Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of data clustering for automated feedback generation about student well-being

Investment in the well-being of today’s schoolchildren is an important investment in the future. We believe that learning does not happen in the absence of well-being. This data-oriented research studies how automation utilizing data analysis algorithms could help provide the students with feedback and guidance about their well-being related issues. We implemented a system that combines data processing methods and research-based knowledge to serve that purpose. Our target was to develop an automated feedback system utilizing information from a large data set collected from well-being surveys from students, as well as research-based well-being knowledge. The system can be used to provide automated feedback for students who answer a well-being survey.
Practice in Caption Generation with Keras: The Design and Evaluation for Attention Models

Hard-attention model has been proposed for automatically generating captions for images. Currently this model is implemented in many ways. For instance, its original implementation is written in Theano and most other implementations are written using Tensorflow library. In this paper, we use Keras, a modular framework for constructing deep neural networks, to implement this model. We also propose two attention models, called Attention-L and Attention-C, are slightly modified from the original attention model. The benchmark dataset Flickr30 is used to compare these three attention models, and the results demonstrate Attention-C model is more likely to obtain the better scores than that of other two models.
Healthcare Android apps: a tale of the customers’ perspective

Healthcare mobile apps are becoming a reality for users interested in keeping their daily activities under control. In the last years, several researchers have investigated the effect of healthcare mobile apps on the life of their users as well as the positive/negative impact they have on the quality of life. Nonetheless, it remains still unclear how users approach and interact with the developers of those apps. Understanding whether healthcare mobile app users request different features with respect to other applications is important to estimate the alignment between the development process of healthcare apps and the requests of their users. In this study, we perform an empirical analysis aimed at (i) classifying the user reviews of healthcare open-source apps and (ii) analyzing the sentiment with which users write down user reviews of those apps. In doing so, we define a manual process that enables the creation of an extended taxonomy of healthcare users' requests. The results of our study show that users of healthcare apps are more likely to request new features and support for other hardware than users of different types of apps. Moreover, they tend to be less critical of the defects of the application and better support developers when debugging.
Leveraging mutants for automatic prediction of metamorphic relations using machine learning

An oracle is used in software testing to derive the verdict (pass/fail) for a test case. Lack of precise test oracles is one of the major problems in software testing which can hinder judgements about quality. Metamorphic testing is an emerging technique which solves both the oracle problem and the test case generation problem by testing special forms of software requirements known as metamorphic requirements. However, manually deriving the metamorphic requirements for a given program requires a high level of domain expertise, is labor intensive and error prone. As an alternative, we consider the problem of automatic detection of metamorphic requirements using machine learning (ML). For this problem we can apply graph kernels and support vector machines (SVM). A significant problem for any ML approach is to obtain a large labeled training set of data (in this case programs) that generalises well. The main contribution of this paper is a general method to generate large volumes of synthetic training data which can improve ML assisted detection of metamorphic requirements. For training data synthesis we adopt mutation testing techniques. This research is the first to explore the area of data augmentation techniques for ML-based analysis of software code. We also have the goal to enhance black-box testing using white-box methodologies. Our results show that the mutants incorporated into the source code corpus not only efficiently scale the dataset size, but they can also improve the accuracy of classification models.
Risk-based data validation in machine learning-based software systems

Data validation is an essential requirement to ensure the reliability and quality of Machine Learning-based Software Systems. However, an exhaustive validation of all data fed to these systems (i.e. up to several thousand features) is practically unfeasible. In addition, there has been little discussion about methods that support software engineers of such systems in determining how thorough to validate each feature (i.e. data validation rigor). Therefore, this paper presents a conceptual data validation approach that prioritizes features based on their estimated risk of poor data quality. The risk of poor data quality is determined by the probability that a feature is of low data quality and the impact of this low (data) quality feature on the result of the machine learning model. Three criteria are presented to estimate the probability of low data quality (Data Source Quality, Data Smells, Data Pipeline Quality). To determine the impact of low (data) quality features, the importance of features according to the performance of the machine learning model (i.e. Feature Importance) is utilized. The presented approach provides decision support (i.e. data validation prioritization and rigor) for software engineers during the implementation of data validation techniques in the course of deploying a trained machine learning model and its software stack.
Does learning by doing have a positive impact on teaching model checking?

The theory of education Learning by doing exposed by Dewey theorized that learning should be relevant and practical, not just passive and theoretical. The aim of this study is twofold: firstly to report on the effort related to the improvement of usability of a model checker tool by master students enrolled in the Software Engineering and Distributed Systems in Internet sections in our university, and secondly to investigate if the integration of research-based assignment into teaching and learning favorably influence learning. The results of quantitative analysis (perceptions and opinions of the students collected through surveys and an independent t-test statistical test) acknowledge the effectiveness and efficiency of learning by doing approach in teaching model checking, both concepts and tool’s usability. The results of qualitative analysis (discussions, perceptions and opinions of both students and teacher) recognize the importance of learning by doing activities (poster creating and presenting, project-based assignment, research based assignment) in teaching/learning model checking.
Early Diagnosis of Alzheimer's Disease Using Deep Learning

Alzheimer's disease (AD) leads to memory loss and impairment, which may cause further symptoms. It affects lives of patients seriously and is not curable, but early confirmation of AD may be helpful to start proper treatment so as to avoid further brain damage. Over the past decades, machine learning methods have been applied to the classification of AD with results based on manually prepared features and a classifier having a multiple-step architecture. Recently, with the development of deep learning, the end to- end process of neural networks has been employed for pattern classification. In this paper, we focus on early diagnosis of AD based on convolutional neural networks (ConvNets) by using magnetic resonance imaging (MRI). Image slices of gray matter and white matter from MRI have been used as the inputs for classification. Ensemble learning methods have been employed after the convolutional operations for improving the classification by combining outputs of deep learning classifiers [27]. Three base ConvNets were designed, implemented, and compared in this paper. Our method was evaluated based on a dataset from the Alzheimer's Disease Neuroimaging Initiative for the early diagnosis of this illness. In particular, the accuracy rates of our classifications have reached up to 97.65% for AD/mild cognitive impairment and 88.37% for mild cognitive impairment/normal control.
Machine learning techniques for code smells detection: an empirical experiment on a highly imbalanced setup

Frederico Caram Luiz LAIS – Laboratory for Advanced Information Systems, FUMEC University Belo Horizonte, Brazil fredcaram@gmail.com Bruno Rafael de Oliveira Rodrigues LAIS – Laboratory for Advanced Information Systems, FUMEC University Belo Horizonte, Brazil brunorodriguesti@yahoo.com. br Fernando Silva Parreiras LAIS – Laboratory for Advanced Information Systems, FUMEC University Belo Horizonte, Brazil fernando.parreiras@fumec.br
A heuristic fuzz test generator for Java native interface

It is well known that once a Java application uses native C/C++ methods through the Java Native Interface (JNI), any security guarantees provided by Java might be invalidated by the native methods. So any vulnerability in this trusted native code can compromise the security of the Java program. Fuzzing test is an approach to software testing whereby the system being tested is bombarded with inputs generated by another program. When using fuzzer to test JNI programs, how to accurately reach the JNI functions and run through them to find the sensitive system APIs is the pre-condition of the test. In this paper, we present a heuristic fuzz generator method on JNI vulnerability detection based on the branch predication information of program. The result in the experiment shows our method can use less fuzzing times to reach more sensitive windows APIs in Java native code.
An Approach to Real Time Parking Management using Computer Vision

Automating vehicle statistics provides vital information that can be used in predicting the flow of traffic. Object detection based systems that use computer vision have produced drastic improvements in results over a sensor based approach. The methodology proposed in the paper follows an approach to perform this operation in real time and is currently being used in estimating the density of parking spaces, amongst other applications. The paper describes a 4 layer architecture for parking management which involves a HAAR based frame extraction from live video feed followed by a YOLOv2(You Only Look Once) deep neural network approach that supports real time detection of vehicles. The third layer emphasizes on the use of a mechanism that measures the number of vehicles entering a parking space by following the path traced by the centroid which is followed by a number plate recognition system that can retrace mishappenings to their source. The detection system developed using this model has been extensively tested on real time traffic in Bangalore and has generated accuracies close to 95% with video data that has been cross verified manually, making it much more effective than sensor based models.
Test coverage criteria for RESTful web APIs

Web APIs following the REST architectural style (so-called RESTful web APIs) have become the de-facto standard for software integration. As RESTful APIs gain momentum, so does the testing of them. However, there is a lack of mechanisms to assess the adequacy of testing approaches in this context, which makes it difficult to automatically measure and compare their effectiveness. In this paper, we first present a set of ten coverage criteria that allow to determine the degree to which a test suite exercises the different inputs (i.e. requests) and outputs (i.e. responses) of a RESTful API. We then arrange the proposed criteria into eight Test Coverage Levels (TCLs), where TCL0 represents the weakest coverage level and TCL7 represents the strongest one. This enables the automated assessment and comparison of testing techniques according to the overall coverage and TCL achieved by their generated test suites. Our evaluation results on two open-source APIs with real bugs show that the proposed coverage levels nicely correlate with code coverage and fault detection measurements.
A Secure Cloud-Assisted Certificateless Group Authentication Scheme for VANETs in Big Data Environment

Nowadays, the construction of efficient intelligent transportation system (ITS) has become a new trend for metropolitan cities with increasingly large populations. As one of the most significant component of ITS, the vehicular ad hoc networks (VANETs) are capable of building temporary vehicular sensor networks for efficient and dynamic information exchange between vehicles and road side units (RSUs). As a matter of fact, the traditional VANETs have limited computing and storing capabilities, which restrict the rapid development VANETs services provided to the drivers. Hence, with the rapid development of big data facilities, the cloud-assisted VANETs structure is proposed in order to enhance the capabilities of VANETs. In addition, due to the inherent wireless communication characteristics, data transmissions of VANETs suffer from charted and uncharted security risks and attacks. Thus proper security strategies should be adopted to guarantee secure communication and driver privacy. Emphasizing on the above issues, we develop an efficient cloud-assisted certificateless grouping authentication scheme for VANETs. In our design, vehicle anonymity is provided during the entire communication process. Note that most of the current authenticating schemes assume the secure channel between the RSU and vehicles in order for initial key message transmission, which is not necessary in our scheme.
Yawn: A CPU Idle-state Governor for Datacenter Applications

Idle-state governors partially turn off idle CPUs, allowing them to go to states known as idle-states to save power. Exiting from these idle-sates, however, imposes delays on the execution of tasks and aggravates tail latency. Menu, the default idle-state governor of Linux, predicts periods of idleness based on the historical data and the disk I/O information to choose proper idle-sates. Our experiments show that Menu can save power, but at the cost of sacrificing tail latency, making Menu an inappropriate governor for data centers that host latency-sensitive applications. In this paper, we present the initial design of Yawn, an idle-state governor that aims to mitigate tail latency without sacrificing power. Yawn leverages online machine learning techniques to predict the idle periods based on information gathered from all parameters affecting idleness, including network I/O, resulting in more accurate predictions, which in turn leads to reduced response times. Preliminary benchmarking results demonstrate that Yawn reduces the 99th latency percentile of Memcached requests by up to 40%.
Building a conceptual architecture and stakeholder map of a system-of-systems for disaster monitoring and early-warning: A case study in Brazil

Monitoring systems are essential for prompt action in case of a disaster, as well as for understanding these systems as constituent systems within a System-of-Systems (SoS) can provide new and unique features that cannot be provided by any individual system separately. Furthermore, the identification of existing stakeholders also plays an important role, as constituent systems may be associated with multiple requirements. Therefore, this work presents two artifacts -a conceptual architecture and a stakeholder map - of a SoS for disaster monitoring and early-warning. A Design Science Research (DSR) within a Brazilian early-warning center has been conducted for designing and evaluating the artifacts. An artifact generalization approach has been also employed for generalizing the artifacts from a specific to a broader and generic scenario. Study findings showed that a SoS for disaster monitoring and early-warning should comprise nine constituents, which are used by four groups of stakeholders.
A Non-Deterministic Method to Construct Ensemble-Based Classifiers to Protect Decision Support Systems Against Adversarial Images: A Case Study

In recent years, Deep Learning has presented impressive performance when solving complex image classification and recognition tasks in decision support systems. Nonetheless, studies have demonstrated Deep Learning models are susceptible to attacks conducted with adversarial images, i.e. images containing subtle perturbations in order to induce models to misclassification. The main existing countermeasures against adversarial images have shown ineficiency, permitting attackers to map their internal operation more easily. Therefore, this work aims to evaluate a defense method called MultiMagNet which randomly incorporates at runtime multiple defense components, implemented as autoencoders, in order to introduce an expanded form of non-determinism behavior for hindering evasions of adversarial nature. Experiments on CIFAR-10 dataset showed MultiMagNet was able to detect images generated by different attack algorithms.
Hermes: A Natural Language Interface Model for Software Transformation

Software maintenance is a costly task and error-prone for both software developers and users as well. By knowing how and what software requirements need to be changed, end users could perform maintenance assisted by tools. However, current literature lacks for tools that support automated maintenance in real-world scenarios and allow users interaction via natural language. Even worse, the current tools are unable to understand the semantic of requests, as well as perform the necessary transformations in the maintenance software. This paper, therefore, proposes Hermes, a natural language interface model for software transformation. It combines computational linguistics techniques and logic programming to perform automated maintenance requests in software. Hermes interacts with end user through state of the art language parsers and domain ontologies by interpreting the semantics of changes requests to build a typed graph that change the software. Hermes was evaluated through an empirical study with 8 participants to investigate its performance, the level of acceptance, and usability. The collected data show that Hermes was accurate, producing a high elevated correctness number of hits by finding correct transformations and has been highly accepted by the users. The results are encouraging and show the potential for using Hermes to properly produce software maintenance requests.
Transmitting acoustic phenomena and aural illusions: Examples from Athanasius Kircher’s Phonosophia anacamptica

This article continues the preliminary exploration of the sonic observations of Athanasius Kircher, namely, the first book of his Phonurgiæ Nova (1673), the Phonosophia anacamptica (Tronchin, Durvilli and Tarabusi). This book analyzes in particular sound propagation in enclosed spaces. In this work, Kircher also describes the statua citofonica, or ‘talking statue’, in relation to the magical effect created by aural illusion and the famous ancient Egyptian acoustical phenomenon and cultural sound-mark associated with the Colossus of Memnon, the famous lost sound phenomenon of the northern of the two colossi of Amenhotep III in the Theban Necropolis, near Luxor, Egypt, that reportedly emitted a sound with the first and final daily touch of sunlight at dawn and dusk. This article aims to describe, analyse and comment on the 17th century contributions of Athanasius Kircher by understanding his Phonosophia anacamptica and the contemporary intellectual context in which he worked. This article differs from our former work; for example, in this article, an earlier example of Francis Bacon’s methods of enquiry into acoustic phenomena illustrates differences with Kircher’s own line of inquiry. We show how Kircher accepts and embraces a Baroque fascination for the ‘marvellous world’ and the allegiance between science and magic, specifically with the mysterious magical affect of sound through the construction of interior listening strategies, or aural espionage, as in the Heidelberg Palace, the Villa Simonetta, and via talking statues such as the Colossus of Memnon.
On the complexity of checking transactional consistency

Transactions simplify concurrent programming by enabling computations on shared data that are isolated from other concurrent computations and are resilient to failures. Modern databases provide different consistency models for transactions corresponding to different tradeoffs between consistency and availability. In this work, we investigate the problem of checking whether a given execution of a transactional database adheres to some consistency model. We show that consistency models like read committed, read atomic, and causal consistency are polynomial-time checkable while prefix consistency and snapshot isolation are NP-complete in general. These results complement a previous NP-completeness result concerning serializability. Moreover, in the context of NP-complete consistency models, we devise algorithms that are polynomial time assuming that certain parameters in the input executions, e.g., the number of sessions, are fixed. We evaluate the scalability of these algorithms in the context of several production databases.
Growth of common friends in a preferential attachment model

Abstract The number of common friends (or connections) in a graph is a commonly used measure of proximity between two nodes. Such measures are used in link prediction algorithms and recommendation systems in large online social networks. We obtain the rate of growth of the number of common friends in a linear preferential attachment model. We apply our result to develop an estimate for the number of common friends that can significantly reduce the cost of computing these measures. We also observe a phase transition in the limiting behavior of the number of common friends; depending on the range of the parameters of the model, the growth is either power-law, or, logarithmic, or static with the size of the graph.
Is This the Right Place? Geometric-Semantic Pose Verification for Indoor Visual Localization

Visual localization in large and complex indoor scenes, dominated by weakly textured rooms and repeating geometric patterns, is a challenging problem with high practical relevance for applications such as Augmented Reality and robotics. To handle the ambiguities arising in this scenario, a common strategy is, first, to generate multiple estimates for the camera pose from which a given query image was taken. The pose with the largest geometric consistency with the query image, e.g., in the form of an inlier count, is then selected in a second stage. While a significant amount of research has concentrated on the first stage, there has been considerably less work on the second stage. In this paper, we thus focus on pose verification. We show that combining different modalities, namely appearance, geometry, and semantics, considerably boosts pose verification and consequently pose accuracy. We develop multiple hand-crafted as well as a trainable approach to join into the geometric-semantic verification and show significant improvements over state-of-the-art on a very challenging indoor dataset.