Software Tasks Research Articles

Effective Noise Reduction in NDR Systems: A Simple Yet Powerful Apriori-Based Approach.

Noise (un-important) alerts are generally considered a major challenge in intrusion detection systems/sensors because they require more analysts to review and may cause disruption to systems that are shut down to avoid the consequences of a compromise. However, in real-world situations, many alerts could be raised for automatic tasks being completed by some software or regular tasks by users doing their daily job. This paper proposes an approach to reduce the number of noise alerts, assuming that frequent long-term security alerts can be considered noise if their frequency is meeting some criteria, such as the minimum occurrence ratio. We prove that to effectively reduce the level of noise alerts in Network Detection and Response (NDR) systems, we are able to use simpler algorithms; sometimes, the answer is in simpler solutions, and not always in complex solutions. We study data from a real customer of a Danish NDR solution and propose an Apriori-based approach to find frequent noisy alerts. Our comparison of the detected noise before and after applying our solution shows high performance in reducing noise alerts for most of the alert types for a real customer. Our experiments show that our method can filter more than 40% of the alerts by setting the minimum occurrences to 70%. Moreover, our results show that we were able to filter out more than 90% for some alert categories.

Exploring Synergies, Differences, and Impacts of Agile and DevOps on Software Development Efficiency

Agile and DevOps are now the preferred methods for satisfying the ever-changing demands for better software. Improvements in efficiency, adaptability, and collaboration are the aims of newly proposed solutions. It examines the impact of these factors on software development efficiency, focussing special attention to the ways in which they differ and complement one another. The focus of DevOps is continuous integration and delivery with operational automation, whereas Agile is on iterative improvement, consumer cooperation, and adaptation. Acquire an understanding of how these methods can be combined or utilised independently with the goal to achieve maximum performance in software tasks. One method that ranks the importance of factors including improvement velocity, quality, cooperation, and automation in relation to their impact on efficiency is the Multivocal Software Factor Prioritisation Development Approach (MSFPDA). By giving these considerations priority, this method integrates the most beneficial features of both methodologies. With the goal to maximise the stability, adaptability, automation, and scalability of both Agile and DevOps, MSFPDA employs comment looping structures, iterative workflows, and continuous monitoring. The simulation analysis is used by the granting studies to evaluate MSFPDA's performance in exceptional improvement circumstances. By identifying and prioritising critical components, MSFPDA can improve software program efficiency, according to this assessment. Blending Agile and DevOps presents a variety of issues, such as managing automation complexity, assuring non-stop delivering without sacrificing agility, and harmonising the team's tradition. This methodology for software enhancement has enormous ability, and the results of the simulations demonstrate how MSFPDA can be utilised in real-world scenarios, improving multiple processes.

A Design Flow Based on Docker and Kubernetes for ROS-based Robotic Software Applications

Human-centered robotic applications are becoming pervasive in the context of robotics and smart manufacturing, and such a pervasiveness is even more expected with the shift to Industry 5.0. The always increasing level of autonomy of modern robotic platforms requires the integration of software applications from different domains to implement artificial intelligence, cognition, and human-robot/robot-robot interaction. Developing and (re)configuring such a multi-domain software to meet functional constraints is a challenging task. Even more challenging is customizing the software to satisfy non-functional requirements such as real-time, reliability, and energy efficiency. In this context, the concept of Edge-Cloud continuum is gaining consensus as a solution to address functional and non-functional constraints in a seamless way. Containerization and orchestration are becoming a standard practice, as they allow for better information flow among different network levels as well as increased modularity in the use of multi-domain software components. Nevertheless, the adoption of such a practice along the design flow, from simulation to the deployment of complex robotic applications by addressing the de facto development standards (e.g., ROS - Robotic Operating System) is still an open problem. We present a design methodology based on Docker and Kubernetes that enables containerization and orchestration of ROS-based robotic SW applications for heterogeneous and hierarchical HW architectures. The methodology aims at (i) integrating and verifying multi-domain components since early in the design flow, (ii) mapping software tasks to containers to minimize the performance and memory footprint overhead, (iii) clustering containers to efficiently distribute load across the edge-cloud architecture by minimizing resource utilization, and (iv) enabling multi-domain verification of functional and non-functional constraints before deployment. The article presents the results obtained with a real case of study, in which the design methodology has been applied to program the mission of a Robotnik RB-Kairos mobile robot in an industrial agile production chain. We have obtained reduced load on the robot’s HW with minimal performance and network overhead, thanks to the optimized distributed system.

A modified method of self-recovery of distributed software in heterogeneous computer systems

The object of research is the distributed computing process in heterogeneous computer systems. The subject of the research is methods of self-healing for distributed software on heterogeneous computer systems. The goal is to increase the efficiency of distributed data processing systems with support for the functional stability of the computing process by developing a modified method of self-healing of distributed software. Tasks: to investigate the existing methods of restoring the distributed computing process, to draw conclusions about their advantages and disadvantages; on the basis of mathematical models of tasks, computing resources and existing methods of resource allocation, develop a modification of the method of self-recovery of distributed software taking into account management strategies, finding the best solution for the selected criteria, reducing energy consumption during the execution of tasks; conduct a number of experiments comparing the developed method with existing ones. Research methods are based on the use of set theory, general systems theory, and simulation modeling theory. The results of the experiments obtained during the simulation of the allocation of software tasks to computing resources in a simulated simulation environment and the simulation of the computing process during self-recovery in case of resource failures confirm the effectiveness of the proposed method. Conclusion: the application of the method in distributed computing control systems does not increase the time the system spends on performing the task in the absence of failures, at the same time, in the presence of failures, it allows to restore the functionality of the software task faster and reduces the execution time by 8–17%, and energy consumption by 7–12%. There is also an increase in efficiency with an increase in the size of the tasks and the probability of failures. The development of technologies for automated or automatic use of methods of resource allocation and self-recovery can be indicated as areas for future research.

A Comparison between the Effectiveness of computerized Cognitive Rehabilitation Training and transcranial Direct Current Stimulation on Dialysis Patients' Executive Functions.

Executive function impairments are among the most common dialysis side effects. The present study aims to compare the efficiency of transcranial Direct Current Stimulation (tDCS) with computerized Cognitive Rehabilitation Training (cCRT) on dialysis patients' executive functions. The present study, a quasi-experimental effort, adopted a pre-test/post-test method that included a control (sham) group. The study sample consisted of 30 participants, selected through the convenience sampling method, and categorized into three groups of cCRT, tDCS, and sham participants. The cCRT participants were asked to complete 8 tasks in Captain's Log MindPower Builder software. The tDCS participants were treated with a 0.06 mA/cm2 current with the anodal electrode on F3 and the cathodal electrode on Fp2. For the sham participants, the electrodes were put on the same regions but there was no current stimulation. The treatment lasted for 10 sessions carried out every other day. The results of MANCOVA showed no significant difference between the sham group and the cCRT group in any of the executive function items. . However, between the sham group and the tDCS group was detected a significant difference in spatial working memory (p \< 0.05) and a marginally significant in cognitive flexibility (p = 0.091). No significant difference was reported between cCRT and tDCS groups in any item. According to the findings of the study, given the efficacy of tDCS on spatial working memory and cognitive flexibility for dialysis patients, it can be used to improve these skills.

A Concrete Syntax Transformation Approach for Software Language Processing

In this paper we present an approach for software language processing tasks such as code generation, reverse-engineering and refactoring, based upon text-to-text (T2T) transformations expressed using the concrete syntax of the source and target software languages. The goal of the approach is to provide simpler and more usable techniques for specifying such tasks, compared to model-to-model (M2M) or model-to-text (M2T) transformation approaches. We evaluate the approach on language processing tasks of domain-specific language (DSL) tool support, software abstraction, model transformation, reverse engineering and program translation, and show that it can be effective for these tasks.

CuMONITOR: Continuous Monitoring of Microarchitecture for Software Task Identification and Classification

The interactions between software and hardware are increasingly important to computer system security. This research collected microprocessor control signal sequences to develop machine learning models that identify software tasks. In contrast with prior work that relies on hardware performance counters to collect data for task identification, this research is based on creating additional digital logic to record sequences of control signals inside a processor’s microarchitecture. The proposed approach considers software task identification in hardware as a general problem, with attacks treated as a subset of software tasks. Three lines of effort are presented. First, a data collection approach is described to extract sequences of control signals labeled by task identity during actual (i.e., non-simulated) system operation. Second, experimental design selects hardware and software configurations to train and evaluate machine learning models. The machine learning models significantly outperform a naïve classifier based on Euclidean distances from class means. Various experiment configurations produced a range of balanced accuracy scores. Third, task classification is addressed using decision boundaries defined with thresholds chosen by an optimization strategy to develop non-neural network classifiers. When implemented in hardware, the non-neural network classifiers could require less digital logic to implement compared to neural network models.

UI semantic component group detection: Grouping UI elements with similar semantics in mobile graphical user interface

Texts, widgets, and images on a UI page do not work separately. Instead, they are partitioned into groups to achieve certain interaction functions or visual information. Existing studies on UI elements grouping mainly focus on a specific single UI-related software engineering task, and their groups vary in appearance and function. In this case, we propose our semantic component groups that pack adjacent text and non-text elements with similar semantics. In contrast to those task-oriented grouping methods, our semantic component group can be adopted for multiple UI-related software tasks, such as retrieving UI perceptual groups, improving code structure for automatic UI-to-code generation, and generating accessibility data for screen readers. To recognize semantic component groups on a UI page, we propose a robust, deep learning-based vision detector, UISCGD, which extends the SOTA deformable-DETR by incorporating UI element color representation and a learned prior on group distribution. The model is trained on our UI screenshots dataset of 1988 mobile GUIs from more than 200 apps in both iOS and Android platforms. The evaluation shows that our UISCGD achieves 6.1% better than the best baseline algorithm and 5.4 % better than deformable-DETR in which it is based.

A multipurpose numerical method for imaging studies and tomographic reconstruction

A multipurpose software, called Revolt, has been developed to fully exploit the imaging capabilities of Triple-GEM 2D cameras for X-rays and neutrons detection. Both tomographic inversion techniques and synthetic data production methods are based on the modeling of a transport matrix between the 2D spatially resolved signal on a detector and the 3D signal emission in the experimental space. The core task of the Revolt software is to provide a transport matrix between the two quantities via a numerical-geometrical approach. The method is based on the analytical evaluation of detector pixels Line Of Sight generated via a Monte Carlo method to include obstacle shading on the detector image. The Revolt implementation and validation are described in this work, which provides a solid base for future application of tomographic inversion techniques in the context of fusion plasma physics.

TeReKG: A temporal collaborative knowledge graph framework for software team recommendation

Successful software development requires a cohesive team with the right mix of technical skills and the ability to collaborate effectively. However, forming a software team that can execute tasks with precision and efficiency requires a deep understanding of each member’s competence, experience, and cooperation history. Previously, automated software team selection has evaluated technical skills, cohesion, and cooperation history. However, the previous method had some limitations. Particularly, local features directly calculated from team members were subjective to the researchers’ views, and the method ignored the temporal aspect of open-source software development. To overcome these limitations, this paper proposes a knowledge-graph software team recommendation framework called TeReKG. This framework encapsulates temporal collaboration patterns and uses a temporal knowledge graph to encode software collaboration history, technical abilities, task dependencies, and project structure. TeReKG was against state-of-the-art team recommendation algorithms using three popular open-source software projects: Moodle, Apache, and Atlassian. The evaluation results show that TeReKG outperforms the state-of-the-art baselines in both single-role and team recommendation tasks. These findings demonstrate that knowledge graph embedding can be effectively utilized in automated recommendation tasks in software engineering. Additionally, this highlights the potential for knowledge graphs to capture global information that can benefit various software development applications, including impact prediction of software repositories, code clone detection, and source code retrieval.

Ontological Modeling and Clustering Techniques for Service Allocation on the Edge: A Comprehensive Framework

Nowadays, we are in a world of large amounts of heterogeneous devices with varying computational resources, ranging from small devices to large supercomputers, located on the cloud, edge or other abstraction layers in between. At the same time, software tasks need to be performed. They have specific computational or other types of requirements and must also be executed at a particular physical location. Moreover, both services and devices may change dynamically. In this context, methods are needed to effectively schedule efficient allocations of services to computational resources. In this article, we present a framework to address this problem. Our proposal first uses knowledge graphs for describing software requirements and the availability of resources for services and computing nodes, respectively. To this end, we proposed an ontology that extends our previous work. Then, we proposed a hierarchical filtering approach to decide the best allocation of services to computational nodes. We carried out simulations to evaluate four different clustering strategies. The results showed different performances in terms of the number of allocated services and node overload.

An Overview of Software-Defined Radio Technology in CubeSat Communications

Small satellites are becoming more popular because they are cost-effective, easy to assemble, and use commercially available parts. However, traditional ground stations that communicate with small satellites require a lot of hardware and are expensive to build. This makes it challenging to get the most out of small satellites' information. Software Defined Radios (SDRs) have been developed to reduce the cost of these ground stations by doing many of the tasks in software. Many universities and organizations are developing SDR ground stations to communicate with satellites in different orbits. Communication with satellites is critical to their development and operation. This paper proposes a ground station that uses SDR technology to communicate with one or more small satellites in Low Earth Orbit (LEO). The station is cost-effective, portable, and easily scaled, allowing it to acquire data from satellites.

ChamelIoT: a tightly- and loosely-coupled hardware-assisted OS framework for low-end IoT devices

The evergrowing Internet of Things (IoT) ecosystem continues to impose new requirements and constraints on every device. At the edge, low-end devices are getting pressured by increasing workloads and stricter timing deadlines while simultaneously are desired to minimize their power consumption, form factor, and memory footprint. Field-Programmable Gate Arrays (FPGAs) emerge as a possible solution for the increasing demands of the IoT. Reconfigurable IoT platforms enable the offloading of software tasks to hardware, enhancing their performance and determinism. This paper presents ChamelIoT, an agnostic hardware operating systems (OSes) framework for reconfigurable IoT devices. The framework provides hardware acceleration for kernel services of different IoT OSes by leveraging the RISC-V open-source instruction set architecture (ISA). The ChamelIoT hardware accelerator can be deployed in a tightly- or loosely-coupled approach and implements the following kernel services: thread management, scheduling, synchronization mechanisms, and inter-process communication (IPC). ChamelIoT allows developers to run unmodified applications of three well-established OSes, RIOT, Zephyr, and FreeRTOS. The experiments conducted on both coupling approaches consisted of microbenchmarks to measure the API latency, the Thread Metric benchmark suite to evaluated the system performance, and tests to the FPGA resource consumption. The results show that the latency can be reduced up to 92.65% and 89.14% for the tightly- and loosely-coupled approaches, respectively, the jitter removed, and the execution performance increased by 199.49% and 184.85% for both approaches.

Simulation and Implementation of Signal Processing for LFM Radar Using DSK 6713

This research aims to propose a comprehensive simulation and implementation methodology for LFM (Linear Frequency Modulated) Radar Signal Processing and its application, using digital signal processing techniques on the DSP Starter Kit (DSK) 6713 board. The motivation behind this study is to develop control software based on MATLAB R14 and SIMULINK to model various system software tasks, including detection, A/D conversion, Fast Fourier Transform (FFT), modulation, accumulation, decision-making, and target detection. The simulations are categorized into two groups: ideal beat frequency and parameterized beat frequency. We introduce several important terminologies for consideration, including pulse compression, SNR, matched filter, Doppler effect, and more. The use of real-time data exchange (RTDX) will facilitate the generation of input data and enable real-time calculations for outputs, leading to the creation of machine code for the DSP chip. This process aims to ensure data verification calculations and enhance the credibility and performance of the proposed methodology. By conducting thorough simulations, verification, and practical testing, the study demonstrates the satisfactory credibility and performance of the developed method. Using this research, we aim to contribute to the advancement of LFM Radar Signal Processing and enable its efficient implementation using digital signal processing techniques on the DSP Starter Kit (DSK) 6713 board.

Real-Time USB Networking and Device I/O

Multicore PC-class embedded systems present an opportunity to consolidate separate microcontrollers as software-defined functions. For instance, an automotive system with more than 100 electronic control units (ECUs) could be replaced with one or, at most, several multicore PCs running software tasks for chassis, body, powertrain, infotainment, and advanced driver assistance system (ADAS) services. However, a key challenge is how to handle real-time device input and output (I/O) and host-level networking as part of sensor data processing and control. A traditional microcontroller would commonly feature one or more Controller Area Network (CAN) buses for real-time I/O. CAN buses are usually absent in PCs, which instead feature higher bandwidth Universal Serial Bus (USB) interfaces. This article shows how to achieve real-time device I/O and host-to-host communication over USB, using suitably written device drivers and a time-aware POSIX-like “tuned pipe” abstraction. This allows developers to establish task pipelines spanning one or more hosts, with end-to-end latency and throughput guarantees for sensor data processing, control, and actuation.

The efficiency of multithreaded computing in casting simulation software

The utilization of computer simulation software for casting process simulation is becoming essential in the advancement of casting technology in aviation and other high-tech engineering fields. With the increase in the number of computational cores in modern CPUs, the use of multi-threaded computations is becoming increasingly relevant. In this study, the efficiency of multi-threaded computations in modeling casting processes was evaluated using finite element method casting simulation software ProCast and PoligonSoft, which utilize parallel computing architectures with distributed (DMP) and shared (SMP) memory, respectively. Computations were performed on Intel and AMD-based computers, varying the number of computational threads from 4 to 32. The calculation efficiency was evaluated by measuring the calculation speed increase in the filling and solidification of GP25 castings made of ML10 alloy, as well as the complex task of filling and solidification modeling nickel superalloy casing castings with radiation heat transfer simulation. The results indicate that the minimum computation time in ProCast software is observed when using 16 computational threads. This pattern holds true for both computing systems (Intel and AMD processors), and increasing the number of threads beyond this point does not make a practical difference. The performance decrease in this scenario can be attributed to the low-performance energy-efficient cores in systems based on Intel processors or the decrease in core frequency and full loading of physical cores in systems based on AMD processors. Multi-threading the modeling task in PoligonSoft software is less efficient than in ProCast, which is a result of the shared-memory architecture used in PoligonSoft. Despite the significant difference in parallel efficiency, the task of GP25 casting solidification in both PoligonSoft and ProCast is solved in a time close enough to be considered sufficient.

Integrating FPGA Acceleration in the DNAssim Framework for Faster DNA-Based Data Storage Simulations

DNA-based data storage emerged in this decade as a promising solution for long data durability, low power consumption, and high density. However, such technology has not yet reached a good maturity level, requiring many investigations to improve the information encoding and decoding processes. Simulations can be key to overcoming the time and the cost burdens of the many experiments imposed by thorough design space explorations. In response to this, we have developed a DNA storage simulator (DNAssim) that allows simulating the different steps in the DNA storage pipeline using a proprietary software infrastructure written in Python/C language. Among the many operations performed by the tool, the edit distance calculation used during clustering operations has been identified as the most computationally intensive task in software, thus calling for hardware acceleration. In this work, we demonstrate the integration in the DNAssim framework of a dedicated FPGA hardware accelerator based on the Xilinx VC707 evaluation kit to boost edit distance calculations by up to 11 times with respect to a pure software approach. This materializes in a clustering simulation latency reduction of up to 5.5 times and paves the way for future scale-out DNA storage simulation platforms.

Towards the Analysis and Completion of Syntactic Structure Ellipsis for Inline Comments

The ellipsis of the syntactic structure is a common phenomenon in ordinary textual documents. Existing studies have found that despite syntactic ellipsis could help avoid repetition of normative documents, it could also, for example, lead to ambiguity and hamper the understandability of document contents. As a fundamental component of software, code comments are generally written by developers in a non-structured way just like normative documents. This naturally inspires us to explore whether syntactic ellipsis is also a common phenomenon in code comments and what potential negative effects would such ellipsis have on software tasks such as code/comments comprehension activities. Such explorations, in our opinion, are expected to facilitate the research on code comments and comments-related software tasks. To this end, we conduct the first large-scale study to explore the syntactic structure ellipsis problem of code comments, with a focus on Java inline comments. Specifically, we construct a data set of 1,000 Java projects with 1,307,457 inline comments and associated codes. Based on this data set, we first study the prevalence of syntactic structure ellipsis in inline comments. We find that syntactic structure ellipsis is quite common in inline comments where 83.6% comments have structure ellipsis (such as subject/predicate omissions). Then, we investigate the effects of syntactic structure ellipsis on code/comment understanding activities. As a result, we find that there indeed exists a negative relationship between them, with a medium effect size. Based on these findings, we further propose neural network based approaches to complete the ellipsis parts for the inline comments. With our approach, we could achieve: 1) a medium improvement in assisting code/comment understanding activities, and 2) a substantial improvement of 11.3% in comment-assisted code abbreviation extension task.

AI and Arduino –Based Smart Home Robot Assistant

The creation of genuine conversation between humans and machines is the primary objective of artificial intelligence (AI). To increase the connection between humans and machines, numerous IT firmshave developed various types of Virtual Personal Assistants (VPAs) based on their applications and use cases, including Microsoft's Cortana, Apple's Siri, Amazon Alexa, and Google Assistant. Using Python, we have developed our own virtual personal assistant that is only available for Windows and can be accessed on any Windows explorer version, including Windows 7, 8, and 10. Python is used as a programming language because it has several important libraries that are used to carry out tasks. Our virtual assistant can identify and respond to the user's voice utilizing python installation packages. Because of how simple it is, home automation has become considerably more popular and in demand in recent years. Recently, work in this area has taken place, and a few systems that either use voice assistants or applications to manage electrical appliances have been developed. To administer the smart home and its various apps, an embedded system is employed as a local server. The findings show a variety of activities that the robot can take to answer the user's questions. The activities performed can be voice responses, mechanical moves, and motions, software tasks, or simply text messages.

Configuration Information System for online processing and data monitoring in the NICA experiments

The collection, storage and processing of experimental data are an integral part of modern high-energy physics experiments. The Configuration Information System (CIS) based on the Configuration Database (ConfigDb) is an essential element of a complex of information systems developed for the NICA experiments. The CIS is used to store and provide data on the configuration of the experiment hardware and software systems when collecting data from the detectors in online mode. The ConfigDb stores both a set of various configuration parameters and descriptions of a sequence of online software tasks to be started and run during experiment sessions. The system allows loading configuration information, activating hardware setups and launching all necessary software applications with the required parameters on the specified distributed nodes. The CIS architecture mainly contains the ConfigDb and Configuration Manager, which uses the Dynamic Deployment System API for managing tasks and providing intercommunication. The manager loads configuration data from the database, starts software tasks in a described sequence and supports task process management. The client-server architecture of the CIS is presented. The client has been implemented as a Web service to manage configuration parameters by users.