Hasil untuk "Computer software"

R. Axelrod

T. Chou

M. R. Lamprecht, D. Sabatini, Anne E Carpenter

Careful visual examination of biological samples is quite powerful, but many visual analysis tasks done in the laboratory are repetitive, tedious, and subjective. Here we describe the use of the open-source software, CellProfiler, to automatically identify and measure a variety of biological objects in images. The applications demonstrated here include yeast colony counting and classifying, cell microarray annotation, yeast patch assays, mouse tumor quantification, wound healing assays, and tissue topology measurement. The software automatically identifies objects in digital images, counts them, and records a full spectrum of measurements for each object, including location within the image, size, shape, color intensity, degree of correlation between colors, texture (smoothness), and number of neighbors. Small numbers of images can be processed automatically on a personal computer and hundreds of thousands can be analyzed using a computing cluster. This free, easy-to-use software enables biologists to comprehensively and quantitatively address many questions that previously would have required custom programming, thereby facilitating discovery in a variety of biological fields of study.

Andreas Holzinger

Nathaniel Valière

K. Boudreau

R. P. Amann, D. Waberski

Sohaib Ahmed, Muhammad Zeeshan, David Parsons et al.

This article brings together the concepts of emerging technologies, game-based learning (GBL), and inquiry learning to conduct a research study undertaken in a maritime science museum. Over the last decade, the potential benefits of emerging technologies have enabled game-based inquiry activities in formal and informal pedagogical contexts. The use of ontologies has also grown significantly in representing learning content. In the science museum literature, there are a few applications found wherein ontologies are used for generating adaptive learning content. However, no study has been found in the literature that targets GBL for museum inquiry activities through emerging technologies using an ontology-driven approach. This paper outlines the results and analyses of research conducted on an ontology-driven GBL inquiry application, MUSEON. For evaluation purposes, the M3 evaluation framework was used and tested with 86 random visitors to explore visitors’ perspectives regarding the effectiveness of MUSEON. The results were encouraging as 71.6% of visitors were satisfied with their learning experiences in a game-based environment. Further, the experimental group performed well (74.6% score) in comparison with the control group (56.4% score) during inquiry learning activities about the maritime science museum exhibits.

Jian Liu, Yuxin Chen, Hao Ding

The rapid proliferation of Internet of Things (IoT) devices has dramatically increased the demand for efficient data processing, making caching a critical solution for achieving high-performance and cost-effective storage in edge environments. However, small-scale edge devices often suffer from severe resource constraints. Furthermore, there is a scarcity of academic analyses addressing how various caching algorithms perform in such environments. To bridge this knowledge gap, we have proposed a cache simulation framework, CacheSim, as an open-source software solution for caching evaluation. CacheSim provides comprehensive metrics, including hit rate, performance, CPU usage, and power consumption, offering researchers valuable insights into the efficiency of different caching strategies. Through this platform, we aim to stimulate innovation in caching algorithms, encouraging the development of techniques optimized for the unique challenges posed by edge devices.

Lekshmi Murali Rani

The study of behavioral and social dimensions of software engineering (SE) tasks characterizes behavioral software engineering (BSE);however, the increasing significance of human-AI collaboration (HAIC) brings new directions in BSE by presenting new challenges and opportunities. This PhD research focuses on decision-making (DM) for SE tasks and collaboration within human-AI teams, aiming to promote responsible software engineering through a cognitive partnership between humans and AI. The goal of the research is to identify the challenges and nuances in HAIC from a cognitive perspective, design and optimize collaboration/partnership (human-AI team) that enhance collective intelligence and promote better, responsible DM in SE through human-centered approaches. The research addresses HAIC and its impact on individual, team, and organizational level aspects of BSE.

LI Junwei, LIU Quan, XU Yapeng

As an important research content of hierarchical reinforcement learning,temporal abstraction allows hierarchical reinforcement learning agents to learn policies at different time scales,which can effectively solve the sparse reward problem that is difficult to deal with in deep reinforcement learning.How to learn excellent temporal abstraction policy end-to-end is always a research challenge in hierarchical reinforcement learning.Based on the Option framework,Option-Critic can effectively solve the above problems through policy gradient theory.However,in the process of policy learning,the OC framework will have the degradation problem that the action distribution of the internal option policies becomes very similar.This degradation problem affects the experimental performance of the OC framework and leads to poor interpretability of the Option.In order to solve the above problems,mutual information knowledge is introduced as the internal reward,and an Option-Critic algorithm with mutual information optimization is proposed.The MIOOC algorithm combines the proximal policy Option-Critic algorithm to ensure the diversity of the lower level policies.In order to verify the effectiveness of the algorithm,the MIOOC algorithm is compared with several common reinforcement learning methods in continuous experimental environments.Experimental results show that the MIOOC algorithm can speed up the learning speed of the model,improve its experimental performance,and its Option internal strategy is more discriminative.

XU Xianzhe, CHEN Jingqiang

There has been a significant surge in the number of scientific papers published in recent years,which makes it challen-ging for researchers to keep up with the latest advancements in their fields.To stay updated,researchers often rely on reading the contributions section of papers,which serves as a concise summary of the key research findings.However,it is not uncommon for authors to inadequately present the innovative content of their articles,making it difficult for readers to quickly grasp the essence of the research.To address this issue,we propose a novel task of contribution summarization to automatically generate contribution summaries of scientific papers.One of the challenges of this task is the lack of relevant datasets.Therefore,we construct a scientific contribution summarization corpus(SCSC).Another issue lies in the fact that currently available abstractive or extractive models tend to suffer from either excessive redundancy or a lack of coherence between sentences.To meet the demand of ge-nerating concise and high-quality contribution sentences,we present MSSRsum,a multi-step sentence selecting-and-rewriting model.Experiments show that the proposed model outperforms baselines on SCSC and arXiv datasets.

Musaddiq Al Ali, Masatoshi Shimoda, Marc Naguib

This study explores the integration of computer vision with topology optimization for additive manufacturing, with a focus on maximizing eigenfrequency in a design domain. Utilizing custom-developed photogrammetry software, high-resolution images are processed to generate detailed 3D models, which are subsequently converted to STL files with precision. Adaptive meshing in COMSOL 5.3 Multiphysics, controlled through a MATLAB 2023 API, ensures optimal mesh resolution. Prioritizing resource conservation in extraterrestrial environments, the original volume is reduced by 50% while preserving structural integrity. The design domain undergoes rigorous topology optimization in MATLAB, supported by COMSOL’s advanced FEM simulation. The optimized design exhibits a 57% performance improvement and a 50% weight reduction, maintaining the desired vibration characteristics, validating the efficacy of the modifications. Moreover, the case with an eccentric mass shows a significant 64% increase in eigenfrequency.

LI Yuhang, TAN Ruixiong, CHAI Yunpeng

In cloud databases,there are numerous configuration options,including internal database parameters and virtual machine resource configuration for the environment deployment,which collectively determine the database’s read/write performance and resource consumption.In the cloud environment with elastic resources,users are concerned about both the database’s service performance and resource consumption costs.However,due to the large number of configuration options and rapid workload changes,finding the optimal combination of configurations becomes challenging.To address the online tuning scenario with dynamically changing workloads,this paper proposes CoTune,a fast tuning method for coordinating cloud database resources and parameters.This method focuses on OLTP workloads and iteratively adjusts the configurations of virtual machine resources and database parameters to minimize resource consumption while ensuring service quality.The method introduces several key innovations:firstly,it adopts a three-stage approach within each tuning cycle to adjust resource quotas and database parameters,prioritizing service quality;secondly,it classifies the impact of database parameters on different resources,reducing the search space and enabling rapid parameter adjustments;and finally,it incorporates a reinforcement learning model for database parameter tuning,with a specific reward function designed to quickly obtain reward values and accelerate the tuning frequency.Experimental results demonstrate that,compared to approaches that simultaneously tune resources and parameters or solely focus on resource tuning,the proposed method reduces resource consumption while maintaining service quality.Through rapid iterative tuning,it effectively addresses the challenges posed by workload variations and achieves more efficient resource utilization in dynamic workload environments.

Heye Huang, Yicong Liu, Jinxin Liu et al.

This study presents a general optimal trajectory planning (GOTP) framework for autonomous vehicles (AVs) that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently. Firstly, we employ the fifth-order Bezier curve to generate and smooth the reference path along the road centerline. Cartesian coordinates are then transformed to achieve the curvature continuity of the generated curve. Considering the road constraints and vehicle dynamics, limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate system. Furthermore, in selecting the optimal trajectory, we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’ behavior and summarizing their manipulation characteristics of “seeking benefits and avoiding losses.” Finally, by integrating the idea of receding-horizon optimization, the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility, optimality, and adaptability. Extensive simulations and experiments are performed, and the results demonstrate the framework’s feasibility and effectiveness, which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic participants. Moreover, we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’ manipulation.

Rasmus Ulfsnes, Nils Brede Moe, Viktoria Stray et al.

Generative AI (GenAI) has fundamentally changed how knowledge workers, such as software developers, solve tasks and collaborate to build software products. Introducing innovative tools like ChatGPT and Copilot has created new opportunities to assist and augment software developers across various problems. We conducted an empirical study involving interviews with 13 data scientists, managers, developers, designers, and frontend developers to investigate the usage of GenAI. Our study reveals that ChatGPT signifies a paradigm shift in the workflow of software developers. The technology empowers developers by enabling them to work more efficiently, speed up the learning process, and increase motivation by reducing tedious and repetitive tasks. Moreover, our results indicate a change in teamwork collaboration due to software engineers using GenAI for help instead of asking co-workers which impacts the learning loop in agile teams.

Jhonny Mertz, Ingrid Nunes

Monitoring software systems at runtime is key for understanding workloads, debugging, and self-adaptation. It typically involves collecting and storing observable software data, which can be analyzed online or offline. Despite the usefulness of collecting system data, it may significantly impact the system execution by delaying response times and competing with system resources. The typical approach to cope with this is to filter portions of the system to be monitored and to sample data. Although these approaches are a step towards achieving a desired trade-off between the amount of collected information and the impact on the system performance, they focus on collecting data of a particular type or may capture a sample that does not correspond to the actual system behavior. In response, we propose an adaptive runtime monitoring process to dynamically adapt the sampling rate while monitoring software systems. It includes algorithms with statistical foundations to improve the representativeness of collected samples without compromising the system performance. Our evaluation targets five applications of a widely used benchmark. It shows that the error (RMSE) of the samples collected with our approach is 9-54% lower than the main alternative strategy (sampling rate inversely proportional to the throughput), with 1-6% higher performance impact.

Sandun Dasanayake, Jouni Markkula, Markku Oivo

Context: Successfully addressing stakeholder concerns that are related to software system development and operation is crucial to achieving development goals. The importance of using a systematic approach to addressing these concerns throughout the software development life cycle is growing as more and more systems are employed to handle critical tasks. Objective: The goal of this study is to provide an overview of addressing concerns across the software development life cycle. Method: A systematic mapping study was conducted using a pre-defined protocol. Four digital databases were searched for research literature and primary studies were selected after a three round selection process conducted by multiple researchers. Results: The extracted data are processed and the results are reported from different viewpoints. The results are also analyzed against our research goals. Conclusion: We show that there is a considerable variation in the use of terminologies and addressing concerns in different phases of the software development life cycle.

Pierre-Olivier Côté, Amin Nikanjam, Rached Bouchoucha et al.

Context: An increasing demand is observed in various domains to employ Machine Learning (ML) for solving complex problems. ML models are implemented as software components and deployed in Machine Learning Software Systems (MLSSs). Problem: There is a strong need for ensuring the serving quality of MLSSs. False or poor decisions of such systems can lead to malfunction of other systems, significant financial losses, or even threats to human life. The quality assurance of MLSSs is considered a challenging task and currently is a hot research topic. Objective: This paper aims to investigate the characteristics of real quality issues in MLSSs from the viewpoint of practitioners. This empirical study aims to identify a catalog of quality issues in MLSSs. Method: We conduct a set of interviews with practitioners/experts, to gather insights about their experience and practices when dealing with quality issues. We validate the identified quality issues via a survey with ML practitioners. Results: Based on the content of 37 interviews, we identified 18 recurring quality issues and 21 strategies to mitigate them. For each identified issue, we describe the causes and consequences according to the practitioners' experience. Conclusion: We believe the catalog of issues developed in this study will allow the community to develop efficient quality assurance tools for ML models and MLSSs. A replication package of our study is available on our public GitHub repository

Kashumi Madampe, Rashina Hoda, John Grundy

Background: Requirements changes (RCs) are inevitable in Software Engineering. Research shows that emotional intelligence (EI) should be used alongside agility and cognitive intelligence during RC handling. Objective: We wanted to study the role of EI in-depth during RC handling. Method: We conducted a socio-technical grounded theory study with eighteen software practitioners from Australia, New Zealand, Singapore, and Sri Lanka. Findings: We found causal condition (software practitioners handling RCs), intervening condition (mode of work), causes (being aware of own emotions, being aware of others' emotions), direct consequences (regulating own emotions, managing relationships), extended consequences (sustaining productivity, setting and sustaining team goals), and contingencies: strategies (open and regular communication, tracking commitments and issues, and ten other strategies) of using EI during RC handling. We also found the covariances where strategies co-vary with the causes and direct consequences, and ease/ difficulty in executing strategies co-vary with the intervening condition. Conclusion: Open and regular communication is key to EI during RC handling. To the best of our knowledge, the framework we present in this paper is the first theoretical framework on EI in Software Engineering research. We provide recommendations including a problem-solution chart in the form of causes, direct consequences, and mode of work against the contingencies: strategies for software practitioners to consider during RC handling, and future directions of research.