Recently, Human Activity Recognition (HAR) has become an important research area because of its wide range of applications in several domains such as health care, elder care, sports monitoring systems, etc. The use of wearable sensors — specifically the use of inertial sensors such as accelerometers and gyroscopes — has become the most common approach to recognize physical activities because of their unobtrusiveness and ubiquity. Overall, the process of building a HAR system starts with a feature extraction phase and then a classification model is trained. In the work of Siirtola et al. is proposed an intermediate clustering step to find the homogeneous groups of activities. For the recognition step, an instance is assigned to one of the groups and the final classification is performed inside that group. In this work we evaluate the clustering-based approach for activity classification proposed by Siirtola with two additional improvements: automatic selection of the number of groups and an instance reassignment procedure. In the original work, they evaluated their method using decision trees on a sports activities dataset. For our experiments, we evaluated seven different classification models on four public activity recognition datasets. Our results with 10-fold Cross Validation showed that the method proposed by Siirtola with our additional two improvements performed better in the majority of cases as compared to using the single classification model under consideration. When using Leave One User Out Cross Validation (user independent model) we found no differences between the proposed method and the single classification model.

Personal and ubiquitous sensing technologies such as smartphones have allowed the continuous collection of data in an unobtrusive manner. Machine learning methods have been applied to continuous sensor data to predict user contextual information such as location, mood, physical activity, etc. Recently, there has been a growing interest in leveraging ubiquitous sensing technologies for mental health care applications, thus, allowing the automatic continuous monitoring of different mental conditions such as depression, anxiety, stress, and so on. This paper surveys recent research works in mental health monitoring systems (MHMS) using sensor data and machine learning. We focused on research works about mental disorders/conditions such as: depression, anxiety, bipolar disorder, stress, etc. We propose a classification taxonomy to guide the review of related works and present the overall phases of MHMS. Moreover, research challenges in the field and future opportunities are also discussed.

The technology trend of context-aware computer systems carries the promise of more flexible automated systems, with a high degree of adaptation to the user’s situation, but it implies as a precondition that the context information (such as the place, time, activity, preferences, etc.) is indeed ava ilable. One very important aspect of the user context is the activity in which the human is currently involved. Human Activity Recognition (HAR) has become a trending topic in the last years because of its potential applications in pervasive health care, assisted living, exercise monitoring, etc. Most of the works on HAR either require from the user to label the activities as they are performed so the system can learn them, or rely on a trained device that expects a “typical” ideal user. The first approach is impractical, as the training process easily becomes time consuming, expensive, etc., while the second one drops the HAR precision for many non-typical users. In this work we propose a “crowdsourcing” method for building personalized models for HAR by combining the advantages of both user-dependent and general models, finding class similarities between the target user and the community users. We evaluated our approach on 4 different public datasets and showed that the personalized models outperformed the user-dependent and user-independent models when labeled data is scarce.

Increase in workload across many organizations and consequent increase in occupational stress are negatively affecting the health of the workforce. Measuring stress and other human psychological dynamics is difficult due to subjective nature of selfreporting and variability between and within individuals. With the advent of smartphones, it is now possible to monitor diverse aspects of human behavior, including objectively measured behavior related to psychological state and consequently stress. We have used data from the smartphone's built-in accelerometer to detect behavior that correlates with subjects stress levels. Accelerometer sensor was chosen because it raises fewer privacy concerns (e.g., in comparison to location, video, or audio recording), and because its low-power consumption makes it suitable to be embedded in smaller wearable devices, such as fitness trackers. About 30 subjects from two different organizations were provided with smartphones. The study lasted for eight weeks and was conducted in real working environments, with no constraints whatsoever placed upon smartphone usage. The subjects reported their perceived stress levels three times during their working hours. Using combination of statistical models to classify selfreported stress levels, we achieved a maximum overall accuracy of 71% for user-specific models and an accuracy of 60% for the use of similar-users models, relying solely on data from a single accelerometer.