Reviewerly/exHarmony huggingface dataset:
Is the Overfitting in a Neural Network a Reliable Model for the Recognition of Activities of Daily Living?

Recently human behaviour recognition based on mobile devices have become attractive to facilitate Ambient Assisted Living (AAL). Due to the diversities of data acquired from a large range of sensors that are available in the off-the-shelf mobile devices, the overfitting problems are highly concerned in the training of Artificial Neural Networks (ANN) for the recognition of Activities of Daily Living (ADL) and the detection of the associated environments. The purpose of this paper is to explore the correlation and causation of these ANN. We also aim to address problems such as how to avoid the overfitting and in what way these ANN models will affect the accuracy of the predictions from the automatic recognition of ADL and the detection of the environments. Several tests have been performed based on three different types of ANN created for our previously proposed framework. From the results, we can see that the implemented ANN models with Deep Neural Networks (DNN) implementation can provide reliable predictions, which is the type of ANN that has a higher probability for overfitting.
Improved C4.5 algorithm based on k-means

When the traditional C4.5 algorithm deals with the big data with a large number of multidimensional continuous attribute values, it may cause the issue of low classification accuracy with the related discretization method. This paper proposes a novel method to discretize continuous data based on th e k-means algorithm. The method generates data clusters by combining continuous, unfeatured data with corresponding class labels, and then takes the approximate boundary points of the cluster as the candidate splitting-points of the continuous attribute. Based on this, the information gain ratio is calculated. Experimental results show that, the proposed K-C4.5 algorithm improves the classification accuracy of the decision tree in comparison with the traditional one.
Fox and Geese

The technological evolution in the mobile sector has experienced an exponential growth in the last years. The number of functionalities has grown, and of course the way we use these devices has changed. Starting with calls and messages, introducing cameras, implementing Internet connectivity, and finally introducing the sensor technology, we arrive to today's smartphones. Following this trend, a popular application as gaming has extended its domain from home computers and consoles to the mobile realm. While there are many developers working on modern game genres, we devised a mobile game that join the rediscovery of ancient board games with the pleasure to play on a smart device. Through the development of the digital version of a game that was played centuries ago, we have created a very attractive tool able to raise the interest of children toward ancient history. We hence discuss here our attempt to digitally rediscover and enhance an ancient board game called "Fox and Geese"
Identifying Diagnostically Complex Cases Through Ensemble Learning

Computer-Aided Diagnosis systems have been used as second readers in the medical imaging diagnostic process. In this study, we aim to identify cases that are hard to diagnose and lead to interpretation variability among medical experts. We propose a combination of image features and advanced machine learning classifiers to predict the degree of malignancy and determine the level of diagnostic difficulty by looking where these classifiers collectively fail. Using the NIH/NCI Lung Image Database Consortium (LIDC) dataset and four ensemble learning algorithms (bagging, random forest, AdaBoost, and a heterogeneous ensemble with decision trees, support vector machines, and k-nearest neighbors), our results show that we can not only detect difficult cases, but we are also able to identify what imaging characteristics or features make these cases hard to diagnostically interpret.
Introducing the Notion of ‘Contrast’ Features for Language Technology

In this paper, we explore whether there exist 'contrast' features that help recognize if a text variety is a genre or a domain. We carry out our experiments on the text varieties that are included in the Swedish national corpus, called Stockholm-Umeå Corpus or SUC, and build several text classification models based on text complexity features, grammatical features, bag-of-words features and word embeddings. Results show that text complexity features and grammatical features systematically perform better on genres rather than on domains. This indicates that these features can be used as 'contrast' features because, when in doubt about the nature of a text category, they help bring it to light.
Scalable Least Square Twin Support Vector Machine Learning

Machine Learning (ML) on massive scale datasets, called Big Data, has become a challenge for traditional computing and storage technologies. Henceforth, massive scale ML is an emerging domain of research. Least Square Twin Support Vector Machine (LSTSVM) is a faster variant of Support Vector Machine (SVM). However, it suffers from scalability issues and shows computational and/or storage bottlenecks on massive datasets. Proposed work designs a scalable solution to LSTSVM called Distributed LSTSVM (DLSTSVM). DLSTSVM is designed using distributed parallel computing on top of cluster of multiple machines. After applying horizontal partitioning on massive datasets, DLSTSVM trains it in distributed parallel fashion and finds two non-parallel hyper-planes as decision boundaries for two different classes. MapReduce paradigm is utilized to execute parallel computation on partitioned data in a way that averts memory constraints. Proposed technique achieves computational and storage scalability without losing prediction accuracy.
Graphical visualization of behavioural patterns in relation to indoor environment quality and energy use

In this paper, the authors provide a general overview on the methodological framework behind the monitoring and evaluation strategies of Mobistyle project that are used as reference for the demonstration cases. The strategies identify parameters that need to be evaluated during several phases regarding the impact (energy, IEQ, health, behavioural patterns) and the strategy (effectiveness of the process) of the project, and how these parameters can be numerically evaluated. In particular, the paper focuses on the graphical visualization method for behavioural patterns analysis in relation to indoor environment quality and energy use. The proposed approach is illustrated based on measured data from one Mobistyle Project case study i.e. a hotel for long term stay located in Turin, Italy.
A Fast On-Chip SVM-Training System With Dual-Mode Configurable Pipelines and MSMO Scheduler

On-chip training of support vector machine (SVM) is limited by its low speed and large resource cost. In this paper, a novel integrated circuit implementation of the modified sequential minimal optimization (MSMO) algorithm for fast on-chip SVM training is carried out to speed up the training process of hardware systems. The dual-mode configurable pipelines with the table-driven-based Gaussian kernels are implemented to accelerate the prediction error updating process of the MSMO algorithm and to adapt to both linear and nonlinear datasets with different feature vector dimensions. Efficient MSMO scheduler is proposed to effectively control the computation and data flows of the system. The presented design is verified on a field-programmable gate array using three datasets with different feature vector dimensions. The experimental results show that the design improves the training speed and adaptability of the hardware system without excessively increasing chip area or power consumption, which facilitates the low-cost fast on-chip SVM training.
Visual-GPS: Ego-Downward and Ambient Video Based Person Location Association

In a crowded and cluttered environment, identifying a particular person is a challenging problem. Current identification approaches are not able to handle the dynamic environment. In this paper, we tackle the problem of identifying and tracking a person of interest in the crowded environment using egocentric and third person view videos. We propose a novel method (Visual-GPS) to identify, track, and localize the person, who is capturing the egocentric video, using joint analysis of imagery from both videos. The output of our method is the bounding box of the target person detected in each frame of the third person view and the 3D metric trajectory. At glance, the views of the two cameras are quite different. This paper illustrates an insight into how they are correlated. Our proposed method uses several difference clues. In addition to using RGB images, we take advantage of both the body motion and action features to correlate the two views. We can track and localize the person by finding the most "correlated" individual in the third view. Furthermore, the target person's 3D trajectory is recovered based on the mapping of the 2d-3D body joints. Our experiment confirms the effectiveness of ETVIT network and shows 18.32 % improvement in detection accuracy against the baseline methods.
Modelling using unified software development process (USDP) method for repository system in Mulawarman University

Abstract Lack of academic document storage or academic electronic-based data in Mulawarman University is the basis of this research. As a Republic Indonesia Regulation in No. 82/2012 on Electronic System and Transaction Act. 3. Therefore, Mulawarman University is required to provide electronic document or electronic-based data storage thus easy for those who need it. In this study, Unified Software Development Process (USDP) methodology as a modeling system has been implemented. Meanwhile, PHP and MySQL as a system development tool have been explored. A document storage system or website-based data called a repository information system has been created as a result of this research. Furthermore, this repository has ten types documents that can be stored and retrieved, consisting of journals, books, proceedings, articles, university regulatory, guidance, reports, course materials, letters, and other academic data for Mulawarman University.
InterPoser: Visualizing Interpolated Movements for Bouldering Training

Bouldering is an urban form of rock climbing that requires precise and complex movement. Similarly to other sports, the simplest way to learn bouldering skill is to mimic professional's motion. However, ordinary beginner boulders cannot learn to coaches, so that they learn by themselves or tutorial videos. Even if they managed, bouldering has a communication difficulty between a trainee and a trainer, that is, climbers cannot mimic the trainer's movement in parallel. Accordingly, we considered a video feedback system would be useful for beginners and suggested InterPoser: a novel visualization system for intermediate motion between a beginner climber and a more experienced. InterPoser receives two videos of different subjects climbing the sample problem and generates an intermediate movement. In addition, this motion is transferred into realistic images of the climber. The proposed system is expected to support beginner to acquire more detailed observation and understanding of the motion.
Data Hiding with Digital Authentication in Spatial Domain Image Steganography

In now a day, Image Steganography is one of the most popular and highly secured wireless communication technique. Digital authentication in image steganography is a special extension in twenty-first century. The approach “steganography” can be categorized into two major wings—spatial domain and frequency domain. Among them, the spatial domain is very common and easy to implement. In this paper, an image steganography scheme is developed in spatial domain with proper digital authentication. The basic idea of integer theory is used to develop this particular data hiding model. More specifically, the concept of triangular number and perfect number is used to develop the system with maintaining all the necessary requirements of information security, i.e., confidentiality, integrity, and availability of data among authorized users. The speciality of this approach is keyless transposition and freeness from different powerful attacks like PRNG (Pseudo Random Number Generator), visual attack, chi-square, and histogram. The efficacy of the proposed method is analyzed with different parameters of data hiding and digital authentication.
[DC] Self-Adaptive Technologies for Immersive Trainings

Online learning is the preferred option for professional training, e.g. Industry 4.0 or e-health, because it is more cost efficient than on-site organisation of realistic training sessions. However, current online learning technologies are limited in terms of personalisation, interactivity and immersiveness that are required by applications such as surgery and pilot training. Virtual Reality (VR) technologies have the potential to overcome these limitations. However, due to its early stage of research, VR requires significant improvements to fully unlock its potential. The focus of this PhD is to tackle research challenges to enable VR for online training in three dimensions: (1) dynamic adaptation of the training content for personalised trainings, by incorporating prior knowledge and context data into self-learning algorithms; (2) mapping of sensor data onto what happens in the VR environment, by focusing on motion prediction techniques that use past movements of the users, and (3) investigating immersive environments with intuitive interactions, by gaining a better understanding of human motion in order to improve interaction. The designed improvements will be characterised though a prototype VR training platform for multiple use cases. This work will not only advance the state of the art on VR training, but also on online e-learning applications in general.
Algorithm Using Deep Learning for Recognition of Japanese Historical Characters in Photo Image of Historical Book

In Japan, there are vast amount of classical books written in cursive Japanese that cannot be read by modern people. It is difficult to recognize each cursive Japanese separately, because they are written connected. Furthermore, there are many types of shape of characters. Therefore, an efficient method to convert them into modern characters automatically is required. Some methods recognizing a block of a few characters using deep learning have been studied so far. However, every page in a Japanese historical book is stored by a photo image; therefore, it is desirable to recognize all characters in a photo image at once. In this paper, we propose a method using deep learning to recognize cursive Japanese in a photo image without separating a block of characters manually. Furthermore, we evaluate the performance of the proposed algorithm using photo images of an actual book.
Integrating runtime data with development data to monitor external quality: challenges from practice

The use of software analytics in software development companies has grown in the last years. Still, there is little support for such companies to obtain integrated insightful and actionable information at the right time. This research aims at exploring the integration of runtime and development data to analyze to what extent external quality is related to internal quality based on real project data. Over the course of more than three months, we collected and analyzed data of a software product following the CRISP-DM process. We studied the integration possibilities between runtime and development data, and implemented two integrations. The number of bugs found in code has a weak positive correlation with code quality measures and a moderate negative correlation with the number of rule violations found. Other types of correlations require more data cleaning and higher quality data for their exploration. During our study, several challenges to exploit data gathered both at runtime and during development were encountered. Lessons learned from integrating external and internal data in software projects may be useful for practitioners and researchers alike.
Generating Synthetic Humans for Learning 3D Pose Estimation

We generate synthetic annotated data for learning 3D human pose estimation using an egocentric fisheye camera. Synthetic humans are rendered from a virtual fisheye camera, with a random background, random clothing, random lighting parameters. In addition to RGB images, we generate ground truth of 2D/3D poses and location heat-maps. Capturing huge and various images and labeling manually for learning are not required. This approach will be used for the challenging situation such as capturing training data in sports.
A Study on Features for Improving Performance of Chinese OCR by Machine Learning

This paper discusses a method to improve the performance of Chinese OCR by choosing a proper feature vector and synthetic classification. We compare two groups of features which are used to implement Chinese OCR System and demonstrate that the first group of features is more useful for static Chinese OCR System. By now feature extractions have been done either for local features or for global features. Classifications have been done by single classification. We propose synthetic features extraction and classification in this paper. We find that the result is improved by machine learning method. Later we apply the result in the area of off- and on-line signature verification system.
Variability Modeling and Implementation with EASy-Producer

EASy-Producer is an open-source research toolset for engineering product lines, variability-rich software ecosystems, and dynamic software product lines. In this tutorial, we will introduce its (textual) variability modeling capabilities realized by the Integrated Variability Modeling Language (IVML) and its model-based development and implementation capabilities, which are realized by the Variability Instantiation Language (VIL) and the Variability Template Language (VTL).
A VR System for Alleviating a Fear of Heights Based on Vital Sensing and Placebo Effect

Acrophobia is a phobia that persons fear for heights and tend to have negative images like falling down from their standing positions. Persons in acrophobia avoid going high places, so they have little experience in such places. Making them experience high places as much as possible is an effective treatment to improve the symptoms. Placebo effect is a well-known phenomenon that person’s psychological belief influences his/her physical and mental status. In this paper, we propose a VR simulation system to overcome acrophobia based on a placebo effect by making persons virtually experience high places. We show a method for effectively presenting the heights to viewers according to their fear strength and elaborate our implementation approach of the proposed system.
Combination of features through weighted ensembles for image classification

Image classification is a multi-class problem that is usually tackled with ensembles of binary classifiers. Furthermore, one of the most important challenges in this field is to find a set of highly discriminative image features for reaching a good performance in image classification. In this work we propose to use weighted ensembles as a method for feature combination. First, a set of binary classifiers are trained with a set of features and then, the scores are weighted with distances obtained from another set of feature vectors. We present two different approaches to weight the score vector: (1) directly multiplying each score by the weights and (2) fusing the scores values and the distances through a Neural Network. The experiments have shown that the proposed methodology improves classification accuracy of simple ensembles and even more it obtains similar classification accuracy than state-of-the-art methods, but using much less parameters.
Real-time performance analysis of retinex algorithm on embedded boards for robotics application

Retinex enhancement is an extremely powerful algorithm in the domain of image/video enhancement due to its ability to deliver effective enhancement It differs from the regular enhancement under very bad illumination, as well as, bad weather conditions. However, its algorithmic complexity is very high, therefore, it cannot be used for real-time enhancement. This paper presents the work done on accelerating performance of the Retinex algorithms, SSR and MSR, using multithreading software optimization and hardware optimization, on the embedded platforms- UDOO x86 Ultra and Nvidia Jetson Tegra K1 and also the integration of these boards with a mobile robot, for tracking in cluttered environment, where the velocity control of the robot is achieved using MPC.
Augmented Reality for Urban Utility Infrastructure: A UK Perspective

Research on Augmented Reality (AR) in the Architecture, Engineering and Construction (AEC) industry is still new. As part of a comprehensive study on the application of AR technologies for urban utility infrastructure, this paper sets out to contribute to the state-of-the-art in this area by presenting the results of an industry survey in the UK. The results of the survey conformed, in principles, to majority of findings of the previous research in the field, but also revealed some new or contradictory patterns. Geo-locationing and geo-tagging are still major concerns and have not yet been completely resolved. Relying on global systems does not look like the most reliable option and local systems are required to either replace or jointly work with global systems. With respect to non-AR issues, it is crucial that the quality and content of infrastructure and utilities data are improved and ideally stored centrally in a nationally procured database.
Exploration on the Application of Blockchain in the Security System of Smart Park

How to solve the traditional security system of Smart Park in the centralized management mode, existing access control is copied, easy loss of data, tampering with information and so on. Based on the distributed storage, intelligent contract, non-tampering, traceability and other characteristics of Blockchain technology, this article will go deep into the security business of Smart Park, and solve a series of problems existing in the processes of intelligent management in the Smart Park traditional security system.
A Novel Bounded-Error Piecewise Linear Approximation Algorithm for Streaming Sensor Data in Edge Computing

Many studies show that many Data compression schemes, like Bounded-Error Piecewise Linear Approximation (BEPLA) methods, have been proposed to lower the length sensor data, aiming to mitigating data transmission energies. When an error bound is given, these data compression schemes consider how to represent original sensor data by using fewer line segments. In this paper, besides BEPLA, we further deal with resolution reduction, which called Swing-RR (Resolution Reduction) sets a new restriction on the position of line segment endpoints. Our simulating results on existing datasets indicate that the length of compressed data is actually lowered.
Research on the Security Data Transmission based on Linux

Abstract This paper develops system software in application layer based on Linux operating system. Linux operation system in this paper. Different transmission modes are adopted in different parts to improve the transmission efficiency in the network. The data capture encryption method is used for the file data, and the storage transmission method is used for the image data. It designs an identity authentication protocol and key exchange protocol based on digital certificate and digital sign technology, to avoid the sophistication and filch and improve the reliability of the identity authentication and the security of the key exchange. The common algorithms of symmetric encryption, asymmetric encryption and information digest are studied by using OpenSSL standard library function to realize data encryption module of the system.