Reviewerly/exHarmony huggingface dataset:
GLT-T: Global-Local Transformer Voting for 3D Single Object Tracking in Point Clouds

Current 3D single object tracking methods are typically based on VoteNet, a 3D region proposal network. Despite the success, using a single seed point feature as the cue for offset learning in VoteNet prevents high-quality 3D proposals from being generated. Moreover, seed points with different importance are treated equally in the voting process, aggravating this defect. To address these issues, we propose a novel global-local transformer voting scheme to provide more informative cues and guide the model pay more attention on potential seed points, promoting the generation of high-quality 3D proposals. Technically, a global-local transformer (GLT) module is employed to integrate object- and patch-aware prior into seed point features to effectively form strong feature representation for geometric positions of the seed points, thus providing more robust and accurate cues for offset learning. Subsequently, a simple yet effective training strategy is designed to train the GLT module. We develop an importance prediction branch to learn the potential importance of the seed points and treat the output weights vector as a training constraint term. By incorporating the above components together, we exhibit a superior tracking method GLT-T. Extensive experiments on challenging KITTI and NuScenes benchmarks demonstrate that GLT-T achieves state-of-the-art performance in the 3D single object tracking task. Besides, further ablation studies show the advantages of the proposed global-local transformer voting scheme over the original VoteNet. Code and models will be available at https://github.com/haooozi/GLT-T.
Self Correspondence Distillation for End-to-End Weakly-Supervised Semantic Segmentation

Efficiently training accurate deep models for weakly supervised semantic segmentation (WSSS) with image-level labels is challenging and important. Recently, end-to-end WSSS methods have become the focus of research due to their high training efficiency. However, current methods suffer from insufficient extraction of comprehensive semantic information, resulting in low-quality pseudo-labels and sub-optimal solutions for end-to-end WSSS. To this end, we propose a simple and novel Self Correspondence Distillation (SCD) method to refine pseudo-labels without introducing external supervision. Our SCD enables the network to utilize feature correspondence derived from itself as a distillation target, which can enhance the network's feature learning process by complementing semantic information. In addition, to further improve the segmentation accuracy, we design a Variation-aware Refine Module to enhance the local consistency of pseudo-labels by computing pixel-level variation. Finally, we present an efficient end-to-end Transformer-based framework (TSCD) via SCD and Variation-aware Refine Module for the accurate WSSS task. Extensive experiments on the PASCAL VOC 2012 and MS COCO 2014 datasets demonstrate that our method significantly outperforms other state-of-the-art methods. Our code is available at https://github.com/Rongtao-Xu/RepresentationLearning/tree/main/SCD-AAAI2023.
Information Processing Capacity of Spintronic Oscillator

Physical reservoir computing is a framework that enables energy‐efficient information processing by using physical systems. Nonlinear dynamics in physical systems provide a computational capability that is unique to reservoirs. It is, however, difficult to find an appropriate task for a reservoir because of the complexity of nonlinear information processing. The information processing capacity has recently been used to clarify systematically the tasks that are solved by reservoirs; it quantifies the memory capacity of reservoirs in accordance with the order of nonlinearity. Herein, an experimental evaluation of the information processing capacity of a spintronic oscillator consisting of nanostructured ferromagnets is reported. The spintronic reservoir state is electrically manipulated by adding a delayed‐feedback circuit. The total capacity reaches a maximum of 5.6 at the edge of the echo state property. A trade‐off between the linear and nonlinear components of the capacity is also found. The result can be used to better understand the nonlinear information processing in reservoirs and to find good matches between reservoirs and tasks. As an example, a function‐approximation task is performed and it is found that it can be efficiently solved when the reservoir state is appropriately tuned so that its information processing capacity matches that of the task.
The Role of Art Education Teachers in Developing Arts-based Environmental Education among Elementary School Students in Najran Region

Arts-based environmental education is a form of learning that aims to develop environmental understanding and responsibility in learners. The student becomes more receptive to the perceptions and observations of the sense of the environment and the use of artistic methods to express personal environmental experiences and ideas. Technical experiences improve the student's ability to know and understand the high value of learning about the environment and how to preserve it and its natural and innate wealth and resources. Therefore, this study aimed to identify the role of Art Education teachers in developing environmental education based on arts among elementary school students, and the differences in their responses according to gender, years of experience, and educational qualification. In this study, the descriptive survey method was followed; an electronic questionnaire was applied to a sample of (119) male and female teachers of Art Education in governmental elementary schools in Najran region in the south of the Kingdom of Saudi Arabia. Means, standard deviations, and t-tests for independent samples were used to draw the results. The study concluded that the means for the domains of the study tool ranged between (2.78-3.03), with a mean level. The domain of implementation came in the first place, followed by evaluation in the second place, and then planning in the third place. The mean of the role of Art Education teachers in developing environmental education based on arts among elementary school students as a whole was (2.93), at an average level. The results also showed statistically significant differences in the responses of the study sample about the role of Art Education teachers in developing environmental education based on arts among elementary school students, due to the effect of the years of experience; the results came in favor of the category ten years or more. In addition, the educational qualification variable impacted the participants’ responses, which were in favor of the higher studies category. However, there were no statistically significant differences due to the gender variable. The study recommended the need to improve the mean level shown by the study results concerning the level of the role of Art Education teachers in developing environmental education based on arts among elementary school students. The improvement is based on the adoption by those in charge of educational policies in the Ministry of Education of a strategic plan to raise the level of competence and competence of Art Education teachers in the elementary stage. As a result, teachers can activate teaching practices (planning, implementation, and evaluation) to employ environmental issues in producing and designing artistic and creative works that enhance the value of students' love for the environment and attitudes toward preserving it.
E-modules with Android Appy Pie Based on Socio-Scientific Issues to Improve Students' Critical Thinking Skills

Learning is limited and not maximized in the use of technology in every school. In addition, learning that is still focused on the teacher as a learning resource, causes students to be less critical in understanding the existing problems, so that technology is needed, with the application of socio-scientific issue-based E-modules. The purpose of this study is to determine the effect of implementing E-modules based on socio-scientific issues in improving students' critical thinking skills. The method used in this research is quantitative research with a quasi-experimental approach. The research design used a pretest-posttest control group design, with class X MIPA 1 as the experimental class and class X MIPA 4 as the control class. The results showed that there was an increase in students' critical thinking skills in the experimental class which was higher than the control class with the average difference between the pretest and posttest results in the control class. This increase can be seen from the statistical paired samples as significant output. The results of the student response questionnaire showed a good category. The conclusion in this study is that the application of E-modules using android appy pie based on socio-scientific issues has an effect on increasing students' critical thinking skills.
Color image encryption algorithm based on 5D memristive chaotic system and group scrambling

A 5D memristive chaotic system is proposed in this paper. The essential characteristics and dynamic behavior of the obtained system are analyzed with the help of numerical simulation methods such as phase diagram, bifurcation diagram, and Lyapunov exponent spectrum. Since the chaotic sequences generated by this system have high randomness, a new color image encryption algorithm is designed based on this system, combining our proposed grouping scrambling algorithm and existing DNA technology. The algorithm uses SHA-3 512 to generate the initial value and encrypt it through three related operations: group scrambling, bidirectional diffusion, and DNA algorithm. The results show that the image encryption scheme proposed in this paper has strong security by comparing the histogram, correlation, information entropy, data loss, and differential attack of ciphertext images.
Hybridization of the swarming and interior point algorithms to solve the Rabinovich–Fabrikant system

Abstract In this study, a trustworthy swarming computing procedure is demonstrated for solving the nonlinear dynamics of the Rabinovich–Fabrikant system. The nonlinear system’s dynamic depends upon the three differential equations. The computational stochastic structure based on the artificial neural networks (ANNs) along with the optimization of global search swarming particle swarm optimization (PSO) and local interior point (IP) algorithms, i.e., ANNs-PSOIP is presented to solve the Rabinovich–Fabrikant system. An objective function based on the differential form of the model is optimized through the local and global search methods. The correctness of the ANNs-PSOIP scheme is observed through the performances of achieved and source solutions, while the negligible absolute error that is around 10 −05 –10 −07 also represent the worth of the ANNs-PSOIP algorithm. Furthermore, the consistency of the ANNs-PSOIP scheme is examined by applying different statistical procedures to solve the Rabinovich–Fabrikant system.
A Hybrid Secure and Optimized Execution Pattern Analysis Based O-HMACSHA 3 Resource Allocation in Cloud Environment

According to the analysis, several task scheduling methods have been implemented, such as the Particle Swarm Optimization (PSO) method, which has enhanced the performance of cloud data centers (DCs) in terms of various scheduling metrics.The scheduling issue in cloud computing (CC) is well-known to be NP-hard, with the main challenge arising from the exponential increase in the no. of possible outcomes or groupings as the problem size grows.Therefore, the key aim is to determine secure and optimal solutions for scheduling consumer tasks.In this study, a proposed method called Optimized-Hybrid Medium Access Control Secure Hash Algorithm 3 (O-HMACSHA3) is introduced for CC.The investigation aims to address the issue of reliable resource scheduling access for different tasks in the cloud environment, with a focus on reducing turnaround time (TAT) and energy consumption (EC).The proposed method utilizes optimization with PSO to achieve soft security in resource scheduling.To evaluate its effectiveness, the research method is compared with other task scheduling methods, including PSO and Fruit Fly-Based Simulated Annealing Optimization (FSAO) method, in terms of EC and time.The findings indicate significant improvements in performance metrics, with energy consumption reduced to 47.7 joules and TAT decreased to 316 milliseconds compared to existing methods.This is in contrast to the proposed method, which resulted in 57.3 joules and 479 milliseconds, respectively, for 20 tasks.
Techniques for combining fast local decoders with global decoders under circuit-level noise

Abstract Implementing algorithms on a fault-tolerant quantum computer will require fast decoding throughput and latency times to prevent an exponential increase in buffer times between the applications of gates. In this work we begin by quantifying these requirements. We then introduce the construction of local neural network (NN) decoders using three-dimensional convolutions. These local decoders are adapted to circuit-level noise and can be applied to surface code volumes of arbitrary size. Their application removes errors arising from a certain number of faults, which serves to substantially reduce the syndrome density. Remaining errors can then be corrected by a global decoder, such as Blossom or union find, with their implementation significantly accelerated due to the reduced syndrome density. However, in the circuit-level setting, the corrections applied by the local decoder introduce many vertical pairs of highlighted vertices. To obtain a low syndrome density in the presence of vertical pairs, we consider a strategy of performing a syndrome collapse which removes many vertical pairs and reduces the size of the decoding graph used by the global decoder. We also consider a strategy of performing a vertical cleanup, which consists of removing all local vertical pairs prior to implementing the global decoder. By applying our local NN decoder and the vertical cleanup strategy to a d = 17 surface code volume, we show a <?CDATA $10^6 \times$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>6</mml:mn> </mml:msup> <mml:mo>×</mml:mo> </mml:math> speedup of the minimum-weight perfect matching decoder. Lastly, we estimate the cost of implementing our local decoders on field programmable gate arrays.
The FeatureCloud Platform for Federated Learning in Biomedicine: Unified Approach

Background Machine learning and artificial intelligence have shown promising results in many areas and are driven by the increasing amount of available data. However, these data are often distributed across different institutions and cannot be easily shared owing to strict privacy regulations. Federated learning (FL) allows the training of distributed machine learning models without sharing sensitive data. In addition, the implementation is time-consuming and requires advanced programming skills and complex technical infrastructures. Objective Various tools and frameworks have been developed to simplify the development of FL algorithms and provide the necessary technical infrastructure. Although there are many high-quality frameworks, most focus only on a single application case or method. To our knowledge, there are no generic frameworks, meaning that the existing solutions are restricted to a particular type of algorithm or application field. Furthermore, most of these frameworks provide an application programming interface that needs programming knowledge. There is no collection of ready-to-use FL algorithms that are extendable and allow users (eg, researchers) without programming knowledge to apply FL. A central FL platform for both FL algorithm developers and users does not exist. This study aimed to address this gap and make FL available to everyone by developing FeatureCloud, an all-in-one platform for FL in biomedicine and beyond. Methods The FeatureCloud platform consists of 3 main components: a global frontend, a global backend, and a local controller. Our platform uses a Docker to separate the local acting components of the platform from the sensitive data systems. We evaluated our platform using 4 different algorithms on 5 data sets for both accuracy and runtime. Results FeatureCloud removes the complexity of distributed systems for developers and end users by providing a comprehensive platform for executing multi-institutional FL analyses and implementing FL algorithms. Through its integrated artificial intelligence store, federated algorithms can easily be published and reused by the community. To secure sensitive raw data, FeatureCloud supports privacy-enhancing technologies to secure the shared local models and assures high standards in data privacy to comply with the strict General Data Protection Regulation. Our evaluation shows that applications developed in FeatureCloud can produce highly similar results compared with centralized approaches and scale well for an increasing number of participating sites. Conclusions FeatureCloud provides a ready-to-use platform that integrates the development and execution of FL algorithms while reducing the complexity to a minimum and removing the hurdles of federated infrastructure. Thus, we believe that it has the potential to greatly increase the accessibility of privacy-preserving and distributed data analyses in biomedicine and beyond.
ReuseKNN: Neighborhood Reuse for Differentially Private KNN-Based Recommendations

User-based KNN recommender systems ( UserKNN ) utilize the rating data of a target user’s k nearest neighbors in the recommendation process. This, however, increases the privacy risk of the neighbors, since the recommendations could expose the neighbors’ rating data to other users or malicious parties. To reduce this risk, existing work applies differential privacy by adding randomness to the neighbors’ ratings, which unfortunately reduces the accuracy of UserKNN . In this work, we introduce ReuseKNN , a novel differentially private KNN-based recommender system. The main idea is to identify small but highly reusable neighborhoods so that (i) only a minimal set of users requires protection with differential privacy and (ii) most users do not need to be protected with differential privacy since they are only rarely exploited as neighbors. In our experiments on five diverse datasets, we make two key observations. Firstly, ReuseKNN requires significantly smaller neighborhoods and, thus, fewer neighbors need to be protected with differential privacy compared with traditional UserKNN . Secondly, despite the small neighborhoods, ReuseKNN outperforms UserKNN and a fully differentially private approach in terms of accuracy. Overall, ReuseKNN leads to significantly less privacy risk for users than in the case of UserKNN .
Large Language Models as Recommendation Systems in Museums

This paper proposes the utilization of large language models as recommendations systems for museums. Since the aforementioned models lack the notion of context, they can’t work with temporal information that is often present in recommendations for cultural environments (e.g. special exhibitions or events). In this respect, the current work aims at enhancing the capabilities of large language models through a fine-tuning process that incorporates contextual information and user instructions. The resulting models are expected to be capable of providing personalized recommendations, aligned with user preferences and desires. More specifically, Generative Pre-trained Transformer 4, a knowledge-based large language model is fine-tuned and turned into a context-ware recommendation system, adapting its suggestions based on user input and specific contextual factors such as location, time of visit, and other relevant parameters. The effectiveness of the proposed approach is evaluated through certain user studies, which ensure an improved user experience and engagement within the museum environment.
Trajectory determination for pipelines using an inspection robot and pipeline features

Geographic trajectory of a pipeline is important information for pipeline maintenance and leak detection.Although accurate trajectory of a ground pipeline usually can be directly measured by using global positioning system technology, it is much difficult to determine trajectory for an underground pipeline where global positioning system signal cannot be received.In this paper, a new method to determine trajectory for an underground pipeline by using a pipeline inspection robot is proposed.The robot is equipped with a low-cost inertial measurement unit and odometers.The kinematic model, measurement model and error propagation model are established for estimating position, velocity and attitude of the robot.The path reconstruction algorithm for the robot is proposed to improve accuracy of trajectory determination based on pipeline features.The experiment is given to illustrate that the position errors of the proposed method are less than 40% of that of the standard extended Kalman filter.
A Framework to Estimate the Key Point Within an Object Based on a Deep Learning Object Detection

Automatic identification of key points within objects is crucial in various application domains. This paper presents a novel framework for accurately estimating the key point within an object by leveraging deep neural network-based object detection. The proposed framework is built upon a training dataset annotated with four non-overlapping bounding boxes, one of which shares a coordinate with the key point. These bounding boxes collectively cover the entire object, enabling automatic annotation if region annotations around the key point exist. The trained object detector is then utilized to generate detection results, which are subsequently post-processed to estimate the key point. To validate the effectiveness of the framework, experiments were conducted using two distinct datasets: cross-sectional images of a parawood log and pupil images. The experimental results demonstrate that our proposed framework surpasses previously proposed approaches in terms of precision, recall, F1-score, and other domain-specific metrics. The improvement in performance can be attributed to the unique annotation strategy and the fusion of object detection and key point estimation within a unified deep learning framework. The contribution of this study lies in introducing a novel framework for closely estimating key points within objects based on deep neural network-based object detection. By leveraging annotated training data and post-processing techniques, our approach achieves superior performance compared to existing methods. This work fills a critical gap in the field by integrating object detection and key point estimation, which has received limited attention in previous research. Our framework provides valuable insights and advancements in key point estimation techniques, offering potential applications in precise object analysis and understanding. Doi: 10.28991/HIJ-2023-04-01-08 Full Text: PDF