Reviewerly/exHarmony huggingface dataset:
FPB: Improving Multi-Scale Feature Representation Inside Convolutional Layer Via Feature Pyramid Block

Multi-scale features exist widely in biomedical images. For example, the scale of lesions may vary greatly according to different diseases. Effective representation of multi-scale features is essential for fully perceiving and understanding objects, which guarantees the performance of models. However, in biomedical image tasks, the insufficiency of data may prevent models from effectively capturing multi-scale features. In this paper, we propose Feature Pyramid Block (FPB), a novel structure to improve multi-scale feature representation within a single convolutional layer, which can be easily plugged into existing convolutional networks. Experiments on public biomedical image datasets prove consistent performance improvement with FPB. Furthermore, the convergence speed is faster and the computational costs are lower when using FPB, which proves high efficiency of our method.
How Contextual Data Influences User Experience with Scholarly Recommender Systems: An Empirical Framework

Since the advent of Recommender Systems (RSs), many papers have been published with the majority focusing on creating more accurate algorithms. The more accurate the algorithm is, the better the recommendation is predicted to be for users. Recently, RSs researchers pointed out that the embedding of the recommendation methods in the User Experience (UX) dramatically affects the recommender systems' value to users. This paper proposes a framework to explore how contexts influence UX with Scholarly recommender systems and identifies relevant contexts to be incorporated in the UX. We first review existing models and theories of UX that are most applicable to RSs and identify gaps in existing work. The framework clarifies how contexts can influence UX with SRSs and enriches our conceptual understanding of how contextual information influences UX of scholarly recommender systems. It can serve as a foundation for further theoretical and empirical investigation. An experiment evaluating the user experience is performed using the quantitative method of Partial Least Squares (PLS) Regression and Structural Equation Modeling (SEM) to examine the developed conceptual framework.
Improving Generalisation in Learning Assistance by Demonstration for Smart Wheelchairs

Learning Assistance by Demonstration (LAD) is concerned with using demonstrations of a human agent to teach a robot how to assist another human. The concept has previously been used with smart wheelchairs to provide customised assistance to individuals with driving difficulties. A basic premise of this technique is that the learned assistive policy should be able to generalise to environments different than the ones used for training; but this has not been tested before. In this work we evaluate the assistive power and the generalisation capability of LAD using our custom teleoperation and learning system for smart wheelchairs, while seeking to improve it by experimenting with different combinations of dimensionality reduction techniques and machine learning models. Using Autoencoders to reduce the dimension of laserscan data and a Gaussian Process as the learning model, we achieved a 23% improvement in prediction performance against the combination used by the latest work on the field. Using this model to assist a driver exposed to a simulated disability, we observed a 9.8% reduction in track completion times when compared to driving without assistance.
Teaching immersive media at the "dawn of the new everything"

In this paper, we discuss the design and implementation of a college-level course on immersive media at a performing arts institution. Focusing on the artistic applications of modern virtual reality technologies, the course aims to offer students a practice-based understanding of the concepts, tools and techniques involved in the design of audiovisual immersive systems and experiences. We describe the course structure and outline the intermixing of practical exercises with critical theory. We provide details of the design projects and discussion tasks assigned throughout the semester. We then discuss the outcome of a course evaluation session conducted with students. Finally, we identify the main challenges and opportunities for educators dealing with modern immersive media technologies with the hope that the findings offered in this paper can support the design and delivery of similar courses in a range of music and arts curricula.
Optimizing Deep Learning Based Channel Estimation using Channel Response Arrangement

The techniques used in deep learning for channel estimation are generally model-centric. These models have changed significantly over the years with each iteration yielding a better estimator than the last. Fundamentally, channel estimation works by exploiting correlations in an array of complex numbers, in particular the channel gains for a fading channel. In this paper, we study the effects of the spatial arrangement of channel response and input data, on channel estimation. With the right spatial arrangement, we improved the performance of our convolutional neural network that was used for estimation. Additionally, we optimized the training procedure simultaneously. We experimentally validate the importance of spatial arrangement of data in obtaining an accurate deep learning model for the channel.
Live Demonstration: Vision-Based Real-Time Fall Detection System on Embedded System

In this paper, we proposed an implementation of fall detection system on Raspberry Pi with the Intel̅ Neural Compute Stick 2. Firstly, we used skeleton extraction algorithm to obtain the important skeleton information. Secondly, we proposed a robustness neural network using pruning method to reduce the parameter and calculation and combined with the neural compute stick to execute the module. The final result will transfer to the Raspberry Pi to display on the monitor. As a result, it can be implemented on the smaller embedded system.
Low Resolution Handwritten Digit String Recognition based on Object Detection Network

A novel object detection network is proposed in this paper for low resolution handwritten digit string recognition. It is composed of a convolutional neural network (CNN) and two independent output branches for classification and bounding box regression. The network is designed to effectively extract the features from low resolution images. Non-categorized non-maximum suppression (NMS) and mini-batch fine-tuning (MB-FT) are used to improve accuracy further. The experiments are conducted on a new dataset collected by a tablet and HDSRC 2014 benchmark datasets, and the high metrics are obtained. Furthermore, its prediction speed reaches 65 FPS achieving real-time recognition.
Automated Clustering and Knowledge Acquisition Support for Beginners

Although automated machine learning (AutoML) is receiving attention in the field of data science, most AutoML open source software focuses on supervised learning tasks, and little attention has been given to unsupervised learning tasks. Moreover, AutoML has a disadvantage in that it tends to deprive users of a chance to acquire knowledge about machine learning and data science because it usually works as a black box. The purpose of this study is to help inexperienced data scientists and machine learning engineers conduct clustering data analysis, which is an unsupervised learning task, while simultaneously enabling them to acquire knowledge about clustering data analysis by extending our existing AutoML software, the machine learning support system (MALSS). The MALSS helps with clustering data analysis by automatically determining the optimal number of clusters, which is one of the main purposes of clustering data analysis. Furthermore, the MALSS helps users to acquire knowledge about clustering data analysis by generating a report after automated clustering data analysis is conducted. We validated the effectiveness of our approach by using open datasets and by running an experiment on a crowdsourcing platform.
Classification of Daily Human Activities Using Wearable Inertial Sensor

Classification of daily human activities using wearable inertial sensors is presented. Two sensing devices namely the accelerometer sensor mounted on arduino controller and shimmer device are used for acquiring data. Data are acquired from thirty eight healthy subjects without any form of disabilities. Variation in classification accuracy considering data obtained from shimmer device, accelerometer sensor and combination of shimmer & accelerometer data are analysed. Performance of two classifiers namely the KNN classifier and SVM classifier in classifying actions are tested. Various experimental analyses proves that among the data considered for classification, combination of shimmer data and accelerometer data provided better results. Also KNN classifier is found to perform better with an average overall accuracy of 95.6% which is around 6% higher that the accuracy obtained with SVM classifier.
Smart e-Health Security and Safety Monitoring with Machine Learning Services

This research provides security and safety extensions to a blockchain based solution whose target is e-health. The Advanced Blockchain platform is extended with intelligent monitoring for security and machine learning for detecting patient treatment medication safety issues. For the reasons of stringent HIPAA, HITECH, EU-GDPR and other regional regulations dictating security, safety and privacy requirements, the e-Health blockchains have to cover mandatory disclosure of violations or enforcements of policies during transaction flows involving healthcare. Our service solution further provides the benefits of resolving the abnormal flows of a medical treatment process, providing accountability of the service providers, enabling a trust health information environment for institutions to handle medication safely, giving patients a better safety guarantee, and enabling the authorities to supervise the security and safety of e-Health blockchains. The capabilities can be generalized to support a uniform smart solution across industry in a variety of blockchain applications.
Cell Segmentation by Modified U-Net Architecture for Biomedical Images

Biomedical image segmentation is one of the main and fast growing field in medical image processing domain. Deep neural networks is one of the popular field used for image segmentation. Convolutional neural networks(CNNs) in deep neural networks have shown good performance for biomedical image segmentation. However, a strong notion exists that large number of annotated images are required for training of CNNs. Therefore, in this paper we have come up with a modified U-Net architecture for limited number of annotated data with an intersection over union score of 92.54%. The architecture uses rectified-adam optimizer(advanced version of adam) for minimizing the loss function which helps us to come close to global optima. We have also compared the performance of various optimizers on the proposed network.
A Quantum Computer Operating System

The growth of classical computers can be credited in no small part to the development of operating systems designed to provide program portability between different hardware configurations. Quantum computing should learn from classical computing history in order to expedite their development. The approach presented in this paper results in basic parallel execution of quantum programs. The approach utilises the information required for sequential execution including program dependancies, program mappings and qubit connectivities reshaping it to present novel data in the form of a serializability conflict graph. Utilising this graph the quantum system can easily determine parallelisable programs as well as how to parallelise them. This premiere system has been implemented and an attempted comparison has been made to other simulators. Because this system requires no new information, the comparison demonstrates that the cost of employing this strategy is largely similar to employing current sequential techniques, with the added novel benefit of parallel execution.
Open source threat intelligence discovery based on topic detection

This paper analyzes open source threat data and identity emerging threat topics in real time. Based on the general topic detection technology, this paper deeply studies the feature extraction method in the security field. This paper studies the keyword feature extraction and entity feature extraction method. The experimental results show that the accuracy and F value of the proposed method are all above 0.98.
Performance of reconstruction factors for a class of new complex continuous wavelets

In this paper, we determine the factors necessary for the reconstruction of the signal from its continuous wavelet transform performed using the new complex continuous wavelet family by making use of admissible conditions and studied some of its properties. We also make a comparative assessment of its performance with the existing complex continuous wavelets, such as Morlet, Paul and DOG, in terms of reconstruction capability. The reconstruction was performed on three data sets, namely, a signal with a mixture of low and high frequencies, a non-stationary signal (synthetic) and an ECG signal. The results show that the proposed family of wavelets reconstruction capability is comparable with Morlet, Paul and DOG wavelets. Further, we investigate an alternate reconstruction formula without making use of admissibility condition and compare its efficiency of reconstruction with the standard (restricted) Morlet wavelet.
Face Verification Using 802.11 waveforms

Verification of an identity based on the human face radar signature in mmwave is studied. The chipset for Qualcomm 802.11ad/y <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Qualcomm 802.11 is a product of Qualcomm Technologies, Inc. and/or its subsidiaries networking that is cable of operating in a radar mode is used. A dataset with faces of 200 different persons was collected for the testing. Our preliminary study shows promising results for the application of autoencoder for the setup at hand. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Qualcomm 802.11 is a product of Qualcomm Technologies, Inc. and/or its subsidiaries.
Coevolutionary Learning of Neuromodulated Controllers for Multi-Stage and Gamified Tasks

Neural networks have been widely used in agent learning architectures; however, learning multiple context dependent tasks simultaneously or sequentially is problematic when using them. Behavioural plasticity enables humans and animals alike to respond to changes in context and environmental stimuli, without degrading learnt knowledge; this can be achieved by regulating behaviour with neuromodulation - a biological process found in the brain. We demonstrate that modulating activity-propagating signals when evolving neural networks enables agents to learn context-dependent and multi-stage tasks more easily. Further, we show that this benefit is preserved when agents occupy an environment shared with other neuromodulated agents. Additionally we show that neuromodulation helps agents that have evolved alone to adapt to changes in environmental stimuli when they continue to evolve in a shared environment.
A Novel Random Neural Network-based Fall Activity Recognition

The past few decades have witnessed a sharp increase in life expectancy. As a result, the proportion of elderly people is increasing worldwide. Consequently, Dementia and Parkinson's disease are expected to rise, thereby increasing the risk of critical events such as falls for elderly people. This has prompted many researchers to develop a wide range of solutions for fall detection and prevention. However, these solutions are either inaccurate or impractical due to hardware complexity. In this paper, we have proposed a novel Random Neural Network (RNN) based fall detection scheme. Results obtained from the proposed RNN-based scheme are compared with traditional machine learning methods such as Support Vector Machine (SVM) and traditional Artificial Neural Network (ANN) etc. From the results, it is evident that the proposed scheme has a higher accuracy of 98%. Additionally, several other parameters such as precision, recall, specificity, and F-measure show that the proposed algorithm has better generalisation capabilities when compared with other traditional machine learning schemes. Furthermore, the proposed RNN is also compared with a recent scheme and the obtained results demonstrate the superiority of the proposed scheme.
Efficient Search for the Number of Channels for Convolutional Neural Networks

Latest algorithms for automatic neural architecture search perform remarkably but few of them can effectively design the number of channels for convolutional neural networks and consume less computational efforts. In this paper, we propose a method for efficient automatic search which is special to the widths of networks instead of the connections within neural architectures. Our method, functionally incremental search based on function-preserving, will explore the number of channels for almost any convolutional neural network rapidly while controlling the number of parameters and even the amount of computations (FLOPs). On CIFAR-10 and CIFAR-100 classification, our method using minimal computational resources (0.41 ~ 1.29 GPU-days) can discover more effective rules of the widths of networks to improve the accuracy (a ~ 1.08 on CIFAR-10 and b ~ 2.33 on CIFAR-100) with fewer number of parameters.
Hand Gesture Recognition Based on Multi-Classification Adaptive Neuro-Fuzzy Inference System and pMMG

In this paper, a multi-classification adaptive neuro-fuzzy inference system combining neural-network and a TSK fuzzy system is proposed to recognize six commonly used gestures. Several techniques including mini-batch gradient descent with L2 regularization, DropRule and AdaBound are integrated to improve the generalization ability of the system and the efficiency of training. Numerical results show that the average classification accuracy of the multiple classifier systems is 95.12%, and this value is higher than some other multiple classifier systems (CNN, LDA, etc.) using MMG signals as the inputs for hand gesture recognition.
Computer-Assisted Self-Training for Kyudo Posture Rectification Using Computer Vision Methods

To some individuals, particularly archery students, perfecting the art of Kyudo is of utmost importance. These devoted students are always trying to correct their posture because it plays a significant role in effectively shooting at the target. However, due to the lack of attention from instructors, students are often forced to train on their own without any guidance. It is difficult for students to analyze their own faults because the shoulders, hips, and feet should be in line with another, parallel to the floor and straight to the target. The proposed solution is, therefore, a system that aims to assist students in correcting their posture. The system will classify the technique presented by the user and using PoseNet, the system will output coordinates and draw a skeleton structure of the user’s technique along with the instructor’s technique. The coordinates will then be measured for similarity and appropriate feedback is provided to the user. The results for classification, using CNN and SVM showed an accuracy of 81.25% and 80.2%, respectively. The results indicate the feasibility of the approach, however, improvement is required in certain areas. Recommendations for improving the approach are discussed.
WiFi-based In-home Fall-detection Utility: Application of WiFi Channel State Information as a Fall Detection Service

Falls are a leading cause of injury and death in older generations. An in-home fall monitoring system increases safety while allowing for continued independence. The WiFi-based In-home Fall-detection Utility monitors a user by harnessing WiFi Channel State Information, distinguishes falls from normal daily activities, and, if a fall occurs, automatically notifies the user's selected emergency contacts.
Qamface: Quadratic Additive Angular Margin Loss For Face Recognition

The angular-based softmax losses and their variants achieve great success in face recognition based on deep learning. ArcFace [1] which directly maximize decision boundary in angular space is one of the most popular and effective loss function. In this paper, we analyze the inherent limitations of ArcFace, including the non-monotonic logit and gradient curve, and inappropriate trend of loss value. To address these problems, we propose a novel loss function named the Quadratic Additive Angular Margin Loss (QAMFace). It takes the value of the angle through a quadratic function rather than cosine function as the target logit. Our QAMFace is easy to implement and only adds negligible computational overhead. Experiments on several relevant benchmarks show that QAMFace performs better in convergence on feature embedding, and consistently outperforms the state-of-the-art face recognition methods. Our codes will be released soon. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Incremental Fast Subclass Discriminant Analysis

This paper proposes an incremental solution to Fast Subclass Discriminant Analysis (fastSDA). We present an exact and an approximate linear solution, along with an approximate kernelized variant. Extensive experiments on eight image datasets with different incremental batch sizes show the superiority of the proposed approach in terms of training time and accuracy being equal or close to fastSDA solution and outperforming other methods.
An Explanation Method for Black-Box Machine Learning Survival Models Using the Chebyshev Distance

A new modification of the explanation method SurvLIME called SurvLIME-Inf for explaining machine learning survival models is proposed. The basic idea behind SurvLIME as well as SurvLIME-Inf is to apply the Cox proportional hazards model to approximate the black-box survival model at the local area around a test example. The Cox model is used due to the linear relationship of covariates. In contrast to SurvLIME, the proposed modification uses \(L_{\infty }\)-norm for defining distances between approximating and approximated cumulative hazard functions. This leads to a simple linear programming problem for determining important features and for explaining the black-box model prediction. Moreover, SurvLIME-Inf outperforms SurvLIME when the training set is very small. Numerical experiments with synthetic and real datasets demonstrate the SurvLIME-Inf efficiency.
Algorithmic Enablers for Compact Neural Network Topology Hardware Design: Review and Trends

This paper reports the main State-Of-The-Art algorithmic enablers for compact Neural Network topology design, while relying on basic numerical experiments. Embedding insensor intelligence to perform inference tasks generally requires a proper definition of a Neural Network architecture dedicated to specific purposes under Hardware limitations. Hardware design constraints known as power consumption, silicon surface, latency and maximum clock frequency cap available resources related to the topology, i.e., memory capacity and algorithmic complexity. We propose to categorize into 4 types the algorithmic enablers that force the hardware constraints as low as possible while keeping the accuracy as high as possible. First, Dimensionality Reduction (DR) is used to reduce memory needs thanks to predefined, hardware-coded patterns. Secondly, low-precision Quantization with Normalization (QN) can both simplify hardware components as well as limiting overall data storage. Thirdly, Connectivity Pruning (CP) involves an improvement against over-fitting while limiting needless computations. Finally, during the inference at the feed-forward pass, a Dynamical Selective Execution (DSE) of topology parts can be performed to limit the activation of the entire topology, therefore reducing the overall power consumption.