Reviewerly/exHarmony huggingface dataset:
DHP: A Joint Video Download and Dynamic Bitrate Adaptation Algorithm for Short Video Streaming

With the development of multimedia technology and the upgrading of mobile terminal equipment, short video platforms and applications are becoming more and more popular. Compared with traditional long video, short video users tend to slide from current viewing video more frequently. Unviewed preloaded video chunks cause a large amount of bandwidth waste and do not contribute to improving the user QoE. Since bandwidth savings conflict with user QoE improvements, it is very challenging to satisfy both. To solve this problem, this paper proposes DHP, a joint video download and dynamic bitrate adaptation algorithm for short video streaming. DHP makes the chunk download decision based on the maximum buffer model and retention rate, and makes the dynamic bitrate adaptation decision according to past bandwidth and buffer size. Experimental results show that DHP can reduce the bandwidth waste by at most 66.74% and improve the QoE by at most 42.5% compared to existing solutions under various network conditions.
Research on parallel parking path planning and tracking control of unmanned vehicles

Aiming at the problems of discontinuous curvature of the planned path, difficulty in finding the optimal reference path and high control accuracy requirements in tracking control for unmanned vehicles in the process of parallel parking, an optimal reference path planning method based on arc line and multi-objective optimization function and a path tracking control strategy based on nonlinear model predictive control are proposed. Firstly, the upper and lower boundaries of the exercisable area are obtained by the tangent of the arc and the straight line, and the multi-objective optimization function is designed to obtain the optimal arc-line combination, and the optimal reference path is obtained by polynomial curve fitting. Secondly, a path-following controller is designed using nonlinear model predictive control. Finally, a comparative analysis is carried out in MATLAB/Simulink and Carsim with the controller based on pure tracking design. The results show that the optimal reference path curvature obtained by the proposed method is continuous without sudden change, and the designed tracking controller has better tracking accuracy.
A new improved clustering method of incomplete data based on K-nearest neighbors

Clustering algorithm has been widely studied and applied in machine learning. Most of the clustering algorithms are only suitable for complete data sets; however, real-world data sets may have missing values for a variety of reasons. To solve this problem, this article propose a fuzzy C-means (FCM) clustering algorithm based on K-nearest neighbors(KNN) imputation. Unlike the two-stage strategy that executes imputation and clustering separately, this article take missing values as the optimization variable of the objective function to make imputation and clustering negotiate with each other to achieve the optimal. The proposed algorithm has a good effect on clustering for incomplete data. The experimental results on UCI data sets can verify that the proposed approach is obviously superior to several other approaches.
Unsupervised Encoder-Decoder Model for Anomaly Prediction Task

For the anomaly detection task of video sequences, CNN-based methods have been able to learn to describe the normal situation without abnormal samples at training time by reconstructing the input frame or predicting the future frame, and then use the reconstruction error to represent the abnormal situation at testing time. Transformers, however, have achieved the same spectacular outcomes as CNN on many tasks after being utilized in the field of vision, and they have also been used in the task of anomaly detection. We present an unsupervised learning method based on Vision Transformer in this work. The model has an encoder-decoder structure, and the Memory module is used to extract and enhance the video sequence's local pattern features. We discover anomalies in various data sets and visually compare distinct scenes in the data set. The experimental results suggest that the model has a significant impact on the task of dealing with anomalies.
Semantic segmentation of vehicle vision based on two-branch Enet network

Semantic segmentation has important research significance in environment perception in unmanned field. By improving the Enet model, adding spatial branches, turning the original model into a two-branch structure, cropping the original structure as semantic branches, and using the attention mechanism to fuse the spatial and semantic branch feature maps to retain the image spatial information and improve the grid effect. The improved algorithm is tested on dataset Cityscapes and dataset BDD100K, and the average cross-merge ratio is improved by 2.75% and 2.41%, respectively. The implementation of the two-branch Enet network makes it easier to obtain information on the drivable area of the vehicle, obstacles ahead, etc.
Bilstm Personalised-Style Poetry Generation Algorithm

Chinese poetry plays an important role in people's daily lives, promoting an understanding of history and culture, enriching their emotions, and enhancing their aesthetic skills. It is an integral part of China's cultural heritage. In natural language processing, Chinese poetry generation is challenging because its unique rhyme format requires expressing certain meanings and achieving perfect harmony between syllables, tones, and rhymes. An improved long and short-term memory network framework is used in this paper to generate each line of the poem. The innovation of this paper is that a style dictionary is provided for different dataset sources, the generated poems are scored based on the style dictionary for their similarity to different styles, and the final poem with the highest similarity is then generated. The results of our comprehensive evaluation of human judgments indicate that our proposed method improves the similarity of the generated poems to the target style and is somehow comparable to the quality of human poetry.
Research on Multi-Target Tracking Technology Based on Particle Filter

Using CNN deep convolutional neural network model to extract image sample features, this paper designs an image multi-target tracking algorithm combining particle filter and Softmax classifier. The possible transformation state factors of the tracking target are considered in the design of the particle filter, the possible transformation states are defined by the Bayesian probability operation method, the particle filter is introduced into the CNN model, and the extracted particle image block is analyzed by the Softmax classifier. Features to achieve accurate classification. The single-target and multi-target tracking accuracy of the proposed algorithm is evaluated through four experiments. The experimental results verify that the premise of improving the accuracy of image multi-target tracking is to obtain accurate feature information of the tracked image and achieve accurate classification. The accuracy of single-target and multi-target tracking has reached more than 94%.
CDTP: A Copyright-preserving Decentralized Data Trading Platform Based on Blockchain

The conventional centralized data trading system confront the problem that the Trusted Third Party (TTP) may be dishonest, which harms the fairness and transparency of the system. Besides, we notice that most data trading systems lack distinguishing between the copyright and the use-right in trading. To address these issues, we propose a novel blockchainbased data trading system with copyright-preserving, called CDTP, mainly including two blockchains and an agreement. The copyright chain, one of the blockchains, is designed for registering and trading copyrights stored in the form of atomic transactions. It adopts an auction-based Byzantine agreement, namely ABFT. Another is use-right blockchain, which records use-right transactions and stores data, combined with IPFS-based storage. Moreover, we carry out experiments to simulate the nerformance of ABFT when it is under attacks.
A multi-objective optimization model of robot path planning under different scenarios

With the rapid development of logistics industry, path planning of mobile robots has been widely concerned. The main purpose of this paper is to solve the problem of path planning for autonomous mobile robots in static environment. The problem can be solved by determining a collision-free path that satisfies the selected criteria of shortest distance and path smoothness. The proposed multi-objective path planning model aims at scientific and reasonable path planning for mobile robots to increase the path smoothness and reduce the moving distance. Firstly, according to the characteristics of obstacles, two groups of different robot workspace scenarios were set from the degree and density of obstacles. Secondly, a multi-objective nonlinear programming model is established to maximize path smoothness and minimize path distance. Finally, different scenarios are simulated, and a fast non-dominated sorting genetic algorithm based on elite strategy (NSGAII) is used to solve the model. Simulation results show that the multi-objective path planning model established in this paper can generate an optimal feasible path even in a complex environment, so as to overcome the shortcomings of traditional methods such as grid method. Moreover, compared with recent path planning techniques, the proposed multi-objective evolutionary algorithm is highly competitive in path optimization.
Trapdoor Sanitizable and Redactable Signatures with Unlinkability, Invisibility and Strong Context-Hiding

In trapdoor sanitizable signatures (TSS) (ACNS’08), a signer can partially delegate its signing ability to someone. When signing a message, the signer chooses its sanitizable parts. Each signature is associated with a trapdoor, enabling any entity arbitrarily to modify the sanitizable parts while retaining validity of the signature. In previous TSS, the sanitizable parts are permanently sanitizable. We formalize sanitization-controllable TSS, where the sanitizable parts can be partially (and irreversibly) changed into fixed. We formally define its security notions, including unlinkablity (any sanitized signature and its trapdoor cannot be linked to their original ones), invisibility (each signature leaks no information about its sanitizable parts) and strong context-hiding (SCH) (any sanitized signature and its trapdoor distribute like fresh ones). We propose a generic transformation from a downgrade-controllable downgradable affine MAC (DAMAC), which is a generalization of DAMAC (CT-RSA’19). Our TSS scheme is the first TSS scheme satisfying unlinkability or invisibility. In redactable signatures (ICISC’01), we can partially black out a signed message. We formalize disclosure-controllable trapdoor redactable signatures (TRS). We propose a generic transformation from a sanitization-controllable TSS. Our TRS scheme is the first unlinkable and disclosure-controllable (T)RS scheme.
A Multi-Modal Knowledge Graph of Intersection Traffic State

Knowledge mapping has become a key method to solve urban computing tasks, such as traffic flow, taxi demand and air quality prediction. Although the existing data is explosive growth, there are relatively few effective data, and data recognition is difficult, so it is impossible to use such data to build a good knowledge map model. The existing massive traffic data includes image and text modals. If these two kinds of modal data are fused together, the problem of less effective data can be solved. Therefore, exploring a correct road traffic information scheme that integrates multimodal information has become the focus of this paper. In this paper, a multi-modal road traffic state knowledge graph is designed to extract vehicle image features and traffic intersection text information features respectively. On this basis, we built an intersection traffic multimodal knowledge graph suitable for structured and semi-structured data. We carry out relevant experiments based on the real intersection data. The experimental verification shows that the method can successfully build the knowledge graph, the data of the image mode and text mode at the same intersection are highly consistent, which lays a better foundation for the later application of multimodal knowledge graph.
Improving Speech Representation Learning via Speech-level and Phoneme-level Masking Approach

Recovering the masked speech frames is widely applied in speech representation learning. However, most of these models use random masking in the pre-training. In this work, we proposed two kinds of masking approaches: (1) speech-level masking, making the model to mask more speech segments than silence segments, (2) phoneme-level masking, forcing the model to mask the whole frames of the phoneme, instead of phoneme pieces. We pre-trained the model via these two approaches, and evaluated on two downstream tasks, phoneme classification and speaker recognition. The experiments demonstrated that the proposed masking approaches are beneficial to improve the performance of speech representation.
Combining spatial attention and cross-layer bilinear pooling for fine-grained image classification

To address the problems of large intra-class difference and small inter-class-class difference in fine-grained images and the difficulty of obtaining effective feature representations, this paper proposes a method combining spatial attention and cross-layer bilinear pooling for fine- grained image classification, which can learn a more powerful fine-grained feature representation. In the paper, extract the underlying image features through ResNet50 network and inspired by the multi-scale detection algorithm of small-size objects, a spatial attention mechanism is used to achieve adaptive adjustment of the spatial weights of each layer features in fusion, which promotes more effective fusion of higher-level features and lower-level features to complement each other. In addition, cross-layer bilinear pooling is proposed in the paper to integrate the features of each layer after two- by-two interaction in order to explore the correlation of features between layers. Finally, by evaluating on three publicly available datasets, the experimental results indicate that the proposed method is superior to the existing classical methods in terms of classification.
On forward sufficient dimension reduction for categorical and ordinal responses

We introduce a forward sufficient dimension reduction method for categorical or ordinal responses by extending the outer product of gradients and minimum average variance estimator to categorical and ordinal-categorical generalized linear models. Previous works in this direction extend forward regression to binary responses, and are applied in a pairwise manner for multi-category data, which is less efficient than our approach. Like other forward regression-based sufficient dimension reduction methods, our approach avoids the relatively stringent distributional requirements necessary for inverse regression alternatives. We show the consistency of our proposed estimator and derive its convergence rate. We develop an algorithm for our methods based on repeated applications of available algorithms for forward regression. We also propose a clustering-based tuning procedure to estimate the bandwidth. The effectiveness of our estimator and related algorithms is demonstrated via simulations and applications.
Transformer-Based Cross-Modal Recipe Embeddings with Large Batch Training

Cross-modal recipe retrieval aims to exploit the relationships and accomplish mutual retrieval between recipe images and texts, which is clear for human but arduous to formulate. Although many previous works endeavored to solve this problem, most works did not efficiently exploit the cross-modal information among recipe data. In this paper, we present a frustratingly straightforward cross-modal recipe retrieval framework, Transformer-based Network for Large Batch Training (TNLBT) achieving high performance on both recipe retrieval and image generation tasks, which is designed to efficiently exploit the rich cross-modal information. In our proposed framework, Transformer-based encoders are applied for both image and text encoding for cross-modal embedding learning. We also adopt several loss functions like self-supervised learning loss on recipe text to encourage the model to further promote the cross-modal embedding learning. Since contrastive learning could benefit from a larger batch size according to the recent literature on self-supervised learning, we adopt a large batch size during training and have validated its effectiveness. The experimental results showed that TNLBT significantly outperformed the current state-of-the-art frameworks in both cross-modal recipe retrieval and image generation tasks on the benchmark Recipe1M by a huge margin. We also found that CLIP-ViT performs better than ViT-B as the image encoder backbone. This is the first work which confirmed the effectiveness of large batch training on cross-modal recipe embedding learning.
Defense Mechanisms Against Audio Adversarial Attacks: Recent Advances and Future Directions

With the popularity of speech and speaker recognition systems in recent years, voice interfaces are increasingly integrated into various Internet of Things (IoT) devices. However, studies have demonstrated that such systems are vulnerable to attacks using manipulated inputs. During the last few years, defense mechanisms have been studied and discussed from various aspects to protect voice systems from such attacks. Notwithstanding, there is lacking survey focus on the defense mechanism of audio adversarial examples. In this paper, we provide a comprehensive survey on state-of-the-art defense methods by illuminating their main concepts, reviewing the recent progress with a novel taxonomy, and discussing the future directions. It promises to bring awareness to the security problems in speech and speaker recognition systems and encourages people to propose more robust defenses against audio adversarial examples.
Component Extraction for Deep Learning Through Progressive Method

Machine learning has shown great impact in a lot of applications. Within all types of tools, deep learning should be one of the most important techniques thanks to its ability to capture the correlation between the input features and output results. However, the relatively long training time and high computation complexity remain a big problem in deep learning. In addition, the impossibility to explain the model makes it harder for us to look for alternatives to fix the bad fitting results. Therefore, this paper aims at improving the deep learning model training result by proposing a principal component extraction algorithm. Compared with the previous Principal Component Analysis (PCA) methods, this algorithm creatively consider not only the original input components but also the computed variables in the first hidden layer in neural network so as to capture more representative components. The experiment shows that compared to previous PCA method, this can better capture the principal components from all input variables.
A novel approach for content-based mining and indexing using hierarchical ranking

The development of multimedia technologies is becoming more popular; users are not satisfied with traditional information retrieval techniques. People shared ideas and information with others by using methods of communication such as eye contact, movement, interaction through gestures, verbal and written communication. In data recovery, ranking is a key concept. Despite the importance of highlight choosing, calculations for picking ranking models have been thoroughly explored, but this is not the case for highlight choice. Numerous factor-determination methods used in the grouping are directly applicable to ranking. It recovers the natural color, surface, and shape information of images for effective component extraction using edge identification, which is frequently used in signal processing and picture pressure. It has observed the use of information-digging techniques for finding covered-up designs in the enormous dataset and addressing problems in various scientific domains. This paper inputs maximum number of files, and it has voice-based statements or keywords in it. Voice commands convert the voice into text and start searching the text into a file. This will be done by content-based mining, which will be comparable to word-based mining or data mining, using a hierarchy pattern to decide its priority irrespective of its supportive words.
Improving Imbalanced Text Classification with Dynamic Curriculum Learning

Recent advances in pre-trained language models have improved the performance for text classification tasks. However, little attention is paid to the priority scheduling strategy on the samples during training. Humans acquire knowledge gradually from easy to complex concepts, and the difficulty of the same material can also vary significantly in different learning stages. Inspired by this insights, we proposed a novel self-paced dynamic curriculum learning (SPDCL) method for imbalanced text classification, which evaluates the sample difficulty by both linguistic character and model capacity. Meanwhile, rather than using static curriculum learning as in the existing research, our SPDCL can reorder and resample training data by difficulty criterion with an adaptive from easy to hard pace. The extensive experiments on several classification tasks show the effectiveness of SPDCL strategy, especially for the imbalanced dataset.
Architecture Search for Deep Neural Network

Deep learning has become a popularly used tool in large amount of applications. Given its ability to explore the input and output relationship, deep learning can perform well in terms of prediction. However, one important drawback in this framework is that the model cannot be easily trained due to huge search space and large number of parameters. In response to such problem, this paper proposes a novel way to build a deep neural network. Specifically, it tries to consider an unbalanced structure of deep neural network by expanding the number of nodes in the beginning to extract as much information as possible and then shrinking quickly to converge to the final result. The experiment results show that our proposed structure can output equally good output with much faster time compared with traditional methods.
Detection of DoH Tunnels with Dual-Tier Classifier

DNS over HTTPS (DoH) has been deployed to provide confidentiality in the DNS resolution process. However, encryption is a double-edged sword in providing security while increasing the risk of data tunneling attacks. Current approaches for plaintext DNS tunnel detection are disabled. Due to the diversity of tunneling tool variations and the low proportion of tunneled traffic in real situations, detecting malicious behaviors is becoming more and more challenging. In this paper, we propose a novel behavior-based model with Dual-Tier Tunnel Classifier (DTC) for tool-level DoH tunneling detection. The major advantage of DTC is that it can not only capture existing tunneling tools but also explore unknown ones in the wild. In particular, DTC considers data imbalance, which improves robustness of the model in the open environment. Our method has been proven successful in both closed and open scenarios, achieving 99.99 % accuracy in detecting known malicious DoH traffic, 96.93% accuracy in unknown and 95.31 % accuracy in identifying malicious DoH tunnel tools.