Reviewerly/exHarmony huggingface dataset:
Nonlinear Process Monitoring Based on Global Preserving Unsupervised Kernel Extreme Learning Machine

Recently, the unsupervised extreme learning machine (UELM) technique as a nonlinear data mining approach has been employed to diagnose nonlinear process faults. However, during the dimensionality reduction of process observation data, UELM only aims at mining the local structure feature information of process data and lacks of the ability of preserving the global structure analysis of process data, which would perform unsatisfactorily for monitoring nonlinear process. Furthermore, the choice of the optimal number of hidden layer nodes in UELM method is still a challenging problem. To handle these two tough problems, a new fault detection approach on the basis of global preserving unsupervised kernel extreme learning machine (GUKELM) technique is proposed to monitor nonlinear process effectively. In GUKELM technique, the data global structure preserving framework is naturally incorporated into standard UELM to capture the local structure information as well as to preserve the global structure feature information of nonlinear process observation data during the dimensionality reduction. Meanwhile, to tackle the strong nonlinearity of process observation data, the kernel trick is utilized to successfully solve the challenging issue of setting the optimal number of hidden layer nodes. Based on extracted process data low dimensional feature information from GUKELM method, support vector data description algorithm is adopted to construct the monitoring statistic to detect process fault. Finally, the feasibility and effectiveness of the proposed process monitoring strategy is illustrated through a numerical nonlinear system and the continuous stirred tank reactor process which is a typical nonlinear process.
Reputation Evaluation with Malicious Feedback Prevention Using a HITS-Based Model

The reputation of web services is calculated by aggregating user feedback ratings. Though reputation is a subjective metric, it can be considered as a good indicator about service's Quality of Experience, and henceforth, it can be used for recommending services in an open ecosystem. In this work, we propose a three-phase process for evaluating web service reputation by aggregating user feedback ratings. The relationship between users and services is modeled as a bi-partite graph where an adapted HITS (Hypertext Induced Topic Search) algorithm is employed to distinguish between honest and malicious users in Phase I. Then, this model is used to evaluate, in Phase III, the reputation of web services from user ratings after punishing malicious users in Phase II. An experiment on a dataset of real Web services was conducted to validate the effectiveness of the proposed model in evaluating Web service reputation.
Easy Clinic: Smart Sensing Application in Healthcare

In the recent few years there was an enormous advancement in the field of sensors and body area network (BAN) technologies. Those technologies led to many beneficial applications in different areas. For instance, in healthcare there was a recognizable evolution of using body sensors to collect and evaluate body parameters. Those sensors can be used to monitor and analyse vital signs like body temperature, blood pressure, sugar level, heart rate and much more which are currently used in a large scale throughout the world hospitals. In this paper, a healthcare smart sensing mobile application is proposed. This application is able to collect data coming from the body temperature sensor through the network then process it based on some scientific analysis to give the mobile user the needed information and the decision to take in if any sudden emergency situation happened.
Research on Edge Detection Operator of a Convolutional Neural Network

With the rapid development of image technology, edge detection technology is becoming more and more diverse. In order to fully improve the edge detection effect, it is necessary to innovate in the detection algorithm. In recent years, deep learning has become an emerging research direction in the field of machine learning, and convolutional neural network <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> (CNN) is a deep learning model that has been widely studied and applied. Since CNN can automatically detect the multi-directionality of image features, it is widely used in image classification and recognition. The traditional edge detection operators are based on gradients, which can only be detected from several fixed directions, resulting in the loss of edge information in other directions. For the above, edge detection algorithm is proposed based on CNN in this paper. The algorithm is as follows: Firstly, because the convolutional network can automatically extract image features. In this paper, CNN is constructed under the framework of tensorflow to obtain the edge detection operator after training. The prepared data set is input into the network for training, and the training result is saved on matlab for verification. In addition, two comparison experiments were carried out in the experiment. The first set of experiments uses six different sets of optimizers to train the network and generate corresponding edge detection operators. They are used to perform edge processing on the image of the test set. By comparing the result of processing, the optimizer suitable for this paper was selected as Adam Optimizer and Gradient Descent Optimizer. The second set of experiments was performed by comparing the edge detection operator generated by Adam Optimizer with the classical operator. The experimental results show that the edge detection operator based on convolutional neural network detects more abundant edge information than the classical operator, and also achieves higher effect on the edge detection of the target.
Computer-aided cancer classification system using a hybrid level-set image segmentation

In computer vision, image segmentation plays an indispensable role. Image segmentation is used to find objects and their boundary limits in images. In medical imaging, tasks such as finding pathological regions and automatic detection of diseases are a complicated problem for computer science and image processing. This problem is intricate because of insufficient clarity of images. High segmentation accuracy, efficiency, reliability, and ability to handle noise or inhomogeneous intensity are some of the desired characteristics of a good segmentation algorithm for medical images. This study proposes an automatic computer-aided system that has been developed by using a level-set region-based active contour segmentation algorithm for edge detection of 2D medical images with and without inhomogeneous intensity. In this method, cancer images are segmented in the transformed domain, which helps to reduce the noise. The proposed segmentation algorithm combines the local geometric properties and local image fitting properties to correctly identify the pixels corresponding to different regions, in images with or without intensity inhomogeneity. The performance of the proposed method is analyzed with panoramic radiograph, Matrix Laboratory. In the experiments, the proposed method outperforms the baselines.
Fluent Editor and Controlled Natural Language in Ontology Development

Ontologies facilitate concise communication across applications and people, by means of precisely defined terms that provide a shared and common understanding. However, typically, the development of an ontology entails the commitment of multiple users and requires significant skills and knowledge in this area. To face this problem, in this paper we discuss the features and capabilities of Fluent Editor and, based on a real-life example, present the properties of Controlled Natural Language (CNL). Firstly, the editor is designed to be user-friendly and is customized with a ribbon and self-extensible windows. Secondly, it actively supports the user when typing sentences by providing built-in features such as syntax highlighting, a hint box, a predicted word list, marking errors, parsing and serializing, and reformatting and validating options. Thirdly, the built-in graphics engine produces high quality images of ontology diagrams, represented on a 2-D view-port, and easy to manipulate and change. Fourthly, CNL allows users with even basic English skills to start typing sentences with little or no preliminary effort in defining terms and the relations between them, and prevents the user from entering any grammatically or morphologically incorrect sentence. The evidenced success of the development of ontologies in many business areas, using CNL aided by Fluent Editor on the one hand, and results from similar studies documented in the literature on the other, seem to indicate that together the language and the tool form a mature approach to developing and maintaining complex information structures. In this case, there is substantial evidence to indicate that the adoption of the above technologies to teach the foundations of knowledge engineering covering artificial intelligence applications, ought to be considered as a good choice also for classroom settings.
Word Sense Disambiguation of Hindi Text using Fuzzified Semantic Relations and Fuzzy Hindi WordNet

Word Sense Disambiguation (WSD) refers to the task of finding intended meaning of a word in the text. We have used Fuzzy Hindi WordNet (FHWN) which is an extension of Hindi WordNet with added fuzzy semantic relations between the synsets. However, fuzziness in semantic relations of Hindi WordNet was not considered in FHWN. Also, it is mentioned in literature that the sematic relations are fuzzy and some relations are more important than the others. In this work, we have amalgamated these two concepts and extended FHWN. We assigned membership values to the semantic relations of FHWN. With added fuzziness, resource becomes closer to human usage of language. Thereafter, we computed various centrality measures and the sense with highest value is deduced as the correct sense. The experimental results proved the proposed method leads to better performance than Fuzzy Hindi WordNet and proves the value for the research.
Extreme Learning Machine Ensemble for CSI based Device-free Indoor Localization

A bstract-Device-free localization is a new and developing technology which estimates an object's locations without requiring it to equip any devices. Channel State Information (CSI), containing more fine-grained information than Received Signal Strength Indication (RSSI), is a natural candidate for localization application and has been studied in many works. Extreme Learning Machine (ELM) is a fast and robust algorithm, but it has only one hidden layer, which limits its capacity. One of the most popular ways of improving accuracy is to use multiple different models to obtain better predictive performance. In this paper, we propose a device-free localization approach using an ensemble of ELMs in which each ELM has the same number of hidden nodes. The proposed approach models the localization task as a regression problem. First, we leverage a modified driver to collect CSI and extract phase information. The Principal Component Analysis (PCA) is then applied to reduce the dimensionality of the phase features. After that, the processed features are fed into an ensemble of ELMs to output their respective predictions. The final prediction is an average combination of them. We conducted experiments in a typical indoor environment to verify its performance, and the results demonstrated the effectiveness of our approach and of CSI.
Recognizing Experts on Social Media

Knowing who is an expert on social media is a challenging yet important task, especially in a world where misleading information is commonplace and where social media is an important information source for knowledge seekers. In this paper we investigate expertise heuristics by comparing features of experts versus non-experts in big data settings. We employ a large set of features to classify experts and non-experts using data collected on two social media platform (Twitter and reddit). Our results show a good ability to predict who is an expert, especially using language-based features, validating that heuristics can be developed to differentiate experts from novices organically, based on social media use. Our results contribute to the development of expertise location and identification systems as well as our understanding on how experts present themselves on social media.
Learning sparse discriminant low-rank features for low-resolution face recognition

In this paper, we propose a novel approach for low-resolution face recognition, under uncontrolled settings. Our approach first decomposes a multiple of extracted local features into a set of representative basis (low-rank matrix) and sparse error matrix, and then learns a projection matrix based on our proposed sparse-coding-based algorithm, which preserves the sparse structure of the learned low-rank features, in a low-dimensional feature subspace. Then, a coefficient vector, based on linear regression, is computed to determine the similarity between the projected gallery and query image's features. Furthermore, a new morphological pre-processing approach is proposed to improve the visual quality of images. Our experiments were conducted on five available face-recognition datasets, which contain images with variations in pose, facial expressions and illumination conditions. Experiment results show that our method outperforms other state-of–the-art low-resolution face recognition methods in terms of recognition accuracy.
Personalized Online Learning of Whole-Body Motion Classes using Multiple Inertial Measurement Units

Online action classification is an important field of research, enabling the particularly interesting application scenario of controlling wearable devices which actively support the user's motions. The majority of machine learning applications of real-world systems are based on pre-trained average-user models without any personalization. Our long-term goal is to provide a system that adapts to its user's personal behavior patterns on the fly and in real-time. Ideally, we want to initiate a continuous collaboration between the system and the user where both alternatively adjust to each other to maximize the system's utility. Such tasks are not feasible with static models. In this paper, we investigate the potential and benefits of personalized online learning in the task of online action classification. We record motion sequences of different subjects wearing the Xsens bodysuit, which incorporates multiple inertial measuring units, enabling a fine-grained discrimination of motions. On this basis, we first perform a feature selection, showing that only a few sensors are necessary to achieve a high classification performance. Subsequently, we compare the recognition capabilities of offline average user models against personalized models trained in an online way. Our experiments conclude that personalized models require only few data to outperform average user systems and are particularly valuable for applications with limited computational hardware which rely on the raw sensor inputs only.
An optimized finite-difference scheme based on the improved PSO algorithm for wave propagation

The finite-difference (FD) scheme is widely used to model acoustic and elastic wave propagation. However, the numerical dispersion will affect the accuracy and efficiency of the modeling due to approximate the differential operator with the difference operator. In this abstract, a new optimized FD scheme is proposed based on the improved particle swarm optimization (PSO) algorithm. We improve the PSO algorithm by introducing the strategies of local learning and global learning. Then, the improved PSO algorithm is used to obtain the optimized FD scheme. Dispersion analyses and numerical modeling demonstrate that the optimized FD scheme can suppress numerical dispersion more efficiently and achieves higher accuracy. Presentation Date: Monday, September 16, 2019 Session Start Time: 1:50 PM Presentation Start Time: 2:15 PM Location: 304A Presentation Type: Oral
An Empirical Relative Value Learning Algorithm for Non-parametric MDPs with Continuous State Space

We propose an empirical relative value learning (ERVL) algorithm for non-parametric MDPs with continuous state space and finite actions and average reward criterion. The ERVL algorithm relies on function approximation via nearest neighbors, and minibatch samples for value function update. It is universal (will work for any MDP), computationally quite simple and yet provides arbitrarily good approximation with high probability in finite time. This is the first such algorithm for non-parametric (and continuous state space) MDPs with average reward criteria with these provable properties as far as we know. Numerical evaluation on a benchmark problem of optimal replacement suggests good performance.
On the Behaviour of Differential Evolution for Problems with Dynamic Linear Constraints

Evolutionary algorithms have been widely applied for solving dynamic constrained optimization problems (DCOPs) as a common area of research in evolutionary optimization.Current benchmarks proposed for testing these problems in the continuous spaces are either not scalable in problem dimension or the settings for the environmental changes are not flexible.Moreover, they mainly focus on non-linear environmental changes on the objective function.While the dynamism in some real-world problems exists in the constraints and can be emulated with linear constraint changes.The purpose of this paper is to introduce a framework which produces benchmarks in which a dynamic environment is created with simple changes in linear constraints (rotation and translation of constraint's hyperplane).Our proposed framework creates dynamic benchmarks that are flexible in terms of number of changes, dimension of the problem and can be applied to test any objective function.Different constraint handling techniques will then be used to compare with our benchmark.The results reveal that with these changes set, there was an observable effect on the performance of the constraint handling techniques.
An Educational Augmented Reality Application for Elementary School Students Focusing on the Human Skeletal System

Augmented Reality (AR) as a new field regarding Human Computing Interaction (HCI) has been gaining momentum in the last few years. Being able to project interactive graphics into real-life environments can be applied in various fields, research and commercial goals. In the field of education, textbooks are still considered to be the primary tool used by students to learn about new topics. Since AR requires interaction and exploration, it brings a ludic component that is hard to replicate using regular textbooks. The application we developed allows elementary school students to interact with a fully three-dimensional human skeleton model, using specialized virtual buttons. Students can understand this complex structure and learn the names of important bones just by using a tablet, a picture and their hands. Results show that the majority of students consider that our AR application helped them visualize and learn more about the human skeletal system. Additionally, the data we gathered shows that there was a 16% increase in correct responses regarding bone names after using our AR application. Our AR application successfully helped the students learn about the human skeletal system by introducing them to AR technologies.
The Security Intention Meeting Series as a way to increase visibility of software security decisions in agile development projects

To achieve a level of security that is just right, software development projects need to strike a balance between security and cost. This necessitates making such decisions as to what security activities to perform in development and which security requirements should be given priority. Current evidence indicates that in many agile development projects, software security is dealt with in a more or less "accidental" way based on individuals' security awareness and interest. This approach is unlikely to lead to an optimal security level for the product. This paper suggests Security Intention Recap Meetings as a recurring organisational tool for evaluating current practices regarding the security intentions of a software project, and to make decisions on how to move forward. These meetings involve key decision makers in the project, such as the product owner and the project manager, with the purpose of making security decisions visible and deliberate and to monitor their results
Depth map estimation methodology for detecting free-obstacle navigation areas

This paper presents a vision-based methodology which makes use of a stereo camera rig and a one dimension LiDAR to estimate free obstacle areas for a quadrotor navigation. The presented approach fuses information provided by a depth map from a stereo camera rig, and the sensing distance of the 1D-LiDAR. Once the depth map is filtered with a Weighted Least Squares filter (WLS), the information is fused through a Kalman filter algorithm. To determine if there is a free space large enough for the quadrotor to pass through, our approach marks an area inside the disparity map by using the Kalman Filter output information. The whole process is implemented in an embedded computer Jetson TX2 and coded in the Robotic Operating System (ROS). Experiments demonstrates the effectiveness of our approach.
Open source intelligence (OSINT) for conflict monitoring in contemporary South Africa: Challenges and opportunities in a big data context

With the advent of the information age, Open Source Intelligence (OSINT) has gained special prominence in the Intelligence Community (IC). However, the era of big data has brought numerous challenges in handling OSINT, in particular because big data comprise large volumes of unstructured data that are generated continuously. In this article, we discuss the use of OSINT in the era of big data, in particular how it relates to compiling event data, but also show that large projects with an international focus such as the Integrated Crisis Early Warning System (ICEWS) are inadequate to study political instability in the current South African context. We shift our collection efforts from mainstream media to the analysis of WhatsApp messages, since this platform has recently gained popularity and it is widely used in South Africa. We show how we build an automated data pipeline that provides near real time data on the occurrence of mass violence in South Africa. For this analysis, we make use of Natural Language Processing (NLP) toolkits in Python and build interactive dashboards to monitor the state of mass violence in South Africa.