Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Integrated Desktop GIS and Web GIS for Power Grid Dispatching and Control System

With the strengthening of the integrated features of the power grid operation, the global monitoring and analysis decision-making requirements around the main power grid and the external environment of Zones I and III are gradually increasing. Considering the visualization requirements and deployment scenarios of the power grid GIS, and the consistency of display on the human-machine display platform of the power grid dispatching and control system spanning Zones I and III, an integrated design based on the principles of integration and modularization was developed. This design relies on the lightweight human-machine interaction frameworks and new generation of service buses. It implements a unified solution for Desktop GIS and Web GIS. The implementation includes interoperate between CIM/G files and GIS interface programs, configuration-based loading of grid maps, and automatic avoidance of feature annotations. Through program reuse between Zones I and III, the GIS-related monitoring scenarios of the power grid applications are supported.
Language-enhanced RNR-Map: Querying Renderable Neural Radiance Field maps with natural language

We present Le-RNR-Map, a Language-enhanced Renderable Neural Radiance map for Visual Navigation with natural language query prompts. The recently proposed RNR-Map employs a grid structure comprising latent codes positioned at each pixel. These latent codes, which are derived from image observation, enable: i) image rendering given a camera pose, since they are converted to Neural Radiance Field; ii) image navigation and localization with astonishing accuracy. On top of this, we enhance RNR-Map with CLIP-based embedding latent codes, allowing natural language search without additional label data. We evaluate the effectiveness of this map in single and multi-object searches. We also investigate its compatibility with a Large Language Model as an "affordance query resolver". Code and videos are available at the link https://intelligolabs.github.io/Le-RNR-Map/.
All-pairs Consistency Learning for Weakly Supervised Semantic Segmentation

In this work, we propose a new transformer-based regularization to better localize objects for Weakly supervised semantic segmentation (WSSS). In image-level WSSS, Class Activation Map (CAM) is adopted to generate object localization as pseudo segmentation labels. To address the partial activation issue of the CAMs, consistency regularization is employed to maintain activation intensity invariance across various image augmentations. However, such methods ignore pair-wise relations among regions within each CAM, which capture context and should also be invariant across image views. To this end, we propose a new all-pairs consistency regularization (ACR). Given a pair of augmented views, our approach regularizes the activation intensities between a pair of augmented views, while also ensuring that the affinity across regions within each view remains consistent. We adopt vision transformers as the self-attention mechanism naturally embeds pair-wise affinity. This enables us to simply regularize the distance between the attention matrices of augmented image pairs. Additionally, we introduce a novel class-wise localization method that leverages the gradients of the class token. Our method can be seamlessly integrated into existing WSSS methods using transformers without modifying the architectures. We evaluate our method on PASCAL VOC and MS COCO datasets. Our method produces noticeably better class localization maps (67.3% mIoU on PASCAL VOC train), resulting in superior WSSS performances.
Design of Stepping Motor Driver Based on Electric Cylinder Application

The most production mode of enterprises is converted into industrial automation and intelligence, the use of traditional hydraulic cylinders and cylinders will be restricted by their own characteristics, but the electric cylinders have unparalleled advantages. How to improve the application of the electric cylinder for the development of modern technology and the complex environment in the field? The driver of stepping motor is designed in this paper, according to the characteristics of electric cylinder. By using RS485 bus control, the stepping motor can be operated by the spontaneous pulse set by the driver according to the parameters of the upper computer of which the response is faster and the stepping motor could hardly lose step.
IoT System Based on Iris Recognition

The Internet of Things is increasingly accessible on the World Wide Web, and WoT provides a new space that makes managing things very easy. The fields of the Internet of Things and because of its many possibilities have been applied very widely in the manufacture and areas of our daily lives. In this paper, it was proposed to use irises to increase data security. The client implements a preprocessing process that consists of the following steps: the first step is to convert color images to the gray level luminosity algorithm, the second step blurring the images by using the median filter algorithm, and step three the images convert to converted to binary images according to a threshold, step four detects the edges by using contour algorithm, and finally resizing the images by using the bilinear interpolation algorithm. The Server cloud side consists of the following steps: the first step is the features extraction which will be used in the sift algorithm to increase the accuracy. The second step uses the eliminated structure of the image and the arrangement of features in the image. The third step uses the k-mean algorithm to extract the algorithm, the fourth step is the classification that uses the algorithm j48, and the algorithm naive Bayes. The Iris recognition dataset is used to evaluate this system, which is the MMU dataset consisting of 450 images of 46 people. The recognition rate for a dataset is 99.99%.
HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization

Sparse neural networks are a key factor in developing resource-efficient machine learning applications. We propose the novel and powerful sparse learning method Adaptive Regularized Training (ART) to compress dense into sparse networks. Instead of the commonly used binary mask during training to reduce the number of model weights, we inherently shrink weights close to zero in an iterative manner with increasing weight regularization. Our method compresses the pre-trained model "knowledge" into the weights of highest magnitude. Therefore, we introduce a novel regularization loss named HyperSparse that exploits the highest weights while conserving the ability of weight exploration. Extensive experiments on CIFAR and TinyImageNet show that our method leads to notable performance gains compared to other sparsification methods, especially in extremely high sparsity regimes up to 99.8% model sparsity. Additional investigations provide new insights into the patterns that are encoded in weights with high magnitudes. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Sign Language Translating into Speech Using Cost-Effective Glove and Smartphone Application for Deaf People

Communication “is a process of formation and exchange of information, ideas, views, feelings, facts, etc.,” is fundamental to living and surviving among people to arrive at a public understanding. Implementing communication, ensuring its occurrence, and protecting it is essential to human rights. The mobile phone is the best communication tool, but our ears are the most crucial. Listening is the effective key to communication. Better hearing allows people to participate in their communities by succeeding in school, getting good jobs, etc. an integrated smart glove with Arduino and smartphones have been designed. In this paper, Particular motions and signs used by people worldwide who are deaf are used and sent to the smart glove linked to the phone by Bluetooth. Better hearing allows people to participate in their communities by succeeding in school, getting good jobs, etc. By addressing hearing loss, people can promote greater educational attainment and higher levels of economic productivity. Thus, to guarantee that disadvantaged people can receive the hearing look after they need, this work would ease people's life, providing them with better facilities. It will give adequate information to get a better conversation understanding.
Sparse Linear Concept Discovery Models

The recent mass adoption of DNNs, even in safety-critical scenarios, has shifted the focus of the research community towards the creation of inherently intrepretable models. Concept Bottleneck Models (CBMs) constitute a popular approach where hidden layers are tied to human understandable concepts allowing for investigation and correction of the network's decisions. However, CBMs usually suffer from: (i) performance degradation and (ii) lower interpretability than intended due to the sheer amount of concepts contributing to each decision. In this work, we propose a simple yet highly intuitive interpretable framework based on Contrastive Language Image models and a single sparse linear layer. In stark contrast to related approaches, the sparsity in our framework is achieved via principled Bayesian arguments by inferring concept presence via a data-driven Bernoulli distribution. As we experimentally show, our framework not only outperforms recent CBM approaches accuracy-wise, but it also yields high per example concept sparsity, facilitating the individual investigation of the emerging concepts. Our code and models are available at: https://github.com/konpanousis/ConceptDiscoveryModels.
ZiCo-BC: A Bias Corrected Zero-Shot NAS for Vision Tasks

Zero-Shot Neural Architecture Search (NAS) approaches propose novel training-free metrics called zero-shot proxies to substantially reduce the search time compared to the traditional training-based NAS. Despite the success on image classification, the effectiveness of zero-shot proxies is rarely evaluated on complex vision tasks such as semantic segmentation and object detection. Moreover, existing zero-shot proxies are shown to be biased towards certain model characteristics which restricts their broad applicability. In this paper, we empirically study the bias of state-of-the-art (SOTA) zero-shot proxy ZiCo across multiple vision tasks and observe that ZiCo is biased towards thinner and deeper networks, leading to sub-optimal architectures. To solve the problem, we propose a novel bias correction on ZiCo, called ZiCo-BC. Our extensive experiments across various vision tasks (image classification, object detection and semantic segmentation) show that our approach can successfully search for architectures with higher accuracy and significantly lower latency on Samsung Galaxy S10 devices.
A Comparative Study of Text Classification Using Artificial Intelligence Models

The presented study provides experiments for carrying out empirical studies related to deep learning, especially convolution networks, concerning novel dataset classification (video and audio). The study will utilize the architecture of the text classification problem in the document domain with the datasets related to documents collected from many projects on real document cases. The process of building a new dataset and the existence of such data followed the urgent need that is specialized in educational lectures. Test and evaluate the two new datasets (Audio and Video) with the published dataset Book dataset by using deep learning methods in classification and machine learning algorithms, then compare the Accuracy results for each dataset with both classification methods, which present that new datasets bring better results than Book dataset which are (95% and 90 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) for new datasets and 83% for Book dataset by using Deep learning, in machine learning algorithms the results are (91%, 77%, and 46 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ). Finally, the two classification techniques are used to maximize the benefits of the classification process. In the selection process, the system will choose deep learning for classifying the datasets than using machine learning algorithms.
Extract-and-Adaptation Network for 3D Interacting Hand Mesh Recovery

Understanding how two hands interact with each other is a key component of accurate 3D interacting hand mesh recovery. However, recent Transformer-based methods struggle to learn the interaction between two hands as they directly utilize two hand features as input tokens, which results in distant token problem. The distant token problem represents that input tokens are in heterogeneous spaces, leading Transformer to fail in capturing correlation between input tokens. Previous Transformer-based methods suffer from the problem especially when poses of two hands are very different as they project features from a backbone to separate left and right hand-dedicated features. We present EANet, extract-and-adaptation network, with EABlock, the main component of our network. Rather than directly uti-lizing two hand features as input tokens, our EABlock uti-lizes two complementary types of novel tokens, SimToken and JoinToken, as input tokens. Our two novel tokens are from a combination of separated two hand features; hence, it is much more robust to the distant token problem. Using the two type of tokens, our EABlock effectively extracts interaction feature and adapts it to each hand. The proposed EANet achieves the state-of-the-art performance on 3D interacting hands benchmarks. The codes are available at https://github.com/jkpark0825/EANet.
BiLMa: Bidirectional Local-Matching for Text-based Person Re-identification

Text-based person re-identification (TBPReID) aims to retrieve person images represented by a given textual query. In this task, how to effectively align images and texts globally and locally is a crucial challenge. Recent works have obtained high performances by solving Masked Language Modeling (MLM) to align image/text parts. However, they only performed uni-directional (i.e., from image to text) local-matching, leaving room for improvement by introducing opposite-directional (i.e., from text to image) localmatching. In this work, we introduce Bidirectional LocalMatching (BiLMa) framework that jointly optimize MLM and Masked Image Modeling (MIM) in TBPReID model training. With this framework, our model is trained so as the labels of randomly masked both image and text tokens are predicted by unmasked tokens. In addition, to narrow the semantic gap between image and text in MIM, we propose Semantic MIM (SemMIM), in which the labels of masked image tokens are automatically given by a state-of-the-art human parser. Experimental results demonstrate that our BiLMa framework with SemMIM achieves state-of-the-art Rank@1 and mAP scores on three benchmarks.
Entropic Score metric: Decoupling Topology and Size in Training-free NAS

Neural Networks design is a complex and often daunting task, particularly for resource-constrained scenarios typical of mobile-sized models. Neural Architecture Search is a promising approach to automate this process, but existing competitive methods require large training time and computational resources to generate accurate models. To overcome these limits, this paper contributes with: i) a novel training-free metric, named Entropic Score, to estimate model expressivity through the aggregated element-wise entropy of its activations; ii) a cyclic search algorithm to separately yet synergistically search model size and topology. Entropic Score shows remarkable ability in searching for the topology of the network, and a proper combination with LogSynflow, to search for model size, yields superior capability to completely design high-performance Hybrid Transformers for edge applications in less than 1 GPU hour, resulting in the fastest and most accurate NAS method for ImageNet classification. Code available here <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
DFM-X: Augmentation by Leveraging Prior Knowledge of Shortcut Learning

Neural networks are prone to learn easy solutions from superficial statistics in the data, namely shortcut learning, which impairs generalization and robustness of models. We propose a data augmentation strategy, named DFM-X, that leverages knowledge about frequency shortcuts, encoded in Dominant Frequencies Maps computed for image classification models. We randomly select X% training images of certain classes for augmentation, and process them by retaining the frequencies included in the DFMs of other classes. This strategy compels the models to leverage a broader range of frequencies for classification, rather than relying on specific frequency sets. Thus, the models learn more deep and task-related semantics compared to their counterpart trained with standard setups. Unlike other commonly used augmentation techniques which focus on increasing the visual variations of training data, our method targets exploiting the original data efficiently, by distilling prior knowledge about destructive learning behavior of models from data. Our experimental results demonstrate that DFM-X improves robustness against common corruptions and adversarial attacks. It can be seamlessly integrated with other augmentation techniques to further enhance the robustness of models. Codes are available at https://github.com/nis-research/dfmX-augmentation.
Hierarchical Spatiotemporal Transformers for Video Object Segmentation

This paper presents a novel framework called HST for semi-supervised video object segmentation (VOS). HST extracts image and video features using the latest Swin Transformer and Video Swin Transformer to inherit their inductive bias for the spatiotemporal locality, which is essential for temporally coherent VOS. To take full advantage of the image and video features, HST casts image and video features as a query and memory, respectively. By applying efficient memory read operations at multiple scales, HST produces hierarchical features for the precise reconstruction of object masks. HST shows effectiveness and robustness in handling challenging scenarios with occluded and fast-moving objects under cluttered backgrounds. In particular, HST-B outperforms the state-of-the-art competitors on multiple popular benchmarks, i.e., YouTube-VOS (85.0%), DAVIS 2017 (85.9%), and DAVIS 2016 (94.0%).
Facial Action Unit-Based Deepfake Video Detection Using Deep Learning

Deepfake videos are becoming more realistic, making them a menace. As a result of the development of deep learning techniques such as Generative adversarial networks (GAN), Deepfake has become closer to the truth. Widespread use of falsified videos and images on social media requires accurate detection. An identity switch (DeepFake) and an expression swap create facial modifications. This paper can detect deepfakes that are perfectly created. Traditional detection approaches that observe artifacts and pixel irregularities cannot keep up with modern technology. The paper is divided into two stages. In the first stage, the paper extracts facial action units from a person and creates a profile for him. This profile represents the behavior of his facial expressions, which differ from one person to another. This was done by building a deep learning network and training it based on a dataset. The second stage is testing, which involves taking videos, extracting facial action units, and testing them on the network to classify them as fake or real. The network has proven its ability to classify with high accuracy of %95.75 compared to traditional methods.
Reinforcement learning for instance segmentation with high-level priors

Instance segmentation is a fundamental computer vision problem which remains challenging despite impressive recent advances due to deep learning-based methods. Given sufficient training data, fully supervised methods can yield excellent performance, but annotation of groundtruth remains a major bottleneck, especially for biomedical applications where it has to be performed by domain experts. The amount of labels required can be drastically reduced by using rules derived from prior knowledge to guide the segmentation. However, these rules are in general not differentiable and thus cannot be used with existing methods. Here, we revoke this requirement by using stateless actor critic reinforcement learning, which enables non-differentiable rewards. We formulate the instance segmentation problem as graph partitioning and the actor critic predicts the edge weights driven by the rewards, which are based on the conformity of segmented instances to high-level priors on object shape, position or size. The experiments on toy and real data demonstrate that a good set of priors is sufficient to reach excellent performance without any direct object-level supervision.
Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection

While state-of-the-art object detection methods have reached some level of maturity for regular RGB images, there is still some distance to be covered before these methods perform comparably on Infrared (IR) images. The primary bottleneck towards accomplishing this goal is the lack of sufficient labeled training data in the IR modality, owing to the cost of acquiring such data. Realizing that object detection methods for the RGB modality are quite robust (at least for some commonplace classes, like person, car, etc.), thanks to the giant training sets that exist, in this work we seek to leverage cues from the RGB modality to scale object detectors to the IR modality, while preserving model performance in the RGB modality. At the core of our method, is a novel tensor decomposition method called TensorFact which splits the convolution kernels of a layer of a Convolutional Neural Network (CNN) into low-rank factor matrices, with fewer parameters than the original CNN. We first pre-train these factor matrices on the RGB modality, for which plenty of training data are assumed to exist and then augment only a few trainable parameters for training on the IR modality – to avoid over-fitting, while encouraging them to capture complementary cues from those trained only on the RGB modality. We validate our approach empirically by first assessing how well our TensorFact decomposed network performs at the task of detecting objects in RGB images vis-á-vis the original network and then look at how well it adapts to IR images of the FLIR ADAS v1 dataset. For the latter, we train models under scenarios that pose challenges stemming from data paucity. From the experiments, we observe that: (i) TensorFact shows performance gains on RGB images; (ii) further, this pre-trained model, when fine-tuned, outperforms a standard state-of-the-art object detector on the FLIR ADAS v1 dataset by about 4% in terms of mAP 50 score.
S2RF: Semantically Stylized Radiance Fields

We present our method for transferring style from any arbitrary image(s) to object(s) within a 3D scene. Our primary objective is to offer more control in 3D scene stylization, facilitating the creation of customizable and stylized scene images from arbitrary viewpoints. To achieve this, we propose a novel approach that incorporates nearest neighborhood-based loss, allowing for flexible 3D scene reconstruction while effectively capturing intricate style details and ensuring multi-view consistency.