Reviewerly/exHarmony huggingface dataset:
Highly Aged and After Cloud

There have been more and more people who will live longer year by year. This phenomenon will become a global expansion in the number of highly aged people. It is said that the highly aged people will be likely to get a sudden bodily worse condition after the age of 75-80 in Japan. (This phenomenon may be also said for all of human) After the bodily worse conditions, all that they can do at present is to see a doctor, which is not so easy matter for those highly aged people. Because it is very difficult to find out a proper medical doctor with an appropriate atmosphere (; easy access, not much burden, proper medical treatment, after care, etc.) for an individual highly aged person. So, in this chapter, try to find out what can be done by using the present high technologies of “After Cloud; including AI, VR, and AR” in order not to cause more and further troubles for those highly aged people.
Library Research Support Services

This chapter addresses library research services at the Universitat Politècnica de Catalunya · BarcelonaTech (UPC), an institution engaged with science and technology that clearly emerges as a research university. Due to the attributes of the UPC, its library, publications, and archives services differ from traditional libraries and have been transformed over its lifetime to offer services to researchers that fit their current and future needs. Librarians are actively seeking personalized solutions to meet researchers' needs. The Research Service Charter of the UPC libraries includes a range of assets related to UPC researchers as producers of new scientific output and as primary agents of its dissemination.
Free-Obstacle Path Finding for Assistant Robot Based on Image Skeletonization

This document explores an autonomous navigation approach based on the generation of free-obstacle navigation diagrams by means of using the skeletonization technique and other image processing algorithms. These diagrams are turned into a path by means of a vector of points and performed by a hybrid robotic platform, composed of a humanoid robot and one with omni-directional locomotion. Four different configurations (origin, obstacles and destination) were studied in order to test the algorithm performance, computing the mean squared error and the average of relative error for each point in the path. For all of the tested configurations, the proposed algorithm chooses the shortest path through the navigation diagram. The final point and mean point-by-point relative errors were obtained for each of the 4 proposed configurations, where the average values over all of them were 1.11% for the final point relative error and 1.23% for the mean relative error, which validates the use of the algorithm for the autonomous generation of navigation paths for the hybrid robotic platform under study.
Image Translation with Attention Mechanism based on Generative Adversarial Networks

In this paper, we present a novel approach for image translation based on Generative Adversarial Networks (GANs). We apply the self-attention mechanism to improve the quality of the generated images as well as focus on not only local feature representation but also global structural correlation. Meanwhile, we adopt the idea of cyclic image translation in CycleGAN. Moreover, in order to stabilize the training process and reduce the probability of appearing unusual gradients, two techniques spectral normalization and TTUR applied during training process. In the experimental section, we compare our method with the original CycleGAN in terms of subjective evaluation, objective scoring, classification verification and computational cost. Both subjective and objective results show that our model can generate high quality and abundant diversity images that uses unpaired unlabeled samples. Two techniques used during the training have prompt the convergence speed of the network and help the model use less time to get the optimal point. Lots of comparisons against CycleGAN demonstrate that our proposed method is superior to the original one.
Password Authentication Attacks at Scale

Whilst there is much literature on password authentication attacks, there has been little study of real online brute force attacks. This paper provides analysis of the most prolific attackers and provides analysis of distributed brute force attackers using unsupervised learning technique for clusterization. Compromised credential checking has recently emerged as an approach to improve password security by deterring users from using re-using breached passwords. Analysing an implementation on a large online gaming site, a $$184\%$$ median increase in daily password change events in the first 30 days of rollout and an ongoing $$117\%$$ increase in the following 20 days is observed.
BUGVILLA: Calibrating Bug Reports with Correlated Developers, Tracking Bug Reports, and Performance Analysis

A bug is defined as the error in code because of which a system cannot produce the desired output. Whenever a bug occurs, a bug report is created. To find the cause of a bug, developers will execute the whole code continuously and analyze the code depending upon the report they received. So the bug reports should be accurate as the developers depend on the bug report received to solve a particular bug. Bug reports are the documents which provide total information of a particular bug with different accuracy. We need to manage the bugs which arise while working on a software project. Developers usually take the help of spreadsheets and fill them with new bugs which are raised. But when the list of bugs increases to a large number and all developers working on a single spreadsheet to update bugs may create ambiguity. Every software project reaches this point, especially during testing and deployment when users tend to find an application’s bugs. So, a tool for tracking bug reports will be useful instead of a spreadsheet. Where tracking bug reports is an important feature which helps to observe the activities of software developers. Moreover, it is important to observe that a bug will be assigned to the developer who is an expert in solving that particular type of developer. This paper introduces a new tool for tracking bug reports, using which we can assign bugs to their respective developers, view bug status, check bug history, check the total performance of developers, and also communicate with developers by means of a group chat.
Information Literacy Cultivation and Course Informationization Construction of University Management Course

As informationization enters all aspects of people’s lives, China’s Internet has entered the era of industrial integration, and informationization and education have further integrated. The changing times brought about by information technology and related technological innovations are a huge challenge that must be faced by current university management courses, and also a major opportunity for the reform of university management courses. Management is a professional foundation course that usually starts in the first year. Students who have just entered the school lack a rational and emotional understanding of the rules of business management and the operation of the market economy. The purpose of this article is to study the informatization of management courses in colleges and universities, and analyze the information literacy training path for management students. In this regard, this paper discusses the realization of the informationization construction model of management courses. The program is based on the “people-oriented” principle to promote self-study and information literacy for management students. From the sample survey of 4,150 college students and graduate students in this paper, we can conclude that the construction of information technology in management courses will increase the interest of students by 10.4% and improve students’ autonomous learning ability.
Assessing the Impact of Various Image Corruptions on the Recognition of Traffic Signs by Machine Learning

Evaluating the traffic conditions correctly is a critical task for safe autonomous driving. Recognizing the traffic signs flawlessly is one big component. It may seem like a simple task given that there are a limited number of traffic signs available. However, the traffic sign images may become corrupted when the weather condition is bad, there is some blockage from the tree or when the traffic sign itself was damaged. This study, by evaluating the robustness of traffic sign recognition against these scenarios, provides valuable insights into image corruption and corruption of datasets in general for machine learning. It raises potential concern for applying a well-performed machine learning algorithm to an unseen new test data in a new environment, and would emphasize the importance in training the model on enhanced training sets with various types of preassigned image corruption.
Introduction on Sensor-Based Human Activity Analysis: Background

The constant growth of sensor-based systems and technologies for the detection of human activities has made notable progress in the field of human-computer interaction. The continuation of Internet connectivity into daily objects and physical devices has made it possible for the researchers to use IoT sensors for healthcare, elderly people monitoring, fitness tracking, working activity monitoring, and so on. The prominent application fields of sensor-based activity monitoring systems are many, but not limited to, pattern recognition, machine learning, context awareness, and human-centric sensing. If a salient investigation is performed on this topic by fellow researchers, this can create a vital turn in the way of interaction among people and mobile devices. In this book, we have bestowed a comprehensive survey showing the various aspects of human activity recognition based on wearable, environmental, and smartphone sensors. After discussing the background, numerous factors have been analyzed for the data pre-processing part regarding noise filtering and segmentation methods. The list of sensing devices, sensors, and application tools listed in this book can be used for the activity data collection efficiently. Moreover, a detailed analysis of more than 150 benchmark datasets and dataset repositories in this book includes information about sensors, attributes, activity classes, etc. These datasets sum up several types of sensor-based daily activities, medical activities, fitness activities, transportation activities, device usage, fall detection, and hand gesture data. In addition to these, we have shown the feature extraction and classical machine learning methods in detail. Moreover, the overview of different types of classification problems has been given along with the discussion on several performance evaluation techniques showing their advantages and limitations. Furthermore, we have also discussed the importance of deep learning methods to solve the problem of shallow learning using hand-crafted features in conventional pattern recognition approaches. Finally, we have presented a summary of activity recognition methods focused on recent works in several benchmark datasets, and mentioned some future challenges regarding data collection, design issues, and other prospects.
A New Proposed Pendulum-Like with Attraction-Repulsion mechanism Algorithm for Solving Optimization Problems

Abstract This work aims to contribute to the domain of new modification in electromagnetism-like (EM) algorithm, which will be referred to in this work as pendulum-like with attraction-repulsion mechanism algorithm (PA). PA algorithm has been constructed with the purpose of solving inverse kinematics (IK) problem. The proposed new algorithm mimics the concepts of a simple physical pendulum and is derived as a local search in order to promote the performance of the EM algorithm. Comparisons between the suggested PA with the available algorithms, such as genetic algorithm (GA) and EM were conducted using two mathematical test functions. On top of the testing, the PA algorithm is tested to solve the problem of IK for a four degree-of-freedom (DOF) planar robot. The simulation results indicate that the PA algorithm outperforms other approaches in terms of accuracy and the speed of convergence through the errors and the decreasing of objective with iterations as mention in presented tables and figures, on top of being able to successfully solve the IK problem with multiple robot configurations.
Efficient Lossy Audio Compression Using Vector Quantization (ELAC-VQ)

Compression is the technique for effective utilization of space in servers as well as in personal computers. Most significantly, being multimedia compression. In this paper, the focus is on the audio compression method. Audio compression method has two types: lossy and lossless compression. Vector quantization is an effective way of lossy compression technique. The important tasks in vector quantization are codebook generation and searching. Simple Codebook generation algorithm is used which enhances the compression process. The proposed method is Efficient Lossy Audio Compression using vector quantization (ELAC-VQ). A centroid-based compression reduces the operation of the comparison with the codebook and helps to improve the performance. At the time of decompression is simple to audio compression. The experimental results show that ELAC-VQ approach reduces the computational complexity, increases the compression percentage and speeds up the vector quantization process. The universal codebook generation is the key which will reduce the overhead of vector finding. These can be preprocessed to reduce runtime processing in compression. To achieve better results as well as for validation, clustering is used for generalized codebook generation.
Poster Abstract: Parallel VM Placement with Provable Guarantees

Efficient on-demand deployment of VMs is at the core of cloud infrastructure but the existing resource management approaches are too slow to fulfill this promise. Parallel resource management is a promising direction for boosting performance, but when applied naïvely, it significantly increases the communication overhead and the decline ratio of deployment attempts. We propose a new dynamic and randomized algorithm, APSR, for parallel assignment of VMs to hosts in a cloud environment. APSR is guaranteed to satisfy an SLA containing decline ratio constraints, and communication overheads constraints. Furthermore, via extensive simulations, we show that APSR obtains a higher throughput than other commonly employed policies (including those used in OpenStack) while achieving a reduction of up to 13x in decline ratio and a reduction of over 85% in communication overheads.
Unsupervised Automatic Keyphrases Extraction on Italian Datasets

Keyword/keyphrase extraction is an important research activity in text mining, natural language processing, and information retrieval. A large number of algorithms, divided into supervised or unsupervised methods, have been designed and developed to solve the problem of automatic keyphrases extraction. The aim of the chapter is to critically discuss the unsupervised automatic keyphrases extraction algorithms, analyzing in depth their characteristics. The methods presented will be tested on different datasets, presenting in detail the data, the algorithms, and the different options tested in the runs. Moreover, most of the studies and experiments have been conducted on texts in English, while there are few experiments concerning other languages, such as Italian. Particular attention will be paid to the evaluation of the results of the methods in two different languages, English, and Italian.
Performance Evaluation in Activity Classification: Factors to Consider

After building the model to recognize activities from sensor data, it is essential to investigate the effectiveness of the model. The evaluation of the performance for machine learning methods can be performed using some evaluation matrices. This chapter properly explains the evaluation matrices namely accuracy, precision, recall, F1 score, balance classification rate, confusion matrix, and so on. Graphical performance measures namely ROC curve, cumulative gains, and lift charts have been explained too. This chapter also represents some essential concepts related to precision and recall trade-off, and accuracy as a performance measure. The contents of this chapter will be useful not only for human activity recognition, but also for other classification-related researches.
Analysis of Hollywood’s Film Production Using Background Subtraction Visual Activity Index Based on Computer Vision Algorithm

The amount of available video content is increasing every day. Apart from the classic channels of video content distribution, such as television and satellite broadcasting, more and more video content is distributed over the Internet. For some decades, some authors have analysed the properties of the available video content and noted certain trends, and above all, a growing visual activity. This paper describes two measures of visual activity (Visual Activity Index and Average Shot Length), as well as papers that analyse their influence. Also, a new measure based on computer vision algorithms is proposed, and this measure is compared with Visual Activity Index. This proposed measure is more resistant to noise than Visual Activity Index, and this insensitivity is based on selected background subtraction algorithm that has this feature in its base for the proposed measure. The proposed measure called the Background Subtraction Visual Activity Index (BGSVAI) is used at the end of the paper to measure the visual activity of 50 films, which received the Award for Best Picture from the Academy of Motion Picture Arts and Sciences from 1965 to 2014. Also, the visual activity of these films was analysed in the mentioned period.
Producción fonográfica, “Greenland”

This undergraduate thesis is about the full music composition and production of 12 tracks or songs that are in the spectrum of Pop Music, mixed with Electronic Music aesthetics.Elements as melody, texture, counterpoint and timbre takes a huge relevance in the album development.The album has strong influences from artists like Daft Punk, Madeon, Porter Robinson, Mr. Oizo, Post Malone, Phoenix, Zedd, J. Balvin, Bag Raiders, and others.
Ten Unsafe Assumptions When Teaching Topics in Software Engineering

Software engineering is a branch of systems engineering and, to be successful, software engineering students must work in a systems-focussed manner. Instructors, including the author, routinely assume that students have the requisite skills for this or can learn them quickly. This article identifies ten common assumptions that are unsafe to make and, if made, impact negatively on the ability of a student to acquire the essential foundation on which to build their understanding of the technical aspects of software engineering. The ten unsafe assumptions are that students understand how to decompose problems, that they know that systems have to be specified at different levels of abstraction, that they know how to bridge different levels of abstraction, that they understand how software and hardware reflect these different levels, that they can follow instructions and pay attention to detail, that they can easily follow oral or written explanations, that they are able to stress test their own software, that they understand the relevance of professional practice, that they are adept at self-criticism, and they understand the relevance of examples. In each case, we identify the implications for teaching practice of not making these assumptions.
Research on prediction model of construction period of overhead line project based on BP neural network

Abstract With the stable development of economy and society, the power load increases year by year, so the power transmission company needs to build enough transmission lines to ensure the power supply. Overhead line project is the most commonly used form of transmission line, but the overhead line project often encounters the situation that cannot be completed on time, which affects the satisfaction of users’ electricity demand. Therefore, it is necessary to reasonably predict the construction period of the overhead line project, so as to provide a reasonable reference for the formulation of the production plan of the grid company. Firstly, this paper analyses the key factors that affect the construction period of overhead line project. Based on the BP neural network method, the construction period prediction model of overhead line project is established. The accuracy of the prediction is high.
RANCANG BANGUN GAME EDUKASI PERAKITAN KOMPUTER UNTUK SISWA SEKOLAH MENENGAH PERTAMA BERBASIS ANDROID

Digital education is a technological development that provides a means to provide learning in the form of educational games so that learning seems more practical, easier to understand and more interesting. Learning about computer assembly is known as the activity of assembling all computer hardware components precisely into the casing in accordance with the location of components that are already available as well as a means of introducing the components needed in computer assembly. The teaching and learning process at SMP IT Nurul Muhajirin using manuals downloaded by teachers from the internet with conventional methods this method makes students have difficulty in introducing devices to the computer, because the teacher explains the material in front of the class and students only listen to the material delivered by the teacher. The method used in the design of this Android-based computer assembly education game is the Game Development Life Cyle (GDLC) and Unified Modeling Language (UML) method, making games using Construct 2 software, assisted with Adobe Photoshop. The results of this study are android-based computer assembly education games that can be used as alternative learning media in IT Nurul Muhajirin Middle School, to increase students insights and knowledge about computers and how to assemble computers.
Towards Code Review Guideline in a Classroom

Software companies generally adopt code review to identify errors and suggest improvements to code, and share knowledge in the team. Companies assume a pre-knowledge on their engineers to undertake the activity. This could be difficult for freshly graduated students as in an academic environment code review is not often exercised: it is not an individual activity and requires substantial interaction among students, educators, deliverance, and acceptance of feedback, timely actions upon feedback as well as the ability to agree on a solution in the wake of diverse viewpoints. This paper proposes a guideline to code reviewing in a classroom. We report on the lessons learnt after applying the proposed guidelines to a large course in Computer Science. Students’ feedback suggests that the process has been well received with some points to be improved.