Reviewerly/exHarmony huggingface dataset:
Comparison of quantum and classical methods for labels and patterns in Restricted Boltzmann Machines

Abstract Classification and data reconstruction using a restricted Boltzmann machine (RBM) is presented. RBM is an energy-based model which assigns low energy values to the configurations of interest. It is a generative model, once trained it can be used to produce samples from the target distribution. The D-Wave 2000Q is a quantum computer which has been used to exploit its quantum effect for machine learning. Bars-and-stripes (BAS) and cybersecurity (ISCX) datasets were used to train RBMs. The weights and biases of trained RBMs were used to map onto the D-Wave. Classification as well as image reconstruction were performed. Classification accuracy of both datasets indicates comparable performance using D-Wave’s adiabatic annealing and classical Gibb’s sampling.
Application of Red Deer Algorithm in Optimizing Complex functions

The Red Deer algorithm (RDA), a recently developed population-based meta-heuristic algorithm, is examined in this paper with the optimization task of complex functions. The RD algorithm blends evolutionary algorithms' survival of the fittest concept with heuristic search techniques' productivity and richness. It is critical to assess this algorithm's performance in comparison with other well-known heuristic methods. The findings are presented along with additional recommendations for increasing RDA performance based on the analysis. The readers of this paper will gain a grasp of the RD algorithm and its optimization ability to determine whether this algorithm is appropriate for their particular business, research, or industrial needs.
Unsupervised symbol emergence for supervised autonomy using multi-modal latent Dirichlet allocations

In future Mars exploration scenarios, astronauts orbiting the planet will control robots on the surface with supervised autonomy to construct infrastructure necessary for human habitation. Symbol-based planning enables intuitive supervised teleoperation by presenting relevant action possibilities to the astronaut. While our initial analog experiments aboard the International Space Station (ISS) proved this scenario to be very effective, the complexity of the problem puts high demands on domain models. However, the symbols used in symbolic planning are error-prone as they are often hand-crafted and lack a mapping to actual sensor information. While this may lead to biased action definitions, the lack of feedback is even more critical. To overcome these issues, this paper explores the possibility of learning the mapping between multi-modal sensor information and high-level preconditions and effects of robot actions. To achieve this, we propose to utilize a Multi-modal Latent Dirichlet Allocation (MLDA) for unsupervised symbol emergence. The learned representation is used to identify domain-specific design flaws and assist in supervised autonomy robot operation by predicting action feasibility and assessing the execution outcome. The approach is evaluated in a realistic telerobotics experiment conducted with the humanoid robot Rollin' Justin.
Performance Evaluation of IMM-based Nonlinear Filters for a Highly Maneuvering Aircraft

We propose trajectory estimation of a highly maneuvering fighter aircraft using an air moving target indicator (AMTI) radar on another aircraft and conduct performance evaluation of a number of nonlinear filters. The goal is to demonstrate the proof of concept and choose a suitable filter, which can be used onboard the aircraft for real-time trajectory estimation. Target state estimation accuracy, real-time state estimation, and filter stability are important factors for choosing a filter by the system designer. The interacting multiple model (IMM) filter, which has been shown as an efficient estimator for a maneuvering target, is used in this study with the extended Kalman filter (EKF), unscented Kalman filter (UKF), and cubature Kalman filter (CKF). The maneuvering target trajectory has a sequence of nearly constant velocity (NCV) and nearly constant turn (NCT) motions. Since the NCT model and AMTI measurement model are nonlinear in the target state, the filtering problem is nonlinear. We have analyzed scenarios with 3g, 4g, 5g, 6g, and 8g accelerations of the target. Due to space constraints, in this paper we present sample results for the 4g and 6g cases. Numerical results show that when AMTI measurements with operational measurement accuracies are used, the performances of the IMM-UKF and IMM-CKF are nearly the same and are only slightly better than that of the IMM-EKF. As expected the IMM-EKF has the lowest CPU time and the CPU times of the IMM-UKF and IMM-CKF are about 1.6 times higher. Based on these findings, the IMM-CKF is recommended for use in the onboard computer system.
Signatures of intrinsic decoherence and weak measurement on quantum correlations

Abstract In this article, we consider a pair of spin-1/2 particles with squeezing coupling serving as the physical carrier of quantum information. We then examine the dynamics of quantum correlation quantified by the entanglement and measurement-induced nonlocality (MIN) under the intrinsic decoherence. The impact of intrinsic decoherence on the dynamical behaviors of quantum correlations is investigated. We show that the MIN quantities are more robust, while intrinsic decoherence cause sudden death in entanglement. Besides, we highlight the role of spin squeezing coupling and external magnetic field on quantum correlation measures. Finally, we investigate the impact of weak measurement on MIN.
An Unobtrusive Fall Detection System Using Low Resolution Thermal Sensors and Convolutional Neural Networks

Human activity recognition has many potential applications. In an aged care facility, it is crucial to monitor elderly patients and assist them in the case of falls or other needs. Wearable devices can be used for such a purpose. However, most of them have been proven to be obtrusive, and patients reluctate or forget to wear them. In this study, we used infrared technology to recognize certain human activities including sitting, standing, walking, laying in bed, laying down, and falling. We evaluated a system consisting of two 24×32 thermal array sensors. One infrared sensor was installed on side and another one was installed on the ceiling of an experimental room capturing the same scene. We chose side and overhead mounts to compare the performance of classifiers. We used our prototypes to collect data from healthy young volunteers while performing eight different scenarios. After that, we converted data coming from the sensors into images and applied a supervised deep learning approach. The scene was captured by a visible camera and the video from the visible camera was used as the ground truth. The deep learning network consisted of a convolutional neural network which automatically extracted features from infrared images. Overall average F1-score of all classes for the side mount was 0.9044 and for the overhead mount was 0.8893. Overall average accuracy of all classes for the side mount was 96.65% and for the overhead mount was 95.77%. Our results suggested that our infrared-based method not only could unobtrusively recognize human activities but also was reasonably accurate.
Memory Efficient Knowledge Base Question Answering with Chatbot Framework

The goal of "Knowledge-Based Question Answering with Chatbot Framework" is to target and improve the ability of chatbot’s existing framework to better recognize and answer informal and natural language patterns via well-structured relational information between entities stored in knowledge bases. Knowledge extraction is not always a one-step process as there may be follow-up questions to the previous answer. The flaw in existing Question Answering systems is that they answer each question independently, adding redundancy for the user to repeat their previous question even if a follow-up was already asked.In this research paper, we are proposing a Knowledge Base Question Answering (KB-QA) system using a chatbot framework to tame this problem. Our approach of KB-QA is capable of conversing with live users utilizing the context of the user’s interaction with the Knowledge Base in the current session. Our approach consists of an ensemble of entity resolution, entity prediction, question answering, and co-reference resolution models. We have conducted quantitative experiments and comparisons with existing state-of-the-art methods, on the Web-Question dataset. Using an F1-score metric, the effectiveness of our memory-efficient approach is evident from the results when evaluated with other conventional methods.
A Visual Feedback Supported Intelligent Assistive Technique for Amyotrophic Lateral Sclerosis Patients

Among diverse intelligent assistive systems developed for amyotrophic lateral sclerosis (ALS) patients, headwear eye tracking based ones trigger broad interests due to their merits such as noninvasive, cost effective, and high operation freedom. However, with headwear eye trackers, patients are easy to feel tired during human–machine interactivities (HMIs), and the operation accuracy is not satisfied compared with its counterparts. To address these two issues, herein, a visual feedback technique is developed which allows users to recognize machine's vision by positioning a laser spot to the user watched object, according to the location information interpreted from user's eye movement. Through the visual feedback technique, users not only obtain real‐time feedback, but also can fine‐tune the laser spot to the desired location before performing further operations. Experimental results demonstrate that the presented work can successfully reduce user's fatigue and boost operation accuracy by 25.1% and 27.6%, respectively, therefore, advancing the field of intelligent assistive technologies.
Touchless Control by Hand Gesture Sensing with Radar Sensor and Machine Learning

A low-cost, simple, and reliable touchless human-computer interaction (HCI) using a low-cost continuous wave (CW) radar sensor, advanced radar signal processing, and machine learning, is proposed and implemented in this presentation. This touchless HCI is based on gesture representation by micro-Doppler spectrograms from short-time Fourier transform (STFT) processing of radar signals and gesture classification is based on an ensemble of machine learning methods with a majority voting scheme. For lower cost and more reliable purposes, a flexible menu selection strategy with fewer hand gesture types is proposed and demonstrated. This reliable menu selection interface can be applied in lifts, restaurants, or other places to reduce the spreading of the virus.
Exploring Performance of a Spread Spectrum-Based Audio Watermarking System Using Convolutional Coding

This paper presents a spread spectrum-based audio watermarking system, where the message embedding is performed in the time domain. Similarly, as in a communication system, a channel coding is used to improve the performance of the audio watermarking system. Our goal here is to explore the performance using convolutional coding in a Spread Spectrum-based audio watermarking system to get the boundaries and the best configuration in the system. To this end, data payload, imperceptibility, and robustness are tested. Usually the number of bits used for quantization, improves the performance. However, we demonstrated here that the increasing number of bits more than 8 will not affect the performance. This is important result which may result in better use of computational resources.
MV-FractalDB: Formula-driven Supervised Learning for Multi-view Image Recognition

The paper proposes a method for automatic multi-view dataset construction based on formula-driven supervised learning (FDSL). Although data collection and human annotation of 3D objects are labor-intensive, we automatically generate their training data and labels in the proposed multi-view dataset. To create a large-scale multi-view dataset, we employ fractal geometry, which is considered the background information of many objects in the real world. We project in a circle from the rendered 3D fractal models to construct the Multi-view Fractal DataBase (MV-FractalDB), which is then used to make a pre-trained CNN model. According to the experimental results, the MV-FractalDB pre-trained model surpasses the accuracies with self-supervised methods (e.g., SimCLR and MoCo) and is close to supervised methods (e.g., ImageNet) in terms of performance rates on multi-view image datasets. We demonstrate the potential of FDSL for multi-view image recognition.
Automatic analysis of infant engagement during play: An end-to-end learning and Explainable AI pilot experiment

Infant engagement during play is an active area of research, related to the development of cognition. Automatic detection of engagement could benefit the research process, but existing techniques used for automatic affect detection are unsuitable for this scenario, since they rely on the automatic extraction of facial and postural features trained on clear video capture of adults. This study shows that end-to-end Deep Learning methods can successfully detect engagement of infants, without the need of clear facial video, when trained for a specific interaction task. It further shows that attention mapping techniques can provide explainability, thereby enabling trust and insight into a model’s reasoning process.
Quantum-inspired Hierarchical Attention Mechanism for Question Answering

The questions and answers in the question answering (QA) datasets are composed of multiple sentences. This requires the model to be able to find key words in the sentence and find key sentences in the paragraph for question answering classification. The hierarchical attention mechanism is very suitable for such QA classification tasks, focusing on key words at the word level and focusing on key sentences at the sentence level. Inspired by the concept of quantum theory, we introduce the weak measurement under two-state vector formalism for the modeling of word-level attention mechanism (WMATT). We introduce a density matrix for modeling the sentence-level attention mechanism (DMATT) in the model. Our two-way LSTM model is called WMATT-DMATT BiLSTM, which is based on word-level attention mechanism of weak measurement and sentence-level attention mechanism of density matrix. Experiments show that our model is comparable to the benchmark models in experimental results on multiple QA datasets. And the performance of our model achieves the state of the art on the WikiQA dataset compared to the baseline models. From the experimental point of view, the attention mechanism based on weak measurement is suitable for the micro level, corresponding to the word level. The attention mechanism based on the density matrix is suitable for macro level, corresponding to sentence level.
Deep learning for cryptanalysis attack on IoMT wireless communications via smart eavesdropping

The Internet of Medical Things (IoMT) faces grave protection concerns due to weakness related to the leakage of sensitive data controlled by detectors and transferred to the cloud through open wireless connections.In most IoMTs, patients information is encrypted by terminal devices, and the published cryptanalysis works demonstrate apparent limitations because they are performed for the theoretical model without any real implementation scenario on wireless encryption protocols, which poses a performance gap between the hypothetical investigation and practical employment of these attacks. To overcome these drawbacks, we suggest an approach based on a deep learning model for an attack simulation on specific IoMT protocols that discovers the private key of IoMT wireless communications using a smart eavesdropping conceptual scenario on multilevel layers of IoMT protocol stack for its implementation. We detail the attack process for building the datasets from wireless packet protocols with the aim of training the proposed cryptanalysis model.
Edge enhanced deep learning system for IoT edge device security analytics

Abstract The processing of locally harvested data at the physically accessible edge devices opens a new avenue of security threats for edge enhanced analytics. Cryptographic algorithms are used to secure the data being processed on the edge device. However, the implementation weakness of the algorithms on the edge devices can lead to side‐channel attack vulnerability, which is exacerbated with the application of machine‐learning techniques. This research proposes a deep learning‐based system integrated at the edge device to identify the side‐channel leakages. To design such a deep learning‐based system, one of the challenges is formulating the suitable attack model for the underlying target algorithm. Based on the previous findings, three machine learning‐based side‐channel attack models are curated and investigated for the edge device security evaluations. As a test case, the standard elliptic‐curve cryptographic algorithm is selected. Moreover, quantitative analysis is provided for the best attack model selection using standard machine‐learning evaluation metrics. A comparative analysis is performed on the raw unaligned data samples and reduced feature‐engineered samples using edge enhanced security analytics. The investigation concludes that the vulnerable algorithm implementation can lead to the secret key recovery from the edge device, with 96% accuracy, using a neural‐network‐based algorithm to analyse side‐channel attacks.
An improved density peaks clustering algorithm by automatic determination of cluster centres

The fast search and find of density peaks clustering (FDP) is an algorithm that can gain satisfactory clustering results with manual selection of the cluster centres. However, this manual selection is difficult for larger and more complex datasets, and it is easy to split a cluster into multiple subclusters. We propose an automatic determination of cluster centres algorithm (A-FDP). On the one hand, a new decision threshold is designed in A-FDP combined with the InterQuartile Range and standard deviation. We select the points larger than the decision threshold as the cluster centres. On the other hand, these cluster centres are made as nodes to construct the connected graphs. These subclusters are merged by finding the connected components of the connected graph. The results show that the A-FDP can obtain better clustering results and have higher accuracy than other classical clustering algorithms on synthetic and UCI datasets.
YOLO-ResNet: A New Model for Rebar Detection

Based on the YOLOv3 model, this paper proposes a YOLO-ResNet model for rebar detection. YOLO-ResNet model is based on ResNet-18 (Residual Network 18), retains the first four downsamplings, and adds CBAM (Convolutional Block Attention Module) attention model to the input and output parts to obtain a $52\times 52$-sized feature map for rebar detection by feature fusion process. Firstly, this paper clusters the width and height of the rebar in the training set to obtain new anchor prior boxes, then replaces the Darknet-53 network of YOLOv3 with an improved backbone network, and finally introduces the FocalLoss to penalize the easily divided negative samples so as to make the model converge faster. Compared with YOLOv3, the number of convolutional layers of YOLO-ResNet is reduced by 45 layers, the total training parameters are reduced by 94.35%, the training time of the model is reduced by an average of 40% under different training times, the GPU memory usage during training is reduced by 56%, and the model weight size has been reduced by 94%. We use 4 evaluation indicators of TrainTime, BestLoss, mAP and Accuarcy for the model. The experimental results show that the YOLO-ResNet model has better performance, less training time, lower GPU memory usage, and smaller model weight files than the YOLOv3 model without reducing the detection accuracy in the rebar detection.
Accessing electronic health records in critical incidents using context-aware attribute-based access control

In emergency situations, different actors involved in first aid services should be authorized to retrieve information from the patient’s Electronic Health Records (EHRs). The research objectives of this work involve the development and implementation of methods to characterise emergency situations requiring extraordinary access to healthcare data. The aim is to implement such methods based on contextual information pertaining to specific patients and emergency situations and also leveraging personalisation aspects which enable the efficient access control on sensitive data during emergencies. The Attribute Based Access Control paradigm is used in order to grant access to EHRs based on contextual information. We introduce an ABAC approach using personalized context handlers, in which raw contextual information can be uplifted in order to recognize critical situations and grant access to healthcare data. Results indicate that context-aware ABAC is a very effective method for detecting critical situations that require emergency access to personal health records. In comparison to RBAC implementations of emergency access control to EHRs, the proposed ABAC implementation leverages contextual information pertaining to the specific patient and emergency situations. Contextual information increases the capability of ABAC to recognize critical situations and grant access to healthcare data.
Recognition of Sign Language Based on Hand Gestures

The target of SLR or sign language recognition is to interpret the sign language into text, respectively. So the deaf and mute people can communicate with ordinary people easily. Sign language recognition has a tremendous social impact; however, it is challenging due to the significant variations and complexity in the hand actions. There are many existing methods for recognizing sign language that uses handcraft features for describing the motion of sign language and then, based on the features it makes the classification models. To approach the problem, we have discussed considering the KNN that can conveniently extract the features. The proposed model can be validated on a real data set.
Hardware Acceleration of Elliptic-Curve based Crypto-Algorithm, ECDSA and Pairing Engines

We have been working on design of hardware accelerators for various Elliptic-Curve based Crypto-Algorithm, such as ECDSA and Pairing Engines. Here, we present the designs of ECDSA using P-256 Short Weierstrass curve, which has the smallest gate count and the fastest performance ever reported. We also discuss the design of the pairing engine and designed an optimal ate pairing engine on the BN curve over 254-bit primes. In this paper, we demonstrated two designs: a pairing engine with optimized datapath and a pairing processor with a two-layer sequencer for multi-pairing. All these designs were designed and fabricated in 65nm CMOS SOTB process and demonstrated the measured performance.
Hybrid Analog-Digital Sensing Approach for Low-power Real-time Anomaly Detection in Drones

With the rapid growth of the use of Machine Learning (ML) techniques in Unmanned Aerial Vehicles (UAVs), there is an opportunity to use ML techniques to detect and prevent anomalous behavior in drones. However, limited drone power thwarts successful implementation of contemporary power-hungry ML techniques. Therefore, we propose a hybrid analog-digital system to solve the problem of continuous anomaly detection. In this paper, a series of pure analog ML methods including SVMs (linear, polynomial, Radial Basis Function (RBF) and Sigmoid kernels) as well as pure analog fully-connected Neural Network (NN) are presented. We validate our method with sensor data from a series of drone experiments to detect and identify causes of failure in real-time. The results show that RBF kernel provides at least 88.17 % and at most 99.99 % accuracy under different time window and crash-like scenarios with an extremely low False Negative (FN) ratio with sensor data especially in the z-axis.