Reviewerly/exHarmony huggingface dataset:
Fitness landscape analysis of convolutional neural network architectures for image classification

The global structure of the hyperparameter spaces of neural networks is not well understood and it is therefore not clear which hyperparameter search algorithm will be most effective. In this paper we analyze the landscapes of convolutional neural network architecture search spaces to provide insight into appropriate search algorithms for these spaces. Using a classical fitness landscape analysis approach (fitness distance correlation) and a more recent tool (local optima networks) we study the global structure of these spaces. Our analysis on six image classification datasets reveals that the landscapes are multi-modal, but with relatively few local optima from which it is not hard to escape with a simple perturbation operator. This led us to explore the performance of iterated local search, which we found to more effectively search the training landscapes than three evolutionary algorithm variants. Evolutionary algorithms, however, outperformed iterated local search in terms of generalization on problems with larger discrepancies between the training and testing landscapes.
New Findings From Explainable SYM‐H Forecasting Using Gradient Boosting Machines

Abstract In this work, we develop gradient boosting machines (GBMs) for forecasting the SYM‐H index multiple hours ahead using different combinations of solar wind and interplanetary magnetic field (IMF) parameters, derived parameters, and past SYM‐H values. Using Shapley Additive Explanation values to quantify the contributions from each input to predictions of the SYM‐H index from GBMs, we show that our predictions are consistent with physical understanding while also providing insight into the complex relationship between the solar wind and Earth's ring current. In particular, we found that feature contributions vary depending on the storm phase. We also perform a direct comparison between GBMs and neural networks presented in prior publications for forecasting the SYM‐H index by training, validating, and testing them on the same data. We find that the GBMs yield a statistically significant improvement in root mean squared error over the best published black‐box neural network schemes and the Burton equation.
Unidirectional RGB-T salient object detection with intertwined driving of encoding and fusion

The U-shaped encoder–decoder architecture based on CNNs has been rooted in salient object detection (SOD) tasks, and it have revealed two drawbacks while driving the rapid development of saliency detection. (1) The inherent characteristics of CNNs dictate that it is difficult to learn long-range dependencies and model global correlations. (2) For the common purpose of improving the performance of saliency detection, the encoder and decoder should complement each other and work together. However, the existing encoder–decoder architecture treats encoder and decoder independently of each other. Specifically, the encoder is responsible for extracting features and the decoder fuses multi-level or multi-modal features to produce prediction maps. That is, the encoder alone needs to be responsible for the decoder, while the valuable information after the decoder fusion will not facilitate feature extraction. Therefore, we propose a unidirectional RGB-T salient object detection network with intertwined driving of encoding and fusion to solve the above problems. Firstly, we introduce transformer (SegFormer) as the backbone of the network to deal with the problem that CNNs are difficult to establish long-range dependence. Secondly, we constructed a unidirectional architecture where encoding and fusion are intertwined and mutually driving, which discards the drawbacks of encoder–decoder architecture to make the network more powerful and concise. Based on the unidirectional architecture, the proposed Local Detail-driven Fusion Module (LDFM) uses the fused features of the previous level to drive the cross-modal fusion at the current level. Meanwhile, the proposed Local Detail-driven Weighting Module (LDWM) uses the fused features to drive the cross-modal weighting. They will drive more effective features to be fed into the next level of the encoding block. Comprehensive experiments have verified the superior performance of our method on the RGB-T saliency detection task.
In the nexus of integrity and surveillance: Proctoring (re)considered

Abstract Background The Covid‐19 pandemic disrupted higher education in many ways, such as the move to Emergency Remote Online Teaching and Learning (EROTL), often including a move to online assessments and examinations. With evidence of increased academic dishonesty in unproctored online assessment, institutions sought ways to ensure academic and institutional integrity and reputation. In doing this, many institutions selected and implemented online proctoring solutions. Objectives This article maps considerations of online proctoring solutions in the nexus between ensuring academic and institutional integrity and reputation, and addressing stakeholder concerns regarding invasive surveillance and the impacts on student privacy. Methods The study involved a PRISMA‐informed systematic review of three digital libraries, namely Clarivate's Web of Science, Elsevier's Scopus, and Springer's SpringerLink, for peer‐reviewed journal articles and conference proceedings. After screening, a final corpus of 27 articles was analysed. Results and Conclusions The findings include evidence that, in the midst of the Covid‐19 pandemic, higher education institutions were largely influenced by cost, usability and efficiency in choosing online proctoring solutions to ensure academic and institutional integrity. Student privacy was either considered in terms of data protection and transparency, or not at all. This article aims to provide valuable insight into the criteria used to select online proctoring solutions to ensure academic and institutional integrity in online examination environments. Student privacy appears not to have the consideration it warrants.
Anomaly Detection Using Siamese Network with Attention Mechanism for Few-Shot Learning

Automated inspection using deep-learning has been attracting attention for visual inspection at the manufacturing site. However, the inability to obtain sufficient abnormal product data for training deep- learning models is a problem in practical application. This study proposes an anomaly detection method based on the Siamese network with an attention mechanism for a small dataset. Moreover, attention branch loss (ABL) is proposed for Siamese network to render more task-specific attention maps from attention mechanism. Experimental results confirm that the proposed method with the attention mechanism and ABL is effective even with limited abnormal data.
Theoretical Design of Optimal Molecular Qudits for Quantum Error Correction

We pinpoint the key ingredients ruling decoherence in multispin clusters, and we engineer the system Hamiltonian to design optimal molecules embedding quantum error correction. These are antiferromagnetically coupled systems with competing exchange interactions, characterized by many low-energy states in which decoherence is dramatically suppressed and does not increase with the system size. This feature allows us to derive optimized code words, enhancing the power of the quantum error correction code by orders of magnitude. We demonstrate this by a complete simulation of the system dynamics, including the effect of decoherence driven by a nuclear spin bath and the full sequence of pulses to implement error correction and logical gates between protected states.
Object Detection Learning for Intelligent Self Automated Vehicles

Robotics is a part of today's communication that makes human life simpler in the day-to-day aspect.Therefore, we are supporting this cause by making a smart city project that is based on Artificial Intelligence, image processing, and some touch of hardware such as robotics.In particular, we advocate a self automation device (i.e., autonomous car) that performs actions and takes choices on its very own intelligence with the assist of sensors.Sensors are key additives for developing and upgrading all forms of self-sustaining cars considering they could offer the information required to understand the encircling surroundings and consequently resource the decision-making process.In our device, we used Ultrasonic and Camera Sensor to make the device self-sustaining and assist it to discover and apprehend the objects.As we recognize that the destiny is shifting closer to the Autonomous vehicles that are the destiny clever vehicles predicted to be driver-less, efficient, and crash-fending off best city vehicles of the destiny.Thus, the proposed device could be capable of stumbling on the item/object for easy and green running to keep away from crash and collisions.It could additionally be capable of calculating the gap of the item/object from the autonomous car and making the corresponding decisions.Furthermore, the device can be tracked-able by sending the location continuously to the mobile device through IOT using GPS and Wi-Fi Module.Interestingly.Additionally, the device is also controlled with the voice using Bluetooth.
Resilient Unscented Kalman Filtering Fusion With Dynamic Event-Triggered Scheme: Applications to Multiple Unmanned Aerial Vehicles

In this article, the resilient unscented Kalman filtering fusion issue is investigated for a class of nonlinear systems under the dynamic event-triggered mechanism where each sensor node transmits the measurement information to its corresponding local filter in an intermittent way. Compared with its static counterpart, the dynamic event-triggered scheme is capable of scheduling the frequency of data transmission in a more efficient way, thereby better reducing communication burden and energy consumption. In addition, for each local filter, the variation of the filter gain is characterized by a multiplicative noise term. To cope with the intractable problem of computing the cross covariance between local filters, the sequential covariance intersection fusion strategy is introduced into the proposed distributed fusion framework. Finally, the proposed algorithm is applied to a maneuvering target tracking scenario with multiple unmanned aerial vehicles, and both numerical simulations and hardware experiments are provided to elucidate the effectiveness and practicality of the proposed filtering scheme.
An efficient mixture sampling model for gaussian estimation of distribution algorithm

Estimation of distribution algorithm (EDA) is a stochastic optimization algorithm based on probability distribution model and has been widely applied in global optimization. However, the random sampling of Gaussian EDA (GEDA) usually suffers from the poor diversity and the premature convergence, which severely limits its performance. This paper analyzes the shortcomings of the random sampling and develops an efficient mixture sampling model (EMSM). EMSM can explore more promising regions and utilize the unsuccessful mutation vectors, which achieves a good tradeoff between the diversity and the convergence. Moreover, the feasibility analysis of EMSM is studied. A new GEDA variant named EMSM-EDA is developed, which combines EMSM with enhancing Gaussian estimation of distribution algorithm (EDA2). The experimental results on IEEE CEC2013 and IEEE CEC2014 test suites demonstrate that EMSM-EDA is efficient and competitive.
Learning Latent Graph Dynamics for Visual Manipulation of Deformable Objects

Manipulating deformable objects, such as ropes and clothing, is a long-standing challenge in robotics, because of their large degrees of freedom, complex non-linear dynamics, and self-occlusion in visual perception. The key difficulty is a suitable representation, rich enough to capture the object shape, dynamics for manipulation and yet simple enough to be estimated reliably from visual observations. This work aims to learn latent Graph dynamics for DefOrmable Object Manipulation (G-DOOM). G-DOOM approximates a deformable object as a sparse set of interacting keypoints, which are extracted automatically from images via unsupervised learning. It learns a graph neural network that captures abstractly the geometry and the interaction dynamics of the keypoints. To handle object self-occlusion, G-DOOM uses a recurrent neural network to track the keypoints over time and condition their interactions on the history. We then train the resulting recurrent graph dynamics model through contrastive learning in a high-fidelity simulator. For manipulation planning, G-DOOM reasons explicitly about the learned dynamics model through model-predictive control applied at each keypoint. Preliminary experiments of G-DOOM on a set of challenging rope and cloth manipulation tasks indicate strong performance, compared with state-of-the-art methods. Although trained in a simulator, G-DOOM transfers directly to a real robot for both rope and cloth manipulation <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Demo video available online at https://youtu.be/oCfbNMx2sQI.
Towards Broad Learning Networks on Unmanned Mobile Robot for Semantic Segmentation

This article investigates the real-time semantic segmentation in robot engineering applications based on the Broad Learning System (BLS), and a novel Multi-level Enhancement Layers Network (MELNet) based on BLS framework is proposed for real-time vision tasks in a complex street scene on the unmanned mobile robot. This network mainly solves two problems: (1) mitigating the contradiction between accuracy and speed while maintaining low model complexity, and (2) accurately describing objects based on their shape despite their different sizes. Firstly, the BLS architecture is expanded to the deep network with trainable parameters. This trainable network could adjust its weights in a complex environment, and mitigate the adverse impact of the environment on the complex tasks. Secondly, enhancement layers with the extended enhancement layers could extract both detailed information and semantic information. Moreover, an Upsampling Atrous Spatial Pyramid Pooling (UPASPP) is designed to fuse detail and semantic information to describe object features properly. Finally, in the case of the MNIST dataset and Cityscapes dataset, we get high accuracy with 8.01M parameters and quicker inference speed on a single GTX 1070 Ti card. At the same time, the unmanned mobile robot (BIT-NAZA) is employed to evaluate semantic performance in real-world situations. This reveals that MELNet could be run adequately on the embedded device and effectively operate in the real-robot system.
Novel Resolution of Unit Commitment Problems Through Quantum Surrogate Lagrangian Relaxation

Unit commitment (UC) problems faced by Independent System Operators on a daily basis are becoming increasingly complex due to the recent push for renewables and the consideration of sub-hourly UC to accommodate the increasing variability in the net load. A disruptive solution methodology to address the growing complexity is therefore required. Quantum computing offers a promise to overcome the combinatorial complexity through the use of the so-called “qubits.” To make the best use of near-term quantum computers to solve UC problems with a much larger number of binary variables than the number of qubits available, this paper devises a novel solution methodology based on a synergistic combination of quantum computing and Surrogate Lagrangian Relaxation (SLR) to solve UC problems. Our new contributions include: 1) A Quantum-SLR (QSLR) algorithm incorporating quantum approximate optimization algorithm (QAOA) into the SLR method, which overcomes the fundamental difficulties of previous LR-based quantum methods such as zigzagging of multipliers and the need to know or estimate the optimal dual value for convergence; 2) A Distributed QSLR framework (D-QSLR) capable of coordinating local quantum/classical computing resources with those within neighborhoods and, in the meantime, protecting data privacy; 3) A Quantized UC model to obtain accurate commitment unit subproblems decision by using a quantum machine; and 4) A time-unit-decomposed quantum UC approach to overcoming the quantum resources’ limitations. Promising quantum test results validate the effectiveness of QSLR and the scalability of the UC-oriented D-QSLR algorithm, which demonstrate QSLR’s enormous potential in UC optimization.
Preserving Privacy Using Blockchain Technology in Autonomous Vehicles

With transportation and automation being some of the major fields of interest in research in the current world, Autonomous Vehicles (AVs) and Intelligent Transportation System (ITS) have been taking the spotlight especially in the current era where mobilization and automation are proliferating. One of the major fields of research and development in the transportation field is the networking of various smart vehicles together for enhanced data acquisition and automated driving. This paper talks about one of the major challenges which is commonly found in developments in the related field which is privacy preservation. Privacy is a term which describes the safety and security of the information retrieved from the users for computation. When developing applications which indulge in automation and networking, privacy is one of the major parameters to be considered because there are high chances of attacks, data breaches and leakages. This paper discusses a possible solution of integrating the Blockchain technology with the systemic framework for encrypting and hashing the data and securing the information from any sort of mishaps and glitches. The paper also throws insights on ITS, AVs and Internet of Vehicles (IoV) and how Blockchain can be integrated to the ITS framework.
Evaluating Cybersickness of Walking on an Omnidirectional Treadmill in Virtual Reality

Cybersickness is a type of motion sickness that may occur during a virtual reality (VR) experience. Many studies have proposed solutions to mitigate cybersickness, while navigating a virtual environment with controllers or walking over a floor. However, reducing the levels of cybersickness while physically walking on an omnidirectional treadmill has been largely overlooked. In this article, we performed a within-subject study, where 34 novice participants underwent four visual conditions while walking in a virtual maze over an omnidirectional treadmill. In the control condition, the movement speed was reduced of the half compared to a standard navigation speed, a movement speed smoothing was added, and the user’s virtual body was represented. The other three conditions changed one of the visual parameters of the control condition: in the standard speed condition, the speed reduction was not performed; for the no smoothing condition, the smoothing was not performed; and for the no avatar condition, the user’s avatar was removed. Results showed that the standard speed condition was reported to induce a significant level of cybersickness compared to the control and no avatar conditions. Nevertheless, standard speed was also the condition most preferred to navigate a virtual environment. This suggests the need to find a tradeoff between the easiness to move quickly in a virtual environment and the cybersickness that can be induced. We provide a discussion of the obtained results and their implications for the design of VR experiences while users walk upon an omnidirectional treadmill.
Adversarial Deception Against SAR Target Recognition Network

Synthetic Aperture Radar (SAR) automatic target recognition (ATR) technology is one of the key technologies to achieve intelligent interpretation for SAR images. With the rapid development of deep learning, deep neural networks have been successively used in SAR ATR and show priority in comparison with the conventional methods. Recently, more and more attention is paid to the robustness of deep learning based SAR ATR methods. The reason is that maliciously modified and imperceptible adversarial images can deceive the SAR ATR methods which are based on the deep neural networks. In this paper, we propose a novel SAR ATR adversarial deception algorithm, which fully considers the characteristics of SAR data. Our method can obtain the satisfactory perturbations with a higher deception success rate, a higher recognition confidence, and a smaller perturbation coverage than other state-of-the- art methods for the SAR images. Experimental results using the MSTAR dataset and OpenSARShip dataset demonstrate the effectiveness of our method. The proposed adversarial deception method can be used in the applications such as SAR dataset protection, SAR sensor design and SAR image quality evaluation.
A Divide-and-Conquer Approach to Dicke State Preparation

We present a divide-and-conquer approach to deterministically prepare Dicke states |D n k (i.e., equal-weight superpositions of all n-qubit states with Hamming Weight k) on quantum computers.In an experimental evaluation for up to n = 6 qubits on IBM Quantum Sydney and Montreal devices, we achieve significantly higher state fidelity compared to previous results [Mukherjee et.al.TQE'2020, Cruz et.al.QuTe'2019].The fidelity gains are achieved through several techniques: Our circuits first "divide" the Hamming weight between blocks of n/2 qubits, and then "conquer" those blocks with improved versions of Dicke state unitaries [Bärtschi et.al.FCT'2019].Due to the sparse connectivity on IBM's heavy-hex-architectures, these circuits are implemented for linear nearest neighbor topologies.Further gains in (estimating) the state fidelity are due to our use of measurement error mitigation and hardware progress.
A Cognitive Similarity-Based Measure to Enhance the Performance of Collaborative Filtering-Based Recommendation System

Advances in technology and high Internet penetration are leading to a large number of businesses going online. As a result, there is a substantial increase in the number of customers making online purchases and the number of items available online. However, with so many options available to choose from, users have to face the information overload problem. Several techniques have been developed to handle this, but the performance of the recommendation system (RS) has been recorded unprecedentedly. The collaborative filtering (CF) of RS is the most prevalent technique, which suggests personalized items to users based on their past preferences. The efficacy of this technique mainly depends on the similarity calculation, which the traditional or cognitive approach can ascertain. In the traditional approach, a similarity measure utilizes the user’s ratings on an item to compute the similarity. Most similarity measures in this approach suffer from either data sparsity and/or cold-start problems. To address both of them, a new similarity measure based on the Jaccard and Gower coefficients, the efficient Gowers–Jaccard–Sigmoid Measure (EGJSM), is proposed in this article. It also includes a nonlinear sigmoid function to penalize the bad ratings. The performance of EGJSM is evaluated by conducting experiments on benchmark datasets, and the results depict that the proposed technique outperforms several existing methods. Along with this, a cognitive similarity (CgS) measure has been proposed, which considers cognitive features such as genre and year of release along with rating information, to calculate similarity. The CgS method also outperforms the proposed EGJSM method and produces almost 4% and 1% lower mean absolute error (MAE) and root-mean-squared error (RMSE) values than that.