1,721,042 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
ECSA 2010 Workshops Summary
Full text linkSince 2004 in St. Andrews (Scotland, U.K.), ECSA the European Conference on Software Architecture (formerly EWSA, the European Workshop on Software Architecture) has been considered as an important meeting point for researchers and practitioners on the topic of software architecture. ECSA has matured from a workshop format to a full software engineering conference in the subfield of software architecture. This year, ECSA has become more ambitious and expanded its scope and schedule up to four full days. The program includes a series of tutorials, a doctoral mentoring program, and four full-day workshops. New and existing software challenges have led to a variety of trends in software architecture research, which makes the conference and workshops more attractive and promotes the discussion on current and emerging topics. Based on the scientific and technical interest of the topics, the innovativeness of workshop topics, and the capacity of the conference workshop program, the workshop co-chairs selected four workshops from the nine submitted proposals. We summarize the aims and goals of each workshop and the contributions accepted for the four workshops: • 2nd International Workshop on Software Ecosystems (EcoSys). Piers Campbell, Faheem Ahmed, Jan Bosch, Sliger Jansen. • 1st International Workshop on Measurability of Security in Software Architectures (MeSSa). Reijo Savola, Teemu Kranstén, Antti Evesti. • 8th Nordic Workshop on Model Driven Software Engineering (NW-MODE). Andrzej Wasowski, Dragos Truscan, Ludwik Kuzniarz. • 1st International Workshop on Variability in Software Product Line Architectures (VARI-ARCH). Alexander Helleboogh, Paris Avgeriou, Nelis Boucke, Patryck Heymans. The ECSA 2010 Workshop co-chairs would like to thanks all workshop organizers for their effort and enthusiasm to attract submission in different software architecture research topics and make the ECSA 2010 workshops a success. © 2010 ACM.status: Publishe
An Empirical Study on the Performance and Energy Usage of Compiled Python Code
No full textPython is a popular programming language known for its ease of learning and extensive libraries. However, concerns about performance and energy consumption have led to the development of compilers to enhance Python code efficiency. Despite the proven benefits of existing compilers on the efficiency of Python code, there is limited analysis comparing their performance and energy efficiency, particularly considering code characteristics and factors like CPU frequency and core count. Our study investigates how compilation impacts the performance and energy consumption of Python code, using seven benchmarks compiled with eight different tools: PyPy, Numba, Nuitka, Mypyc, Codon, Cython, Pyston-lite, and the experimental Python 3.13 version, compared to CPython. The benchmarks are single-threaded and executed on an NUC and a server, measuring energy usage, execution time, memory usage, and Last-Level Cache (LLC) miss rates at a fixed frequency and on a single core. The results show that compilation can significantly enhance execution time, energy and memory usage, with Codon, PyPy, and Numba achieving over 90% speed and energy improvements. Nuitka optimizes memory usage consistently on both testbeds. The impact of compilation on LLC miss rate is not clear since it varies considerably across benchmarks for each compiler. Our study is important for researchers and practitioners focused on improving Python code performance and energy efficiency. We outline future research directions, such as exploring caching effects on energy usage. Our findings help practitioners choose the best compiler based on their efficiency benefits and accessibility.</p
Model-based enhancement of software performance for precision critical systems
Full text linkArchitectural level analysis of a software system for its quality attributes is a proven cost-effective approach. This is particularly significant for performance, which defines multiple aspects of the quality of the system.
In this paper we outline the contribution of a PhD, which provides architecture viewpoint based modeling and analysis support for parallelism and flow latency aspects of the performance, in legacy systems. The main contribution of the PhD includes Parallelism Viewpoint and Flow Latency Viewpoint.
We use the proposed viewpoints to find parallelism and flow latencies specific performance bottlenecks of an industrial case, a precision critical electron microscope software system.
The preliminary results of using Parallelism Viewpoint for our example case show that the viewpoint provides a profound insight into the thread-model of the system, which helps in reducing the excessively used parallelism in the system.sponsorship: Distrinet, Katholieke Universiteit Leuvenstatus: Publishe
Towards quality-centric design and evaluation of big data cyber security analytics systems
Full text linkBig Data Cyber Security Analytics (BDCA) systems are a new breed of software systems that leverage big data technologies to collect, store, and analyse a large volume of security events data for detecting cyber-attacks. To detect sophisticated and complex cyber-attacks, many organizations are rapidly adopting BDCA systems to analyse a large volume of security events data in diverse formats from multiple sources such as network devices, software applications and honeypots. BDCA systems are a complex class of software systems that are expected to fulfil a certain class of quality attributes such as response time, accuracy, and scalability. Given the increasing volume, velocity, and heterogeneity of security events data, BDCA systems present unique design challenges and new research and development opportunities for providing suitable design and evaluation support. However, most of the research efforts have focused on algorithmic solutions (e.g., data filtering and feature selection) for optimizing response time, accuracy, and scalability of BDCA systems. Hence, there is an important need of research efforts aimed at providing suitable design knowledge (e.g., architectural tactics and design guidelines) for BDCA systems. More research efforts also need to be invested in exploiting the optimization opportunities (e.g., selection of components) offered by the architectural design of BDCA systems for optimizing response time, accuracy, and scalability. This thesis aims at contributing to the growing body of design and evaluation knowledge for BDCA systems by gathering/devising, implementing, and evaluating a set of quality-centric design approaches for optimizing response time, accuracy, and scalability of BDCA systems. This thesis advances the domain of BDCA systems’ design and evaluation knowledge by making the following contributions. • Design, conduct, and report a systematic literature review of the state-of-the-art BDCA systems to identify the most important quality attributes and codify architectural tactics for BDCA systems • Quantify the impact of architectural tactics on the accuracy and response time of a BDCA system through a systematically designed experimentation, which leads to the formulation of tactics-specific guidelines for designing BDCA systems • Present and evaluate a design approach for determining an architecture for a BDCA system at design time that offers optimal accuracy and response time • Present and evaluate an architecture-driven adaptation approaches for (re)composing a BDCA system at runtime with a set of components to ensure optimal accuracy and response time in the face of changes in the operating environment of the system • Present and evaluate a scalable fuzzy rule based approach to correlate security event data with the components of a BDCA system for (re)composing a BDCA system at runtime to ensure optimal accuracy and response time in the face of changes in the operating environment of the system. • Investigate the scalability of a BDCA system with the default and modified configuration setting of the underlying big data framework (i.e., Apache Spark) to explore and subsequently exploit configuration setting for optimizing scalabilityThesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202
Improved Labeling of Security Defects in Code Review by Active Learning with LLMs
No full textMining high-quality datasets of security defects is important for cybersecurity. In this paper, we focus on mining a dataset of reviews that discuss potential security defects in code or other artifacts. Mining such datasets often involves labeling, and this is challenging because security defects are rare.We investigate the use of active learning with a fine-tuned large language model to make the mining and labeling of such datasets more effective. Our simulations demonstrate that active learning can increase the effectivity of human annotators by a factor of 13. This means we can produce datasets with 13 times more defects than found in random samples of the same size. We conducted an empirical study on over four million unlabeled reviews from GitHub, showing that active learning increases the effectiveness by a factor bigger than 6. In total, 246 out of 1298 labeled reviews can be identified as discussing security defects. We do not depend on a keyword list for upfront candidate selection but dynamically evolve an LLM for this.Our work holds the potential to inspire future research in this area, resolving rare class and imbalance problems at the root where they appear, by adjusting the mining and labeling of the datasets. Our final dataset and model are publicly available.</p
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
- …
