Reviewerly/exHarmony huggingface dataset:
Method Agnostic Model Class Reliance (MAMCR) Explanation of Multiple Machine Learning Models

Various Explainable Artificial Intelligence (XAI) methods provide insight into the machine learning models by quantitatively analysing the contribution of each variable to the model’s predictions globally or locally. The contribution of variables identified as (un)important by one method’s explanation may not be identified as the same by another method’s explanation for the same machine learning (ML) model. Similarly, the important feature of many well-performing ML models that fit equally well on the same data may not be the same as each other. While this is the case, providing the explanation based on a single model in the lens of a specific explanation method would be biased over the model/method. Hence, a framework is proposed to describe the consensus variable importance across multiple explanation methods for many almost-equallyaccurate models as a method agnostic explanation for the model class reliance. Empirical experiments are carried out on the COMPAS dataset to verify whether an inadmissible feature becoming an (un)important feature is consistent across multiple explanation methods and to get a consensus explanation. The results demonstrate the efficiency of the method agnostic model class reliance explanation in discovering the unbiased explanation of almost-equally-accurate models of the model class.
Smart Conveyor Belt Sushi Bill Payment with a Mobile Shot

Organization must automate wherever and whenever they can, particularly during today's global changes in daily lifestyles. Trends regarding the use of technology, especially AI has emerged as a key enabler for disruptive innovation. This paper thus presents the application programming interface of object detector implemented with YOLOv4 and OpenCV for classifying the prices of sushi plates distinguished by colors. The object detector is part of the smart cross-platform mobile application to facilitate billing process for conveyor belt sushi business. The frontend is developed with Flutter to build single codebase for UIs. To handle the variants of image colors resulting from the use of different mobile cameras, color transfer is used for transferring the image dataset colors to images captured by users. Microservices architecture is adopted for the backend. Orchestration of YOLOv4, OpenCV and Spring Boot REST API will create APIs to calculate food cost, generate QR code for bill payment, and maintain customer membership benefits. The constructed object detection model achieved the precision of 97%, recall of 97%, F1-score of 97% and mAP of 97.3%. The smart billing system presented in this work would accelerate the workflow, increase productivity, reduce waste and drive moving for contactless society.
Combining Latent Semantic Analysis and Pre-trained Model for Vietnamese Text Readability Assessment

Together with the rapid development of text processing, readability assessment is an important and challenging task of measuring how easy or difficult it is to read a text. Despite the foundation and enhancement of this task in high-resource languages such as English where there are a ton of NLP tools and corpus, this task is not an advantage for low-resource languages, especially Vietnamese. Most previous studies for Vietnamese text readability assessment focus on shallow text characteristics, which have yet to address deeper readability features. In our study, we propose a novel finding in Vietnamese to create the construction of features reflecting in terms of semantics. In view of this, we notice that the difficulty level of terms affects the difficulty level of knowledge that strongly involves text comprehension. Particularly, our approach based on the difficulty distribution of terms in a text generated by Latent Semantic Analysis (LSA) technique decreases the dependence of experts in annotating and discovering the typical feature in a narrow domain. Our proposed feature is efficient to be considered as a new and automatic feature for Vietnamese text readability assessment. Furthermore, LSA is a statistical approach that is more stable and feasible for low-resource languages. In addition, we also integrate PhoBERT, a pre-trained language model for Vietnamese, to generate the bidirectional contextual representation of a word for Vietnamese long-sequence as a semantic feature. Through the experiments in Vietnamese readability dataset, our proposed approach achieves promising performance against the strong competitive baselines. The best performance with up to an accuracy of 94.52% and a weighted F1 score of 94.09%.
Wav2sv: End-to-end Speaker Embeddings Learning from Raw Waveforms based on Metric Learning for Speaker Verification

With the application of deep learning in the field of speaker recognition, the performance of speaker recognition systems has been greatly improved. However, most current work still relies on handcrafted features, existing raw waveform-based systems fail to utilize the multi-scale feature and multi-level information efficiently. Besides, the speaker embedding generated by speaker identification is used to complete speaker verification through similarity discrimination, resulting in a domain mismatch problem. To address these problems, we propose an end-to-end system called Wav2sv, which uses a stack of strided convolution layers as a feature encoder, SE-Res2Blocks and dense connection between each frame layer as the frame aggregator; and obtain the speaker embedding with a metric learning objective. This new end-to-end system can automatically learn the most suitable speaker embedding from raw waveform based on metric learning for speaker verification. Our simulation results on VoxCeleb1 indicate that the proposed approach achieves an EER of 4.75%, which is 18% superior to the Wav2spk baseline. Our work demonstrates the great potential of extracting speaker embeddings from raw waveforms.
Memory Usage Comparison in an Android Application: Basic Object-Oriented Programming vs Decorator Design Pattern

This paper explores how an Android application consumes memory when it is developed by two approaches: basic object-oriented programming versus decorator design pattern. Somehow programmers develop applications based on their coding experience without concern of memory usage especially in mobile applications which memory consumption is a critical issue. The deployed Android application of a list of temples which join a Candle Festival is selected as a case study. New functions are assumingly required by users so that the application could provide more services. Two class diagrams are designed based on basic object-oriented programming and based on decorator design pattern before implementation of the two approaches. The problem is what is the better approach for handling an increasing number of services after the application has been deployed. So that the application still consumes less memory as possible. The case-study applications retrieve information from a free REST API. Memory usage is measured by using an emulator and a physical device. The result shows that an Android application which developed by using decorator design pattern consumes more memory although its codes are cleaner and easier to read. Therefore, when a developer applies decorator design pattern, they should pay more attention on unexpected effect of the design pattern. Research in impact of design patterns should also be investigated more.
Credit card fraud detection based on self-paced ensemble neural network

Along with the significant increase in the number of credit cards, the number of credit card frauds worldwide is increasing day by day. At the same time, the development of Internet technology has led to the emergence of new fraud methods. The traditional credit card fraud detection methods can no longer meet the needs of the current credit card financial industry development. Identifying fraudulent credit card transactions effectively, quickly and accurately has become a major concern for banks. Methods combining expert rules and statistical analysis, decision tree methods, anomaly detection methods, and feature engineering methods are used in credit card fraud detection research. Among the many methods, deep learning is a new artificial intelligence method that has developed rapidly in recent years and is widely used in credit card fraud detection research. This paper uses a self-paced ensemble neural network (SP-ENN) model to learn credit card fraud transactions by dividing the datasets with different hardness, then identifying these transactions by neural networks, and finally performing a comprehensive evaluation. It was found that this model significantly outperforms other up-sampling or integration models in detecting credit card fraud data.
Hybrid approach for text similarity detection in Vietnamese based on Sentence-BERT and WordNet

In this paper, we explore the task of similarity detection, which determines whether two sentences have the same meaning. Although the task has shown to be important in many natural language processing applications, not much work has been done in Vietnamese. We present an approach based on Sentence-BERT (SBERT) model. Leveraging the pre-trained model and combining it with linguistic knowledge (WordNet), we then tested it on two popular Vietnamese datasets: vnPara and VNPC. Our best model achieves 97.62% F1 score on vnPara and 95.31% F1 score on VNPC.
Long- and Short-Term Storage Enhanced Video Object Recognition Model

The object recognition technology in video scene has been applied more and more in many fields, but the recognition accuracy and speed of video object recognition model are limited due to the large amount of video data and repeated information. In this paper, a long- and short-term storage enhanced video object recognition model is proposed to solve the above problems. Firstly, multi-frame aggregation is carried out for video frames by the proposed model to establish long-term video frames and short-term video frames, which can reduce model computation without losing key feature information. Besides, the attention mechanism is used to redistribute the similarity weights among long-term video frames, short-term video frames and key frames. Thirdly, the long- and short-term storage module(LSS) is designed for feature enhancement, which stores the image features of long and short-term video frames and fuses them during key frame recognition. Finally, the whole video object recognition model is optimized and adjusted, then verified by simulation and experiment. The simulation and experiment show that the model achieves the recognition mAP of 77.12% and the detection time reaches 14.772 mins. Compared with the state-of-the-art (SOTA) model, the proposed model can improve the recognition accuracy, shorten the detection time, and has better performance advantage.
WSSA: Weakly Supervised Semantic-based approach for Sentiment Analysis

In this work, we propose a Weakly Semantic-based approach for Sentiment Analysis (WSSA), a novel approach that analyzes sentiment by considering weak labels from different sources (sentiment analysis tools) and aggregates them based on features such as the consistency between sources, the semantic equivalence between documents, and experts’ domain knowledge in order to improve the sentiments analysis tools results. The aggregation is achieved using a Probabilistic Soft Logic reasoner to infer the documents’ polarity.
Stock price prediction under multi-frequency model - based on attention state-frequency memory network

Stock price forecasting is the most typical forecasting task in financial time series. However, stock prices are influenced by a variety of factors, with short-term and long-term trading activity being among the most important factors affecting price changes. The trading activity in these different frequency patterns is eventually reflected in the stock price data. Therefore, if the model identifies information about the potential multiple frequencies of the time series data in the process of prediction, it can enable the model to better learn the features of the time series data. In addition, stock price data are non-linear and non-stationary in nature, which makes it a challenging task to predict future trends. We used the attention state-frequency memory neural network (A-SFM) to accomplish the above objectives. The A-SFM realizes the decomposition of frequency by Fourier transform, and learns the state and frequency information by the powerful learning ability of neural network. In addition, we add an attention mechanism to the structure of the model to extract and learn the important parts of the price information. In this paper, we use the A-SFM model to capture the state and frequency information inherent in the series from past financial market data and use this information to make short- and long-term forecasts.
Hybrid video coding scheme based on VVC and spatio-temporal attention convolution neural network

In this paper, we propose a hybrid video coding framework. The framework is built on the basis of VVC (Versatile Video Coding) video coding standard and constructs an implicitly aligned multi-frame fusion model to accomplish subjective video quality enhancement. The proposed framework mainly optimizes video compression efficiency from two perspectives. First is the sequence-level dynamic rate control algorithm, which assigns the appropriate bitrate to each video to obtain the highest overall video quality. Second is the MAQE, a multi frame implicit alignment video quality enhancement model, which performs motion alignment through multiple convolutional kernels of different sizes, uses a residual aggregation layer to fuse features of different frames, and then uses an enhanced attention module to adaptively deflate features based on spatiotemporal contextual features, so as to more effectively fuse feature of multiple frames and obtain higher quality reconstructed frames. The proposed method is validated on two tracks of 0.1M code rate and 1M code rate on CLIC-2022 video compression task, Experimental results show that the proposed method achieves PSNR of 30.301 and 37.251 and obtains MS-SSIM of 0.9368 and 0.9875. This paper is a comprehensive presentation of the scheme used by the Night-Watch team of the CLIC-2022 video track.
Compositional Mixture Representations for Vision and Text

Learning a common representation space between vision and language allows deep networks to relate objects in the image to the corresponding semantic meaning. We present a model that learns a shared Gaussian mixture representation imposing the compositionality of the text onto the visual domain without having explicit location supervision. By combining the spatial transformer with a representation learning approach we learn to split images into separately encoded patches to associate visual and textual representations in an interpretable manner. On variations of MNIST and CIFAR10, our model is able to perform weakly supervised object detection and demonstrates its ability to extrapolate to unseen combination of objects.
ImageSig: A signature transform for ultra-lightweight image recognition

This paper introduces a new lightweight method for image recognition. ImageSig is based on computing signatures and does not require a convolutional structure or an attention-based encoder. It is striking to the authors that it achieves: a) an accuracy for 64 X 64 RGB images that exceeds many of the state-of-the-art methods and simultaneously b) requires orders of magnitude less FLOPS, power and memory footprint. The pretrained model can be as small as 44.2 KB in size. ImageSig shows unprecedented performance on hardware such as Raspberry Pi and Jetson-nano. ImageSig treats images as streams with multiple channels. These streams are parameterized by spatial directions. We contribute to the functionality of signature and rough path theory to stream-like data and vision tasks on static images beyond temporal streams. With very few parameters and small size models, the key advantage is that one could have many of these "detectors" assembled on the same chip; moreover, the feature acquisition can be performed once and shared between different models of different tasks - further accelerating the process. This contributes to energy efficiency and the advancements of embedded AI at the edge.
Medical Compound Figure Detection Using Inductive Transfer and Ensemble Learning

Mehtarizadeh, Mehdi Reza Zare, MohammadInformation retrieval in medical publications can be assisted with content-based image retrieval. Figures have become an inseparable part of publications. Around 40% of figures that appear in medical publications are compound and composed of two or more individual figures. Using medical image processing models, valuable metadata can be extracted from these figures which can assist image retrieval. However, a model that was originally designed to analyse a single figure is not able to analyse a compound figure. Thus, Compound Figure Detection (CFD) methods are used to first determine whether a figure is compound or not. Then, relative models and algorithms can be applied to individual figures. In this work, a model based on deep learning and inductive transfer is proposed to realise CFD. The model relies on pre-trained Convolutional Neural Networks as feature extractors. The model uses feature vectors as visual features (or visual words) and makes a dictionary of available visual features within the dataset. Every image is described based on the frequency of visual words it contains. Various versions of the model then form an ensemble of models to better generalise on unseen data. It is shown that the ensembled final model achieved 97.27% accuracy on the ImageCLEF2016 medical task test dataset.
Research on the Digital Image Processing Method Based on Parallel Computing

In the continuous innovation of science and technology, most scientific problems need to deal with more data information, and the actual processing speed and quality directly determine the problem-solving process. Although the current single core processing technology got rapid development both at home and abroad, but still can't meet the demand of scientific problem, and the theory of parallel computing technology effectively solve the above problems, in the practical use of Chinese super cool platform can achieve the processing capacity of one hundred million times per second, and with the innovation of practical technology is still in constant breakthroughs. Therefore, based on the understanding of digital image processing methods and research trends, according to the concepts related to parallel computing and unified computing equipment architecture, this paper deeply discusses the core image processing technology methods and experimental results, thus proving the application value of parallel computing technology.
Research on Manipulator Control Based on RGB-D Sensor

At present, the most mature way of teaching control of industrial robot manipulator is based on the control of teaching pendant joystick. With the development of human–computer interaction technology, more and more interaction technologies can be studied, such as voice control, gesture control and so on. This paper is devoted to the realization of new interaction methods and control logic, so that the manipulator can be controlled by gesture without contact. The implementation process of this method is as follows: firstly, get depth data by Kinect sensor [1], and use filter to reduce data jitter. Secondly, the user in the depth data is separated from the background to reduce the interference and the amount of calculation. Third, match depth data coordinate system and RGB image coordinate system. Use relative pixel coordinate in RGB image to detect the gesture. Fourth, the obtained coordinate data is transmitted to the rapid program of industrial robot through socket. Finally, the proposed method was tested and the results showed that: users can control manipulator to move and grasp objects by gesture smoothly. Through experiment, we can evaluate the performance and disadvantages of our system. The gesture recognition rate is more than 80% (only one gesture is below 90%. The system can lock the user as an operator target. After practice, the recognition rate of a freshman operator can be higher than 75%. What’s more, this system has stability when program runs long time. The performance degradation is less than 5% after 30 min of operation. Though it doesn’t have a universal protocol in the field of gesture recognition, we still believe the technology of HRI will impact our life in the future, and be expected to extend widely. We develop this system for extensibility. Our method is suitable for remote control and application scenarios under special working conditions, and has a good market application prospect.
Focused Feature Differentiation Network for Image Quality Assessment

Image quality assessment (IQA) intended to assess the perceptual quality of images has been an essential problem in both human and machine vision. Recently, with the help of deep neural network (DNN), IQA algorithms can extract more valuable differences between the distorted and reference images than the traditional algorithms, and thus the performance of DNN-based algorithms is more satisfactory than that of previous algorithms. However, the accuracy for different distorted images preference rating of the existing DNN-based quality assessment methods will be decreased when multiple distorted images are quite similar to each other or to the reference image. To tackle this problem, we propose a focused feature differentiation network (FFDN) to highlight the feature maps with greater distorted and reference differentiation. Furthermore, we use the multi-scale feature fusion module to fuse the focused differentiation features at different scale receptive fields. To further improve the accuracy of our method, we predict the mean opinion score and differentiation score by stages and combine them with different self-learning weights. Finally, we convert the weighted score into different image preference degrees. Experimental results on the validation dataset of CLIC2022 and test dataset of CLIC2021 show that the accuracy of our model FFDN is higher than other excellent quality assessment methods.
CoDo: Contrastive Learning with Downstream Background Invariance for Detection

The prior self-supervised learning researches mainly select image-level instance discrimination as pretext task. It achieves a fantastic classification performance that is comparable to supervised learning methods. However, with degraded transfer performance on downstream tasks such as object detection. To bridge the performance gap, we propose a novel object-level self-supervised learning method, called Contrastive learning with Downstream background invariance (CoDo). The pretext task is converted to focus on instance location modeling for various backgrounds, especially for downstream datasets. The ability of background invariance is considered vital for object detection. Firstly, a data augmentation strategy is proposed to paste the instances onto background images, and then jitter the bounding box to involve background information. Secondly, we implement architecture alignment between our pretraining network and the mainstream detection pipelines. Thirdly, hierarchical and multi views contrastive learning is designed to improve performance of visual representation learning. Experiments on MSCOCO demonstrate that the proposed CoDo with common backbones, ResNet50-FPN, yields strong transfer learning results for object detection.