Reviewerly/exHarmony huggingface dataset:
Solving Generalized Polyomino Puzzles Using the Ising Model

In the polyomino puzzle, the aim is to fill a finite space using several polyomino pieces with no overlaps or blanks. Because it is an NP-complete combinatorial optimization problem, various probabilistic and approximated approaches have been applied to find solutions. Several previous studies embedded the polyomino puzzle in a QUBO problem, where the original objective function and constraints are transformed into the Hamiltonian function of the simulated Ising model. A solution to the puzzle is obtained by searching for a ground state of Hamiltonian by simulating the dynamics of the multiple-spin system. However, previous methods could solve only tiny polyomino puzzles considering a few combinations because their Hamiltonian designs were not efficient. We propose an improved Hamiltonian design that introduces new constraints and guiding terms to weakly encourage favorable spins and pairs in the early stages of computation. The proposed model solves the pentomino puzzle represented by approximately 2000 spins with >90% probability. Additionally, we extended the method to a generalized problem where each polyomino piece could be used zero or more times and solved it with approximately 100% probability. The proposed method also appeared to be effective for the 3D polycube puzzle, which is similar to applications in fragment-based drug discovery.
SemIE: Semantically-aware Image Extrapolation

We propose a semantically-aware novel paradigm to perform image extrapolation that enables the addition of new object instances. All previous methods are limited in their capability of extrapolation to merely extending the already existing objects in the image. However, our proposed approach focuses not only on (i) extending the already present objects but also on (ii) adding new objects in the extended region based on the context. To this end, for a given image, we first obtain an object segmentation map using a state-of-the-art semantic segmentation method. The, thus, obtained segmentation map is fed into a network to compute the extrapolated semantic segmentation and the corresponding panoptic segmentation maps. The input image and the obtained segmentation maps are further utilized to generate the final extrapolated image. We conduct experiments on Cityscapes and ADE20K-bedroom datasets and show that our method outperforms all baselines in terms of FID, and similarity in object co-occurrence statistics. Project url: https://semie-iccv.github.io/
Full-Velocity Radar Returns by Radar-Camera Fusion

A distinctive feature of Doppler radar is the measurement of velocity in the radial direction for radar points. However, the missing tangential velocity component hampers object velocity estimation as well as temporal integration of radar sweeps in dynamic scenes. Recognizing that fusing camera with radar provides complementary information to radar, in this paper we present a closed-form solution for the point-wise, full-velocity estimate of Doppler returns using the corresponding optical flow from camera images. Additionally, we address the association problem between radar returns and camera images with a neural network that is trained to estimate radar-camera correspondences. Experimental results on the nuScenes dataset verify the validity of the method and show significant improvements over the state-of-the-art in velocity estimation and accumulation of radar points.
Consumer preference analysis based on text comments and ratings: A multi-attribute decision-making perspective

With the popularity of social media, extracting consumer preferences from online consumer-generated content is of vital importance for product/service providers to develop tailored marketing strategies. However, existing approaches face difficulties analyzing consumer preferences over different attributes of alternatives (restaurants, hotels, etc.), which hinders product/service providers from comprehensively understanding consumer choice decisions. To address this issue, we solve for the consumer preferences over the attributes represented by attribute weights based on consumers' historical data, including text comments and overall ratings. Specifically, for each comment and a corresponding rating, we first employ sentiment analysis to calculate values of the attributes, and then develop a quadratic programming model to solve for the weights. Based on a stream of a consumer's text comments and overall ratings, we can correspondingly obtain a stream of weights indexed by the comment time. We then model this stream of weights as hesitant judgments and employ a hesitant multiplicative programming method to solve for the final weights that fit the consumer's preferences over attributes at the highest satisficing level. In the application of recommendation, our approach not only provides insights into the consumer's preferences but also has higher prediction power compared with some state-of-the-art methods.
The Role of Ethical AI in Fostering Harmonic Innovations that Support a Human-Centric Digital Transformation of Economy and Society

As widely recognized, artificial intelligence is deeply impacting on all aspects of our daily life. Technologies like machine learning, deep learning, intelligent process automation, natural language processing and understanding, intelligent document processing, conversational systems, chatbots, and computer vision are changing the way we work, spend our leisure and entertainment time, access health care systems to get treated, buy and use financial and insurance services, make our purchasing in physical and online stores, travel for work and tourism, live in our cities and homes, book a restaurant or order food.
EDA++: Estimation of Distribution Algorithms With Feasibility Conserving Mechanisms for Constrained Continuous Optimization

Handling nonlinear constraints in continuous optimization is challenging, and finding a feasible solution is usually a difficult task. In the past few decades, various techniques have been developed to deal with linear and nonlinear constraints. However, reaching feasible solutions has been a challenging task for most of these methods. In this article, we adopt the framework of estimation of distribution algorithms (EDAs) and propose a new algorithm (EDA++) equipped with some mechanisms to deal with nonlinear constraints. These mechanisms are associated with different stages of the EDA, including seeding, learning, and mapping. It is shown that, besides increasing the quality of the solutions in terms of objective values, the feasibility of the final solutions is guaranteed if an initial population of feasible solutions is seeded to the algorithm. The EDA with the proposed mechanisms is applied to two suites of benchmark problems for constrained continuous optimization and its performance is compared with some state-of-the-art algorithms and constraint-handling methods. Conducted experiments confirm the speed, robustness, and efficiency of the proposed algorithm in tackling various problems with linear and nonlinear constraints.
Sum-rank product codes and bounds on the minimum distance

The tensor product of one code endowed with the Hamming metric and one endowed with the rank metric is analyzed. This gives a code which naturally inherits the sum-rank metric. Specializing to the product of a cyclic code and a skew-cyclic code, the resulting code turns out to belong to the recently introduced family of cyclic-skew-cyclic codes. A group theoretical description of these codes is given, after investigating the semilinear isometries in the sum-rank metric. Finally, a generalization of the Roos and the Hartmann-Tzeng bounds for the sum rank-metric is established, as well as a new lower bound on the minimum distance of one of the two codes constituting the product code.
Client-optimized algorithms and acceleration for encrypted compute offloading

Homomorphic Encryption (HE) enables secure cloud offload processing on encrypted data. HE schemes are limited in the complexity and type of operations they can perform, motivating client-aided implementations that distribute computation between client (unencrypted) and server (encrypted). Prior client-aided systems optimize server performance, ignoring client costs: client-aided models put encryption and decryption on the critical path and require communicating large ciphertexts. We introduce Client-aided HE for Opaque Compute Offloading (CHOCO), a client-optimized system for encrypted offload processing. CHOCO reduces ciphertext size, reducing communication and computing costs through HE parameter minimization and through “rotational redundancy”, a new HE algorithm optimization. We present Client-aided HE for Opaque Compute Offloading Through Accelerated Cryptographic Operations (CHOCO-TACO), an accelerator for HE encryption and decryption, making client-aided HE feasible for even resource-constrained clients. CHOCO supports two popular HE schemes (BFV and CKKS) and several applications, including DNNs, PageRank, KNN, and K-Means. CHOCO reduces communication by up to 2948× over prior work. With CHOCO-TACO client enc-/decryption is up to 1094× faster and uses up to 648× less energy.
Oscillation Phenomenon in Trust-Region-Based Sequential Convex Programming for the Nonlinear Trajectory Planning Problem

The trust-region-based sequential convex programming (TSCP) method is designed to solve the nonlinear trajectory planning problem using successive linearization and trust region. The TSCP method has a good real-time performance suitable for onboard aerospace applications. However, the main challenge in practical applications is its poor convergence. In this article, we reveal an oscillation phenomenon, which is an important factor affecting the convergence. The main contribution of this article is threefold: 1) the oscillation phenomenon is proved to be an inherent property of the TSCP method for the nonlinear trajectory planning problem; 2) the oscillation condition that determines whether oscillation occurs is obtained; and 3) the oscillation-condition-based remedy to address the oscillation is presented. The effectiveness and robustness of the proposed remedy have been verified via a complex reentry trajectory planning problem.
White-box content camouflage attacks against deep learning

Deep learning has achieved remarkable success in a wide range of computer vision tasks. However, recent researches suggest that deep learning systems are vulnerable to a variety of attacks. Security concerns have been raised regarding the training or inference phase of deep learning models in the last few years, and the research field about the vulnerability of the pre-processing components in these models is still developing. In this paper, we systematically examine white-box content camouflage attacks on five types of pre-processing modules in deep learning systems: scaling, sharpening, Gamma correction, contrast adjustment, and saturation adjustment. We assume that an attacker's goal is to generate camouflage examples that show inconsistent visual semantics before and after pre-processing. Under the white-box setting (where the pre-processing algorithms and their parameters are known), we formulate content camouflage attacks as an optimization problem in which perceptual losses in the source and target images are smoothly calculated by a multi-scale discriminator to improve the camouflaging effect of the attack example. We evaluate our content camouflage attacks by conducting a series of experiments on two example groups as well as two real-world datasets, i.e., CIFAR-10 and FER-2013. The experimental results show that with good camouflaging ability, our attacks are effective against deep learning systems, and outperform prevalent scaling camouflage attacks by generating examples with better quality and a higher attack success rate. The proposed camouflage attacks are also extended to the four commonly used pre-processing algorithms, and yield good results. Furthermore, we discuss the effect of varying the parameters of several image pre-processing algorithms under our attacks and analyze' the reasons for their vulnerability.
A Video Analytics System for Person Detection Combined with Edge Computing

Ensuring citizens’ safety and security has been identified as the number one priority for city authorities when it comes to the use of smart city technologies. Automatic understanding of the scene, and the associated provision of situational awareness for emergency situations, are able to efficiently contribute to such domains. In this study, a Video Analytics Edge Computing (VAEC) system is presented that performs real-time enhanced situation awareness for person detection in a video surveillance manner that is also able to share geolocated person detection alerts and other accompanied crucial information. The VAEC system adopts state-of-the-art object detection and tracking algorithms, and it is integrated with the proposed Distribute Edge Computing Internet of Things (DECIoT) platform. The aforementioned alerts and information are able to be shared, though the DECIoT, to smart city platforms utilizing proper middleware. To verify the utility and functionality of the VAEC system, extended experiments were performed (i) in several light conditions, (ii) using several camera sensors, and (iii) in several use cases, such as installed in fixed position of a building or mounted to a car. The results highlight the potential of VAEC system to be exploited by decision-makers or city authorities, providing enhanced situational awareness.
Robust cubature Kalman filter based on maximum correntropy and resampling-free sigma-point update framework

The problem of nonlinear filtering for GNSS/INS under non-Gaussian noise is studied in this paper, where an improved maximum correntropy cubature Kalman filter (MCCKF) is proposed by utilizing resampling-free sigma-point update framework (SUF). The MCCKF has better robustness than CKF in case non-Gaussian noise appears. However, only the Gaussian moments are circulated in MCCKF, which is less efficient when the nonlinearity of dynamic function is strong. The proposed algorithm named RMCCKF, imports the re-scaled measurement and state prediction covariance that produced by maximum correntropy criterion into the direct construction of posterior sigma points. By constraining the posterior covariance of MCCKF and modifying the sigma points directly, the resampling-free SUF spreads extra information of dynamic model over the posterior sigma points update. The proposed algorithm is verified by using numerical simulation and field test on GNSS/INS. Experiment results indicate that, RMCCKF achieves better performance than CKF and MCCKF, especially when the filtering problem is of high dimension. On the GNSS/INS of land vehicle navigation, RMCCKF not only improves the position but also reduces the heading error from 1.77 degree to 0.26 degree, compared with MCCKF.
Collision Avoidance Considering Iterative Bézier Based Approach for Steep Slope Terrains

The Agri-Food production requirements needs a more efficient and autonomous processes, and robotics will play a significant role in this process.Deploying agricultural robots on the farm is still a challenging task.Particularly in slope terrains, where it is crucial to avoid obstacles and dangerous steep slope zones.Path planning solutions may fail under several circumstances, as the appearance of a new obstacle.This work proposes a novel open-source solution called AgRobPP-CA to autonomously perform obstacle avoidance during robot navigation.AgRobPP-CA works in real-time for local obstacle avoidance, allowing small deviations, avoiding unexpected obstacles or dangerous steep slope zones, which could impose a fall of the robot.Our results demonstrated that AgRobPP-CA is capable of avoiding obstacles and high slopes in different vineyard scenarios, with low computation requirements.For example, in the last trial, AgRobPP-CA avoided a steep ramp that could impose a fall to the robot.
Human-robot interaction in higher education for predicting student engagement

In recent years, Human-Robot Interaction (HRI) has become emerging innovation in robotics research and development. Research in Human-Robot Interaction (HRI) aims to understand, design, and evaluate robotic systems intended for human use. Robots and humans must communicate for interaction to occur. Human-robot communication can take various forms, but the proximity of the two parties heavily influences these forms.HRI innovations entail learning, designing, and evaluating robotics systems that communicate with people. Robots connecting and speaking with humans are becoming increasingly common at home, customer service, education, and healthcare. Despite the fact that there are now many robotic instruments that teach students how to learn various subjects and activities, efficient integration of the prediction model is still under development. Therefore, this research introduces a student engagement prediction model using HRI (SEPM-HRI) in higher education. This model uses the supervised learning algorithm for student engagement prediction. The experimental results show that the proposed model can implement in a real-time scenario with the expected prediction accuracy of 98.78%. The experimental results show that the proposed SEPM-HRImodel enhances an accuracy ratio of 98.78%, interaction ratio of 95.62%, prediction ratio of 96.35%, a learning rate of 94.26%, a student engagement ratio of 93.14%, and a student participation ratio of 92.86%. This model gives the mean square error rate of 30.21% compared to other existing approaches.
Contact-Aware Retargeting of Skinned Motion

This paper introduces a motion retargeting method that preserves self-contacts and prevents interpenetration. Self-contacts, such as when hands touch each other or the torso or the head, are important attributes of human body language and dynamics, yet existing methods do not model or preserve these contacts. Likewise, interpenetration, such as a hand passing into the torso, are a typical artifact of motion estimation methods. The input to our method is a human motion sequence and a target skeleton and character geometry. The method identifies self-contacts and ground contacts in the input motion, and optimizes the motion to apply to the output skeleton, while preserving these contacts and reducing interpenetration. We introduce a novel geometry-conditioned recurrent network with an encoder-space optimization strategy that achieves efficient retargeting while satisfying contact constraints. In experiments, our results quantitatively outperform previous methods and we conduct a user study where our retargeted motions are rated as higher-quality than those produced by recent works. We also show our method generalizes to motion estimated from human videos where we improve over previous works that produce noticeable interpenetration.
Learning to Learn Variational Quantum Algorithm

Variational quantum algorithms (VQAs) use classical computers as the quantum outer loop optimizer and update the circuit parameters to obtain an approximate ground state. In this article, we present a meta-learning variational quantum algorithm (meta-VQA) by recurrent unit, which uses a technique called "meta-learner." Motivated by the hybrid quantum-classical algorithms, we train classical recurrent units to assist quantum computing, learning to find approximate optima in the parameter landscape. Here, aiming to reduce the sampling number more efficiently, we use the quantum stochastic gradient descent method and introduce the adaptive learning rate. Finally, we deploy on the TensorFlow Quantum processor within approximate quantum optimization for the Ising model and variational quantum eigensolver for molecular hydrogen (H2), lithium hydride (LiH), and helium hydride cation (HeH+). Our algorithm can be expanded to larger system sizes and problem instances, which have higher performance on near-term processors.
Joint Estimation of Azimuth and Distance for Far-Field Multi Targets Based on Graph Signal Processing

Target position estimation is one of the important research directions in array signal processing. In recent years, the research of target azimuth estimation based on graph signal processing (GSP) has sprung up, which provides new ideas for the Direction of Arrival (DoA) application. In this article, by extending GSP-based DOA to joint azimuth and distance estimation and constructing a fully connected graph signal model, a multi-target joint azimuth and distance estimation method based on GSP is proposed. Firstly, the fully connection graph model is established related to the phase information of a linear array. For the fully connection graph, the Fourier transform method is used to solve the estimated response function, and the one-dimensional estimation of azimuth and distance is completed, respectively. Finally, the azimuth and distance estimation information are combined, and the false points in the merging process are removed by using CLEAN algorithm to complete the two-dimensional estimation of targets. The simulation results show that the proposed method has a smaller mean square error than the Multiple Signal Classification (MUSIC) algorithm in azimuth estimation under the condition of a low signal-to-noise ratio and more accurate response values than the MUSIC algorithm in distance estimation under any signal-to-noise ratio in multi-target estimation.
Keep CALM and Improve Visual Feature Attribution

The class activation mapping, or CAM, has been the cornerstone of feature attribution methods for multiple vision tasks. Its simplicity and effectiveness have led to wide applications in the explanation of visual predictions and weakly-supervised localization tasks. However, CAM has its own shortcomings. The computation of attribution maps relies on ad-hoc calibration steps that are not part of the training computational graph, making it difficult for us to understand the real meaning of the attribution values. In this paper, we improve CAM by explicitly incorporating a la-tent variable encoding the location of the cue for recognition in the formulation, thereby subsuming the attribution map into the training computational graph. The resulting model, class activation latent mapping, or CALM, is trained with the expectation-maximization algorithm. Our experiments show that CALM identifies discriminative attributes for image classifiers more accurately than CAM and other visual attribution baselines. CALM also shows performance improvements over prior arts on the weakly-supervised object localization benchmarks. Our code is available at https://github.com/naver-ai/calm.
Deep Permutation Equivariant Structure from Motion

Existing deep methods produce highly accurate 3D reconstructions in stereo and multiview stereo settings, i.e., when cameras are both internally and externally calibrated. Nevertheless, the challenge of simultaneous recovery of camera poses and 3D scene structure in multiview settings with deep networks is still outstanding. Inspired by projective factorization for Structure from Motion (SFM) and by deep matrix completion techniques, we propose a neural network architecture that, given a set of point tracks in multiple images of a static scene, recovers both the camera parameters and a (sparse) scene structure by minimizing an unsupervised reprojection loss. Our network architecture is designed to respect the structure of the problem: the sought output is equivariant to permutations of both cameras and scene points. Notably, our method does not require initialization of camera parameters or 3D point locations. We test our architecture in two setups: (1) single scene reconstruction and (2) learning from multiple scenes. Our experiments, conducted on a variety of datasets in both internally calibrated and uncalibrated settings, indicate that our method accurately recovers pose and structure, on par with classical state of the art methods. Additionally, we show that a pre-trained network can be used to reconstruct novel scenes using inexpensive fine-tuning with no loss of accuracy.