Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Semantic and Strutural Features Learning based on Graph for Just-in-Time Defect Prediction

: Change-level defect prediction which is also known as just-in-time defect prediction, will not only improve the software quality and reduce costs, but also give more accurate and earlier feedback to developers than traditional ﬁle-level defect prediction. To build just-in-time defect prediction models, most existing approaches focused on using manually traditional features (metrics of code change), and exploited different machine learning. However, those approaches fail to capture the semantic differences between code changes and the dependency information within programs; and consequently do not cover all types of bugs. Such information has an important role to play in improving the accuracy of the defect prediction model. In this paper, to bridge this research gap, we propose an end to end deep learning framework that extracts features from the code change automatically. To this purpose, we present the code change by code property sub-graphs (CP-SG) extracted from code property graphs (CPG) that merges existing concepts of classic program analysis, namely abstract syntax tree (AST), control ﬂow graphs (CFG) and program dependence graphs (PDG). Then, we apply a deep graph convolutional neural network (DGCNN) that takes as input the selected features. The experimental results prove that our approach can signiﬁcantly improve the baseline method DBN-based features by an average of 20.86 percentage points for within-project and 32.85 percentage points for cross-project.
Threat Modelling with the GDPR towards a Security and Privacy Metrics Framework for IoT Smart-farm Application

: This paper considers a balance between privacy and security provisions for IoT devices constrained by processing ability, energy consumption, and storage. Risk-driven testing is invoked in conjunction with the seven GDPR principles towards a metrics framework suitable for such an energy-conscious network within the domain of IoT-oriented smart-farms. The resulting metrics framework demonstrates how the inﬂuence of privacy can minimise processing requirements, whilst threat modeling assures security. The research concludes that several redundant security provisions can be replaced with privacy alternatives that improve energy efﬁciency.
Implementing Test Driven Development in the Big Data Domain: A Movie Recommendation System as an Exemplary Case

: As a consequence of the ongoing digitalization in today’s society, the amount of data that is being produced is rapidly increasing. Moreover, not only the volume of the data is growing, but there are also more complex types of data and, depending on the use case, it is also necessary to integrate heterogenous data into one analysis. Since traditional ways of dealing with data are oftentimes overstrained by those new challenges, novel approaches and technologies have been developed. In its entirety, this phenomenon is summarized under the term big data. However, quality assurance in the big data realm is still not mature and this even more applies to the actual testing. Therefore, it is necessary to explore new approaches. One rather recent proposition was the application of the test driven development methodology to the big data domain. To further evaluate its feasibility and go beyond a purely theoretical point of view, the publication at hand discusses the test driven implementation of a movie recommendation system as an exemplary case. In doing so, it facilitates the general understanding of the topic, helps in judging the approach’s feasibility and provides some practical insights concerning its actual application.
De facto or de jure Harmonisation: How Much Dis(harmonised) Are the Entities in an IFRS Environment?

: This paper uses exploratory analysis to seek evidence of accounting practices in a harmonized environment that mitigates the comparability of financial reporting by selecting an exemplary topic from different cases. The topics include the treatment given by entities to those cases not prescribed in the standards, but traditionally used by entities, cases prescribed by standards, but in a way that is not specific or clear enough, and, finally, cases where standards allow alternative accounting treatments. The consolidated reports and accounts of the entities included in the main Euronext Lisbon index for the years 2019 and 2020 were assessed. It was found that the accounting practices adopted by the entities are diverse, with different implications within the options that are reflected in the recognition and presentation of expenses and incomes. This type of research allows broadening the discussion around the implementation of effective measures to reduce the subjectivity associated with the adoption and application of standards to reach higher levels of de facto harmonisation or convergence. It is expected that the proposed analysis can contribute to drawing the attention of standard-setters and regulators of financial reporting to the potential constraints associated with the high flexibility of, or gaps in, International Financial Reporting Standards.
Parameter choices for sparse regularization with the ℓ1 norm

We consider a regularization problem whose objective function consists of a convex fidelity term and a regularization term determined by the ℓ1 norm composed with a linear transform. Empirical results show that the regularization with the ℓ1 norm can promote sparsity of a regularized solution. The goal of this paper is to understand theoretically the effect of the regularization parameter on the sparsity of the regularized solutions. We establish a characterization of the sparsity under the transform matrix of the solution. When the objective function is block-separable or an error bound of the regularized solution to a known function is available, the resulting characterization can be taken as a regularization parameter choice strategy with which the regularization problem has a solution having a sparsity of a certain level. When the objective function is not block-separable, we propose an iterative algorithm which simultaneously determines the regularization parameter and its corresponding solution with a prescribed sparsity level. Moreover, we study choices of the regularization parameter so that the regularization term can alleviate the ill-posedness and promote sparsity of the resulting regularized solution. Numerical experiments demonstrate that the proposed algorithm is effective and efficient, and the choices of the regularization parameters can balance the sparsity of the regularized solution and its approximation to the minimizer of the fidelity function.
Towards an Adaptive Intelligent Assessment Framework for Collaborative Learning

: Assessing in an online collaborative learning environment is a complex task due to the variety of elements and factors that intervene in how a group of learners collaborates to achieve an assessment task. This paper aims to improve both learners’ and group performance at a given activity or a set of activities by adapting the assessment process to the learner level. To that end, we propose a general framework to illustrate our adaptive approach for assessment in an online collaborative learning environment. To do so, we take the concept of adaptation, generally based on three models: the learner model, the domain model, and the adaptive model, as a point of departure and extend it by designing two other new models that are an assessment model and a group learner model. To present our assessment model, we are based on IMS/QTI standard and ontology for the formalization of the question. We aim to combine collaborative learning, assessment, and adaptation to provide an adaptive assessment, an adaptive group composition, and an adaptive collaboration.
Attention! Transformer with Sentiment on Cryptocurrencies Price Prediction

: Cryptocurrencies have won a lot of attention as an investment tool in recent years. Specific research has been done on cryptocurrencies’ price prediction while the prices surge up. Classic models and recurrent neural networks are applied for the time series forecast. However, there remains limited research on how the Transformer works on forecasting cryptocurrencies price data. This paper investigated the forecasting capability of the Transformer model on Bitcoin (BTC) price data and Ethereum (ETH) price data which are time series with high fluctuation. Long short term memory model (LSTM) is employed for performance comparison. The result shows that LSTM performs better than Transformer both on BTC and ETH price prediction. Furthermore, in this paper, we also investigated if sentiment analysis can help improve the model’s performance in forecasting future prices. Twitter data and Valence Aware Dictionary and sEntiment Reasoner (VADER) is used for getting sentiment scores. The result shows that the sentiment analysis improves the Transformer model’s performance on BTC price but not ETH price. For the LSTM model, the sentiment analysis does not help with prediction results. Finally, this paper also shows that transfer learning can help on improving the Transformer’s prediction ability on ETH price data.
Classification Techniques Use to Empirically Validate Redundancy Metrics as Reliability Indicators based on Fault-proneness Attribute

: Software metrics are proposed as quantitative measures of internal quality factors like cohesion and complexity. For the external ones such as reliability and maintainability, they are usually predicted by means of various metrics of internal attributes. In this context, we have focused on a suite of four entropy-based software redundancy metrics considered as software reliability indicators. Despite their important purpose, they are manually computed and only theoretically validated. Hence, we have implemented an empirical approach for assessing these metrics, using a set of programs retrieved from real software projects. Given that software reliability as external attribute, cannot be directly evaluated, we employ other measurable quality factors representing direct reﬂections of this attribute. Among them, defect density and fault-proneness are widely used as means to measure and predict software reliability based on software metrics. The basic idea is to generate an empirical dataset embodying for each program, the values of the redundancy metrics and the values of one of these measurable attributes. In our previous work, we have studied their relationship with the defect density attribute in order to validate them as useful reliability indicators. Promising results indicating the usefulness of these metrics as defect density indicators are obtained. Classifying modules (functions or classes) as defective or not defective is also an important reliability indicator. Literature review shows that software reliability counts on its fault-prone modules and more trusted software consists of less fault-prone units. Therefore, we aim in this paper to propose an empirical approach to validate the redundancy metrics as signiﬁcant reliability indicators. The validation is carried out using the accuracy measure and results show that the fault proneness attribute can be predicted using the redundancy metrics with a good accuracy rate of 0.82.
Machine Learning in Software Development Life Cycle: A Comprehensive Review

: This research concludes an overall summary of the publications so far on the applied Machine Learning (ML) techniques in different phases of Software Development Life Cycle (SDLC) that includes Requirement Analysis, Design, Implementation, Testing, and Maintenance. We have performed a systematic review of the research studies published from 2015-2021 and revealed that Software Requirements Analysis phase has the least number of papers published; in contrast, Software Testing is the phase with the greatest number of papers published.
Tools for the Confluence of Semantic Web and IoT: A Survey

: The last decade has meant a whole revolution in the context of Internet of Things (IoT), becoming one of the most important areas of research nowadays. The Semantic Web emerged as a way to add semantics to data to enable advanced reasoning. Due to this, the integration of both areas represents a beneﬁcial convergence for more ”semantic-aware” devices and services. In this paper we present a review to analyze the state of the art of the conﬂuence of the Semantic Web and IoT: extracting an overview of the ad-hoc implemented tools, their categorization, and the supported sub-domains on which they exert their inﬂuence.
Channel capacity of next generation large scale MIMO systems

Information rate that can be transferred over a given bandwidth is limited by the information theory. Capacity depends on many factors such as the signal to noise ratio (SNR), channel state information (CSI) and the spatial correlation in the propagation environment. It is very important to increase spectral efficiency in order to meet the growing demand for wireless services. Thus, Multiple input multiple output (MIMO) technology has been developed and applied in most of the wireless standards and it has been very successful in increasing capacity and reliability. As the demand is still increasing, attention now is shifting towards large scale multiple input multiple output (MIMO) which has a potential of bringing orders of magnitude of improvement in spectral and energy efficiency. It has been shown that users channels decorrelate after increasing the number of antennas. As a result, inter-user interference can be avoided since energy can be focused on precise directions. This paper investigates the limits of channel capacity for large scale MIMO. We study the relation between spectral efficiency and the number of antenna N. We use time division duplex (TDD) system in order to obtain CSI using training sequence in the uplink. The same CSI is used for the downlink because the channel is reciprocal. Spectral efficiency is measured for channel model that account for small scale fading while ignoring the effect of large scale fading. It is shown the spectral efficiency can be improved significantly when compared to single antenna systems in ideal circumstances.
Food waste in the phase of domestic consumption: the causes and preventive actions

This work is part of a research field still little explored both nationally and internationally and in which different theoretical approaches and methodologies for quantitative analysis of the dynamics of food waste are used. Most of the research, globally, focuses on the formation of waste in the stages of production and distribution. Few studies regarded, however, an analysis of food waste in the phase of domestic consumption. Therefore, this paper aims to fill this gap by providing the results of a survey, carried out at a representative sample of the population, to quantify, qualify and identify the causes of food waste as well as actions that consumers put in place to reduce or, even better, to prevent it. Said survey was conducted on a representative sample of 500 individuals. The questionnaire was launched in April 2014 and remained on-line until June 2014. The same has been diffused through the social network "Facebook".
Building Trust by Supporting Situation Awareness: Exploring Pilots’ Design Requirements for Decision Support Tools

Supporting pilots with a decision support tool (DST) during high-workload scenarios is a promising and potentially very helpful application for AI in aviation. Nevertheless, design requirements and opportunities for trustworthy DSTs within the aviation domain have not been explored much in the scientific literature. To address this gap, we explore the decision-making process of pilots with respect to user requirements for the use case of diversions. We do so via two prototypes, each representing a role the AI could have in a DST: A) Unobtrusively hinting at data points the pilot should be aware of. B) Actively suggesting and ranking diversion options based on criteria the pilot has previously defined. Our work-in-progress feedback study reveals four preliminary main findings: 1) Pilots demand guaranteed trustworthiness of such a system and refuse trust calibration in the moment of emergency. 2) We may need to look beyond trust calibration for isolated decision points and rather design for the process leading to the decision. 3) An unobtrusive, augmenting AI seems to be preferred over an AI proposing and ranking diversion options at decision time. 4) Shifting the design goal toward supporting situation awareness rather than the decision itself may be a promising approach to increase trust and reliance.
Controller Synthesis for Manufacturing Systems in the Situation Calculus

Manufacturing is transitioning from a mass production model to a service model in which facilities ‘bid’ for products. To decide whether to bid for a previously unseen product, a facility must be able to synthesize, on the ﬂy, a process plan controller that delegates abstract manufacturing tasks in a supplied process recipe to the available manufacturing resources. First-order representations of the state are commonly considered in reasoning about action in AI. Here we show that we can leverage the wide literature on the Situation Calculus automatically synthesize such controllers. We identify two important decidable cases—ﬁnite domains and bounded action theories—for which we provide practical synthesis techniques.
Expressive speech synthesis from Broadcast News

Speech Synthesis is the computer process of converting text to voice. This project consists in the synthesis of voices that can tell news with an appropriate expression, since it is important to achieve expressiveness on the generated speech in order to obtain natural sounding voices. Conventional Speech Synthesis systems use as training data audios signals, specifically recorded for voice models training. Nevertheless, in this project the data was obtained from a news TV station, in order to test a different database in the speech synthesis. An important part of the work done in this TFG has been preparing data later used in synthesis. The audio and its transcriptions were labeled so as to differentiate the expressions recorded: explaining good or bad news, or talking about relevant or trivial topics. A phonetic segmentation of the database was obtained in order to create the models used in the speech synthesis. After preparing all the audio and transcriptions data, statistical-parametric models were estimated and used to synthesize test voices, in order to evaluate the previous setup work. All the project has been developed in a Linux environment, using Ogmios, AHOCoder and HTS-toolkit as main software. The results obtained after synthesizing the voices shows that the data preparation process is correct, but the voices synthesized had not the enough quality. This is due to the adaptation of the voices towards heterogeneous samples, originated by the amount of different speakers used to train the models.
The AIM and EM Algorithms for Learning from Coarse Data

Statistical learning from incomplete data is typically performed under an assumption of ignorability for the mechanism that causes missing values. Notably, the expectation maximization (EM) algorithm is based on the assumption that values are missing at random. Most approaches that tackle non-ignorable mechanisms are based on speciﬁc modeling assumptions for these mechanisms. The adaptive imputation and maximization (AIM) algorithm has been introduced in earlier work as a general paradigm for learning from incomplete data without any assumptions on the process that causes observations to be incomplete. In this paper we give a thorough analysis of the theoretical properties of the AIM algorithm, and its relationship with EM. We identify conditions under which EM and AIM are in fact equivalent, and show that when these conditions are not met, then AIM can produce consistent estimates in non-ignorable incomplete data scenarios where EM becomes inconsistent. Convergence results for AIM are obtained that closely mirror the available convergence guarantees for EM. We develop the general theory of the AIM algorithm for discrete data settings, and then develop a general discretization approach that allows to apply the method also to incomplete continuous data. We demonstrate the practical usability of the AIM algorithm by prototype implementations for parameter learning from continuous Gaussian data, and from discrete Bayesian network data. Extensive experiments show that the theoretical diﬀerences between AIM and EM can be observed in practice, and that a combination of the two methods leads to robust performance for both ignorable and non-ignorable mechanisms.
Implementation of Convolutional Autoencoder and Learnings on Image Compression

Autoencoders are a neural network family and being well-known for its advantage on compressing data and denoising the corrupted data. Among various techniques of implementing autoencoders, the convolutional autoencoders are efficient as it is capable of combining the benefits of convolutional architectures with the traditional autoencoders. By building a convolutional autoencoder and a convolutional neural network, we have found that how much information the image data will lose during the compression is depending on the rate of compression. The CIFAR-10 dataset is used for our training and testing due to the fact that an image data is better for the visualization of information compression. Besides, an adequate normalization technique is also implemented in order to fasten the calculation in training.
The efficacy of cognitive behavior stress management on functional dyspepsia

Abstract Background: Functional dyspepsia and digestive disorders are common, debilitating and costly. Little information is available about the role of stress management in terms of cognitive-behavioral treatment of dyspepsia. We performed a protocol for systematic review and meta-analysis to evaluate the effectiveness of cognitive behavior stress management for the treatment of functional dyspepsia. Methods: A comprehensive search of several databases from 1966 to March 2022 will be conducted. The databases include Ovid Medline In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Ovid PsycINFO, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, and Scopus. The primary outcome for this study was the rate of successful treatment (presence of no more than mild pain or discomfort after treatment). The secondary outcomes were improvement of dyspepsia at short-term (<1 year) and long-term (≥1 year) follow up, improvement in quality of life, and development of treatment-related adverse events. The risk of bias in each included study will be assessed utilizing the Cochrane Collaboration's risk of bias tool. The Review Manager 5.3 (Cochrane Collaboration, Oxford, UK) will be used to analyze the data. Results: We will synthesize the current studies to evaluate the effectiveness and safety of cognitive behavior stress management on functional dyspepsia. Conclusion: The result of this review will provide more reliable references to help clinicians make decisions when dealing with functional dyspepsia.