Miracle-dz/UniFAR-FTUs huggingface dataset:
Code Reviews for Visual Programming Languages

Visual languages such as OutSystems are being progressively more used by large teams that collaborate on enterprise grade projects. This results in new challenges when it comes to ensuring the quality of the applications built with this languages, without sacrificing productivity. An increasingly common way to improve quality is performing distributed lightweight tool supported code reviews. A code review consists of an analysis of the code by one or more developer. The objective for this is to ensure that the changes do not introduce bugs and adhere to the established coding best practices. With this practice, companies can offer higher quality and easier to maintain software. However, the current tools and processes for code review are focused on textual programming languages and opening a bigger gap for the visual ones. The main goal of this dissertation is to develop a specialized tool to promote and making visual programming languages code review more accessible. An easy-to-follow process was also created to support the tool. At the moment, the tool can be used with OutSystems’ language and UML class diagram, however, new languages can easily be added to the solution. We examined the code review method currently used by developers vs. the new method created concerning defect detection rates and user experience. Also conducted individual interviews with 30 computer science engineers. Results were collected with the help of questionnaires and analyzed using multiple descriptive statistics. The new code review method was significantly better than the current method used, reaching average usability improvements of 47.75%, tasks load reduction of 15,43%. Generalization of results is limited since the analyzed techniques were applied only to a small set, consisted of 30 testers. The result supported the evidence concerning the advantage of the new code review method using the developed tool over the current method of reviewing visual artifacts.
SUCCESS FACTORS OF COLLABORATION IN THE CONTEXT OF DEVOPS

Context: Collaboration is an important aspect of DevOps. However, researchers continue to report problems with collaboration between development and operations. Objective: We aim to contribute to a better understanding of collaboration in the context of DevOps, especially to aid in adopting DevOps. Method: We conducted a literature review on collaboration and corresponding success factors. For practice we classified the factors into factors influencing the likelihood of collaboration and factors influencing the performance of collaboration, to aid in the adoption of DevOps. We will validate the factors of collaboration in a DevOps context using an inductive approach, i.e. semi-structured interviews and document studies. Results: We developed an initial framework of success factors and classified the found success factors. Subsequently we conducted two interviews. We learned that most factors are considered as important, however few factors could be confirmed and no more factors are added. We will interview the other respondents to prove the applicability of the framework. Which may identify promising avenues for further development of the framework. Conclusions: This is study is not finalized yet, therefore we cannot fully answer the main research question.
Energy Efficiency Analysis by Game-Theoretic Approach in the Next Generation Network

This paper presents a game-theoretic scheme with anti-coordinated players by incorporating adaptation of femto base station (FBS) transmit power, attenuation of interference and utility function for open access mode and closed access mode, respectively. The deployment of femtocells in the networks is to produce improved energy efficiency (EE) and optimized response of payoff function. Additionally, the operating principle of the spectrum sharing scheme has been discussed in which FBS as a player acquire knowledge from utility responses of their strategic communications and revise their strategies at each level of the game process. Here, an FBS is regarded as a player in the game to select those users who are satisfied to a greatest extent and besides an FBS plays a role of mentor. Thereafter, the equilibrium concept has been invoked to aid the anti-coordinated players for the strategies. Finally, validated the simulation results are with its rarely studied extension in cognitive-femtocell networks.
Identification of Retinoblastoma Using the Extreme Learning Machine

Retinoblastoma is cancer on the retina that often occurs in infants and children that can cause blindness and even death. The cause of retinoblastoma is due to the mutation of the RB1 gene which keeps retinal cells reproducing until the tumor grows on the retina. In general, to identify a retinoblastoma the doctor uses an ophthalmoscope that shines brightly through the pupil to examine the back of the eye and see the presence of white or yellowish white tumor lesions in the eye. In addition, the examination is also done by analyzing the retinal fundus image from the fundus camera. The image of the fundus camera is re-analyzed by a doctor or expert to determine whether or not there is retinoblastoma. Therefore, a system is needed to help the expert to diagnose retinoblastoma. The method used in this study is the extreme learning machine. Retinal fundus images are used as input images to identify retinoblastoma. Before being identified, pre-processing of the image is carried out, it consists of scaling, green channel, contrast stretching, thresholding, and feature extraction using the zoning method. From this study, it was concluded that the proposed method had the ability to identify retinoblastoma with an accuracy of 92%.
Design of an Efficient Current Mode Full-Adder Applying Carbon Nanotube Technology

In this article a new design of a current mode full-adder is proposed through the field effect transistors based on carbon nanotubes. The outperformance of the current mode full-adder constructed by CNTFET compared to that of constructed by CMOS is observable in the simulation and comparisons. This circuit operates based on triple input majority function. The simulation is run by HSPICE software according to the model proposed in Stanford University for CNTFETs at 0.65 V power supply voltage. The proposed circuit outperforms compared to the previous current mode full-adders in terms of speed, accuracy and PDP.
Activity and Fall Detection in the Habitational Environment: Subsystem Interface

This report describes the design and implementation of an interface subsystem for a fall detection system using a pressure based floor sensor. The goal of the fall detection system is to detect and alarm when an elderly person has fallen. The subsystem covers communication between hardware and software, using the WiFi protocol MQTT. The communication is fast enough so that it does not limit other subsystem performance and allows for high expandability to accommodate the hardware’s modular design. The interface module receives a probability of fall occurrence from the separate processing module, from which an average is taken over a 30 second time window to avoid false positives. Alarming is done through the use of text-to-speech voice API to make automated phone calls that retrieve user input to confirm or deny that help is on the way. A habit tracker proof of concept is provided that could improve the accuracy of fall detection by checking if the client is showing anomalies during a given day. A user interface is implemented using the Kivy environment. This environment has its own language which allows for separation of functionality and layout, which keeps the interface elements separate and leads to ease of use. Overall, this results in a strong alternative for fall detection that could be used to improve the time an elderly person can live at home safely without the need to move to a nursing home.
Reconsidering Service Failures and Service Recovery Strategies: Introducing Mechanism of Value Restoration (MVR)

Although Service-Dominant (S-D) Logic is an emerging school of thought in marketing literature, recovery strategies after service failures continue to be conceptualized under the good-dominant logic paradigm. This paper proposes a different conceptualization for service failures and recoveries strategies in the S-D Logic framework. Thus, we conceptualize service failures as co-destruction moments and service recovery strategies as Mechanism of Value Restoration (MVR) by applying Prahalad and Ramaswamy’s DART model. Key-words:Service-Dominant Logic, Services failures, Service Recovery Strategies
Supervisory Control of Multilevel Discrete-Event Systems with a Bus Structure

High-tech systems often contain a few components with many dependencies across the system, acting as system-level integrating components. In a hierarchical system decomposition, such so-called bus components tend to be placed in the top node. In supervisory control synthesis, this results in a significant increase of the state space. In this paper, a method is proposed to transform a set of plant models and requirement models into a tree-structured multilevel discrete-event system with a bus structure. After recording the dependencies within the system, the bus and the non-bus plant models are identified and separated. Subsequently, each set of plant models is clustered separately, resulting in a multilevel discrete-event system. Finally, these two systems are merged into a single multilevel discrete-event system where bus plant models are distributed over the multilevel discrete-event system. Experimental results of several models available in the literature are reported to assess the applicability of the proposed method.
Controlled Release Behaviors of Tribenuron Methyl Herbicide and Potassium Nitrate Fertilizer from Natural Polymer Blended Poly(Vinyl Alcohol) Hydrogel Microspheres

Polyvinyl Alcohol (PVA) hydrogel microspheres for controlled release of fertilizer (KNO3) and Tribenuron Methyl Herbicide (TBM) were prepared by blending the naturally occurring Alginate (AL) and Pectin (PE). The cross-linking in PVA hydrogel microspheres was carried out by varying the amount of glutaraldehyde from 0.5-1.0 wt%. To control the loading and release properties of PVA hydrogel microspheres, a small amount of NaCl (0.2 wt%) was also added in fromulation of microspheres. The blending of PE and AL in PVA hydrogel microspheres was confirmed with FT-IR spectra and by increased thermal stability PVA hydrogel as determined by Differential Scanning Calorimetry (DSC) and Thermogravimetric (TGA) analysis. The PVA hydrogel microspheres were characterized for degree of swelling and for loading and cumulative release of fertilizer (KNO3) and herbicide (TBM), which were carried out in solutions of different pH and at different temperatures. The Scanning Electron Micrographs (SEM) of PVA hydrogel microspheres were recorded before and after the release of loaded agrochemicals and used to confirm the shape and homogeneous blending of PE/AL in PVA hydrogels. The enhanced brittle morphology form SEM images of PVA hydrogel microspheres after the release of loaded agrochemicals has suggested the controlled release of agrochemicals through a mechanism of degradation of polymer chains. Thus natural polymer blended PVA hydrogel microspheres are found to be a potential candidate for the formulation of controlled release systems for the application of agrochemicals in soils and to overcome the environmental problem, which usually encountered on applications of fertilizers and herbicides in agricultural fields by conventional methods.
Remaining useful life prediction for the electro-hydraulic actuator based on improved relevance vector machine

The electro-hydraulic actuator plays a significant role in the automatic flight control system, which is critical to the flight security, so it is vital to monitor the state of health and predict the remaining useful life for the electro-hydraulic actuator. Relevance vector machine is flourishing in the field of remaining useful life prediction and gradually applied to the prediction of complex systems or components. However, the general relevance vector machine cannot achieve on-line prediction efficiently due to its high computational complexity, besides, the sparse relevance vector machine model which is only based on historical data set could cause a great prediction error in the long term. To deal with these plights, an optimized incremental learning and on-line training algorithm based on the relevance vector machine is presented taking full advantage of the on-line updating samples to improve the precision of prediction, and in order to remedy the problem of computational complexity and improve the computational efficiency, the “sample entropy” is introduced as an effective signature of the electro-hydraulic actuator’s health to effectively reduce the size of training samples. The effectiveness of the proposed on-line training approach is evaluated through an experiment of an electro-hydraulic actuator, and the results show a satisfactory prediction accuracy as well as an improvement in the computational efficiency for remaining useful life prediction.
A Review on Gas Turbine Gas-Path Diagnostics: State-of-the-Art Methods, Challenges and Opportunities

Gas-path diagnostics is an essential part of gas turbine (GT) condition-based maintenance (CBM). There exists extensive literature on GT gas-path diagnostics and a variety of methods have been introduced. The fundamental limitations of the conventional methods such as the inability to deal with the nonlinear engine behavior, measurement uncertainty, simultaneous faults, and the limited number of sensors available remain the driving force for exploring more advanced techniques. This review aims to provide a critical survey of the existing literature produced in the area over the past few decades. In the first section, the issue of GT degradation is addressed, aiming to identify the type of physical faults that degrade a gas turbine performance, which gas-path faults contribute more significantly to the overall performance loss, and which specific components often encounter these faults. A brief overview is then given about the inconsistencies in the literature on gas-path diagnostics followed by a discussion of the various challenges against successful gas-path diagnostics and the major desirable characteristics that an advanced fault diagnostic technique should ideally possess. At this point, the available fault diagnostic methods are thoroughly reviewed, and their strengths and weaknesses summarized. Artificial intelligence (AI) based and hybrid diagnostic methods have received a great deal of attention due to their promising potentials to address the above-mentioned limitations along with providing accurate diagnostic results. Moreover, the available validation techniques that system developers used in the past to evaluate the performance of their proposed diagnostic algorithms are discussed. Finally, concluding remarks and recommendations for further investigations are provided.
METRICS AND TOOLS THAT ARE AVAILABLE FORTESTING SCALABILITY

Scalability covers the ability of an application to cope up with heavier processing load as desired. Other than that, it also covers how small one can start a system. Testing scalability is a very challenging task among all the other quality attributes i.e. performance, reliability, usability, accessibility and security. However empirically there is not much research work done on this specific domain. The focus of this study is to identify the all the metrics and tools that are in practice for measuring the scalability of a specific system. Moreover, finding out the challenges that are being faced while performing these measurement practices. To collect information regarding the identification of metrics and tools for testing scalability, a systematic mapping study was conducted. Through this method we tried to get a generalized data from many resources over the internet.
Efficient User Guidance for Validating Participatory Sensing Data

Participatory sensing has become a new data collection paradigm that leverages the wisdom of the crowd for big data applications without spending cost to buy dedicated sensors. It collects data from human sensors by using their own devices such as cell phone accelerometers, cameras, and GPS devices. This benefit comes with a drawback: human sensors are arbitrary and inherently uncertain due to the lack of quality guarantee. Moreover, participatory sensing data are time series that exhibit not only highly irregular dependencies on time but also high variance between sensors. To overcome these limitations, we formulate the problem of validating uncertain time series collected by participatory sensors. In this article, we approach the problem by an iterative validation process on top of a probabilistic time series model. First, we generate a series of probability distributions from raw data by tailoring a state-of-the-art dynamical model, namely Generalised Auto Regressive Conditional Heteroskedasticity (GARCH), for our joint time series setting. Second, we design a feedback process that consists of an adaptive aggregation model to unify the joint probabilistic time series and an efficient user guidance model to validate aggregated data with minimal effort. Through extensive experimentation, we demonstrate the efficiency and effectiveness of our approach on both real data and synthetic data. Highlights from our experiences include the fast running time of a probabilistic model, the robustness of an aggregation model to outliers, and the significant effort saving of a guidance model.
A Fast method for Denoising the signal based on CIC filter

: This paper presents use of cascaded integrated comb filter and is mainly used to denoise the signal easily . It keeps the continuous record of the filters and noise can be removed from the signal and then performing the cascaded process on the signal. Recently non uniform filter bank technique used. The input signal can be reconstructed same as the output signal . The main drawback in this method is there is no difference between the input and output signal and also there is no possibility to calculate any metrics. So, to overcome the above problem we prefer the cascaded integrated comb filter (CIC) . Acquiring the input signal and applying CIC in order to denoise the signal. ,In this CIC the process carried out in seven phases as the output of first phase connected to the input of second phase and the process continues up to seven phases then we obtain the denoised signal. The metric calculation can also be done using MAE (mean absolute error) In the extension, we use cascaded integrated comb filter and is mainly used to denoise the signal easily . It keeps the continuous record of the filters and noise can be removed from the signal and then performing the cascaded process on the signal.
Estimating attraction by quantifying bodily coordination using wearable sensors

In this thesis, prediction of romantic, social and sexual attraction between two people using bodily coordination features is studied. Attraction is one type of interest that can occur between interacting people and understanding the modeling of it can help understand how to model other types of interests in human-human interactions because similar methods can be used to model the non-verbal behavior that reveals interest. Previous research in psychology and social signal processing fields showed that synchrony and convergence of both audio features and body movements of people during an interaction are indicators of interest. However, audio features require recording people's voices during interactions and it can be disturbing for people especially during more personal conversations. In addition, capturing nonverbal cues from video mostly requires recording people with a camera from front and it might make people more aware of being recorded and intervene with the naturality of the interaction. Moreover, processing the videos to extract specific nonverbal behavior can be costly. Based on these ideas, we decided to use motion channel and hypothesize that movement synchrony and convergence features can be used to automatically quantify and predict attraction. We propose a novel method of estimating romantic, social and sexual attraction between two people by quantifying their bodily coordination using wearable sensors in a speed-date setting. We developed simple synchrony and convergence features, inspired from the literature and specifically adapted to be extracted from accelerometer data. To our knowledge, this is the first time that motion convergence is used for estimating attraction. Our features could predict one-way social attraction with a 73% Area under the ROC curve (AUC), out-performing previous work in a similar setting. We also showed that prediction performance increased when the male and female data are separated, aligning with the theories in psychology studies. We could also predict mutual romantic attraction with an AUC of 80%. We found that different types of attraction can be estimated better using different feature types, more specifically we could predict social attraction better using movement correlation features whereas for romantic and sexual interest mimicry features were better indicators. Moreover, features extracted from different types of signals recorded from accelerometers showed varying performances for different attraction types. Additionally, asymmetric features out-performed the symmetric features and our synchrony features showed better performance than convergence features. Finally, we a have seen that motion convergence can occur in people having an interaction regardless of attraction.
EXTENDED THE SAM MODEL - PROPOSED A SYNCHRONIZE MODEL -

Integration between business and information technology (IT) has become one of the top priorities for today's organizations, known as "strategic alignment", which has resulted in several models. The model that has attracted the most attention in this area is the Strategic Alignment Model (SAM) proposed by (Henderson and Venkatraman, 1993a). Any organization needs to be flexible and dynamic to support internal activities and external exchanges. The SAM model does not convene several points on this dynamic integration. Our article will result in giving the limits and weakness of the SAM model by propagating the studies impeded by the model and concluding with a proposal of the revisited SAM model which will lead to the part of the implementation of the business and IT strategy and the infrastructure part, which must meet the requests to strategic and operational objectives with a synchronization that anticipates each time if there is a verification to do or to renew again the strategy and realign the business IT in a constantly evolving and dynamic way.
INVESTIGATION OF USER ADAPTABILITY AND SHARING PATTERNS ON MULTIPLE SOCIAL MEDIA PLATFORMS

Emergence of new features on social media platforms have become a daily occurrence. Stories, live videos, memories and various other emerging features are a crucial part of most of the popular social media platforms. Through this study, we conduct a qualitative assessment of user adaptation to these revolutionizing social media features. The aim of this study is to understand user preferences based on platforms, their adaptability and usage of these features. This study derives conclusions based on a semi-structured interview conducted on 18 users in their early twenties in the UAE. The study derives conclusions on changes in user behavior on Facebook, user strategies for managing different audiences and content across social media platforms, views on cross-linking and cross-sharing of social platforms and using social media as a common portal to keep track of daily happenings. The study finds that users treat social media platforms distinctly while tending to share different contents on each platform depending on the nature of the content and the audience of each platform.