Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Software Project Outcomes: Using Machine Learning and Open Source Data to Employ Software Project Performance Determinants

Many factors can influence the ongoing management and execution of technology projects. Some of these elements are known a priori during the project planning phase. Others require real-time data gathering and analysis throughout the lifetime of a project. These real-time project data elements are often neglected, misclassified, or otherwise misinterpreted during the project execution phase resulting in increased risk of delays, quality issues, and missed business opportunities. The overarching motivation for this research endeavor is to offer reliable improvements in software technology management and delivery. The primary purpose is to discover and analyze the impact, role, and level of influence of various project related data on the ongoing management of technology projects. The study leverages open source data regarding software performance attributes. The goal is to temper the subjectivity currently used by project managers (PMs) with quantifiable measures when assessing project execution progress. Modern-day PMs who manage software development projects are charged with an arduous task. Often, they obtain their inputs from technical leads who tend to be significantly more technical. When assessing software projects, PMs perform their role subject to the limitations of their capabilities and competencies. PMs are required to
Manchu Text Extract Under the Image Background

Based on the structure and characteristic of Manchu,this paper elaborates a new stroke extraction method. Firstly,the digital image processing method is used to extract the Manchu character from an image background picture,perform such preprocessing methods as de- noising,thinning,pruning and so on. Then the mid- axis of Manchu character,termination point,interior points and intersection points are found. Moreover,the critical point on the mid- axis is achieved. The critical point by using the stroke growth algorithm is used to separate the stroke so as to achieve the purpose of Manchu text stroke extraction. To verify the feasibility of the method,this paper carries out a text extraction on handwritten Manchu,printed Manchu and Manchu in the color image. Simulation results show that the method is effective in separating the Manchu stroke and will lay a solid foundation for the further study of Manchu identification.
A COMPUTERIZED SYSTEM FOR DESIGNING OPTIMAL TREE-LINING IN PLANTING AREAS

A computerized system for the selection of planting lining directions is to optimize tree planting areas. Therefore, this paper focuses on some issues of lining and the development strategies of system. Lining direction handle 60 0 from the baseline is a common practice; however various areas coordinates, analyzing other lining directions (1 0 – 90 0 ) may promote better the number of trees. The highest tree number by optimal lining direction indicates the area is fully utilized. However, to analyze all possible directions, by manual approach consume large time and effort leads to unanswered decision of optimal result determination. Thus an application named Tree-Lining Planning by Computerized System (LP-CS) does offer opportunities to determine tree numbers for the directions. The datasets by various coordinates represents areas were generated for the purposes of analysis. The generated results show that different lining angles promote better number of trees than 60 0 lining direction. This indicates other directions might have tendency to be best lining direction because of variations in the area’s coordinates.
Recognition of musical symbols in scores using neural networks

1 Resum . 2 Resumen . 3 Acknowledgements . 5 Revision history and approval record . 6 Table of contents . 7 List of Figures . 9 List of Tables: . 11
Architectures v/s Microservices

As it evolves, technology has always found a better way to build applications and improve their efficiency. New techniques have been learned by adapting old technologies and observing how markets shift towards new trends to satisfy their customers and shareholders. By taking Service Oriented Architecture (SOA) and evolving techniques in cloud computing, Web 2.0 emerged with a new pattern for designing an architecture evolved from the conventional monolithic approach known as microservice architecture (MSA). This new pattern develops an application by breaking the substantial use into a group of smaller applications, which run on their processes and communicate through an API. This style of application development is suitable for many infrastructures, especially within a cloud environment. These new patterns advanced to satisfy the concepts of domain-driven, continuous integration, and automated infrastructure more effectively. MSA has created a way to develop and deploy small scalable applications, which allows enterprise-level applications to dynamically adjust to their resources. This paper discusses what that architecture is, what makes it necessary, what factors affect best-fit architecture choices, how microservices-based architecture has evolved, and what factors are driving service-based architectures, in addition to comparing SOA and microservice. By analyzing a few popular architectures, the factors which help in choosing the architecture design will be compared with the MSA to show the benefits and challenges that may arise as an enterprise shifts their developing architecture to microservices.
Proceedings of The 3rd Workshop on Multi-word Units in Machine Translation and Translation Technology (MUMTTT 2017)

Putting the Horses before the Cart: Identifying Multiword Expressions before Translation Translating multiword expressions (MWEs) is notoriously difficult. Part of the challenge stems from the analysis of non-compositional expressions in source texts, preventing literal translation. Therefore, before translating them, it is crucial to locate MWEs in the source text. We would be putting the cart before the horses if we tried to translate MWEs before ensuring that they are correctly identified in the source text. This paper discusses the current state of affairs in automatic MWE identification, covering rule-based methods and sequence taggers. While MWE identification is not a solved problem, significant advances have been made in the recent years. Hence, we can hope that MWE identification can be integrated into MT in the near future, thus avoiding clumsy translations that have often been mocked and used to motivate the urgent need for better MWE processing. iv Proceedings of The 3rd Workshop on Multi-word Units in Machine Translation and Translation Technology (MUMTTT 2017), London, 14 November 2017. Computational and Corpus-based Phraseology Recent advances and interdisciplinary approaches MUMTTT 2017 Academic Programme Tuesday, 14th November 2017 Session 1: Multi-words in Machine Translation Multi-word Adverbs How well are they handled in Parsing and Machine Translation? Martin Volk and Johannes Graën Chinese Separable Words in SMT Gongbo Tang and Fabienne Cap Out of the tailor or still in the woods? An empirical study of MWEs in MT Fabienne Cap Tuesday, 14th November 2017
RSA, Public-Key Cryptography, and Authentication Protocols

In the modern electronic age, secure communication is vital to the efficient workings in all areas of life. From governmental proceedings and military devices to personal banking and email, securely communicating between parties has become a necessity that has furthered research in the field of cryptography. This thesis is an introduction to a particular type of cryptography, public-key cryptography, and examines the basics of RSA and ElGamal cryptosystems. Worked-out examples for these types of encryption, decryption and authentication protocols are explored, as well.
Study on biometric authentication systems, challenges and future trends: A review

User authentication in computer system is done based on certain security measures like passwords, keys, id cards, pin etc. however, the misuse and theft of these security measures are also increasing due to day by day advancement of technology. This led to the birth of biometric security systems in which the authentication of an individual is done based on a feature vector derived from physiological and/or behavioral characteristics of a human being by using biometrics technology, an individual can be authenticated based on “who he is” rather. In order to help beginners chose their research area or a particular biometric technology, all the emerging biometric technologies and challenges in each technology have been discussed in this paper in detail. Future trends in biometric technologies are also discussed.
Short-term load forecasting using machine learning and periodicity decomposition

The accuracy of electricity consumption forecasts is of paramount importance in energy planning, it provides strong support for the effective energy demand management. In this work, we proposed a load forecast through the decomposition of the historical time series in relation to the historical evolution of each hour of the day. The output of these decomposition were served as input to different algorithms of machine learning. We tested our model by five machines learning methods, the achieved results are examined with three of the most commonly used evaluation measures in forecasting. The obtained results were very satisfactory.
Using Bandwidth Estimation to Optimize Buffer and Rate Selection for Streaming Multimedia over IEEE 802.11 Wireless Networks

As streaming techniques and wireless access networks become more widely deployed, a streaming multimedia connection with the “last mile” being a wireless network is becoming increasingly common. However, since current streaming techniques are primarily designed for wired networks, streaming multimedia applications can perform poorly in wireless networks. Recent research has shown that the wireless network conditions, such as the wireless link layer rate adaptation, contending traffic, and interference can significantly degrade the performance of streaming media applications. This performance degradation includes increased multimedia frame losses and lower image quality caused by packet loss, and multiple rebuffering events that stop the media playout. This dissertation presents the model, design, implementation and evaluation of an application layer solution for improving streaming multimedia application performance in IEEE 802.11 wireless networks by using enhanced bandwidth estimation techniques. The solution includes two parts: 1) a new Wireless Bandwidth estimation tool (WBest) designed for fast, non-intrusive, accurate estimation of available bandwidth in IEEE 802.11 networks, which can be used by streaming multimedia applications to improve the performance in wireless networks; 2) a Buffer and Rate Optimization for Streaming (BROS) algorithm using WBest to guide the streaming rate selection and initial buffer optimization. WBest and BROS are implemented and incorporated into an emulated streaming client-server system, Emulated Streaming (EmuS), in Linux and evaluated under a variety of wireless conditions. The evaluations show that with WBest and BROS, the performance of streaming multimedia applications in wireless networks can be significantly improved in terms of multimedia frame loss, rebuffer events and buffer delay.
Algorithmic Rectification of Visual Illegibility under Extreme Lighting

Image and video enhancement, a classical problem of signal processing, has remained a very active research topic for past decades. This technical subject will not become obsolete even as the sensitivity and quality of modern image sensors steadily improve. No matter what level of sophistication cameras reach, there will always be more extreme and complex lighting conditions, in which the acquired images are improperly exposed and thus need to be enhanced. The central theme of enhancement is to algorithmically compensate for sensor limitations under ill lighting and make illegible details conspicuous, while maintaining a degree of naturalness. In retrospect, all existing contrast enhancement methods focus on heightening of spatial details in the luminance channel to fulfil the goal, with no or little consideration of the colour fidelity of the processed images; as a result they can introduce highly noticeable distortions in chrominance. This long-time much overlooked problem is addressed and systematically investigated by the thesis. We then propose a novel optimization-based enhancement algorithm, generating optimal tone mapping that not only makes maximal gain of contrast but also constrains tone and chrominance distortion, achieving superior output perceptual quality against severe underexposure and/or overexposure.
A secure mHealth application for attention deficit and hyperactivity disorder

Nowadays, many people have smartphones, the fact that encourages the development of new tools to address different problems. One of its consequences is the recent growth of mHealth, a term that refers to the practice of medicine based on the use of mobile devices for medical and health purposes. This work describes a new mHealth tool to improve memory and cognitive abilities through gamification and serious games. In particular, a mobile application here is proposed to help children that suffer from attention deficit hyperactivity disorder (ADHD). This application integrates the four profiles involved in this disorder: children, parents, teachers, and medical staff. With it, parents can discover if their children suffer the disorder, and children can improve their cognitive abilities through games of different types. Besides, the security aspect of the proposal is emphasized to highlight its importance in mHealth. Thus, the developed tool includes various cryptographic mechanisms to protect the confidentiality of communications and the authenticity of users and data.
Learning by Association

Neural networks usually require vast amounts of labeled training data. In this dissertation, we propose an approach to solve this problem: "Learning bay Association". This training schedule is suitable for any embedding learning task such as classification, domain adaptation or clustering. The key idea is to “associate” training examples for which labels are known with unlabeled examples. A novel cost function facilitates state-of-the-art results with significantly less labeled training data.
Disk I/O Control Mechanism for Online and Batch Processing

Some IT systems, including banking systems, perform workload cycles on a daily basis. These workloads are composite workloads of online and batch processing. To separate the impact of these two workloads, typical banking systems employ individual computers dedicated to each type of workload. Since each dedicated system is designed to fit the peak times of their respective tasks, it is difficult to optimize the scale of the system as a whole. In this paper, we propose an effective method for maximizing disk I/O performance under composite workloads. Our basic idea depends on the fact that online workloads and batch workloads (1) have different peak times in a day, and (2) have different I/O patterns and lengths of disk access.
Conditional Variational Auto-Encoder for Text-Driven Expressive AudioVisual Speech Synthesis

In recent years, the performance of speech synthesis systems has been improved thanks to deep learning-based models, but generating expressive audiovisual speech is still an open issue. The variational auto-encoders (VAE)s are recently proposed to learn latent representations of data. In this paper, we present a system for expressive text-to-audiovisual speech synthesis that learns a latent embedding space of emotions using a conditional generative model based on the variational auto-encoder framework. When conditioned on textual input, the VAE is able to learn an embedded representation that captures emotion characteristics from the signal, while being invariant to the phonetic content of the utterances. We applied this method in an unsuper-vised manner to generate duration, acoustic and visual features of speech. This conditional variational auto-encoder (CVAE) has been used to blend emotions together. This model was able to generate nuances of a given emotion or to generate new emotions that do not exist in our database. We conducted three perceptive experiments to evaluate our findings.
Controlled Modification of Generated (Style)GAN Latent Vectors

StyleGAN is a neural network architecture that is able to generate photo-realistic images. The diversity of generated images are ensured by latent vectors. These latent vectors encodes important features of generated images. They provide us insight-full information about properties of image generation in StyleGAN, which may also occur similarly in other neural network architectures. Using pre-trained StyleGAN models, we have conducted several experiments to show properties on StyleGAN based on results of style modification. The experiments have shown the influence of noise and styles in StyleGAN and how latent vectors can be manipulated in generated images.
Sentiment Analysis on Product Features Based on Lexicon Approach Using Natural Language Processing

Sentiment analysis has played an important role in identifying what other people think and what their behavior is. Text can be used to analyze the sentiment and classified as positive, negative or neutral. Applying the sentiment analysis on the product reviews on e-market helps not only the customer but also the industry people for taking decision. The method which provides sentiment analysis about the individual product’s features is discussed here. This paper presents the use of Natural Language Processing and SentiWordNet in this interesting application in Python: 1. Sentiment Analysis on Product review [Domain: Electronic]2. sentiment analysis regarding the product’s feature present in the product review [Sub Domain: Mobile Phones]. It usesa lexicon based approach in which text is tokenized for calculating the sentiment analysis of the product reviews on a e-market. The first part of paper includessentiment analyzer whichclassifiesthe sentiment present in product reviews into positive, negative or neutral depending on the polarity. The second part of the paper is an extension to the first part in which the customer review’s containing product’s features will be segregated and then these separated reviews are classified into positive, negative and neutral using sentiment analysis. Here, mobile phones are used as the product with features as screen, processors, etc. This gives a business solution for users and industries for effective product decisions.
ENACT: An Efficient and Extensible Entity-Actor Framework for Modular Robotics Software Components

Recent advances towards generic robot programs require efﬁcient sharing of copious information among many functional modules. Current solutions favor extensibility over efﬁciency and thus limit attainable functionality. In contrast, we contribute a software framework that efﬁciently implements the extensible entity-aspect-actor paradigm. Our framework remains safe for multi-threading, scales to distributed systems, handles inconsistent data and supports history logging. Evaluation through scenarios in industrial and service robotics attests claimed beneﬁts. We conclude that our framework is ﬁt for use in robotics, with signiﬁcance also for computer games, simulations, and VR applications.
Automatic Detection of Quotations in Multilingual News

We present fully functional software that identifies direct speech quotations as part of its automatic analysis of more than 20,000 news articles per day in currently 11 languages. The system currently identifies over 2600 quotations per day, together with the person who made the quotation and – where applicable – the persons or organisations mentioned in the quotation. The most recent quotations from and about each person are listed on this person’s dedicated information page, which is updated daily. As another component of the system also identifies variants of each name, the quotes can be assigned to the same person even if his or her name is spelled differently, allowing users to view all quotations from or about any of the currently 615,000 person names in the system’s database in any of these languages. This automatic news analysis system is publicly accessible at http://press.jrc. it/NewsExplorer/.
Overlapping Community Detecting Based on Complete Bipartite Graphs in Micro-Bipartite Network Bi-Egonet

Community detection has become a hot topic in complex networks. It plays an important role in information recommendation and public opinion control. Bipartite network, as a special complex network, reflects the characteristics of a kind of network in our life truly and objectively. Therefore, detecting community structure in bipartite networks is of great significance and has practical value. In this paper, we first introduce two concepts: a) a micro-bipartite network model Bi-EgoNet which can be used to analyze bipartite network from a micro view to reduce the complexity of structure in bipartite networks, and b) a complete bipartite graph that is a special bipartite graph with the indivisible property. Then, we propose a novel overlapping community detection algorithm based on a complete bipartite graph in micro-bipartite network Bi-EgoNet (CBG&BEN), which combines advantages of both a complete bipartite graph and Bi-EgoNet to get an optimal community structure. The CBG&BEN is evaluated on accuracy and effectiveness in several synthetic and real-world bipartite networks. The CBG&BEN is compared with other excellent existing algorithms, and our experimental results demonstrated that CBG&BEN is better at detecting overlapping community structure in bipartite networks.
Ordonnancement distribué d'instructions Ordonnancement distribué d'instructions

This article presents an algorithm to perform a distributed computation of the in- structions, suited to high degree superscalar microarchitectures. The method relies on a parti- tionning of both the register file and the reservation stations in order to decrease the number of register file access ports and the number of stations comparators. Matching the results with the depending sources is no more global but point to point thanks to an identification of the instruc- tions and their components. The method, by limiting the access resources to each renaming register to four ports allows, despite an increase of the number of registers, to keep the access time beyond the cycle time.