Reviewerly/exHarmony huggingface dataset:
Design of Fall Detection System using Computer Vision Technique

Fall detection becomes an important topic in the homecare system. Compared to the wearable sensor, video-based fall detection system is more convenient and relaxed. In this paper, we propose a real-time and high accuracy fall detection system based on the video sensing stream. Firstly, we propose a fast and high-performance foreground segmentation method, which only uses the hue image to get the human information, and is it performed with simple adaptive background model. We also solve the problem of light and shadow change and achieve better results on PETS2001, PETS2006, and CDW 2014 dataset, compared with other algorithms. Based on this high performance segmentation technique, we can develop a fall detection system. The decision of fall detection is mainly based on the shape of human and the center of gravity. In our self-made falling sequences, the experiment results show that the accuracy of our proposed method can achieve 96% on average. Furthermore we can achieve real-time performance on embedded system with any GPU acceleration supported.
Named Entity Recognition on Indonesian Tweets using Hidden Markov Model

Tweets are short messages that can load up to 140 characters, posted by Twitter users. To meet this limit, users usually use abbreviation to express their thoughts, so that produce unstructured and inappropriate sentences grammar. As a result, it is difficult to identify which entity are needed, such as people's names, locations, and organizations. On this research, developed an entity named recognition system on Indonesian-language tweets using Hidden Markov Model with the addition of the POS Tag feature. Using metrics evaluation, the biggest F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score is 64.06%. Our source code and data are available at: https://github.com/dirazarine/Named-Entity-Recognition-on-Indonesian-Tweets-using-Hidden-Markov-Model.
CAOS: a tool for OpenStack accounting management

The analysis and understanding of resources utilization in shared infrastructures, such as cloud environments, is crucial in order to provide better performance, administration and capacity planning. The management of resource usage of the OpenStack-based cloud infrastructures hosted at INFN-Padova, the Cloud Area Padovana and the INFN-PADOVA-STACK instance of the EGI Federated cloud, started with the deployment of Ceilometer, the OpenStack component responsible for collecting and managing accounting information. However, by using Ceilometer alone we found some limiting problems related to the way it handles information: among others, the imbalance between storage and data retention requirements, and the complexity in computing custom metrics. In this contribution we present a tool, called CAOS, which we have been implementing to overcome the aforementioned issues. CAOS collects, manages and presents the data concerning resource usage of our OpenStack-based cloud infrastructures. By gathering data from both the Ceilometer service and Open-Stack API, CAOS enables us to track resource usage at different levels (e.g. per project), in such a way that both current and past consumption ofresources can be easily determined, stored and presented.
Crime Scene Investigation Image Retrieval using a Hierarchical Approach and Rank Fusion

A two-phase hierarchical approach for Crime Scene Investigation(CSI) image retrieval was proposed. In the first phase, the approach uses multi-class classifier to classify the class label of the query image. In the second phase, according to the classified label, a smaller dataset or sub-database only including images with the same label as the query image is picked out from the image database. Then Query By Example(QBE) approach is used to retrieve images from the sub-database. Furthermore, fusion results are returned by different representations for the same query based on rank fusion model. Pilot experimental study was carried out on a database with 18,004 CSI images, which are collected from the actual cases and divided into 10 semantic categories labeled by public security officer. Experimental results showed that retrieval accuracy based on the proposed approach is encouraged.
Towards the Integration of Blockchain and IoT for Security Challenges in IoT

Internet of things (IoT) technology plays a vital role in the current technologies because IoT develops a network by integrating different kinds of objects and sensors to create the communication among objects directly without human interaction. With the presence of internet of things technology in our daily comes smart thinking and various advantages. At the same time, secure systems have been a most important concern for the protection of information systems and networks. However, adopting traditional security management systems in the internet of things leads several issues due to the limited privacy and policies like privacy standards, protocol stacks, and authentication rules. Usually, IoT devices has limited network capacities, storage, and computing processors. So they are having more chances to attacks. Data security, privacy, and reliability are three main challenges in the IoT security domain. To address the solutions for the above issues, IoT technology has to provide advanced privacy and policies in this large incoming data source. Blockchain is one of the trending technologies in the privacy management to provide the security. So this chapter is focused on the blockchain technologies which can be able to solve several IoT security issues. This review mainly focused on the state-of-the-art IoT security issues and vulnerabilities by existing review works in the IoT security domains. The taxonomy is presented about security issues in the view of communication, architecture, and applications. Also presented are the challenges of IoT security management systems. The main aim of this chapter is to describe the importance of blockchain technology in IoT security systems. Finally, it highlights the future directions of blockchain technology roles in IoT systems, which can be helpful for further improvements.
Studying the Benefits and Challenges of Immersive Dataflow Programming

Creating Virtual Reality (VR) applications normally requires advanced knowledge of imperative programming, 3D modeling, reactive programming, and geometry. Immersive authoring tools propose to reduce the learning curve of VR programming by allowing users to create VR content while immersed in VR. Immersive authoring can take advantage of many of the features that make VR applications intuitive and natural to use-users can manipulate programming primitives through direct manipulation, immediately see the output of their code, and use their innate spatial reasoning capabilities when viewing a program. In this paper, we investigate the benefits and challenges of immersive dataflow authoring. We implemented an immersive authoring tool that enables dataflow programming in VR and conducted a series of retrospective interviews. We also describe design implications for future immersive authoring tools.
Predicting the Success of Ensemble Algorithms in the Banking Sector

The banking sector, like other service sector, improves in accordance with the customer's needs. Therefore, to know the needs of customers and to predict customer behaviors are very important for competition in the banking sector. Data mining uncovers relationships and hidden patterns in large data sets. Classification algorithms, one of the applications of data mining, is used very effectively in decision making. In this study, the c4.5 algorithm, a decision trees algorithm widely used in classification problems, is used in an integrated way with the ensemble machine learning methods in order to increase the efficiency of the algorithms. Data obtained via direct marketing campaigns from Portugal Banks was used to classify whether customers have term deposit accounts or not. Artificial Neural Networks and Support Vector Machines as Traditional Artificial Intelligence Methods and Bagging-C4.5 and Boosted-C.45 as ensemble-decision tree hybrid methods were used in classification. Bagging-C4.5 as ensemble-decision tree algorithm achieved more powerful classification success than other used algorithms. The ensemble-decision tree hybrid methods give better results than artificial neural networks and support vector machines as traditional artificial intelligence methods for this study.
Introduction to model management and analytics

Model management and analytics (MMA) aims to use models and related artifacts to derive relevant information to support decision making processes of organizations. Various different models as well as analytics approaches could be identified. In addition, MMA systems will have different requirements and as such apply different architecture design configurations. In this chapter we will discuss the key concepts related to MMA by referring also to existing data analytics approaches. We present and discuss the current key data analytics reference architectures in the literature and the key requirements for MMA. Subsequently, we provide the reference architecture of MMA using multiple architecture viewpoints. We illustrate the framework using a real-world example.
Perception of Spatial Relationships in Impossible Spaces

Impossible spaces have been used to increase the amount of virtual space available for real walking within a constrained physical space. In this technique, multiple virtual rooms are allowed to occupy overlapping portions of the physical space, in a way which is not possible in real euclidean space. Prior work has explored detection thresholds for impossible spaces, however very little work has considered other aspects of how impossible spaces alter participants’ perception of spatial relationships within virtual environments. In this paper, we present a within-subjects study (n = 30) investigating how impossible spaces altered participants perceptions of the location of objects placed in different rooms. Participants explored three layouts with varying amounts of overlap between rooms and then pointed in the direction of various objects they had been tasked to locate. Significantly more error was observed when pointing at objects in overlapping spaces as compared to the non-overlapping layout. Further analysis suggests that participants pointed towards where objects would be located in the non-overlapping layout, regardless of how much overlap was present. This suggests that, when participants are not aware that any manipulation is present, they automatically adapt their representation of the spaces based on judgments of relative size and visible constraints on the size of the whole system.
Challenges and Opportunities in the Digital Transformation of the Higher Education Institutions: The Case of Mexico

In this chapter, we describe the challenge and experience to spread out information and communication technologies (ICT) and how to implement them progressively, in order to improve administrative, academic, and pedagogical issues. As a case study, institutional strategies to tackle these issues in the higher education institutions (HEI) that belong to the Instituto Politécnico Nacional of Mexico (IPN) are described. The main challenge of HEI to incorporate cutting-edge technology in order to form professionals according to educational models and needs of the worldwide is pointed out. Thus, the intended focus of this work is to elevate the discourse and generate awareness about information and communication technologies to translate the language for being legible and understandable to deans, directors, and general authorities of HEI so that they can clearly make decisions with respect to the benefits that current technologies bring to the education institutions, and thus they can keep in mind investing important economic resources in future and mature technologies. Nowadays, HEI of the IPN are facing problems regarding the allocation of budgetary resources, so the strategies and analytical discussion of how ICT can envision the growth of HEI is a very timely issue to be presented to the Mexican government.
EmoWei : Emotion-Oriented Personalized Weight Management System Based on Sentiment Analysis

A number of online communities and commercial apps exist to assist people with weight management. However, these systems are limited to logging and tracking meals or workouts without considering one's emotional state, which is known to have a strong impact on health (e.g., stress-related eating). To confirm the feasibility of monitoring emotion from personal logs such as online posts, we first conducted a Recurrent Neural Network (RNN) based sentiment analysis on 17,735 weight loss-related tweets and 200 posts from an online weight management community called FatSecret in comparisons to general tweets. The results suggest that we can infer one's emotion based on their written text and their progress in managing weight. Based on the findings, we propose EmoWei, a new weight management system that integrates users' emotions to provide personalized assistance to achieve their weight loss goals with minimum stress.
Virtual cycling effort is dependent on power update rate

Abstract Cycling ergometer protocols are commonly integrated with a virtual reality environment (VRE), especially because of its static position that also allows multiple exercise experiments. Concerning VRE scenarios, visually delayed situations like the ones produced at excessive low update rates can also affect the sense of presence and physiological responses. However, the main interface between the subject and a cycling VRE is the power applied over the crank, and there are only a few experiments to evaluate the effect of delayed situations on this particular interface. Thus, this work aims to investigate the effects of the power update rate (PUR) over the subject's performance on an avatar‐based simulator during a drafting task. A custom cycling VRE was built, and 21 male recreational cyclists (175.9 ± 7.5 cm; 76.5 ± 13.9 kg) were tested at six different PUR levels from 100 to 3000 ms. As a result, PUR affects performance scores (virtual distance, efficiency, and heart rate, p &lt; 0.01) at the given VRE conditions. The case‐by‐case analysis of the groups reveals that higher update rates always lead to a statistical equivalent or superior performance. Nevertheless, no parameter shows any group difference between 500 ms and lower PUR. These results suggest that virtual cycling protocols should consider PUR and other delay‐related mechanisms as possible intervening factors over physiological responses and performance scores.
UML Acceptance

Unified Modeling Language (UML) has been adopted as a standard modeling language in the software industry for the graphical representation of analysis and design models. Due to its importance, UML is taught in most undergraduate majors in Software Engineering and Computing. However, little is known about the students' perception regarding the UML diagrams. Their perceptions may influence the diagrams' adoption since the students are future software engineers. This paper aims to start filling this gap by better understanding students' perception regarding UML diagrams acceptance. To achieve this goal, we applied a Focus Group to foster a discussion with undergraduate students about five UML diagrams. Besides, we applied the Technology Acceptance Model to assess the students' acceptance regarding these diagrams. Based on the results of this study, students considered use cases and class diagrams useful for designing systems, although they stated that their modeling was not easy. We also noticed that the perceived usefulness was the construct that most influenced the students' intention to use these diagrams. Our research benefits instructors interested in understanding the students' perceptions about UML, enlightening the points that they need to reinforce to show the benefits of the modeling language. Still, the instructors are invited to reflect on other teaching strategies to show the importance of these diagrams in the software development process.
A Comparison of Apache Spark Supervised Machine Learning Algorithms for DNA Splicing Site Prediction

Thanks to next-generation sequencing techniques, a very big amount of genomic data are available. Therefore, in the last years, biomedical databases are growing more and more. Analyzing this big amount of data with bioinformatics and big data techniques could lead to the discovery of new knowledge for the treatment of serious diseases. In this work, we deal with the splicing site prediction problem in DNA sequences by using supervised machine learning algorithms included in the MLlib library of Apache Spark, a fast and general engine for big data processing. We show the implementation details and the performance of those algorithms on two public available datasets adopting both local and cloud environments, emphasizing the importance of this last environment for its scalability and elasticity of use. We compare the performance of the algorithms with U-BRAIN, a general-purpose learning algorithm originally designed for the prediction of DNA splicing sites. Results show that, among the Spark algorithms, all have good prediction accuracy (>0.9)—that is comparable with the one of U-BRAIN—and much lower execution time. Therefore, we can state that Apache Spark machine learning algorithms are promising candidates for dealing with the DNA splicing site prediction problem.
Just-noticeable difference binary pattern for reduced reference image quality assessment

Just-noticeable difference (JND) is defined as the smallest intensity change in an image that can be noticed by the human vision system (HVS). Any perceptible distortion level must be greater than the JND. Based on this observation, a local binary pattern (LBP) is developed for image quality assessment. First, the JND map of the image is computed. The spatial and relative intensity relationships among pixels in a local neighborhood are employed to generate the proposed LBP based on the JND map. Then, image contrast is used as a weighting factor for the LBP histogram generation to characterize the structural and contrast information of the image. Finally, the contrast and structure changes due to image distortion are measured by calculating the similarity between contrast-weighted histograms of the reference and distorted images. Support vector regression is employed to pool the similarity to predict the quality. Experimental results on benchmark databases demonstrate that the proposed LBP can effectively and accurately measure image quality, which is consistent with the HVS. The proposed method achieves high consistency with subjective perception using 18 reference values and performs better than other state-of-the-art reduced reference image quality assessment methods.
Quality Image Enhancement from Low Resolution Camera using Convolutional Neural Network

Images captured with various cameras have different qualities especially low-resolution images which generally have deficiencies in terms of quality. The uses of Convolutional Neural Network (CNN) in Super Resolution has been widely applied and has impressive performance results. It encourages the use of CNN to translate the captured images by low-resolution into a better resolution. In this paper, we propose a new architecture to perform image translation from low-resolution into a higher one using DenseNet with Skip-connections. The dataset used is taken from the DPED (DSLR-Photo Enhancement Dataset) which contains images captured from different cameras simultaneously. To get results that can be used by all camera models, a transfer learning scenario is added. Using our architecture, we were able to achieve results with average PSNR of 20,86 dB and 0,9307 SSIM which was better than the comparison architecture.
Knowledge is Power: Systematic Reuse of Privacy Knowledge for Threat Elicitation

Privacy threat modeling is difficult. Identifying relevant threats that cause privacy harm requires an extensive assessment of common potential privacy issues for all elements in the system-under-analysis. In practice, the outcome of a threat modeling exercise thus strongly depends on the level of experience and expertise of the analyst. However, capturing (at least part of) this privacy expertise in a reusable threat knowledge base (i.e. an inventory of common threat types), such as LINDDUN's and STRIDE's threat trees, can greatly improve the efficiency of the threat elicitation process and the overall quality of identified threats. In this paper, we highlight the problems of current knowledge bases, such as limited semantics and lack of instantiation logic, and discuss the requirements for a privacy threat knowledge base that streamlines threat elicitation efforts.
Towards Minimising Timestamp Usage In Application Software

With digitisation, work environments are becoming more digitally integrated. As a result, work steps are digitally recorded and therefore can be analysed more easily. This is especially true for office workers that use centralised collaboration and communication software, such as cloud-based office suites and groupware. To protect employees against curious employers that mine their personal data for potentially discriminating business metrics, software designers should reduce the amount of gathered data to a necessary minimum. Finding more data-minimal designs for software is highly application-specific and requires a detailed understanding of the purposes for which a category of data is used. To the best of our knowledge, we are the first to investigate the usage of timestamps in application software regarding their potential for data minimisation. We conducted a source code analysis of Mattermost, a popular communication software for teams. We identified 47 user-related timestamps. About half of those are collected but never used and only 5 are visible to the user. For those timestamps that are used, we propose alternative design patterns that require significantly reduced timestamp resolutions or operate on simple enumerations. We found that more than half of the usage instances can be realised without any timestamps. Our analysis suggests that developers routinely integrate timestamps into data models without prior critical evaluation of their necessity, thereby negatively impacting user privacy. Therefore, we see the need to raise awareness and to promote more privacy-preserving design alternatives such as those presented in this paper.
Multi-density Clustering Based Hierarchical Path Planning

Path planning is one of the key abilities in artificial intelligence. Many different approaches exist, focusing on finding the shortest path. Obstacle density, which is often ignored by conventional approaches, has a great influence on path quality especially in off-road environment. Because obstacles along a path compromise driving safety and frequent obstacle avoidance increases vehicle control difficulty while decreases traveling speed. In this paper, a simple and efficient approach of hierarchical path planning algorithm based on multi-density clustering is proposed, aiming at finding a clear and short path to achieve an integrated goal of obstacle avoidance and path length shortness. A simulation evaluation shows that our proposed approach can find short path bypassing regions with dense obstacles efficiently.