Reviewerly/exHarmony huggingface dataset:
Relational Sequence Encoding Graph Attention Network for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to classify all the aspects’ sentiment polarity in a sentence. However, most of the related research didn’t take full advantage of the dependency relations between words, the interaction between the node and its corresponding dependency relations is not taken into consideration, which causes the aspect words mistakenly focus on other context word. To solve these problems, a graph attention network model with relational sequence encoding (RSE-GAT) is proposed in this paper. The model constructs an aspect-rooted syntactic dependency tree, which constructs the representation of virtual connections and relation between aspect words and other words by dependency sequence encoding. At the same time, the composition operator is applied to enhance the interaction between word embedding and relation embedding. Finally, GAT is utilized for feature extraction. The experimental results on three public datasets demonstrate that the proposed RSE-GAT model achieves better results. Compared with the classical GCN-based model ASGCN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[5]</sup> , the accuracy on RES14, LAP14 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[9]</sup> and Twitter <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[10]</sup> is increased by 3.05%, 2.05% and 3.68% respectively.
A scene text detection algorithm based on feature filtering and fusion algorithm

Text instances in scene text images often present characteristics of complex background, arbitrary shape, varying orientation and light conditions, etc. Therefore, scene text detection has been a challenging research direction in recent years. In order to improve the performance of scene text detection algorithms, in particular on irregular-shape text instances, we propose the feature guided adaptive network (FGANet for short) for scene text detection, which jointly utilizes feature filtering and adaptive fusion mechanisms to strengthen text detection performance. In specific, FGANet designs a module that is able to filter out the channels of low efficacy from the feature maps extracted from the backbones; it devises another module that adaptively fuses the shallow features with visual details and deep features having more semantic meanings to enhance the perception ability on texts. Experiments on several benchmark scene text datasets show that FGANet achieves significantly better scene text detection performance.
Interference-Aware Latency Prediction With Kernels For Deep Neural Network

With the popularity of artificial intelligence applications, deep neural network (DNN) inference workloads are becoming more common in cloud servers. To improve GPU utilization, a GPU executes multiple workloads simultaneously, inevitably leading to resource contention and increasing inference latency. We propose a kernel-based latency prediction method that can more accurately predict the latency in the case of mutual interference between multiple workloads. The method uses the kernel parameters decomposed during the DNN inference to predict the latency of each kernel. It obtains the impact of interference on each model by the amount of data exchanged between the L1 cache, L2 cache, and GPU memory during the execution of each model. We conduct experiments on popular models. The results show that compared with the state-of-the-art multi-model coexistence prediction method, our method reduces the average error by 52% when predicting the latency of a single model and by 62%, 51%, and 58% when predicting the co-location of two, three, and four models.
End-to-End recognition approach for Cognitive Impaired speech using Sequential Conv-Nets

It is essential to find a way to early diagnosis of Mental Cognitive Disorders (MCD) in order to enable preventative care and prompt therapy to stop future progression. Automatic speech recognition software (ASR) giving transcriptions could possibly enhance communication of speech in the present. ASR is especially beneficial for those with increasing conditions that limit the comprehensibility of speech issues with motor function. ASR services typically have training on normal speech and might not be ideal for speech impairment, putting up a roadblock to using augmented help tools. This paper presents the recognition of cognitive impaired speech using Sequential 2-dimensional Conv-Nets. Convolutional networks efficiently explore and exploits temporal, cepstral and spectral structures of the speech signals. The network has been trained and tested on Spectrograms of Mel-Filters banks taking its time and frequency variants. The experiment was performed on 15000 spoken responses of digits (0-9) uttered by Dementia patients. The outcome of the trained model showed a validation accuracy of 91.27% with a loss of 0.5%.
Research on end to end low resource speech synthesis based on meta learning

With the development of deep learning, much progress has been made in end-to-end speech synthesis. The success is obvious especially in quality, pronunciation accuracy, and fluency ,the speech is close to human’s, its accuracy is below human’s tolerance. All these achievements are easy to achieve in the scene of the abundant corpus, however, there are difficulties in corpus collection for 95% languages in today's world and it is difficult to get good results when facing corpus shortage yet. We propose a meta-learning approach to solve the difficulties in low-resource scenarios by learning knowledge from other languages (with a large dataset of high quality). Our model can share information between languages,makes the model trained on a small corpus a good model using the knowledge from other languages. The speech synthesized with our model sounds more natural and achieve a higher level on quality. The resultant speech is close to human’s speech in terms of fluency. We add a parameter generator to the basic end-to-end speech system and our model has smaller parameters than the multilingual model with separate parameters for each language. Experiments show that our model can share information efficiently between languages.
Fuzzy Neuron Model – An Element Of Deep Learning For Stress Estimation

Understanding functions of neuron are crucial steps in deep learning and neural networks development. In this paper, the authors have presented the structure, and topology of neurons. Mathematics model offers the direct way to test hypothesis and understanding mechanism responsible for neuronal and deep learning algorithm development. Fuzzy logic model, its basic operations and transformation are also discussed, which can consider the uncertainty, vagueness, etc. in the stressed signal.
Indoor Fall Detection Based on Yolov5 And Openpose

The current traditional fall behavior detection method constructs a fall detection model by pose annotation of the acquired images, which leads to low detection accuracy due to the lack of lightweight processing of the model. In this regard, indoor fall detection based on Yolov5 and openpose is proposed. The Yolov5 network structure is constructed and the path aggregation network is added on top of it. The skeletal joint points of human body are extracted using the openpose algorithm, and the fall behavior is determined based on the skeletal joint point information obtained in real time, and finally the model is lightened. In the experiment, the proposed model is verified for detection accuracy. The analysis of the experimental results shows that when the proposed model is used for indoor fall behavior detection, the MAP value of the model is high and has a high inspection accuracy.
Web Spam Detection based on Single Page Semantic Features

In order to solve the problem of high difficulty and large amount of computation in feature extraction for web spam detection, a method for extracting semantic features only based on the HTML script of the current page is proposed. Firstly, the domain name is segmented by a memorization search algorithm combining depth-first search and dynamic programming. Secondly, The Latent Dirichlet Distribution is used to extract subject words of the web page. Lastly, three single-page semantic similarity features are calculated based on Word2Vec and word mover distance. Combining the single-page semantic similarity features with single-page statistical features, classification algorithms such as random forest are used to build classification models for web spam detection. The experimental results show that the AUC value of single-page content extraction based on semantic and statistical features for classification reaches 83.1%, which is about 7% higher than that of the control method.
FLC: Empower Large-Scale Multi-Label Text Classification via Free Label Correlations Derived from Massive Raw Texts

Large-scale multi-label text classification (LMTC) aims at tagging texts with its most relevant subset of labels from a large candidate label set. State-of-the-art LMTC methods are mostly based on deep neural networks trained using a cross-entropy loss. These methods require massive human labeled training data to capture the underlying label correlations implicitly, which could be a burden in real-world applications. We observe that many correlated labels are missed by human annotators, but their label names in fact co-occur often in document contexts. In this paper, we propose a novel text classification framework FLC, incorporating “free” label correlations derived from massive raw text corpora. Specifically, we design a principled similarity measurement to evaluate the label correlation based on the co-occurrence statistics of their corresponding label names. Based on the derived label correlations, we add a complementary loss to make the overall loss more robust when correlated labels were predicted by the classifier but missed by human annotators. Experiments on two real-world datasets demonstrate the superiority of FLC over state-of-the-art methods, especially when the human labels are not perfect and the training data is limited. Moreover, the computational overhead of employing FLC is negligible.
Design of Lightweight Infrared Image Enhancement Network Based on Adversarial Generation

Most of the images collected by infrared devices have problems such as blurred edges and low signal-to-noise ratio, and it is necessary to enhance their details and remove noise to make up for the lack of hardware conditions. Based on the deep convolutional adversarial generative network, this paper designs an end-to-end lightweight infrared image enhancement network by introducing the residual module, which realizes the mapping of low-quality blurred images to high-quality clear images. At the same time, the high-low quality infrared image pairs dataset is used for experimental comparative analysis. The results show that compared with the existing infrared image enhancement network, the structure designed in this paper can simplify the complexity of the model and accelerate the training speed of the network without reducing the image enhancement quality.
A Joint Event Extraction Framework based on Conditional Information Fusion Layers

With the rapid development of the Internet, the text published by various online social media is increasing day by day. How to select useful information from the redundant text quickly and accurately is the problem we have to face today. Event extraction aims to solve this problem mentioned above. To improve the effect of trigger and argument extraction, we propose a joint model based on conditional information fusion layers. In the trigger extraction module, we introduce the type information fusion layer to reduce the difficulty of extracting triggers. In the argument extraction module, we introduce the trigger information fusion layer to make the input of argument extraction carry both event type information and trigger information. Finally, we validate the model in the Chinese large-scale event extraction labeled dataset provided by Baidu. Experiments show that our model's F1-score performance is about 1% higher than common models such as BERT and so on in trigger extraction subtask, and about 3% higher than common methods such as BERT-MRC and so on in argument extraction subtask.
A Bilingual Machine Transliteration System for Sanskrit-English Using Rule-Based Approach

Machine Transliteration is a big challenging area in an increasingly multilingual ecosphere due to its dangerous position in various downstream natural language processing application systems. When a word is transliterated, it is shifted from one script to another. In contrast to a translation, which clarifies the meaning of a word written in a different language, a transliteration only conveys the word's pronunciation by utilizing a familiar alphabet. This paper proposes a technique for creating a bilingual automated tool to type Sanskrit using English orthography/alphabets and converting Sanskrit text into the script of English which helps in reading the Sanskrit text for those who are not aware about the orthography of Sanskrit language. The system receives input via the QWERTY keyboard, which produces the equivalent Sanskrit text and inversely user can give Sanskrit text as input to get equivalent text using the script of English language. The goal is to create an easy-to-use and robust automated solution that allows end-users to effortlessly type Sanskrit shlokas or sentences using an English keyboard. The suggested approach is unique in that it is based on the language's Unicode and works for the low-resource ancient language Sanskrit. The primary applications of the designed tool are to help user to read Sanskrit text using the script/orthography of English, to create parallel corpora for the Sanskrit language translation process, to create e-versions of manuscripts written in Sanskrit language as the majority of ancient knowledge is not available on the internet, and to make it easy to learn Sanskrit language by any individual or students at the school level. This tool encourages humans to use their original language.
Research on Learning method of text graph representation based on knowledge enhancement

In order to solve the problem that traditional text representation learning methods ignore the structure information of the text itself and the key information with high correlation degree not mentioned in the text, a text graph representation learning method based on knowledge enhancement is proposed. Firstly, Bert, bidirectional long short-term memory network and conditional random field are fused to extract entities from financial texts, and then mapped to external large-scale financial maps for knowledge enhancement to obtain the external entity set associated with texts. The external entity set is added to the keyword generation model to realize the node construction of text representation graph. The concept fusion between nodes is realized by the community detection algorithm. According to the co-occurrence relationship of concepts in the text and the correlation relationship between concepts and sentences, the edge is built and the weight is given, so as to realize the construction of text graph representation. Experimental results show that the proposed text graph representation method performs well in the task of text similarity calculation, and performs well on CNSE and CNSS with F1 reaching 82.05 and 88.96 respectively.
Neighbor-items Aware Graph Neural Networks for Session based Recommendation for large rotating units

A lot of results have been achieved so far for session-based graph neural network recommendations, which aim to predict user behavior based on anonymous sequences of user actions. However, a large number of graph neural network-based session recommendations focus on the current session, which is short in length and contains limited information in most cases. Therefore, in this paper, we propose a novel graph neural network recommendation model based on neighbor item awareness. The model designs a session-aware encoder that efficiently aggregates neighbor item information with the help of global session information, and achieves session information enhancement while reducing the introduction of noise. Specifically, NA-GNN constructs a global session graph and a current session graph to model the influence of neighboring items on items and sessions: (1) Global session graph, by creating links to related items in all sessions and constructing root-mean-square-valued session perceptrons to explore the influence of neighboring sessions on items, and finally fusing out session representations of neighboring item features. (2) Current session graph, exploring item embedding by modeling pairwise item transitions in the current session. And, in this paper, we fuse the session representation with neighboring item information and the current session representation through an attention mechanism. Extensive experiments on two real-world datasets show that our approach consistently outperforms state-of-the-art methods.
Safe and Efficient Mobile Robot Path Planning in Open World Environments

An open world environment in mobile robot path planning generally refers to a working space with unpredefined/unknown obstacles. It is a challenging task for path planning in open world environments, especially those having multiple dynamic obstacles with time-varying moving speed. In this context, safety become the key performance indicator, resulting in most of global path planning algorithms (e.g., A*, RRT*, etc) no longer in force due to their inadequate adaptability to dynamic environments. Therefore, we propose in this paper a novel safety-aware RRT* algorithm for the path planning of mobile robots in open world environments with dynamic obstacles. We leverage the safety-field model (firstly developed for self-driving cars with constrained path, i.e. vehicle lane) to mobile robot path planning (with unconstrained moving routes in the working space in general). In particular, a fine-grained energy map is developed as a safety indicator illustrating the real-time risk to the working space caused by dynamic obstacles in terms of distance away from the robot (which presents the potential energy), moving direction and moving speed (which present the kinetic energy). This is in strong contrast to the artificial potential field model that is widely utilized in the obstacle avoidance in dynamic environments, which only considers the potential energy and provides a coarse risk indication to robots. In order to improve the efficiency of the proposed safety-aware RRT* algorithm, we further propose a pruning strategy for the sampling process of RRT* based on the fine-grained energy map to guide the rapidly-exploring random tree growing in a safe direction. The parent node re-selection and rewiring process of the proposed algorithm also incorporates the safety-field based energy map. Experimental results show that the proposed safety-aware RRT* algorithm can provide excellent safety and efficiency in dynamic environments.
Path planning of cotton bale handling robot based on improved gray wolf optimization algorithm

China has traditionally been the world’s largest cotton producer, cotton importer and cotton consumer. The transportation of cotton from the processing plant to the warehouse is usually completed by manually operated handling equipment. Labor volume of cotton bale handling is large and the working environment is bad. In order to enhance the efficiency of the bale handling robot, a grey wolf optimization algorithm based on Chebyshev mapping and dynamic weight is proposed. First of all, for the problem of substantial randomness and extreme instability of the initial population of Gray Wolf algorithm, Chebyshev map is used to initialize the population. Using the monotonic increasing characteristic of tangent function interval to improve the convergence factor, the convergence speed in the initial stage of the algorithm needs to be improved. The improved weight strategy is adopted to join the non elite wolf to lead the population optimization, which improves the convergence rate of the algorithm and the performance of jumping out of local optimization. The experimental results show that the improved algorithm significantly improves the optimization performance, has a faster convergence speed and higher optimization accuracy. In the simulation of discrete complex obstacle scenes, the minimum and average distance of the algorithm is better than the comparison algorithm.
An Abstractive Text Summarization Method Based on Improved ConvS2S

In the abstractive text summary model, because of the asymmetry between the input document and the output summary, and because the models of the summary generate by stages according to the time step, the neural network often generates words that are not related to the original document or even do not exist in the vocabulary when generating a summary. To solve this problem, ConvS2S model uses a combination of convolutional coding and multi-head attention mechanism, it solves this problem by parallel computing at different locations of the document to obtain multiple features, and then generating the full summary in parallel. In order to better solve this problem, we introduced the improved pointer network into the ConvS2S model, and improved the ROUGE score on LCSTS dataset and Gigaword dataset, especially on ROUGE-1 boost with 1.4 and 1.3 respectively, which reflects the effectiveness of our model.
Research on data security sharing mechanism and application based on blockchain and privacy computing

The rapid growth and massive accumulation of various types of data have driven all walks of life to achieve high-quality development, and have had a major and profound impact on social governance and people’s lives. However, there are many problems in the traditional data transaction and sharing, such as the loss of data ownership and the leakage of original data information. How to ensure the invisibility of data and the determination of data ownership while sharing data is an urgent problem. Based on this, combining the advantages of emerging technologies such as blockchain and privacy computing, this paper proposes a data security sharing architecture and mechanism based on blockchain and privacy computing to ensure the effective protection of personal information in the process of data sharing, to realize the safe circulation and sharing of data.
D-Resnet: A Static Gesture Segmentation Recognition Network Based on Feature Fusion

Nowadays, gesture recognition based on vision plays an increasingly important role in fields such as augmented reality. However, most of the existing researches focus on the ideal lighting conditions, while gesture recognition in complex lighting conditions is not highly concerned, which also leads to the low recognition rate in this scene. To address the above problems, this paper proposes a static gesture segmentation recognition network with feature fusion: first, the input image is pre-processed with noise, then the feature map of the image is extracted by using DeepLabV3, then the pre-processed image is masked by the obtained feature maps, and the masked result is fused with the pre-processed image as the input of the network, and then Resnet18 is used for gesture recognition. The experimental results in both datasets show that the proposed method in this paper has higher recognition accuracy under different lighting conditions compared to Resnet and its variants.