Miracle-dz/UniFAR-FTUs huggingface dataset:
Review on Marketing of Software Products

With the advent of the information age, the software industry as one of the imposrtant pillars of the information industry, has played a vital role in technological progress and social development. For software companies, the level of marketing management directly affects the core competitiveness of the company. Software products are one of the key factors affecting enterprise development. This thesis will start with the specific characteristics of the software product itself, and analyze the three aspects of the software marketing strategy and promotion channels through the analysis of market demand.
Am I going to Heaven?: First step climbing the Stairway to Heaven Model Results from a Case Study in Industry

Context: Nowadays, software development organizations have adopted agile practices and data-driven software development aiming at competitive advantage. Moving from traditional to agile and data-driven software development requires changes in the organization culture and structure, which may not be easy. Stairway to Heaven Model (StH) describes this evolution path in five stages. Objective: We aimed to investigate how Systems Theory tools, GUT Matrix and reference ontologies can help organizations in the first transition of StH, i.e., moving from traditional to agile development. Method: We performed a participative case study in a Brazilian organization that develops software in partnership with a European organization. We applied Systems Theory tools (systemic maps and archetypes) to understand the organization and identify undesirable behaviors and their causes, and also GUT matrices to decide about which ones should be addressed first; we defined strategies to change the undesirable behaviors by implementing agile practices, and we used reference ontologies to share a common understanding about agile concepts. Results: By understanding the organization, a decision was made to implement a combination of agile and traditional practices. The implemented strategies improved software quality and project time and cost. Problems due to misunderstanding agile concepts were solved, allowing the organization to experience agile culture and foresee changes in its business model. Conclusion: Systems Theory tools and GUT Matrix aid organizations to move from traditional to agile development by supporting better understand the organization, find leverage points of change and enabling to define strategies aligned to the organization characteristics and priorities. Reference ontologies can be useful to establish a common understanding about agile, enabling teams to be aware of and, thus, more committed to agile practices and concepts.
From Safety Requirements to Just-Enough Safety-Centered Architectural Solutions in Agile Contexts

Safety-critical systems can be found in many sectors of our lives, e.g., in medical equipments and vehicles. A agile practices have been increasingly incorporated into the development processes of these systems, mainly due to demands related to time-to-market and budget reduction. At the same time, recent accidents have shown that various failures have been caused by errors or faults introduced during development and resulted from misunderstandings of safety requirements by agile development teams. Moreover, there is still a lack of techniques for ensuring that safety requirements are properly addressed by both software architecture and implementation. To address this gap, this paper presents the SCA3DA metamodel, which leverages the understanding of safety requirements by agile teams, so that these requirements can be included in the just-enough safety-centered software architecture of safety-critical systems. To demonstrate the applicability of this metamodel, we used it in clinical safety interlock scenarios for infusion pumps. Preliminary results indicate that adopting the SCA3DA metamodel is also feasible in industry projects.
MDE in the Wild: An Exploratory Analysis on What Developers are Discussing from Q&A Platforms

Model-Driven Engineering (MDE) is an approach that considers models as first-class entities used in the software analysis, design and implementation. Although MDE has become popular in both academia and industry as an alternative for tackling the growing complexity of modern software, MDE has not been largely adopted in the software development process. A possible way to mitigate those problems consists of understanding how MDE has been applied in practice and what are the main barriers found by developers. Hence, this paper presents an exploratory study to analyse the discussions about MDE from two Q&A platforms: Stack Overflow and Software Engineering Stack Exchange. One hundred fourteen discussions have been analysed under four perspectives: discussion type, application domain, tools, and developer interest. As a result, we identified that 69, 30% of the discussions regard technical aspects, from which metamodelling is the most discussed topic, and Xtext is the most discussed tool. In addition, we observed that discussions in which developers suggest MDE-related tools tend to attract more views and answers.
An Approach for Selecting FLOSS Projects for Education

Context: The use of Free/Libre/Open Source Software (FLOSS) projects in Software Engineering Education (SEE) provides an opportunity for learning the theory and practicing technical and soft skills, narrowing the theory-practice gap usually present in undergraduate software engineering courses. However, selecting a FLOSS project for pedagogical use has its challenges. The selection should consider technical, social and pedagogical criteria, and the search process is often manual and ad-hoc. Objective: We present an approach to the selection of FLOSS projects for SEE based on technical and social criteria, and evaluate it from the students' perspective. Method: We performed a literature review to identify the criteria for selecting FLOSS projects used in SEE research. A set of selection criteria was documented and operationalized so that automatic search could be performed in open source projects repositories. We implemented a tool called FlossSearch.Edu to support the selection of FLOSS projects, based on the combination of one or more criteria, from a randomly populated repository of cloned GitHub projects. A case study was conducted with undergraduate students. They used the tool to select FLOSS projects based on criteria defined by the professor and answered a survey based on the Technology Acceptance Model (TAM). Results: FlossSearch.Edu played an important role in the process of selecting FLOSS projects. Most participants that used FlossSearch.Edu stated that the tool was useful, easy to use and that they intended to use it in the future.
Kinematic Modeling and Cable Distribution Optimization of a Flexible Mechanism for In-situ Grinding of Aero-engine Blades

The in-situ grinding work of aero-engine blades while still attached on aircrafts requires a type of slender and flexible robotic arm. This paper proposes an in-situ grinding mechanism driven by cables and supported by discrete elastic backbones. By means of geometric analysis and coordinate transformation under D-H convention, the forward and inverse kinematics models of the mechanism are established and verified. The workspace is simulated by Monte Carlo method and the result shows that the designed mechanism meets the requirements of performing certain in-situ grinding work. Finally, the distribution of the driving cables is optimized in order to reduce the travel range demand on each actuator.
Agile Global Software Development: A Systematic Literature Review

Global Software Development (GSD) continues to grow substantially and it is fast becoming the norm and fundamentally different from local Software Engineering development. Withal, agile software development (ASD) has become an appealing choice for companies attempting to improve their performance although its methods were originally designed for small and individual teams. The current literature does not provide a cohesive picture of how the agile practices are taken into account in the distributed nature of software development: how to do it, who, and what works in practice. This study aims to highlight how ASD practices are applied in the context of GSD in order to develop a set of techniques that can be relevant in both research and practice. To answer the research question, "how are agile practices adopted in agile global software development teams? " We conducted a systematic literature review of the ASD and GSD literature. A synthesis of solutions found in seventy-six studies provided 48 distinct practices that organizations can implement, including "collaboration among teams", "agile architecture", "coaching", "system demo" and "test automation". These implementable practices go some way towards providing solutions to manage GSD teams, and thus to embrace agility.
Usability and User eXperience Evaluation of Conversational Systems: A Systematic Mapping Study

Conversational Systems (CSs) are software that uses the user's voice to perform some action. Before these systems were available to users, they must be evaluated. In this sense, Usability and User eXperience (UX) evaluations contribute to the verification of software quality, since they evaluate several aspects such as efficiency, effectiveness, immersion, and user satisfaction. Therefore, the goal of our Systematic Mapping Study (SMS) is to identify the evaluation technologies (methods, techniques, models, among others) used by researchers and professionals to evaluate Usability and UX of CSs. We selected 39 papers for data extraction and, based on these works, we identified 31 different evaluation technologies. Besides, our SMS extracted the characteristics of technologies, CSs, and empirical studies described in the papers. Our results identify a lack of evaluation technologies of CSs that unite the concepts of Usability and UX and undergo empirical evaluations. Moreover, we observed researchers tend to create their questionnaires according to the needs of the study. Overall, our SMS presents data about the researched topic, describing the gaps, and contributing to the scientific community that evaluates Usability and UX of CSs.
Sloth

Many web applications store persistent data in databases. During execution, such applications spend a significant amount of time communicating with the database for retrieval and storing of persistent data over the network. These network round-trips represent a significant fraction of the overall execution time for many applications (especially those that issue a lot of database queries) and, as a result, increase their latency. While there has been prior work that aims to eliminate round-trips by batching queries, they are limited by (1) a requirement that developers manually identify batching opportunities, or (2) the fact that they employ static program analysis techniques that cannot exploit many opportunities for batching, as many of these opportunities require knowing precise information about the state of the running program. In this article, we present Sloth, a new system that extends traditional lazy evaluation to expose query batching opportunities during application execution, even across loops, branches, and method boundaries. Many such opportunities often require expensive and sophisticated static analysis to recognize from the application source code. Rather than doing so, Sloth instead makes use of dynamic analysis to capture information about the program state and, based on that information, decides how to batch queries and when to issue them to the database. We formalize extended lazy evaluation and prove that it preserves program semantics when executed under standard semantics. Furthermore, we describe our implementation of Sloth and our experience in evaluating Sloth using over 100 benchmarks from two large-scale open-source applications, in which Sloth achieved up to a 3 × reduction in page load time by delaying computation using extended lazy evaluation.
Flipped Classroom in Software Engineering: A Systematic Mapping Study

Context. Software Engineering (SE) teaching is evolving continually, with new methods being developed and evaluated. In this sense, it is important to gain more knowledge of how such methods are actually implemented. Objective. The aim of this study is to systematically examine the literature on the use of the flipped classroom method in SE teaching. Method. To achieve the study objective, we conducted a Systematic Mapping Study (SMS) starting with 769 studies. After the filtering process, we extracted data from 26 primaries studies, which meet the study selection criteria. Results. We found papers from 2008 to 2020, most of them published in SE conferences. In fifteen papers, the content is delivered to the students before class, nine of them using a specific system developed to this task. We found that the in-class activities follow three main strategies: (1) project-based learning (38.3%); (2) problem-based learning and self-direct learning (50.0%); and (3) team-based learning (7.7%). Reviewed studies reported challenges in implementing FC in ES course such as overworked and time-constrained professors and difficulty in sustaining student motivation. Also, we found studies reporting improvements in student learning and motivation Conclusion. Based on our findings, we conclude the use of an active method has proved to be useful for in-class practical activities, especially related to the software development field. We also observed that adaptive educational content delivery has not been explored in software engineering studies with flipped classes.
Improving Single Shot Detector for Industrial Cracks by Feature Resolution Analysis

Although the single shot detector (SSD) is effective for object detection in natural images, it is not suitable for special tasks such as the industrial crack detection. The difficulty lies in the wide diversity of crack sizes and shapes that is usually unpredictable. To solve this problem, we improve the SSD model by feature resolution analysis. The classical SSD network extracts several convolutional feature layers with degressive scales, and then classifies and locates targets by a series of prior boxes with default sizes and aspect ratios regarding to each scale. Therefore, the key is whether the design of these prior boxes is consistent with the real target characteristics. In this paper, we improve the architecture of SSD network via statistically analyzing the distribution of sizes and shapes from our collected crack samples. According to the resolution analysis of the targets at each feature scale, only a fewer number of valid feature layers are carefully extracted, and some more accurate prior boxes are designed relative to each scale. Finally, experimental results demonstrate that the proposed method could not only achieve significantly better prediction accuracy, but also acquire higher computational efficiency, which outperform the state-of-the-art methods.
Heartbeat Classification Using ANFIS System and QRS Complex Features Extraction

The present work 1 proposes the development of an intelligent system that can classify ECG beats. This system employs two modules: a feature extraction module and a classification module. Feature extraction module extracts four QRS complex features; two of these features are the fractional slopes extracted from the frequency domain and the two remaining features are timing information's. In the second module five ANFIS classifier trained with the backpropagation gradient descent method in combination with the least squares method are used to classify five types of beats. The experiments show that the method has achieved 92.34% of total classification accuracy.
Efficient Deep CNN-BiLSTM Model for Network Intrusion Detection

The need for Network Intrusion Detection systems has risen since usage of cloud technologies has become mainstream. With the ever growing network traffic, Network Intrusion Detection is a critical part of network security and a very efficient NIDS is a must, given new variety of attack arises frequently. These Intrusion Detection systems are built on either a pattern matching system or AI/ML based anomaly detection system. Pattern matching methods usually have a high False Positive Rates whereas the AI/ML based method, relies on finding metric/feature or correlation between set of metrics/features to predict the possibility of an attack. The most common of these is KNN, SVM etc., operate on a limited set of features and have less accuracy and still suffer from higher False Positive Rates. In this paper, we propose a deep learning model combining the distinct strengths of a Convolutional Neural Network and a Bi-directional LSTM to incorporate learning of spatial and temporal features of the data. For this paper, we use publicly available datasets NSL-KDD and UNSW-NB15 to train and test the model. The proposed model offers a high detection rate and comparatively lower False Positive Rate. The proposed model performs better than many state-of-the-art Network Intrusion Detection systems leveraging Machine Learning/Deep Learning models.
Research on Brain-inspired Vision Based on Dynamic Vision Sensor Cameras

In this paper, we explored many applications of brain-inspired vision, in which the data was acquired based on a dynamic vision sensor camera, that is PSEE300EVK from French. Specifically, we explored the following three aspects: (1) Converting large-scale artificial convolution neural network into spiking neural network, which can process large-scale datasets and save network resources without dropping much precision. We proposed reliable solutions for the difference between the two networks, and it can be generalized to other deep network transformations. (2) Recognizing pedestrians and vehicles spiking data flow in the autopilot scenario. Specifically, we transformed Cityscapes dataset into two modes spiking data, one called event processing mode, another called contrast detection mode. (3) Constructing a structured light 3D acquisition system and 3D image recognition algorithm based on the PSE300EVK camera. Tests show that the algorithm used in this paper can effectively reduce the error between the deep artificial convolutional neural network and the deep spiking convolutional neural network, and it has good ability of generalization. In this article we push forward application of brain-inspired vision in actual scenes and look forward the further development, besides, it helps deploy power-efficient neuromorphic spiking neuron chips in embedded applications.
Integrated Control of Path Following and Lateral Stability for Unmanned Autonomous Vehicle with Decentralized Driving System

As a basis of hierarchical control structure, this paper proposes an integrated control approach of path following and lateral stability for unmanned autonomous vehicle with decentralized driving system. In the upper layer controller, the Sliding Mode Control theory is used to reduce the path following heading error and lateral offset while ensuring the vehicle stability. In the lower layer controller, the optimal tire force allocation method is used to execute the control demand from the upper controller. The CarSim-Simulink co-simulation results verify the effectiveness of the proposed approach.
A Fabric Image Segmentation Pipeline to Find the Yarn Weaving Pattern from a Single Photograph

Rendering high quality woven fabric images need special rendering techniques that differ from standard approaches. Since a woven fabric consists of vertically and horizontally crossing elliptical yarns and yarns, consist of cylindrical microfibers, a suitable, revised rendering approach is needed. Therefore, a rendering model for woven fabric images is proposed "Microcylinder Appearance Model". The model needs some parameters to produce a realistic rendering of different fabric types such as silk, linen, velvet, and polyester satin. In the original article, authors derived these parameters via eye inspection. Our ultimate aim is to automatize this process. In this article, we propose a pipeline that does the first step of this automatization, i.e. segmenting the captured image via image processing techniques and finding the yarn crossing points. The segmented image can further be utilized to extract parameters of cylindrical shading models in future works.
Garlic Under Various Drying Study and Its Impact on Allicin Retention

Garlic (Allium sativum) has been cultivated for centuries all over the world on account of its culinary and medicinal properties. The present work has been carried out with the emphasis on selection of an efficient and cost-effective drying technique for garlic with highest allicin retention. For comparative study, drying of garlic samples were experimented with using different methods, namely heat pump dryer, hot air dryer, solar cabinet dryer, infra red dryer, vacuum dryer, freeze dryer, and fluidized bed dryer having different operating conditions. A comparison of the efficiency of various drying methods was based on the parameters, e.g., drying kinetics, allicin content, and color analysis. In addition, the effect of garlic slice thickness on the above-mentioned parameters was also studied. Drying of garlic shows a typical falling rate period in sliced form. It was observed that dried garlic samples using heat pump drying, vacuum drying, freeze drying, and heat pump cum fluidized bed drying showed lesser color change and excellent allicin retention compared to the other methods.
Doctoral Symposium: Dependable Distributed Content-Based Publish/Subscribe Systems

Content-based publish/subscribe systems provide an eﬃ-cient communication paradigm that allows decoupling of information producers and consumers across location and time. Distributed overlay-based publish/subscribe systems, while scalable, face many problems that hinders their applicability in scenarios requiring dependable communication. In this paper, we discuss three important dimensions of de-pendability in distributed content-based publish/subscribe systems, namely, availability, reliability and maintainability.