Reviewerly/exHarmony huggingface dataset:
Quantum Geometric Transformation Based on QIRHSI Quantum Color images

The negative effects caused by geometric distortion can be removed to the maximum extent possible through appropriate geometric transformations, allowing us to focus on the image content itself in subsequent processing and recognition. Therefore, geometric transformations are often used as a pre-processing step for other image processing applications. In this paper, quantum algorithms are designed to implement geometric transformations, including two-point swapping, circular translation, flipping transformations and right-angle rotation, across a quantum image representation QIRHSI (Quantum Image Representation based on HSI color space) which is built on HSI (Hue-Saturation-Intensity) color space. The above geometric transformations are realized by quantum circuits composed of elementary quantum gates. By analyzing the complexity of the fundamental quantum gates needed for the above geometric transformations, it is found that the global transformations (circular translation, flip transformation and right-angle rotation) are lower than the local transformation (two-point swapping). The proposed geometric transforms are used to facilitate the applications of quantum images with low complexity and high efficiency.
Hands-On, Instructor-Light, Checked and Tracked Training of Trainers in Java Fork-Join Abstractions

As part of a 3-day workshop on training faculty members in concurrency, we developed a module for hands-on training in Java Fork-Join abstractions that had several related novel pedagogical and technical components: (1) Source and runtime checks that (a) tested whether test-aware code created by the trainees met the expected requirements and (b) logged their results in the local file system and the IBM cloud. (2) Editable worked example code along with a guide on how to understand the underlying concepts behind the code and experiment with the code. (3) The ability to follow the guide (a) synchronously, with graduate student help, in a session devoted to this module, and (b) asynchronously, on one's own, before or after the synchronous session. (4) Assignments trainees could do after experimenting with the worked example. (5) Zoom recording of the entire synchronous session. Fourteen faculty members across the country attended the session and had varying amounts of knowledge of Java and automatic assessment. Data gathered from check logs and a Zoom recording, together with novel visualizations of them, provide information to evaluate our pedagogical model and differentiate the participants.
How different is different? Systematically identifying distribution shifts and their impacts in NER datasets

Abstract When processing natural language, we are frequently confronted with the problem of distribution shift. For example, using a model trained on a news corpus to subsequently process legal text exhibits reduced performance. While this problem is well-known, to this point, there has not been a systematic study of detecting shifts and investigating the impact shifts have on model performance for NLP tasks. Therefore, in this paper, we detect and measure two types of distribution shift, across three different representations, for 12 benchmark Named Entity Recognition datasets. We show that both input shift and label shift can lead to dramatic performance degradation. For example, fine-tuning on a wide spectrum dataset (Ontonotes) and testing on an email dataset (Cerec) that shares labels leads to a 63-points drop in F1 performance. Overall, our results indicate that the measurement of distribution shift can provide guidance to the amount of data needed for fine-tuning and whether or not a model can be used "off-the-shelf" without subsequent fine-tuning. Finally, our results show that shift measurement can play an important role in NLP model pipeline definition.
An image encryption algorithm based on multi-level discrete wavelet transform and hyperchaotic system

An encryption scheme based on multi-level discrete wavelet transform (DWT) and hyperchaotic system is proposed in this paper. Firstly, several random key streams are produced by a 4D hyperchaotic system. Secondly, the multiple wavelet transform is applied to obtain wavelet coefficients that carry the approximate information of the image. Thirdly, the wavelet coefficients are encrypted using the random chaotic sequences and then converted to an image via multi-level inverse discrete wavelet transform (IDWT). Finally, a diffusion process is applied to the pixels to enhance the randomness in the cipher image. The security analysis results show that our scheme is highly secure and has good statistical properties.
A Fine-grained Attention Mechanism Model of Knowledge Graph for Recommendation

Data sparsity and cold start are very challenging and popular problems in the field of recommendation. This paper establishes a personalized model of knowledge graph fine-grained attention mechanism for recommendation(KGFARS). At the user side, the attention mechanism is introduced to capture the weight assigned by use to item, and the information of items is aggregated to learn user’s embedding. A fine-grained attention mechanism algorithm is proposed on the item side, which essentially consists of a two-stage preference: 1) the preference of user and relationship which between a given item and a neighborhood; 2) the preference of user and corresponding neighborhood under the relationship. In addition, a multi-channel information aggregation function is designed for the fusion process of item and neighborhoods, which allows similar entities to transmit more information and improve the generalization ability of the recommendation model. Finally, based on the Top-k recommendation task, we select BPR loss for training to verify the effectiveness and potential of the proposed personalized recommendation model on public data sets.
A Multi-objective Evolutionary Algorithm for Formation Structure Optimization of Unmanned Aerial Vehicles Performing Search Task

In order to solve the target search problem of the multiple Unmanned Aerial Vehicles (UAVs) under the condition of implicit communication, this paper firstly establishes a multi-objective optimization model of formation structure. The objective of this model is to maximize the invulnerability of information transmission topology and formation search efficiency. The constraints such as topology connectivity and safety distance are considered. Then, this paper proposes a multi-objective evolutionary algorithm CLUinD&O-MOEA based on NSGA-II, which combines the clustering method and improved crowding distance, in order to improve the efficiency of exploring the irregular frontier of this problem. In the early stage of the iteration process, the Self-Organizing Maps (SOM) method is used to cluster the population in the decision space, and each mating male parent is selected from the same cluster to improve the global search ability of the algorithm. At the end of the iteration process, the population is clustered in the target space, so that the algorithm can converge to the real PF faster. At the same time, this paper improves the calculation method of crowding distance, which combines the crowding distance of the solution in the target space and the decision space to improve the diversity of the population. In addition, a disturbance mechanism was designed for CLUinD&O-MOEA to solve the redundancy problem caused by the symmetry of formation geometry. Finally, the experimental results show that CLUinD&O-MOEA is better than the comparison algorithms CA-MOEA and NSGA-II-DE in solving the formation structure optimization problem, and can effectively improve the solving efficiency.
Modified Gaussian Sum Mixed-Degree CKF for Relative Motion Estimation of Noncooperative Spacecraft

To improve the capability of acquiring relative motion information between non-cooperative spacecraft in proximity capture, the problem of relative state estimation is investigated in this paper. Considering the kinematic coupling caused by the transformation matrix introduced in the observation equation when the measurable center of form does not coincide with the unknown center of mass, the dynamical and measurement models are established respectively first. Then, in the presence of a strong nonlinear model and non-Gaussian measurement noise, a modified Gaussian sum mixed-degree spherical simplex-radial cubature Kalman filter (GS-MSSRCKF) is proposed to estimate the relative motion. Furthermore, the algorithm is modified to avoid the fault of GS-MSSRCKF caused by excessive measurement outliers. Finally, when GS-MSSRCKF is compared to a few filtering methods that have been previously suggested, the simulation results show an improvement in accuracy and a decrease in calculation time.
Ridge-regression Echo State Network for Effluent Ammonia Nitrogen Prediction

Echo state network (ESN) is trained by linear regression, which is an effective training method. However, ESN may have the ill-posed phenomenon. To solve this phenomenon, a ridge-regression echo state network (RESN) is given. We use the ridge-regression method to train RESN. The selection of regularization parameter is leave-one-out cross validation. The RESN can well deal with the ill-posed phenomenon. Experimental results show that RESN has high accuracy than some ESNs for effluent ammonia nitrogen.
Seeker Optimization Algorithm based Path Planning of Warehouse Robot

Path optimization is one of the most important techniques for improving the efficiency of intelligent warehouse system, and which is considered by using Seeker Optimization Algorithm (SOA) in this paper. Firstly, an intelligent warehouse environment is constructed based on grid map. Then, A dichotomous randomized intermediate point selection strategy combining with a multi-directional search mechanism for path search is adopted in SOA, which is proposed for avoiding the problems of poor adaptation of the algorithm to specific maps and path redundancy. Finally, simulation validates effectiveness of the SOA by comparing it with Ant Colony optimization (ACO) and genetic algorithms (GA).
Multi-Robot Exploration in Unknown Environments via Multi-Agent Deep Reinforcement Learning

Mobile robots have been widely used in hazardous environments to obtain information about surroundings for humans. The volume and efficiency of sensing data can be significantly increased if multiple mobile robots collaboration are exploited. In recent years, deep learning and reinforcement learning techniques have been applied to the field of robotics, which perform well in many tasks including exploration in unknown environments. In this paper, to address the multi-robot exploration problem, a multi-agent deep reinforcement learning (MADRL) based method with the centralized training and decentralized execution (CTDE) architecture was proposed. Extensive experimental results show that our method significantly improves the multi-robot exploration performance in unknown environments. On average, the proposed method uses 12.9% less travel distance and reduces 5.8% overlapping areas when compared with traditional methods.
Research on particle swarm optimized image alignment algorithm based on improved SURF features

Image feature extraction and matching is an essential work in computer image processing. This technique aligns two or more images of the same scene obtained by different sensors under different or the same imaging conditions to determine the relationship between them. Based on the SURF algorithm, this paper adopts the local two-dimensional entropy to improve the uniqueness of the extracted feature points. Then the Euclidean distance in the traditional algorithm is changed to the Manhattan distance, and the distance evaluation function is introduced for pre-processing. Finally, the particle swarm algorithm is applied to optimize the feature point matching process. Through theoretical analysis and experimental contrast, the improved SURF algorithm has significantly improved both matching speed and accuracy, demonstrating the algorithm's reliability and effectiveness.
An Efficient Transformer-based Approach for Joint Nuclei Detection and Segmentation in Whole Slide Tissue Images

The detection and segmentation of cell nuclei in whole slide tissue images plays an important role in disease diagnosis and treatment. Automatic detection and segmentation of nuclei is very challenging due to high nuclei density, low contrast, overlapping and adhesion between cells. Recently, Transformer-based object detection and instance segmentation methods have made great progress on traditional computer vision datasets. These Transformer-based approaches are effective by removing the need for many hand-crafted components in the network, and post-processing steps like non-maximum suppression (NMS). However, such approaches consume tremendous amount of memory in detection and segmentation of cell nuclei in whole slide tissue images due to large number of cells in the images. Also, those methods may suffer from inferior performance on small cell instances. Inspired by Deformable DETR which makes use of small set of key sampling points in the attention module to reduce the computation and the usage of multi-scale feature maps, we propose an efficient Transformer-based approach for joint nuclei detection and segmentation in whole slide tissue images. Specifically, In Transformer decoder, it directly outputs the object detection results and instance segmentation masks. We evaluate our proposed method on the public MoNuSeg dataset, and the experimental results show that our method achieves promising performance on nuclei detection and segmentation.
Fast computation of permutation equivariant layers with the partition algebra

Linear neural network layers that are either equivariant or invariant to permutations of their inputs form core building blocks of modern deep learning architectures. Examples include the layers of DeepSets, as well as linear layers occurring in attention blocks of transformers and some graph neural networks. The space of permutation equivariant linear layers can be identified as the invariant subspace of a certain symmetric group representation, and recent work parameterized this space by exhibiting a basis whose vectors are sums over orbits of standard basis elements with respect to the symmetric group action. A parameterization opens up the possibility of learning the weights of permutation equivariant linear layers via gradient descent. The space of permutation equivariant linear layers is a generalization of the partition algebra, an object first discovered in statistical physics with deep connections to the representation theory of the symmetric group, and the basis described above generalizes the so-called orbit basis of the partition algebra. We exhibit an alternative basis, generalizing the diagram basis of the partition algebra, with computational benefits stemming from the fact that the tensors making up the basis are low rank in the sense that they naturally factorize into Kronecker products. Just as multiplication by a rank one matrix is far less expensive than multiplication by an arbitrary matrix, multiplication with these low rank tensors is far less expensive than multiplication with elements of the orbit basis. Finally, we describe an algorithm implementing multiplication with these basis elements.
Unwrapping phase method based on rearranged periodic fragments

For 3D reconstruction method Fringe Projection Profilometry (FPP), the calculation of absolute phase is a very important step, and the accuracy of absolute phase directly affects the accuracy of 3D reconstruction. The phase unwrapping problem can be roughly divided into two steps: calculating the wrapping phase and calculating the periods. The former can be solved by using trigonometry, while the latter requires additional consideration. In this paper, a waveform encoding method is proposed, which independently calculates the period each pixel through decoding without additional information, such as surrounding area information or geometric information. In addition, this paper also extends the method to a certain extent, which can be used in a wider range of scenarios. (Abstract)
Improving Accented Speech Recognition with Multi-Domain Training

Thanks to the rise of self-supervised learning, automatic speech recognition (ASR) systems now achieve near-human performance on a wide variety of datasets. However, they still lack generalization capability and are not robust to domain shifts like accent variations. In this work, we use speech audio representing four different French accents to create fine-tuning datasets that improve the robustness of pre-trained ASR models. By incorporating various accents in the training set, we obtain both in-domain and out-of-domain improvements. Our numerical experiments show that we can reduce error rates by up to 25% (relative) on African and Belgian accents compared to single-domain training while keeping a good performance on standard French.
A Robust Estimation Method for Automatic Registration of Remote Sensing Imagery

Image registration is a fundamental process in the processing and analysis of remote sensing imagery. Given the enormous increase in the availability and variety of modern remote sensors, automated registration of satellite images has become an important pre-processing task. A wide range of registration techniques has been developed for various types of applications and data. The three main stages involved in image registration are the generation of matching points, estimation of mapping functions, and resampling. The classic robust estimator, random sample consensus (RANSAC) algorithm has been the method of choice to accomplish this task. However, the speed of convergence of RANSAC depends on a number of factors like percentage of inliers, complexity of the transformation model etc. and there have been a few improved versions of RANSAC in literature. The ability to deduce an accurate transformation model in the presence of outliers is crucial to achieve accurate image registration results. This paper presents a new robust estimator, ProLoSAC, which performs filtering of matching points to estimate the mapping function accurately while maintaining the speed of RANSAC algorithm for image registration. We have applied the proposed algorithm for the automatic registration of multi-sensor multi-resolution remote sensing images namely, Resoucesat-1 AWIFS, GeoEye-1, Landsat-8, Pleiades-1A and AISA hyperspectral images. The validation of results carried out using various error metrics indicates that the proposed algorithm is superior to the existing approach for satellite image registration.
Optimal UAV Hangar Locations for Emergency Services Considering Restricted Areas

With unmanned aerial vehicle(s) (UAV), swift responses to urgent needs (such as search and rescue missions or medical deliveries) can be realized. Simultaneously, legislators are establishing so-called geographical zones, which restrict UAV operations to mitigate air and ground risks to third parties. These geographical zones serve particular safety interests but they may also hinder the efficient usage of UAVs in time-critical missions with range-limiting battery capacities. In this study, we address a facility location problem for up to two UAV hangars and combine it with a routing problem of a standard UAV mission to consider geographical zones as restricted areas, battery constraints, and the impact of wind to increase the robustness of the solution. To this end, water rescue missions are used exemplary, for which positive and negative location factors for UAV hangars and areas of increased drowning risk as demand points are derived from open-source georeferenced data. Optimum UAV mission trajectories are computed with an A* algorithm, considering five different restriction scenarios. As this pathfinding is very time-consuming, binary occupancy grids and image-processing algorithms accelerate the computation by identifying either entirely inaccessible or restriction-free connections beforehand. For the optimum UAV hangar locations, we maximize accessibility while minimizing the service times to the hotspots, resulting in a decrease from the average service time of 570.4 s for all facility candidates to 351.1 s for one and 287.2 s for two optimum UAV hangar locations.
Solving Rehabilitation Scheduling problems via a Two-Phase ASP approach

A core part of the rehabilitation scheduling process consists of planning rehabilitation physiotherapy sessions for patients, by assigning proper operators to them in a certain time slot of a given day, taking into account several legal, medical and ethical requirements and optimizations, e.g., patient's preferences and operator's work balancing. Being able to efficiently solve such problem is of upmost importance, in particular after the COVID-19 pandemic that significantly increased rehabilitation's needs. In this paper, we present a two-phase solution to rehabilitation scheduling based on Answer Set Programming, which proved to be an effective tool for solving practical scheduling problems. We first present a general encoding, and then add domain specific optimizations. Results of experiments performed on both synthetic and real benchmarks, the latter provided by ICS Maugeri, show the effectiveness of our solution as well as the impact of our domain specific optimizations. Under consideration in Theory and Practice of Logic Programming (TPLP).
Bird’s Nest Detection Algorithm for Transmission Line Towers Based on Deep Learning

The transmission line’s ability to operate safely will be substantially compromised by the presence of the nest of birds atop the tower of either a transmission line. During recent decades, A popular area of study now is the efficient location and detection of bird nests on transmission line towers. As deep learning technology has advanced and hardware processing power has increased, using deep learning technology can be gained by utilizing deep learning technology for bird’s nest target recognition. This research suggests a technique for detecting bird nests on transmission system towers through YOLOv5. The method consists of input, backbone, neck, and prediction. By introducing pre-training weights and various data enhancement methods, After multiple network training sessions, it is feasible to find and recognize the bird’s nest on the power system’ tower. The outcomes reveal that the YOLOv5 model’s mean average precision (mAP) of the bird’s nest on the top of power tower can reach 85.6%, and the model is only 14.4 MB in size. This work suggested a YOLOv5-based method for determining bird nests on transmission line towers that can meet the demands of high precision and low volume while also showing promise for future development.
A concrete product derivation in software product line engineering: a practical approach

Software Product Lines enable the development of a perfect family of products by reusing shared assets in a systematic manner.Product derivation is a critical activity in software product line engineering and one of the most pressing issues that a software product line must address.This work introduces an approach for automating the derivation of a product from a software product line.The software product line is part of a product family that evolved from a non-structured approach to managing variability.The automated derivation approach relies on product configurations and the refactoring of feature models.The approach was deployed and evaluated in the automotive domain using a real-world software product line.The outcome demonstrates that the approach generates a product in an automated and successful manner.