Reviewerly/exHarmony huggingface dataset:
A method of computer library information data management based on network analysis

In order to improve the classification accuracy in the process of digital management of Library and information data, a method of information data management based on network analysis is proposed. First, based on the relevant theories of Library and information management, the target data set is obtained from the data source through data collection and extraction. Then the target data is preprocessed by cleaning, integration and transformation, so as to obtain the data set required for model construction. Finally, the network analysis method is used to establish the data evaluation model and management model, and to evaluate the availability of data. Simulation experiments show that the internal consistency of the data processed by the proposed model is maintained at more than 75%, the accuracy of data classification is high, and the fit between the evaluation index and the evaluation model is maintained at more than 0.83, which can meet the requirements of practical application.
Diffuse and Smooth: Beyond Truncated Receptive Field for Scalable and Adaptive Graph Representation Learning

As the scope of receptive field and the depth of Graph Neural Networks (GNNs) are two completely orthogonal aspects for graph learning, existing GNNs often have shallow layers with truncated-receptive field and far from achieving satisfactory performance. In this article, we follow the idea of decoupling graph convolution into propagation and transformation processes, which generates representations over a sequence of increasingly larger neighborhoods. Though this manner can enlarge the receptive field, it has two critical problems unsolved: how to find the suitable receptive field to avoid under-smoothing or over-smoothing? and how to balance different diffusion operators for better capturing the local and global dependencies? We tackle these challenges and propose a S calable, A daptive G raph C onvolutional N etworks ( SAGCN ) with Transformer architecture. Concretely, we propose a novel non-heuristic metric method that quickly finds the suitable number of diffusing iterations and produces smoothed local embeddings that enable the truncated receptive field to become scalable and independent of prior experience. Furthermore, we devise smooth2seq and diffusion-based position schemes introduced into Transformer architecture for better capturing local and global information among embeddings. Experimental results show that SAGCN enjoys high accuracy, scalability and efficiency on various open benchmarks and is competitive with other state-of-the-art competitors.
A Comprehensive Roadmap on Bangla Text-based Sentiment Analysis

The effortless expansion of Internet access has eventually transformed the dissemination behavior toward E-Mode. Thus, the usage of online or, more specifically, “Digital” texts has expanded abruptly. “Bangla,” the seventh most spoken language globally, has no different nature. Communication in the Bangla language has also been exposed on the Internet, which describes the feelings of individuals in any specific context. These enormously generated data from diverse sources have drawn the interest of the researchers working in the Natural Language Processing domain. Despite its relatively complicated structure, a lesser amount of annotated data, as well as a limited number of frameworks and approaches, exist. This lacking of resources has kept several stones unturned in this diverse, emotion-rich, and widely spoken language. To bridge the lacking and absence of resources, this article aims to provide a generalized deduced working procedure in this domain. To do so, the existing research work in the domain of sentiment analysis using Bangla text has been collected, evaluated, and summarized. Also, in this article, the techniques used in pre-processing, feature extraction, and eventually used algorithms have been identified and discussed. Considering these facts, this research work sketches a tentative blueprint of sentiment analysis using Bangla text. Additionally, this article discusses existing regional language corpora such as Tamil, Urdu, and Hindi, as well as English and methodologies used to extract emotional essence from Bangla language comparing other languages. That will assist in determining the probable chosen path of exploring Bangla in a deeper aspect. Moreover, this work has deduced and presented a generalized framework that will direct aspiring researchers to decide the pathway of choosing data vis-à-vis methodologies based on their interests.
PK-BERT: Knowledge Enhanced Pre-trained Models with Prompt for Few-Shot Learning

The amount of data in some fields are scarce because they are difficult or expensive to obtain. The general practice is to pre-train a model on similar data sets and fine-tune the models in downstream tasks by transfer learning. The pre-trained models could learn the general language representation from large-scale corpora but their downstream task may be different from the pre-trained tasks in form and type. It also lacks related semantic knowledge. Therefore, we propose PK-BERT—Knowledge Enhanced Pre-trained Models with Prompt for Few-shot Learning. It (1) achieves few-shot learning by using small samples with pre-trained models; (2) constructs the prefix that contains the masked label to shorten the gap between downstream task and pre-trained task; (3) uses the explicit representation to inject knowledge graph triples into the text to enhance the sentence information; and (4) uses masked language modelling (MLM) head to convert the classification task into generation task. The experiments show that our proposed model PK-BERT achieves better results.
symKrypt: A Lightweight Symmetric-Key Cryptography for Diverse Applications

Symmetric-key cryptography is used widely due to its capability to provide a strong defense against diverse attacks; however, it is prone to cryptanalysis attacks. Therefore, we propose a novel and highly secure symmetric-key cryptography, symKrypt for short, to defend against diverse attacks and provide tighter security than the conventional cryptography. Our proposed algorithm uses multiple private keys to encrypt a single block of a message. To generate the private keys, we again propose a true-random number generator, called Grando, and a pseudo-random number generator, called Prando. Moreover, symKrypt keeps secret about the bit mixing of the original message with the private keys. Also, the number of private keys is kept secret. In addition, the private keys are generated dynamically based on the initial inputs using a pseudo-random number generator which is highly unpredictable and secure. In this paper, we theoretically analyze the capabilities of symKrypt and provide experimental demonstration using millions of private keys to prove its correctness. Furthermore, we demonstrate the proposed pseudo-random number generator algorithm experimentally in NIST SP 800-22 statistical test suite. Our propose random number generators, Grando and Rando, pass all 15 tests in the NIST SP 800-22 test suite. To the best of our knowledge, symKrypt is the first model to use multiple private keys in encryption yet lightweight and powerful.
Analyzing Chinese text with clause relevance structure

Discourse structure is generally represented as hierarchical structure, the two most well known representations are rhetorical structure theory (RST) and Penn discourse treebank (PDTB). The main problem of the hierarchical structure is that it can not describe the direct semantic relevance between the elementary discourse units (EDU), especially the non-adjacent and cross-level EDUs. Discourse dependency structure (DDS) has been put forward in recent years to describe the head-dependent relation between the EDUs. However, the judgment process of the head can not be answered theoretically. This problem is particularly serious in Chinese discourse analysis, because Chinese lacks the form differences between the main clauses and the subordinate clauses. In this paper, we propose clause relevance structure to represent the discourse structure. Compared with the hierarchical discourse structure and DDS, the clause relevance structure can effectively describe the direct semantic association between discontinuous and cross-level clauses in a text, and the construction of structure is not presupposed by the head recognition. We propose the judgment criteria and formal constraints of the clause relevance structure, and built a human-annotated corpus on Chinese text. Based on the Chinese corpus, we explore the automatic recognition of clause relevance structure. The clause relevance recognition task is formalized as a classification problem and performed by the BERT-based model. A bidirectional LSTM layer is added on the top of the BERT to improve the performance, and the recognition accuracy (90.77%) is achieved by the BERT-LSTM model. Experimental results show that the long distance clause pairs are the main difficulties in the clause relevance recognition, and these difficulties mainly focus on the positive examples, while the clause pairs with short distance are especially difficult to be correctly recognized as negative relevance.
Enumerating Discrete Resonant Rossby/Drift Wave Triads and Their Application in Information Security

We propose a new parametrization of the resonant Rossby/drift wave triads to develop an algorithm to enumerate all resonant triads in a given grid of wavenumbers. To arrive at such a parametrization, we have employed tools from arithmetic/algebraic geometry to project resonant triads on a certain class of conics. Further, we extend the newly developed algorithm for the enumeration of quasi-resonant triads and experimentally show that the said algorithm is robust to design the network of quasi-resonances. From the experimental results, we observed that the new algorithm enumerates all triads in low computation time when compared with the existing methods. Finally, we apply this work to information security by constructing a total order on the enumerated resonant triads to design a substitution box (S-box) generator. Via extensive analyses over several indicators (nonlinearity, algebraic complexity, linear and differential approximation probabilities, strict avalanche criteria, and bit independence criterion) we show that the newly developed S-box outperforms the S-boxes constructed by most of the existing schemes.
IvCDS: An End-to-End Driver Simulator for Personal In-Vehicle Conversational Assistant

An advanced driver simulator methodology facilitates a well-connected interaction between the environment and drivers. Multiple traffic information environment language processing aims to help drivers accommodate travel demand: safety prewarning, destination navigation, hotel/restaurant reservation, and so on. Task-oriented dialogue systems generally aim to assist human users in achieving these specific goals by a conversation in the form of natural language. The development of current neural network based dialogue systems relies on relevant datasets, such as KVRET. These datasets are generally used for training and evaluating a dialogue agent (e.g., an in-vehicle assistant). Therefore, a simulator for the human user side is necessarily required for assessing an agent system if no real person is involved. We propose a new end-to-end simulator to operate as a human driver that is capable of understanding and responding to assistant utterances. This proposed driver simulator enables one to interact with an in-vehicle assistant like a real person, and the diversity of conversations can be simply controlled by changing the assigned driver profile. Results of our experiment demonstrate that this proposed simulator achieves the best performance on all tasks compared with other models.
A multi-satellite co-tracking method for a single space target based on adaptive distributed spherical simplex information-weighted consensus filter

A multi-satellite co-tracking method for a single non-cooperative target is proposed to extend the arc of observation as well as to improve the accuracy of tracking. Firstly, the motion of the target is considered to be affected by J2 perturbation, and the model of multi-satellite co-tracking a single space target is designed with only measured angles. Then, a multi-satellite co-tracking method for a single space target based on an adaptive distributed spherical simplex information-weighted consensus filter (ADSSICF) is proposed. The spherical simplex is used to reduce the computational cost of the information filter, and to improve the efficiency of tracking. The angle-only measurement equation is linearized by the statistical linear regression method, and linearization errors are compensated for by updating measurement noise. By designing an adaptive consensus algorithm, only a small number of iterations are needed to achieve network consensus, to improve the convergence speed of the consensus algorithm, and prove the stability of ADSSICF. Finally, a simulation of four low-orbit satellites tracking a single space target is established. The focus of this paper is on the real-time performance and tracking accuracy of the multi-satellite co-tracking a single space target with angle-only based on ADSSICF. The performance of ADSSICF is verified from three indicators: the consensus and convergence of algorithm, the accuracy of state estimation.
Responsive and intelligent service recommendation method based on deep learning in cloud service

The rapid expansion of the cloud service market is inseparable from its widely acclaimed service model. The rapid increase in the number of cloud services has resulted in the phenomenon of service overload. Service recommendations based on services' function attributes are important because they can help users filter services with specific functions, such as the function of guessing hobbies on shopping websites and daily recommendation functions in the listening app. Nowadays, cloud service market has a large number of services, which have similar functions, but the quality of service (QoS) is very different. Although the recommendation based on services' function attributes satisfies users' basic demands, it ignores the impact of the QoS on the user experience. To further improve users' satisfaction with service recommendations, researchers try to recommend services based on services' non-functional attributes. There is sparsity of the QoS matrix in the real world, which brings obstacles to service recommendation; hence, the prediction of the QoS becomes a solution to overcome this obstacle. Scholars have tried to use collaborative filtering (CF) methods and matrix factorization (MF) methods to predict the QoS, but these methods face two challenges. The first challenge is the sparsity of data; the sparsity makes it difficult for CF to accurately determine whether users are similar, and the gap between the hidden matrices obtained by MF decomposition is large; the second challenge is the cold start of recommendation when new users (or services) participate in the recommendation; its historical record is vacant, making accurately predicting the QoS value be more difficult. To solve the aforementioned problems, this study mainly does the following work: 1) we organized the QoS matrix into a service call record, which contains user characteristic information and current QoS. 2) We proposed a QoS prediction method based on GRU-GAN. 3) We used the time series data for quality predictions and compared some QoS prediction methods, such as CF and MF. The results showed that the prediction results based on GRU-GAN are far superior to other prediction methods under the same data density. We aim to help the engineering community promote their findings, shape the technological revolution, improve multidisciplinary collaborations, and collectively create a better future.
Tight Upper Bounds on Distinct Maximal (Sub-)Repetitions in Highly Compressible Strings

For [Formula: see text], maximal [Formula: see text]-repetitions ([Formula: see text]-subrepetitions) are fractional powers in strings with exponent of at least [Formula: see text] (and [Formula: see text], respectively) which are non-extendable with respect to their minimum period. In this paper, we show that in a string [Formula: see text] with string attractor [Formula: see text] there are at most [Formula: see text] distinct (unpositioned) extended maximal [Formula: see text]-repetitions. Also for any natural number [Formula: see text] the string contains at most [Formula: see text] distinct extended maximal [Formula: see text]-subrepetitions without [Formula: see text]th powers. We further prove that for fixed [Formula: see text] and [Formula: see text], both upper bounds are tight up to a constant factor.
Integrated Two Variant Deep Learners for Aspect-Based Sentiment Analysis: An Improved Meta-Heuristic-Based Model

Several traditional methods were tested on standard datasets to evaluate client emotions transmitted via internet portals. Customers, on the other hand, continue to have difficulty obtaining aspect-oriented viewpoints voiced by other customers, and the accuracy of the current model is insufficient. The suggested Aspect-Based Sentimental Analysis (ABSA) starts with pre-processing, which includes “stop word and punctuation removal, lower case conversion, and stemming.” Aspect extraction, which entails dividing the nouns and adjectives, as well as verbs and adverbs, is the following step. The weighted polarity features from the “Vader sentiment intensity analyzer, as well as the word2vector and Term Frequency-Inverse Document Frequency (TF-IDF)” are concatenated. OIDL stands for Optimized Integrated Deep Learning, which combines two types of deep learners. The first is the combination of concatenated features with “Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN),” while the second is the combination of concatenated features with RNN. The Improved Coyote Optimization Algorithm (ICOA) improves both deep learners, and the conclusion of sentiment analysis result is considered both models. Thus, the suggested model surpasses standard methodologies regarding precision and accuracy, according to the results of the experiments.
Compact source for quadripartite deterministically entangled optical fields

Since entangled multiple optical fields were identified as the building blocks of quantum networks, the quadripartite entangled optical fields have been produced by using four degenerate optical parametric amplifiers or two nondegenerate optical parametric amplifiers (NOPAs). However, realizing an efficient and compact source for multiple quantum users has remained an outstanding challenge, hindering their practical applications. Here, we proposed a compact and feasible scheme to deterministically entangle four spatially separated optical fields, employing only a single NOPA. Accordingly, two-sided output NOPA-based optical fields were coupled on a beam splitter network to form the quadripartite entangled state, causing the deterministic generation of both the Greenberger–Horne–Zeilinger (GHZ) and the linear cluster states in this compact entanglement source. We also obtained the optimal experimental parameters based on the simulation results, thereby providing a direct reference for experimental implementation. Our findings propose that the resultant GHZ and linear cluster states can be potentially applied in quantum-enhanced information science, specifically in quantum secret sharing, controlled quantum teleportation networks, and quantum-entangled atomic ensemble networks.
An Auto-Grading Oriented Approach for Off-Line Handwritten Organic Cyclic Compound Structure Formulas Recognition

Auto-grading, as an instruction tool, could reduce teachers' workload, provide students with instant feedback and support highly personalized learning.Therefore, this topic attracts considerable attentions from researchers recently.To realize the automatic grading of handwritten chemistry assignments, the problem of chemical notations recognition should be solved first.The recent handwritten chemical notations recognition solutions belonging to the end-to-end trainable category suffered from the problem of lacking the accurate alignment information between the input and output.They serve the aim of reading notations into electrical devices to better prepare relevant edocuments instead of auto-grading handwritten assignments.To tackle this limitation to enable the auto-grading of handwritten chemistry assignments at a fine-grained level.In this work, we propose a component-detectionbased approach for recognizing off-line handwritten Organic Cyclic Compound Structure Formulas (OCCSFs).Specifically, we define different components of OCCSFs as objects (including graphical objects and text objects), and adopt the deep learning detector to detect them.Then, regarding the detected text objects, we introduce an improved attention-based encoder-decoder model for text recognition.Finally, with these detection results and the geometric relationships of detected objects, this article designs a holistic algorithm for interpreting the spatial structure of handwritten OCCSFs.The proposed method is evaluated on a self-collected data set consisting of 3000 samples and achieves promising results.
3D Vehicle Detection Algorithm Based on Multimodal Decision-Level Fusion

3D vehicle detection based on LiDAR-camera fusion is becoming an emerging research topic in autonomous driving.The algorithm based on the Camera-LiDAR object candidate fusion method (CLOCs) is currently considered to be a more effective decision-level fusion algorithm, but it does not fully utilize the extracted features of 3D and 2D.Therefore, we proposed a 3D vehicle detection algorithm based on multimodal decision-level fusion.First, project the anchor point of the 3D detection bounding box into the 2D image, calculate the distance between 2D and 3D anchor points, and use this distance as a new fusion feature to enhance the feature redundancy of the network.Subsequently, add an attention module: squeeze-and-excitation networks, weight each feature channel to enhance the important features of the network, and suppress useless features.The experimental results show that the mean average precision of the algorithm in the KITTI dataset is 82.96%, which outperforms previous state-ofthe-art multimodal fusion-based methods, and the average accuracy in the Easy, Moderate and Hard evaluation indicators reaches 88.96%, 82.60%, and 77.31%, respectively, which are higher compared to the original CLOCs model by 1.02%, 2.29%, and 0.41%, respectively.Compared with the original CLOCs algorithm, our algorithm has higher accuracy and better performance in 3D vehicle detection.
An Analysis Model of Learners’ Online Learning Status Based on Deep Neural Network and Multi-Dimensional Information Fusion

The learning status of learners directly affects the quality of learning.Compared with offline teachers, it is difficult for online teachers to capture the learning status of students in the whole class, and it is even more difficult to continue to pay attention to students while teaching.Therefore, this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information fusion.Specifically, a facial expression recognition model and an eye state recognition model are constructed to detect students' emotions and fatigue, respectively.By integrating the detected data with the homework test score data after online learning, an analysis model of students' online learning status is constructed.According to the PAD model, the learning state is expressed as three dimensions of students' understanding, engagement and interest, and then analyzed from multiple perspectives.Finally, the proposed model is applied to actual teaching, and procedural analysis of 5 different types of online classroom learners is carried out, and the validity of the model is verified by comparing with the results of the manual analysis.