Programming and Computer Software

An experimental work on the analysis of effectiveness of neural network models applied to the classification of adverse drug reactions at the entity level is described. Aspect-level sentiment analysis, which aims to determine the sentimental class of a specific aspect conveyed in user opinions, has been actively studied for more than 10 years. A number of neural network architectures have been proposed. Even though the models based on these architectures have much in common, they differ in certain components. In this paper, the applicability of the neural network models developed for the aspect-level sentiment analysis to the problem of the classification of adverse drug reactions is studied. Extensive experiments on English language texts of biomedical topic, including health records, scientific literature, and social media have been conducted. The proposed models mentioned above are compared with one of the best model based on the support vector machine method and a large set of features.

Data center is the most effective way of providing computational resources to a large number of users. The software-defined model is a modern approach to the creation of the computing infrastructure for the data center, which allows user tasks to be processed in acceptable time and at acceptable cost. This paper formulates the general design requirements for the interagency data center and describes some problems and methods of planning and building software-defined data centers (deployment of computing systems optimized for maximum hardware utilization, software support for different classes of tasks, etc.).

Nowadays, ensuring software security is of paramount importance. Software failures can have significant consequences, and malicious vulnerability exploitation can inflict immense losses. Large corporations pay particular attention to the investigation of computer security incidents. Code-reuse attacks based on return-oriented programming (ROP) are gaining popularity each year and can bypass even modern operating system protection mechanisms. Unlike ordinary shellcode, where instructions are placed sequentially in memory, a ROP chain consists of multiple small instruction blocks (called gadgets) and uses the stack to chain them together. This makes the analysis of ROP exploits more difficult. The main goal of this work is to simplify reverse engineering of ROP exploits. A method for analyzing code-reuse attacks that allows one to split the chain into gadgets, restore the semantics of each particular gadget, and restore the prototypes and parameter values of the system calls and functions invoked during the execution of the ROP chain is proposed. The semantics of each gadget is determined by its parameterized type. Each gadget type is defined by a postcondition (Boolean predicate) that must always be true after the gadget execution. The proposed method was implemented as a software tool and tested on real-world ROP exploits found on the Internet.

This paper is based on a dissertation “Techniques for organizing shared access to distributed memory pages in cloud computing systems” defended at the Igor Sikorsky Kyiv Polytechnic Institute in 2017. The paper describes distributed page processing in Oracle Real Application Clusters (Oracle RAC) and compares it with other well-known processing methods. The comparison includes analysis of different architectures (including shared nothing, shared disk, and replication-based architectures) in terms of SQL query processing and asserts the soundness of the distributed page approach (also known as global cache fusion) to cloud database management systems (DBMSs). As a result of analyzing the global cache fusion approach, the main drawback of Oracle RAC systems—increasing queue problem—is revealed; it causes the impossibility to process queries once their rate exceeds a certain threshold inversely proportional to the packet delivery time between nodes. To eliminate the increasing queue problem when accessing distributed pages, a new access method is proposed that introduces an additional page state—unloading state—which improves the efficiency of distributed page processing by reducing the number of transfers between nodes during hot page processing. In addition to cloud DBMSs, the proposed method can also be used in other cloud systems with page-organized distributed memory architecture.

The Multi-Bulk Synchronous Parallel (Multi-BSP) model is a recently proposed parallel programming model for multicore machines that extends the classic Bulk Synchronous Parallel model. Multi-BSP aims to be a useful model to design algorithms and estimate their running time. This model heavily relies on the right computation of parameters that characterize the hardware. Of course, the hardware utilization also depends on the specific features of the problems and the algorithms applied to solve them. This article introduces a semi-automatic approach for solving problems applying parallel algorithms using the Multi-BSP model. First, the specific multicore computer to use is characterized by applying an automatic procedure. After that, the hardware architecture discovered in the previous step is considered in order to design a portable parallel algorithm. Finally, a fine tuning of parameters is performed to improve the overall efficiency. We propose a specific benchmark for measuring the parameters that characterize the communication and synchronization costs in a particular hardware. Our approach discovers the hierarchical structure of the multicore architecture and compute both parameters for each level that can share data and make synchronizations between computing units. A second contribution of our research is a proposal for a Multi-BSP engine. It allows designing algorithms by applying a recursive methodology over the hierarchical tree already built by the benchmark, focusing on three atomic functions based in a divide-and-conquer strategy. The validation of the proposed method is reported, by studying an algorithm implemented in a prototype of the Multi-BSP engine, testing different parameter configurations that best fit to each problem and using three different high-performance multicore computers.

Content Distribution Networks (CDN) are key for providing worldwide services and content to end-users. In this work, we propose three multiobjective evolutionary algorithms for solving the problem of designing and optimizing cloud-based CDNs. We consider the objectives of minimizing the total cost of the infrastructure (including virtual machines, network, and storage) and the maximization of the quality-of-service provided to end-users. The proposed model considers a multi-tenant approach where a single cloud-based CDN is able to host multiple content providers using a resource sharing strategy. The proposed evolutionary algorithms address the offline problem of provisioning infrastructure resources while a greedy heuristic method is proposed for addressing the online problem of routing contents. The experimental evaluation of the proposed methods is performed over a set of realistic problem instances. Results indicate that the proposed approach is effective for designing and optimizing cloud-based CDNs reducing total costs by up to 10.3% while maintaining an adequate quality of service.

For the circulated programming frameworks evolvement, Object Oriented (OO) approach is utilized by architects with originators in the point of reference period which results in Distributed Object Oriented (DOO) frameworks. The main aspect of DOO frameworks remains as the equipped scattering of programming classes among different hubs. The essential plan of DOO applications has no top-class circulation, henceforth rebuilding must be finished. The DOO programming rebuilding is done by means of a proposed versatile strategy called Neural Network (NN), to strengthen the exhibition further. At first, Class Dependency Graph (CDG) is developed, in which the hubs speak to the classes, and furthermore the associations between the hubs speak to the conditions between the classes. Presently, the components of articles, strategies, factors, lines, and import connected with the classes in the CDG are extricated and given as contributions to the NN for the preparation procedure. Presently, bunching of the prepared highlights is finished by which the OO framework is sectioned into subsystems with low coupling utilizing Class Dependency Based Clustering (CDBC) strategy. Presently, the grouped classes are amassed into bunch diagrams utilizing K‑Medoid bunching method lastly, the mapping is finished with the made parcels to the fixed accessible machines utilizing Recursive K Means grouping in the focused on circulated design. Reenactment results uncovered that the proposed work yields upgraded results in a useful manner contrasted with the current systems.

Smart contracts are a special type of programs running inside a blockchain. Immutable and transparent, they provide means to implement fault-tolerant and censorship-resistant services. Unfortunately, its immutability causes a serious challenge of ensuring that a business logic and implementation is correct upfront, before publishing in a blockchain. Several big accidents have indeed shown that users of this technology need special tools to verify smart contract correctness. Existing automated checkers are able to detect only well known implementation bugs, leaving the question of business logic correctness far aside. In this work, we present a symbolic model-checking technique along with a formal specification method for a subset of Solidity programming language that is able to express both state properties and trace properties; the latter constitutes a weak analogy of temporal properties. We evaluate the proposed technique on the MiniDAO smart contract, a young brother of notorious TheDAO. Our Proof-of-Concept was able to detect a non-trivial error in the business logic of this smart contract in a few seconds.