On Multiple Hypothesis Testing with Rejection Option

We study the problem of multiple hypothesis testing (HT) in view of a rejection option. That model of HT has many different applications. Errors in testing of M hypotheses regarding the source distribution with an option of rejecting all those hypotheses are considered. The source is discrete and arbitrarily varying (AVS). The tradeoffs among error probability exponents/reliabilities associated with false acceptance of rejection decision and false rejection of true distribution are investigated and the optimal decision strategies are outlined. The main result is specialized for discrete memoryless sources (DMS) and studied further. An interesting insight that the analysis implies is the phenomenon (comprehensible in terms of supervised/unsupervised learning) that in optimal discrimination within M hypothetical distributions one permits always lower error than in deciding to decline the set of hypotheses. Geometric interpretations of the optimal decision schemes are given for the current and known bounds in multi-HT for AVS's.Comment: 5 pages, 3 figures, submitted to IEEE Information Theory Workshop 201

Distributed Tracing for Troubleshooting of Native Cloud Applications via Rule-Induction Systems

Diagnosing IT issues is a challenging problem for large-scale distributed cloud environments due to complex and non-deterministic interrelations between the system components. Modern monitoring tools rely on AI-empowered data analytics for detection, root cause analysis, and rapid resolution of performance degradation. However, the successful adoption of AI solutions is anchored on trust. System administrators will not unthinkingly follow the recommendations without sufficient interpretability of solutions. Explainable AI is gaining popularity by enabling improved confidence and trust in intelligent solutions. For many industrial applications, explainable models with moderate accuracy are preferable to highly precise black-box ones. This paper shows the benefits of rule-induction classification methods, particularly RIPPER, for the root cause analysis of performance degradations. RIPPER reveals the causes of problems in a set of rules system administrators can use in remediation processes. Native cloud applications are based on the microservices architecture to consume the benefits of distributed computing. Monitoring such applications can be accomplished via distributed tracing, which inspects the passage of requests through different microservices. We discuss the application of rule-learning approaches to trace traffic passing through a malfunctioning microservice for the explanations of the problem. Experiments performed on datasets from cloud environments proved the applicability of such approaches and unveiled the benefits

Excitation of Phonons in Solids and Nanostructures by Intense Laser and XUV Pulses and by Low Energy Atomic Collision

German Academic Exchange Service (DAAD), German Federal Ministry of Education and Research (BMBF

ASSESSMENT AS A SIGNIFICANT COMPONENT IN EDUCATIONAL PROCESS

The article discusses assessment as a significant component of educational process. The assessment mechanisms and peculiarities are listed in the framework of the recent reformations in educational sphere. In this context assessment is investigated as educational outcome or everlasting and constant evidence collection, analysis and usage. The authors prove the fact that assessment should be for the learner and with the learner as an ongoing process

Generalizing Fowler-Nordheim Tunneling Theory for an Arbitrary Power Law Barrier

Herein, the canonical Fowler-Nordheim theory is extended by computing the zero-temperature transmission probability for the more general case of a barrier described by a fractional power law. An exact analytical formula is derived, written in terms of Gauss hypergeometric functions, that fully capture the transmission probability for this generalized problem, including screened interaction with the image potential. First, the quality of approximation against the so far most advanced formulation of Fowler-Nordheim, where the transmission is given in terms of elliptic integrals, is benchmarked. In the following, as the barrier is given by a power law, in detail, the dependence of the transmission probability on the exponent of the power law is analyzed. The formalism is compared with results of numerical calculations and its possible experimental relevance is discussed. Finally, it is discussed how the presented solution can be linked in some specific cases with an exact quantum-mechanical solution of the quantum well problem.Comment: 10 pages, 8 figure

Incident Management for Explainable and Automated Root Cause Analysis in Cloud Data Centers

Effective root cause analysis (RCA) of performance issues in modern cloud environ- ments remains a hard problem. Traditional RCA tracks complex issues by their signatures known as problem incidents. Common approaches to incident discovery rely mainly on expertise of users who define environment-specific set of alerts and  target detection of problems through their occurrence in the monitoring system. Adequately modeling of all possible problem patterns for nowadays extremely sophisticated data center applications is a very complex task. It may result in alert/event storms including large numbers of non-indicative precautions. Thus, the crucial task for the incident-based RCA is reduction of redundant recommendations by prioritizing those events subject to importance/impact criteria or by deriving their meaningful groupings into separable situations. In this paper, we consider automation of incident discovery based on rule induction algorithms that retrieve conditions directly from monitoring datasets without consuming the sys- tem events. Rule-learning algorithms are very flexible and powerful for many regression and classification problems, with high-level explainability. Since annotated or labeled data sets are mostly unavailable in this area of technology, we discuss data self-labelling principles which allow transforming originally unsupervised learning tasks into classification problems with further application of rule induction methods to incident detection