Reviewerly/exHarmony huggingface dataset:
Evaluation of Random Forest for Complex Human Activity Recognition Using Wearable Sensors

The recognition of human activities plays a central role in bridging the gap between the raw sensor signals and high-level pervasive applications. However, the complex nature of human behavior presents a great challenge to the choice of representative features and a discriminant classification model and further makes it quite difficult to develop an accurate and robust activity recognizer. Random forest benefits from the idea of bootstrap and random feature sampling, while only preliminary experimentation for simple activity recognition has been previously reported and few studies, as far as we know, have comprehensively and systematically discussed its performance, especially in wearable-based complex activity recognition. The objective of this study is to present the first empirical results obtained with random forest and to compare its performance with other commonly used prediction models and dimensionality reduction methods. We conduct extensive comparative experiments on three benchmarked datasets. The results indicate the superiority of random forest-based activity recognizers over five individual classifiers, three ensemble models, one feature extractor as well as two feature selectors in terms of recognition performance across scenarios. Besides, the parameter sensitivity analysis recommends the default setting for the parameters and the time cost analysis indicates its applicability to practical applications.
Towards a self-sufficient face verification system

The absence of a previous collaborative manual enrolment represents a significant handicap towards designing a face verification system for face re-identification purposes. In this scenario, the system must learn the target identity incrementally, using data from the video stream during the operational authentication phase. So, manual labelling cannot be assumed apart from the first few frames. On the other hand, even the most advanced methods trained on large-scale and unconstrained datasets suffer performance degradation when no adaptation to specific contexts is performed. This work proposes an adaptive face verification system, for the continuous re-identification of target identity, within the framework of incremental unsupervised learning. Our Dynamic Ensemble of SVM is capable of incorporating non-labelled information to improve the performance of any model, even when its initial performance is modest. The proposal uses the self-training approach and is compared against other classification techniques within this same approach. Results show promising behaviour in terms of both knowledge acquisition and impostor robustness.
Residual-Aided End-to-End Learning of Communication System Without Known Channel

Leveraging powerful deep learning techniques, the end-to-end (E2E) learning of communication system is able to outperform the classical communication system. Unfortunately, this communication system cannot be trained by deep learning without known channel. To deal with this problem, a generative adversarial network (GAN) based training scheme has been recently proposed to imitate the real channel. However, the gradient vanishing and overfitting problems of GAN will result in the serious performance degradation of E2E learning of communication system. To mitigate these two problems, we propose a residual aided GAN (RA-GAN) based training scheme in this paper. Particularly, inspired by the idea of residual learning, we propose a residual generator to mitigate the gradient vanishing problem by realizing a more robust gradient backpropagation. Moreover, to cope with the overfitting problem, we reconstruct the loss function for training by adding a regularizer, which limits the representation ability of RA-GAN. Simulation results show that the trained residual generator has better generation performance than the conventional generator, and the proposed RA-GAN based training scheme can achieve the near-optimal block error rate (BLER) performance with a negligible computational complexity increase in both the theoretical channel model and the ray-tracing based channel dataset.
Provably Safe Trajectory Optimization in the Presence of Uncertain Convex Obstacles

Real-world environments are inherently uncertain, and to operate safely in these environments robots must be able to plan around this uncertainty. In the context of motion planning, we desire systems that can maintain an acceptable level of safety as the robot moves, even when the exact locations of nearby obstacles are not known. In this paper, we solve this chance-constrained motion planning problem using a sequential convex optimization framework. To constrain the risk of collision incurred by planned movements, we employ geometric objects called ε-shadows to compute upper bounds on the risk of collision between the robot and uncertain obstacles. We use these ε-shadow-based estimates as constraints in a nonlinear trajectory optimization problem, which we then solve by iteratively linearizing the non-convex risk constraints. This sequential optimization approach quickly finds trajectories that accomplish the desired motion while maintaining a user-specified limit on collision risk. Our method can be applied to robots and environments with arbitrary convex geometry; even in complex environments, it runs in less than a second and provides provable guarantees on the safety of planned trajectories, enabling fast, reactive, and safe robot motion in realistic environments.
Quality and reliability studies in software defect management: a literature review

Purpose This paper is focused at studying the current state of research involving the four dimensions of defect management strategy, i.e. software defect analysis, software quality, software reliability and software development cost/effort. Design/methodology/approach The methodology developed by Kitchenham (2007) is followed in planning, conducting and reporting of the systematic review. Out of 625 research papers, nearly 100 primary studies related to our research domain are considered. The study attempted to find the various techniques, metrics, data sets and performance validation measures used by researchers. Findings The study revealed the need for integrating the four dimensions of defect management and studying its effect on software performance. This integrated approach can lead to optimal use of resources in software development process. Research limitations/implications There are many dimensions in defect management studies. The authors have considered only vital few based on the practical experiences of software engineers. Most of the research work cited in this review used public data repositories to validate their methodology and there is a need to apply these research methods on real datasets from industry to realize the actual potential of these techniques. Originality/value The authors believe that this paper provides a comprehensive insight into the various aspects of state-of-the-art research in software defect management. The authors feel that this is the only research article that delves into the four facets namely software defect analysis, software quality, software reliability and software development cost/effort.
A Methodology for High-efficient Federated-learning with Consortium Blockchain

This paper we propose a distributed computing architecture, the federated Learning based on the consortium blockchain. With decentralized distributed training, the federated learning modeling process is more robust, it can solve the current fairness and security problems of federated learning. We research the modeling efficiency problems in the Consortium Blockchain Federated Learning (CBFL) architecture at model training process, using model compression to improve the modeling efficiency, and we analyze Practical Byzantine Fault Tolerance consensus algorithm commonly used in the consortium chain, and propose the improvement of PBFT algorithm on the consensus efficiency and mechanism fault tolerance. The experiment results show that the improved CBFL has better practicability.
SoccerMix: Representing Soccer Actions with Mixture Models

Analyzing playing style is a recurring task within soccer analytics that plays a crucial role in club activities such as player scouting and match preparation. It involves identifying and summarizing prototypical behaviors of teams and players that reoccur both within and across matches. Current techniques for analyzing playing style are often hindered by the sparsity of event stream data (i.e., the same player rarely performs the same action in the same location more than once). This paper proposes SoccerMix, a soft clustering technique based on mixture models that enables a novel probabilistic representation for soccer actions. SoccerMix overcomes the sparsity of event stream data by probabilistically grouping together similar actions in a data-driven manner. We show empirically how SoccerMix can capture the playing style of both teams and players and present an alternative view of a team's style that focuses not on the team's own actions, but rather on how the team forces its opponents to deviate from their usual playing style.
Practical graph signal sampling with log-linear size scaling

Graph signal sampling is the problem of selecting a subset of representative graph vertices whose values can be used to interpolate missing values on the remaining graph vertices. Optimizing the choice of sampling set using concepts from experiment design can help minimize the effect of noise in the input signal. While many existing sampling set selection methods are computationally intensive because they require an eigendecomposition, existing eigendecomposition-free methods are still much slower than random sampling algorithms for large graphs. In this paper, through optimizing sampling sets towards the D-optimal objective from experiment design, we propose a sampling algorithm that has complexity comparable to random sampling algorithms, while reaching accuracy similar to existing eigendecomposition-free methods for a broad range of graph types.
Ship Detection in Large Scale Sar Images Based on Bias Classification

With the development of imaging technology, ship target detection in large scenes has become a research hotspot. The patches without sea area sent to detector greatly increase the computational cost and there are many false alarms in land area. Based on above, this paper proposes a ship detection method based on bias classification. Patches of large scale SAR images without sea area will no longer be sent to the detector, which greatly reduces false alarms in land area. The proposed method is implemented in the anchor-free network framework named CenterNet. The experimental results show that the bias classification method proposed in this paper can effectively reduce false alarms in land area.
Deep Neural Network Detection for Pulsed Radar-Embedded M-PSK Communications

In this paper, we investigate the demodulation performance of radar-embedded communications, by utilizing deep neural network (DNN) machine learning to extract the communications symbols. We compare its symbol error rate (SER) performance with a detection method that uses a least squares estimator (LSE) to estimate the magnitude and phase of the radar signal and is coupled with maximum likelihood detection (MLD). We choose M-PSK modulation (specifically QPSK and 8-PSK) for illustration. When estimating and subtracting the radar amplitude prior to demodulation, SER improvements depend on collection time for both DNN machine learning and MLD demodulation methods. In this work, we develop three DNN demodulators for radar-embedded communications. The first two techniques perform very close to the traditionally optimal MLD detector while the third, which is designed for various radar-to-communications ratio (RCR), outperforms the MLD and is shown to be robust. Two interesting results to note when training the DNN: a) there is a near ideal communications signal-to-noise power ratio (C-SNR) and b) there is a near ideal radar-to-communications power ratio (RCR) in which to train the DNN for robust demodulation of radar-embedded communications that employ QPSK and 8-PSK.
Neural Collaborative For Music Recommendation System

Abstract In music there are various types of genres and every person has their own choice of the type of music they want to hear. Recommendation system is an important feature in an application, especially with the large number of choices for a particular item. With a good recommendation system, users will be helped by the suggestions given and can improve the user experience of the application. It is better provided by using collaborative filtering (CF) approach by recommending products related to one’s preferences history. However, CF approach still lacking in integrating complex users data. Therefore, hybrid technique could be the solution to polish the CF approach. Combining neural network and CF also called NCF thought to be better than CF alone. This study focuses on collaborative filtering approach combined with neural network or called neural collaborative filtering. In this study, we use 20,000 users, 6,000 songs, and 470,000 transaction ratings then predict the score using CF and NCF approach. The aim of this study is to differentiate recommendation systems with the use of CF alone and NCF. Through this research, it was found that NCF is better than user-based collaborative filtering in gather those playlist they really want to hear, but requires more time to build it.
Entropy information‐based heterogeneous deep selective fused features using deep convolutional neural network for sketch recognition

An effective feature representation can boost recognition tasks in the sketch domain. Due to an abstract and diverse structure of the sketch relatively with a natural image, it is complex to generate a discriminative features representation for sketch recognition. Accordingly, this article presents a novel scheme for sketch recognition. It generates a discriminative features representation as a result of integrating asymmetry essential information from deep features. This information is kept as an original feature-vector space for making a final decision. Specifically, five different well-known pre-trained deep convolutional neural networks (DCNNs), namely, AlexNet, VGGNet-19, Inception V3, Xception, and InceptionResNetV2 are fine-tuned and utilised for feature extraction. First, the high-level deep layers of the networks were used to get multi-features hierarchy from sketch images. Second, an entropy-based neighbourhood component analysis was employed to optimise the fusion of features in order of rank from multiple different layers of various deep networks. Finally, the ranked features vector space was fed into the support vector machine (SVM) classifier for sketch classification outcomes. The performance of the proposed scheme is evaluated on two different sketch datasets such as TU-Berlin and Sketchy for classification and retrieval tasks. Experimental outcomes demonstrate that the proposed scheme brings substantial improvement over human recognition accuracy and other state-of-the-art algorithms.
Diabetes Type 2: Poincar�Data Preprocessing for Quantum Machine Learning

Quantum Machine Learning (QML) techniques have been recently attracting massive interest. However reported applications usually employ synthetic or well-known datasets. One of these techniques based on using a hybrid approach... | Find, read and cite all the research you need on Tech Science Press
Fractional R閚yi Entropy Image Enhancement for Deep Segmentation of Kidney MRI

Recently, many rapid developments in digital medical imaging have made further contributions to health care systems. The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses. Many factors like image contrast and quality affect the result of image segmentation. Due to that, image contrast remains a challenging problem for image segmentation. This study presents a new image enhancement model based on fractional Rényi entropy for the segmentation of kidney MRI scans. The proposed work consists of two stages: enhancement by fractional Rényi entropy, and MRI Kidney deep segmentation. The proposed enhancement model exploits the pixel’s probability representations for image enhancement. Since fractional Rényi entropy involves fractional calculus that has the ability to model the non-linear complexity problem to preserve the spatial relationship between pixels, yielding an overall better details of the kidney MRI scans. In the second stage, the deep learning kidney segmentation model is designed to segment kidney regions in MRI scans. The experimental results showed an average of 95.60% dice similarity index coefficient, which indicates best overlap between the segmented bodies with the ground truth. It is therefore concluded that the proposed enhancement model is suitable and effective for improving the kidney segmentation performance.
Basic Models of Descriptive Image Analysis

This paper is devoted to the basic models of descriptive image analysis, which is the leading branch of the modern mathematical theory of image analysis and recognition.
Interactive Machine Learning for Embodied Interaction Design: A tool and methodology

As immersive technologies are increasingly being adopted by artists, dancers and developers in their creative work, there is a demand for tools and methods to design compelling ways of embodied interaction within virtual environments. Interactive Machine Learning allows creators to quickly and easily implement movement interaction in their applications by performing examples of movement to train a machine learning model. A key aspect of this training is providing appropriate movement data features for a machine learning model to accurately characterise the movement then recognise it from incoming data. We explore methodologies that aim to support creators’ understanding of movement feature data in relation to machine learning models and ask how these models hold the potential to inform creators’ understanding of their own movement. We propose a 5-day hackathon, bringing together artists, dancers and designers, to explore designing movement interaction and create prototypes using new interactive machine learning tool InteractML.
A Privacy Preserving Hybrid Neural-Crypto Computing-Based Image Steganography for Medical Images

With advancements in X-ray technology, there is an increase in the number of digital images used in the diagnosis of a patient. Whether it be a simple X-ray, MRI, CT scan or even a photo taken from a camera, the rise in the use of digital images has increased sharply. Though this has eased the entire process, it has brought the threat of cyber-attacks and breaches. The proposed method bridges this existing gap by incorporating suitable security mechanisms to preserve the privacy and confidentiality of medical diagnostic information of an individual. The approach utilizes neural networks to perform image steganography and combines it with a cryptographic algorithm (RSA) to secure medical images. The proposed method uses a two-level security providing a lower loss of 0.002188 on medical images improving upon existing image steganography techniques.
MuSeM: Detecting Incongruent News Headlines using Mutual Attentive Semantic Matching

Measuring congruence between two texts has several useful applications, such as detecting the prevalent deceptive and misleading news headlines on the web. Many works have proposed machine learning based solutions such as text similarity between the headline and body text to detect the incongruence. Text similarity based methods fail to perform well due to different inherent challenges such as relative length mismatch between the news headline and its body content and non-overlapping vocabulary. On the other hand, more recent works that use headline guided attention to learn a headline derived contextual representation of the news body also result in convoluting overall representation due to the news body's lengthiness. This paper proposes a method that uses inter-mutual attention-based semantic matching between the original and synthetically generated headlines, which utilizes the difference between all pairs of word embeddings of words involved. The paper also investigates two more variations of our method, which use concatenation and dot-products of word embeddings of the words of original and synthetic headlines. We observe that the proposed method outperforms prior-arts significantly for two publicly available datasets.
Prior-based bayesian pairwise ranking for one-class collaborative filtering

In many real-world applications, only user-item interactions (one-class feedback) can be observed. The recommendation methods have been studied for personalized ranking with one-class feedback in recent years. Pairwise ranking methods have been widely used for dealing with the one-class problem with the assumption that users prefer their observed items over unobserved items. However, existing some items that users have not seen yet. It is unsuitable for treating all unobserved items of the user as negative feedback. In this paper, we propose a Prior-based Bayesian Pairwise Ranking (PBPR) model, which relaxes the simple pairwise preference assumption in previous works by further considering the pairwise preference between two unobserved items. Moreover, we calculate users' potential preference scores on unobserved items, i.e., prior information, based on historical interactions. The prior information can be used to measure the fine-grained preference difference between any two unobserved items of each user. Through extensive experiments on real-world datasets, we demonstrate the effectiveness of our proposed recommendation method.
Regularization-Based Coflow Scheduling in Optical Circuit Switches

To improve the application-level data efficiency, the scheduling of coflows, defined as a collection of parallel flows sharing the same objective, is prevailing in recent data centers. Meanwhile, optical circuit switches (OCS) are gradually applied to provide high data rate with low power consumption. However, so far few research outputs have covered the flow, let alone the coflow, scheduling in the context of OCS. In this work, we investigate coflow scheduling in OCS-based data centers. We first derive a novel operation called regularization processed respectively on the flow traffic demands and the flow start times, which can be efficiently implemented and reduce the circuit reconfiguration frequency dramatically. We then propose a 2-approximation algorithm, called Reco-Sin, for single coflow scheduling to minimize the coflow completion time (CCT). For multiple coflows, we derive Reco-Mul to minimize the total weighted CCT, which can transform any non-preemptive multi-coflow scheduling in packet switches to a scheduling scheme in OCS. Reco-Mul can achieve a constant approximation under the assumption that no tiny flows will be transmitted in OCS. To get rid of this assumption, we present another multiple coflow scheduling scheme, named Reco-Mul+, which has an approximation ratio of O(K). Here, K is the total number of coflows. Extensive simulations based on Facebook data traces show that our approaches outperform state-of-the-art schemes significantly, i.e., one single coflow can be finished up to 1.97× faster with Reco-Sin, and multiple coflows can be completed up to more than 2× faster with Reco-Mul and Reco-Mul+.