Reviewerly/exHarmony huggingface dataset:
Privacy-Enhanced Emotion Recognition Approach for Remote Health Advisory System

Speech recognition also known as voice recognition involves in identifying the words in the speech and translates it into machine-readable form. Speech recognition applications are currently being used for health care and special needs’ assistance such as transcription of medical reports, conversational systems, command and control. The main benefit of this technology is that it can be accessed from anywhere at any time. Hence, it is now being popularly adopted for remote health advisory system especially for elderly patients. This system involves in sending a patient’s voice from a remote location for whom medical advice is required. The caregiver or the therapists provide medical advice based on the recognized voice. In the literature, many techniques exist for automatic speech recognition. However, the existing works do not much concentrate on providing technique for speech analysis and classification useful for remote health advisory system. And moreover, the existing works have not focussed on the privacy issue that arises while sending patient’s voice to a remote server. Thus, in this paper a privacy-enhanced voice recognition approach for health advisory system has been proposed to solve the limitations in the existing works.
Binocular stereo vision calibration based on constrained sparse beam adjustment algorithm

In order to realize high-precision three-dimensional measurement of railway train head, a high-precision camera calibration algorithm for binocular stereo vision system based on constrained sparse beam adjustment is proposed in this paper. By analyzing the digital model of binocular vision and the principle of three-dimensional measurement, a binocular stereo vision measurement system based on time-domain coding is built. The edge preservation filtering algorithm based on mean square deviation is used to denoise and filter the visual image. The image is enhanced by histogram equalization. A FAST algorithm based on accelerated segmentation detection is proposed to extract feature information. To solve the problem of camera calibration, a binocular vision calibration method based on coplanar intersecting circle is proposed. The coordinates of circular points are calculated by using the extracted elliptic contour of the intersecting circle, and then the internal parameters of the two cameras and the position relationship between them are obtained. The calibration algorithm needs no manual intervention and has high accuracy. Aiming at the distortion correction, the camera calibration is transformed into a global optimization problem, and the sparse beam adjustment algorithm based on constraints is used to optimize the parameters. Compared with Zhang's calibration method, the results show that the average absolute relative error of the parameters in the two cameras is less than 3.45e−6, the average re-projection error is about 0.1 pixels, and the data accuracy is high, which proves the feasibility and theoretical correctness of the experimental method of the system.
What do participants on digital media platforms produce? Tracking the development of Hello Sunday Morning from a blog, to a social network, to a self-tracking app

This article analyses the development of the participatory online social movement and health intervention Hello Sunday Morning (HSM) from 2009 to 2016. HSM has developed a media platform comprised of blogs, social networking and self-tracking apps with the aim of changing individual and cultural drinking practices. The case of HSM enables us to reflect on the participatory culture of digital media platforms. I argue that participants don’t just construct the sociality of the platform – its practices of speaking and being heard – they also play a crucial role in generating exercises that are coded into the platform’s interfaces, protocols and algorithms, and they produce data that informs the platform’s ongoing development. The article draws on interviews with participants, analysis of the platform since 2009, and my own critical reflections on participating in research, evaluation and design activities.
TDP: Two-dimensional perceptron for image recognition

Convolutional neural network (CNN) is widely applied to different areas due to good recognition performance. However, convolution operation is a complex computation and consumes the bulk of processing time for CNN. It is still a hot problem how to develop a novel model with good recognition performance for deep learning. Here, we propose a novel model, namely, two-dimensional perceptron (TDP), to get direct input of two-dimensional data for further processing. A TDP has a new network architecture and an innovative computation process of hidden neurons. In cases with the same number of hidden neurons, compared with multilayer perceptron (MLP), TDP achieves good recognition performance with 1×-36× speedup and a decrease of parameters by exceeding 97% on MNIST and COIL-20 datasets. Meanwhile, TDP obtains 1%–32% improvement of recognition accuracy in comparison to CNN on CIFAR-10 and SVHN datasets. Furthermore, on INFUSE dataset, TDP has an increase of F1 score by up to almost 11% in comparison with MLP and CNN. The results indicate that TDP is a promising and novel model with excellent recognition performance.
Saliency Detection Based on Local and Global Information Fusion

A saliency detection model based on local and global information fusion is proposed to improve the accuracy. Firstly, the saliency map reflecting local edge information is acquired based on the superpixel segmentation, saliency estimation and multi-scale linearly combination. Then the saliency map with better global information is obtained based on full convolutional network. Finally, two saliency maps are fused by the guided filtering and Hadamard product operation to improve the robustness of the saliency detection. The experimental results on MASR1000, ECSSD, and CDSS datasets show that the proposed fusion model could have higher Precision-Recall Curves and F-measure, and detect the salient object more accurately.
Predicting the success of news

Traditional recommendation systems have limited possibilities to optimise business value in editorial decision making in news production, as they select the recommendations only from the content whose production has been decided editorially in the daily news process or content from existing content inventories. This paper explores an approach to use predictive analytics to make it possible to optimise story assignment and editing in daily editorial work based on selected business objectives already before publishing. In this case study exploration, we use the `constructive approach' as a method to provide solutions to concrete business problems with a scientific approach. We contribute by experimenting a novel method combining elements from several scientific domains like strategic management and system dynamics. We conclude that with language analysis using recurrent neural networks, we were able to predict the success of a news story published on a digital channel in a way that fulfils the `weak market test' criteria of the constructive approach. A company with whom the model was developed considered it valuable enough to decide to move it from exploration to be further developed and used in real news production.
A Watermarking Technique Based on File Page Objects for PDF

This paper presents a novel algorithm which is based on the Portable Document Format (PDF) document structure. The PDF document page objects are utilized for watermarking. The secret message is compressing by using Huffman coding, then suitable page objects of PDF document are used for hiding the secret message. Since the embedded watermark information is stored in PDF document page objects, so it will not affect the content, as well as the format of the PDF documents. The experimental result shows that the proposed algorithm is robust with detection accuracy can be up to 97%. The watermark is extracted with high probability, which illustrates that the proposed algorithm is robust and imperceptible. The experiments prove that the proposed method is suitable for PDF documents protection.
Discriminative Auto-Encoder With Local and Global Graph Embedding

In order to exploit the potential intrinsic low-dimensional structure of the high-dimensional data from the manifold learning perspective, we propose a global graph embedding with globality-preserving property, which requires that samples should be mapped close to their low-dimensional class representation data distribution centers in the embedding space. Then we propose a novel local and global graph embedding auto-encoder(LGAE) to capture the geometric structure of data, its cost function have three terms, a reconstruction loss to reproduce the input data based on the learned representation, a local graph embedding regularization to enforce mapping the neighboring samples close together in the embedding space, a global embedding regularization to enforce mapping samples close to their low-dimensional class representation distribution centers. Thus in the learning process, our LGAE can map samples from same class close together in the embedding space, as well as reduce the scatter within-class and increase the margin between-class, it will also detect the local and global intrinsic geometric structure of data and discover the latent discriminant information in the embedding space. We build stacked LGAE for classification tasks and conduct comprehensive experiments on several benchmark datasets, the results confirm that our proposed framework can learn discriminative representation, speed up the network convergence process, and significantly improve the classification performance.
Machine Learning Approach to Evaluate News Search Engines

News search engines are the specialized search services devoted for searching news online. Reading news using hardcopy print news papers has now become old fashioned and people prefers online news reading. News search engines like Google news and Bing news are available which facilitates the news search. In this paper we propose a machine learning approach to compare Google news and Bing news in terms of retrieval effectiveness both considering sole relevancy parameters as well as incorporating freshness with the relevance. Experimental results show that Bing news is better for relevance retrieval and Google news have the higher average NDCG value when we incorporates freshness also with the sole relevance parameters.
$S^{3}$ Net: Trained on a Small Sample Segmentation Network for Biomedical Image Analysis

Fully convolutional networks (FCNs) are powerful methods to extract hierarchies of features that have achieved remarkable success in various biomedical image analysis tasks. However, the successful training of FCN requires more than hundreds of pixel-level annotated training samples, which poses a challenge for biomedical image processing tasks. In this paper, we present S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net, a network that makes more efficient use of available annotated samples on biomedical image segmentation. S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net is essentially a deeply-supervised encoder-decoder network where the decoder has been redesigned to efficiently restore multi-level encoded feature resolution in a single step. We have conducted extensive experiments on the 2015 MICCAI Gland Challenge dataset. Compared with other methods, S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net achieves a 0.781 dice-score using 11 images for training, higher than U-Net++ 18.6% and U-Net 12.9%, which verifies the performance of S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net trained on a small sample. Further, with training on 85 images, S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net achieves a 0.910 dice-score by using resnet50 as the encoder, which is higher than state-of-the-art semantic segmentation results by 4%.
Conceptual Model for Human Anatomy Learning Based Augmented Reality on Marker Puzzle 3D Printing

Learning medicine not only requires students to master a variety of abilities but also must follow some doctoral standards. In general, the learning process within the Faculty of Medicine students is still conducted by using cadaver. However, several obstacles were encountered when using that media. To overcome the limitations, the use of Augmented Reality (AR) technology has become a medium used for learning. A systematic review method of the study and research of human anatomy on AR in the field of medicine is presented. Based on this review, a model for developing human anatomy learning media using AR that uses 3D printing object marker puzzles was created. The concept model is expected to be able to overcome some of the problems. Potential challenges in developing human anatomy learning models using 3D printing puzzle markers present more specific information and location of a part of human anatomy.
Tracking of a Gunning Jet Using Particle Filtering in Infrared Image Sequences

Vessels for the liquid metal have an inner lining of refractory bricks to withstand the high temperatures of molten steel. To counteract the wear of the lining, gunning is used as a common procedure in steelmaking to prolong the usability of the lining. During gunning, a concretelike, moist refractory mass is applied to the lining surface by pumping it through a nozzle and pointing the spouting mass jet at the desired area. The optimal machine settings, e.g., pressure levels and mixture ratio, are crucial to the maintenance quality. These parameters are reflected in the gunning jet appearance and can be observed and interpreted by experienced operators. Video tracking of the gunning jet facilitates remote observation and is key to automated feature analysis. In this article, we focus on tracking the jet using infrared images. In particular, we use a particle filter with online learning to update the reference template. Template matching is used for tracker stabilization and object verification. We use a parametric model of the gunning jet for feature extraction and dynamic reference template generation. For tracker initialization, we combine template matching with geometric- and temperature-based constraints. We provide experiments on real and synthetic data to demonstrate the capabilities of the presented tracker.
Implications of Seasonal and Spatial Albedo Variation on the Energy Output of Bifacial Solar Farms: A Global Perspective

The energy gain of a bifacial solar farm (compared to its monofacial counterpart) is primarily determined by the magnitude of the ground albedo. There is a persistent suspicion that the seasonal and spatial variability of albedo will lead to dramatic fluctuation and significant loss of overall bifacial energy output, and a high-precision albedo measurement is a prerequisite for bifacial LCOE calculations. In this paper, we assess the hypothesis by calculating the energy output of bifacial solar farms across the world based on satellite-derived seasonal/spatial albedo data. Our results show that energy output is actually not a sensitive function of seasonal albedo, a low-resolution time-averaged albedo would produce comparable (within 1%) results. We conclude that high-quality economic viability and LCOE-assessment is possible even with low-resolution albedo information.
Tiltrotors Position Tracking Controller Design Using Deep Reinforcement Learning

Abstract In this paper, a quad-tiltrotors air-water trans-domain robot is introduced. The nonlinear dynamic behaviours with uncertainties require a robust controller for multi-tasks. For this robot, controllers are designed using deep reinforcement learning method solving position and attitude control when operating as a UAV in the air. A ROS combining Gazebo simulation platform is designed to train the robot. The simulation results show the tiltrotors robot gets capabilities of spots tracking as a quad-rotors, and trajectory tracking as both the quad-rotors and tiltrotors.
Automated Contrast Enhancement of the MRI Video Imaging

Contrast enhancement of the Magnetic Resonance Imaging (MRI) videos is a powerful tool that helps to illustrate different essential details. In this work, a new automated video contrast enhancement method was introduced and was applied on the cardiac MRI video. This technique was based on fuzzy image technique, which consists of three stages: fuzzification, modification of membership equation, and defuzzification. In the second stage, the parameters of the membership function were selected depending on evaluation of maximum fuzzy entropy. The quality of results was evaluated by comparison with other methods using quantitative metrics. The results demonstrate an improved contrast of MRI video using this method, where the contrast is 50% more than input MRI video and 10% higher compared other method.
Discovering Malicious URLs Using Machine Learning Techniques

Security specialists have been developing and implementing many countermeasures against security threats, which is needed because the number of new security threats is further and further growing. In this chapter, we introduce an approach for identifying hidden security threats by using Uniform Resource Locators (URLs) as an example dataset, with a method that automatically detects malicious URLs by leveraging machine learning techniques. We demonstrate the effectiveness of the method through performance evaluations.
HEliOS

Demand for fast data sharing among smart devices is rapidly increasing. This trend creates challenges towards ensuring essential security for online shared data while maintaining the resource usage at a reasonable level. Existing research studies attempt to leverage compression based encryption for enabling such secure and fast data transmission replacing the traditional resource-heavy encryption schemes. Current compression-based encryption methods mainly focus on error insensitive digital data formats and prone to be vulnerable to different attacks. Therefore, in this paper, we propose and implement a new Huffman compression based Encryption scheme using lightweight dynamic Order Statistic tree (HEliOS) for digital data transmission. The core idea of HEliOS involves around finding a secure encoding method based on a novel notion of Huffman coding, which compresses the given digital data using a small sized "secret" (called as secret_intelligence in our study). HEliOS does this in such a way that, without the possession of the secret intelligence, an attacker will not be able to decode the encoded compressed data. Hence, by encrypting only the small-sized intelligence, we can secure the whole compressed data. Moreover, our rigorous real experimental evaluation for downloading and uploading digital data to and from a personal cloud storage Dropbox server validates efficacy and lightweight nature of HEliOS.
Creative Coding and Interaction Design for Media Multiplicities

Media multiplicities are media artworks that employ multiple networked digital devices to create holistic aesthetic effects. Examples include the networked light artworks of Squidsoup, the Spaxels drone-mounted light performances, DrawBots, Siftables and many others. In multiplicitous media artworks, each individual device is a programmable node connected to other nodes via a network connection, and may combine any number of sensors and actuators. A number of development technologies support artists and designers to configure and create media multiplicities, but this domain offers new challenges for creative practitioners. This workshop aims to bring together experts in creative coding and interaction design to discuss and conceptualise frameworks for the practice of media multiplicities. Open challenges include: speed of setup; ease of hardware configuration; speed of code deployment; ability to model and simulate works in VR; network connectivity and stability; and understanding network, computation and power constraints.
A generalized mechanism beyond NLP for real-time detection of cyber abuse through facial expression analytics

Abuse in cyber space is a problem requiring immediate attention. Unfortunately, despite advances in Natural Language Processing techniques, there are clear limitations in detecting instances of cyber abuse today. Challenges arising due to different languages that teens communicate with today, and usage of codes along with code mixing and code switching make the design of a comprehensive approach very hard. Existing NLP based approaches for detecting cyber abuse thus suffer from a high degree of false negatives and positives. In this paper, we investigate a new approach to detect instances of cyber abuse. Our approach is motivated by the premise that abusers tend to have unique facial expressions while engaging in an actual abuse episode, and if we are successful, such an approach will be language-agnostic. Here, using only four carefully identified facial features without any language processing, and realistic experiments with 15 users, our system proposed in this paper achieves 98% accuracy for same-user evaluation and up to 74% accuracy for cross-user evaluation in detecting instances of cyber abuse.
Blockchain Based School Operational Funding Recording System Design

School operational funding, we called it Bantuan Operasional Sekolah (BOS) in Bahasa, is financial support for education given to every school for the school needed. BOS was started to be implemented in Indonesia since 2005 until now. On the BOS implementation, there were many issues, such as corruption, school could not manage it, it was not be included by local government on annual budget, etc. Considering on the issues and looking at BOS characteristic, accountable and transparent, therefore it should be procedurally monitored.One of the procedural monitoring solutions is by using blockchain technology which is transparent and decentralization. By implementing the blockchain, every entities could monitor each others. This research would elaborate how to plan BOS recording system using blockchain technology and implement the blockchain using hyperledger framework, and also analyze its functional and performance testing process. It would prove that blockchain technology with its beneficial side could support BOS implementation by preventing any fraud acts from any stakeholders.