Reviewerly/exHarmony huggingface dataset:
Probing Geometric Excitations of Fractional Quantum Hall States on Quantum Computers

Intermediate-scale quantum technologies provide new opportunities for scientific discovery, yet they also pose the challenge of identifying suitable problems that can take advantage of such devices in spite of their present-day limitations. In solid-state materials, fractional quantum Hall phases continue to attract attention as hosts of emergent geometrical excitations analogous to gravitons, resulting from the nonperturbative interactions between the electrons. However, the direct observation of such excitations remains a challenge. Here, we identify a quasi-one-dimensional model that captures the geometric properties and graviton dynamics of fractional quantum Hall states. We then simulate geometric quench and the subsequent graviton dynamics on the IBM quantum computer using an optimally compiled Trotter circuit with bespoke error mitigation. Moreover, we develop an efficient, optimal-control-based variational quantum algorithm that can efficiently simulate graviton dynamics in larger systems. Our results open a new avenue for studying the emergence of gravitons in a new class of tractable models on the existing quantum hardware.
A Novel Robust Hexarotor Capable of Static Hovering in Presence of Propeller Failure

This paper presents a novel open source design of the Y-shaped hexarotor Unammend Aerial Vehicle (UAV), and proves both in theory and real experiments its robustness to the failure of any of its propellers.An intuitive geometrical interpretation of UAV static hovering ability is presented, through which the robustness of different coplanar/collinear hexarotor designs is analyzed.Following the presented geometrical interpretation, we also show the conditions that allow the Star-shaped hexarotor to be robust to the failure of some of its propellers, while showing its incapability to static hover in the case of the failure of any of its propellers.Finally, the efficiency of the Y-shaped and Star-shaped hexarotors are tested experimentally, and conclusions on the advantages and disadvantages of the two designs are drawn.
Meta-Active Learning in Probabilistically Safe Optimization

When a robotic system is faced with uncertainty, the system must take calculated risks to gain information as efficiently as possible while ensuring system safety. The need to safely and efficiently gain information in the face of uncertainty spans domains from healthcare to search and rescue. To efficiently learn when data is scarce or difficult to label, active learning acquisition functions intelligently select a data point that, if the label were known, would most improve the estimate of the unknown model. Unfortunately, prior work in active learning suffers from an inability to accurately quantify information-gain, generalize to new domains, and ensure safe operation. To overcome these limitations, we develop Safe MetAL, a probabilistically-safe, active learning algorithm which meta-learns an acquisition function for selecting sample efficient data points in safety critical domains. The key to our approach is a novel integration of meta-active learning and chance-constrained optimization. We (1) meta-learn an acquisition function based on sample history, (2) encode this acquisition function in a chance-constrained optimization framework, and (3) solve for an information-rich set of data points while enforcing probabilistic safety guarantees. We present state-of-the-art results in active learning of the model of a damaged UAV and in learning the optimal parameters for deep brain stimulation. Our approach achieves a 41% improvement in learning the optimal model and a 20% speedup in computation time compared to active and meta-learning approaches while ensuring safety of the system.
GA-STT: Human Trajectory Prediction With Group Aware Spatial-Temporal Transformer

Human trajectory prediction is a crucial yet challenging problem, which is of fundamental importance to robotics and autonomous driving vehicles. The core challenge lies in effectively modeling the socially aware spatial interaction and complex temporal dependencies among crowds. However, previous methods usually model spatial and temporal information separately and only use individual features, while ignoring group-level motion characteristics. We propose a novel trajectory prediction framework termed GA-STT, a group aware spatial-temporal transformer network to address these issues. Specifically, we first get the individual representations supervised by group-based annotations. Then, we model the complex spatial-temporal interactions with spatial and temporal transformers separately and fuse spatial-temporal embedding through the cross-attention mechanism. Results on the publicly available ETH/UCY datasets show that our model outperforms the state-of-the-art method by 19.4% in average displacement error(ADE) and 16.9% in final displacement error(FDE) and successfully predicts complex spatial-temporal interactions.
Prohibited Items Detection in Baggage Security Based on Improved YOLOv5

To resolve the problems of high item overlap and complex background in the security detection of X-ray luggage images, this paper proposes a network model based on improved YOLOv5. A transformer is used in the last convolution module of the Backbone network, which effectively improves the feature extraction ability of backbone, and introduces the global attention mechanism module in Head branch, which solves the problem of complex background in X-ray baggage image and inhibits the interference of background. Finally, the adaptive spatial feature fusion algorithm is adopted in the prediction part to improve the accurate prediction ability of the model. The mean Average Precision (mAP) of the algorithm on OPIXray data set reached 91%, and the detection speed reached 47 FPS.
Blockchain Technology in the Public Sector by Leveraging the Triumvirate of Security

Blockchain technology has become a trend in various sectors, both industrial and public sectors. There have been many questions regarding this blockchain technology in terms of competition in the public sector. Mainly among these are the concerns that become issues around governance and control in distributed ledgers. From an information security point of view this article investigates the influence of blockchain technology on public domain processes. It contains an outline of the development of the provision of public services and one-way information by the government to the community, the exchange of information and communication between the public and the government through the current blockchain application, and the current development of blockchain technology. We use a triad, Confidentiality-Integrity-Accessibility to guide our discussion of the interrelationships of security, operation, and regulation of this generation. By leveraging the triad model, we offer a framework for public managers who may also be able to recommend blockchain technology, and we observe the positive benefits that arise from the denial of a distributed ledger. In particular, we look at the benefits of blockchain technology which has to do with disclaimers to help public managers understand how to leverage blockchain generation for an application process.
A Robustly Optimized BMRC for Aspect Sentiment Triplet Extraction

Aspect sentiment triplet extraction (ASTE) is a challenging subtask in aspect-based sentiment analysis.It aims to explore the triplets of aspects, opinions and sentiments with complex correspondence from the context.The bidirectional machine reading comprehension (BMRC) can effectively deal with ASTE task, but several problems remains, such as query conflict and probability unilateral decrease.Therefore, this paper presents a robustly optimized BMRC method by incorporating four improvements.The word segmentation is applied to facilitate the semantic learning.Exclusive classifiers are designed to avoid the interference between different queries.A span matching rule is proposed to select the aspects and opinions that better represent the expectations of the model.The probability generation strategy is also introduced to obtain the predicted probability for aspects, opinions and aspect-opinion pairs.We have conducted extensive experiments on multiple benchmark datasets, where our model achieves the stateof-the-art performance. 1
Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection

Despite numerous studies of deep autoencoders (AEs) for unsupervised anomaly detection, AEs still lack a way to express uncertainty in their predictions, crucial for ensuring safe and trustworthy machine learning systems in high-stake applications. Therefore, in this work, the formulation of Bayesian autoencoders (BAEs) is adopted to quantify the total anomaly uncertainty, comprising epistemic and aleatoric uncertainties. To evaluate the quality of uncertainty, we consider the task of classifying anomalies with the additional option of rejecting predictions of high uncertainty. In addition, we use the accuracy-rejection curve and propose the weighted average accuracy as a performance metric. Our experiments demonstrate the effectiveness of the BAE and total anomaly uncertainty on a set of benchmark datasets and two real datasets for manufacturing: one for condition monitoring, the other for quality inspection.
Edge Deployment Framework of GuardBot for Optimized Face Mask Recognition With Real-Time Inference Using Deep Learning

Deep learning based models on the edge devices have received considerable attention as a promising means to handle a variety of AI applications. However, deploying the deep learning models in the production environment with efficient inference on the edge devices is still a challenging task due to computation and memory constraints. This paper proposes a framework for the service robot named GuardBot powered by Jetson Xavier NX and presents a real-world case study of deploying the optimized face mask recognition application with real-time inference on the edge device. It assists the robot to detect whether people are wearing a mask to guard against COVID-19 and gives a polite voice reminder to wear the mask. Our framework contains dual-stage architecture based on convolutional neural networks with three main modules that employ (1) MTCNN for face detection, (2) our proposed CNN model and seven transfer learning based custom models which are Inception-v3, VGG16, denseNet121, resNet50, NASNetMobile, XceptionNet, MobileNet-v2 for face mask classification, (3) TensorRT for optimization of all the models to speedup inference on the Jetson Xavier NX. Our study carries out several analysis based on the models&#x2019; performance in terms of their frames per second, execution time and images per second. It also evaluates the accuracy, precision, recall &#x0026; F1-score and makes the comparison of all models before and after optimization with a main focus on high throughput and low latency. Finally, the framework is deployed on a mobile robot to perform experiments in both outdoor and multi-floor indoor environments with patrolling and non-patrolling modes. Compared to other state-of-the-art models, our proposed CNN model for face mask recognition based on the classification obtains 94.5%, 95.9% and 94.28% accuracy on training, validation and testing datasets respectively which is better than MobileNet-v2, Xception and InceptionNet-v3 while it achieves highest throughput and lowest latency than all other models after optimization at different precision levels.
A Modular and Portable Black Box Recorder for Increased Transparency of Autonomous Service Robots

Autonomous service robots have great potential to support humans in tasks they cannot perform due to, amongst others, time constraints, work overload, or staff shortages. An important step for such service robots to be trusted or accepted by society is the provision of transparency. Its purpose is not only to communicate what a robot is doing according to the human interaction partners' needs, it should also regard social and legal requirements. A black box recorder (inspired by flight recorders) increases the system's transparency by facilitating the investigation of the cause of an incident, clarifying responsibilities, or improving the user's understanding about the robot. In this work we propose the needed requirements of such a black box recorder for increased transparency of autonomous service robots, based on the related work. Further, we present a new modular and portable black box recorder design meeting these requirements. The applicability of the system is evaluated based on real-world robot data, using the realized open-source reference implementation.
DEEPFAKE Image Synthesis for Data Augmentation

Deepfake videos and images have increased in recent years at exponential rates, and refer to the transfer of features of interest from source image (or video) to target image (or video), such that the target modality appears to animate the source almost close to the real.The forensic professionals, policy makers, and the public alike are interested in the role of Deepfakes is playing to confuse viewers in spreading misinformation by undermining the truth.In the past decade, computer vision has made significant advances using the latest state-of-art-methods of image processing techniques.Supervised Deep learning models produce super-human results in a variety of computer vision and machine learning applications.The field of medical imaging is scarce in terms of a reliable data set that is extensive enough to train distinct models.One way to tackle this problem is to use a Generative Adversarial Network to synthesise DEEPFAKE images to augment the data.DEEPFAKE images can be a useful means in various applications like translating to different useful and sometimes malicious modalities, unbalanced datasets or increasing the amount of datasets.In this paper the data scarcity has been addressed by using Progressive Growing Generative Adversarial Networks (PGGAN).However, PGGAN consists of convolution layer that suffers from the training-related issues.PGGAN requires a large number of convolution layers in order to obtain highresolution image training, which makes training a difficult task.In this work, a subjective self-attention layer has been added with convolution layers for efficient feature learning and the use of spectral normalization for training stabilization -the two tasks resulting into what is referred to as Enhanced-GAN.The Enhanced-GAN performance is compared to PGGAN performance using the parameters of AM Score and Mode Score.In addition, the strength of Enhanced-GAN and PGGAN synthesis is evaluated using the U-net model for segmentation tasks.Dice Coefficient metrics show that U-net trained on Enhanced-GAN DEEPFAKE data optimized with real data performs better than PGGAN DEEPFAKE data with real data.
MFNet: Multi-Feature Fusion Network for Real-Time Semantic Segmentation in Road Scenes

Although high-accuracy networks have been applied to semantic segmentation at present, their inference speeds remain slow. A trade-off between accuracy and speed is demanded for real-time applications. To approach this problem, we propose Multi-Feature Fusion Network (MFNet) with real-time efficient prediction capacity. MFNet adopts three branches (attention, semantic and spatial information) to capture low-level and high-level features. Additionally, MFNet exerts asymmetric factorized (AF) blocks to extract local and long-range features. As a result, without any pre-training or post-processing, MFNet using only 1.34 M parameters, achieves 72.1% mean intersection over union (mIoU) on the Cityscapes test set at a speed of 116 frames per second (FPS), with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$512\times 1024$ </tex-math></inline-formula> high resolution on a single Titan Xp graphics card. Our network’s performance stands out from other state-of-the-art networks on four datasets (Cityscapes, CamVid, KITTI, and Gatech).
Optimal echo state network parameters based on behavioural spaces

The quantitative analysis of neural networks is a critical issue to improve performance. In this work, we extend the ideas of this adaptation method to the more commonly used behavioural spaces. The aim is to construct an echo state network (ESN) behavioural space through the generalization rank, kernel rank, and memory capacity. After deriving behavioural spaces, we investigate the influencing factors and methods of reducing the search complexity of behavioural spaces. This investigation reveals that the rule converges to the expected space distributions, even in a random ESN. We propose an optimization algorithm that adopts the novel search genetic algorithm (NSGA), which combines the novelty and quality of individuals. In the novel search process, because the time needed to construct the behaviour space is much lower than the network training time, the network optimization efficiency is greatly improved. According to the characteristics of the behaviour space distribution, we propose a method to shrink the search space to solve the problem of a large search space. This method overcomes the difficulties in traditional ESN parameter selection and the long optimization time of a genetic algorithm and provides behavioural space theory to clarify the influence of reservoir performance on tasks.
Detection of Pancreatic Cancer in CT Scan Images Using PSO SVM and Image Processing

A diagnosis of pancreatic cancer is one of the worst cancers that may be received anywhere in the world; the five-year survival rate is very less. The majority of cases of this condition may be traced back to pancreatic cancer. Due to medical image scans, a significant number of cancer patients are able to identify abnormalities at an earlier stage. The expensive cost of the necessary gear and infrastructure makes it difficult to disseminate the technology, putting it out of the reach of a lot of people. This article presents detection of pancreatic cancer in CT scan images using machine PSO SVM and image processing. The Gaussian elimination filter is utilized during the image preprocessing stage of the removal of noise from images. The <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> means algorithm uses a partitioning technique to separate the image into its component parts. The process of identifying objects in an image and determining the regions of interest is aided by image segmentation. The PCA method is used to extract important information from digital photographs. PSO SVM, naive Bayes, and AdaBoost are the algorithms that are used to perform the classification. Accuracy, sensitivity, and specificity of the PSO SVM algorithm are better.
Trade Facilitation and Agriculture Sector Performance in Sub-Saharan Africa: Insightful Policy Implications for Economic Sustainability

This study investigates the impact of trade facilitation though costs, documents, and time to import and export—on agricultural sector performance (ASP) for a panel of 33 Sub-Saharan African (SSA) countries from 2005 to 2019. The empirical analysis is based on a dynamic system of generalized method of moments. The following findings are established. First, higher import costs, documents, and time significantly improve ASP, while exports negatively impact ASP. Second, while import costs promote trade in agricultural products through substitution effects, export costs impede it. Third, among other factors, improvements in human capital, gross fixed capital formation, population growth, and trade openness significantly improve agricultural sector performance. Fourth, the results of the robustness check further emphasize the importance of TF on ASP from various dimensions. The study concludes that improving TF procedures by reducing costs, documents, and time in cross-border trade remains crucial to boosting agriculture sector performance in SSA.
Enhancement of human 3D pose estimation using a novel concept of depth prediction with pose alignment from a single 2D image

2D poses are insufficient to estimate human activities under the geometric ambiguities, occlusion, diverse appearance, and viewpoints inside the image. As a result, 3D posing has emerged as an attractive research area in recent decades due to its ability to distinguish human activities effectively. Human skeletal 3D posture is the foundation for 3D avatar creation, the construction of human 3D meshes, recognition of human actions or activities, augmented reality, etc. Therefore, estimating the correct human 3D skeleton or pose with minimum errors is an essential task. This research paper proposes a depth predictor module; an innovative and creative approach of depth prediction for evaluating the human 3D pose from a single 2D image. However, no predicted 3D pose is entirely free from errors. Therefore, it needs to reduce the inaccuracy after predicting the 3D pose. For this purpose, an enhancement approach, pose alignment , is used to reduce the positioning inaccuracies of the anticipated 3D pose. The specialty of the proposed model is that it does not require any training dataset, and it effectively estimates 3D poses under any scale variations. This proposed method is evaluated on different datasets: Human3.6M, HumanEva-I, NTU RGB + D, MPII, UAV-human and the Articulated Free Fall dataset. In terms of errors, the proposed approach achieves the minimum 3D pose error compared to the existing 3D pose estimation techniques. The proposed depth prediction approach reduces the average MPJPE value to 48.30, and the pose alignment module further reduces it to 40.61. In addition, the proposed model takes an inference time of 1.806 s per frame for a single person. • We introduced a novel concept for human 3D pose estimation from a single RGB image. • The specialty of this framework is that there is no need for any training dataset. • This is an algorithm-based system to estimate the 3D pose of the human body. • We introduced a Depth Predictor module, our novel concept to predict depth. • Proposed a pose alignment technique to enhance the accuracy of the predicted 3D pose. • This approach is novel and very interesting in the field of computer vision.
A random approximate reduct-based ensemble learning approach and its application in software defect prediction

Software defect prediction (SDP) is an important research topic in software engineering. It can optimize the allocation of testing resources by indicating the defect-prone software modules. In recent years, ensemble learning has been frequently used in SDP. Ensemble learning can effectively improve the generalization ability of individual learners, but how to increase the diversity of individual learners is still an open issue. In this paper, we propose a random approximate reduct-based ensemble algorithm called ERAR. To increase the diversity of learners generated by ERAR, a novel technique, called random approximate reduct (RAR), is proposed to perturb the attribute space, and the resampling technique is used to perturb the instance space. RAR is derived from the random subspace method and the granular decision entropy-based reduct in rough set theory. We then investigate the application of ERAR in SDP. In order to alleviate the class imbalance issue in SDP, we further design a hybrid mechanism by combining ERAR with the Synthetic Minority Over-sampling Technique (SMOTE). Experimental results on 20 benchmark data sets show that the hybrid mechanism can provide competitive solutions for SDP.