Reviewerly/exHarmony huggingface dataset:
Recognizing Functional Pragmatics in Chinese Discourses on Enhancing Paragraph Representation and Deep Differential Amplifier

Discourse functional pragmatics recognition focuses on identifying the functions of discourse paragraphs, which is a significant research direction in natural language processing. To obtain better paragraph representation and alleviate the issue of imbalanced data distribution, we propose a Chinese discourse Functional Pragmatics Recognition model (FPR) based on enhancing paragraph representation and deep differential amplifier. More specifically, we first combine two different encoding methods to obtain paragraph encoding which contains much richer word-level information. And then we apply a deep differential amplifier to amplify the difference between paragraphs, which consists of residual structure and iterative structures. Additionally, we give more attention to the minority classes by adjusting the weights of the minority and majority classes in the objected function. Experimental results on MCDTB 2.0 show that our model FPR outperforms the state-of-the-art models.
One-Dimensional Feature Supervision Network for Object Detection

Self-attention mechanisms have been widely used in object detection tasks to distinguish the importance of different channels and reinforce important information in features, and also leads to the exciting results at all scales. However, most of the self-attentive mechanisms, as well as their variants, focus only on the channel dimension and thus easily ignore the wide and high dimensions of the feature map that play an important role in capturing local contextual information. To alleviate this problem, in this paper we propose an one-dimensional feature supervision network for object detection (1DSNet). Specifically, we first propose an one-dimensional feature supervision module (1DSM). It uses a lightweight one-dimensional feature vector to weight the features from the width and height perspectives, respectively, for jointly reinforcing the important information in the features. Moreover, in order to improve the representation of multi-scale feature context information, we construct a receptive field dilated pyramid pooling (RFD-SPP) that can obtain a larger field of view based on the spatial pyramid pooling. Finally, experimental results demonstrate that our proposed 1DSNet is effective and competitive when compared with some representative methods.
Improving Neural Machine Translation by Retrieving Target Translation Template

In the neural machine translation (NMT) paradigm, transformer-based NMT has achieved great progress in recent years. It is based on the standard end-to-end structure, and acquires translation knowledge through the attention mechanism from the parallel corpus automatically without human intervention. Inspired by the process of translating sentences by human translators and the successful application of translation template in statistical machine translation, this paper proposes a novel approach to incorporate the target translation template into the Transformer-based NMT model. Firstly, the template extraction method derives the parallel templates corpus from the constituency parse tree. Secondly, given a sentence to be translated, a fuzzy matching strategy is proposed to calculate the most possible target translation template from the parallel template corpus. Finally, an effective method is proposed to incorporate the target translate template into the Transformer-based NMT model. Experimental results on three translation tasks demonstrate the effectiveness of the proposed approach and it improves the translation quality significantly.
A Simple Mixed-Supervised Learning Method for Salient Object Detection

Weakly supervised salient object detection aims to address the limitations of fully supervised methods that heavily rely on pixel-level data. However, the sparse nature of weak labels often results in suboptimal detection accuracy. Drawing inspiration from human visual attention mechanisms, we propose a Mixed-Supervised Learning method to mitigate this issue. Mixed-Supervised Learning refers to training a neural network with hybrid data. Specifically, we propose a two-stage training strategy. In stage I, the model is supervised by a large number of scribble annotations so that it can roughly locate salient objects. In stage II, a small number of pixel-level labels are used for learning to endow the model with detail decoding capability. Our training strategy decomposes the SOD task into two sub-tasks, object localization and detail refinement, and we design a corresponding network, LRNet, which includes the supplementary detail information, a Feature Attention module (FA), and a Detail Refinement module (DF). The two-stage training strategy is simple and generalizable. Extensive experiments demonstrate the effectiveness of the training strategy, and our model detection accuracy surpasses the existing state-of-the-art models of weakly supervised learning, even reaching fully supervised results. Besides, experiments on COD and RSI SOD tasks demonstrate the generality of our method. Our code will be released at https://github.com/nightmengna/LRNet .
Minimizing Peak Memory Footprint of Inference on IoTs Devices by Efficient Recomputation

Deploying Deep Neural Networks (DNNs) on tiny IoT devices brings excellent convenience to daily life. Nowadays, the small data memory capacity of tiny IoT devices based on Micro-Controller Units (MCUs) poses a great challenge for DNN inference, since once the size of the data memory of an MCU is less than the peak memory requirements of inference a DNN, the DNN cannot be deployed on the MCU. In this paper, we introduced the recomputation method into DNN inference for reducing data memory footprint. By saving activations of some non-divergence nodes instead of these of divergence nodes on one hand, and restoring the necessary activations by recomputation on the other hand, less peak memory footprint could be achieved, thus resolving the above challenge of insufficient data memory. We propose an efficient inference scheduling algorithm based on the idea of recomputation and evaluate our proposed approach using some popular DNNs including InceptionV1, InceptionV3, and ResNet. Relative to TensorFlow Lite Micro, a widely-used framework for intelligent applications on MCUs, our algorithm achieves a 1.07x−1.25x reduction in peak memory footprint, and 0.01x−0.23x higher than the state-of-the-art algorithm.
Knowledge Graph Completion for Power Grid Main Equipment Using Pretrained Language Models

The safe and stable operation of power systems relies on the timely diagnosis of defects in power grid equipment. To achieve this, knowledge graph (KG) can be used to model power grid equipment defect knowledge, and knowledge graph embedding (KGE) can be utilized to embed KG into low dimensional vector spaces for deep learning models. However, pre-trained language model-based KGE methods may not perform as well as structure-based methods due to their limitations in explicitly representing domain-specific knowledge and supplementary information about entities. In this study, a hybrid KGE model called PLMSM was proposed to address this issue. PLMSM combines pre-trained language models with structure-based models to input entities and their supplementary information into a pre-trained language model to obtain their embeddings, which are then combined with the embeddings generated by a structure-based model for entity completion tasks. The model was optimized through efficient negative sampling and addressed the issue of inaccurate predictions caused by long-tail entities in the power grid defects KG. The experimental results showed that PLMSM achieved good performance in Entity completion tasks on the power grid equipment defects KG. This proposed model has potential applications in power grid equipment defect diagnosis and maintenance.
UCLD-Net: Decoupling Network via Unsupervised Contrastive Learning for Image Dehazing

From traditional algorithms based on handcrafted prior to learning algorithms based on neural networks, the image dehazing technique has gone through great development. The handcrafted prior-based methods need to first estimate the transmission map and atmosphere light in the atmospheric scattering model separately, and then calculate the final haze-free image, which often leads to a gradual accumulation of errors. In contrast, in the end-to-end neural network-based methods, supervised learning with labels is a major element for the improvement of the dehazing effect. But in the physical situation, paired (hazy, haze-free) images are difficult to collect, which limits the application scope of supervised dehazing. To address this deficiency, we propose a Decoupling Network for image dehazing via Unsupervised Contrastive Learning mechanism which is widely used in self-supervised representation learning, named UCLD-Net. Specifically, we use the estimated transmission map and atmosphere light to design the structure of UCLD-Net and introduce prior knowledge to construct its loss function. It is demonstrated by the experiments that UCLD-Net achieves comparable results in the dehazing experiments on the benchmark RESIDE dataset, which sufficiently verifies its effectiveness.
Learning from Patterns via Pre-trained Masked Language Model for Semi-supervised Automated Essay Scoring

Automated essay scoring is one of the key applications of natural language processing technology in the field of education. Currently, pre-trained language models for automated essay scoring systems have no significant advantage over classical models, and pre-trained language models are underutilized for this task. Moreover, in real-world scenarios, supervised models that lack annotated data often perform poorly. To address the issue that the pre-trained language models are not fully applied, we propose a novel prompt tuning model PTAES in this paper, and we convert the essay scoring procedure into a cloze-style question, after which we design pairs of natural language pattern-verbalizer. Further, we propose a joint model that combines the prompt tuning based model and the pre-trained fine-tuning based model, attempting to address the issue of inadequately supervised models in low-resource situations. Experimental findings indicate that, in supervised, semi-supervised, and zero-shot scenarios, our model can achieve state-of-the-art results, and our method further increases the value of the pre-trained language model in automated essay scoring.
Web-Based Writing Learning Application of Basic Hanacaraka Using Convolutional Neural Network Method

The Javanese script, known as Hanacaraka, or Carakan, is one of the traditional Indonesian scripts developed and used on the island of Java. The government's efforts to preserve the use of Javanese language and script by making Javanese a compulsory subject of local content at the education level in Central Java and East Java. In the basic competence of writing, the Javanese script has a complicated shape so that students have difficulty writing and recognizing Javanese script writing. Through this research a web-based basic Javanese writing learning application was designed that can recognize handwriting digitally which aims to help learn basic hanacaraka writing for beginners, especially students at the basic education level in Central Java and East Java. Handwriting Recognition is a system that can recognize handwritten characters and convert them into text that can be read and understood by machines or computers. The handwriting recognition process in this study uses the Convolutional Neural Network (CNN) algorithm which has the capability and ability to recognize patterns in images. Based on the tests that have been carried out between the two architectural models that have been made, the performance of the CNN model that will be used from various experiments has an accuracy of 98.29% and a loss of 0.0746 on the training data. As well as producing an average accuracy value of 99.52%, an average error rate of 0.48%, an overall accuracy of 95.03% and an overall error rate of 4.97%.
Comparative Analysis of Phishing Tools on Social Media Sites

Social networks, often referred to as social media are a form of information technology development. Social networks are used by society in obtaining information, being a means of long-distance communication, as well as distributing information. However, behind the development of social media which has experienced significant developments, there are problems related to information security. Vulnerable to leakage of credential data and fraud becomes a negative impact due to the development of social media, one of the scams that often occurs on social media is phishing. Such scams are attempts to obtain, steal, or dig into someone's data through emails, text messages, and social media posts. A kun social media is targeted by perpetrators of phishing crimes because the account consists of sensitive data of social media users, one of the social media accounts that can be exposed the phishing attack is Facebook. Therefore, the problem studied in this study is how criminals use three different types of phishing tools to commit crimes. Social media users now understand how phishing works, where criminals use phishing tools to access social media accounts. The data obtained from this study is a comparison of the performance of phishing tools used in committing information crimes on the Facebook social media site, with the existence of these tools that are easy to use for phishing can be a supporting factor for perpetrators of phishing to user accounts freely.
A mathematical taxonomy towards fuzzy based optimized cloud sales services

In the highly working system based on demand service model for end users. The on-demand service model consists of memory storage, hardware management, information processing and various IoT applications using mathematical programming logics with optimized fuzzy model. By using the fuzzy model, the end users get the best optimal cloud services. For providing cloud services in an optimized way the fuzzy logic is playing an important role. The developed algorithm is efficient in terms of cost and time for the cloud service providers. The simulation of our algorithm is done in cloud simulator.
ODD-VGAN: Optimised Dual Discriminator Video Generative Adversarial Network for Text-to-Video Generation with Heuristic Strategy

Generating videos is a novel area of computer vision research that is still far from being addressed. The reason for the same being that videos are very complex in nature where both spatial and temporal coherence needs to be taken care of. Compared to the unconditional video generation, an automated video generation from the text description is an even more difficult task, in which maintaining semantic consistency and visual quality are very crucial. The video generation from the text description seems to be non-trivial owing to the intrinsic complexity that occurs in the frames and video framework. The conditional generative models are required to be implemented for this challenging task of text-to-video generation. “Generative adversarial networks (GANs)” have had a lot of success in producing images conditioned over the natural language description. But, it is yet to be employed for producing realistic videos from text that are temporally and spatially coherent and semantically consistent with the text descriptions. Thus, a new Optimised Dual Discriminator Video Generative Adversarial Network (ODD-VGAN) for text-to-video generation is suggested in this paper. The hyper-parameters of ODD-VGAN are optimised using the improved reptile search algorithm (IRSA). The efficiency of the proposed approach is demonstrated by both qualitative and quantitative experimental results.
Pengembangan Media Pembelajaran Teks Negosiasi Berbasis Audio Visual Untuk Siswa Kelas X SMA Swasta Al-Hidayah Medan

This research aimed to develop audio-visual-based learning media for negotiating texts using the CapCut application for Class X students at SMAS Al-Hidayah Medan. The objectives were to assess the feasibility and effectiveness of the developed learning media. The study followed the Thiagarajan 4-D model: defining, designing, developing, and disseminating. Material experts evaluated the media and deemed it decent with a score of 73.5%. Design experts rated it very decent with a score of 90%. The media was then tested on Indonesian language teachers and Class X(1) students, receiving high ratings of 95% and 92% respectively. The data indicated the validity and feasibility of the learning media. Students' scores increased after using the media, reaching the minimum passing grade. Consequently, the audio-visual-based negotiating text learning media was deemed both feasible and effective for the learning process.
Pengembangan E-Modul Sebagai Bahan Ajar Bahasa Indonesia Materi Teks Ceramah Kelas XI IPA SMAS Al-Washliyah Pasar Senen

This study aimed to develop and test the feasibility of e-modules as Indonesian language teaching materials for Class XI SMAS in Al-Washliyah Pasar Senen. The research utilized a Research and Development (R&amp;D) approach, following the ADDIE model, encompassing Analysis, Design, Development, Implementation, and Evaluation. The sample consisted of 19 students from class XI IPA out of a total population of 45 students. The results indicated that the e-module met the appropriate criteria, with material and design experts validating its content and structure. The teacher evaluation demonstrated a high feasibility level (94%), while students responded positively (92%) to the e-module. Consequently, the findings conclude that the e-module is highly suitable as a teaching material for Class XI SMAS Al-Washliyah.
Penggunaan Media Audio Visual untuk Memotivasi Belajar Siswa Kelas III MI Miftahul Ulum Surabaya

This descriptive qualitative study investigates the utilization and challenges associated with audio-visual media in enhancing the motivation of third-grade students at MI Miftahul Ulum Surabaya. Data collection involved observations, interviews, questionnaires, and documentation. Findings demonstrate that the use of audio-visual media effectively captures students' attention, increasing their motivation to actively engage in learning and facilitating comprehension of instructional content. However, challenges arise from limited access to necessary tools and the time-consuming preparation process for audio-visual materials. Thus, careful and thorough preparation is essential to ensure the optimal use of audio-visual media, enabling more effective learning experiences for students. Addressing these constraints can contribute to the successful integration of audio-visual resources in educational settings.
Classification of Engineering Journals Quartile using Various Supervised Learning Models

In scientific research, journals are among the primary sources of information. There are quartiles or categories of quality in journals which are Q1, Q2, Q3, and Q4. These quartiles represent the assessment of journal. A classification machine learning algorithm is developed as a means in the categorization of journals. The process of classifying data to estimate an item class with an unknown label is called classification. Various classification algorithms, such as K-Nearest Neighbor (KNN), Naïve Bayes, and Support Vector Machine (SVM) are employed in this study, with several situations for exchanging training and testing data. Cross-validation with Confusion Matrix values of accuracy, precision, recall, and error classification is used to analyzed classification performance. The classifier with the finest accuracy rate is KNN with average accuracy of 70%, Naïve Bayes at 60% and SVM at 40%. This research suggests assumption that algorithms used in this article can approach SJR classification system.
Introduction

Abstract One of the main issues of global social media governance relates to the definition of the rules governing online content moderation worldwide. One could think that it would be sufficient for online platforms to refer to existing international human rights standards. However, a more careful analysis shows that international law provides exclusively general principles and that a single human rights standard does not exist. Since their inception, major social media platforms have set their own rules; yet this normative autonomy too has raised serious concerns. The current situation exposes a dilemma for online content governance that seriously affects the operations of social media companies and impacts on the exercise of fundamental rights by users as well as digital policy strategies.
Robust Anti-forensics on Audio Forensics System

Audio forensics systems are effective evidence to prove whether audio is real or not. Recently, there have been studies on anti-forensics technology that can deceive audio forensics systems. However, the anti-forensics technology loses its effectiveness after being compressed. In this paper, we first study the influence of known and unknown compression on audio anti-forensics technology, and enhance the robustness of audio anti-forensics technology. In this paper, the compression algorithm and compression approximation algorithm are added to the iterative process of generating anti-forensics adversarial examples to generate anti-forensics adversarial examples that can resist compression, and solve the problem that audio anti-forensics adversarial examples cannot reduce the accuracy of audio forensics system under compression. Three compression algorithms, AAC-HE, AAC-LC and MP3, as well as a pre-trained compression approximation algorithm, were added to the iterative process of generating audio anti-forensics adversarial examples to generate audio anti-forensics adversarial examples, which were uploaded and downloaded through the actual network platform (Himalaya) and input into the audio forensics system. Experimental results show that the proposed method is effective in resisting compression.
GAN-Powered Model &amp;Landmark-Free Reconstruction: A Versatile Approach for High-Quality 3D Facial and Object Recovery from Single Images

In recent years, 3D facial reconstructions from single images have garnered significant interest. Most of the approaches are based on 3D Morphable Model (3DMM) fitting to reconstruct the 3D face shape. Concurrently, the adoption of Generative Adversarial Networks (GAN) has been gaining momentum to improve the texture of reconstructed faces. In this paper, we propose a fundamentally different approach to reconstructing the 3D head shape from a single image by harnessing the power of GAN. Our method predicts three maps of normal vectors of the head’s frontal, left, and right poses. We are thus presenting a model-free method that does not require any prior knowledge of the object’s geometry to be reconstructed. The key advantage of our proposed approach is the substantial improvement in reconstruction quality compared to existing methods, particularly in the case of facial regions that are self-occluded in the input image. Our method is not limited to 3d face reconstruction. It is generic and applicable to multiple kinds of 3D objects. To illustrate the versatility of our method, we demonstrate its efficacy in reconstructing the entire human body. By delivering a model-free method capable of generating high-quality 3D reconstructions, this paper not only advances the field of 3D facial reconstruction but also provides a foundation for future research and applications spanning multiple object types. The implications of this work have the potential to extend far beyond facial reconstruction, paving the way for innovative solutions and discoveries in various domains.