Similarity Calculation Algorithm Research Articles

COIN: A Container Workload Prediction Model Focusing on Common and Individual Changes in Workloads

Recently, containers have become the primary deployment form for cloud applications. Predicting container workload accurately is critical to ensure the quality of service (QoS) and cost-efficiency of the applications and meet service level agreements (SLAs) with users. However, facing multiple challenges, including model unavailability due to insufficient data, model maladaptation due to dynamic workload changes, and model non-generalization due to changeable workload patterns in container workload prediction, existing methods have not yet provided a united and effective solution. To this end, we propose a novel integrated forecasting model named COIN that combines COmmon and INdividual changes in container workloads to ensure the availability, adaptivity, and generality of the prediction model based on transfer learning and online learning. Besides, we present a container similarity calculation algorithm for real cloud scenarios, which combines the static and dynamic information of containers and comprehensively depicts the similarity between containers. Through experiments based on two public datasets, the COIN model achieves a higher accuracy than existing state-of-the-art solutions, demonstrating the effectiveness and robustness of our proposed model, which provides a new solution to container workload prediction.

An Efficient and Privacy-Preserving Route Matching Scheme for Carpooling Services

With the popularity of intelligent terminals and the advances of mobile Internet, carpooling service, which reduces the travel cost of each user by allowing multiple users to share one car, has received considerable attention and makes our life more convenient. However, the vigorous development of carpooling services still faces severe challenges in users’ location or route privacy. In this article, we propose an efficient and privacy-preserving route matching scheme called TAROT for carpooling services. With TAROT, users can enjoy high-quality carpooling services while without revealing sensitive location and route information. Specifically, based on a Goldwasser–Micali-based equality determination algorithm (GMEDA), we design an accurate similarity computation algorithm (ASCA), which allows users to get accurate carpooling results over ciphertexts. Meanwhile, the reverse Minhash (RM) method is also designed to construct a dissimilar route filter algorithm (DRFA), which can filter out dissimilar routes in advance and reduce computational costs and communication overheads. Security analysis shows that TAROT can protect users’ location privacy. In addition, TAROT is also evaluated with many random maps, and the simulation results demonstrate that TAROT is highly efficient.

Efficient Personalized Recommendation Based on Federated Learning with Similarity Ciphertext Calculation

With the popularity of big data, people get less useful information because of the large amount of data, which makes the Recommender System come into being. However, the privacy and accuracy of the Recommender System still have great challenges. To address these challenges, an efficient personalized recommendation scheme is proposed based on Federated Learning with similarity ciphertext calculation. In this paper, we first design a Similarity calculation algorithm based on Orthogonal Matrix in Ciphertext (SOMC), which can compute the Similarity between users’ demand and Items’ attributes under ciphertext with a low calculation cost. Based on SOMC, we construct an efficient recommendation scheme by employing the Federated Learning framework. The important feature of the proposed approach is improving the accuracy of recommendation while ensuring the privacy of both the users and the Agents. Furthermore, the Agents with good performance are selected according to their Reliability scores to participate in the federal recommendation, so as to further make the accuracy of recommendation better. Under the defined threat model, it is proved that the proposed scheme can meet the privacy requirements of users and Agents. Experiments show that the proposed scheme has optimized accuracy and efficiency compared with existing schemes.

API Recommendation Based on WII-WMD

Improving software development efficiency based on existing APIs is one of the hot researches in software engineering. Understanding and learning so many APIs in large software libraries is not easy and software developers prefer to provide only requirements descriptions to get the right API. In order to solve this problem, this paper proposes an API recommendation method based on WII-WMD, an improved similarity calculation algorithm. This method firstly structures the text, and then fully mines the semantic information in the text. Finally, it calculates the similarity between the user's query problem and the information described in the API document. The experiment results show that the API recommendation based on WII-WMD can improve the efficiency of the API recommendation system.

Empirical Research of Hot Topic Recognition and its Evolution Path Method for Scientific and Technological Literature

With the advent of big data era, the recognition of hot topics and the analysis of their evolution path in the frontier of a certain field of scientific and technological literature have received widespread attention from the academic community. It can not only reveal the development trend in a certain field of scientific and technological literature, but also discover the evolution law of topic content in different development stages of the field. However, there are still some problems in some current research methods, such as inaccurate recognition of hot topics and unclear evolution path, which seriously affect the comprehensiveness and accuracy of the analysis. To solve the above problems, this paper uses Latent Dirichlet Allocation (LDA) model to propose a hot topic recognition and evolution analysis method in scientific and technological literature field, which aims to reveal the evolution law of topic content level in different development stages of the field, such as inheritance, merging, division, and other topic evolution trends, so as to provide decision support for domain knowledge innovation services. Main research process is as follows. Firstly, LDA is used to extract global topics and stage topics. Secondly, similarity calculation algorithm is used to filter topics. Thirdly, novelty and support are used to identify hot topics. Fourthly, three paths of inheritance evolution, merging evolution and division evolution are formed for hot topics. Finally, the effectiveness of the method is verified by using 47,896 scientific and technological literature data in the field of intelligent algorithms in Web of Science as an empirical example.

Efficient and Privacy-Preserving Speaker Recognition for Cybertwin-Driven 6G

With the introduction of cybertwin, a new approach to represent human or things in the cyberspace, it is foreseeable that vehicles will be able to offer more and more services in the future. Naturally, considering the safety of drivers, speaker recognition will be widely used in vehicle scenarios. Speaker recognition technologies are experiencing increasing popularity due to the unique and indissoluble link between individuals and their voices. However, the coming cybertwin-driven 6G brings speaker recognition technologies unprecedented challenges, especially in preventing the disclosure of voiceprint. To address these challenges, an efficient and privacy-preserving speaker recognition scheme for cybertwin-driven 6G, referred to as NEATEN, is proposed in this article. With NEATEN, the speaker identity can be recognized at multiple security levels without leaking the voiceprint data. More concretely, based on the random projection data perturbation, voiceprint perturbation algorithms in two phases and the corresponding ciphertext-based similarity computation algorithm are proposed. By using these algorithms, our efficient and accurate speaker recognition scheme can be achieved. Orthogonal to the previous works of biometric identification based on the Euclidean distance, NEATEN makes progress on the non-Euclidean distance, such as cosine distance and complicated distance. Detailed analysis shows that NEATEN can resist various known security threats. Experiments conducted on TIMIT and Voxceleb data sets have demonstrated that NEATEN is highly accurate and efficient, and can be flexibly deployed in a real cybertwin-driven 6G vehicle environment.

API Recommendation Based on WII-WMD#

Improving software development efficiency based on existing APIs is one of the hot researches in software engineering. Understanding and learning so many APIs in large software libraries is not easy and software developers prefer to provide only requirements descriptions to get the right API. In order to solve this problem, this paper proposes an API recommendation method based on WII-WMD, an improved similarity calculation algorithm. This method firstly structures the text, and then fully mines the semantic information in the text. Finally, it calculates the similarity between the user's query problem and the information described in the API document. The experiment results show that the API recommendation based on WII-WMD can improve the efficiency of the API recommendation system.

API Recommendation Based on WII-WMD

Improving software development efficiency based on existing APIs is one of the hot researches in software engineering. Understanding and learning so many APIs in large software libraries is not easy and software developers prefer to provide only requirements descriptions to get the right API. In order to solve this problem, this paper proposes an API recommendation method based on WII-WMD, an improved similarity calculation algorithm. This method firstly structures the text, and then fully mines the semantic information in the text. Finally, it calculates the similarity between the user's query problem and the information described in the API document. The experiment results show that the API recommendation based on WII-WMD can improve the efficiency of the API recommendation system.

Synthetic Network and Search Filter Algorithm in English Oral Duplicate Correction Map

Combining the communicative language competence model and the perspective of multimodal research, this research proposes a research framework for oral communicative competence under the multimodal perspective. This not only truly reflects the language communicative competence but also fully embodies the various contents required for assessment in the basic attributes of spoken language. Aiming at the feature sparseness of the user evaluation matrix, this paper proposes a feature weight assignment algorithm based on the English spoken category keyword dictionary and user search records. The algorithm is mainly based on the self-built English oral category classification dictionary and converts the user’s query vector into a user-English-speaking type vector. Through the calculation rules proposed in this paper, the target user’s preference score for a specific type of spoken English is obtained, and this score is assigned to the unrated item of the original user’s feature matrix as the initial starting score. At the same time, in order to solve the problem of insufficient user similarity calculation accuracy, a user similarity calculation algorithm based on “Synonyms Cilin Extended Edition” and search records is proposed. The algorithm introduces “Synonyms Cilin” to calculate the correlation between the semantic items, vocabulary, and query vector in the user query record to obtain the similarity between users and finally gives a user similarity calculation that integrates user ratings and query vectors method. For the task of Chinese grammar error correction, this article uses two methods of predicting the relationship between words in the corpus, Word2Vec and GloVe, to train the word vectors of different dimensions and use the word vectors to represent the text features of the experimental samples, avoiding sentences brought by word segmentation. On the basis of word vectors, the advantages and disadvantages of CNN, LSTM, and SVM models in this shared task are analyzed through experimental data. The comparative experiment shows that the method in this paper has achieved relatively good results.

Design and Implementation of English Intelligent Communication Platform Based on Similarity Algorithm

Intelligent communication processing in English aims to obtain effective information from unstructured text data using various text processing techniques. Text vector representation and text similarity calculation are important fundamental tasks in the whole field of natural language processing. In response to the shortcomings of existing sentence vector representation models and the singularity of text similarity algorithms, improved models and algorithms are proposed based on a thorough study of related domain technologies. This paper presents an in-depth and comprehensive study of text vectorization representation and text similarity calculation algorithms in the field of natural language processing. The existing text vectorized representation models and text similarity computation algorithms are described, and their shortcomings are summarized to provide a basis for the background and significance of this paper, as well as to provide ideas for improvement directions. It is experimentally verified that the sentence vector model proposed in this paper achieves higher accuracy than the SIF sentence vector model for text classification tasks. In the task of text similarity computation, it achieves better results in three evaluation metrics: accuracy, recall, and F1 value. The algorithm also improves the computational efficiency of the model to a certain extent by removing feature words with low feature contribution. The algorithm first improves the deficiencies of the traditional word-shift distance algorithm by defining multifeature fusion weights and realizes a text similarity calculation algorithm based on multifeature weighted fusion with better similarity calculation results. Then, a linear weighting model is constructed to further combine the similarity calculation results of the hierarchical pooled IIG-SIF sentence vectors to realize the multimodel fusion text similarity calculation algorithm.

Intention Understanding in Human-Robot Interaction Based on Visual-NLP Semantics.

With the rapid development of robotic and AI technology in recent years, human–robot interaction has made great advancement, making practical social impact. Verbal commands are one of the most direct and frequently used means for human–robot interaction. Currently, such technology can enable robots to execute pre-defined tasks based on simple and direct and explicit language instructions, e.g., certain keywords must be used and detected. However, that is not the natural way for human to communicate. In this paper, we propose a novel task-based framework to enable the robot to comprehend human intentions using visual semantics information, such that the robot is able to satisfy human intentions based on natural language instructions (total three types, namely clear, vague, and feeling, are defined and tested). The proposed framework includes a language semantics module to extract the keywords despite the explicitly of the command instruction, a visual object recognition module to identify the objects in front of the robot, and a similarity computation algorithm to infer the intention based on the given task. The task is then translated into the commands for the robot accordingly. Experiments are performed and validated on a humanoid robot with a defined task: to pick the desired item out of multiple objects on the table, and hand over to one desired user out of multiple human participants. The results show that our algorithm can interact with different types of instructions, even with unseen sentence structures.

Deep learning embedder method and tool for mass spectra similarity search

Spectral similarity calculation is widely used in protein identification tools and mass spectra clustering algorithms while comparing theoretical or experimental spectra. The performance of the spectral similarity calculation plays an important role in these tools and algorithms especially in the analysis of large-scale datasets. Recently, deep learning methods have been proposed to improve the performance of clustering algorithms and protein identification by training the algorithms with existing data and the use of multiple spectra and identified peptide features. While the efficiency of these algorithms is still under study in comparison with traditional approaches, their application in proteomics data analysis is becoming more common. Here, we propose the use of deep learning to improve spectral similarity comparison. We assessed the performance of deep learning for spectral similarity, with GLEAMS and a newly trained embedder model (DLEAMSE), which uses high-quality spectra from PRIDE Cluster. Also, we developed a new bioinformatics tool (mslookup - https://github.com/bigbio/DLEAMSE/) that allows users to quickly search for spectra in previously identified mass spectra publish in public repositories and spectral libraries. Finally, we released a human database to enable bioinformaticians and biologists to search for identified spectra in their machines. Significance statementSpectral similarity calculation plays an important role in proteomics data analysis. With deep learning's ability to learn the implicit and effective features from large-scale training datasets, deep learning-based MS/MS spectra embedding models has emerged as a solution to improve mass spectral clustering similarity calculation algorithms. We compare multiple similarity scoring and deep learning methods in terms of accuracy (compute the similarity for a pair of the mass spectrum) and computing-time performance. The benchmark results showed no major differences in accuracy between DLEAMSE and normalized dot product for spectrum similarity calculations. The DLEAMSE GPU implementation is faster than NDP in preprocessing on the GPU server and the similarity calculation of DLEAMSE (Euclidean distance on 32-D vectors) takes about 1/3 of dot product calculations. The deep learning model (DLEAMSE) encoding and embedding steps needed to run once for each spectrum and the embedded 32-D points can be persisted in the repository for future comparison, which is faster for future comparisons and large-scale data. Based on these, we proposed a new tool mslookup that enables the researcher to find spectra previously identified in public data. The tool can be also used to generate in-house databases of previously identified spectra to share with other laboratories and consortiums.

A Maintenance Task Similarity-Based Prior Elicitation Method for Bayesian Maintainability Demonstration

Prior distribution elicitation is a challenging problem for a Bayesian inference-based mean time to repair (MTTR) demonstration because if inaccurate prior information is introduced into the prior distribution, the results become unreliable. This paper proposes a novel maintenance task representation model based on the similarity of attributed maintenance items. A novel similarity computation algorithm for maintenance tasks is then formulated on the basis of this model. Optimistic and pessimistic values are ascertained from the time data for similar maintenance tasks to obtain a prior distribution. The main idea is to separate maintenance tasks into distinct items and use attribute sets to extract key features. Each pair of items is then compared to quantify the differences between reference and candidate tasks. Candidate tasks with an acceptable difference from the reference task are taken as prior information sources for constructing the prior distribution. A case study involving a high-frequency (HF) transceiver MTTR Bayesian demonstration shows that the proposed method can effectively obtain maintenance tasks similar to those of information sources for prior distribution elicitation.

An Automatic Question Answering Method for Small-Scale Corpus

It is a growing trend for automatic question answering system to be prominent in the development process of society. There are many methods trying to address this problem, but with deficiencies—relatively developed methods based on template matching need a lot of manual work writing templates, and machine learning based methods need plenty of work collecting a large number of corpus, bring huge burden on small-scale scenery. Facing these problems, we propose an automatic question answering methods meeting the needs of small-scale corpus. This method consists of combining an improved text similarity calculation algorithm and an intention recognition method based on slot filling. We conduct experiments on the problem sets of related fields, and it shows a good performance of the proposed automatic question answering method. Our methods make small scene applications for dialog systems more practical.

Does government information release really matter in regulating contagion-evolution of negative emotion during public emergencies? From the perspective of cognitive big data analytics

The breeding and spreading of negative emotion in public emergencies posed severe challenges to social governance. The traditional government information release strategies ignored the negative emotion evolution mechanism. Focusing on the information release policies from the perspectives of the government during public emergency events, by using cognitive big data analytics, our research applies deep learning method into news framing framework construction process, and tries to explore the influencing mechanism of government information release strategy on contagion-evolution of negative emotion. In particular, this paper first uses Word2Vec, cosine word vector similarity calculation and SO-PMI algorithms to build a public emergencies-oriented emotional lexicon; then, it proposes a emotion computing method based on dependency parsing, designs an emotion binary tree and dependency-based emotion calculation rules; and at last, through an experiment, it shows that the emotional lexicon proposed in this paper has a wider coverage and higher accuracy than the existing ones, and it also performs a emotion evolution analysis on an actual public event based on the emotional lexicon, using the emotion computing method proposed. And the empirical results show that the algorithm is feasible and effective. The experimental results showed that this model could effectively conduct fine-grained emotion computing, improve the accuracy and computational efficiency of sentiment classification. The final empirical analysis found that due to such defects as slow speed, non transparent content, poor penitence and weak department coordination, the existing government information release strategies had a significant negative impact on the contagion-evolution of anxiety and disgust emotion, could not regulate negative emotions effectively. These research results will provide theoretical implications and technical supports for the social governance. And it could also help to establish negative emotion management mode, and construct a new pattern of the public opinion guidance.

Improved Synthetic Weighted Algorithm of Ontology-Based Semantic Similarity Computation

To solve the problems of incomplete consideration and low precision in existing domain ontology semantic similarity computation, an improved synthetic weighted algorithm of ontology-based semantic similarity computation is proposed, mixing path coincidence degree, the shortest distance, and concept property methods. First, the depths of lowest common ancestor (LCA) and an ontology tree are added to the formula of path coincidence degree for distinguishing the influence of LCA depth on similarity when multiple inheritances occur. Second, the analysis of similarity algorithm based on the shortest distance cannot distinguish two situations with the same path distance. One is when the density of LCA is different. The other is a depth difference in the concept pair. So, the number of direct subnodes of the LCA and the depth difference are added to the formula of the shortest distance. Meanwhile, the switch of density factor is set to ensure similarity calculation results between [0,1]. Then, a synthetic weighted algorithm of semantic similarity computation is constructed using the weighting path coincidence degree, the shortest distance, and the concept property. Finally, this algorithm and the other three algorithms in the literature are used to calculate semantic similarity in tea ontology. The results show that this algorithm is closest to expert experience.

Digital twin-based process reuse and evaluation approach for smart process planning

With the advances in new-generation information technologies, smart process planning is becoming the focus for smart process planning with less time and lower cost. Big data-based reusing and evaluating the multi-dimensional process knowledge is widely accepted as an effective strategy for improving competitiveness of enterprises. However, there was little research on how to reuse and evaluate process knowledge with dynamical changing machining status. In this paper, we propose a novel digital twin-based approach for reusing and evaluating process knowledge. First, the digital twin-based process knowledge model which contains the geometric information and real-time process equipment status is introduced to represent the purpose and requirement of machining planning. Second, the process big data is constructed based on the three-layer and its association rules for accumulating process knowledge. Moreover, the similarity calculation algorithm of the scene model is proposed to filter the unmatched process knowledge. For accurately reusing the process knowledge, the process reusability evaluation approach of the candidate knowledge set is presented based on the real-time machining status and the calculated confidence. Finally, the diesel engine parts are applied in the developed prototype module to verify the effectiveness of the proposed method. The proposed method can promote the development and application of the smart process planning.

Microblog topic evolution computing based on LDA algorithm

AbstractResearch on topic evolution of Microblog is an effective way to analyze network public opinions. This paper proposes a method for mining changing of Microblog topics with time, and realizes topic evolution through topic extraction and topic relevance calculation. Firstly, latent Dirichlet allocation (LDA) model is used to automatically extract topics from different time slices; secondly, a similarity calculation algorithm is designed to calculate relevance of topic content through normalization of similarities among characteristic words and co-occurrence relations, to get evolutionary relationship among sub-topics of different time slices; thirdly, using probability distribution of blog article-topic to calculate topic intensity in each time slice, and then gets evolutionary relationship of topic intensity over time. Experiments show that the proposed topic evolution analysis model can effectively detect the evolution of topic content and intensity of real blogs.

A privacy-preserving friend recommendation scheme in online social networks

Recently, online social networks (OSNs), which can offer many innovative services, are on the rise. As making friends is a basic way to create user's social relationship, friend recommendation is proposed to help users expand their social circles in OSNs. However, traditional friend recommendation process poses several crucial privacy breaches in OSNs, such as identity theft and relationship privacy leakage. Aimed to solve this problem, different from traditional friend recommendation schemes, based on the common interests by characterizing user's social behaviors/activities, we identify the threat model, and then propose a k-degree anonymous friend recommendation (KFR) scheme. Firstly, we abstract OSN as a hypergraph and then propose an edge segmentation algorithm to hide user's identity privacy and social relationship privacy. Subsequently, based on users’ common interests, we design a similarity calculation algorithm. Finally, combined the similarity calculation algorithm with the segmentation tree (ST) technique, a novel k-degree anonymous friend recommendation scheme is proposed. The experiments carried out on real datasets show that the proposed scheme is scalable and effective.

An image authentication technology based on depth residual network

ABSTRACTThe traditional image authentication technique generally determines the image attribution by extracting specific features and combining the similarity calculation algorithm. Because of the selected features dimensions, characterization and other factors, the accuracy and speed of image authentication have been restricted. In this paper, Recog-Net, an end-to-end image authentication model based on convolution neural network has been proposed. Deep residual network is chosen as the features extractor. Mahalanobis distance and threshold method are used to complete the image authentication. Experiments show that the performance of the extractor's features, compared with the traditional features and the features of other convolution neural network architectures, is more excellent, with a high degree of generality, recognition rate and robustness, still having these advantages even after a substantial compression. The Recog-Net for image authentication is able to accurately authenticate the images tampered with certain range.