<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>Convolutional neural networks (CNN) is a contemporary technique for computer vision applications, where pooling implies as an integral part of the deep CNN. Besides, pooling provides the ability to learn invariant features and also acts as a regularizer to further reduce the problem of overfitting. Additionally, the pooling techniques significantly reduce the computational cost and training time of networks which are equally important to consider. Here, the performances of pooling strategies on different datasets are analyzed and discussed qualitatively. This study presents a detailed review of the conventional and the latest strategies which would help in appraising the readers with the upsides and downsides of each strategy. Also, we have identified four fundamental factors namely network architecture, activation function, overlapping and regularization approaches which immensely affect the performance of pooling operations. It is believed that this work would help in extending the scope of understanding the significance of CNN along with pooling regimes for solving computer vision problems.</abstract>

Implications of Pooling Strategies in Convolutional Neural Networks: A Deep Insight

<abstract><title style='display:none'>Abstract</title> The management of distributed systems infrastructure requires dedicated set of tools. The one tool that helps visualize current operational state of all systems and notify when failure occurs is available within monitoring solution. This paper provides an overview of monitoring approaches for gathering data from distributed systems and what are the major factors to consider when choosing a monitoring solution. Finally we discuss the tools currently available on the market.</abstract>

Tools for Distributed Systems Monitoring

<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>In this paper, a project and implementation of the parallel RANSAC algorithm in CUDA architecture for point cloud registration are presented. At the beginning, a serial state of the art method with several heuristic improvements from the literature compared to basic RANSAC is introduced. Subsequently, its algorithmic parallelization and CUDA implementation details are discussed. The comparative test has proven a significant program execution acceleration. The result is finding of the local coordinate system of the object in the scene in the near real-time conditions. The source code is shared on the Internet as a part of the Heuros system.</abstract>

Parallel RANSAC for Point Cloud Registration

<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>
This paper is devoted to the study of a multi-criteria scheduling problem on unrelated processors with machines’ learning effect, with the goal of minimizing makespan, machine cost and maximal flow-time simultaneously, which is an NP-hard problem. An improved particle swarm optimization algorithm equipped with the overloaded operators, as well as a procedure of Levy flight, is proposed to generate the Pareto-optimal solutions. The experimental results show that the Levy flight strategy can effectively improve the performance of the algorithm, which can generate more non-dominated solutions, and slightly reduce the execution time of the process.
</abstract>

Multi-criteria Scheduling in Parallel Environment with Learning Effect

<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

<abstract>
<title style='display:none'>Abstract</title>
It is well known that airport security is an important component of homeland security, since airports are highly vulnerable to terrorist attacks. In order to improve the overall security of the domestic airport network, some work studied the budget allocation of domestic airport network protection. They established a minimax optimization model and designed an exact cutting plane algorithm to solve the problem. However, the exact algorithm can not solve large-scale problems in an acceptable time. Hence, this paper designs a heuristic cutting plane algorithm for solving budget allocation of large-scale domestic airport network protection. Finally, numerical experiments are carried out to demonstrate the feasibility and effectiveness of the new algorithm.
</abstract>

A Heuristic Cutting Plane Algorithm For Budget Allocation of Large-scale Domestic Airport Network Protection

<abstract>
<title style='display:none'>Abstract</title>
The online 3D bin packing problem(3D-BPP) is widely used in the logistics industry and is of great practical significance for promoting the intelligent transformation of the industry. The heuristic algorithm relies too much on manual experience to formulate more perfect packing rules. In recent years, many scholars solve 3D-BPP via deep reinforcement learning(DRL) algorithms. However, they ignore many skills used in manual packing, one of the most important skill is workers put the item aside if the item is packed improperly. Inspired by this skill, we propose a DRL algorithm with a buffer zone. Firstly, we define the wasted space and the buffer zone. And then, we integrate them into the DRL algorithm framework. Importantly, we compare the bin utilization with di erent thresholds of wasted space and di erent buffer zone sizes. Experimental results show that our algorithm outperforms existing heuristic algorithms and DRL algorithms.
</abstract>

Online Three-Dimensional Bin Packing: A DRL Algorithm with the Buffer Zone

<abstract>
<title style='display:none'>Abstract</title>
Automatic crack detection in construction facilities is a challenging yet crucial task. However, existing deep learning (DL)-based semantic segmentation methods for this field are based on fully supervised learning models and pixel-level manual annotation, which are time-consuming and labor-intensive. To solve this problem, this paper proposes a novel crack semantic segmentation network using weakly supervised approach and mixed-label training strategy. Firstly, an image patch-level classifier of crack is trained to generate a coarse localization map for automatic pseudo-labeling of cracks combined with a thresholding-based method. Then, we integrated the pseudo-annotated with manual-annotated samples with a ratio of 4:1 to train the crack segmentation network with a mixed-label training strategy, in which the manual labels were assigned with a higher weight value. The experimental data on two public datasets demonstrate that our proposed method achieves a comparable accuracy with the fully supervised methods, reducing over 65% of the manual annotation workload.
</abstract>

Automatic Crack Detection Using Weakly Supervised Semantic Segmentation Network and Mixed-Label Training Strategy

<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

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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...