Reviewerly/exHarmony huggingface dataset:
Deinterleaving of Pulse Streams With Denoising Autoencoders

Analyzing radar signals is an important task in operating electronic support measure systems. The received signals in the real electromagnetic environment often originate from multiple emitters and must be separated for further processing. Pulses from important target emitters with known parameters should be picked out first. To solve the problem, time-of-arrival (TOA) deinterleaving may be performed to extract signals from a certain emitter by learning the pulse repetition interval (PRI) modulation that makes up the signal. However, conventional deinterleaving methods only work with simple PRI modulations; their performance degrades in noisy environments. A novel approach based on denoising autoencoders for TOA deinterleaving was developed in this article. The inner patterns of pulse-of-interest sequences were learned by the proposed denoising autoencoders to generate output sequences from well-trained autoencoders. Simulation results show that the proposed method outperforms conventional methods, especially in environments with high lost and spurious pulse ratios.
Communicative and social consequences of interactions with voice assistants

The growing prevalence of artificial intelligence and digital media in children’s lives provides them with the opportunity to interact with novel non-human agents such as robots and voice assistants. Previous studies show that children eagerly adopt and interact with these technologies, but we have only limited evidence of children’s distinction between artificial intelligence and humans. In this study, the communication patterns and prosocial outcomes of interactions with voice assistants were investigated. Children between 5 and 6 years (N = 72) of age solved a treasure hunt in either a human or voice assistant condition. During the treasure hunt, the interaction partner supplied information either about their knowledge of or experience with the objects. Afterwards, children were administered a sharing task and a helping task. Results revealed that children provided voice assistants with less information than humans and that only the type of information given by a human interaction partner was related to children’s information selection. Sharing was influenced by an interaction between type of information and interaction partner, showing that the type of information shared influenced children’s decisions more when interacting with a human, but less when interacting with a voice assistant. Children in all conditions enjoyed the treasure hunt with the interaction partner. Overall, these results suggest that children do not impose the same expectations on voice assistants as they do on humans. Consequently, cooperation between humans and cooperation between humans and computers differ.
ARDA

Automatic machine learning (AML) is a family of techniques to automate the process of training predictive models, aiming to both improve performance and make machine learning more accessible. While many recent works have focused on aspects of the machine learning pipeline like model selection, hyperparameter tuning, and feature selection, relatively few works have focused on automatic data augmentation. Automatic data augmentation involves finding new features relevant to the user's predictive task with minimal "human-in-the-loop" involvement. We present ARDA, an end-to-end system that takes as input a dataset and a data repository, and outputs an augmented data set such that training a predictive model on this augmented dataset results in improved performance. Our system has two distinct components: (1) a framework to search and join data with the input data, based on various attributes of the input, and (2) an efficient feature selection algorithm that prunes out noisy or irrelevant features from the resulting join. We perform an extensive empirical evaluation of different system components and benchmark our feature selection algorithm on real-world datasets.
Automated Semantic Segmentation of Red Blood Cells for Sickle Cell Disease

Red blood cell (RBC) segmentation and classification from microscopic images is a crucial step for the diagnosis of sickle cell disease (SCD). In this work, we adopt a deep learning based semantic segmentation framework to solve the RBC classification task. A major challenge for robust segmentation and classification is the large variations on the size, shape and viewpoint of the cells, combining with the low image quality caused by noise and artifacts. To address these challenges, we apply deformable convolution layers to the classic U-Net structure and implement the deformable U-Net (dU-Net). U-Net architecture has been shown to offer accurate localization for image semantic segmentation. Moreover, deformable convolution enables free-form deformation of the feature learning process, thus making the network more robust to various cell morphologies and image settings. dU-Net is tested on microscopic red blood cell images from patients with sickle cell disease. Results show that dU-Net can achieve highest accuracy for both binary segmentation and multi-class semantic segmentation tasks, comparing with both unsupervised and state-of-the-art deep learning based supervised segmentation methods. Through detailed investigation of the segmentation results, we further conclude that the performance improvement is mainly caused by the deformable convolution layer, which has better ability to separate the touching cells, discriminate the background noise and predict correct cell shapes without any shape priors.
Investigating Bias in Facial Analysis Systems: A Systematic Review

Recent studies have demonstrated that most commercial facial analysis systems are biased against certain categories of race, ethnicity, culture, age and gender. The bias can be traced in some cases to the algorithms used and in other cases to insufficient training of algorithms, while in still other cases bias can be traced to insufficient databases. To date, no comprehensive literature review exists which systematically investigates bias and discrimination in the currently available facial analysis software. To address the gap, this study conducts a systematic literature review (SLR) in which the context of facial analysis system bias is investigated in detail. The review, involving 24 studies, additionally aims to identify (a) facial analysis databases that were created to alleviate bias, (b) the full range of bias in facial analysis software and (c) algorithms and techniques implemented to mitigate bias in facial analysis.
Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition

Face recognition success rate is influenced by illumination, expression, posture change, and other factors, which is due to the low generalization ability of a single convolutional neural network. A new face recognition method based on parallel ensemble learning of convolutional neural networks (CNN) and local binary patterns (LBP) is proposed to solve this problem. It also helps to improve the low pedestrian detection rate caused by occlusion. First, the LBP operator is employed to extract features of the face texture. After that, 10 convolutional neural networks with 5 different network structures are adopted to further extract features for training, to improve the network parameters and get classification result by using the Softmax function after the layer is fully connected. Finally, the method of parallel ensemble learning is used to generate the final result of face recognition using majority voting. By this method, the recognition rates in the ORL and Yale-B face datasets increase to 100% and 97.51%, respectively. In the experiments, the proposed approach is illustrated not only enhances its tolerance to illumination, expression, and posture but also improves the accuracy of face recognition and the poor generalization performance of the model, which is normally caused by the learning algorithm being trapped in a local minimum. Moreover, the proposed method is combined with a pedestrian detection model as a hybrid model for improving the detection rate, which shows in the result that the detection rate is improved by 11.2%. In summary, the proposed approach greatly outperforms other competitive methods.
Combinatorial Learning of Robust Deep Graph Matching: An Embedding Based Approach

Graph matching aims to establish node correspondence between two graphs, which has been a fundamental problem for its NP-hard nature. One practical consideration is the effective modeling of the affinity function in the presence of noise, such that the mathematically optimal matching result is also physically meaningful. This paper resorts to deep neural networks to learn the node and edge feature, as well as the affinity model for graph matching in an end-to-end fashion. The learning is supervised by combinatorial permutation loss over nodes. Specifically, the parameters belong to convolutional neural networks for image feature extraction, graph neural networks for node embedding that convert the structural (beyond second-order) information into node-wise features that leads to a linear assignment problem, as well as the affinity kernel between two graphs. Our approach enjoys flexibility in that the permutation loss is agnostic to the number of nodes, and the embedding model is shared among nodes such that the network can deal with varying numbers of nodes for both training and inference. Moreover, our network is class-agnostic. Experimental results on extensive benchmarks show its state-of-the-art performance. It bears some generalization capability across categories and datasets, and is capable for robust matching against outliers.
Real-Time Anomaly Recognition Through CCTV Using Neural Networks

Nowadays, there has been a rise in the amount of disruptive and offensive activities that have been happening. Due to this, security has been given principal significance. Public places like shopping centres, avenues, banks, etc are increasingly being equipped with CCTVs to guarantee the security of individuals. Subsequently, this inconvenience is making a need to computerize this system with high accuracy. Since constant observation of these surveillance cameras by humans is a near-impossible task. It requires workforces and their constant attention to judge if the captured activities are anomalous or suspicious. Hence, this drawback is creating a need to automate this process with high accuracy. Moreover, there is a need to display which frame and which parts of the recording contain the uncommon activity which helps the quicker judgment of that unordinary action being unusual or suspicious. Therefore, to reduce the wastage of time and labour, we are utilizing deep learning algorithms for Automating Threat Recognition System. Its goal is to automatically identify signs of aggression and violence in real-time, which filters out irregularities from normal patterns. We intend to utilize different Deep Learning models (CNN and RNN) to identify and classify levels of high movement in the frame. From there, we can raise a detection alert for the situation of a threat, indicating the suspicious activities at an instance of time.
Privacy-Preserving Federated Deep Learning with Irregular Users

Federated deep learning has been widely used in various fields. To protect data privacy, many privacy-preservingapproaches have been designed and implemented in various scenarios. However, existing works rarely consider a fundamental issue that the data shared by certain users (called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">irregular users</i> ) may be of low quality. Obviously, in a federated training process, data shared by many <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">irregular users</i> may impair the training accuracy, or worse, lead to the uselessness of the final model. In this article, we propose PPFDL, a Privacy-Preserving Federated Deep Learning framework with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">irregular users</i> . In specific, we design a novel solution to reduce the negative impact of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">irregular users</i> on the training accuracy, which guarantees that the training results are mainly calculated from the contribution of high-quality data. Meanwhile, we exploit Yao's garbled circuits and additively homomorphic cryptosystems to ensure the confidentiality of all user-related information. Moreover, PPFDL is also robust to users dropping out during the whole implementation. This means that each user can be offline at any subprocess of training, as long as the remaining online users can still complete the training task. Extensive experiments demonstrate the superior performance of PPFDL in terms of training accuracy, computation, and communication overheads.
Information-theoretic aspects of the generalized amplitude-damping channel

The generalized amplitude damping channel (GADC) is one of the sources of noise in superconducting-circuit-based quantum computing. It can be viewed as the qubit analogue of the bosonic thermal channel, and it thus can be used to model lossy processes in the presence of background noise for low-temperature systems. In this work, we provide an information-theoretic study of the GADC. We first determine the parameter range for which the GADC is entanglement breaking and the range for which it is anti-degradable. We then establish several upper bounds on its classical, quantum, and private capacities. These bounds are based on data-processing inequalities and the uniform continuity of information-theoretic quantities, as well as other techniques. Our upper bounds on the quantum capacity of the GADC are tighter than the known upper bound reported recently in [Rosati et al., Nat. Commun. 9, 4339 (2018)] for the entire parameter range of the GADC, thus reducing the gap between the lower and upper bounds. We also establish upper bounds on the two-way assisted quantum and private capacities of the GADC. These bounds are based on the squashed entanglement, and they are established by constructing particular squashing channels. We compare these bounds with the max-Rains information bound, the mutual information bound, and another bound based on approximate covariance. For all capacities considered, we find that a large variety of techniques are useful in establishing bounds.
Using EEG and Deep Learning to Predict Motion Sickness Under Wearing a Virtual Reality Device

Virtual Reality (VR) research has been widely applied in many fields. VR promises to deliver the experience that is beyond the user's imagination. One of the advantages of VR is the feeling it gives of being there. VR can provide experiences impossible in the real world, such as flying, diving in deep water, exploring outer space, or living with dinosaurs. Despite the improvements in the software and hardware, the problem of motion sickness remains. We implement a deep learning model to train and predict motion sickness. A questionnaire is a well-known method to measure motion sickness. The weakness of the questionnaire is the measurement carried out after the user experiences motion sickness symptoms. By using the deep learning and EEG, the system will learn and classify motion sickness. The system learns the user's EEG pattern when they begin to feel the sickness symptoms. The system will be trained using deep learning to identify the sickness patterns in the future. By the EEG patterns, the system can predict the sickness symptoms before it occurs. Our model outperforms traditional models in loss values, accuracy, and F-measure metrics in Roller Coaster. With other datasets, our model also performs well. Our model can achieve 82.83% accuracy from the dataset. We also found that the time steps to predict motion sickness during 5 minute periods is a suitable configuration.
Face Recognition System Using Machine Learning Algorithm

Face recognition in artificial intelligence is a frequent problem. This application was extensively used in our daily life. Several smartphones were opening phones with facial identification to safeguard private details and used on Facebook to identify instantly when users of Facebook appear in pictures. Several approaches for face recognition have already been suggested until now, but it remains very difficult in real-world situations. A primary technique to differentiate people depends on various conditions, for example, partial facial occlusion, illumination, and posture variety. This paper aims at designing a face recognition approach utilizing machine learning algorithm and principal component analysis (PCA). It is experimented using linear discriminant analysis, multilayer perceptron, Naive Bayes, and support vector machine. Moreover, it has achieved recognition accuracy of 97% & 100% by using PCA and linear discriminant analysis.
Impact of Feature Selection Methods on the Predictive Performance of Software Defect Prediction Models: An Extensive Empirical Study

Feature selection (FS) is a feasible solution for mitigating high dimensionality problem, and many FS methods have been proposed in the context of software defect prediction (SDP). Moreover, many empirical studies on the impact and effectiveness of FS methods on SDP models often lead to contradictory experimental results and inconsistent findings. These contradictions can be attributed to relative study limitations such as small datasets, limited FS search methods, and unsuitable prediction models in the respective scope of studies. It is hence critical to conduct an extensive empirical study to address these contradictions to guide researchers and buttress the scientific tenacity of experimental conclusions. In this study, we investigated the impact of 46 FS methods using Naïve Bayes and Decision Tree classifiers over 25 software defect datasets from 4 software repositories (NASA, PROMISE, ReLink, and AEEEM). The ensuing prediction models were evaluated based on accuracy and AUC values. Scott–KnottESD and the novel Double Scott–KnottESD rank statistical methods were used for statistical ranking of the studied FS methods. The experimental results showed that there is no one best FS method as their respective performances depends on the choice of classifiers, performance evaluation metrics, and dataset. However, we recommend the use of statistical-based, probability-based, and classifier-based filter feature ranking (FFR) methods, respectively, in SDP. For filter subset selection (FSS) methods, correlation-based feature selection (CFS) with metaheuristic search methods is recommended. For wrapper feature selection (WFS) methods, the IWSS-based WFS method is recommended as it outperforms the conventional SFS and LHS-based WFS methods.
An Intelligent Terminal Based Privacy-Preserving Multi-Modal Implicit Authentication Protocol for Internet of Connected Vehicles

The Internet of connected Vehicles (IOV) can collect, process, compute and release the information of intelligent transportation systems. IOV is an integrated service system that can support the applications for automatic driving, intelligent transport and information services. As the number of incidents on IOV has been on the rise in the past few years, IOV security is becoming increasingly important in the IOV architecture. One of the most notable risks of IOV faces is intelligent terminal security. The vehicle's intelligent terminal can be used to launch for further attacks on the on-board operating system to penetrate into the internal network of connected vehicle, and consequently threaten the safety of the vehicle. Thus, it is of paramount importance that we protect the security of the intelligent terminal. We propose two intelligent terminal based privacy-preserving multi-modal implicit authentication protocols to protect the security of the intelligent terminal in IOV. The proposed protocols use the password and the vehicle owner's behavior features as the authentication factors to protect the security of the intelligent terminal. Since the vehicle owner's behavior features are sensitive and the privacy information of the user must be protected, we also consider the privacy protection of the behavior features. Our protocols do not reveal any information about the vehicle owner's behavior features to the authentication server and the adversary except the ciphertext size of the feature vector. We analyze the security of our proposed protocol and compare them with other related protocols in terms of computation and communications costs. Our results demonstrate that our proposed protocols yield better security and efficiency.
Collective Remote Attestation at the Internet of Things Scale: State-of-the-Art and Future Challenges

In recent years, the booming of Internet of Things (IoT) has populated the world with billions of smart devices that implement novel services and applications. The potential for cyberattacks on IoT systems have called for new solutions from the research community. Remote attestation is a widely used technique that allows a verifier to identify software compromise on a remote platform (called prover). Traditional challenge-response remote attestation protocols between the verifier and a single prover face a severe scalability challenge when they are applied to large scale IoT systems. To tackle this issue, recently researchers have started developing attestation schemes, which we refer to as Collective Remote Attestation (CRA) schemes, that are capable of remotely performing attestation of large networks of IoT devices. In this paper, after providing the reader with a background on remote attestation, we survey and analyze existing CRA schemes. We present an analysis of their advantages and disadvantages, as well as of their effectiveness against a reference attacker model. We focus our attention on CRA schemes' characteristics and adversarial mitigation capabilities. We finally highlight open research issues and give possible directions for mitigating both the limitations of existing schemes, and new emerging challenges. We believe this work can help guiding the design of current and future proposals for CRA.