Reviewerly/exHarmony huggingface dataset:
Symmetry-adapted encodings for qubit number reduction by point-group and other Boolean symmetries

Abstract A symmetry-adapted fermion-to-spin mapping or encoding that is able to store information about the occupancy of the n spin-orbitals of a molecular system into a lower number of n − k qubits in a quantum computer (where the number of reduced qubits k ranges from 2 to 5 depending on the symmetry of the system) is introduced. This mapping reduces the computational cost of a quantum computing simulation and at the same time enforces symmetry constraints. These symmetry-adapted encodings (SAEs) can be explicitly seen as a block-diagonalization of the Jordan–Wigner qubit Hamiltonian, followed by an orthogonal projection. We provide the form of the Clifford tableau for a general class of fermion-to-qubit encodings, and then use it to construct the map that block-diagonalizes the Hamiltonian in the SAEs. The algorithm proposed does not require any further computations to obtain this map, which is derived directly from the character table of the molecular point group. An implementation of the algorithm is presented as an open-source Python package, QuantumSymmetry , a user guide and code examples. QuantumSymmetry uses open-source quantum chemistry software PySCF for Hartree–Fock calculations, and is compatible with quantum computing toolsets OpenFermion and Qiskit . QuantumSymmetry takes arbitrary user input such as the molecular geometry and atomic basis set to construct the qubit operators that correspond in the appropriate SAE to fermionic operators on the molecular system, such as the second-quantized electronic structure Hamiltonian. QuantumSymmetry is used to produce numerical examples of variational quantum algorithm simulations to find the ground state energy for a number of example molecules, for both Unitary Coupled Clusters with Singles and Doubles and Adaptive Derivative Assembled Pseudo-Trotter Variational Quantum Eigensolver ansätze. We show that, beyond the advantage given by the lower qubit count, the proposed encodings consistently result in shallower and less complex circuits with a reduced number of variational parameters that are able to reach convergence faster and without any loss of computed accuracy.
Privacy-Preserving Multi-Party Access Control for Third-Party UAV Services

Third-Party Unmanned Aerial Vehicle (UAV) Services, a.k.a. Drone-as-a-Service (DaaS), are an increasingly adopted business model, which enables possibly unskilled users, with no background knowledge, to operate drones and run automated drone-based tasks. Although these services provide significant advantages, the resources provided by drones are typically owned by multiple parties. Thus, Third-Party UAV services require adopting multi-party access control solutions. In this context, the leakage of the access control policies specified by the data owners might disclose confidential information and, thus, they should be protected as well. In this work, we propose a privacy-preserving multi-party access control solution tailored to the application scenarios of Third-Party UAV Services. Our solution advances an existing privacy-preserving multi-party access control framework based on Secure Function Evaluation to fit the distributed and heterogeneous nature of drone deployments. Through an extensive experimental evaluation, we demonstrate our solution can perform private policy evaluation on constrained devices in a reasonable time while requiring limited communication, memory, and energy overhead.
Demo: A Multimodal Behavioral Biometric Scheme for Smartphone User Authentication (MBBS)

In this demo paper, we introduce MBBS - a tetra-model behavioral biometric authentication scheme for smartphones. MBBS leverages four modalities: the way a smartphone user (i) swipes on the touchscreen, (ii) taps any combination of ''text-independent" 8-digit numbers, (iii) writes his name on the touchscreen, and (iv) the hand's micro-movements he makes during this entry process, to authenticate users. Additionally, MBBS includes a Generative Adversarial Network (GAN) powered data augmentation architecture to enhance overall accuracy and security. To this end, we aim to demonstrate the effectiveness of MBBS firstly on ''real'' users' samples and later on the augmented samples comprising of ''real'' and ''GAN-generated'' samples, on a real Android device. MBBS is likely to enjoy maximum usability since it does not require users to remember any secret. Further, it exploits the users' familiarity with the processes and it increases the accuracy (by employing GAN in real time) without requiring a large sample size from users. Preliminary results in terms of performance, security, and usability analysis also show a positive opinion about our developed mechanism.
Data Sharing in Social Networks

In the context of multi-user cooperative systems and, in particular, in social networks, personal data is uploaded to user profiles and shared with other users. These data are often jointly owned and associated with different degrees of sensitivity according to the users. Controlling access to such multi-owner data, under the authority of different users, is challenging. Traditional access control policies are not expressive enough to determine whether a data disclosure meets the privacy expectations of the different involved parties. In this work, we propose a fine-grained access control model for multi-user cooperative systems and apply it to the context of social networks. We consider compound objects and extend attribute-based access control with provenance information to specify additional access control constraints. We also present a prototype implementation and provide an experimental evaluation to demonstrate the feasibility of the proposed model.
Vehicle Anomaly Detection by Attention-Enhanced Temporal Convolutional Network

Intelligent transportation system (ITS) is the general trend in the field of transportation. As an important development direction to promote the realization of ITS, connected and automated vehicles (CAVs) have attracted extensive attention from many scholars. However, most CAVs system are vulnerable to various spoofing attacks. To solve this problem, an Attention-Enhanced Temporal Convolutional Network (TCN) for anomaly detection of vehicle data is proposed in this paper. Firstly, the Squeeze-and-Excitation Networks (SE-net) is used to automati-cally obtain the importance degree of each feature channel, and then according to this importance degree, the useful features are promoted and the features that are not useful for the current task are suppressed. Then, the multi-layer TCN model with attention branch is used to fully extract the data features, and the abnormal detection results are obtained. To verify the proposed model, we conducted experiments on the SPMD dataset. The experimental results show that Attention-Enhanced TCN has good detection performance for vehicle abnormal data, which is superior to the current state-of-the-arts.
Trajectory tracking control strategy based on intelligent optimization for space underactuated manipulator

For the space underactuated manipulator(SUM), this paper researches a intelligent optimization algorithm to plan the trajectory and design the tracking controller. The SUM system is composed of two planar underactuated sub-systems, which can realize horizontal and vertical movements respectively. The control target is to move the end point to the specified position, which means that all joints of the two sub-systems are controlled to the target state. For this control target, we design the feasible trajectory for the active links of the SUM by using particle swarm optimization(PSO) to achieve all joints reaching the specified target state simultaneously. Finally, we design the sliding mode variable structure controller for the active joints to track the planning trajectory. Based on the above control strategy, the control objective of the SUM is realized. The numerical simulation results demonstrate the effectiveness of the proposed control strategy.
A Practical Vision-Aided Multi-Robot Autonomous Navigation using Convolutional Neural Network

In this paper, a low-cost practical approach for the collision avoidance of a multi-robot system by using a single camera. A convolutional neural network (CNN) is applied to obtain an estimation of the depth of the image at the output of a monocular camera, assisting the team of mobile robots to detect obstacles in an unknown environment, determining navigation strategies, overcoming the limitation of the onboard LiDAR sensor. An avoidance controller was designed over a modified artificial potential field (APF) method, leading robots to avoid obstacles to reach the goal point. This paper provides an alternative solution for range measuring and environment sensing, replacing common distance sensors such as LiDAR sensors and ultrasonic sensors. The camera captures more data about the environment while being relatively cheaper than most sensors. An open-source CNN machine learning model called MiDaS is applied to help estimate the depth of detected obstacles from the input image. Simulations and experiments with three TurtleBot3 mobile robots were conducted to validate the proposed algorithms. Experimental studies have been carried out to test the effectiveness of the proposed approach in the paper.
Research on Dynamic Labels in Network Pruning

Convolutional neural network compression technology plays an extremely important role in model transplantation and deployment, especially in mobile and embedded hardware platforms with small memory and low computing power, compression technology is even more critical. Convolutional neural network channel pruning technology has developed rapidly in recent years, and a number of excellent pruning algorithms have emerged. The channel pruning technology has gradually developed from the earliest static pruning to dynamic pruning, which adopts different pruning schemes for different inputs. However, the current dynamic pruning scheme needs to introduce multiple modules to predict the mask to prune the feature maps, and some schemes also introduce multiple hyperparameters in the loss function to balance the model accuracy and pruning rate, which leads to The model has difficulty converging during training. We propose a dynamic pruning method, each convolution structure configures a simple prediction module, and generating dynamic labels through the input's norm and similarity to guide the prediction module training, which will not bring new parameters to the loss function. We conducted related experiments on multiple models on the Cifar10 datasets. The experiments on ResNet56 show that our scheme is 1.3% higher than the most advanced scheme in terms of compression rate under the premise of the same accuracy.
Multimode DALSTM model for anomaly detection of nuclear reactor core

The nuclear reactor core is a life-critical system, whose reliable operation ensures the safety of a nuclear power plant. The reactor core temperature is a direct representation of the reactor core state. Therefore, the detection and analysis of core temperature anomalies is of great importance. However, multiple temperature variables are nonlinear and complexly coupled, showing different distribution characteristics with the variations of operating condition. For this reason, a multi-mode anomaly monitoring strategy for core temperature in nuclear power plant is proposed. To realize the nonlinear feature extraction of multiple temperature variables, a deep auto-encoder and long short-term memory (DALSTM) network are constructed. Considering the complex temperature distribution characteristics, the nonlinear features are clustered to divide the data into different subspaces. In this way, the distribution features are similar in the same subspace. Finally, local DALSTM models of core temperature in different subspaces are developed, and a multimode DALSTM monitoring strategy is designed. The monitoring statistics of the local models reflect the detailed variations of the process and achieve fine-grained detection of anomalous behavior. The effectiveness of the proposed model is verified by real operation data. The experimental results show that the method can achieve fast and accurate anomaly detection for reactor core temperature.
A StyleGAN3-Based Data Augmentation Method for Ceramic Defect Detection

Deep learning is currently the mainstream method for ceramic defect detection, and it requires a large number of defect samples to train the network. However, collecting these defect samples is very time-consuming and deep learning suffers from few-shot learning problems. In this study, a StyleGAN3-based data augmentation method for ceramic defect detection was proposed which can generate ceramic defect samples and thus reduce the data collection work. Experiments show that our method uses less training time, has a more stable training process, and can improve the accuracy of the detection network.
Differential Privacy Framework using Secure Computing on Untrusted Servers

Smart cities use cutting-edge technologies to monitor the real world and improve various aspects of human life, such as traffic, agriculture, electric power grid, and medical care. However, protecting users' privacy is crucial in smart-city applications for service coordination. Any leakage of users' sensitive information may prevent users from sharing their data, and thereby hinder the progress of smart cities. Differential privacy (DP) is one of the approaches that protects sensitive information in a dataset, and strongly resists background knowledge attacks. This study proposes a central differential privacy scheme using secure computing methods. After receiving the ciphertext of the data, the central data curator in this system generates a perturbed dataset in the encrypted domain. The unmodified data of each user is not exposed to the data curator, therefore the data users do not need a trusted data curator. Moreover, compared with the existing models in which significant amount of noise is locally added before sending the data to the data curator, the proposed scheme adds considerably less noise while maintaining the same privacy requirement, resulting in enhanced data utility. Model evaluation demonstrates that our system effectively protects the privacy of individuals and provides accurate data for statistical analysis.
A Lite Fireworks Algorithm with Fractal Dimension Constraint for Feature Selection

As the use of robotics becomes more widespread, the huge amount of vision data leads to a dramatic increase in data dimensionality. Although deep learning methods can effectively process these high-dimensional vision data. Due to the limitation of computational resources, some special scenarios still rely on traditional machine learning methods. However, these high-dimensional visual data lead to great challenges for traditional machine learning methods. Therefore, we propose a Lite Fireworks Algorithm with Fractal Dimension constraint for feature selection (LFWA+FD) and use it to solve the feature selection problem driven by robot vision. The “LFWA+FD” focuses on searching the ideal feature subset by simplifying the fireworks algorithm and constraining the dimensionality of selected features by fractal dimensionality, which in turn reduces the approximate features and reduces the noise in the original data to improve the accuracy of the model. The comparative experimental results of two publicly available datasets from UCI show that the proposed method can effectively select a subset of features useful for model inference and remove a large amount of noise noise present in the original data to improve the performance.
Online Learning-based Trust Prediction for Reliable and Energy-efficient Transmission

Industrial wireless communication networks (IWCNs) have been widely applied in data interaction between field sensors and edge computing units. Nevertheless, harsh industrial environments and malicious attacks cause data loss and delay, which makes it challenging to satisfy the reliability and timeliness requirements of IWCNs. Considering the limited communication energy budget, computation capacity, and multiple unreliable factors, traditional reliable transmission policies become less efficient for IWCNs. To handle these issues, in this paper, we introduce an online learning-based trust model and present a trust-delay aware energy-efficient transmission scheme (TDEETs) to reduce communication energy consumption while satisfying data reliability and control stability constraints. Firstly, a novel trust prediction mechanism based on online extreme learning machine (ELM) with a forgetting factor is proposed. Then, with the aid of low-complexity trust prediction, the optimal path selection strategy and retransmission policy are designed by online solving the optimization problem. Finally, numerical examples demonstrate the effectiveness of the proposed trust prediction mechanism and the transmission performance improvement using TDEETs.
Reinforcement Learning with Reward Shaping and Hybrid Exploration in Sparse Reward Scenes

High precision modeling in industrial systems is difficult and costly. Model-free intelligent control methods, represented by reinforcement learning, have been applied in industrial systems broadly. The hard evaluated of production states and the low value density of processing data causes sparse rewards, which lead to an insufficient performance of reinforcement learning. To overcome the difficulty of reinforcement learning in sparse reward scenes, a reinforcement learning method with reward shaping and hybrid exploration is proposed. By perfecting the rewards distribution in the state space of environment, the reward shaping can make the state-value estimation of reinforcement learning more accurate. By improving the rewards distribution in time dimension, the hybrid exploration can make the iteration of reinforcement learning more efficient and more stable. Finally, the effectiveness of the proposed method is verified by simulations.
FitJaw Mobile, a virtual reality device applied to dentistry: An analysis based on two patient treatments

Virtual reality (VR) has improved significantly in the last decade and has been applied to different fields, including medicine, dentistry, and physiotherapy. VR has been used for the innovative treatment of painful conditions, especially when traditional exercise therapies were unsuccessful because of patient noncompliance.The purpose of this study was to analyze the use of VR as an aid when using exercises to manage temporomandibular disorders (TMDs).Two White women diagnosed with TMDs of muscular origin (one with muscular pain and the other with reduced mouth opening) were referred to the Department of Prosthodontics of the University of Seville, where they were enrolled in an exercise program using the VR software program FitJaw Mobile. Both had been treated the previous year with an occlusal device for TMD of muscular origin, but their symptoms had not improved.For both patients, the functional movement limitation and the chronic pain improved noticeably.The use of VR when doing jaw exercises can improve outcomes and compliance.
Partial conditioning for inference of many-normal-means with Hölder constraints

Inferential models have been proposed for valid and efficient prior-free probabilistic inference. As it gradually gained popularity, this theory is subject to further developments for practically challenging problems. This paper considers the many-normal-means problem with the means constrained to be in the neighborhood of each other, formally represented by a Hölder space. A new method, called partial conditioning, is proposed to generate valid and efficient marginal inference about the individual means. It is shown that the method outperforms both a fiducial-counterpart in terms of validity and a conservative-counterpart in terms of efficiency. We conclude the paper by remarking that a general theory of partial conditioning for inferential models deserves future development.
Dictionary-enabled efficient training of ConvNets for image classification

Convolutional networks (ConvNets) are computationally expensive but well known for their performance on image data. One way to reduce their complexity is to explore inherited data sparsity. However, since the gradients involved in ConvNets require dynamic updates, applying data sparsity in the training step is not straightforward. Dictionary-based learning methods can be useful since they encode the original data in a sparse form. This paper proposes a new dictionary-based training paradigm for ConvNets by exploiting redundancy in the training data while keeping the distinctive features intact. The ConvNet is then trained on the reduced, sparse dataset. The new approach significantly reduces the training time without compromising accuracy. To the best of our knowledge, this is the first implementation of ConvNet on dictionary-based sparse training data. The proposed method is validated on three publicly available datasets â€“MNIST, USPS, and MNIST FASHION. The experimental results show a significant reduction of 4.5 times in the overall computational burden of vanilla ConvNet for all the datasets. Whereas the accuracy is intact at 97.21% for MNIST, 96.81% for USPS, and 88.4% for FASHION datasets. These results are comparable to state-of-the-art algorithms, such as ResNet-{18,34,50}, trained on the full training dataset.
Solving NP-Complete Akari games with deep learning

This work proposes a deep learning-based agent that solves 10 × 10 board size puzzle games of the NP-Complete Akari genre, while learning tabula rasa, that is, never exposed to any rules of the game. A six-model ensemble is trained to form a single solution policy. Lamp placements are chosen sequentially until the final resolution or failure. For the training and testing of the agent, over 3 million games were randomly produced with at least one known solution. The proposed agent solves 98.1% of Akari games that were never shown to it during training, revealing that the agent can learn implicit rules in NP-complete games. These approaches and training procedures proposed can guide future research on tabula rasa neural network agents for solving games or NP-Complete problems.
Exploring network reliability by predicting link status based on simplex neural network

Complex networks are graph-based structures with non-trivial topological features that frequently occur in real systems. Link prediction plays an important role in various real-world networks application, such as recommendation systems, protein structure prediction, packet forwarding strategy optimization, etc. The existing link prediction approaches mainly focus on superficial heuristic features, while ignoring high-order structure information. In this paper, we propose a deep-learning based model, named Weisfeiler–Lehman Simplex Neural Network (WL-SNN), which can learn the high-order simplex information of the network. In particular, we design a third-order Laplace operator to extract the simplicial features and utilize the graph convolutional network to compensate for the possible deficiencies of the model resulting from the single-channel features. Furthermore, we use the Weisfeiler–Lehman algorithm to extract closed subgraphs of the target, which significantly enhances the adaptability of the model to large-scale networks. Experimental results on six real-world networks show that our approach achieves comparable performance in the link prediction task as well as in the stability analysis of the network.
You Don't know Jack about Application Performance

You don't need to do a full-scale benchmark any time you have a performance or capacity planning problem. A simple measurement will provide the bottleneck point of your system: This example program will get significantly slower after eight requests per second per CPU. That's often enough to tell you the most important thing: if you're going to fail.