Abstract E-learning provides an individualized course path which provides a user the convenience of pacing ones way through a particular course. One of the key privileges it offers is the flexibility of the course and consistent delivery of the material. The proposed system predicts the user’s domain knowledge with the help of the lectures knowledge that a particular user completes and also a lecture’s

In this paper, we consider the final boundary value problem for non-local Kirchhoff’s model of parabolic type with discrete random noise. We first discuss the instability of solutions. Then we present the regularized solution by the trigonometric method in non-parametric regression associated with the truncated expansion method. In addition, under a prior assumption on the exact solution, the convergence

Abstract Frequent itemset mining is considered a popular tool to discover knowledge from transactional datasets. It also serves as the basis for association rule mining. Several algorithms have been proposed to find frequent patterns in which the apriori algorithm is considered as the earliest proposed. Apriori has two significant bottlenecks associated with it: first, repeated scanning of input dataset

Performance analysis of Java applications requires a deep understanding of the Java virtual machine and the system on which it is running. An unexpected latency can be caused by a bug in the source code, a misconfiguration or an external factor like CPU or disk contention. Existing tools have difficulties finding the root cause of some latencies because they do not efficiently collect performance data

The Static Single Assignment (SSA) form is an intermediate representation used for the analysis and optimization of programs in modern compilers. The ϕ-function placement is the most computationally expensive part of converting any program into its SSA form. The most widely-used ϕ-function placement algorithms are based on computing dominance frontiers (DF). However, this kind of algorithms works under

Abstract When engineering complex systems, models are typically used to represent various systems aspects. These models are often heterogeneous in terms of modeling languages, provenance, number or scale. As a result, the information actually relevant to engineers is usually split into different kinds of interrelated models. To be useful in practice, these models need to be properly integrated to provide

Abstract Data of sequential nature arise in many application domains in the form of, e.g., textual data, DNA sequences, and software execution traces. Different research disciplines have developed methods to learn sequence models from such datasets: (i) In the machine learning field methods such as (hidden) Markov models and recurrent neural networks have been developed and successfully applied to

Rough polyharmonic splines (RPS) is a variational method which has recently been developed for linear divergence-form operators with arbitrary rough coefficients. RPS method does not rely on concepts of ergodicity or scale-separation, but on compactness properties of the solution space. In this paper, we extend RPS approach method for the optimal control problem governed by parabolic systems with rough

Software developers are increasingly using machine learning APIs to implement 'intelligent' features. Studies show that incorporating machine learning into an application increases technical debt, creates data dependencies, and introduces uncertainty due to non-deterministic behaviour. However, we know very little about the emotional state of software developers who deal with such issues. In this paper

Automatic source code analysis in key areas of software engineering, such as code security, can benefit from Machine Learning (ML). However, many standard ML approaches require a numeric representation of data and cannot be applied directly to source code. Thus, to enable ML, we need to embed source code into numeric feature vectors while maintaining the semantics of the code as much as possible. code2vec

The delivery of a framework in place for secure application development is of real value for application development teams to integrate security into their development life cycle, especially when a mobile or web application moves past the scanning stage and focuses increasingly on the remediation or mitigation phase based on static application security testing (SAST). For the first time, to the author's

One of the biggest strength of many modern programming languages is their rich open source package ecosystem. Indeed, modern language-specific package managers have made it much easier to share reusable code and depend on components written by someone else (often by total strangers). However, while they make programmers more productive, such practices create new health risks at the level of the ecosystem:

This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening. The main contributions described here are threefold: 1) The design of an advanced visual interface for multimodal diagnosis of breast cancer (BreastScreening); 2) Insights from the field comparison of single vs multimodality screening of breast cancer diagnosis

As collaborative coding environments make it easier to contribute to software projects, the number of developers involved in these projects keeps increasing. This increase makes it more difficult for code reviewers to deal with buggy contributions. Collaborative environments like GitHub provide a rich source of data on developers' contributions. Such data can be used to extract information about developers

Standard automatic methods for recognizing problematic development commits can be greatly improved via the incremental application of human+artificial expertise. In this approach, called EMBLEM, an AI tool first explore the software development process to label commits that are most problematic. Humans then apply their expertise to check those labels (perhaps resulting in the AI updating the support

A major determinant of the quality of software systems is the quality of their requirements, which should be both understandable and precise. Most requirements are written in natural language, which is good for understandability but lacks precision. To make requirements precise, researchers have for many years advocated the use of ``formal'' approaches. Many such approaches exist, differing in their

As machine learning is applied more widely, data scientists often struggle to find or create end-to-end machine learning systems for specific tasks. The proliferation of libraries and frameworks and the complexity of the tasks have led to the emergence of "pipeline jungles" - brittle, ad hoc ML systems. To address these problems, we introduce the Machine Learning Bazaar, a new framework for developing

Automatic source code summarization is the task of generating natural language descriptions for source code. Automatic code summarization is a rapidly expanding research area, especially as the community has taken greater advantage of advances in neural network and AI technologies. In general, source code summarization techniques use the source code as input and outputs a natural language description

Automatic source code analysis in key areas of software engineering, such as code security, can benefit from Machine Learning (ML). However, many standard ML approaches require a numeric representation of data and cannot be applied directly to source code. Thus, to enable ML, we need to embed source code into numeric feature vectors while maintaining the semantics of the code as much as possible. code2vec

Weak memory presents a new challenge for program verification and has resulted in the development of a variety of specialised logics. For C11-style memory models, our previous work has shown that it is possible to extend Hoare logic and Owicki-Gries reasoning to verify correctness of weak memory programs. The technique introduces a set of high-level assertions over C11 states together with a set of

An E-graph is a data structure that can efficiently encode the congruence closure of an equivalence relation over many expressions. E-graphs are used in theorem provers for communicating equalities among theories. Another strand of work proposed their use for rewrite-driven program optimization with a technique called equality saturation. In this work, we expand on the idea of equality saturation and

Decidability and synthesis of inductive invariants ranging in a given domain play an important role in many software and hardware verification systems. We consider here inductive invariants belonging to an abstract domain $A$ as defined in abstract interpretation, namely, ensuring the existence of the best approximation in $A$ of any system property. In this setting, we study the decidability of the

Modern Hardware Description Languages (HDLs) such as SystemVerilog or VHDL are, due to their sheer complexity, insufficient to transport designs through modern circuit design flows. Instead, each design automation tool lowers HDLs to its own Intermediate Representation (IR). These tools are monolithic and mostly proprietary, disagree in their implementation of HDLs, and while many redundant IRs exists

A major determinant of the quality of software systems is the quality of their requirements, which should be both understandable and precise. Most requirements are written in natural language, which is good for understandability but lacks precision. To make requirements precise, researchers have for many years advocated the use of ``formal'' approaches. Many such approaches exist, differing in their

Several emerging technologies for byte-addressable non-volatile memory (NVM) have been considered to replace DRAM as the main memory in computer systems during the last years. The disadvantage of a lower write endurance, compared to DRAM, of NVM technologies like Phase-Change Memory (PCM) or Ferroelectric RAM (FeRAM) has been addressed in the literature. As a solution, in-memory wear-leveling techniques

This paper presents the basic concepts and the module structure of the Distributed and Unified Numerics Environment and reflects on recent developments and general changes that happened since the release of the first Dune version in 2007 and the main papers describing that state [1, 2]. This discussion is accompanied with a description of various advanced features, such as coupling of domains and cut

Translating biomedical ontologies is an important challenge, but doing it manually requires much time and money. We study the possibility to use open-source knowledge bases to translate biomedical ontologies. We focus on two aspects: coverage and quality. We look at the coverage of two biomedical ontologies focusing on diseases with respect to Wikidata for 9 European languages (Czech, Dutch, English

We consider the problem of better modeling query-cluster interactions to facilitate query focused multi-document summarization (QFS). Due to the lack of training data, existing work relies heavily on retrieval-style methods for estimating the relevance between queries and text segments. In this work, we leverage distant supervision from question answering where various resources are available to more

A COVID-19 pandemic has already proven itself to be a global challenge. It proves how vulnerable humanity can be. It has also mobilized researchers from different sciences and different countries in the search for a way to fight this potentially fatal disease. In line with this, our study analyses the abstracts of papers related to COVID-19 and coronavirus-related-research using association rule text

The main goal of this research is to produce a useful software for United Nations (UN), that could help to speed up the process of qualifying the UN documents following the Sustainable Development Goals (SDGs) in order to monitor the progresses at the world level to fight poverty, discrimination, climate changes. In fact human labeling of UN documents would be a daunting task given the size of the

A recent introduction of Transformer deep learning architecture made breakthroughs in various natural language processing tasks. However, non-English languages could not leverage such new opportunities with the English text pre-trained models. This changed with research focusing on multilingual models, where less-spoken languages are the main beneficiaries. We compare pre-trained multilingual BERT

Recently several datasets have been proposed to encourage research in Question Answering domains where commonsense knowledge is expected to play an important role. Recent language models such as ROBERTA, BERT and GPT that have been pre-trained on Wikipedia articles and books have shown reasonable performance with little fine-tuning on several such Multiple Choice Question-Answering (MCQ) datasets.

In this work, we formulate the NER task as a multi-answer question answering (MAQA) task and provide different knowledge contexts, such as entity types, questions, definitions and definitions with examples. This formulation (a) enables systems to jointly learn from varied NER datasets, enabling systems to learn more NER specific features, (b) can use knowledge-text attention to identify words having

Perhaps the simplest type of multilingual transfer learning is instance-based transfer learning, in which data from the target language and the auxiliary languages are pooled, and a single model is learned from the pooled data. It is not immediately obvious when instance-based transfer learning will improve performance in this multilingual setting: for instance, a plausible conjecture is this kind

Scholarly resources, just like any other resources on the web, are subject to reference rot as they frequently disappear or significantly change over time. Digital Object Identifiers (DOIs) are commonplace to persistently identify scholarly resources and have become the de facto standard for citing them. We investigate the notion of persistence of DOIs by analyzing their resolution on the web. We derive

Apache Flink is an open-source system for the scalable processing of batch and streaming data. The Flink does not natively support the efficient processing of spatial data streams, which is the requirement of many applications dealing with the spatial data. Besides Flink, other scalable spatial data processing platforms including GeoSpark, Spatial Hadoop, GeoMesa and Parallel Secondo do not support

Sparse and irregularly sampled multivariate time series are common in clinical, climate, financial and many other domains. Most recent approaches focus on classification, regression or forecasting tasks on such data. In forecasting, it is necessary to not only forecast the right value but also to forecast when that value will occur in the irregular time series. In this work, we present an approach

Missing numerical values are prevalent, e.g., owing to unreliable sensor reading, collection and transmission among heterogeneous sources. Unlike categorized data imputation over a limited domain, the numerical values suffer from two issues: (1) sparsity problem, the incomplete tuple may not have sufficient complete neighbors sharing the same/similar values for imputation, owing to the (almost) infinite

RDF (Resource Description Framework) is a standard language to represent graph databases. Query languages for RDF databases usually include primitives to support path queries, linking pairs of vertices of the graph that are connected by a path of labels belonging to a given language. Languages such as SPARQL include support for paths defined by regular languages (by means of Regular Expressions). A

Today's data analytics displays an overwhelming diversity along many dimensions: data types, platforms, hardware acceleration, etc. As a result, system design often has to choose between depth and breadth: high efficiency for a narrow set of use cases or generality at a lower performance. In this paper, we pave the way to get the best of both worlds: We present Modularis-an execution layer for data

Relational databases are valuable resources for learning novel and interesting relations and concepts. In order to constraint the search through the large space of candidate definitions, users must tune the algorithm by specifying a language bias. Unfortunately, specifying the language bias is done via trial and error and is guided by the expert's intuitions. We propose AutoBias, a system that leverages

Abstract A blockchain is resistant to modification based on the consensus of the network majority, which requires a large amount of communication among distributed nodes. Existing data dissemination protocol solves the wrong block problem also at a high communication cost. This paper investigates the error-correcting tamper-proofing data storage problem and proposes a three-layer framework to store

Abstract The high possibility of attack is greatly attributed to the broadcast nature of the communication medium in which the sensor nodes operate; this makes eavesdropping of messages possible on the network. This paper proposes a privacy-aware sociopsychological trust and reputation management (PSTRM) model. The paper presents a model that models the ability of a node as a continuum based on the

Abstract In this paper, we propose PAIRS3: a possibilistic classification approach based on artificial immune recognition system (AIRS) and the possibility theory. PAIRS3 is applied to address shortcomings in student attainment rates of course learning outcomes by predicting effective remedial actions through learning from assessment rubrics instances. For most of assessment rubric instances, it is

Abstract Motion simulators have been of significant importance for the aviation sector in training pilots. However, the present boom in the utilization of robotics for virtual reality (VR) gaming has given rise to a new application of motion simulators. Motion cueing algorithms (MCA) play a key role in mapping the motions from a gaming scenario to the workspace of a simulator. This workspace is small

Abstract Handling large-scale software variability is still a challenge for many organizations. After decades of research on variability management concepts, many industrial organizations have introduced techniques known from research, but still lament that pure textbook approaches are not applicable or efficient. For instance, software product line engineering—an approach to systematically develop

BPMN collaboration models are commonly used to describe the behaviour and interactions of processes in an inter-organisational context. An important role in this kind of models is played both by the message flow, and by sub-processes. The interplay between these features of BPMN models can conceal subtle or unexpected effects, which makes the design activity error-prone, thus leading to the possible

Recently, artificial intelligence techniques have been widely applied to address various specialized tasks in software engineering, such as code generation, defect identification, and bug repair. Despite the diffuse usage of static analysis tools in automatically detecting potential software defects, developers consider the large number of reported alarms and the expensive cost of manual inspection

Finite element analysis (FEA), introduced more than half a century ago, requires a (qualified) analyst to generate the necessary input data, verify the output and post process the analysis results for a meaningful conclusion. The required expertise and labor efforts have precluded the use of FEA in daily medical practice by orthopedic surgeons for example. Patient-specific analyses of the mechanical

Over the past years, more secondary (Systematic Literature Reviews and Systematic Mappings) and tertiary studies have been conducted. Their conduction is considered a quite large task and labor-intensive since it involves a detailed process including a protocol development, which is one of the most challenging phase reported by the software engineering research community. In this scenario, we propose

Advancements in mobile device computing power have made interactive web applications possible, allowing the web browser to render contents dynamically and support low-latency communication with the server. This comes at a cost to the developer, who now needs to reason more about correctness of communication patterns in their application as web applications support more complex communication patterns

This paper describes a static verification framework for the message-passing fragment of the Go programming language. Our framework extracts models that over-approximate the message-passing behaviour of a program. These models, or behavioural types, are encoded in Promela, hence can be efficiently verified with Spin. We improve on previous works by verifying programs that include communication-related

As the popularity of Smart Televisions (TVs) and interactive Smart TV applications (apps) has recently grown, the usability of these apps has become an important quality characteristic. Previous studies examined Smart TV apps from a usability perspective. However, these methods are mainly manual, and the potential of automated model-based testing methods for usability testing purposes has not yet been

In this work, we apply anomaly detection to source code and bytecode to facilitate the development of a programming language and its compiler. We define anomaly as a code fragment that is different from typical code written in a particular programming language. Identifying such code fragments is beneficial to both language developers and end users, since anomalies may indicate potential issues with

Software engineers have a wide variety of tools and techniques that can help them improve the quality of their code, but still, a lot of bugs remain undetected. In this paper we build on the idea that if a particular fragment of code is changed too often, it could be caused by some technical or architectural issues, therefore, this fragment requires additional attention from developers. Most teams

Session type systems have been given logical foundations via Curry-Howard correspondences based on both intuitionistic and classical linear logic. The type systems derived from the two logics enforce communication correctness on the same class of pi-calculus processes, but they are significantly different. Caires, Pfenning and Toninho informally observed that, unlike the classical type system, the

Advancements in mobile device computing power have made interactive web applications possible, allowing the web browser to render contents dynamically and support low-latency communication with the server. This comes at a cost to the developer, who now needs to reason more about correctness of communication patterns in their application as web applications support more complex communication patterns

Duality is a central concept in the theory of session types. Since a flaw was found in the original definition of duality for recursive types, several other definitions have been published. As their connection is not obvious, we compare the competing definitions, discuss tradeoffs, and prove some equivalences. Some of the results are mechanized in Agda.

This paper describes a static verification framework for the message-passing fragment of the Go programming language. Our framework extracts models that over-approximate the message-passing behaviour of a program. These models, or behavioural types, are encoded in Promela, hence can be efficiently verified with Spin. We improve on previous works by verifying programs that include communication-related

The original paper on Mixed Sessions introduce the side A of the tape: there is an encoding of classical sessions into mixed sessions. Here we present side B: there is a translation of (a subset of) mixed sessions into classical session types. We prove that the translation is a minimal encoding, according to the criteria put forward by Kouzapas, P\'erez, and Yoshida.