Customer churn is a problem virtually all companies face, and the ability to predict it reliably can be a cornerstone for successful retention campaigns. In this study, we propose an approach to customer churn prediction in non-contractual B2B settings that relies exclusively on invoice-level data for feature engineering and uses multi-slicing to maximally utilize available data. We cast churn as a binary classification problem and assess the ability of three established classifiers to predict it when using different churn definitions. We also compare classifier performance when different amounts of historical data are used for feature engineering. The results indicate that robust models for different churn definitions can be derived by using invoice-level data alone and that using more historical data for creating some of the features tends to lead to better performing models for some classifiers. We also confirm that the multi-slicing approach to dataset creation yields better performing models compared to the traditionally used single-slicing approach.

Video quality as perceived by human observers is the ground truth when Video Quality Assessment (VQA) is in question. It is dependent on many variables, one of them being the content of the video that is being evaluated. Despite the evidence that content has an impact on the quality score the sequence receives from human evaluators, currently available VQA databases mostly comprise of sequences which fail to take this into account. In this paper, we aim to identify and analyze differences between human cognitive, affective, and conative responses to a set of videos commonly used for VQA and a set of videos specifically chosen to include video content which might affect the judgment of evaluators when perceived video quality is in question. Our findings indicate that considerable differences exist between the two sets on selected factors, which leads us to conclude that videos starring a different type of content than the currently employed ones might be more appropriate for VQA.

We introduce the term “prolonged emergency remote teaching” to accentuate the temporal aspect of the current educational crisis. In-depth lived experiences of students and teaching staff in one engineering education institution were examined, in order to depict what being involved in an exclusively online communication feels like from the personal perspective, thus examining e-learning sustainability during the COVID-19 pandemic. Interviews with samples of students and teaching staff were conducted, and then psychological phenomenology was employed to produce rich descriptions of their personal experiences and impressions. Both the students and the faculty employees observed e-learning during the Fall semester of 2020 as challenging and artificial. Although trying to function as everything is in order, as the semester unfolded, motivation deteriorated, communication turned more and more superficial, and limitations became more prominent. For both sides, although there were some conveniences, the educational process yielded sub-optimal results and was hard to sustain; intellectual and social capital of both sides appeared to be underutilized. We present conclusions and recommendations, suggesting that modern technologies should help both sides to adapt to the new reality, instead of feeling stuck in an online “limbo”.

For decades, computed tomography (CT) images have been widely used to discover valuable anatomical information. Metallic implants such as dental fillings cause severe streaking artifacts which significantly degrade the quality of CT images. In this paper, we propose a new method for metal‐artifact reduction using complementary magnetic resonance (MR) images. The method exploits the possibilities which arise from the use of emergent trimodality systems. The proposed algorithm corrects reconstructed CT images. The projected data which is affected by dental fillings is detected and the missing projections are replaced with data obtained from a corresponding MR image. A simulation study was conducted in order to compare the reconstructed images with images reconstructed through linear interpolation, which is a common metal‐artifact reduction technique. The results show that the proposed method is successful in reducing severe metal artifacts without introducing significant amount of secondary artifacts.

Correlating estimates of objective measures related to the presence of different coding artifacts with the quality of video as perceived by human observers is a non-trivial task. There is no shortage of data to learn from, thanks to the Internet and web-sites such as YouTube tm . There has, however, been little done in the research community to try to use such resources to advance our understanding of perceived video quality. The problem is the fact that it is not easy to obtain the Mean Opinion Score (MOS), a standard measure of the perceived video quality, for more than a handful of videos. The paper presents an approach to determining the quality of a relatively large number of videos obtained randomly from YouTube tm . Several measures related to motion, saliency and coding artifacts are calculated for the frames of the video. Programmable graphics hardware is used to perform clustering: first, to create an artifacts-related signature of each video; then, to cluster the videos according to their signatures. To obtain an estimate for the video quality, MOS is obtained for representative videos, closest to the cluster centers. This is then used as an estimate of the quality of all other videos in the cluster. Results based on 2,107 videos containing some 90,000,000 frames are presented in the paper.