<abstract><title style='display:none'>Abstract</title> Graph coloring is one of the best known, popular and extensively researched subject in the field of graph theory, having many applications and conjectures, which are still open and studied by various mathematicians and computer scientists along the world. In this paper we present a survey of graph coloring as an important subfield of graph theory, describing various methods of the coloring, and a list of problems and conjectures associated with them. Lastly, we turn our attention to cubic graphs, a class of graphs, which has been found to be very interesting to study and color. A brief review of graph coloring methods (in Polish) was given by Kubale in [32] and a more detailed one in a book by the same author. We extend this review and explore the field of graph coloring further, describing various results obtained by other authors and show some interesting applications of this field of graph theory.</abstract>

Poznań University of Technology, Institute of Computing Science

Institute of Bioorganic Chemistry, Polish Academy of Sciences

A survey of graph coloring - its types, methods and applications

<abstract><title style='display:none'>Abstract</title>The economic order quantity (EOQ) model is a pure economic model in classical inventory control theory. The model is designed to find the order quantity so as to minimize total cost under a deterministic setting. In this study, we revise the standard EOQ model to incorporate sustainability considerations that include environmental and social criteria in addition to the conventional economic considerations. We propose models for a number of different policies and analyze these revised models. Based on our analysis, we show how the triple bottom line considerations of sustainability be appended to traditional cost accounting in EOQ model. We also provide a number of useful insights for decision and policy making practices.</abstract>

EOQ Revisited with Sustainability Considerations

<abstract>
<title style='display:none'>Abstract</title>
With a large variety of communication methods and protocols, many software architects face the problem of choosing the best way for services to share information. For communication technology to be functional and practical, it should enable developers to define a complete set of CRUD methods for the processed data. The research team compared this paper’s most commonly used data transfer protocols and concepts: REST, WebSocket, gRPC GraphQL and SOAP. A set of web servers was implemented in Python, each using one of the examined technologies. Then, the team performed an automated benchmark measuring time and data transfer overhead for a set of defined operations: creating an entity, retrieving a list of 100 entities and fetching details of one entity. Tests were designed to avoid the results being interfered with by database connection or docker-compose environment characteristics. The research team has concluded that gRPC was the most efficient and reliable data transfer method. On the other hand, GraphQL turned out to be the slowest communication method of all. Moreover, its server and client libraries caused the most problems with proper usage in a web server. SOAP did not participate in benchmarking due to limited compatibility with Python and a lack of popularity in modern web solutions.
</abstract>

Comparative Review of Selected Internet Communication Protocols

<abstract><title style='display:none'>Abstract</title>The main aim of the presented study was to check whether the well-established measures concerning the attitude towards humanoid robots are good predictors for the uncanny valley effect. We present a study in which 12 computer rendered humanoid models were presented to our subjects. Their declared comfort level was cross-referenced with the Belief in Human Nature Uniqueness (BHNU) and the Negative Attitudes toward Robots that Display Human Traits (NARHT) scales. Subsequently, there was no evidence of a statistical significance between these scales and the existence of the uncanny valley phenomenon. However, correlations between expected stress level while human-robot interaction and both BHNU, as well as NARHT scales, were found. The study covered also the evaluation of the perceived robots’ characteristic and the emotional response to them.</abstract>

Attitude Towards Humanoid Robots and the Uncanny Valley Hypothesis

<abstract><title style='display:none'>Abstract</title> Selecting the proper set of innovation projects is the vital point of the innovation management in a firm. To make a good decision, managers have to consider many conicting objectives in a highly uncertain environment. In this article, the comprehensive decision procedure based on scenario planning and multiple criteria analysis is presented. The main part of the procedure is a two-step model in which Hurwicz's rule and VIKOR-S method are employed to gain the selection of alternatives. To build up the decision base, the classification of the criteria involved in the innovation project selection into five categories is proposed. </abstract>

Scenario Planning + MCDA Procedure For Innovation Selection Problem

<abstract><title style='display:none'>Abstract</title>Dynamic capacity provisioning is a useful technique for handling the workload variations seen in cloud environment. In this paper, we propose a dynamic provisioning technique for multi-tier applications to allocate resources efficiently using queueing model. It dynamically increases the mean service rate of the virtual machines to avoid congestion in the multi-tier environments. An optimization model to minimize the total number of virtual machines for computing resources in each tier has been presented. Using the supplementary variable and the recursive techniques, we obtain the system-length distributions at pre-arrival and arbitrary epochs. Some important performance indicators such as blocking probability, request waiting time and number of tasks in the system and in the queue have also been investigated. Finally, computational results showing the effect of model parameters on key performance indicators are presented.</abstract>

School of Computer Application, KIIT University, Bhubaneswar - 751024, India

School of Computer Engineering, KIIT University, Bhubaneswar - 751024, India

Dynamic Provisioning and Resource Management for Multi-Tier Cloud Based Applications

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<title style='display:none'>Abstract</title>
Recently, numerous machine learning (ML) techniques have been applied to address database performance management problems, including cardinality estimation, cost modeling, optimal join order prediction, hint generation, etc. In this paper, we focus on query optimizer hints employed by users in their queries in order to mask some Query Optimizer deficiencies. We treat the query optimizer hints, bound to previous queries, as significant additional query metadata and learn to automatically predict which new queries will pose similar performance challenges and should therefore also be supported by query optimizer hints. To validate our approach, we have performed a number of experiments using real-life SQL workloads and we achieved promising results.
</abstract>

Learning From User-Specified Optimizer Hints in Database Systems

<abstract>
<title style='display:none'>Abstract</title>
To address the issue of insufficient accuracy in consumer recommendation systems, a new biased network inference algorithm is proposed based on traditional network inference algorithms. This new network inference algorithm can significantly improve the resource allocation ability of the original one, thereby improving recommendation performance. Then, the performance of this algorithm is verified through comparative experiments with network-based inference algorithms, network inference algorithms with initial resource optimization, and heterogeneous network inference algorithms. The results showed that the accuracy of the new network inference algorithm was 24.5%, which was superior to traditional one. In terms of system performance testing, the recommendation hit rate of the new network inference algorithm increased by 13.97%, which was superior to the other three comparative algorithms. The experimental results indicated that a novel network inference algorithm with bias can improve the performance of consumer recommendation systems, providing new ideas for improving the performance of consumer recommendation systems.
</abstract>

The Differentiation of Residents’ Cultural Consumption Tendency and Consumption Recommendation System Based on Network Inference Algorithm

<abstract>
<title style='display:none'>Abstract</title>
This bibliometric study aims to summarize the academic landscape of non-probabilistic data research, based on an examination of scientific output indexed in Web of Science and Scopus databases. It employs multiple methods to analyse and describe the collected corpus, including co-authorship and keyword co-occurrence networks to investigate patterns of collaboration and predominant research themes. Co-authorship analysis identified several robust research clusters, while keyword later spotlighted key thematic areas in the field. Countries, types of documents, categories, year of publication, citations and other metrics were also produced, and implications discussed. The findings present a structured overview of the non-probabilistic data research landscape, delineating the research trends, prominent authors, and emerging themes.
</abstract>

Theory and Practice on Non-Probabilistic Data and Analysis: a bibliometric review

<abstract>
<title style='display:none'>Abstract</title>
For long-term success, organizations and manufacturing companies must exploit the potential strengths of collective decision making in maintenance management. The maintenance strategy selection issue has been studied in a single decision-maker framework for a long time. This research is one of the first attempts at dealing with the enhancement of maintenance management through the participation of stakeholders in the decision making process. In this context, the author introduces a participatory multi criteria decision model that combines Borda count and PROMETHEE methodology to select the most appropriate maintenance strategy; in accordance with the decision makers’ preferences on a set of strategies evaluated according to conflicting criteria. Therefore, the PROMETHEE II method is used to manage the individual decisions of each stakeholder, while the Borda count is in charge of collectively selecting the best maintenance strategy, taking as a starting point stakeholder’s preferences being established thanks to PROMETHEE II. In the same context, the proposed model was applied to a real scenario: a textile company, and can be easily replicated in other industries.
</abstract>

Hybrid use of Borda count and PROMETHEE method for maintenance strategy selection problem

<abstract>
<title style='display:none'>Abstract</title>
Deep neural networks based image classification systems could suffer from adversarial attack algorithms, which generate input examples by adding deliberately crafted yet imperceptible noise to original input images. These crafted examples can fool systems and further threaten their security. In this paper, we propose to use latent space protect image classification. Specifically, we train a feature searching network to make up the difference between adversarial examples and clean examples with label guided loss function. We name it DefenseFea(input transformation based defense with label guided loss function), experimental result shows that DefenseFea can improve the rate of adversarial examples that achieved a success rate of about 99% on a specific set of 5000 images from ILSVRC 2012. This study plays a positive role in the further investigation of the relationship between adversarial examples and clean examples.
</abstract>

DefenseFea: An Input Transformation Feature Searching Algorithm Based Latent Space for Adversarial Defense

<abstract>
<title style='display:none'>Abstract</title>
Matrix completion is widely used in information science fields such as machine learning and image processing. The alternating direction method of multipliers (ADMM), due to its ability to utilize the separable structure of the objective function, has become an extremely popular approach for solving this problem. But its subproblems can be computationally demanding. In order to improve computational e ciency, for large scale matrix completion problems, this paper proposes an improved ADMM by using convex combination technique. Under certain assumptions, the global convergence of the new algorithm is proved. Finally, we demonstrate the performance of the proposed algorithms via numerical experiments.
</abstract>

An Improved Alternating Direction Method of Multipliers for Matrix Completion

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Foundations of Computing and Decision Sciences

The Journal of Poznan University of Technology

ISSN: 0867-6356 (print version)  Foundations of Computing and Decision Sciences (until 1990 Foundations of Control Engineering) is a quarterly peer-reviewed international...

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ISSN: 0867-6356 (print version)  Foundations of Computing and Decision Sciences (until 1990 Foundations of Control Engineering) is a...