Reviewerly/exHarmony huggingface dataset:
Substation Decision-making Platform Based on Artificial Intelligence

In view of the actual needs faced by the substation maintenance, this paperproposes a kind of substation decision-making platform based on artificialintelligence. The platform formalizes and integrates the basic data, electricaldata and the operational data of the equipment, qualitatively triggers themaintenance task abide by the result of the logistic regression model, providesfurther results of data processing through quantitative analysis, and providesknowledge navigation to the operation guidance of the corresponding equip-ment. The platform matches the electrical data with the inference enginestored in the knowledge base. If the data match the condition of the inferencesuccessfully, the inference is triggered and the action is executed. The resultis provided to the relevant staff as a suggestion to assist the final decision.After the task is completed, the cause, effect and solution of the equipmentfailure are backfilled and expanded into the equipment base as a new instance.
The Design and Implementation of an Integrated Building Environment for Android Systems

In recent years many embedded system products are shipped with Android system installed as their operating systems. Building an installable system image file of Android, however, is not trivial and there is no graphical user interface (GUI) for the official Android build system. Although quite a lot of guiding information and helpful resources are available in addition to the official documentation provided by Google, it is not uncommon that the building process failed due to some subtle mistakes and the developer felt frustrated. When system customization is desired, such as tailoring the source code or removing some unnecessary modules from the source code, the burden is even greater because the building rules of Android system lack enough flexibility and Android system has become bigger and more complicated during the past years. It is not only very challenging but also very difficult, if not impossible, to implement the desired tailoring without help from certain analysis tools. This paper presents the development of a GUI based integrated building environment for Android. The main goal is to help smoothly and easily build a custom system image of Android via providing GUIs and integrating the functions needed and desirable in carrying out the building process. The contributions of this work mainly include: (1) an approach to analyzing the module dependency of Android source code that helps remove unnecessary modules or features in customizing Android system image; (2) the GUIs that help users easily and quickly build an Android system image; and (3) an integrated building environment that helps achieve building automation of Android. This integrated building environment therefore is useful and helpful in building Android system images.
Real-time Path Planning for Autonomous Underwater Vehicle Mobile Docking

In this paper, we study the real-time path planning problem for AUV mobile docking. The proposed algorithm take the terminal heading and velocity into consideration while AUV docking into the underwater mobile platform using the relative velocity and position between AUV and the platform, the algorithm generates the desired speed and desired heading for the AUV. The simulation results and pool experimental results both show that the proposed algorithm can work well.
Research Status of Innovative Industrial Clusters in China Based on Bibliometric Analysis

The development of innovative industrial clusters is an important way to promote the development of innovative enterprises in China. At the same time, it also has definite practical significance to encourage regional innovation. This paper uses the CNKI database as the research platform, and selects the literature in the field of innovative industrial clusters from 2002 to March 1, 2021 as the research object, using bibliometrics method and CiteSpace software tools to analyze the number of publications, cooperative institutions, research hotspots, and trends. Perform visual analysis and systematically comb the progress status of innovative industrial clusters and predict future research trends in China. The research results show that the research on the basic theories of innovation industry clusters is not solid enough in China, and the cooperation between scientific research institutions needs to be strengthened; in addition, in this paper, the research process is divided into the embryonic exploration period and the development period for analysis, and then found it Related research hotspots mainly focus on innovative industrial clusters and industrial cluster theory, innovative industrial cluster development areas, innovative enterprises and government policies.
ECDT: Exploiting Correlation Diversity for Knowledge Transfer in Partial Domain Adaptation

Domain adaptation aims to transfer knowledge across different domains and bridge the gap between them. While traditional knowledge transfer considers identical domain, a more realistic scenario is to transfer from a larger and more diverse source domain to a smaller target domain, which is referred to as partial domain adaptation (PDA). However, matching the whole source domain to the target domain for PDA might produce negative transfer. Samples in the shared classes should be carefully selected to mitigate negative transfer in PDA. We observe that the correlations between different target domain samples and source domain samples are diverse: classes are not equally correlated and moreover, different samples have different correlation strengthes even when they are in the same class. In this study, we propose ECDT, a novel PDA method that Exploits the Correlation Diversity for knowledge Transfer between different domains. We propose a novel method to estimate target domain label space that utilizes the label distribution and feature distribution of target samples, based on which outlier source classes can be filtered out and their negative effects on transfer can be mitigated. Moreover, ECDT combines class-level correlation and instance-level correlation to quantity sample-level transferability in domain adversarial network. Experimental results on three commonly used cross-domain object data sets show that ECDT is superior to previous partial domain adaptation methods.
Cosine-Pruned Medial Axis: A New Method for Isometric Equivariant and Noise-Free Medial Axis Extraction

We present the CPMA, a new method for medial axis pruning with noise robustness and equivariance to isometric transformations. The CPMA leverages the discrete cosine transform to create smooth versions of a shape <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> . We use the smooth shapes to compute a score function <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {F}_{\Omega }$ </tex-math></inline-formula> that filters out spurious branches from the medial axis of the original shape <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> . Our method generalizes to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -dimensional shapes given the properties of the Discrete Cosine Transform. We extensively compare with state-of-the-art pruning methods to highlight the CPMA’s noise robustness and isometric equivariance. We conducted experiments using two 2D datasets — Kimia216 and Animal2000 — and one 3D dataset — the Groningen benchmark. We found that our pruning approach achieves competitive results and yields stable medial axes even in scenarios with significant contour perturbations.
A Decision Tree Approach for Assessing and Mitigating Background and Identity Disclosure Risks

The Facebook/Cambridge Analytica data scandal shows a type of privacy threat where an adversary attacks on a massive number of people without prior knowledge about their background information. Existing studies typically assume that the adversary knew the background information of the target individuals. This study examines the disclosure risk issue in privacy breaches without such an assumption. We define the background disclosure risk and re-identification risk based on the notion of prior and conditional probabilities respectively, and integrate the two risk measures into a composite measure using the Minimum Description Length principle. We then develop a decision-tree pruning algorithm to find an appropriate group size considering the tradeoff between disclosure risk and data utility. Furthermore, we propose a novel tiered generalization method for anonymizing data at the group level. An experimental study has been conducted to demonstrate the effectiveness of our approach.
End-to-End Encrypted Messaging Based on PGP with Forward Secrecy

Abstract In the era of big data, more and more users prefer messaging applications with end-to-end encryption to protect their privacy. However, if a hacker gets the password or encrypting key, it is possible to login into the account, or intercept the network packets and get the original messages. This paper introduces a new decentralized online messaging system that combines PGP and Diffie–Hellman key exchange and gives forward secrecy to end-to-end encrypted messaging. It is not possible for the hacker to decrypt the intercepted messages once the temporary session is ended, even if the hacker gets the PGP private keys.
Short-Term Solar PV Generation Forecast Using Neural Networks and Deep Learning Models

Accurate short-term forecasting of renewable-based generating sources is an essential tool for plants owner and system operator in order to take informed operational decisions. This chapter presents various neural network (NN) and deep learning (DL)-based approaches to forecast the solar PV generation. The random vector functional link (RVFL) model, a variant of NN and long short-term memory (LSTM), a DL-based model are developed to forecast solar PV generation considering a realistic dataset of solar PV plants at IIT Gandhinagar campus. Application of DL-based LSTM and NN-based variants of RVFL models for forecasting solar PV generation for both clear sky and cloudy day is carried out, and a performance comparison between LSTM, RVFL, and wavelet decomposition (WD)-RVFL-based solar PV forecasting algorithms for clear sky is presented. On analysis, it was found that DL-based LSTM model performs better than variants of NN-based RVFL models. As a result, the drawback of shallow NN models over DL models are highlighted. At last, the need for sophisticated DL models over shallow NN models to solve the problem of renewable energy forecasting is stressed.
Lexical data mining‐based approach for the self‐enrichment of <scp>LMF</scp> standardized dictionaries: Case of the syntactico‐semantic knowledge

Summary The LMF ISO standard provides a large cover of lexical knowledge using a fine structure. However, like most of the electronic dictionaries, the available normalized LMF dictionaries comprise only basic morpho‐syntactic and semantic knowledge, such as the meanings of lexical entries through the definitions and the associated examples, and sometimes the indication of the synonyms and antonyms. Other sophisticated knowledge, such as the syntactic behaviors, semantic classes and syntactico‐semantic links, which are scarce, requires a high expertise and its adding to dictionaries is expensive. In fact in this paper, we propose an approach of lexical data mining of the widely available textual content associated with the meanings, notably in the normalized LMF dictionaries, in order to perform the self‐enrichment of these dictionaries. First, we contribute to the enrichment of the syntactic behaviors by linking them to the suitable meanings. Second, we focus on the enrichment of the meanings of LMF lexical entries with semantic classes based on the Gaston Gross semantic classification. Finally, we establish the syntactico‐semantic links based on the results of the syntactic and semantic enrichment processes. The proposed approach has been consolidated by an experimentation carried out on an available normalized LMF dictionary for Arabic language.
Information hiding using matroid theory

Inspired by problems in Private Information Retrieval, we consider the setting where two users need to establish a communication protocol to transmit a secret without revealing it to external observers. This is a question of how large a linear code can be, when it is required to agree with a prescribed code on a collection of coordinate sets. We show how the efficiency of such a protocol is determined by the derived matroid of the underlying linear communication code. Furthermore, we provide several sufficient combinatorial conditions for when no secret transmission is possible.
AutoName: A Corpus-Based Set Naming Framework

We propose AutoName, an unsupervised framework that extracts a name for a set of query entities from a large-scale text corpus. Entity-set naming is useful in many tasks related to natural language processing and information retrieval such as session-based and conversational information seeking. Previous studies mainly extract set names from knowledge bases which provide highly reliable entity relations, but suffer from limited coverage of entities and set names that represent broad semantic classes. To address these problems, AutoName generates hypernym-anchored candidate phrases via probing a pre-trained language model and the entities' context in documents. Phrases are then clustered to identify ones that describe common concepts among query entities. Finally, AutoName ranks refined phrases based on the co-occurrences of their words with query entities and the conceptual integrity of their respective clusters. We built a new benchmark dataset for this task, consisting of 130 entity sets with name labels. Experimental results show that AutoName generates coherent and meaningful set names and significantly outperforms all baselines.
Image Acquisition Time Series Prediction Method Based on Deep Learning

Abstract At present, in-depth learning can be said to be a learning method based on neural network, and time series can be used to achieve prediction results. Therefore, a time series prediction method for image collection based on deep learning is proposed. Based on time series prediction, the image collection in deep learning is analyzed, and the DBN model is combined with GCRBM model to train the model, identify the time series category, and reconstruct the sequence in order to achieve complete time series prediction. The experimental results show that the time series prediction method works well.
Segmentation of pulmonary vessels based on MSFM method

Accurate segmentation of pulmonary blood vessels from CT images is of great significance for lung disease detection and segmentation of other lung structures. Manual segmentation is difficult to accurately segment vascular tissue for various reasons. Therefore, in view of the existing problems and shortcomings of the existing lung vessel segmentation method, a more efficient lung vessel segmentation algorithm is proposed, that is, the multi-template fast marching method (MSFM algorithm). Firstly, it used the hole filling and maximum inter-class variance algorithm for preprocess. In the process, the lung parenchyma is extracted from the chest CT, and then the lung blood vessels are extracted in the lung parenchymal area using the MSFM algorithm. In the extraction process, the threshold and gradient are used to limit the progress of the process and the lung blood vessels are more accurately segmented. Through experimental verification, the accuracy of lung blood vessel segmentation based on MSFM algorithm is improved.
Swing Voters’ Vote Choice Prediction Using Multilevel Logit Model to Improve Election Survey Accuracy

Abstract Public opinion surveys are often used to predict an election result. However, the predictions are not always accurate due to many factors. The presence of swing voters at the time of survey is one of the sources of the inaccuracy. On the other hand, election surveys are also often conducted by using multi-stage random sampling method so that ordinary models such as logit model generally do not provide satisfactory results. The data, hence, is complex and may be approached by multilevel models. The study is conducted to assess the extent to which a prediction of swing voters’ vote choice through a multilevel logit model can improve survey accuracy. The data used in this study was the result of a survey conducted using stratified multistage random sampling method two weeks before the 2019 presidential election. The model with 15 predictors and random effects for villages and neighborhood providing 96.3% accuracy and AUC reached 99.1% in the validation process. Based on the final model, the swing voters in this survey were predicted to vote more for Candidate B (10.4%) than Candidate A (7.5%). The direction of the swing voters’ support different from the loyal voters who prefer Candidate A (49.1%) than Candidate B (33.0%). The prediction of swing voters’ vote choice using multilevel logit model significantly improved the survey accuracy. Before the swing voters’ support was predicted the absolute deviation between the survey result and the election result was quite large, around 6.4%-11.5%. After swing voters’ support was predicted the absolute difference shrank to 1.1%.
English Treebank of Mapúa University Smart Interactive Voice Response System

Treebanks are linguistically annotated corpus of natural language texts represented as a database. It is vital in natural language processing (NLP), which allows the interaction and communication between computers and the human language. It is behind the recent technological innovations of automated and smart assistants. This paper intends to create an English treebank for the usage of Mapua University in their future endeavors to develop Interactive Voice Response (IVR) systems. This study is accomplished by providing students with a connection to a communication server through their phones. Calls are made to the Asterisk server set up in a Raspberry Pi. When the intended audio message is forwarded to the system, it is converted to a text input using a Speech-to-Text algorithm. It then undergoes process and analysis divided into five parts: preprocessing, segmentation, tagging, creation of the grammar tree, and computation for the Probabilistic Context-Free Grammar (PCFG). Based on the testing results for this study, the system yielded a word error rate of 7.36%, a NER F1 score of 71.7%, and a PCFG calculation accuracy of 64.44%.
Object Detection and Semantic Segmentation for a Small Low-cost Vehicle

This contribution presents a demonstrator vehicle for automated driving in different or even unknown and rough terrains. Covering different terrains is an unique feature which separates this approach from the classic automotive industry which mainly focuses on driving on existing roads. This makes our approach highly interesting for commercial vehicles which are not limited to roads as well as those which have to drive on roads without reliable maps such as after catastrophes or simply in non-cartographed areas like construction sites or production plants. The main focus here is the perception. Thus, the object detection in 3D and the semantic segmentation will be covered. The former is needed to not only detect obstacles and their location but also whether to avoid or to run over them. The latter gives information about the surrounding: E.g. which areas of the ground are driveable. This is important if not only the road is considered as viable driving surface. To leave the level of an only academic feasibility, common commercial off-the-shelf (COTS) hardware is used and attention is paid on cost-benefit ratio. For the implementation, different neural networks are compared and finally used.
Privacy Protection of Frequent Sequences based on Uniform Adaptive Local Margin

Abstract In order to enhance the privacy protection of frequent sequences, and improve its mining utility and reduce the impact of data dimension, this paper proposes a consistent adaptive margin for marginal release under local difference privacy, which has the characteristics of improving the effectiveness and efficiency. The algorithm mainly uses the consistent adaptive local margin strategy to index, finds frequent sequences, perturbs transaction data through random response mechanism, and finds all the sensitivities that satisfy local differential privacy. For the purpose of determining the running time of mining frequent sequences, a reasonable local sensitivity traversal data set before and after the interference is selected. According to the combination property of differential privacy, it is proved theoretically that the algorithm satisfies local differential privacy, and the effectiveness of the algorithm is verified by experiments. The experimental results show that the proposed algorithm can protect the local differential privacy of frequent sequences safely and efficiently, and ensure the accuracy of frequent sequences.
Research on the Value Orientation and Implementation Strategy of English Education Based on the Live-streaming Course Platform

The value orientation of contemporary English education is to teach for thinking, which can not only cultivate students' ability to understand, to grasp knowledge and to memorize, but also improve students' ability to read and think, develop students' English thinking, build a good thinking mode, and lay a solid foundation for future development. This study aims to elaborate on the significance of the value orientation of English education and the advantages of live-streaming course platform in in implementing the value orientation, and to propose some corresponding implementing strategies based on prior literature and the researcher experiences. The study concludes that in college English classroom teaching, teachers should make effective teaching plan based on the value orientation of English education and need to build efficient English classrooms with the help of live-streaming courses, improve the efficiency of implementation of the value orientation in English education, guide college students to master their own learning method so as to facilitate their English professional level and all-round development. This study have theoretical implications for English education reform in China facing the new situation of change and practical implications for English classroom teaching via modern technology.