Reviewerly/exHarmony huggingface dataset:
Evolutionary Algorithms Approach For Search Based On Semantic Document Similarity

Advancements in cloud computing and distributed computing have fostered research activities in Computer science. As a result, researchers have made significant progress in Neural Networks, Evolutionary Computing Algorithms like Genetic, and Differential evolution algorithms. These algorithms are used to develop clustering, recommendation, and question-and-answering systems using various text representation and similarity measurement techniques. In this research paper, Universal Sentence Encoder (USE) is used to capture the semantic similarity of text; And the transfer learning technique is used to apply Genetic Algorithm (GA) and Differential Evolution (DE) algorithms to search and retrieve relevant top N documents based on user query. The proposed approach is applied to the Stanford Question and Answer (SQuAD) Dataset to identify a user query. Finally, through experiments, we prove that text documents can be efficiently represented as sentence embedding vectors using USE to capture the semantic similarity, and by comparing the results of the Manhattan Distance, GA, and DE algorithms we prove that the evolutionary algorithms are good at finding the top N results than the traditional ranking approach.
A Zero-Shot Super-Resolution Image Reconstruction Technique Based on Radial Basis Function Neural Networks

This paper addresses the challenge of improving image resolution in special fields, such as medicine and astronomy, where high-quality and high-resolution image sample data is difficult to obtain. We propose a novel zero-shot super-resolution image reconstruction method that leverages machine learning to obtain the mapping relationship between the original image and its degraded image. We can improve image resolution by learning the similar structures present in images across different scales and angles. Our method consists of three processes, namely expansion, deblurring, and edge enhancement, all of which use radial basis function neural networks to learn the mapping relationship between the original image and its degraded image. Our comparative experiments on the Set5 dataset show that our method's performance is comparable to that of the bicubic interpolation algorithm. Moreover, we can further improve image resolution by using our method directly on top of the bicubic interpolation algorithm, and our experimental results demonstrate that our method outperforms the bicubic interpolation algorithm. Importantly, our method is portable and can be combined with other super-resolution algorithms to enhance image resolution further.
PERANCANGAN SISTEM INFORMASI PERTANIAN KABUPATEN TOBA BERBASIS WEB

Agricultural information systems are very important and very influential in conveying information about agriculture to the wider community. At this time information about agriculture in Toba district does not yet have the right system for conveying agricultural information, resulting in information about agriculture not being conveyed to the wider community, especially the people of Toba district properly and quickly. To overcome the above problems, the researchers designed a web-based agricultural information system in Toba district which aims to make it easier for the wider community to find the latest information about agriculture such as types of agricultural products, production yields, prices and agricultural activities in Toba district. This information system was built by utilizing the HTML, CSS and MySQL programming languages to process the database used to use the Sublime Text application as a place for program design.
ANALYSIS OF PRESUPPOSITION USED IN ENCANTO MOVIE SCRIPT

Presupposition is one of the topics in pragmatic studies. In this thesis, the writers focuse more on the discussion of presuppositions in pragmatic studies, where presuppositions are everything that is in one's mind about everything that exists in this world. Presuppositions are needed to understand the meaning of a conversation between the speaker and the listener so that there is no misunderstanding in communication. In this research the writers use a descriptive research method, because all data comes from film script. Presuppositions in Encanto movie taken from the dialogues of the characters in the movie and analyzed based on the theories by Yule (1996) and classifying them into various types of presuppositions. The result of this study shows that there are many variation of presupposition contained in this move script. There are six presuppositions, specifically 5 existential, 14 factive, 1 non-factive, 3 lexical, 23 structural, and 5 counterfactual. Data analysis shows that there are several types of presuppositions contained in this movie. From this research, the writers hope that this thesis can be useful as an additional knowledge for future researchers who discuss pragmatic studies, especially presuppositions.
The role of External Sources on Innovation Performance: Review from theory to empirical research

In recent years, firms' innovation activities have been encouraged in many countries, and the topic of innovation performance has been of interest to researchers. Previous studies have confirmed that internal sources have an impact on innovation performance, while external sources have gained increasing popularity and influence on firms' activities. Despite the growing interest in the impact of external sources on innovation performance, most studies have reached conflicting conclusions, with some showing positive effects and others negative effects. Therefore, the question remains as to whether external sources can affect innovation performance. This study aims to clarify the role of external sources on innovation performance by reviewing, analyzing, and synthesizing results from 56 empirical studies conducted worldwide. The findings highlight the theoretical frameworks used in previous studies and the types of external sources, such as external knowledge, R&amp;D, information technology, and spillovers from foreign enterprises, that affect innovation performance. Finally, the study proposes suggestions for future research.
Intra-country academic mobility as a tool of national development (a case study of political science education)

Introduction: academic mobility plays an important role in the building international scientific and educational communications in the modern system of Russian higher education and acts as a tool of “soft power”. Online learning during the COVID-19 pandemic helped to increase the attractiveness of virtual academic mobility, while there is practically no interest from Russian government in the development of intra-country academic mobility. Objectives: to show the issues and the prospects for the development of intra-country academic mobility in the field of professional political science education. Methods: institutional method, a content analysis, a comparative analysis. Results: analysis of the websites of metropolitan and regional universities included in the academic leadership program “Priority 2030” and carrying out the educational process in the fields of study 41.03.04 and 41.04.04 “Political Science” showed that all higher educational institutions have sections on the academic mobility realization as an international vector of cooperation. However, the development of intra-country academic mobility is in its infancy. Conclusions: the systematic development of academic mobility within the country in the field of professional political science education will contribute to the training of highly qualified specialists capable of analyzing the socioeconomic and political situation in a particular Russian region, which, to a large extent, will allow achieving the main goals of Russia’s national development.
Security and Privacy-Preserving for Automated Electric Vehicle

Due to the limited fossil fuels and the emission pollution caused by combustion, electric vehicles, as the promoters of green transportation, have become indispensable travel tools in daily life. On the other hand, as flourishing as automated vehicles are, autonomous valet parking technology is utilized to address issues such as long waiting periods for finding a parking spot and huge costs. So automated electric vehicles (AEV) have become the frontier hot spot in the field of international automotive engineering and the core competitiveness in the future market. However, some security threats are also introduced. This chapter elaborates on services including autonomous valet parking (AVP) and charging management from the parking mode and charging mode. Then, security requirements are proposed based on the system model and security threats. In addition, this chapter summarizes privacy-preserving strategies for smart parking, long-range autonomous valet parking (LAVP), and charge management. Finally, future research directions are discussed in order to accelerate secure and rapid development of AEV.
Noval Enabling Technology for V2X Network: Blockchain

V2X network is crucial for achieving intelligent transportation systems. However, since V2X involves many participants and transmits sensitive information, it faces various security and privacy issues in practical applications. Blockchain, a distributed ledger technology, can build a safe and reliable environment for V2X. This chapter introduces the fundamentals of blockchain and then provides an extensive discussion on how blockchain is applied in the V2X network. Furthermore, we elaborate on blockchain integration in V2X from trust management, data sharing, resource sharing, transaction application, collaborative application, traffic management and evidence service. Finally, challenging problems on blockchain for V2X, which can be regarded as future research directions and opportunities, are presented.
Industry and Government Collaboration on Security Guardrails for AI Systems: Summary of the AI Safety and Security Workshops

These conference proceedings capture the industry and government perspectives emerging from a series of workshops hosted by the RAND Corporation and the Carnegie Endowment for International Peace in 2023 to inform policymakers and the broader public discussion about artificial intelligence safety and security. Participants identified concerns about national security, potential policies to mitigate risks, and key questions for future research.
Reconstruction of the gaps in malfunction Landsat7 images: Review

The Landsat satellite series program has been developed and managed by the United States Geological Survey (USGS) since 1972, representing the longest temporal record of space-based terrestrial observation and taking into account the primary forces advancing the science concept in the global earth systems. Despite the malfunction images of Landsat 7 in 2003, the malfunction imagery preserves its important value in both technical studies and applications. Thus, there are numerous approaches and algorithms that have been presented to solve the SLC-off trouble by finding appropriate methods and approaches to predict the pixel reflectance values at gap locations accurately. This work aims to familiarize the reader with the concepts related to recovering the gap locations of the Landsat 7 SLC-off imagery approaches.
HW-TSC’s Neural Machine Translation System for CCMT 2023

This paper presents Huawei Translation Service Center (HW-TSC)’s submission to the machine translation tasks of the 19th China Conference on Machine Translation (CCMT 2023). We participate in all machine translation tasks, including five bilingual machine translation tasks, the Belt and Road low-resource language machine translation task, Chinese-centric multilingual machine translation task and Chinese $$\rightarrow $$ English zero-referencing machine translation task. Under different machine translation tasks, we adopt different methods to train the corresponding neural machine translation system. This paper mainly explains the model structure, data size and training method adopted by the translation systems, and gives the comparison of the evaluation results under different training methods.
Transn’s Submission for CCMT 2023 Quality Estimation Task

Machine translation quality estimation is a kind of technique to rate and choose the best translation from several translations of text in source language, which is suitable for the application trend of machine translation in international communication. CCMT 2023 Quality Estimate Task focuses on predicting sentence level score for each sentence pair in English to/from Chinese. This paper describes our methods for predicting Human Translation Edit Rate (HTER) score of sentence pairs in both directions. Various source-translation interactive structures are explored to enhance the representations of source and translation to make a more accurate prediction. On account of the well-known powerful ability of pretrained language models, the combinations between varied pretrained language models and interactive structures are also searched to obtain better model performance. Meanwhile, some pretraining methods and the ensemble method are applied to boost the single model performance on Dev and Test data. Our method gets competitive results in both directions with these augmentations.
Securing Artificial Intelligence Model Weights: Interim Report

As frontier artificial intelligence (AI) models become more capable, protecting them from malicious actors will become more important. If AI systems rapidly become more capable over the next few years, achieving sufficient security will require investments — starting today — well beyond what the default trajectory appears to be. This working paper suggests steps that can be taken now to avoid future problems.
Sparse Bayesian Learning-Based Interval Type-2 Fuzzy Logic Control for Electrospinning Processes

This article develops a closed-loop electrospinning process control system composed of a high-speed industrial camera, an interval type-2 (IT2) fuzzy logic controller (FLC) and a high-precision programmable micropump. A pure data-driven IT2 T-S fuzzy model with a micropump flow input and a fiber diameter output is established by a sparse Bayesian learning (SBL) method, and the closed-loop IT2 FLC is thereby proposed to finely tune the electrospinning fiber diameter according to the technical requirement of the circuit electrospinning process suffered by external disturbances and system uncertainties. Sufficient conditions are derived to guarantee the asymptotical stability of the closed-loop system with the assistance of Lyapunov theory. Experiments on bead-chain structure electrospinning process are conducted to show the effectiveness and superiority of the present SBL-based fuzzy controller.
Reconstruction-Assisted and Distance-Optimized Adversarial Training: A Defense Framework for Remote Sensing Scene Classification

Despite deep neural networks (DNNs) have been widely applied in remote sensing (RS) scene classification and achieved satisfying performance, the vulnerability of DNNs towards adversarial examples significantly degrades their performance. Moreover, the relatively limited labeled samples of RS scene classification make DNNs more likely to overfit, leading to weak generalizability and noise sensitivity. This may result in DNNs being more vulnerable to adversarial examples. Consequently, the defense of adversarial examples is of crucial importance to improve both the generalizability and robustness of DNNs in the RS scene classification task. However, few studies have been conducted on defense for RS scene classification, especially ignoring the intrinsic characteristics of RS images. In this paper, an effective defense framework for RS scene classification, named reconstruction-assisted and distance-optimized adversarial training (RDAT), is proposed to defend adversarial examples. In order to solve the problems caused by high interclass similarity, a distance-optimized (DO) strategy is designed for adversarial training to strengthen the learning of underfitting content, increase the interclass distance, and improve the robustness of the networks. Furthermore, in order to generate high quality samples for adversarial training, a reconstruction-assisted (RA) block is proposed to eliminate adversarial perturbations in adversarial examples. Specifically, in this block, by swin transformer (SwinT) block and multi-scale convolution (MSC) block, SwinT-MSC-UNet (SMUNet) is constructed to fully extract global and multi-scale local features to adapt to the characteristics of RS images with large variance of ground object scales. Extensive experiments on the benchmark datasets, i.e., UC Merced (UCM) and Aerial Image Dataset (AID), have demonstrate that the proposed RDAT can effectively resist multiple adversarial attacks and yield superior results than other defense methods for RS scene classification.
Fully Sparse Transformer 3-D Detector for LiDAR Point Cloud

The 3D object detector usually uses a framework similar to 2D detection and benefits from the advancements of 2D detection tasks. In these frameworks, it is necessary to make the unstructured, sparse point cloud features into dense grids to be compatible with popular 2D operators such as convolution and transformers, which also causes extra computational costs. In this paper, we propose a simple and efficient Fully Sparse TRansformer (FSTR) for LiDAR-based 3D object detection, which is able to combine with state-of-the-art sparse backbones to form a fully sparse, end-to-end, simple, and efficient detection framework. FSTR uses the sparse voxel feature from the sparse backbone as the input token without any custom operators. Further, we introduce the dynamic queries to provide a priori location and context of the foreground for the decoder and drop the high-confidence background tokens to further reduce redundant computations. We propose Gaussian denoising queries to speed up the decoder training and make it more adaptable to the distribution of sparse voxel features. Extensive experiments on the nuScenes benchmark and the Argoverse2 benchmark validate the effectiveness of the proposed method. FSTR outperforms all LiDAR real-time methods by 69.5 mAP and 72.9 NDS on the official benchmark of nuScenes dataset. On the long-range detection benchmark Argoverse2, the proposed method achieves a new state-of-art performance of 39.9 mAP which outperforms the existing LiDAR detectors, even the LiDAR-Camera detectors by a large margin (+9.4 mAP and +7.5mAP), showing the great advantage of the proposed method for long-range detection.
Learning Interpretable Rules for Scalable Data Representation and Classification

Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. However, rule-based models are hard to optimize, especially on large data sets, due to their discrete parameters and structures. Ensemble methods and fuzzy/soft rules are commonly used to improve performance, but they sacrifice the model interpretability. To obtain both good scalability and interpretability, we propose a new classifier, named Rule-based Representation Learner (RRL), that automatically learns interpretable non-fuzzy rules for data representation and classification. To train the non-differentiable RRL effectively, we project it to a continuous space and propose a novel training method, called Gradient Grafting, that can directly optimize the discrete model using gradient descent. A novel design of logical activation functions is also devised to increase the scalability of RRL and enable it to discretize the continuous features end-to-end. Exhaustive experiments on ten small and four large data sets show that RRL outperforms the competitive interpretable approaches and can be easily adjusted to obtain a trade-off between classification accuracy and model complexity for different scenarios.
Honeywords Generation Mechanism Based on Zero-Divisor Graph Sequences

The identity authentication of most applications is based on a symbolic password. However, incidents of password leakage emerge one after another, which brings serious hidden danger to the users’ information security. For decades, various schemes have been proposed to solve the problem of information protection. However, most schemes neglect the timely detection of password leakage. The present paper introduces a password leak detection method based on zero-divisor graph sequences. Specifically, it is to construct an algorithm for generating honeywords with high smoothness. First, we introduce the concept of the zero-divisor graph and construct zero-divisor graph sequences by using the corresponding zero-divisor matrices. Second, the honeywords with high flatness are constructed by using the sequence of zero-divisor graphs. Third, the security analysis verifies the effectiveness of the scheme. Fourth, compared with other honeywords schemes, our scheme has more obvious advantages, in the aspects of honeywords generated flatness, DoS resistance, and storage resources occupied by honeywords.
Joint Contrastive Learning for Factual Consistency Evaluation of Cross-Lingual Abstract Summarization

Current summarization models tend to generate erroneous or irrelevant summaries, i.e., factual inconsistency, which undoubtedly hinders the real-world application of summarization models. The difficulty in language alignment makes factual inconsistency in cross-lingual summarization (CLS) more common and factual consistency checking more challenging. Research on factual consistency has paid little attention to CLS due to the above difficulties, focusing mainly on monolingual summarization (MS). In this paper, we investigate the cross-lingual domain and propose a weakly supervised factual consistency evaluation model for CLS. In particular, we automatically synthesize large-scale datasets by a series of rule-based text transformations and manually annotate the test and validation sets. In addition, we also train the model jointly with contrastive learning to enhance the model’s ability to recognize factual errors. The experimental results on the manually annotated test set show that our model can effectively identify the consistency between the summaries and the source documents and outperform the baseline models.
ClusterFusion: Leveraging Radar Spatial Features for Radar-Camera 3D Object Detection in Autonomous Vehicles

Thanks to the complementary nature of millimeter wave radar and camera, deep learning-based radar-camera 3D object detection methods may reliably produce accurate detections even in low-visibility conditions. This makes them preferable to use in autonomous vehicles’ perception systems, especially as the combined cost of both sensors is cheaper than the cost of a lidar. Recent radar-camera methods commonly perform feature-level fusion which often involves projecting the radar points onto the same plane as the image features and fusing the extracted features from both modalities. While performing fusion on the image plane is generally simpler and faster, projecting radar points onto the image plane flattens the depth dimension of the point cloud which might lead to information loss and makes extracting the spatial features of the point cloud harder. We proposed ClusterFusion, an architecture that leverages the local spatial features of the radar point cloud by clustering the point cloud and performing feature extraction directly on the point cloud clusters before projecting the features onto the image plane. ClusterFusion achieved the state-of-the-art performance among all radar-monocular camera methods on the test slice of the nuScenes dataset with 48.7% nuScenes detection score (NDS). We also investigated the performance of different radar feature extraction strategies on point cloud clusters: a handcrafted strategy, a learning-based strategy, and a combination of both, and found that the handcrafted strategy yielded the best performance. The main goal of this work is to explore the use of radar’s local spatial and point-wise features by extracting them directly from radar point cloud clusters for a radar-monocular camera 3D object detection method that performs cross-modal feature fusion on the image plane.