Reviewerly/exHarmony huggingface dataset:
Distributed stochastic compositional optimization problems over directed networks

We study the distributed stochastic compositional optimization problems over directed communication networks in which agents privately own a stochastic compositional objective function and collaborate to minimize the sum of all objective functions. We propose a distributed stochastic compositional gradient descent method, where the gradient tracking and the stochastic correction techniques are employed to adapt to the networks’ directed structure and increase the accuracy of inner function estimation. When the objective function is smooth, the proposed method achieves the convergence rate $${\mathcal {O}}\left( k^{-1/2}\right) $$ and sample complexity $${\mathcal {O}}\left( \frac{1}{\epsilon ^2}\right) $$ for finding the ( $$\epsilon $$ )-stationary point. When the objective function is strongly convex, the convergence rate is improved to $${\mathcal {O}}\left( k^{-1}\right) $$ . Moreover, the asymptotic normality of Polyak-Ruppert averaged iterates of the proposed method is also presented. We demonstrate the empirical performance of the proposed method on model-agnostic meta-learning problem and logistic regression problem.
Jeffrey imaging revisited

Abstract In ‘The logic of partial supposition’ (Analysis vol. 81), Benjamin Eva and Stephan Hartmann investigate partial imaging, a credence-revision method which combines the partiality familiar from Jeffrey Conditioning(The Logic of Decision, 1983) with the formal notion of imaging familiar from Lewis’s ‘Causal decision theory’ (1981). They argue that because partial imaging is non-monotonic, it ‘fail[s] to provide a plausible account of the norms of partial subjunctive suppositions’. In this note, I present a notion of partial imaging that does satisfy monotonicity, and discuss some of the applications and ramifications. The account frames conditioning as a form of imaging, and rejects Gärdenfors’s principle of linearity in ‘Imaging and conditionalization’ (1982).
Detailed Study of a Proposal for a Computer Based Tutoring Strategy

In this article, we propose a new tutoring strategy to combat school dropout in Morocco.This strategy is based on a collaborative approach involving educational administration and teachers at the national level to ensure equity and equal opportunities for all Moroccan students, on the one hand, and the quality of the training, on the other.Its implementation will be ensured through a computerized system based on the student's learning and evaluation portfolios in order to provide individualized assistance, taking into consideration his/her shortcomings and didactic needs.In this work, after defining tutoring and its types, we will present the Moroccan ministerial strategy in terms of tutoring by discussing the relevance of the tutoring systems prevailing in Morocco and the existing platforms accredited by the Ministry of Education.Then, we will study the portfolio concept and its forms, in addition to some criticisms established on this tool in order to identify the components, ensuring the quality of the tutoring to be taken into account in our proposed IT solution.Finally, we will study in detail the design of our system by modeling the evaluation and remediation processes and examining its functional behavior.
P‐2.1: Research on real‐time performance of road condition display

Road target detection and recognition is of great significance in the current field of automatic driving, and in the road target detection and recognition, the high precision of detection algorithm and fast reasoning speed are very important for safe automatic driving. The YOLOv5 (YOLO, you only look once) target detection algorithm can be used to identify and analyze road targets on the way of vehicles, playing a role in assisting driving and reducing safety risks. By using the improved YOLOv5 target detection algorithm to train the BDD100K dataset, the model obtained can significantly improve the recall rate and thus the accuracy. The improved YOLOv5 algorithm mainly uses K‐means algorithm to find the most appropriate anchors for data sets, and gets more accurate models through real‐time data augmentation training. The results show that, on the BDD100K test set, the MAP‐50 of the improved model can reach 51.8%, and compared with the performance of the original model, the improved model has significantly improved the target detection mAP. Compared with the manual model, the proposed model can detect the target more accurately while guaranteeing the detection speed.
High Density Photonic Reservoir Computing using Optical Fiber and Polymer Waveguide

We present a demo system of FPGA-driven time-delay photonic reservoir computer. The architecture of the reservoir is based on a high bandwidth density optical interconnect technologies using VCSEL and photodiode chips with multi-mode optical fiber, and specially designed polymer waveguides. Using the demo system, we also present the results on voice utterance dataset. This reservoir is promising for a power-efficient board-integrated photonic neural network.
A Novel Object Detector Based on High-Quality Rotation Proposal Generation and Adaptive Angle Optimization

Currently, reliable and accurate oriented detection in remote sensing images still needs to be improved. The wide variation of object shapes and orientations in the remote sensing images usually leads to two issues in two-stage oriented object detectors. One issue is how to generate high-quality rotation proposals. The other is the angular error sensitivity to the aspect ratios in the angle optimization process. In this paper, we propose a novel rotation proposal generation and optimization detector, which is based on high-quality rotation proposal generation and adaptive angle optimization to solve these two issues. The proposed method mainly establishes the geometric relationship guided region proposal networks (GRG-RPN) and the adaptive angle optimization head (AAO-Head) to achieve more accurate oriented object detection. The GRG-RPN only uses a simple network and a small number of horizontal anchors to predict high-quality rotation proposals. This approach was derived via the calculation based on the theoretical analysis of the geometric relationship between the oriented bounding boxes (OBBs) and their external horizontal bounding boxes (EHBBs). The AAO-Head solves the angular error sensitivity to the aspect ratios and achieves adaptive angle optimization using a new regression parameter, which is defined based on the theoretical analysis of the relationship between the Intersection over Union (IoU), the angular errors and the aspect ratios. Experiments show that our method can achieve 2.5% mAP improvement averagely versus the compared SOTA methods, and achieve 0.5% mAP improvement versus the next best method Oriented R-CNN with fewer regression parameters and a simpler regression approach.
P‐2.24: A Survey of Privacy Protection Research based on Virtual Reality

Virtual reality is immersive, personalized, and highly interactive. The data is rich and diverse, and privacy involves a wide range. It is crucial to study the privacy protection of virtual reality. This paper introduces the development process of virtual reality privacy protection in detail. From the form of data generation, the virtual reality privacy protection system is proposed, divided into perception, transmission, application, and interaction. According to the shortcomings of current research, the pending problems and possible solutions faced in virtual reality privacy protection are summarized.
Differentiable Architecture Search Algorithm Based on Global Comparison

Manually building neural network models is a great test of researchers’ knowledge reserves, so using Neural Architecture Search (NAS) to automatically construct neural networks is becoming increasingly popular. This paper uses an improved Differentiable Architecture Search (DARTS) to automatically build a classification network model, DARTS is a gradient-based NAS algorithm. However, DARTS uses local selection, so this paper proposes a selection algorithm based on global selection. The improved algorithm can ensure that all operations connected to the same intermediate node can be fairly compared, ensuring fairness in selection and searching for more types of neural network architectures. DARTS only uses fixed candidate operations to construct the neural network, so the structure of the neural network is relatively single, skip connection will dominate the searched network, and DARTS uses a large number of approximation strategies, which can easily lead to a decrease in the accuracy of the model. For the problems, we add a SENet attention mechanism after cell output, SENet can extract features on the feature layer in the channel dimension, it can not only improve the search performance of the network but also effectively increase the diversity and robustness of the network. The final test error on CIFAR10 reaches 2.48%.
The Automatic Measurement of the Lexical Complexity of Algerian EFL Academic Writings: A Comparative Corpus-Based Study

This research paper aims to gauge the lexical complexity entrenched in the abstracts of Algerian students via artificial intelligence. It represents a comparative study directed toward measuring the lexical density and variation manifested in the abstracts of PhD theses and master dissertations through the use of a developed language analytic parser. A retrospective design was utilised during the study since the addressed independent dichotomous variable (theses and dissertations abstracts) and dependent variables (lexical density and variation) were not manipulated or controlled by the researcher. The data collection procedure was based on the random retrieval of 96 abstracts from the Algerian platform of theses. The analysis involved the use of the web-based lexical complexity analyser (LCA) devised by Ai and Lu (2010) in processing data. Five proxy measures were employed, namely: LD, NDW, NDW–50, NDW–ER50 and NDW–ES50. Multiple inferential statistical tests were employed based on the incorporation of SPSS version 28 as a tool for statistical analysis. A probability value of .05 (p ≤ .05) was set as the threshold of statistical significance for rejecting the null hypothesis. The results indicated the presence of a negligible difference between the lexical density of theses’ and dissertations’ abstracts. More importantly, the mean scores of lexical variation rooted in abstracts of PhD students were significantly greater than those of master students. The four proxy measures used to determine the lexical variation of students’ writing showed that the academic level of students has a substantial effect on the degree of lexical variation. Further studies are still required in the context of Algeria to reach more conclusive results about the relationship between the academic level of students vis-à-vis the lexical richness of writing production.
A Brief Introduction to Transformers as Language Models

Transformers are deep-learning neural network models, currently achieving state-of-the-art results across a diverse range of domains. Here we provide a brief introduction to transformers as generative language models.
Adoption and Optimization of Blockchain Technology in the Regulation of Class II Vaccines

At present, there are many problems and challenges in the management of Class II vaccines, for example, regulatory regulations, the regulatory system and the quality supervision for second-class vaccines are not yet complete. Blockchain technology will play an increasingly important role in the safety supervision of Class II vaccines. Firstly, we have explored the vulnerabilities and causes of second-class vaccine safety supervision in this paper. Then we have studied the impacts of blockchain technology on second-class vaccine supervision and the potential drivers of adoption by reliability theory and fuzzy dynamic decision-making method. Finally, based on the study results, a modular and sustainable supply chain integration optimization design has be proposed for the second-class vaccine safety supervision system, the supervision system based on optimization design has deep traceability and disintermediation to track and monitor the quality of each stage of the entire supply chain, and has characteristics such as intelligence, scalability, and compatibility. This study provides theoretical basis and technical support for embedding blockchain ICT into the multi-agent collaborative supervision of class II vaccines.
From Image Transfer to Object Transfer: Cross-Domain Instance Segmentation Based on Center Point Feature Alignment

Remote sensing images can have significant appearance differences due to various factors such as atmospheric conditions, sensor types, seasons, and capture times. Therefore, when applying a pre-trained instance segmentation deep learning model to newly accessed remote sensing images, the model’s performance tends to decrease significantly. Current mainstream image-based or feature-based domain adaptation methods are not designed specifically for the cross-domain instance segmentation problem. These methods attempt to align the whole images, which may not be optimal for instance segmentation tasks. To address this issue, we propose a cross-domain instance segmentation method based on object-level alignment. Instead of aligning the entire images from both datasets, we only align the features of each object instance, particularly the representative center point features. Our approach mainly consists of an improved contour-based instance segmentation model for object-based domain adaptation, an object-pasting enhancement technique based on Fourier domain adaptation (FDA) that effectively reduces the gap between the source and target domains of the object instances, and a self-training strategy that dynamically generates pseudo-labels for iterative model training. Our experiments on cross-domain building instance segmentation demonstrate that the proposed method achieves a 9.5 intersection over union (IoU) improvement over the current best method. Additionally, experiments on a cross-domain close-range dataset involving transfer between simulated and real street images show that our method significantly outperforms the current best method by 6.5 mean average precision (mAP). These results on remote sensing and close-range datasets validate the universality of our approach.
High-Dimensional Multiple-Measurement-Vector Problem: Mutual Information and Message Passing Solution

In this paper, we study the high-dimensional multiple-measurement-vector (MMV) problem, which typically arises in massive machine-type communications (mMTC) that operates with massive multiple-input multiple-output (MIMO) in a grant-free manner. We derive an expression for the mutual information (MI) of the MMV channel, considering an input that has i.i.d. rows and a row-wise output that is randomly mapped from a linear combination of the input and the weighting. Our derivation follows from the replica method, a non-rigorous but very powerful approach prevailing in physics society, but unlike the classical treatment, the replica symmetry (RS) structure is extended here to comply with the MMV setting. This new structure requires an additional condition of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">simultaneously diagonalizability</i> on the matrix parameters if the row-wise output mapping is non-Gaussian. The MI obtained suggests its first-order condition is pointing towards the fixed-point equation of some message passing algorithm. In the case of a correlated Gaussian channel, the MI further gives birth to a new identity on the celebrated MI-MMSE relation. To find an algorithm that matches the MI’s first-order condition, we apply vector-form message passing, and circumvent the difficulty of non-commutable matrix products. We also derive its state evolution (SE), whose fixed-point equation clearly shows it perfectly agrees with the first-order condition of the MI derived.
PESAC, the Generalized Framework for RANSAC-Based Methods on SIMD Computing Platforms

This paper focuses on the computational optimization of RANSAC. We describe the Parallel Efficient Sample Consensus (PESAC) framework that allows efficient utilization of SIMD extensions and provides memory locality due to a special way of storing the input sequence of correspondences and generating a batch of samples per one main loop iteration. It is inspired by the USAC framework and has a block structure capable of implementing most modern RANSAC-based methods. We enhance it with individual blocks of sample and model restrictors that are aimed at the rejection of “bad” samples and model hypothesis before time-consuming model computation and verification blocks. We also provide a detailed description implementing 2D homography estimation problem in PESAC and benchmark the running time on the MIDV-2020 dataset of identity documents. Comparing to naive implementation, we accelerated our framework by 122 times for the document classification task (with a 6 % increase in accuracy) and by 18 times for document tracking (with a 46 % decrease in tracking failure rate) by using both restrictors and vector processing. This version also outperformed a number of USAC implementations from OpenCV-4.6.0 in runtime and accuracy of estimation (3 times faster, 6 % greater accuracy for the classification task, and 2 times faster, 33 % lower failure rate for tracking if comparing with USAC_MAGSAC).
Physically Plausible 3D Human-Scene Reconstruction From Monocular RGB Image Using an Adversarial Learning Approach

Holistic 3D human-scene reconstruction is a crucial and emerging research area in robot perception. A key challenge in holistic 3D human-scene reconstruction is to generate a physically plausible 3D scene from a single monocular RGB image. The existing research mainly proposes optimization-based approaches for reconstructing the scene from a sequence of RGB frames with explicitly defined physical laws and constraints between different scene elements (humans and objects). However, it is hard to explicitly define and model every physical law in every scenario. This letter proposes using an implicit feature representation of the scene elements to distinguish a physically plausible alignment of humans and objects from an implausible one. We propose using a graph-based holistic representation with an encoded physical representation of the scene to analyze the human-object and object-object interactions within the scene. Using this graphical representation, we adversarially train our model to learn the feasible alignments of the scene elements from the training data itself without explicitly defining the laws and constraints between them. Unlike the existing inference-time optimization-based approaches, we use this adversarially trained model to produce a per-frame 3D reconstruction of the scene that abides by the physical laws and constraints. Our learning-based method achieves comparable 3D reconstruction quality to existing optimization-based holistic human-scene reconstruction methods and does not need inference time optimization. This makes it better suited compared to existing methods, for potential use in robotic applications, such as robot navigation, etc.
Path and Trajectory Planning of a Tethered UAV-UGV Marsupial Robotic System

This letter addresses the problem of trajectory planning in a marsupial robotic system consisting of an unmanned aerial vehicle (UAV) linked to an unmanned ground vehicle (UGV) through a non-taut tether with controllable length. To the best of our knowledge, this is the first method that addresses the trajectory planning of a marsupial UGV-UAV with a non-taut tether. The objective is to determine a synchronized collision-free trajectory for the three marsupial system agents: UAV, UGV, and tether. First, we present a path planning solution based on optimal Rapidly-exploring Random Trees (RRT*) with novel sampling and steering techniques to speed-up the computation. This algorithm is able to obtain collision-free paths for the UAV and the UGV, taking into account the 3D environment and the tether. Then, the letter presents a trajectory planner based on non-linear least squares. The optimizer takes into account aspects not considered in the path planning, like temporal constraints of the motion imposed by limits on the velocities and accelerations of the robots, or raising the tether's clearance. Simulated and field test results demonstrate that the approach generates obstacle-free, smooth, and feasible trajectories for the marsupial system.
P‐2.30: Design and Fabrication of an Optical Path Folding Pancake Virtual Reality Head‐mounted Display

Metaverse has been going viral across the globe, and virtual reality head‐mounted displays (VR‐HMDs) as the basic infrastructure and entrance to cater to this evolution of social interaction are showing rapid development recently. Pancake optics based on optical path folding provides the feasibility of VR‐HMDs in the form of glasses and is wide recognition. The pancake system has become the current mainstream owing to its large exit pupil diameter (EPD), high resolution, novel compact, and lightweight. Owing to the use of polarization elements, the birefringence in the lens will change the polarization state of the light, resulting in uneven brightness and stray light, seriously reducing the optical performance of the system, so it is necessary to control the birefringence of the lens. In this study, we proposed a pancake system based on a 2.1‐inch LCD display which achieves 10 mm EPD at 11 mm, diagonal field of view (FOV) of 96°, overall length (OAL) of the system less than 20 mm, and support diopter adjustment from ‐7D to ‐1D. The residual birefringence of the lens can be effectively suppressed in four stages: 1) controlling the thickness ratio between the center and the edge of the lens during the optical design, 2) setting a reasonable position and size of the injection gate and cooling system during the mold design process, 3) adopting reasonable injection parameters during the precision injection molding process and 4) eliminating the birefringence of the lens with the lens annealing process. Ultra‐precision machining and correction of the mold cores have been employed to achieve high surface accuracy and low birefringence of the lens. The optomechanical design, assembly, and adjustment process are discussed. Finally, the prototype of the pancake system is experimentally tested, and the results demonstrate pretty well.