Institute of Computer Science

The issue of controlling values of various parameters of an evolutionary algorithm is one of the most important and promising areas of research in evolutionary computation: it has a potential of adjusting the algorithm to the problem while solving the problem. In the paper we: 1) revise the terminology, which is unclear and confusing, thereby providing a classification of such control mechanisms, and 2) survey various forms of control which have been studied by the evolutionary computation community in recent years. Our classification covers the major forms of parameter control in evolutionary computation and suggests some directions for further research.

Evolutionary computation techniques have received a great deal of attention regarding their potential as optimization techniques for complex numerical functions. However, they have not produced a significant breakthrough in the area of nonlinear programming due to the fact that they have not addressed the issue of constraints in a systematic way. Only recently have several methods been proposed for handling nonlinear constraints by evolutionary algorithms for numerical optimization problems; however, these methods have several drawbacks, and the experimental results on many test cases have been disappointing. In this paper we (1) discuss difficulties connected with solving the general nonlinear programming problem; (2) survey several approaches that have emerged in the evolutionary computation community; and (3) provide a set of 11 interesting test cases that may serve as a handy reference for future methods.

We generalize the notion of an approximation space introduced in [8]. In tolerance approximation spaces we define the lower and upper set approximations. We investigate some attribute reduction problems for tolerance approximation spaces determined b

Medical care has become one of the most indispensable parts of human lives, leading to a dramatic increase in medical big data. To streamline the diagnosis and treatment process, healthcare professionals are now adopting Internet of Things (IoT)-based wearable technology. Recent years have witnessed billions of sensors, devices, and vehicles being connected through the Internet. One such technology-remote patient monitoring-is common nowadays for the treatment and care of patients. However, these technologies also pose grave privacy risks and security concerns about the data transfer and the logging of data transactions. These security and privacy problems of medical data could result from a delay in treatment progress, even endangering the patient's life. We propose the use of a blockchain to provide secure management and analysis of healthcare big data. However, blockchains are computationally expensive, demand high bandwidth and extra computational power, and are therefore not completely suitable for most resource-constrained IoT devices meant for smart cities. In this work, we try to resolve the above-mentioned issues of using blockchain with IoT devices. We propose a novel framework of modified blockchain models suitable for IoT devices that rely on their distributed nature and other additional privacy and security properties of the network. These additional privacy and security properties in our model are based on advanced cryptographic primitives. The solutions given here make IoT application data and transactions more secure and anonymous over a blockchain-based network.

With prevalent attacks in communication, sharing a secret between communicating parties is an ongoing challenge. Moreover, it is important to integrate quantum solutions with classical secret sharing schemes with low computational cost for the real world use. This paper proposes a novel hybrid threshold adaptable quantum secret sharing scheme, using an m-bonacci orbital angular momentum (OAM) pump, Lagrange interpolation polynomials, and reverse Huffman-Fibonacci-tree coding. To be exact, we employ entangled states prepared by m-bonacci sequences to detect eavesdropping. Meanwhile, we encode m-bonacci sequences in Lagrange interpolation polynomials to generate the shares of a secret with reverse Huffman-Fibonacci-tree coding. The advantages of the proposed scheme is that it can detect eavesdropping without joint quantum operations, and permits secret sharing for an arbitrary but no less than threshold-value number of classical participants with much lower bandwidth. Also, in comparison with existing quantum secret sharing schemes, it still works when there are dynamic changes, such as the unavailability of some quantum channel, the arrival of new participants and the departure of participants. Finally, we provide security analysis of the new hybrid quantum secret sharing scheme and discuss its useful features for modern applications.

People supported by AI-powered decision support tools frequently overrely on the AI: they accept an AI's suggestion even when that suggestion is wrong. Adding explanations to the AI decisions does not appear to reduce the overreliance and some studies suggest that it might even increase it. Informed by the dual-process theory of cognition, we posit that people rarely engage analytically with each individual AI recommendation and explanation, and instead develop general heuristics about whether and when to follow the AI suggestions. Building on prior research on medical decision-making, we designed three cognitive forcing interventions to compel people to engage more thoughtfully with the AI-generated explanations. We conducted an experiment (N=199), in which we compared our three cognitive forcing designs to two simple explainable AI approaches and to a no-AI baseline. The results demonstrate that cognitive forcing significantly reduced overreliance compared to the simple explainable AI approaches. However, there was a trade-off: people assigned the least favorable subjective ratings to the designs that reduced the overreliance the most. To audit our work for intervention-generated inequalities, we investigated whether our interventions benefited equally people with different levels of Need for Cognition (i.e., motivation to engage in effortful mental activities). Our results show that, on average, cognitive forcing interventions benefited participants higher in Need for Cognition more. Our research suggests that human cognitive motivation moderates the effectiveness of explainable AI solutions.

Abstract The discovery of drivers of cancer has traditionally focused on protein-coding genes 1–4 . Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium 5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers 6,7 , raise doubts about others and identify novel candidates, including point mutations in the 5′ region of TP53 , in the 3′ untranslated regions of NFKBIZ and TOB1 , focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available.

The increasing integration of technology into our lives has created unprecedented volumes of data on society’s everyday behaviour. Such data opens up exciting new opportunities to work towards a quantitative understanding of our complex social systems, within the realms of a new discipline known as Computational Social Science. Against a background of financial crises, riots and international epidemics, the urgent need for a greater comprehension of the complexity of our interconnected global society and an ability to apply such insights in policy decisions is clear. This manifesto outlines the objectives of this new scientific direction, considering the challenges involved in it, and the extensive impact on science, technology and society that the success of this endeavour is likely to bring about.

Based on evolutionary computation (EC) concepts, we developed an adaptive evolutionary planner/navigator (EP/N) as a novel approach to path planning and navigation. The EP/N is characterized by generality, flexibility, and adaptability. It unifies off-line planning and online planning/navigation processes in the same evolutionary algorithm which 1) accommodates different optimization criteria and changes in these criteria, 2) incorporates various types of problem-specific domain knowledge, and 3) enables good tradeoffs among near-optimality of paths, high planning efficiency, and effective handling of unknown obstacles. More importantly, the EP/N can self-tune its performance for different task environments and changes in such environments, mostly through adapting probabilities of its operators and adjusting paths constantly, even during a robot's motion toward the goal.

A large number of papers are appearing in the biomedical engineering literature that describe the use of machine learning techniques to develop classifiers for detection or diagnosis of disease. However, the usefulness of this approach in developing clinically validated diagnostic techniques so far has been limited and the methods are prone to overfitting and other problems which may not be immediately apparent to the investigators. This commentary is intended to help sensitize investigators as well as readers and reviewers of papers to some potential pitfalls in the development of classifiers, and suggests steps that researchers can take to help avoid these problems. Building classifiers should be viewed not simply as an add-on statistical analysis, but as part and parcel of the experimental process. Validation of classifiers for diagnostic applications should be considered as part of a much larger process of establishing the clinical validity of the diagnostic technique.

This paper presents preliminary results of a representative survey of the German population focusing on perceptions of risk and ways of coping with COVID-19. Results show that older people estimate the risk of COVID-19 as being less than younger people. Women are more concerned about COVID-19 than men. People especially worry about being infected in places with high public traffic such as public transport and shops or restaurants. Coping strategies are highly problem-focused and most respondents listen to experts’ advice and try to behave calmly and appropriately. People accept that measures to tackle COVID-19 will take time to be effective. Bulk buying and storing of food is mainly justified by a combination of convenience and a perceived need to be prepared for potential quarantine.

Emerging bacterial antibiotic resistance draws attention to bacteriophages as a therapeutic alternative to treat bacterial infection. Examples of phage that combat bacteria abound. However, despite careful testing of antibacterial activity in vitro, failures nevertheless commonly occur. We investigated immunological response of phage antibacterial potency in vivo. Anti-phage activity of phagocytes, antibodies, and serum complement were identified by direct testing and by high-resolution fluorescent microscopy. We accommodated the experimental data into a mathematical model. We propose a universal schema of innate and adaptive immunity impact on phage pharmacokinetics, based on the results of our numerical simulations. We found that the mammalian-host response to infecting bacteria causes the concomitant removal of phage from the system. We propose the notion that this effect as an indirect pathway of phage inhibition by bacteria with significant relevance for the clinical outcome of phage therapy.

Adaptation of parameters and operators is one of the most important and promising areas of research in evolutionary computation; it tunes the algorithm to the problem while solving the problem. In this paper we develop a classification of adaptation on the basis of the mechanisms used, and the level at which adaptation operates within the evolutionary algorithm. The classification covers all forms of adaptation in evolutionary computation and suggests further research.

Semantic and logical problems arising in an incomplete information database are investigated. A simple query language is described, and its semantics, which refers the queries to the information about reality contained in a database, rather than to reality itself, is defined. This approach, called the internal interpretation, is shown to lead in a natural way to the notions of a topological Boolean algebra and a modal logic related to $4 in the same way as referring queries directly to reality (external interpretation) leads to Boolean algebras and classical logic. An axiom system is given for equivalent (with respect to the internal interpretation) transformation of queries, which is then exploited as a basic tool in a method for computing the internal interpretation for a broad class of queries. An interesting special case of the problem of determining the internal interpretation amounts to deciding whether an assertion about reality (a "yes-no" query) is consistent with the incomplete information about reality contained in a database. A solution to this problem, which relies on the classical combinatorial problem of distinct representatives of subsets, is given.

MOTIVATION: Pre-selection of informative features for supervised classification is a crucial, albeit delicate, task. It is desirable that feature selection provides the features that contribute most to the classification task per se and which should therefore be used by any classifier later used to produce classification rules. In this article, a conceptually simple but computer-intensive approach to this task is proposed. The reliability of the approach rests on multiple construction of a tree classifier for many training sets randomly chosen from the original sample set, where samples in each training set consist of only a fraction of all of the observed features. RESULTS: The resulting ranking of features may then be used to advantage for classification via a classifier of any type. The approach was validated using Golub et al. leukemia data and the Alizadeh et al. lymphoma data. Not surprisingly, we obtained a significantly different list of genes. Biological interpretation of the genes selected by our method showed that several of them are involved in precursors to different types of leukemia and lymphoma rather than being genes that are common to several forms of cancers, which is the case for the other methods. AVAILABILITY: Prototype available upon request.

Many types of attribute reduction and decision rules have been proposed in the area of rough sets. It is required to provide their consistent classification. The task is not easy because new proposals address different issues such as: noise in data, compact representation, prediction capability. Usually, when introducing a new knowledge reduction method the authors relate it only to one basic type of knowledge reduction. The main objective of the paper was to find and prove static relationships among classical types of knowledge reduction in inconsistent decision tables in order to provide an underlying classification of knowledge reduction types. Hence, if a newly devised reduction type is a specialization of some known type then all its properties inherited from generalized types will be also known. © 2001 John Wiley & Sons, Inc.

For an intermediate mass Higgs boson with SM-like couplings the CERN LHC allows observation of a variety of decay channels in production by gluon fusion and weak boson fusion. Cross section ratios provide measurements of various ratios of Higgs couplings, with accuracies of order 15% for 100 ${\mathrm{fb}}^{\ensuremath{-}1}$ of data in each of the two LHC experiments. For Higgs boson masses above 120 GeV, minimal assumptions on the Higgs sector allow for an indirect measurement of the total Higgs boson width with an accuracy of 10 to 20 %, and of the $\stackrel{\ensuremath{\rightarrow}}{H}\mathrm{WW}$ partial width with an accuracy of about 10%.

Array comparative genomic hybridization (aCGH) is a powerful tool for the molecular elucidation and diagnosis of disorders resulting from genomic copy-number variation (CNV). However, intragenic deletions or duplications--those including genomic intervals of a size smaller than a gene--have remained beyond the detection limit of most clinical aCGH analyses. Increasing array probe number improves genomic resolution, although higher cost may limit implementation, and enhanced detection of benign CNV can confound clinical interpretation. We designed an array with exonic coverage of selected disease and candidate genes and used it clinically to identify losses or gains throughout the genome involving at least one exon and as small as several hundred base pairs in size. In some patients, the detected copy-number change occurs within a gene known to be causative of the observed clinical phenotype, demonstrating the ability of this array to detect clinically relevant CNVs with subkilobase resolution. In summary, we demonstrate the utility of a custom-designed, exon-targeted oligonucleotide array to detect intragenic copy-number changes in patients with various clinical phenotypes.

Context. Since its proclamation in 2012, microservices-based architecture has gained widespread popularity due to its advantages, such as improved availability, fault tolerance, and horizontal scalability, as well as greater software development agility. Motivation. Yet, refactoring a monolith to microservices by smaller businesses and expecting that the migration will bring benefits similar to those reported by top global companies, such as Netflix, Amazon, eBay, and Uber, might be an illusion. Indeed, for systems that do not have thousands of concurrent users and can be scaled vertically, the benefits of such migration have not been sufficiently investigated, while the existing evidence is inconsistent. Objective. The purpose of this paper is to compare the performance and scalability of monolithic and microservice architectures on a reference web application. Method. The application was implemented in four different versions, covering not only two different architectural styles (monolith vs. microservices) but also two different implementation technologies (Java vs. C#.NET). Next, we conducted a series of controlled experiments in three different deployment environments (local, Azure Spring Cloud, and Azure App Service). Findings. The key lessons learned are as follows: (1) on a single machine, a monolith performs better than its microservice-based counterpart; (2) The Java platform makes better use of powerful machines in case of computation-intensive services when compared to.NET; the technology platform effect is reversed when non-computationally intensive services are run on machines with low computational capacity; (3) vertical scaling is more cost-effective than horizontal scaling in the Azure cloud; (4) scaling out beyond a certain number of instances degrades the application performance; (5) implementation technology (either Java or C#.NET) does not have a noticeable impact on the scalability performance.

In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA.