Miracle-dz/UniFAR-FTUs huggingface dataset:
Reservoir Sampling Based Streaming Method for Large Scale Collaborative Filtering

Collaborative filtering algorithms work on user feedback data (such as purchases, clicks, or ratings.) in order to build models of users and items. User feedback data in real life e-commerce sites can be very large which incurs high costs on maintenance and model building. Parallelization of computation might help but it results in additional costs for extra computing power and maintenance problems of very large datasets still persist. Sampling at this point can be an effective approach for reducing the amount of data. In this work we propose a novel sampling technique for collaborative filtering which can be used to reduce the amount of data considerably. Experimental results on three real life datasets show that the proposed method leads to a significant reduction in the amount of data with little harm to the accuracy of the models. The method works in a streaming fashion which makes it suitable for being used in real time at large-scale e-commerce applications where there is a large flow of continuous user feedback.
Master thesis : New tools for the analysis of Cytomine user viewing behavior

In the medical eld, doctors and researchers should be able to observe and interpret cell samples. The most widely spread method is to observe samples with a microscope. New methods allow for scanning samples into very large and detailed images. These images can be uploaded to the Cytomine web application and doctors can go through the images at ease. The application includes many functionalities, namely the ability to annotate regions of interest. Currently the ULiege MOOC server is open to the general public (see https//www.fun-mooc.fr). Anyone can take part in the MOOC, but it's mostly used for the bene t of the students studying the medical eld at the University of Liege. The students study particular images and are then evaluated at the end of the year. Meanwhile, the Cytomine app has been collecting data on the students' time spent on the website. The main objective is to develop IT tools to analyze data collected. The bulk of the data collected consists of where the students decided to look in the images (Gaze data). Consequently, a tool was implemented to nd correlations between students' behavior and the results they obtain during exams. Using Machine Learning techniques, the goal is to predict a student's grade based on how they used the application. This allows for the analysis of speci c viewing patterns that can give more insight on how the student used Cytomine. Currently, the model contains 395 students' data with over 2000 features. Extra Trees and Random Forest learning techniques have been applied to attempt to predict grades. Otherwise, other tools were implemented to better visualize these patterns. The idea would be to generate Heatmaps of the students' gaze data (Gazemap). These Gazemaps would be included in the application and users can be given access to this information. This could give teachers the ability to keep track of the students' work.
Improving the aerospace image quality using subpixel processing for the Earth's distance monitoring

Based on the averaging principle, an algorithm for subpixel processing of an aerospace image is developed to increase spatial resolution. With it, a high-resolution image can be gotten by processing several original shots with a lower resolution. Input information is a series of aerospace images that are offset relative to one another by half a pixel. There is one generated image with increased resolution appears as the result of the processing.
Enhancing the SZZ Algorithm to Deal with Refactoring Changes

SZZ was proposed by Śliwerski, Zimmermann, and Zeller (hence the SZZ abbreviation) to identify fix-inducing changes, i.e., changes that are likely to induce bugs. Despite the wide adoption of this algorithm, SZZ still faces limitations, which have been recently reported. No existing research work widely surveys how SZZ has been used, extended, and evaluated by the software engineering community. Furthermore, only a few studies have investigated the SZZ improvements. Therefore, this thesis aims to explore the existing limitations that have been documented in the SZZ literature and to enhance the state-of-the-art of SZZ by proposing solutions to some of its limitations. First, we perform a systematic mapping study to determine the current state-of-the-art of the SZZ algorithm. Our results exhibit that majority of the analyzed studies use SZZ as a foundation to conduct their empirical studies (79%), while only a few studies have proposed direct improvements to SZZ (6%) or evaluated it (4%). We further observe that SZZ exhibits several unaddressed limitations, such as the bias related to the refactoring changes. Second, we conducted an empirical study to investigate the relationship between the refactoring changes and the SZZ results. We use RefDiff — a tool that detects code refactoring with high precision — to analyze an extensive dataset, including 31,518 issues of ten systems, with 64,855 bug-fixes and 20,298 fix-inducing changes. We run RefDiff for both bug-fix and fix-inducing changes that were generated by a recent SZZ implementation, meta-change aware SZZ (MASZZ). The results indicate a refactoring ratio of 6.5% for fix-inducing changes and 19.9% for bug-fix changes. We incorporated RefDiff into MA-SZZ and proposed a refactoring aware SZZ implementation (RA-SZZ). RA-SZZ reduces the number of lines flagged as fix-inducing changes by MA-SZZ by 20.8%. These results indicate that refactoring can impact the SZZ results. Using an evaluation framework, we observe that RA-SZZ reduces the disagreement ratio compared to previous implementations; however, our results suggest the SZZ accuracy must still be improved. Finally, we evaluated the RA-SZZ accuracy using a well-accepted dataset, which we validated for evaluating SZZ implementations. Furthermore, we revisited the known RA-SZZ limitations to improve the accuracy of the algorithm by integrating a novel refactoring-detection tool, RMiner. We observed that after refining RA-SZZ, in the median, 44% of the lines that were flagged as fix-inducing are accurate, while only 29% were flagged accurately in case of the MA-SZZ-generated results. We also manually analyzed the RA-SZZ results and observed that there are still refactoring (31.17%) and equivalent changes (13.64%) to be recognized by SZZ. This result reiterates that detecting refactoring indeed increases the SZZ accuracy. Our thesis results contribute to SZZ maturation and indicate that the impact of refactoring upon SZZ may be even higher if further improvements can be made in future studies.
MALAYSIAN ONLINE JOURNAL OF EDUCATIONAL MANAGEMENT (MOJEM)

This research aims at validating a previously developed 5-factor model of changeoriented leadership capability scale in academic settings. Data were collected from Malaysian academic leaders in 25 public and private universities through an online survey distribution platform. The result of data screening procedure highlighted the existence of one outlying case, which was removed from the dataset. In addition, the assumption of multivariate normality was not met. Hence, to run a confirmatory analysis to test the factorial validity of the proposed scale, we applied the robust Maximum-Likelihood (ML) methodology in EQS 6.4 software package to correct the chi-square statistic and generate robust standard errors. The outcome of the analysis shed light on the fact that the change-oriented capability scale developed in academic settings was a 4-factor model. In addition, the fit indices and standard errors generated through conventional ML estimator and the robust method were compared to provide more insights regarding the undue influence of the multivariate non-normal data on fit indices and standard errors. The implications of the findings with respect to the practice, theory, and methodology were discussed as well.
Research of specific field ultra-short text classification based on collaborative filteringalgorithm

In some specific fields, there are a lot of ultra-short texts that need to be categorized. This paper proposes an ultra-short text classification method based on collaborative filtering algorithm aiming at the problems such as short text content, short length, sparse features, and large number of categories in certain fields. First, converting ultra-short text into word frequency vector by doing Chinese word segmentation and calculating word frequency; Secondly, combining relevant data in specific fields, defining the ultra-short texts as users, categories as items, and then constructing a user-item recommendation matrix. Finally, calculating text similarity by using cosine similarity method and obtaining the classification results. The experimental results show that the proposed method can well solve the problem of classification of ultra-short texts in specific fields, and the average accuracy is 9.19% and 3.81% higher than vector space model and topic similarity method respectively.
Embedded Implementation of Distress Situation Identification through Sound Analysis

ABSTRACTObjective: The development of an embedded system capable of detecting distress sounds, e.g. breaking glass or a cry for help, in a person’s home and notifying relevant personnel in the case of a distress situation.Methods: The system is based on a personal computer (PC) equipped with a sound card and microphone that is capable of performing real time analysis of sound signals. Sounds are processed through 4 modules: Sound Event Detection and Extraction, Sound/Speech Classiﬁcation, Sound Recognition and Speech Recognition. Training, testing and validation of the model was performed using 2 databases – a life sound database which we created and a French adapted speech corpus (a large and structured set of texts recorded by hundreds of different French speakers). Results: The system was found to be reliable for detecting and classifying sounds at signal to noise ratios of 10 decibels (dB) or more, with an error rate of 5% or less. However, it was less efﬁcient at sound and speech recognition. The error rate for sound recognition ranged from 9% to 37% at different sound levels. For speech recognition the error rate was 22%. This comprised 6% due to distress words being picked up in a normal sentence (leading to potentially false distress alerts) and 16% due to a distress word not being recognised (result-ing in potentially missed distress alerts). Conclusion: An embedded PC, equipped with a classical sound card and a microphone, is capable of real-time detection and analysis of sounds to detect distress situations. The system requires further reﬁnement to improve its accuracy before it can be evaluated in real-life.
A Method of Geocoordinate Measurement Based on Inertial Navigation of Long Oil and Gas Pipelines

In order to solve the problem of geocoordinate measurement and trajectory distribution of buried long oil and gas pipelines, the positioning accuracy of pipeline is improved. In this paper, the data fusion of the mileage data by Kalman filtering algorithm is used to solve the segmented trajectories of gyroscope and accelerometer observation data collected by inertial navigation system. Calculating the total mileage of 49.902 km, each position error with checkpoint contrast are controlled to less than 1 m. The overall trend with map match exactly, integrated positioning accuracy is 0.928 ‰. It has a certain reference for using inertial navigation technology buried long distance oil and gas pipeline geographic coordinates measurement.
The Effect Of Brand Equity, Marketing Mix, And Lifestyle To Purchase Decision At Maxx Coffee, Bogor

Coffee becomes one of the important commodities both in terms of consumption and in terms of production. Therefore, the interaction in marketing with the system and its activities should be able to get closer between consumers to products and brand name of the company offered. Nowdays, urban society at this time experiencing lifestyle changes. One of the manifestations of modern lifestyle today is the habit of certain community groups who like to consume coffee in coffee shops. Base on that, the purpose of this research is to identify the characteristics of MAXX Coffee consumer, analyze the influence of brand equity, lifestyle, and marketing mix of MAXX Coffee to consumer purchase decision, and to formulate alternative marketing strategy based on factors influencing marketing mix and brand equity MAXX Coffee. This research using Structural equation Modeling (SEM) with 271 respondent based on convenience sampling method during the reseach time. Based on the results of the research, in order to create strong brand equity, the management of MAXX Coffee Bogor can invest through the improvement of several marketing mix indicators, namely price, people, process, and physical. In the context of MAXX Coffee lifestyle focus to follow the perception of today's consumer lifestyle that wants to improve the prestige and image in the environment around consumers.
Data-Driven Cybersecurity Incident Prediction: A Survey

Driven by the increasing scale and high profile cybersecurity incidents related public data, recent years we have witnessed a paradigm shift in understanding and defending against the evolving cyber threats, from primarily reactive detection toward proactive prediction. Meanwhile, governments, businesses, and individual Internet users show the growing public appetite to improve cyber resilience that refers to their ability to prepare for, combat and recover from cyber threats and incidents. Undoubtedly, predicting cybersecurity incidents is deemed to have excellent potential for proactively advancing cyber resilience. Research communities and industries have begun proposing cybersecurity incident prediction schemes by utilizing different types of data sources, including organization’s reports and datasets, network data, synthetic data, data crawled from webpages, and data retrieved from social media. With a focus on the dataset, this survey paper investigates the emerging research by reviewing recent representative works appeared in the dominant period. We also extract and summarize the data-driven research methodology commonly adopted in this fast-growing area. In consonance with the phases of the methodology, each work that predicts cybersecurity incident is comprehensively studied. Challenges and future directions in this field are also discussed.
Improved Alignment Estimation for Autonomous Docking of Mobile Robots

This paper presents the methodology for an improved visual tracking, intended for the autonomous control of mobile robots. We propose the cumulated use of the image processing method and positioning sensors for an improved visual tracking. The method primarily uses detection, tracking, and recognition techniques to locate the targets. The use of such methods will enable the robot to monitor the real-time continuous changes in the orientation and the alignment with respect to the target module. The proposed methodology is implemented on a subsisting genderless coupling mechanism which is integrated into a multi-directional hybrid locomotion module to test the alignment accuracy in an autonomous docking procedure. The long-term objective is to demonstrate the autonomous docking and self-reconfiguration of multiple mobile robots.
Channel modeling and characterization for VLC indoor transmission systems based on MMC ray tracing method

The article presents methodology of obtaining the channel impulse responses (CIRs) on a way of numerical calculation for optical wireless transmission systems working indoor, including also those which work in scope of visible spectrum of light (VLC), based on the modified Monte Carlo ray tracing algorithm (MMC), which allows to determine them with really good quality for any room conditions in a short time. The influence of transmission channel is one of the most important factors, which is taken into consideration when design of wireless transmission systems is done. Using of the CIR is commonly applicable technique for this purpose. Based on the channel impulse response we can among others analyze and predict the link performance in the presence of noise and other factors, which can have significant impact on it final performance.
Efficient CNC Tool Path Planning Using Point Cloud

This paper reports the development of an efficient iso-scallop tool path planning strategy for machining of freeform surfaces on a three axis CNC milling center using the point cloud as the input. Boundary of the point cloud is chosen as the Master Cutter Path, using which the scallop points are computed. Adjacent side tool paths are computed using these scallop points and the path planning process is completed till the entire surface is covered. The system generates post-processed NC program in ISO format which was extensively tested for various case studies. The results were compared with the iso-planar tool path strategy from commercial software. Our system was found to generate efficient tool path in terms of part quality, productivity and storage memory.
Choreography as Design

ABSTRACT Design-based learning is often promoted as a way for students to practice the skills essential for their future success; however, little research in this area has been conducted in the field of dance. One aim of this research is to contribute to the literature by examining the process through which undergraduate students from multiple disciplines come together to solve an innovative design challenge within a choreography course. The major assignment required students to express an issue of concern in their discipline, life, or worldwide through the synthesis of dance, music, visuals, text, and technology into three- to four-minute digital choreography. The researchers conducted qualitative data analysis of artifacts, and in-class discussions of the final presentations. The goal was to determine the extent to which students utilized the design process in their work and to identify ways to enhance the course implementation for future iterations.
Extending a constrained hybrid dynamics solver for energy-optimal robot motions in the presence of static friction

Friction effects impose a requirement for the supplementary amount of torque to be produced in actuators for a robot to move, which in turn increases energy consumption. We cannot eliminate friction, but we can optimize motions to make them more energy efficient, by considering friction effects in motion computations. Optimizing motions means computing efficient joint torques/accelerations based on different friction torques imposed in each joint. Existing friction forces can be used for supporting certain types of arm motions, e.g standing still. Reducing energy consumption of robot's arms will provide many benefits, such as longer battery life of mobile robots, reducing heat in motor systems, etc. The aim of this project is extending an already available constrained hybrid dynamic solver, by including static friction effects in the computations of energy optimal motions. When the algorithm is extended to account for static friction factors, a convex optimization (maximization) problem must be solved. The author of this hybrid dynamic solver has briefly outlined the approach for including static friction forces in computations of motions, but without providing a detailed derivation of the approach and elaboration that will show its correctness. Additionally, the author has outlined the idea for improving the computational efficiency of the approach, but without providing its derivation. In this project, the proposed approach for extending the originally formulated algorithm has been completely derived and evaluated in order to show its feasibility. The evaluation is conducted in simulation environment with one DOF robot arm, and it shows correct results from the computation of motions. Furthermore, this project presents the derivation of the outlined method for improving the computational efficiency of the extended solver.
Prototype for Product Validation Process of IoT System

Third-party verification targeting system products are applied to mission-critical systems. The authors published a method of applying third-party verification to consumer products and of estimating the occurrence location and the cause of defects detected via verification. For development of the Internet of Things (IoT) systems, prototype development is a checkpoint in the development process. We verified prototypes from a third-party perspective and presented a method to calculate the ease of continuing to develop prototypes as they are, as a distance from prototype to completion of development using this result. We also propose a method to confirm product specifications by evaluating the product using limited users suitable for the tester of the prototype, and by clarifying the purpose of use and the implementation method. This method determines product specifications based on evaluation results for prototypes by limited users. The authors applied it to the development of a hydroponic cultivation system and drone deterrence of harmful bird systems. Results confirmed the effectiveness of this method.
An Adaptive Filter for IMU/Encoder Data Fusion for Acceleration Estimation in Robot Arms

This paper develops an adaptive filter for fusing the noisy and biased measurement data from MEMS-based inertial measurement units and encoders for estimation of acceleration in robot arms. A discrete-time second-order model is derived for designing an adaptive Kalman filter (AKF). The output of the AKF is an unbiased but noisy estimate of the acceleration. To cancel the noise, a recursive least square filter is designed to filter the output of the designed AKF. It is shown that the resulting filtered signal is unbiased and noise-cancelled. Experimental results demonstrating the effectiveness of the developed adaptive filter are presented.
Time Series Traffic Speed Prediction Using k-Nearest Neighbour Based on Similar Traffic Data

During the past few years, time series models and neural network models are widely used to predict traffic flow and traffic congestion based on historical data. Historical data traffic from sensors is often applied to time series prediction or various neural network predictions. Recent research shows that traffic flow pattern will be different on weekdays and weekends. We conducted a time series prediction of traffic flow on Monday, using data on weekdays and whole days data. Prediction of short time traffic flows on Monday based on weekdays data using k-NN methods shows a better result, compared to prediction based on all day’s data. We compared the results of the experiment using k-NN and Neural Network methods. From this study, we observed that generally, using similar traffic data for time series prediction show a better result than using the whole data.
Stability Analysis and Stabilization Strategy for Discrete Networked Control Systems

For discrete networked control systems (NCSs) with random and bounded network transmission delay, sufficient conditions which make state-feedback NCSs asymptotically stable are derived by the Lyapunov-Krasovskii functional and linear matrix inequality (LMI). By matrix transformation, sufficient conditions above are converted to controller design methods which make the state-feedback control system asymptotically stable. The simulation proves that the system conservativeness is reduced.