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Software has emerged as a significant part of many domains, including financial service platforms, social networks and vehicle control. Standards organizations have responded to this by creating regulations to address issues such as safety and privacy. In this context, compliance of software with standards has emerged as a key issue. For software development organizations, compliance is a complex...
When manually testing Web sites humans can go with vague, yet general instructions, such as "add the product to shopping cart and proceed to checkout". Can we teach a robot to follow such instructions as well?In this paper I present a novel model, called semantic usage patterns which allows us to capture the general topics behind the individual steps of interactions. These models can be...
In state-of-the-art Software Transactional Memory (STM) systems, threads carry out the execution of transactions as non-interruptible tasks. Hence, a thread can react to the injection of a higher priority transactional task and take care of its processing only at the end of the currently executed transaction. In this article we pursue a paradigm shift where the execution of an in-memory transaction...
Deals with the problem of Russia's military-industrial complex economic influence assessment. The trends of the military-industrial complex changes under the new normal inducing its structural and conceptual transformation are identified. The military-industrial complex definition is brought up to date afterwards. The article lastly presents the results of the Russian military-industrial complex economic...
Keeping pace with the market requires software to be shipped quickly, in rapid release cycles with a strong feedback loop. Managing and controlling these changes become complex, especially in the mobile world. In mobile applications, unlike in the Web world, the user chooses whether to download updates or to ignore them, and rollbacks are typically not an option. This paper describes a centralized...
Teachers today spend a lot of time grading students' work. Yet many times students feel under-appreciated and they lack understanding of the grading criteria, which leads to no improvement of their learning through time. This paper presents a perspective on automatic evaluations of students' work with the use of gamified peer assessment. Using peer assessment, students not only learn what was correct...
The article treats with the integration of an elaborate Lexicon Extended Language which is denoted all in the interconnected reuse process. The eLEL is a lexicon rich in information and allows to describe a software component such as: its identification, its notion, its behavior and its different characteristic. From this lexicon, we proposed a reused environment that included three processes, namely...
Software development teams apply security practices to prevent vulnerabilities in the software they ship. However, vulnerabilities can be difficult to find, and security practices take time and effort. Stakeholders can better guide software development if they have empirical data on how security practices are applied by development teams. The goal of this paper is to inform managers and developers...
Recurrent neural networks are represented as non-linear models of dynamic systems. This kind of neural networks is divided into two groups, which are globally and locally recurrent neural networks. Some types are distinguished among globally recurrent networks. The major approximation properties and features of every distinguished type are emphasized. The represented analysis is useful for choosing...
We present an original concept for patch generation: we propose to do it directly in production. Our idea is to generate patches on-the-fly based on automated analysis of the failure context. By doing this in production, the repair process has complete access to the system state at the point of failure. We propose to perform live regression testing of the generated patches directly on the production...
The application of Information Retrieval (IR) techniquesto software traceability link recovery has been the focusof many studies. These studies have formulated the task ofestablishing valid trace links between two types of softwareartifacts as a retrieval problem, where one type of artifacts isselected as the set of queries and the other as the corpus. Previouswork selected the sets of queries and...
This position paper addresses the issue of startups and technical debt. Early stage startups condition makes creating technical debt an almost mandatory decision. Not managing technical debt can be deadly for a startup as fast product iteration cycle is necessary. We here introduce a technique for managing technical debt based on Visual Thinking. The technique addresses the problem of knowing how...
As a popular topic model, Probabilistic Latent Semantic Analysis (PLSA) has been widely applied in text clustering due to its reliability and practicability. While independence assumption contributes to its practicability, it loses the rich local information between words, which in some cases will result in incoherent topics. In this paper, we propose an enhanced PLSA model embedded with word correlation...
Temporal Pooling (TP) is a recent technique for processing temporal events by forming declarative representations of the complete sequences. In this paper, we examine and extend the functionality of the existing TP algorithm from the Hierarchical Temporal Memory (HTM) framework and introduce the Self-Organising Temporal Pooling (SOTP) architecture. The SOTP draws together the Merge Self-Organising...
Deductive logic and its variants enjoy the common property of monotonicity. For tasks such as inductive reasoning and belief revision, this was eventually deemed a serious flaw, prompting attempts to construct non-monotonic versions of logic. With the introduction of the idea of probabilistic reasoning to AI, particularly with the advent of Bayesian networks (BNs), the aforementioned monotonicity...
In recent years, there has been an increasing interest in music generation using machine learning techniques typically used for classification or regression tasks. This is a field still in its infancy, and most attempts are still characterized by the imposition of many restrictions to the music composition process in order to favor the creation of “interesting” outputs. Furthermore, and most importantly,...
Attention mechanism advances the neural machine translation (NMT) by reducing the confusion introduced by irrelevant words in long sentences. However, the confusion caused by ambiguous words hasn't been handled yet and it may be a bottleneck for the NMT model. This paper validates the hypothesis and proposes a simple and flexible framework, which enables the NMT model to only focus on the relevant...
Regression-based tasks have been the forerunner regarding the application of machine learning tools in the context of data mining. Problems related to price and stock prediction, selling estimation, and weather forecasting are commonly used as benchmarking for the comparison of regression techniques, just to name a few. Neural Networks, Decision Trees and Support Vector Machines are the most widely...
Regularization kernel network (RKN) is an effective and widely used kernel method for nonlinear regression analysis. In this paper, I characterize its bias and propose an approach to correct the bias. This leads to a new method called bias corrected regularization kernel network (BCRKN). Theoretical characterizations and simulation studies are used to verify the effectiveness of this bias corrected...
The contextual bandits can be viewed as a generalization of online classification models, where only the chosen class is observed. The selection of learning experts allows to find the best parametrization of an expert during its learning, within a set of predefined parameters, and reduces the bias of the hypothesis space, and hence improves the performances. As the contextual bandits learn, their...
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