Reviewerly/exHarmony huggingface dataset:
ABSTRACTIVE-BASED AUTOMATIC TEXT SUMMARIZATION ON INDONESIAN NEWS USING GPT-2

Automatic text summarization is challenging research in natural language processing, aims to obtain important information quickly and precisely. There are two main approach techniques for text summary: abstractive and extractive summary. Abstractive Summarization generates new and more natural words, but the difficulty level is higher and more challenging. In previous studies, RNN and its variants are among the most popular Seq2Seq models in text summarization. However, there are still weaknesses in saving memory; gradients are lost in long sentences so resulting in a decrease in lengthy text summaries. This research proposes a Transformer model with an Attention mechanism that can fetch important information, solve parallelization problems, and summarize long texts. The Transformer model we propose is GPT-2. GPT-2 uses decoders to predict the next word using the pre-trained model from w11wo/indo-gpt2-small, implemented on the Indosum Indonesian dataset. Evaluation assessment of the model performance using ROUGE evaluation. The study's results get an average result recall for R-1, R-2, and R-L were 0.61, 0.51, and 0.57, respectively. The summary results can paraphrase sentences, but some still use the original words from the text. Future work increase the amount of data from the dataset to improve the result of more new sentence paraphrases.
E-SCM (SUPPLY CHAIN MANAGEMENT) MANAGEMENT OF EDUCATIONAL OIL SUPPLY AT UD. HASTIN

Abstract: Today's highly competitive trade competition requires business owners to be able to create products and services that are cheaper, better and faster so that they can satisfy consumers. UD. Hastin is a distributor of cooking oil located on Jl. Lt. Gen. Suprapto No. 16 Tanjung Balai – Asahan, North Sumatra. Cooking oil is a very high demand household product. Therefore, it is necessary to have good stock control so that there is no shortage or excess of stock. The problems faced by UD. Hastin currently has limitations in controlling inventory because it is still done manually, which causes slow and inefficient performance. The purpose of this research is to design a Supply Chain Management at UD. Hastin uses the PHP programming language and MySQL database by implementing a web-based system that will make it easier to manage oil supplies. The method used in this research is a qualitative method with literature and field studies. The conclusion, the E-SCM was built at UD. Hastin can avoid shortages and excess stock due to better management, smoother communication with suppliers, and information on UD. Hastin's stock items can be known in real-time. The product resulting from this research is the Stock Management Information System at UD. Hastin. Keywords: edible oil; E-SCM; suppliers; supply Abstrak: Persaingan dagang yang sangat kompetitif saat ini menuntut pemilik usaha untuk dapat menciptakan produk dan jasa yang murah, lebih baik dan lebih cepat sehingga bisa memuaskan konsumen. UD. Hastin merupakan distributor minyak makan yang berlokasi di Jl. Letjend. Suprapto No. 16 Tanjung Balai – Asahan, Sumatera Utara. Minyak makan merupakan produk rumah tangga yang sangat tinggi permintaanya. Oleh karenanya perlu pengendalian stok yang baik agar tidak kekurangan dan atau kelebihan stok. Permasalahan yang dihadapi oleh UD. Hastin saat ini adalah keterbatasan dalam mengontrol stok persediaan karena masih dilakukan secara manual, yang menyebabkan kinerja yang lambat dan kurang efisien. Tujuan dari penelitian untuk merancang Supply Chain Management pada UD. Hastin menggunakan bahasa pemrograman PHP dan database MySQL dengan menerapkan sistem berbasis web yang akan mempermudah dalam pengelolaan persediaan minyak. Metode yang digunakan dalam penelitian ini ialah metode kualitatif dengan studi pustaka dan studi lapangan. Kesimpulannya E-SCM yang dibangun di UD.Hastin mampu menggindari kekurangan dan kelebihan stok karena pengelolaanya yang lebih baik, komunikasi dengan supplier lebih lancer dan informasi stok barang UD.Hastin bisa diketahui secara real time. Dan Produk yang dihasilkan adalah Sistem Informasi Pengelolaan Stok Barang pada UD. Hastin. Kata kunci: E-SCM; minyak makan; persediaan; supplier
Improved Cauchy Reed-Solomon Codes for Cloud Data Retrieval and Secured Data Storage using Role-Based Cryptographic Access and forensic investigation

Doling out client consent strategies to PC frameworks presents a huge test in guaranteeing legitimate approval, especially with the development of open frameworks and scattered stages like the cloud. RBAC has turned into a broadly involved strategy in cloud server applications because of its versatility. Granting access to cloud-stored data for investigating potential wrongdoings is crucial in computer forensic investigations. In cases where the cloud service provider's reliability is questionable, maintaining data confidentiality and establishing an efficient procedure for revoking access upon credential expiration is essential. As storage systems expand across vast networks, frequent component failures require stronger fault tolerance measures. Our work secure data-sharing system combines role (Authorized) based access control and AES encryption technology to provide safe key distribution and data sharing for dynamic groups. Data recovery entails protecting data dispersed over distributed systems by storing duplicate data and applying the erasure code technique. Erasure coding strategies, like Reed-Solomon codes, guarantee disc failure robustness while cutting down on data storage expenses dramatically. They do, however, also result in longer access times and more expensive repairs. Consequently, there has been a great deal of interest in academic and business circles for the investigation of novel coding strategies for cloud storage systems. The objective of this study is to present a novel coding method that utilizes the intricate Cauchy matrix in order to improve Reed-Solomon coding efficiency and strengthen fault tolerance.
Capture More Structured Context by Vision Transformers for Free-Hand Sketch Recognition

Free-hand sketch recognition presents unique challenges that require careful consideration of spatial and temporal properties. Convolutional neural networks, which are widely used for sketch recognition, have limited ability to extract structured context, leading to insufficient learning of spatial properties. In this study, we introduce Vision Transformers to the sketch recognition domain to address this issue. To the best of our knowledge, this is the first attempt to evaluate the applicability and effectiveness of Vision Transformers for sketch recognition. In addition, we also address the problem of semantically different categories appearing similar in sketches. To overcome this challenge, we propose a novel loss function for sketch recognition, termed Sketch Online Label Smoothing (SketchOLS). Our experiments on QuickDraw dataset demonstrate that the proposed approach is a robust and effective solution for sketch recognition. Remarkably, our method outperforms existing models even without the injection of additional temporal information. These results highlight the potential of our approach to enhance free-hand sketch recognition tasks.
Object Detection via Multi-Scale Token Based on Vision Transformer

Visual transformers have achieved impressive performance on object detection. Traditional transformers only focus on multi-scale features between tokens and tokens. However, these methods do not pay attention to the fine-grained features inside a single token, which can lead to the loss of semantic information in the object detection task. To address this issue, we propose a novel network for the above problem, which consists of three components, (1) Internal Multiscale Token Module (IMTM) focuses on the receptive field size of each token and transforms the token dimension size to effectively extract more multiscale features within the self-attention layer, thereby improving the performance and generalization ability of the model. (2) Differential Filter Module (DFM) uses a convolutional network to focus on high-frequency information in the image, helping the Transformer to learn edge features and establish local context, while improving the model performance through residual connections. (3) Feature Fusion Module (FFM) enhances the local and global information extracted by the network by fusing information from different dimensions. Extensive experiments on PASCAL VOC shows that our proposed method can achieve a state-of-the-art performance on object detection.
Continually-Adapted Margin and Multi-Anchor Distillation for Class-Incremental Learning

This paper addresses the problem of class-incremental learning. The model is trained to recognize the classes added incrementally. It thus suffers from the challenging issue of catastrophic forgetting. Stemming from the knowledge distillation idea of attempting to retain the model's knowledge on seen classes while learning the newly-added ones, we advance to further alleviate the catastrophic forgetting via our proposed multi-anchor distillation objective, which is realized by constraining the spatial relationship between the input data and the multiple class embeddings of each seen class in the feature space while training the model. Moreover, since the knowledge distillation for incremental learning generally relies on keeping a replay buffer to store the samples of seen classes, the buffer of limited size brings another issue of class imbalance: the number of samples from each seen class decreases gradually, thus being much smaller than the number of samples from each new class. We therefore propose to introduce the continually-adapted margin into the classification objective for tackling the prediction bias towards new classes caused by the class imbalance. Experiments are conducted on various datasets and settings to demonstrate the effectiveness and superior performance of our proposed techniques in comparison to several state-of-the-art baselines.
Fidelity-preserving Learning-Based Image Compression: Loss Function and Subjective Evaluation Methodology

Learning-based image compression methods have emerged as state-of-the-art, showcasing higher performance compared to conventional compression solutions. These data-driven approaches aim to learn the parameters of a neural network model through iterative training on large amounts of data. The optimization process typically involves minimizing the distortion between the decoded and the original ground truth images. This paper focuses on perceptual optimization of learning-based image compression solutions and proposes: i) novel loss function to be used during training and ii) novel subjective test methodology that aims to evaluate the decoded image fidelity. According to experimental results from the subjective test taken with the new methodology, the optimization procedure can enhance image quality for low-rates while offering no advantage for high-rates.
Video Prediction Based on Multi-Resolution Echo State Networks for a Jar Test

For a jar test in water purification, it is necessary to develop a video prediction method to assist in coagulant dosage decisions. However, such a method has not been established; video prediction methods in other fields require a large amount of training videos. This paper proposes a video prediction method based on multi-resolution echo state networks (ESN) for a jar test. Because the proposed method is constructed based on ESN, we succeed in long-term video prediction even if the amount of training videos is small. Specifically, we develop a new model that performs ESN while observing the input video at different resolutions and effectively fuses the prediction results. The multi-resolution approach enables us to acquire both rough shape changes and fine pattern features, resulting in accurate prediction. In fusion of the multiple prediction results, our method can calculate optimal weights by the least squares method. Experimental results for a jar test confirm that our method outperformed existing video prediction methods based on deep neural networks. In addition, the results on the sky timelapse dataset show the versatility of our method.
StGAN: A Novel Symbolic Signal Decomposition Base on GANs and Swin Transformer

The symbolic imagery signal decomposition is a common problem in digital signal processing. Its main purpose is to divide the symbolic imagery signal into different parts. However, in real-world applications, symbolic imagery signal captured by the camera is usually influenced by complex negative lighting environments such as highlights, reflections, drop shadows, or information loss. To overcome those problems, a generative adversarial network with Swin Transformer (StGAN) is proposed and applied to symbolic imagery signal decomposition and semantic segmentation tasks. In addition, a realistic image dataset taken in complex lighting conditions is proposed for symbolic imagery signal decomposition, which have complex lighting environments and edge information loss, we name it the MLT dataset. We demonstrate StGAN brings significant improvements in performance than some existing methods in the accuracy of symbolic imagery signal decomposition on MLT datasets. Further experiments on Sky and Facades datasets prove that StGAN works well in other tasks, especially when fewer data is involved in training, but it can still ensure good accuracy.
Reference Governors Based on Offline Training of Regression Neural Networks

This paper presents two machine learning-based constraint management approaches based on Reference Governors (RGs). The first approach, termed NN-DTC, uses regression neural networks to approximate the distance to constraints. The second, termed NN-NL-RG, uses regression neural networks to approximate the input-output map of a nonlinear RG. Both approaches are shown to enforce constraints for a nonlinear second order system. NN-NL-RG requires a smaller dataset size as compared to NN-DTC for well-trained neural networks. For systems with multiple constraints, NN-NL-RG is also more computationally efficient than NN - DTC. Finally, promising results are reported by having both approaches implemented on a more complex spacecraft proximity maneuvering and docking application, through simulations.
MEDO: Minimizing Effective Distortions Only for Machine-Oriented Visual Feature Compression

In search for efficient feature compression technologies for machine consumption, MPEG recently issued a call for proposal (CfP) on feature compression for video coding for machine (FCVCM). One issue in feature compression is that the input feature maps generally have high redundancy in them. Various researches to reduce such redundancy have been made. For example, a recent study called L-MSFC (learnable multi-scale feature compression), which effectively combines multi-scale feature fusion and compression in an end-to-end learnable framework, showed up to 98% BD rate gain over the anchor model defined in the FCVCM CfP. Despite these advances in FCVCM, relation between distortions in feature maps and performance of vision tasks has stayed relatively unexplored. In this paper, we propose a novel loss function called MEDO (minimizing effective distortions only) based on our hypothesis that distortions below some threshold do not improve task performance. Experimental results on instance segmentation task show that our MEDO loss on top of L-MSFC improves the overall rate-mAP performance without compromising complexity. Being more practical for real-world uses, we also present an extension to L-MSFC for variable-rate support with a single model.
Data Efficient Incremental Learning via Attentive Knowledge Replay

Class-incremental learning (CIL) tackles the problem of continuously optimizing a classification model to support growing number of classes, where the data of novel classes arrive in streams. Recent works propose to use representative exemplars of learnt classes, and replay the knowledge of them afterward under certain memory constraints. However, training on a fixed set of exemplars with an imbalanced proportion to the new data leads to strong biases in the trained models. In this paper, we propose an attentive knowledge replay framework to refresh the knowledge of previously learnt classes during incremental learning, which generates virtual training samples by blending between pairs of data. Particularly, we design an attention module that learns to predict the adaptive blending weights in accordance with their relative importance to the overall objective, where the importance is derived from the change of the image features over incremental phases. Our strategy of attentive knowledge replay encourages the model to learn smoother decision boundaries and thus improves its generalization beyond memorizing the exemplars. We validate our design in a standard class-incremental learning setup and demonstrate its flexibility in various settings.
Multi-modal Social Group Activity Recognition in Panoramic Scene

Group Activity Recognition (GAR) is a challenging problem in computer vision due to the intricate dynamics and interactions among individuals. The existing methods utilize RGB videos face challenges in panoramic environments with numerous individuals and social groups. In this paper, we propose Multimodal Group Activity Recognition network (MGAR-net), that leverages the combined power of RGB and LiDAR modalities. Our approach effectively utilizes information from both modalities thus robustly and accurately captures individual relationships and detects social groups in face of optical challenges. By harnessing the capability of LiDAR with our new fusion module, called Distance Aware Fusion Module (DAFM), MGAR-net acquires valuable 3D structure information. We conduct experiments on the JRDB-Act dataset, which contains challenging scenarios with numerous people. The results demonstrate that LiDAR data provide valuable information for social grouping and recognizing individual action and group activities, particularly in crowded group settings. For social grouping, our MGAR-net improve performance by about 12% compared to the existing state-of-the-art models in terms of the AP metric.
A Lightweight No-reference Video Quality Assessment Method

Recently, quality assessment for user-generated content (UGC) videos has become a challenging task due to the absence of reference videos and the presence of complex distortions. Prior methods has highlighted the effectiveness of semantic features for quality assessment. However, these models are incapable for real-time prediction and efficient computation in practical applications. In this paper, we design a lightweight no-reference video quality assessment model leveraging pretrained lightweight network for semantic understanding and utilizing a low-level CNN for distortion features. The temporal features and spatial features are extracted respectively in the semantic and low-level scales, and then they are multiplied and integrated to obtain the video-level score. Experiments on UGC video quality databases show that our proposed model achieves comparable accuracy to state-of-the-art benchmarks while providing real-time performance on GPU.
AHFu-Net: Align, Hallucinate, and Fuse Network for Missing Multimodal Action Recognition

In this work, we explore the multimodal action recognition problem, specifically in the context of RGB-Depth modalities scenario, where a subset of the learning modalities is missing at inference time. To address this issue, we construct a hallucination network to generate missing modality information from the available modality at inference time. We propose key components of an effective spatio-temporal encoder for strong unimodal performance with Local Patch Temporal Transformer (LPTT) and Spatial Encoder Transformer (SET), alignment of multi-modal features, and fusion strategy with our Multimodal Bottleneck Transformer Fusion Module (MMBTF). We incorporate these ideas into a novel framework named AHFu-Net (Align, Hallucinate, and Fuse network) for RGB-Depth action recognition. Our experiments demonstrate that AHFu Net achieves state-of-the-art performance while maintaining high accuracy in the case of missing modality on multimodal datasets of NTU-RGB+D and NWUCLA.
Personalized Decision Supports based on Theory of Mind Modeling and Explainable Reinforcement Learning

In this paper, we propose a novel personalized decision support system that combines Theory of Mind (ToM) modeling and explainable Reinforcement Learning (XRL) to provide effective and interpretable interventions. Our method leverages DRL to provide expert action recommendations while incorporating ToM modeling to understand users' mental states and predict their future actions, enabling appropriate timing for intervention. To explain interventions, we use counterfactual explanations based on RL's feature importance and users' ToM model structure. Our proposed system generates accurate and personalized interventions that are easily interpretable by end-users. We demonstrate the effectiveness of our approach through a series of crowd-sourcing experiments in a simulated team decision-making task, where our system outperforms control baselines in terms of task performance. Our proposed approach is agnostic to task environment and RL model structure, therefore has the potential to be generalized to a wide range of applications.
Stability Conditions and Control Designs via Improved Lyapunov Function for Takagi-Sugeno Fuzzy Descriptor Systems

This paper is concerned with control design methods via a new Lyapunov function for fuzzy descriptor systems. Takagi-Sugeno fuzzy descriptor model, that can represent nonlinear systems, are divided into two families: one with same membership functions in both sides of fuzzy systems and another with different membership functions in right and left side of fuzzy systems. Although many papers propose control designs for the above two systems that are derived from Lyapunov stability theory, they are very conservative because they use a Lyapunov function including a single Lyapunov matrix. In this paper, to design less conservative conditions, we propose new Lyapunov functions with all Lyapunov matrices that are multiple. Finally, numerical examples are given to show the effectiveness of our methods.
TOPSIS Cooperative Positioning Quality Evaluation Cloud Model Integrating EWM And AHP

The intelligent development of the integration of space, space, and sea cannot be achieved without navigation and positioning services. The current collaborative positioning solutions are rich and complex, with varying evaluations of positioning performance, and there is an urgent need to evaluate them. This study proposes evaluation methods from the nodes, links, algorithms, and hardware of collaborative positioning systems, provides quality evaluation levels, and constructs an evaluation architecture that integrates tomography analysis (AHP), entropy weight analysis (EWM), ideal advantage and disadvantage distance analysis (TOPSIS), and cloud model theory (CM). This framework has been evaluated and applied as an example through the collaborative positioning experiment of unmanned aerial vehicle clusters. Combining AHP-EWM with multi-dimensional TOPSIS cloud model for evaluation provides a scientific new approach for performance evaluation and provides a scientific basis for decision-making and improvement of actual positioning schemes.