Reviewerly/exHarmony huggingface dataset:
A Simple Framework for XAI Comparisons with a Case Study

The number of publications related to Explainable Artificial Intelligence (XAI) has increased rapidly this last decade. However, the subjective nature of explainability has led to a lack of consensus regarding commonly used definitions for explainability and with differing problem statements falling under the XAI label resulting in a lack of comparisons. This paper proposes in broad terms a simple comparison framework for XAI methods based on the output and what we call the practical attributes. The aim of the framework is to ensure that everything that can be held constant for the purpose of comparison, is held constant and to ignore many of the subjective elements present in the area of XAI. An example utilizing such a comparison along the lines of the proposed framework is performed on local, post-hoc, model-agnostic XAI algorithms which are designed to measure the feature importance/contribution for a queried instance. These algorithms are assessed on two criteria using synthetic datasets across a range of classifiers. The first is based on selecting features which contribute to the underlying data structure and the second is how accurately the algorithms select the features used in a decision tree path. The results from the first comparison showed that when the classifier was able to pick up the underlying pattern in the model, the LIME algorithm was the most accurate at selecting the underlying ground truth features. The second test returned mixed results with some instances in which the XAI algorithms were able to accurately return the features used to produce predictions, however this result was not consistent.
Utilizing Text-based Augmentation to Enhance Video Captioning

Video captioning (VC) is a challenging cross-modality task that requires the model to capture the visual information in the video and to automatically generate the captions accordingly. Literature shows that Transformer-based deep neural networks (DNN) achieve the state-of-arts. Without exception, such DNN models are data-hungry, which hinders the development of the VC models since large-scale VC training datasets need to pay a much higher cost to build compared to the datasets for other tasks such as image recognition or neural machine translation. As a result, data augmentation is a valuable approach to improving the performance of VC models. In this work, we propose two text-based augmentation methods to enlarge the scale of VC datasets, so as to develop better VC models. Our basic ideas lie that when a video is given, a different person may give different descriptions, which leads to a better understanding of the given video. From another view of point, language has flexible and versatile expression properties which can be used to augment training corpora. Specifically, in our work, the pre-training Transformer-based language models, i.e., PEGASUS from Google and translator WMT19 from FAIR, have been employed to generate “new captions.” The various ways to select the proper captions and training strategies have also been fully explored to capitalize on data augmentation. Extensive experiments are conducted on the mainstream VC training datasets: MSVD and MSR-VTT. It is encouraged to see that our data augmentation method consistently boosts LSTM-based and Transformer-based VC models, with improvements of an average of 3.8 and up to 7.9 CIDEr scores.
Adaptive Cuckoo Search Algorithm Based on Dynamic Scaling Factor and Historical Experience

Cuckoo search (CS) algorithm is a simple and efficient bionic algorithm, it has advantages in solving the resources scheduling problem and optimize computer aided design. A novel adaptive CS algorithm, EDCS, combining historical experience and dynamic step size, is proposed to solve the problems of slow convergence and local optimization in the later stage of iteration. Through adaptive adjustment of Lévy flights step size, the algorithm has a large space for optimization in the early stage and thus improves the global search ability. In the later stage, the step size decreases with iteration and the local search ability of the algorithm is enhanced. Aiming at the problem that the overstepping nest caused by the preferential random walks affects the convergence speed and algorithm accuracy, a memory strategy is introduced to make the algorithm in full use of the historical iterative experiences, which improves the stability. The experimental results show that the performance of EDCS algorithm is significantly better than that of the original algorithm.
Application of Blockchain Technology in Asset Securitization

Asset securitization is a structured financial product, and its risks exist in the complexity of the deal structure and the extension of the transaction chain, and simultaneously amplify the information asymmetry between the parties to the final transaction. This situation can lead to exogenous shocks that multiply risk. Companies often engage in asset securitization activities with specific portfolios of assets or cash flows they hold, such as accounts receivable, bank loans, etc., in order to obtain more funds for growth. The current blockchain technology has become an important technology choice for asset securitization with its advantages of decentralization, smart contracts, and tamper-proof. This paper introduces and analyzes the basic technical features of blockchain, and discusses the feasibility of blockchain application in asset securitization and the application process of blockchain securitization. The application of blockchain technology in the example of accounts receivable asset securitization is exemplified to clarify the effect of using blockchain technology and to provide design ideas and references for enterprises to apply blockchain technology to asset securitization.
On the Processing of Interrogative Sentence and Sentence Tense in Chinese-English Machine Translation

This paper mainly discusses machine translation, the automatic translation of sentences in two different languages. It is mainly the product of artificial intelligence combined with computer software and hardware technology, linguistics, mathematics and other disciplines. At present, the computer software and hardware platform supports big data storage and high-speed operation, whose conditions are very good, but the quality of the machine translation is still unsatisfactory, especially the accuracy of some sentences. The research target of this paper is the Chinese-English machine translation. Although there are many related technologies, this paper mainly discusses the processing of interrogative sentences and sentence tenses. This paper focuses on two tasks: (1) The translation processing of interrogative sentences in Chinese-English machine translation, putting forward the method of sentence pattern conversion. (2) The processing of sentence tenses in Chinese-English machine translation.
Optimization of Subgraph Matching over Knowledge Graph Based on Subgraph Indexing

Given a query graph, subgraph matching is the process of finding all the isomorphic graphs over a large data graph. Subgraph is one of the fundamental steps of many graph-based applications including recommendation system, information retrieval, social network analysis, etc. In this paper, we investigate the problem of subgraph matching over power grid knowledge graph. Since knowledge graph is a modelled as a directed, labelled, and multiple edges graph, it brings new challenges for the subgraph matching on knowledge graph. One challenge is that subgraph matching candidate calculation complexity increases with edges increase. Another challenge is that the search space of isomorphic subgraphs for a given region is huge, which needs more system resources to prune the unpromising graph candidates. To address these challenges, we propose subgraph index to accelerate the matching processing of subgraph que-ry. We use domain-specific information to construct index of power grid knowledge and maintain a small portion of search candidates in the search space. Experimental studies on real knowledge graph and synthetic graphs demonstrate that the proposed techniques are efficient compared with counterparts.
Explicit and Implicit Feature Interaction Based on Attention Networks for Click-Through Rate Prediction

Click-through rate (CTR) prediction, which aims to estimate the probability of a user to click on a given item, plays a critical role in both recommender systems and online advertising. Recently, a number of deep neural network-based approaches have been developed in an implicit and bit-wise interaction, which the order of interaction is inconclusive. In this work, a novel model called eXtreme Deep Attention Interaction Network (xAtInt) is proposed to capture meaningful combination features explicitly. On the one hand, xAtInt learn meaningful feature interaction via attention mechanism; on the other hand, it is capable of finding arbitrary degree interaction at a vector-wise. Furthermore, to improve performance, we integrate DNN with xAtInt learning explicit and implicit feature interactions. Extensive experiments are conducted on real-world dataset. The results show that our model present good performance compared existing state-of-the-art approaches.
An optimized Graph Embedding based Knowledge Graph Cleaning Algorithm

Data quality of knowledge graph is a one of the most important guareentees for many knowledge-based applications. We investigate the konwledge graph cleaning problem. We propose a knowledge graph error detection framework and design an optimized embedding based clean algorithm. The framework maps the knowledge graph into an numerical space and keeps the relationship between different nodes. With this framework, both miss data error and errous relationship can be cleaned. Extensive experimental study over different data sets validate the effectiveness of the method.
Deep Hash Model for Similarity Text Retrieval

Text hashing transforms a text into a binary hash code, making similar texts have similar hash codes. Text hashing can reduces storage and improves retrieval efficiency of similar texts retrieval, but integrating semantic characteristics into hash code of text is difficult and is a hot topic. A deep hashing network model based on SE-Inception + Bi-LSTM + Attention mechanism is proposed in this paper. The Bert pre-training language model is used to preprocess text content to obtain the input vector, and the hash layer is added to generate the binary hash code of the text. The proposed model deeply combines semantic information to generate hash code and adds SE module with attention mechanism to improve the performance of neural network. The experimental results showed that the proposed model is superior to other model in extraction accuracy and classification accuracy.
Vision4All — A Deep Learning Fashion Assistance Solution For Blinds

Fashion is an industry that never appears to slow down, and it would be incredible if the blind could participate in this growing trend. By 2050, nearly 120 million individuals are expected to be vision-impaired. In this paper, the proposed Vision4All model assists users in identifying colors, clothing categories, textures, fabric, style, graphic, and text-based content on clothes. We enhanced FashionNet, a deep model that learns clothing characteristics by predicting garment qualities and categories together. To improve prediction accuracy, the ResNet34 architecture replaced the obsolete VGG16 design. A Fine-Grained multilabel classification model was trained by tackling the noisy data problem for attribute prediction. For identifying the range of graphical content printed on clothes, we use Pythia's modular re-implementation of the bottom-up, top-down approach. Our solution allows users to navigate through speech eliminating the requirement for users to rely on their vision. Vision4All is the first complete solution to align with Fashion assistance for the visually impaired.
A Joint Network for Pose Classification and Evaluation Based on Attention Mechanism

In recent years, many studies have focused on pose recognition, with few studies on pose evaluation and even fewer on pose classification. Thus, studies combining pose evaluation and pose classification are almost non-existent. In this paper, a channel attention-based Siamese network was proposed, and two functions of pose classification and pose evaluation were integrated into the same network, which means this proposed model simultaneously implements classifying input poses into the appropriate categories and evaluating the similarity of input poses. Briefly, the proposed model was structured as a basic Siamese network with three convolutional blocks, one of which contains a convolution layer, a pooling layer, a batch normalization layer, and an activation function. After three convolutional blocks, one of the branches of the Siamese network diverged into two fully connected layers to generate two feature vectors, one for classification and the other for evaluation. The other branch connects a fully connected layer to generate a feature vector for pose evaluation. Furthermore, the attention module has been shown to be effective according to our experiments on the Baduanjin dataset. Experimental results also indicate that the performance of both the pose classification task and the pose evaluation task is relatively great in our proposed model.
Research on Compression of Teacher Guidance Network Use Global Differential Computing Neural Architecture Search

Large scale deep neural network has achieved unprecedented performances in many fields, such as computer vision, computer auditory, medical monitor and the others. However, it faces severe challenge for application scenarios with limited hardware resources and high real-time requirements. In our recent work, a globally differentiable deep neural architecture search method is adopted. At the same time, the knowledge of the teacher network is introduced into the network search, and the student network that is more sensitive to the tea-cher network is automatically searched. The student network is used to replace the teacher network for the original task in series resource-constrained devices. Compared with teacher network, student network has tiny size and remarkable performance close to the teacher network with knowledge distillation, so that the neural architecture search method is skillfully turned into a model compression method. We have verified the proposal in cifar10, cifar100 and engineering datasets. Experiments show that this method can greatly reduce the scale of the task network and maintain excellent performance similar to the original network. Additional experimental results on Jetson TX2 and raspberry show that the reasoning time of compressed network decreases significantly.
Boost Speech Recognition without Training

Speech recognition has many application scenarios, such as Interactive Voice Response, and voice commands in games. Many of the voice in these scenarios are short and with high concurrency, so that the performance requirements for speech recognition are very demanding. To boost the performance of speech recognition, one way is to use as much data as possible to train the recognition model to obtain higher accuracy or optimize the structure of the model to make the model smaller and faster. However, doing this is very expensive, it requires us to have very large datasets and a lot of computing resources. To boost the performance of speech recognition without training the model, we have proposed some methods based on our experience, including speech extraction, speech padding, fuzzy pinyin matching, and phonetic similarity matching. Experimental results show that our proposed method indeed boosts the performance of speech recognition. We also discuss the deployment methods we used.
Real-time Face Structure Reproduction Model for Generative Adversarial Networks

In this paper, the image to image translation algorithm based on generative countermeasure network is used to capture and extract the face features in the source image and target image, obtain the mapping function after training the target image model, and transfer the features of the original face image to the face model of the target image. The conditional generative adversarial networks is selected as the solution to the face to face mapping problem. At the same time, a novel method for real-time face reconstruction of single target video sequence is proposed. When studying the selected loss function, we find that the system has wide applicability and strong ease of use, and there is no need to adjust the parameters. The results show that in addition to face capture and feature migration, in many tasks that need to process highly structured images, the pix2pix algorithm based on cGAN is also a highly expected method. This means that this system can not only be more perfect in face feature migration, but also have the possibility to expand the function in other fields of image processing.
Imbalanced Image Classification by An Enhanced Depthwise Separable Convolutions Network

The field of image recognition is developing rapidly, and many high-performance models have been proposed. However, these models encounter some problems when dealing with imbalanced data, which are, the minority classes have low accuracy and the majority classes have high accuracy. This paper proposes an enhanced depthwise separable convolutions network (called EXception), which takes into account both the learning of the minority classes and the majority classes. EXception contains three components. The data augmentation component improves the generalization by cutting and flipping the pictures. The transfer learning component is a pre-trained model (i.e., Xception) on ImageNet. This paper reuses part of the pre-trained parameters of Xception to reduce training time. And the sampling component contains a novel sampling algorithm that can samples the training data. Before the next iteration, according to F1-scores of the previous iteration, the algorithm dynamically changes the number of samples for each class in the next iteration. To a certain extent, the algorithm ensures that the model can fairly learn the features of the majority classes and the minority classes.
Textual Similarity Based on Double Siamese Text Convolutional Neural Networks and Using BERT for Pre-training Model

Textual similarity measurement has become even more important in natural language processing for information retrieval. The improvement in the accuracy of machine translation is a prime reason for developing a two-stage model of fine-tuning Bidirectional Encoder Representation from Transformers (BERT) and training using the Double Siamese Text Convolutional Neural Networks (DSTCNN). The first stage task is improved to use the output of each word of BERT as the input of the DSTCNN model. The DSTCNN model is proposed in the second stage task to determine the sequential and reverse relationship. The design motivation and detailed structure of the DSTCNN model are provided. Also, the STS-B dataset was used for the experiment. The Two-stages model achieved 88.1% Spearman's rank correlation coefficient, which increased by 1.6%, compared with the first-stage model. The experimental results show that using the two-stage model yields an improved performance compared to other algorithms.
Forgery Face Image Detection Based on Improved Capsule Network

Face forgery technologies may have a significant adverse impact on individual privacy and national political security. In this paper, a simple but effective method for detecting forgery face image based on improved capsule network is proposed. More specifically, we first adopt the part of per-trained VGG19 to extract latent features for better classification. Then, the improved capsule network architecture makes use of exponential linear unit (ELU) instead of the traditional rectified linear unit (ReLU) to improve the learning speed and convergence properties. Moreover, the effective attention mechanisms are embedded into the improved capsule network for further improving the detecting accuracy performance. Experimental results on four famous face forgery datasets demonstrate that the proposed framework outperforms other state-of-the-art approaches.
Research on Underground Personnel Behavior Detection Algorithm Based on Lightweight OpenPose

To effectively identify the unsafe behaviour of underground workers in coal mines, this paper designs an intelligent detection system of underground workers’ behaviour based on a lightweight OpenPose algorithm. Firstly, the system obtains the coordinates of key points of human bones, detects the posture of falling, climbing and pushing by constructing different detection algorithms, and deploys the posture detection model to the industrial computer; Secondly, the industrial computer reads the video of the infrared camera and transmits the information of the detected unsafe behaviour to the alarm. The experimental results show that the detection speed can reach the actual application effect of single-channel 30fps or dual-channel 20fps.
A Novel Multi-objective Neural Architecture Search Algorithm via Gaussian Progress Sampling

Multi-objective neural architecture search (NAS) algorithms aim to automatically search the neural architecture suitable for different computing power platforms by using multi-objective optimization methods. The LEMONADE algorithm, which is a representative algorithm of multi-objective NAS algorithms, maintains a population of networks on an approximation of the Pareto front of the multiple objectives, such as predictive performance, number of parameters or FLOPs. To address the irrationality and repeatability of only sampling based on cheap objectives in LEMONADE, we propose a novel multi-objective neural architecture search algorithm via Gaussian Process sampling, dubbed GP-LEMONADE. Meanwhile, to make the sampling process more efficient, we design the online predictor based on Gaussian Process to predict expensive objectives, and sample candidate networks by combining cheap objectives and expensive objectives, so as to ensure the rationality and efficiency of sampling. Experiments show that the GP-LEMONADE algorithm evolves 100 generations and obtains the SOTA model with 3.98% test error. This process only takes 7.38 GPU days, which is 26.75 GPU days shorter than that of LEMONADE. Our methods have improved the performance of the LEMONADE algorithm and ensured the rationality and efficiency of sampling during the evolution, which effectively improving the search efficiency of multi-objective NAS algorithms.
SiamL-MLP: Siamese-like Network for Object Tracking Based on MLP-Mixer

Visual object tracking has always been a research hotspot in the field of computer vision. Recently, some excellent techniques in video object detection can be used to improve the performance of visual object trackers. Therefore, we propose a new Siamese-like network tracker by leveraging the superior representation ability of the recent visual object detection algorithm MLP-Mixer. Specifically, the MLP-Mixer based backbone network is used for feature extraction, which can enhance the feature representation. Furthermore, to connect the target template and the search region, we adopt cross-attention instead of conventional correlation operation to avoid getting stuck in local optimization when predicting target location. Finally, in order to demonstrate the effectiveness of the proposed method, we conduct extensive experiments on four tracking benchmarks, including OTB2015, VOT2016, VOT201S and GOT-10k datasets. Experimental results show that our proposed SiamL-MLP achieves competitive performance on the OTB2015, VOT2016, VOT201S and GOT-10k datasets. Our tracker runs at approximatively 40 FPS on GPU. Code will be available at https://github.com/SilvesterSun/SiamL-MLP.