An honest peer-review process is a key for producing high quality scientific research. However, this process depends on two main factors: (1) the expertise of reviewers in the topic of a submitted paper and (2) the relationships between reviewers and authors. To satisfy the first factor, editors and conferences chairs manually select reviewers. Whereas to prevent any conflict of interest (CoI) between reviewers and authors to satisfy the second factor, reviewers and authors are asked to declare any CoI manually. Such a solution is tedious to all actors and error-prone. To solve this problem and satisfy those two factors, we have developed a novel framework that (1) recommend expert reviewers and (2) resolve the CoIproblem. To develop our framework, we have represented the DBLP citation network dataset as a graph database using Neo4J. A Cypher queries used to select expert reviewers. Various link prediction algorithms, especially the Adamic Adar and the Common Neighbors algorithms, have been utilized to resolve any notential conflict of interest.

Affect detection form physiological signals has been an active research field recently. It has many applications in education, computer games, car driver monitoring, and health. Electrocardiography, electromyography, respiration, and skin conductivity have all been considered as potential physiological channels for recognizing affective states. There is a large amount of literature on physiology-based affect detection that reports on high detection rates of affective states (either discrete emotions, or dimensional valence/arousal) under laboratory conditions. However, one challenge is that physiological measures exhibit considerable variations over time, non-stationary, making classification of future data difficult. In this study, we aim to investigate the changing physiological features over multiple sessions. In our experiments, we use a dataset collected from four participants who viewed a set of images from the International Affective Picture System (IAPS) over multiple days. This study also provides a comparison between self-reported and IAPS ratings for both valence and arousal dimensional affective model. In addition, the study provides an analysis of the performance of individual physiological channels for affect detection. Results indicate that a Deep Neural Network achieved accuracy with 61.12%, 67.14% of detecting self-report compared with IAPS rating for valence and arousal. Results also show that EMG zygomatic features and a fusion of features from all physiological channels have the highest affect detection accuracy for both valence and arousal.