Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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Object-Oriented Build Automation – A Case Study
Get PDFFast and precise build and deployment automation is a fundamental task for every project oriented on rapidly appearing changes. As a rule of thumb, the tools used for this task work as procedural-declarative frameworks – often overlooking the extra requirements for large projects like easy parallelization, precise targeting of specific subsystem or general code readability. In this article we document our findings in build automation as we have abandoned the procedural-declarative approach to object-oriented perspective of our setup environment – all implemented in the .NET build automation framework Cake Frosting. Due to the clear separation of the various layers of our system and our codebase we are able to fire up our new build-deployment routines at ease and at specific part of our ecosystem. As the whole routine is written as a C# console application we can easily manage some aspects of parallel execution (i.e. number of threads) of some tasks which results in great drop of job execution time. To further improve the execution time, we introduce the concept of proof-of-work which is a file that stores the information about the last successful build. Together, all of our concepts resulted in a fast build-deployment routine – as in pessimistic scenario we managed to drop to about 30 % of the original time. We believe that others may benefit from our case study as the concepts proposed here can be easily incorporated to any other project written in .NET (or one that is built using object-oriented command-line application) – though we would not recommend using our approach in small projects (in terms of KLOC)
Comparison of Manual and Automated Feature Engineering for Daily Activity Classification in Mental Disorder Diagnosis
Get PDFMotor activity data allows for analysis of complex behavioral patterns, including the diagnosis of mental disorders, such as depression or schizophrenia. However, the classification of actigraphy signals remains a challenge. The main reasons are small datasets and the need for sophisticated feature engineering. The recent development of AutoML approaches allows for automating feature extraction and selection. In this work, we compare automatic and manual feature engineering for applications in mental health. We also analyze classifier evaluation methods for small datasets. The automated approach results in better classification, as measured with several metrics, and in a shorter, cleaner code, providing software engineering advantages
Finding Effective Compiler Optimization Sequences: A Hybrid Approach
Get PDFThe Optimization Selection Problem is widely known in computer science for its complexity and importance. Several approaches based on machine learning and iterative compilation have been proposed to mitigate this problem. Although these approaches provide several advantages, they have disadvantages that can hinder the performance. This paper proposes a hybrid approach that combines the best of machine learning and iterative compilation. Several experiments were performed using different strategies, metrics and hardware platforms. A thorough analysis of the results reveals that the hybrid approach is a considerable improvement over machine learning and iterative compilation. In addition, the hybrid approach outperforms the best compiler optimization level of LLVM
Optimal Allocation of Charging Stations for Electric Vehicles Using Probabilistic Route Selection
Get PDFElectric vehicles (EVs) are environmentally friendly and are considered to be a promising approach toward a green transportation infrastructure with lower greenhouse gas emissions. However, the limited driving range of EVs demands a strategic allocation of charging facilities, hence providing recharging opportunities that help reduce EV owners' anxiety about their vehicles' range. In this paper, we study a set covering method where self-avoiding walks are utilized to find the most significant locations for charging stations. In the corresponding optimization problem, we derive a lower bound of the number of charging stations in a transportation network to obtain full coverage of the most probable routes. The proposed method is applied to a transportation network of the southern part of Sweden
A Decentralized Authoritative Multiplayer Architecture for Games on the Edge
Get PDFWith the ever growing number of edge devices, the idea of resource sharing systems is becoming more appealing. Multiplayer games are a growing area of interest due to the scalability issues of current client-server architectures. A paradigm shift from centralized to decentralized architectures that would allow greater scalability has gained a lot of interest within the industry and academic community. Research on peer to peer network protocols for multiplayer games was mainly focused on cheat detection. Previously proposed solutions address the cheat detection issues on a protocol level but do not provide a holistic solution for the architecture. Additionally, existing solutions introduce some level of centralization, which inherently introduces single point of failures. We propose a blockchain-based, completely decentralized architecture for edge devices with no single point of failure. Our solution relies on an innovative consensus mechanism based on verifiable delay functions that additionally allows the network to derive verifiable randomness. We present simulation results that show the assignment of players and referees to instances is pseudo-random, which inherently prevents collusion-based cheats and vulnerabilities
More Efficient On-the-Fly Verification Methods of Colored Petri Nets
Get PDFColored Petri Nets (CP-nets or CPNs) are powerful modeling language for concurrent systems. As for CPNs' model checking, the mainstream method is unfolding that transforms a CPN into an equivalent P/T net. However the equivalent P/T net tends to be too enormous to be handled. As for checking CPN models without unfolding, we present three practical on-the-fly verification methods which are all focused on how to make state space generation more efficient. The first one is a basic one, based on a standard state space generation algorithm, but its efficiency is low. The second one is an overall improvement of the first. The third one sacrifices some applicability for higher efficiency. We implemented the three algorithms and validated great efficiency of latter two algorithms through experimental results
Integration of 2D Textural and 3D Geometric Features for Robust Facial Expression Recognition
Get PDFRecognition of facial expressions is critical for successful social interactions and relationships. Facial expressions transmit emotional information, which is critical for human-machine interaction; therefore, significant research in computer vision has been conducted, with promising findings in using facial expression detection in both academia and industry. 3D pictures acquired enormous popularity owing to their ability to overcome some of the constraints inherent in 2D imagery, such as lighting and variation. We present a method for recognizing facial expressions in this article by combining features extracted from 2D textured pictures and 3D geometric data using the Local Binary Pattern (LBP) and the 3D Voxel Histogram of Oriented Gradients (3DVHOG), respectively. We performed various pre-processing operations using the MDPA-FACE3D and Bosphorus datasets, then we carried out classification process to classify images into seven universal emotions, namely anger, disgust, fear, happiness, sadness, neutral, and surprise. Using Support Vector Machine classifier, we achieved the accuracy of 88.5 % and 92.9 % on the MDPA-FACE3D and the Bosphorus datasets, respectively
Variational Algorithms for Workflow Scheduling Problem in Gate-Based Quantum Devices
Get PDFIn this paper we consider the combinatorial optimization problem known as workflow scheduling. We compare three encoding schemes of varying density: one-hot, binary, and domain wall, and test their performance against two well-known hybrid quantum-classical algorithms: Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE). In an attempt to obtain the best results possible, we investigate various parameters of the algorithms and test out other state-of-the-art improvements, such as dedicated QAOA mixers. Ultimately, we prove that, despite its popularity, one-hot encoding is not always the best, and using a denser encoding scheme, such as binary or domain wall, can allow for solving larger instances of workflow scheduling. Additionally, combining the above-mentioned encodings with dedicated QAOA mixers reduces the number of infeasible solutions, leading to better results
What is Your Code Clone Detection and Evolution Research Made Of?
Get PDFOver the past few decades, clone detection and evolution have become a major area of study in software engineering. Clone detection experiments present several challenges to researchers such as accurate data collection, selecting proper code detection algorithms, and understanding clone evolution phenomena. This paper attempts to facilitate clone detection and evolution research by providing a structured and systematic mechanism to conduct experiments. Clone detection experiments usually consist of several tasks such as fetching data from a version control system, performing necessary pre-processing activities, and feeding the data to a clone detection algorithm. Therefore, a particular clone detection experiment can interpret as a meaningful combination of such tasks into a scientific workflow. In this work, the concrete tasks in a code clone detection workflow are referred to as Building Blocks. This paper presents a useful collection of Building Blocks identified based on a systematic literature review, and a conceptual framework of an experimental testbed to facilitate clone detection experiments. The reusability of the Building Blocks was validated using four case studies selected from the literature. The validation results confirm the reusability and the expressiveness of the Building Blocks in new ventures. Besides, the proposed experimental testbed is proven beneficial in conducting and replicating clone detection experiments
Distributed Algorithm for Parallel Edit Distance Computation
Get PDFThe edit distance is the measure that quantifies the difference between two strings. It is an important concept because it has its usage in many domains such as natural language processing, spell checking, genome matching, and pattern recognition. Edit distance is also known as Levenshtein distance. Sequentially, the edit distance is computed by using dynamic programming based strategy that may not provide results in reasonable time when input strings are large. In this work, a distributed algorithm is presented for parallel edit distance computation. The proposed algorithm is both time and space efficient. It is evaluated on a hybrid setup of distributed and shared memory systems. Results suggest that the proposed algorithm achieves significant performance gain over the existing parallel approach