Background Virtually, all organisms on Earth have their own circadian rhythm, and humans are no exception. Circadian rhythms are associated with various human states, especially mood disorders, and disturbance of the circadian rhythm is known to be very closely related. Attempts have also been made to derive clinical implications associated with mood disorders using the vast amounts of digital log that is acquired by digital technologies develop and using computational analysis techniques. Objective This study was conducted to evaluate the mood state or episode, activity, sleep, light exposure, and heart rate during a period of about 2 years by acquiring various digital log data through wearable devices and smartphone apps as well as conventional clinical assessments. We investigated a mood prediction algorithm developed with machine learning using passive data phenotypes based on circadian rhythms. Methods We performed a prospective observational cohort study on 55 patients with mood disorders (major depressive disorder [MDD] and bipolar disorder type 1 [BD I] and 2 [BD II]) for 2 years. A smartphone app for self-recording daily mood scores and detecting light exposure (using the installed sensor) were provided. From daily worn activity trackers, digital log data of activity, sleep, and heart rate were collected. Passive digital phenotypes were processed into 130 features based on circadian rhythms, and a mood prediction algorithm was developed by random forest. Results The mood state prediction accuracies for the next 3 days in all patients, MDD patients, BD I patients, and BD II patients were 65%, 65%, 64%, and 65% with 0.7, 0.69, 0.67, and 0.67 area under the curve (AUC) values, respectively. The accuracies of all patients for no episode (NE), depressive episode (DE), manic episode (ME), and hypomanic episode (HME) were 85.3%, 87%, 94%, and 91.2% with 0.87, 0.87, 0.958, and 0.912 AUC values, respectively. The prediction accuracy in BD II patients was distinctively balanced as high showing 82.6%, 74.4%, and 87.5% of accuracy (with generally good sensitivity and specificity) with 0.919, 0.868, and 0.949 AUC values for NE, DE, and HME, respectively. Conclusions On the basis of the theoretical basis of chronobiology, this study proposed a good model for future research by developing a mood prediction algorithm using machine learning by processing and reclassifying digital log data. In addition to academic value, it is expected that this study will be of practical help to improve the prognosis of patients with mood disorders by making it possible to apply actual clinical application owing to the rapid expansion of digital technology.

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The military of the Republic of South Korea applies infrastructure as a service (IaaS) cloud-computing technology and applications distributed nationwide are transferred to two defense-integrated data centers (DIDCs). However, to improve the quality of the military information service provided by the DIDCs, it is crucial to expand the cloud-computing service from IaaS to the software as a service (SaaS) level. Owing to the in-house planning, development and operation of military applications by the Ministry of National Defense and each military force, these organizations operate several applications that are similar and redundant. Accordingly, SaaS can reduce the costs of IT services and management overhead by eliminating similar and redundant applications and integrating them into common services. In this study, a military service framework (MSF) was designed based on the criteria of business functions and service targets. The MSF was applied to applications currently in operation to create a current military SaaS portfolio, whereas the future military SaaS portfolio was restructured by identifying common services within and between organizations and throughout the military. Consequently, 369 future military SaaS portfolios were created, representing a 36% decrease realized by integrating similar and redundant systems out of 580 applications.

Since early responses are crucial to reduce the damage from unknown Internet attacks, our first consideration while developing a defense mechanism can be on time efficiency and observing (and predicting) the change of network statuses, even at the sacrifice of accuracy. In the recent security field, it is an earnest desire that a new mechanism to predict unknown future Internet attacks needs to be developed. This motivates us to study forecasting toward future Internet atacks, which is referred to as CWF (Cyber Weather Forecasting). In this paper, in order to show that the principle of CWF can be realized in the real-world, we propose a forecasting mechanism called FORE (FOrecasting using REgression analysis) through the real-time analysis of the randomness in the network traffic. FORE responds against unknown worms 1.8 times faster than the early detection mechanism, named ADUR (Anomaly Detection Using Randomness check), that can detect the worm when only one percent of total number of vulnerable hosts are infected. Furthermore, FORE can give us timely information about the process of the change of the current network situation. Evaluation results demonstrate the prediction efficiency of the proposed mechanism, including the ability to predict worm behaviors starting from 0.03 percent infection. To our best knowledge, this is the first study to achieve the prediction of future Internet attacks.

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