Reviewerly/exHarmony huggingface dataset:
Learn to Race: Sequential Actor-Critic Reinforcement Learning for Autonomous Racing

Deep reinforcement learning (DRL) has emerged as a powerful technique for autonomous racing. However, current studies often overlook the utilization of valuable historical information by relying solely on dense layers to generate actions based on the current state. This paper presents a novel approach called Sequential Actor-Critic (Seq-AC) for autonomous racing, which leverages the historical trajectory to enhance learning efficiency. Besides dense layers, we employ Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks to construct the actor and critic networks within the Deep Deterministic Policy Gradients (DDPG) framework, complemented by the use of a continuous memory buffer. Through extensive simulations, we demonstrate that the proposed Seq-AC method surpasses regular DDPG in terms of convergence speed and training results. By incorporating historical information, our approach enables the agent to capture long-term dependencies and make informed decisions. Furthermore, we investigate the impact of time sequence lengths on algorithm performance, shedding light on the optimal choice for effective learning.
DFM4SFM - Dense Feature Matching for Structure from Motion

Structure from motion (SfM) is a fundamental task in computer vision and allows recovering the 3D structure of a stationary scene from an image set. Finding robust and accurate feature matches plays a crucial role in the early stages of SfM. So in this work, we propose a novel method for computing image correspondences based on dense feature matching (DFM) using homographic decomposition: The underlying pipeline provides refinement of existing matches through iterative rematching, detection of occlusions and extrapolation of additional matches in critical image areas between image pairs. Our main contributions are improvements of DFM specifically for SfM, resulting in global refinement and global extrapolation of image correspondences between related views. Furthermore, we propose an iterative version of the Delaunay-triangulation-based outlier detection algorithm for robust processing of repeated image patterns. Through experiments, we demonstrate that the proposed method significantlv improves the reconstruction accuracy.
Analisis Sentimen Fenomena PHK Massal Menggunakan Naive Bayes dan Support Vector Machine

Termination of employment (PHK) on a large scale has a very significant impact on society and the economy. Mass layoffs have led to an increase in the number of unemployed people. Many people who have lost their jobs without a stable source of income struggle to find new jobs. This exacerbated the situation on the labor market and increased the number of unemployed people. Mass layoffs can also reduce economic activity and consumption. The sentiment analysis carried out aims to determine public sentiment regarding the phenomenon of mass layoffs that are currently happening in Indonesia based on positive and negative categories. In this study, the classification method used is the SVM method, which is one of the supervised learning methods in machine learning and also uses Nave Bayes as a comparison method. After classification, the next stage is the testing process using the K-fold cross-validation method. From the various sentiments obtained from Twitter data, it can be concluded that there are around 108 positive sentiments and 333 negative sentiments related to mass layoffs, while the results obtained from the test results using the SVM method show an accuracy of up to 84% while using the Nave Bayes method shows an accuracy of up to 74.1 percent
Penerapan Normalisasi Histogram untuk Peningkatan Kontras Pencahayaan pada Pengamatan Visual CCTV

Low Contrast can cause low image quality and make it difficult for proper image analysis. One technique to improve image quality is to increase the lighting contrast. One method that is often used is histogram normalization, which can increase image contrast by balancing the distribution of pixels across a range of pixel values. The purpose of this research is to apply the histogram normalization method to images and compare the results before and after the normalization process. The images used in this study are self-made images and images from public databases. The results of the study show that normalized histograms can increase image contrast and improve low image quality due to inadequate lighting. Thus, histogram normalization can be used as a technique to improve image quality in various applications, including medical image processing, satellite image processing, and security surveillance.
A Comparative Study of MobileNet Architecture Optimizer for Crowd Prediction

Artificial intelligence technology has grown quickly in recent years. Convolutional neural network (CNN) technology has also been developed as a result of these developments. However, because convolutional neural networks entail several calculations and the optimization of numerous matrices, their application necessitates the utilization of appropriate technology, such as GPUs or other accelerators. Applying transfer learning techniques is one way to get around this resource barrier. MobileNetV2 is an example of a lightweight convolutional neural network architecture that is appropriate for transfer learning. The objective of the research is to compare the performance of SGD and Adam using the MobileNetv2 convolutional neural network architecture. Model training uses a learning rate of 0.0001, batch size of 32, and binary cross-entropy as the loss function. The training process is carried out for 100 epochs with the application of early stop and patience for 10 epochs. Result of this research is both models using Adam's optimizer and SGD show good capability in crowd classification. However, the model with the SGD optimizer has a slightly superior performance even with less accuracy than model with Adam optimizer. Which is model with Adam has accuracy 96%, while the model with SGD has 95% accuracy. This is because in the graphical results model with the SGD optimizer shows better stability than the model with the Adam optimizer. The loss graph and accuracy graph of the SGD model are more consistent and tend to experience lower fluctuations than the Adam model.
Studi Komparasi Algoritma SVM Dan Random Forest Pada Analisis Sentimen Komentar Youtube BTS

Sentiment analysis of YouTube boy group BTS comments uses the NLP approach to detect emotional patterns based on two category labels, namely positive and negative. With NLP, positive or negative polarity in an entity can be allocated as well as predicted high and low performance from various classification sentiments. The machine learning algorithms used to measure the accuracy of sentiment analysis developed are the Support Vector Machine and Random Forest algorithms. The steps taken start from the data collection obtained from the BTS YouTube Comment dataset and then go through the data preprocessing stage. Then proceed to the feature extraction stage by converting text into digital vectors or Bag of Words (BOW) and classified using machine learning algorithms until the evaluation stage. From the results comparison of the evaluated algorithms, the accuracy value between the two algorithms is 96% for training data and 85% for data testing using the SVM algorithm, while for the Random Forest algorithm it is 82% for training data and 80% for data testing. This shows that the SVM algorithm produces a higher accuracy value than the Random Forest for sentiment analysis of YouTube boy group BTS comments.
M-Estimation Based Robust Factor Graph Fusion Method for Integrated Navigation under Non-Gaussian Noise

A robust factor graph fusion method based on Mestimation for the inertial navigation system and global navigation satellite system (INS/GNSS) is proposed in this paper. The factor nodes of the designed factor graph are composed of prior factor nodes and GNSS factor nodes with unary factor properties, and INS factor nodes with binary factor properties, in which the variable nodes are represented by extended navigation states. The traditional factor graph fusion algorithm always considers the measurement noise as a Gaussian distribution, which cannot adapt to the denial environment and carrier maneuvering, resulting in greatly reduced performance. In this paper, measurement noise with non-Gaussian characteristics is modeled as Gaussian mixture noise, which is then processed by M-estimation-based method. The incremental smoothing algorithm is used to enhance the effectiveness of the factor graph fusion method. The navigation accuracy of the proposed robust method for non-Gaussian measurement information is more than 45% higher than that of the fusion algorithm based on the existing factor graph, which has been verified by the experimental simulations.
PENGEMBANGAN LEMBAR KERJA PESERTA DIDIK BERBASIS LIVE WORKSHEET UNTUK MENINGKATKAN AKTIVITAS DAN HASIL BELAJAR SISWA KELAS V

To find out the development, student responses, feasibility and concept mastery of students in the use of Live Worksheet-based LKPD for thematic learning themes 9 sub-themes 2 class V at SD Islam Modern An-Nur, the research method used in this development research is Sugiyono's modification includes 6 steps, namely problem analysis, data collection, product design, design validation, design revision and product testing.The subject of this study was An-Nur Modern Islamic Elementary School with 15 students in class V. The data collection tools used were expert validation questionnaires, student response questionnaires as well as pretest and posttest. The data analysis technique used in this research is descriptive analysis.. The measurement scale that will be used in this study to determine the score of the respondents' answers is to use the Guttman Scale. The tests carried out are by using pretest and posttest. The results of this study obtained the feasibility of a team of media and material experts as much as 89% (Very Eligible). Student responses to live worksheet-based student worksheets obtained an average student response of 96% which, based on the interpretation category, was included in the "very good" category. good". Mastery of students' concepts of Live Worksheet-based Student Worksheets can be seen through the pretest and posttest that have been carried out. From the results of the pretest, the percentage of the average value is 5, while after the posttest, the average value is 8, which is based on the category of interpretation included in the category of "very feasible". Based on these results, student worksheets based on live worksheets can increase student activity and learning outcomes.
Features of the geological structure of the Kogodai VMS deposit in the Kurchum block

The article considers the geological structure of Kogodai copper massive sulfide deposit in the Kurchum block, which is an integral part of the Irtysh shear zone. It is shown that the geological structure of the site involves feldspar quartz-mica and amphibole-feldspar-mica crystalline schists, gneisses with frequent horizons of amphibolites. Mineralized zones confined to the contact of amphibolite bodies with gneisses on terrigenous rocks are noted, granitoids in different phases of intrusion including dikes and various granite and quartz porphyries are also considered. The spatial contiguity of geological structures of different ages and compositions subject to intense metamorphic transformations is emphasized. This article presents the features of the copper massive sulfide deposit, the composition of the ores and the connection with the geological and structural-tectonic factors that contributed to its formation.
Artificial Intelligent Model for Riot and Violence Detection that Largely Affect Societal Health and Local Healthcare System

Violence and Riots are the curse for the society and it largely affects the social fabric of the society, at the same time it also brings medical emergency. Violence and other forms of violences lead to health complications at a very large scale like deterioration in mental health, emotional health, physical health, and other medical issues like Depression and anxiety. Democracy like India has great sensations toward Riots where a lot of different religious societies reside. It seems wonderful that everyone has rights but at the same time everyone also has some Duties. Any activity that involves violence cannot be justified in the name of Protest and Democracy. The rioting is similar to such anti-national activities with an objective to terrorize the local residents, intimidate citizens, and threaten the State government, police, and the authorities. Riots and Violence affect Society's Emotional Health and Local Healthcare System at large scale. Such disruptive activities create a kiosk in the society and victims of such events need healthcare system support immediately. It is very serious that those involved in such disruptive activities need systematic trials against them with proper proof and evidence. It is a very difficult task to collect evidence while such activities are happening especially when violence is done by mobs. CCTV Surveillance is a common way of security for any place that is under threat. It is very difficult to monitor them by human beings so there should be some intelligent system that can identify the patterns of interest like riot and violence. Once the system identifies the pattern of interest, it sends control information to the command center and now that CCTV camera switches to Riot mode to capture image and video to the fullest of its clarity by zooming in and out. These pictures and videos further can be used for compliance of rioters action and to make them under the bar for a long period of time. This Deep Learning model will help us to identify such disruptive activities like fire, riot, and stone pelting done by a group of people. Basically, this model works on activity recognition in the given image. A Deep Convolutional Neural Network (DCNN) will be used to identify such activities from the image (M. Ramzan, A. Abid, H. Ullah Khan, S. Mahmood Awan, A. Ismial, M. Ahmed, M. Ilyas, and A. Mahmood, “A Review on State-of-the-Art Violence Detection Techniques” in IEEE Access August 19, 2019. https://doi.org/10.1109/ACCESS.2019.2932114 ).
Balinese Script Handwriting Recognition Using Faster R-CNN

In Balinese culture, the ability to read Balinese script is one of the challenges young generations face. Advances in machine learning have proposed handwriting detection systems using both traditional and deep learning models. However, the traditional approach is usually impractical and is prone to inaccurate identification results. Convolutional Neural Network (CNN)-based models integrate feature extraction and classification into an end-to-end pipeline to increase performance. This research proposes that recognizing characters through an object detection approach makes an end-to-end process of localizing and classifying several characters simultaneously using the Faster R-CNN. Four CNN models, including ResNet-50, ResNet-101, ResNet-152, and Inception ResNet V2 were tested to detect 28 Balinese characters in a single form covering 18 consonants and 10 digits using Intersection over Union (IoU) thresholds: 0.5 and 0.75. ResNet-50 and Inception ResNet V2 achieve 0.991 mAP at IoU of 0.5, while Inception ResNet V2 excels at IoU of 0.75. Further analysis showed that class “nol” had the lowest Recall due to many undetected ground truths. Meanwhile, class “ba” had the lowest Precision due to its similarity with classes “ga” and “nga”. This research contributes to experimenting with Faster R-CNN in detecting handwritten Balinese scripts.
CLOMO: Counterfactual Logical Modification with Large Language Models

In this study, we delve into the realm of counterfactual reasoning capabilities of large language models (LLMs). Our primary objective is to cultivate the counterfactual thought processes within LLMs and rigorously assess these processes for their validity. Specifically, we introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark. In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship. To effectively evaluate a generation model's counterfactual capabilities, we propose an innovative evaluation metric, the LogicAware Counterfactual Score to directly evaluate the natural language output of LLMs instead of modeling the task as a multiple-choice problem. Analysis shows that the proposed automatic metric aligns well with human preference. Our experimental results show that while LLMs demonstrate a notable capacity for logical counterfactual thinking, there remains a discernible gap between their current abilities and human performance.
Do text-free diffusion models learn discriminative visual representations?

While many unsupervised learning models focus on one family of tasks, either generative or discriminative, we explore the possibility of a unified representation learner: a model which addresses both families of tasks simultaneously. We identify diffusion models, a state-of-the-art method for generative tasks, as a prime candidate. Such models involve training a U-Net to iteratively predict and remove noise, and the resulting model can synthesize high-fidelity, diverse, novel images. We find that the intermediate feature maps of the U-Net are diverse, discriminative feature representations. We propose a novel attention mechanism for pooling feature maps and further leverage this mechanism as DifFormer, a transformer feature fusion of features from different diffusion U-Net blocks and noise steps. We also develop DifFeed, a novel feedback mechanism tailored to diffusion. We find that diffusion models are better than GANs, and, with our fusion and feedback mechanisms, can compete with state-of-the-art unsupervised image representation learning methods for discriminative tasks - image classification with full and semi-supervision, transfer for fine-grained classification, object detection and segmentation, and semantic segmentation. Our project website (https://mgwillia.github.io/diffssl/) and code (https://github.com/soumik-kanad/diffssl) are available publicly.
Active Open-Vocabulary Recognition: Let Intelligent Moving Mitigate CLIP Limitations

Active recognition, which allows intelligent agents to explore observations for better recognition performance, serves as a prerequisite for various embodied AI tasks, such as grasping, navigation and room arrangements. Given the evolving environment and the multitude of object classes, it is impractical to include all possible classes during the training stage. In this paper, we aim at advancing active open-vocabulary recognition, empowering embodied agents to actively perceive and classify arbitrary objects. However, directly adopting recent open-vocabulary classification models, like Contrastive Language Image Pretraining (CLIP), poses its unique challenges. Specifically, we observe that CLIP's performance is heavily affected by the viewpoint and occlusions, compromising its reliability in unconstrained embodied perception scenarios. Further, the sequential nature of observations in agent-environment interactions necessitates an effective method for integrating features that maintains discriminative strength for open-vocabulary classification. To address these issues, we introduce a novel agent for active open-vocabulary recognition. The proposed method leverages inter-frame and inter-concept similarities to navigate agent movements and to fuse features, without relying on class-specific knowledge. Compared to baseline CLIP model with 29.6% accuracy on ShapeNet dataset, the proposed agent could achieve 53.3% accuracy for open-vocabulary recognition, without any fine-tuning to the equipped CLIP model. Additional experiments conducted with the Habitat simulator further affirm the efficacy of our method.
Synchronizing Vision and Language: Bidirectional Token-Masking AutoEncoder for Referring Image Segmentation

Referring Image Segmentation (RIS) aims to segment target objects expressed in natural language within a scene at the pixel level. Various recent RIS models have achieved state-of-the-art performance by generating contextual tokens to model multimodal features from pretrained encoders and effectively fusing them using transformer-based cross-modal attention. While these methods match language features with image features to effectively identify likely target objects, they often struggle to correctly understand contextual information in complex and ambiguous sentences and scenes. To address this issue, we propose a novel bidirectional token-masking autoencoder (BTMAE) inspired by the masked autoencoder (MAE). The proposed model learns the context of image-to-language and language-to-image by reconstructing missing features in both image and language features at the token level. In other words, this approach involves mutually complementing across the features of images and language, with a focus on enabling the network to understand interconnected deep contextual information between the two modalities. This learning method enhances the robustness of RIS performance in complex sentences and scenes. Our BTMAE achieves state-of-the-art performance on three popular datasets, and we demonstrate the effectiveness of the proposed method through various ablation studies.
ChatIllusion: Efficient-Aligning Interleaved Generation ability with Visual Instruction Model

As the capabilities of Large-Language Models (LLMs) become widely recognized, there is an increasing demand for human-machine chat applications. Human interaction with text often inherently invokes mental imagery, an aspect that existing LLM-based chatbots like GPT-4 do not currently emulate, as they are confined to generating text-only content. To bridge this gap, we introduce ChatIllusion, an advanced Generative multimodal large language model (MLLM) that combines the capabilities of LLM with not only visual comprehension but also creativity. Specifically, ChatIllusion integrates Stable Diffusion XL and Llama, which have been fine-tuned on modest image-caption data, to facilitate multiple rounds of illustrated chats. The central component of ChatIllusion is the "GenAdapter," an efficient approach that equips the multimodal language model with capabilities for visual representation, without necessitating modifications to the foundational model. Extensive experiments validate the efficacy of our approach, showcasing its ability to produce diverse and superior-quality image outputs Simultaneously, it preserves semantic consistency and control over the dialogue, significantly enhancing the overall user's quality of experience (QoE). The code is available at https://github.com/litwellchi/ChatIllusion.