Reviewerly/exHarmony huggingface dataset:
The Theme Evolution and Strategic Decision-Making of the Public’s Attention to the Theme of Aging in China Based on Big Data Analysis

Actively responding to population aging has become the new national condition of China’s aging society in the new era. To do a good job of actively coping with aging in the new era, it is necessary to take multiple measures and work together on the basis of clarifying the major realities and problems faced by China’s aging society. From the perspective of big data technology, this paper systematically studies the concerns of China’s aging through the method of artificial intelligence. In addition, with two typical aging indicators, three coupled intelligent algorithms are introduced in this paper. The prediction results show that the multi-dimensional support vector machine optimized by the immune algorithm shows good advantages compared with the other two algorithms. The multi-dimensional support vector machine optimized by the immune algorithm, one of the big data, has a good prediction effect on the performance indicators of China’s aging, which indirectly illustrates the applicability of big data analysis to the concerns of China’s aging.
Determining most suitable listener backchannel type for speaker's utterance

A major hurdle in achieving a dialogue system that enables smooth dialogue is to determine how to generate an appropriate response to a user's utterance. Previous research has focused mainly on estimating whether to make an utterance backchannel in response to the user's utterance. We go one step further by examining, for the first time, the relationship between the type of utterance backchannel to be used and intent and type of the speaker's utterance, known as a dialogue act (DA). Specifically, we propose a new method for classifying utterance backchannels into nine types. We also created a corpus consisting of the DAs of speaker utterances and the backchannel types of listener utterances then used it to analyze the relationship between a speaker's and listener's utterances. Our findings clarify that the occurrence frequencies of a listener's backchannel types significantly depend on the DAs of the speaker's utterances. Since the goal of our research is to construct a dialogue system that generates a more natural backchannel, this classification method, which determines certain types of aids from the speaker's DA, will be beneficial to such a system.
Towards a sentiment-aware conversational agent

We propose an end-to-end sentiment-aware conversational agent based on two models: a reply sentiment prediction model and a text generation model, conditioned on the predicted sentiment and the context of the dialogue. Additionally, we propose to use a sentiment classification model to evaluate the sentiment expressed by the agent during the development of the model. Results show that explicitly guiding the text generation model with a pre-defined set of sentiment sentences leads to clear improvements, regarding the expressed sentiment and the quality of the generated text.
On Quantum Algorithms for Random Walks in the Nonnegative Quarter Plane

It is well known that strong connections exist between random walks (RWs) in the nonnegative quarter plane and the mathematical performance modeling, analysis and optimization of computer systems and communication networks. Examples include adaptive threshold-based load balancing in concert with affinity scheduling (e.g., [16, 10]), scheduling parallel jobs on parallel computer systems (e.g., [15]), and asymptotic scalabilty in wireless and linear loss networks (e.g., [9]).
Improved Fully Convolutional Siamese Networks for Visual Object Tracking Based on Response Behaviour Analysis

Siamese networks have recently attracted significant attention in the visual tracking community due to their balanced accuracy and speed. However, as a result of the non-update of the appearance model and the changing appearance of the target, the problem of tracking drift is a regular occurrence, particularly in background clutter scenarios. As a means of addressing this problem, this paper proposes an improved fully convolutional Siamese tracker that is based on response behaviour analysis (SiamFC-RBA). Firstly, the response map of the SiamFC is normalised to an 8-bit grey image, and the isohypse contours that represent the candidate target region are generated through thresholding. Secondly, the dynamic behaviour of the contours is analysed in order to check if there are distractors approaching the tracked target. Finally, a peak switching strategy is used as a means of determining the real tracking position of all candidates. Extensive experiments conducted on visual tracking benchmarks, including OTB100, GOT-10k and LaSOT, demonstrated that the proposed tracker outperformed the compared trackers such as DaSiamRPN, SiamRPN, SiamFC, CSK, CFNet and Staple and achieved state-of-the-art performance. In addition, the response behaviour analysis module was embedded into DiMP, with the experimental results showing the performance of the tracker to be improved through the use of the proposed architecture.
The extent of partially resolving uncertainty in assessing coherent conditional plausibilities

Handling uncertainty and reasoning under partial knowledge are challenging tasks that require to deal with coherent assessments and their extensions. Plausibility theory is shown to rest upon the principle of partially resolving uncertainty due to Jaffray, together with a systematically optimistic behavior. This means that we allow situations in which the agent may only acquire the information that a non-impossible event occurs, without knowing which is the true state of the world. This leads to assume that a target event is plausibly true if it is compatible with the acquired piece of information. The aim of the paper is to provide coherence conditions for a conditional plausibility assessment (namely, Pl-coherence), by referring to a suitable axiomatic definition based on the Dempster's rule of conditioning. We provide different equivalent notions of Pl-coherence in terms of consistency, betting scheme, and penalization that, as a by-product, highlight different interpretations. We then specialize the Pl-coherence conditions to the subclasses of (finitely additive) conditional probabilities and (finitely maxitive) conditional possibilities.
Reinforcement learning method for target hunting control of multi‐robot systems with obstacles

Aiming at the target encirclement problem of multi-robot systems, a target hunting control method based on reinforcement learning is proposed. First, the Markov game modeling for the multi-robot system is carried out. According to the task of hunting, potential energy models are designed to meet the requirements of arriving at the desired state and avoiding obstacles. The multi-robot reinforcement learning algorithm guided by the potential energy models is presented to perform the hunting, where reinforcement learning principles are combined with the model control. Secondly, based on the potential energy models, the target-tracking hunting strategy and the target-circumnavigation hunting strategy are established. In the former, the consensus tracking of multi-robot systems is achieved by designing the velocity potential energy function. And in the latter, virtual circumnavigation points are added to construct the potential energy function, which realizes the desired circumnavigation. Finally, the effectiveness of target hunting control based on the multi-robot reinforcement learning method is verified by simulation.
Population based training and federated learning frameworks for hyperparameter optimisation and ML unfairness using Ulimisana Optimisation Algorithm

This paper introduces the Ulimisana Optimisation Algorithm enabled Population Based Training (PBT-UOA) framework which allows for hyperparameters to be fine-tuned using a population based meta-heuristic algorithm at the same time as parameters are being optimised. Models are trained until near-convergence on the updated hyperparameters and the parameters of the best performing model are shared to warm start the other models in the next hyperparameter tuning iteration. In the PBT-UOA, all models are trained using the same dataset. This framework performed better than the Bayesian Optimisation algorithm. This paper also introduces the Ulimisana Optimisation Algorithm enabled Federated Learning (FL-UOA) framework which is an extension of the PBT-UOA. This framework is introduced to address the challenges of scattered datasets and privacy that is presented by the increase in connected end-devices. The FL-UOA learns on local data in scattered end-devices without sending datasets to a central server. The training datasets in local end-devices are used to evaluate models trained in other end-devices. The performance metrics are used to update the Social Trust Network (STN) of the FL-UOA framework. The FL-UOA outperformed the classic Federated Learning framework. This STN updating technique was tested in Machine Learning (ML) Unfairness to see how well it functioned as a regularisation term. This was achieved by training different models on subsets that contained datasets representing only specific sensitive groups. Results showed that by updating the hyperparameters while learning the parameters on the dataset scattered across different devices, the FL-UOA, takes advantage of diversified learning and reduces the ML Unfairness for models trained on group specific datasets.
Hierarchical feature aggregation network with semantic attention for counting large‐scale crowd

The purpose of crowd counting is to estimate the number of people in an image. Due to the unconstrained imaging conditions, the scale variation and background occlusion in the images make it still challenging to achieve counting accurately. To tackle the two key challenges, we design a Hierarchical Feature Aggregation Network (HFANet) for accurate crowd counting in complex scenarios. The proposed method can extract multiple features at different levels and then aggregate them hierarchically to generate a high-quality density map. To highlight the crowd regions effectively from the cluttered background, we propose the Semantic Attention module to preserve the useful feature information through the attention mechanism for the low-level features extracted by VGG-16. Meanwhile, we employ convolutional kernels of different sizes to extract multi-scale features and global average pooling operations to preserve contextual information. Furthermore, we design the Feature Aggregation module to integrate the extracted multiple features through a progressive approach, which aims to take full advantage of the complementary properties between low-level and high-level features for efficient features aggregation. Finally, we evaluate the performance of the proposed HFANet on four challenging datasets. Extensive experimental results demonstrate that our proposed approach has better performance compared with most state-of-the-art approaches.
Construction of Intelligent Evaluation Platform Based on Random Matrix iWrite College English Writing

The advantages of iWrite English writing teaching and marking system 3.0 and the case study of English writing in higher education based on this system have a positive effect on forming a new model of English writing teaching in higher education under the background of Internet + and improving and solving the main problems of English writing teaching in higher education. Although the current intelligent marking systems have made a lot of achievements, they have not fundamentally solved the problem of the rationality of intelligent marking of subjective questions. In order to better perceive the sense of speech in English essays in depth and improve the rationality problem of intelligent marking, this study proposes a quantification of N-element sense of speech value based on correlation analysis and a scoring fitting algorithm for English essays based on rationality enhancement. The quantification of perceptual value is done by obtaining the N components of the composition and calculating their support in the corpus. In the daily teaching work, teachers should actively cooperate with the marking system to improve their own teaching level and explore ways and methods of students’ writing training, so as to cultivate more English talents. In addition, word features, sentence features, and chapter structure features were extracted from the essays to fit the English essay scores. Since not all students were able to complete the essays according to the requirements of the questions, a streaming scoring model was used to separate the normal essays from the low-scoring essays. The low-scoring essays were scored using the k-nearest neighbor algorithm, while the normal essays were fitted to the test takers’ scores using the support vector regression algorithm. Statistically, it was found that the essay scores also showed a certain normal distribution. The standard support vector regression algorithm is prone to data skewing problems, so this study addresses this problem by using a rationality enhancement method, which gives a corresponding penalty factor according to the distribution of the data set.
Content‐based image retrieval using color and a novel texture descriptor: orientational‐based local binary pattern

Summary The increasing use of images and multimedia has led, in the past years, to a major growth of data and images to store. With this, comes the importance of having a way to facilitate the indexing and the retrieval of the images. In this article, we present a new method for content‐based image retrieval. The proposed approach is composed of two phases: first, a preselection of relevant images from the initial database based on HSV color space and using color moment information. The second phase relies on using the selected images from the first phase as a new database and extracting the texture feature using a novel descriptor called orientational‐based local binary pattern (OB‐LBP) derived from the traditional LBP texture feature technique. In the proposed descriptor (OB‐LBP) we compare each neighbor of the center pixel (of 3 × 3 window) with its three closest neighbors and form a binary code of three components for each pixel, then based on their directions we generate three images and produce the histogram to extract the final feature descriptor. Corel‐10 k and Wang's databases were used to evaluate the proposed method. The results show a significant improvement in the proposed method over existing methods.
An Evaluation Approach for Physical Education Teaching Practice Quality Using Stochastic Simulation Algorithm

In order to increase the precision of the quality assessment of institution physical education, as well as teaching practice, the assessment system of institution physical education, as well as teaching practice quality, based on stochastic simulation algorithm, is put forward. Then, we construct the constraint parameter model of institution physical education, as well as teaching practice quality assessment, and analyze the hierarchical structure characteristics of institution physical education, as well as teaching practice quality. Subsequently we standardize the data fusion through the developmental characteristic analysis approach of teaching quality assessment and analyze the confidence level of institution physical education, as well as teaching practice quality, in order to construct frequent item set parameters. Moreover, we adopted the combination analysis approach of support and confidence level to analyze the correlation pattern and identify the characteristics, so as to recognize the characteristic analysis of institution physical education, as well as teaching practice quality level. The random simulation and association rule decision-making approaches are implemented to assess the quality level of institution physical education, as well as teaching practice, and the random simulation dynamic detection and the maximum matching degree analysis approach are implemented to assess the quality level of institution physical education, as well as teaching practice. The simulation outcomes confirm that the precision of this approach in evaluating the quality of institution physical education, as well as teaching practice, is considerably high, and the quality level of institution physical education, as well as teaching practice, is significantly increased.
Optimization and Evaluation of Oral English CAF Based on Artificial Intelligence and Corpus

With the development of the times, the exchanges between countries are increasing. China is becoming a superpower, and the number of international cities is increasing. This requires the communication level of Chinese people to be improved. English, as the second largest communication language in China, should be better understood and studied. This paper makes an in-depth discussion on the optimization and evaluation of oral English CAF based on artificial intelligence and corpus and makes an experimental analysis. The results are as follows: (1) introducing oral English teaching based on artificial intelligence and oral English based on a corpus, so as to deepen the public’s cognition of both and make oral English more deeply rooted in people’s hearts and get attention. (2) Analyzing the algorithms of phoneme errors in spoken English. Errors in spoken English are very common. Algorithms can be used to identify them better. When evaluating spoken English, algorithms are needed to evaluate them more accurately. (3) There are many examples of the benefits of artificial intelligence to oral English teaching. By comparison, it is found that the method of evaluating using artificial intelligence is more accurate, a corpus can improve oral English, and CAF optimization is also of great help to oral English.
Saturation and vanishing ideals

We consider an homogeneous ideal I in the polynomial ring $$S=K[x_1,\dots ,$$ $$x_m]$$ over a finite field $$K={\mathbb {F}}_q$$ and the finite set of projective rational points $${{\mathbb {X}}}$$ that it defines in the projective space $${{\mathbb {P}}}^{m-1}$$ . We concern ourselves with the problem of computing the vanishing ideal $$I({{\mathbb {X}}})$$ . This is usually done by adding the equations of the projective space $$I({{\mathbb {P}}}^{m-1})$$ to I and computing the radical. We give an alternative and more efficient way using the saturation with respect to the homogeneous maximal ideal.
Kernel-Based Generalized Median Computation for Consensus Learning

Computing a consensus object from a set of given objects is a core problem in machine learning and pattern recognition. One popular approach is to formulate it as an optimization problem using the generalized median. Previous methods like the Prototype and Distance-Preserving Embedding methods transform objects into a vector space, solve the generalized median problem in this space, and inversely transform back into the original space. Both of these methods have been successfully applied to a wide range of object domains, where the generalized median problem has inherent high computational complexity (typically $\mathcal{NP}$-hard) and therefore approximate solutions are required. Previously, explicit embedding methods were used in the computation, which often do not reflect the spatial relationship between objects exactly. In this work we introduce a kernel-based generalized median framework that is applicable to both positive definite and indefinite kernels. This framework computes the relationship between objects and its generalized median in kernel space, without the need of an explicit embedding. We show that the spatial relationship between objects is more accurately represented in kernel space than in an explicit vector space using easy-to-compute kernels, and demonstrate superior performance of generalized median computation on datasets of three different domains. A software toolbox resulting from our work is made publicly available to encourage other researchers to explore the generalized median computation and applications.
Sequential Anomaly Detection Against Demodulation Reference Signal Spoofing in 5G NR

In fifth generation (5G) new radio (NR), the demodulation reference signal (DMRS) is employed for channel estimation as part of coherent demodulation of the physical uplink shared channel. However, DMRS spoofing poses a serious threat to 5G NR since inaccurate channel estimation will severely degrade the decoding performance. In this correspondence, we propose to exploit the spatial sparsity structure of the channel to detect the DMRS spoofing, which is motivated by the fact that the spatial sparsity structure of the channel will be significantly impacted if the DMRS spoofing happens. We first extract the spatial sparsity structure of the channel by solving a sparse feature retrieval problem, then propose a sequential sparsity structure anomaly detection method to detect DMRS spoofing. In simulation experiments, we exploit clustered delay line based channel model from 3GPP standards for verifications. Numerical results show that our method outperforms both the subspace dimension based and energy detector based methods.
Improving Robustness of Speech Anti-Spoofing System Using Resnext with Neighbor Filters

Since recent advances in speech synthesis techniques, it is important to develop robust speech anti-spoofing systems against all major spoofing attacks. In this paper, we propose a novel spoofing speech detection model by jointing ResNeXt with neighbor filters (NF-ResNeXt) to improve the robustness of speech anti-spoofing models. Inspired by higher-order cepstral coefficients are more difficult to be maintained during the speech synthesis procedure, we present a novel neighbor filter module for extracting the residual features to enhance the robustness of cepstral features. Then, we introduce a neural network architecture based on ResNeXt model for processing the residual features and calculating the scores of speech clips being spoofed. The NF-ResNeXt model is trained on the training set of ASVspoof 2019 logical access (LA) dataset and achieves an equal error rate (EER) of 5.13% on the evaluation dataset, which outperforms the existing state-of-the-art speech anti-spoofing models.
Dual Re-Weighting Network for Multi-Source Domain Adaptation

In this paper, we propose a novel framework called Du-al Re-weighting Multi-source Network (DRMN) to address the task of Multi-source Domain Adaptation (MSDA). Two challenges exist in MSDA: i) the domain discrepancies a-mong the multiple source domains, and ii) the domain mis-match between target and source domains. We propose du-al re- weighting mechanisms including source distribution re-weighting and sample selected re-weighting. Source distribution re-weighting mechanism can match the estimated source label distribution and the unknown target label distribution to adapt the classifier. Sample selected re-weighting mechanism can select highly confident target data as pseudo-labeled sam-ples to integrate the information from different sources, and further improve the classification performance. We find that DRMN can show competitive performance with respect to the state-of-the-art on different real-world datasets.