Reviewerly/exHarmony huggingface dataset:
InterCap: Joint Markerless 3D Tracking of Humans and Objects in Interaction

Humans constantly interact with daily objects to accomplish tasks. To understand such interactions, computers need to reconstruct these from cameras observing whole-body interaction with scenes. This is challenging due to occlusion between the body and objects, motion blur, depth/scale ambiguities, and the low image resolution of hands and graspable object parts. To make the problem tractable, the community focuses either on interacting hands, ignoring the body, or on interacting bodies, ignoring hands. The GRAB dataset addresses dexterous whole-body interaction but uses marker-based MoCap and lacks images, while BEHAVE captures video of body object interaction but lacks hand detail. We address the limitations of prior work with InterCap, a novel method that reconstructs interacting whole-bodies and objects from multi-view RGB-D data, using the parametric whole-body model SMPL-X and known object meshes. To tackle the above challenges, InterCap uses two key observations: (i) Contact between the hand and object can be used to improve the pose estimation of both. (ii) Azure Kinect sensors allow us to set up a simple multi-view RGB-D capture system that minimizes the effect of occlusion while providing reasonable inter-camera synchronization. With this method we capture the InterCap dataset, which contains 10 subjects (5 males and 5 females) interacting with 10 objects of various sizes and affordances, including contact with the hands or feet. In total, InterCap has 223 RGB-D videos, resulting in 67,357 multi-view frames, each containing 6 RGB-D images. Our method provides pseudo ground-truth body meshes and objects for each video frame. Our InterCap method and dataset fill an important gap in the literature and support many research directions. Our data and code are areavailable for research purposes.
Similarity Identification Based on Word Trigrams Using Exact String Matching Algorithms

Several studies regarding excellent exact string matching algorithms can be used to identify similarity, including the Rabin-Karp, Winnowing, and Horspool Boyer-Moore algorithms. In determining similarities, the Rabin-Karp and Winnowing algorithms use fingerprints, while the Horspool Boyer-Moore algorithm uses a bad-character table. However, previous research focused on identifying similarities using these algorithms based on character n-gram. In contrast, identification based on the word n-gram to determine the similarity based on its linguistic meaning, especially for longer strings, had not been covered yet. Therefore, a word-level trigram was proposed to identify similarities based on the word trigrams using the three algorithms and compare each performance. Based on precision, recall, and running time comparison, the Rabin-Karp algorithm results were 100%, 100%, and 0.19 ms, respectively; the Winnowing algorithm results with the smallest window were 100%, 56%, and 0.18 ms, respectively; and the Horspool algorithm results were 100%, 100%, and 0.06 ms. From these results, it can be concluded that the performance of the Horspool Boyer-Moore algorithm is better in terms of precision, recall, and running time.
Development of the concept of training students with special educational needs in institutions of higher education

The aim of the article is to identify the characteristics of the different teaching strategies used in higher education for students with developmental disabilities based on a review of the academic literature on higher education for students with special educational needs and a study of the practical aspects of the issue. Methodology. To conduct the research, analytical and bibliographical methods were applied in reviewing scientific literature on the application of different learning strategies in higher education for students with special educational needs. In addition, an online questionnaire survey of educators and methodologists working with students with special educational needs was used. Results. The main characteristics of modern learning strategies for students with special educational needs have been analysed as a result of the work.
A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of $n$ local cost functions by using local information exchange is considered. This problem is an important component of many machine learning techniques with data parallelism, such as deep learning and federated learning. We propose a distributed primal--dual stochastic gradient descent (SGD) algorithm, suitable for arbitrarily connected communication networks and any smooth (possibly nonconvex) cost functions. We show that the proposed algorithm achieves the linear speedup convergence rate $\mathcal{O}(1/\sqrt{nT})$ for general nonconvex cost functions and the linear speedup convergence rate $\mathcal{O}(1/(nT))$ when the global cost function satisfies the Polyak--{\L}ojasiewicz (P--{\L}) condition, where $T$ is the total number of iterations. We also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global optimum. We demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.
HDAM: Heuristic Difference Attention Module for Convolutional Neural Networks

The attention mechanism is one of the most important priori knowledge to enhance convolutional neural networks. Most attention mechanisms are bound to the convolutional layer and use local or global contextual information to recalibrate the input. This is a popular attention strategy design method. Global contextual information helps the network to consider the overall distribution, while local contextual information is more general. The contextual information makes the network pay attention to the mean or maximum value of a particular receptive field. Different from the most attention mechanism, this article proposes a novel attention mechanism with the heuristic difference attention module, HDAM. HDAM's input recalibration is based on the difference between the local and global contextual information instead of the mean and maximum values. At the same time, to make different layers have a more suitable local receptive field size and increase the exibility of the local receptive field design, we use genetic algorithm to heuristically produce local receptive fields. First, HDAM extracts the mean value of the global and local receptive fields as the corresponding contextual information. Then the difference between the global and local contextual information is calculated. Finally HDAM uses this difference to recalibrate the input. In addition, we use the heuristic ability of genetic algorithm to search for the local receptive field size of each layer. Our experiments on CIFAR-10 and CIFAR-100 show that HDAM can use fewer parameters than other attention mechanisms to achieve higher accuracy. We implement HDAM with the Python library, Pytorch, and the code and models will be publicly available.
Online broad learning system for dynamic data flow

Dynamic data flow is a data model with the characteristics of high speed, high dimensionality, change, and potential infinity, and its data distribution evolves over time. Broad Learning System (BLS) is a learning model with fast training speed, simple structure and high accuracy. It has the characteristics of online learning and rapid adjustment of the model structure. The traditional online BLS algorithm cannot capture the changing trend of the dynamic data flow with time in time, so it is not suitable for dealing with the dynamic data flow problem. This paper proposes a novel online broad learning algorithm (MDO-BLS) that combines sliding window strategy and forgetting factor method. This BLS model has a forgetting mechanism, that is, the model has the ability to learn new samples online while forgetting the knowledge of old samples, so that it can quickly capture the trend of data changes. To verify the performance of the proposed BLS model with forgetting mechanism, experiments are performed using a dynamic regression dataset. The experimental results show that the BLS model with forgetting mechanism is superior to the traditional BLS and some other online algorithm in both prediction accuracy and time overhead, so it is more suitable for application in dynamic data flow environment.
Multi-scale structure aware network for salient object detection

The methods based on convolutional neural network (CNN) have greatly boosted the performance of salient object detection. However, detecting salient objects of multiple scales remains a challenge. To identify salient objects with different scales, we propose a novel multi-scale structure aware network, which can effectively integrate the semantic and detail information of multi-level features to enhance the complementarity between the features at different scales. Specifically, we first propose a global context enhancement module (GCEM) to enhance the relationship between highlevel features in CNN through the atrous spatial pyramid pooling module. Then, we utilize the channel and spatial attention mechanisms to suppress the background noises in above enhanced features. Next, we design feature integration module (FIM) to selectively fuse the low-level detail features, high-level semantic features, and global context information generated by GCEM. Finally, the proposed GCEM and FIM module are progressively cascaded three times to generate final saliency maps. Experimental results on three benchmark datasets demonstrate that our approach achieves superior performance over state-of-the-art methods.
Research on Railway Freight Loading and Securing Safety State Monitoring Based on Internet of Things

In view of lacking modern monitoring technology for safety management in complex transportation environments, this paper proposes to apply narrowband Internet of things technology to goods loading and securing safety status monitoring. A trinity monitoring system based on an embedded development system, including on-board, personal computer and cloud terminal, is designed. Among them, a sensor module is built at the lower computer, data collection was realized by a wireless communication module and a narrowband Internet of things module, and C# language is used at the upper computer to realize multiple functions such as data reading, storage, real-time display and threshold alarm. The system test results verify the effectiveness of the monitoring system. Its rich functions can help railway staff find the potential safety situation during railway freight transportation. Moreover, the historical data can also provide quantitative support for the optimization of loading and securing scheme.
HammingMesh: A Network Topology for Large-Scale Deep Learning

Numerous microarchitectural optimizations unlocked tremendous processing power for deep neural networks that in turn fueled the AI revolution. With the exhaustion of such optimizations, the growth of modern AI is now gated by the performance of training systems, especially their data movement. Instead of focusing on single accelerators, we investigate data-movement characteristics of large-scale training at full system scale. Based on our workload analysis, we design HammingMesh, a novel network topology that provides high bandwidth at low cost with high job scheduling flexibility. Specifically, HammingMesh can support full bandwidth and isolation to deep learning training jobs with two dimensions of parallelism. Furthermore, it also supports high global bandwidth for generic traffic. Thus, HammingMesh will power future large-scale deep learning systems with extreme bandwidth requirements.
Semantic Segmentation in Learned Compressed Domain

Most machine vision tasks (e.g., semantic segmentation) are based on images encoded and decoded by image compression algorithms (e.g., JPEG). However, these decoded images in the pixel domain introduce distortion, and they are optimized for human perception, making the performance of machine vision tasks suboptimal. In this paper, we propose a method based on the compressed domain to improve segmentation tasks. i) A dynamic and a static channel selection method are proposed to reduce the redundancy of compressed representations that are obtained by encoding. ii) Two different transform modules are explored and analyzed to help the compressed representation be transformed as the features in the segmentation network. The experimental results show that we can save up to 15.8\% bitrates compared with a state-of-the-art compressed domain-based work while saving up to about 83.6\% bitrates and 44.8\% inference time compared with the pixel domain-based method.
ANALISIS KUALITAS HASIL PREDIKSI KLASIFIKASI PENGGUNAAN LAHAN MENGGUNAKAN CA MARKOV MODEL BERDASARKAN PETA RENCANA TATA RUANG

Land use changes due to community activities and mobility occur because of the increasingly complex need for land. Spatial analysis is needed to identify land use changes which are subsequently reviewed by the Regional Spatial Plan in accordance with Government Regulation Number 8 of 2013 concerning the accuracy of the RTRW map. In this study, the study area taken was Pati Regency around the South Ring Road which includes four districts. From the acquisition of high resolution satellite imagery data in 2009, 2015 and 2019, predictions were made for the years 2023 and 2030 to determine the development of the area around the South Ring Road. The results of the prediction of land use using CA Markov in 2023 will be compared with the prediction in 2030 to determine the quality of the prediction results of the classification of land use in the prediction year with the same input data interval and exceeding the input data interval by conducting a suitability analysis with the RTRW. In 2023, the category of conformity is 95.41341%, and in 2030 amounting to 95.41340%. This shows that the prediction results of land use change with CA Markov for the same year with the time interval of the input data have insignificant differences with the predicted results with longer intervals when compared to the current RTRW.
Human-computer interaction system based on gesture recognition

In recent years, hand gesture has been widely used in human-computer interaction, virtual reality and other fields as an input channel. However, there are still some problems in gesture interaction, such as low resolution, occlusion and slow recognition rate, which lead to limited use scenarios. Therefore, based on the real scene of human-computer interaction, this paper builds a gesture recognition and object detection network according to the open source algorithms and frameworks, and constructs a human-computer interaction system based on gesture recognition. Experimental results show that the system can switch different modes according to hand movements and positions to realize interface interaction. The object detection and recognition in entertainment scenarios and the mathematical operations of virtual calculators in office scenarios are simulated respectively, which further improves accuracy and real-time performance of gesture recognition in the practical application scenarios.
Object Disparity

Most of stereo vision works are focusing on computing the dense pixel disparity of a given pair of left and right images. A camera pair usually required lens undistortion and stereo calibration to provide an undistorted epipolar line calibrated image pair for accurate dense pixel disparity computation. Due to noise, object occlusion, repetitive or lack of texture and limitation of matching algorithms, the pixel disparity accuracy usually suffers the most at those object boundary areas. Although statistically the total number of pixel disparity errors might be low (under 2% according to the Kitti Vision Benchmark of current top ranking algorithms), the percentage of these disparity errors at object boundaries are very high. This renders the subsequence 3D object distance detection with much lower accuracy than desired. This paper proposed a different approach for solving a 3D object distance detection by detecting object disparity directly without going through a dense pixel disparity computation. An example squeezenet Object Disparity-SSD (OD-SSD) was constructed to demonstrate an efficient object disparity detection with comparable accuracy compared with Kitti dataset pixel disparity ground truth. Further training and testing results with mixed image dataset captured by several different stereo systems may suggest that an OD-SSD might be agnostic to stereo system parameters such as a baseline, FOV, lens distortion, even left/right camera epipolar line misalignment.
A Novel Data Analytic Model for Mining User Insurance Demands from Microblogs

This paper proposes a method based on LDA model and Word2Vec for analyzing Microblog users' insurance demands. First of all, we use LDA model to analyze the text data of Microblog user to get their candidate topic. Secondly, we use CBOW model to implement topic word vectorization and use word similarity calculation to expand it. Then we use K-means model to cluster the expanded words and redefine the topic category. Then we use the LDA model to extract the keywords of various insurance information on the “Pingan Insurance” website and analyze the possibility of users with different demands to purchase various types of insurance with the help of word vector similarity. Finally, the validity of the method in this paper is verified against Microblog user information. The experimental results show that the accuracy, recall rate and F1 value of the LDA-CBOW extending method have been proposed compared with that of the traditional LDA model, respectively, which proves the feasibility of this method. The results of this paper will help insurance companies to accurately grasp the preferences of Microblog users, understand the potential insurance needs of users timely, and lay a foundation for personalized recommendation of insurance products.
Obtuse Lattice Bases

A lattice reduction is an algorithm that transforms the given basis of the lattice to another lattice basis such that problems like finding a shortest vector and closest vector become easier to solve. We define a class of bases called obtuse bases and show that any lattice basis can be transformed to an obtuse basis. A shortest vector $\mathbf{s}$ can be written as $\mathbf{s}=v_1\mathbf{b}_1+\dots+v_n\mathbf{b}_n$ where $\mathbf{b}_1,\dots,\mathbf{b}_n$ are the input basis vectors and $v_1,\dots,v_n$ are integers. When the input basis is obtuse, all these integers can be chosen to be positive for a shortest vector. This property of obtuse bases makes the lattice enumeration algorithm for finding a shortest vector exponentially faster. We have implemented the algorithm for making bases obtuse, and tested it some small bases.
Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis

Machine learning with formal privacy-preserving techniques like Differential Privacy (DP) allows one to derive valuable insights from sensitive medical imaging data while promising to protect patient privacy, but it usually comes at a sharp privacy-utility trade-off. In this work, we propose to use steerable equivariant convolutional networks for medical image analysis with DP. Their improved feature quality and parameter efficiency yield remarkable accuracy gains, narrowing the privacy-utility gap.
Role of automation, Big Data, AI, ML IBN, and cloud computing in intelligent networks

There are more smart devices in our world today than individuals. A growing number of people are linked to the Internet 24 hours a day, in some form or another. A growing number of people own three, four, or more smart devices and rely on them. Smartphones, fitness and health trackers, e-readers, and tablets are just a few examples. It is forecast that on average there will be 3.4 smart devices or connections for every person on earth. The Internet of Things (IoT) is relevant to many industries. IoT systems contribute to the environmental controls, retail, transportation, healthcare, and agriculture industries among many others. According to Statista, the number of IoT devices that are in use across all relevant industries is forecast to grow to more than 8 billion by 2030. As for consumers, important growth areas are the Internet and digital media devices, which include smartphones. This area is also predicted to grow to more than 8 billion by 2030. Other applications with more than 1 million connected devices are connected and autonomous vehicles, IT infrastructure, asset management, and electric utility smart grid.All of this is made possible through intelligent networks. The planet is rapidly becoming covered in networks that allow digital devices to communicate and interconnect. Consider the network mesh as a digital skin that surrounds the earth. Mobile devices, electronic sensors, electronic measuring equipment, medical gadgets, and gauges can all link with this digital skin. They keep track of, communicate with, analyze, and, in some situations, automatically adjust to the data collected and transmitted.
Coordinated Motion Planning Through Randomized k-Opt

This paper examines the approach taken by team gitastrophe in the CG:SHOP 2021 challenge. The challenge was to find a sequence of simultaneous moves of square robots between two given configurations that minimized either total distance travelled or makespan (total time). Our winning approach has two main components: an initialization phase that finds a good initial solution, and a $k$-opt local search phase which optimizes this solution. This led to a first place finish in the distance category and a third place finish in the makespan category.
Feature-Fusion Parallel Decoding Transformer for Image Captioning

Image caption is an important research direction at the intersection of computer vision and natural language processing. It is based on object detection, enabling machines to describe image content in human language, generating sentences with correct grammar. Most of the existing methods employ a Transformer-based structure which achieve the cutting-edge performance. However, most methods focus on improving visual feature information extraction, optimizing and improving between grid features and region features, and improving the performance of the final model. In this paper, we tried to improve the final effect of the model from the perspective of the model structure and the visual features extraction. We proposed Feature-Fusion Parallel Decoding Transformer (FPDT) which adopts parallel decoding mode and uses both grid features and region features. We conducted a large number of experimental studies on the MSCOCO dataset. And FPDT's performance on MSCOCO datasets is also at the cutting edge.
Feature Compression Using Dynamic Switches in Multi-split CNNs

Convolutional neural networks (CNN) are often computationally demanding for mobile devices.Offloading some computation lowers this burden: initial convolutional layers are processed on a smartphone, the resulting high dimensional features are transmitted, and latter layers are processed in the cloud/edge/another device.To improve this process, we propose Dynamic Switch, a convolutional subnetwork enabling anywhere splittable CNNs with multirate feature compression using a single set of network parameters.We achieve 90% feature compression with at most 3% accuracy loss for MobileNet and MSDNet on ImageNet dataset and at most 4.58% on CIFAR100 dataset with MSDNet, ResNet-18, Mo-bileNet/MobileNetv2 and ShuffleNet/ShuffleNetv2.