Miracle-dz/UniFAR-FTUs huggingface dataset:
Guiding Development of Contribution and Community Strategies in Open Source Software Requirements Engineering

Background:For software-intensive organizations, Open Source Software (OSS) may provide a pivotal building block in business models and strategies, product and service offerings, as well as in tool and infrastructure setups. The Requirements Engineering (RE) and development processes of OSS take place inside communities where the focal organization is a stakeholder among many, including competitors. Therefore, to exploit the potential benefits of OSS, an organization has to consider what it shares as OSS and how it engages with the OSS communities. By being too open, an organization may expose itself to risks such as giving away differentiating functionality. On the contrary, being too closed may cause misalignment between an organization's RE process and that of a community.Objective:The objective of this thesis is two-fold. Firstly, to create guidance for organizations in making decisions of what to share as OSS in line with the organization's business goals. Secondly, to create guidance for how an organization can identify OSS communities where they need to have an influence on the RE process, and how they can gain it, in order to achieve its internal agenda.Research Methodology:We used a design science research approach, applying empirical software engineering research methods to investigate the problem context and design and validate solution artifacts that may be used as treatments in the problem context. The relevance of the research has been maintained through a close industry collaboration with several case studies and interview surveys.Results: To address the two objectives, we introduce the two concepts of contribution and community strategy. Contribution strategies answer the questions if a software artifact (e.g., a feature or project) or parts of it should be released as OSS, when in time, and if it should be contributed to an existing OSS community, or if a new community should be established. Community strategies answer the questions what OSS communities an organization considers as important and need to have an influence on in terms of their RE process, and also how this influence may be gained. The thesis offers problem understanding of how organizations reason in terms of these questions, as well as guidance for how the different types of strategies may be developed. In regards to contribution strategies, results also offer guidance on how to create supporting guidelines, processes, and infrastructure on an organizational level.Conclusion:The results of this thesis are captured in a number of frameworks, models, and methods. These artifacts contribute to an understanding of the problem context and provide design knowledge and exemplars that may be transferred and implemented by organizations in a real-world problem context. Evaluation of such a technology transfer is a topic for future work. (Less)
Multi-Level Feature Network With Multi-Loss for Person Re-Identification

Person re-identification has become a challenging task due to various factors. One key to effective person re-identification is the extraction of the discriminative features of a person’s appearance. Most previous works based on deep learning extract pedestrian characteristics from neural networks but only from the top feature layer. However, the low-layer feature could be more discriminative in certain circumstances. Hence, we propose a method, named the multi-level feature network with multiple losses (MFML), which has a multi-branch network architecture that consists of multiple middle layers and one top layer for feature representations. To extract the discriminative middle-layer features and have a good effect on deeper layers, we utilize the triplet loss function to train the middle-layer features. For the top layer, we focus on learning more discriminative feature representations, so we utilize the hybrid loss (HL) function to train the top-layer feature. Instead of concatenating multilayer features directly, we concatenate the weighted middle-layer features and the weighted top-layer feature as the discriminative features in the testing phase. The extensive evaluations conducted on three datasets show that our method achieves a competitive accuracy level compared with the state-of-the-art methods.
Verifikasi Tanda Tangan Online Menggunakan Algoritma Genetika Dan Support Vector Machine

Abstrak Tanda tangan merupakan salah satu alat autentifikasi yang sering digunakan. Banyak hal didunia ini yang diresmikan menggunakan tanda tangan. Setiap orang memiliki karakteristik tanda tangan yang cukup beragam. Pengenalan tanda tangan secara offline masih mungkin memiliki banyak kesalahan karena itu dikembangkan pengenalan tanda tangan secara online dengan menggunakan fitur-fitur dinamis dari tanda tangan. Pada penelitian ini, dibangun dua skema yaitu tanpa pemilihan fitur menggunakan Algoritma Genetika dan tanpa pemilihan fitur. Sistem verifikasi ini menggunakan algoritma Support Vector Machine(SVM) untuk memverifikasi tanda tangan karena SVM sudah terbukti di penelitian sebelumnya dapat menghasilkan akurasi yang baik. Penelitian ini juga ditujukan untuk menemukan fitur-fitur yang penting dalam sebuah tanda tangan dari enam kelompok fitur yang diuji. Dataset yang digunakan adalah dataset SVC2004 yang berisi tanda tangan 5 orang yang masing masing memiliki 20 tanda tangan asli dan 20 tanda tangan palsu yang ditiru oleh professional. Hasil penelitian menunjukkan Algoritma Genetika dapat menghasilkan akurasi 94.40% dan lebih baik 4.21% dibandingkan tanpa melalui pemilihan fitur. Kelompok fitur yang berpengaruh adalah kelompok fitur Geometry dan Miscellaneous karena menghasilkan akurasi yang lebih baik daripada kelompok fitur lainnya. Kata kunci : verifikasi tanda tangan, algoritma genetika, Support Vector Machine(SVM), kelompok fitur Abstract Signatures are one of the most commonly used authentication tools. Many things in this world are inaugurated using signatures. Everyone has signature characteristics that are quite diverse. The verification of offline signatures may still have many errors because it developed the verification of signatures online by using the dynamic features of the signature. In this research, two schemes are built without the feature selection using Genetic Algorithm and without feature selection. This verification system uses the Support Vector Machine (SVM) algorithm to verify the signature because SVM has been proven in previous research to produce good accuracy. The study is also intended to find important features in a signature of the six groups of features tested. The dataset used is a SVC2004 dataset containing 20 authentic signatures and 20 fake signatures imitated by professionals of 5 users. The results showed Genetic Algorithm can produce 94.40% and 4.21% better than without the selection of features. Influential feature groups are Geometry and Miscellaneous feature groups because they produce better accuracy than other feature groups. Keywords: signature verification, Genetic Algorithm, Support Vector Machine(SVM), feature group
Biometric Attendance System using Microcontroller

- the project is totally based on Atmega 8 microcontroller. This project will take the attendance and display it when it is requested from the biometric attendance system. In this project fingerprint of different persons are sensed by the finger print module and it recognizes the person and provides attendance and display it on the LCD display. This is time saver project in which there is no requirement of any person to come and take the attendance of a class or organisation. This project can be implemented in schools or organizations and it’s a cost effective project. It is fast easy and reliable to use.
Rice Straw and Rice Straw Ash for the Removal of Brilliant Green Dye from Wastewater

Aims: The use of economical, simply obtained and green adsorbents has been employed as a perfect alternative to the expensive methods of removing dyes from aqueous solution. The capability of rice straw and rice straw ash to remove brilliant green dye by the adsorption process has been studied. Study Design: Adsorption studies were carried out at different initial dye concentrations, contact time and adsorbent dosages. Place and Duration of Study: Regional center for food and feed, Agricultural research center. Methodology: Adsorption data were modeled using Langmuir, Freundlich, Temkin adsorption isotherms. Results: Freundlich model showed the best fit with the equilibrium data for rice straw; however Langmuir model was better fitted for rice straw ash. Kinetic adsorption records were modeled using pseudo-first-order and pseudo-second-order. It was found that pseudo-second-order was best fit with the equilibrium data. Rice straw and rice straw ash were characterized by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM). Conclusion: Rice straw as an agriculture waste by-product could be used as an alternative to commercial activated carbon as adsorbent due to its availability, removal efficiency and low cost.
Big Data Methods

Advances in data science, such as data mining, data visualization, and machine learning, are extremely well-suited to address numerous questions in the organizational sciences given the explosion of available data. Despite these opportunities, few scholars in our field have discussed the specific ways in which the lens of our science should be brought to bear on the topic of big data and big data's reciprocal impact on our science. The purpose of this paper is to provide an overview of the big data phenomenon and its potential for impacting organizational science in both positive and negative ways. We identifying the biggest opportunities afforded by big data along with the biggest obstacles, and we discuss specifically how we think our methods will be most impacted by the data analytics movement. We also provide a list of resources to help interested readers incorporate big data methods into their existing research. Our hope is that we stimulate interest in big data, motivate future research using big data sources, and encourage the application of associated data science techniques more broadly in the organizational sciences.
Modeling Human Activity With Seasonality Bursty Dynamics

The public's purchase incentive increases dramatically during the holiday season and subsequently returns to normal levels. This seasonality is common in various scenarios and highlights the following questions: how does the public's purchase incentive fluctuate over the course of a year? Which factors are conducive to this seasonal behavior and how can they be modeled? In this paper, we propose a model that explicitly integrates temporal point process theory with the construction of a networked community, to describe the dynamics of collective action propagation with seasonal fluctuation. Furthermore, a database is constructed of sales records for 21 video game consoles and 13 237 video games in France, Germany, Japan, the U.K., the USA, and worldwide from 1989 to 2018. Experimental results suggest that peak desire always appears in the holiday season about one week before Christmas and is about four times higher than consumption desire in a normal period in all areas.
Sunglint correction in airborne hyperspectral images over inland waters

This study assessed sunglint effects in airborne high spatial and high spectral resolution images acquired by the SpecTIR sensor under different view-illumination geometries over the Brazilian Ibitinga reservoir (Case II waters). These effects were corrected using the Goodman et al. (2008) and the Kutser et al. (2009) methods, and a variant that used the continuum removal technique to calculate the oxygen absorption band depth. The performance of each method to removing sunglint effects was evaluated by a quantitative analysis of pre- and post-sunglint correction reflectance values (residual reflectance images). Furthermore, the analysis was supported by inspection of the reflectance differences along transects placed over homogeneous masses of waters or over specific portions of the scenes affected and non-affected by sunglint. Results showed that the algorithm of Goodman et al. (2008) produced better results than the other two methods, as it approached to zero the amplitude of the reflectance values between homogenous water masses free and contaminated by sunglint. The Kutser et al. (2009) method had also good performance, except for the most contaminated sunglint portions of the scenes. When the continuum removal technique was incorporated to the Kutser et al. (2009) method, results varied with the scene and were more sensitive to atmospheric correction artifacts and instrumental signal-to-noise ratio.
Developing students' worksheet based on scientific approach in cell as the system of life

Student’s worksheet scientific approach is a teaching material that contains material, teaches students to experiment by following the steps based on the scientific method. The scientific method is observed, ask, gather information, associate, and communicate. This study aims to determine the feasibility of student’s worksheets based on class VII scientific approach. This type of research was Research and Development (R & D) with the Four D research model. The sample used was Kalianget 2 Public Middle School. The result of the research showed that feasibility of Student Worksheets based on scientific approach obtained very feasible category, with material feasibility percentage of 86.11% (very feasible), format feasibility of 85.42% (very feasible), display feasibility of 85.94% (very feasible). Based on the research, it can be concluded that scientific approach-based student’s worksheet feasibility had been categorized as very feasible to use, and student’s response towards it was positive (categorized as very good).
A New Process for Selecting the Best Background Representatives based on GMM

Background subtraction is an essential step in the process of monitoring videos. Several works have been proposed to differentiate the background pixels from the foreground ones. Mixtures of Gaussian (GMM) are among the most popular models for this problem. However, they suffer from some inconveniences related to light variations and complex scene. This paper proposes an improvement of the GMM by proposing a new technique of ordering the Gaussians distributions in the selection phase of the scene’s best representatives. This approach replaces the usual ranking of Gaussian according to the value of wk,t/σt with sorting according to their covariance measure which is calculated between each pixel and each of these Gaussians. The obtained results on the Wallflower dataset has proven the effectiveness of the proposed approach compared to standard GMM.
Interactive Bike Lane Planning Using Sharing Bikes’ Trajectories

Cycling as a green transportation mode has been promoted by many governments all over the world. As a result, constructing effective bike lanes has become a crucial task to promote the cycling life style, as well-planned bike lanes can reduce traffic congestions and safety risks. Unfortunately, existing trajectory mining approaches for bike lane planning do not consider one or more key realistic government constraints: 1) budget limitations, 2) construction convenience, and 3) bike lane utilization. In this paper, we propose a data-driven approach to develop bike lane construction plans based on the large-scale real world bike trajectory data collected from Mobike, a station-less bike sharing system. We enforce these constraints to formulate our problem and introduce a flexible objective function to tune the benefit between coverage of users and the length of their trajectories. We prove the NP-hardness of the problem and propose greedy-based heuristics to address it. To improve the efficiency of the bike lane planning system for the urban planner, we propose a novel trajectory indexing structure and deploy the system based on a parallel computing framework (Storm) to improve the system's efficiency. Finally, extensive experiments and case studies are provided to demonstrate the system efficiency and effectiveness.
Hierarchical Multiagent Reinforcement Learning for Coordinating Verbal and Non-verbal Actions in Robots

This paper proposes an approach for learning to coor- dinate verbal and non-verbal behaviours in interactive robots. It is based on a hierarchy of multiagent reinforcement learners executing verbal and non-verbal actions in parallel. Our approach is evaluated in a conversational humanoid robot that learns to play Quiz games. First experimental results show evidence that the proposed multia- gent approach can outperform hand-coded coordinated behaviours.
China's high‐tech industry efficiency measurement with virtual frontier data envelopment analysis and Malmquist productivity index

Data envelopment analysis (DEA) is a widely used non‐parametric method in efficiency measurement with multiinputs and multioutputs. Malmquist productivity measures the efficiency change in different periods and decomposes the general efficiency change into technical efficiency change and frontier shift. In this paper, we choose a driving industry in social development, the high‐tech industry, as an example to illustrate a new method, virtual frontier DEA model, in the aspect of improvement of the traditional DEA model. Additionally, we decompose the Malmquist productivity index with virtual frontier DEA model to find out the driving force of high‐tech efficiency change.
4-Port 2-Element MIMO Antenna for 5G Portable Applications

In this paper, a compact, low-profile four-port, two-element antenna for the 5G Internet of Things (IoT) and handheld applications with height <inline-formula> <tex-math notation="LaTeX">$h=3.0$ </tex-math></inline-formula> mm is presented. The antenna structure contains two planar inverted-F antenna (PIFA) elements having the same shapes. Each antenna element has two feeding plates placed at the right angle to each other to make them cross-polarized for the exploitation of polarization diversity, whereas spatial diversity is employed by positioning two antennas diagonally on opposite sides of the antenna structure. For reducing mutual coupling, the etching of rectangular slots on each side of the ground plane beneath the top plate of each element has been done to stop the flow of current between two ports of the same antenna element. Maximum isolation achieved among ports is less than −25 dB, and envelope correlation coefficient is below 0.009 in bands of interest. The minimum frequency range covered by the four ports of this antenna is from around 2.7 to 3.6 GHz for <inline-formula> <tex-math notation="LaTeX">$S_{11}< -10$ </tex-math></inline-formula> dB, thus covering expected future 5G band (3300–3600 MHz), and may be used for small portable and handheld the IoT and cellular applications as a diversity/MIMO antenna.
Modified Self-Organizing Map Algorithm for Brain Tumor Detection and Analysis Using Magnetic Resonance Brain Images

medical image processing play an important role to help radiologists and support their decisions in diagnosis of the patient, magnetic resonance imaging (MRI) has ability to diagnosis the small details in the human body with a high resolution; in this paper, we propose modified self-organizing map algorithm (MSOM) for brain tumor detection and analysis using magnetic resonance brain images the significance of the (MSOM) algorithm is ability to detect tumor area in the magnetic resonance brain image (MRI) clearly with a high accuracy and best performance according of different values, the advantage of method proposed can segment and detect different types of MRI brain images FLAIR, T1 and T2-weight images with same performance and accuracy, the (MSOM) method start through input magnetic resonance brain image (MRI) and preprocessing applied to remove the noise from the image, applied modified selforganizing map (MSOM), applied tumor area, performance of the method, finally the applied of modified self-organizing map (MSOM) gave a best results us shown in the results.
Digital Image Compression Using Adaptive Btc Method Based On Weber – Fechner’s Measure

Using Weber-Fechner's law in digital image compression is an efficient method for decreasing computational complexity and improving compression results in low bit rates with the same reconstructed image quality for conventional coding method (without using Weber-Fechner's condition). Our purpose here is to increase the compression ratios (low bit rates) by using a new coding system based on the condition of WeberFechner's law which is proposed to classify low, constant and high detail blocks in an image, then low or constant detail blocks are compressed by only sending the mean of the original image block, while the high detail blocks are compressed using AMBTC or AMBTC+VQ system, then send the two reconstruction values and the bit map when we use the AMBTC method but when we use the AMBTC+VQ methods, we send the two reconstruction values and the index of the codeword. As documented in experimental results, using Weber-Fechner's condition in AMBTC or AMBTC+VQ the bit rate and computational complexity are much lower than using the conventional for AMBTC or AMBTC+VQ methods with the same image quality.
Workload Analysis of Active Safety Processor

Active Safety (AS) is an important feature to protect drivers and passengers in vehicles, however, the growing complexity of AS functions increases the CPU workload of the AS processor. This thesis attempts to 1) identify the signals that cause more CPU workload than others and 2) predict this CPU workload. In order to achieve the purpose, regression models of CPU workload in real-time contexts are proposed. Based on these models, different feature selection techniques are used to rank the contributions of signals. As a result, given the sampled input signals to all the software modules that are embedded running on the CPU, a model for predicting CPU workload in a real-time context is proposed. Signals and the associated contribution coefficients are listed using different feature selection methods and criteria. Volvo Car Corporation will be able to trace back to the functions that generate signals with high contribution coefficients according to the list of signals, and improve the corresponding AS functions to decrease the CPU workload.
Energy Detection Based Sensing and Primary User Traffic Classification for Cognitive Radio

Keywords— World Academy of Science, Engineering and Technology International Journal of Electronics and Communication Engineering Vol:10, No:6, 2016 749 International Scholarly and Scientific Research & Innovation 10(6) 2016 scholar.waset. org/1307-6892/10004695 In te rn at io na l S ci en ce I nd ex , E le ct ro ni cs a nd C om m un ic at io n E ng in ee ri ng V ol :1 0, N o: 6, 2 01 6 w as et . o rg /P ub lic at io n/ 10 00 46 95 Probability of detection Probability of false alarm A. FFT-Based Energy Detector KFFT N n , , . ,N probability of detection probability of false alarm N PF World Academy of Science, Engineering and Technology International Journal of Electronics and Communication Engineering Vol:10, No:6, 2016 750 International Scholarly and Scientific Research & Innovation 10(6) 2016 scholar.waset. org/1307-6892/10004695 In te rn at io na l S ci en ce I nd ex , E le ct ro ni cs a nd C om m un ic at io n E ng in ee ri ng V ol :1 0, N o: 6, 2 01 6 w as et . o rg /P ub lic at io n/ 10 00 46 95
Dispersion of solute with chemical reaction in blood flow

A mathematical model is developed to study the influence of an externally applied magnetic field and chemical reaction on the flow characteristics of blood in the presence of mild stenosis. The equations of momentum are solved under appropriate boundary conditions using Hankel transform. Taylor's dispersion model [17] is used to obtain dispersion of solute in blood flow. The effects of various parameters entering into the problems are discussed numerically and explained graphically.