Reviewerly/exHarmony huggingface dataset:
VCSOD: A Video Conversion Scheme Based on Salient Object Detection Algorithm

Short videos on mobile phones are becoming more and more popular. How to convert horizontal screen videos into high-quality vertical screen videos in batches has become a difficult point in production. In this paper, we propose a video conversion scheme based on salient object detection algorithm. A 3D fully-convolutional network architecture is employed in the detection of consecutive frames. A lightweight image salient detection model is used for single-frame detection. By filtering and clustering the target coordinate points detected by the continuous frame and single frame respectively, the key coordinate value of the salient object in the continuous frame is obtained. Use these key coordinate points to smoothly synthesize vertical video. This solution can be applied to industrial production on a large scale, saving labor costs and mass-producing higher-quality vertical-screen videos.
Comparative Study of Bayesian Optimization Process for the Best Machine Learning Hyperparameters

Bayesian optimization is important algorithm that uses two essential components, namely the surrogate model and the acquisition function. They are used to approximate the unknown objective function. This optimization is used as a hyperparameter tuning technique for the four machine learning algorithms to increase their performance. In this work, we applied Bayesian optimization to choose the best hyperparameters for a set of ML algorithms namely RF, SVM, KNN and LR. For this, we used a heart disease dataset. In this context, we obtained the best hyperparameters with accuracy for each machine learning algorithm optimized by BO-GP and BO-TPE. The results demanstrate the highest accuracy in BO-GP and BO-TPE are respectively LR is 89.01% and SVM is 89.01%. Then, the setting of hyperparameters allows to find the best hyperparameters that is improved accuracy for each algorithm.
Efficient Human Pose Estimation via Multi-Head Knowledge Distillation

Nowadays, neural networks are becoming increasingly swelling to achieve high accuracy and better adaptability. For the task of human pose estimation, heavy neural networks are applied to achieve higher performance. But more extensive networks would lead to lower inference speed and consume more computing resources. How to acquire preferable accuracy even with a smaller model has become a valuable research subject. In this paper, we applied Knowledge Distillation to improve the performance with lightweight model. But still, the performance disparities between teacher networks and student networks exist. To further narrow the gap, we proposed the Multi-Headed Architecture to promote the accuracy of smaller models, raising performances to a similar level to larger models.
Decision-based adversarial attack for speaker recognition models

As a biometric technology, speaker recognition is widely used in finance, criminal investigation, and other fields due to its convenience and high accuracy. Speaker recognition models are vulnerable to spoofing attacks and adversarial attacks. Thus, the security of speaker recognition models has received much attention. However, few works focus on the decision-based adversarial attacks for speaker recognition systems (SRS), in which the adversary can only access the final decisions of the black-box models. In this paper, we proposed Biased-Aha, a decision-based attack method that combined query history information and prior gradient from the substitution model to launch an efficient attack. Specifically, to generate the adversarial example, the perturbation is determined by following the sampling direction for successful queries and avoiding the sampling direction for failed queries, combined with the gradient direction from the substitution model. The experiment results show that Biased-Aha takes a high attack success rate and high efficiency. For the speaker recognition models, Gaussian Mixture Models (GMM) and ivector, Biased-Aha outperforms the state-of-the-art decision-based adversarial attacks.
Structure Design of Log Management Platform That Supports Smooth Expansion of Anomaly Detection Capability

With the popularity of cloud services and more and more traditional businesses on the cloud, the log management platform as an important cloud service is suffering from the impact of more new anomaly detection tasks. Traditional log management platforms have some problems in anomaly detection, such as high maintenance costs and inflexible detection tools. This paper proposes an architecture of a log management platform that supports the smooth expansion of anomaly detection capability. This architecture allows users to upload the anomaly detection model, and the online operation of the model has been realized through the model online loading module, to achieve the smooth expansion of the anomaly detection capability.
Research on Intelligent Steel Logistics Order Allocation Decision Problem

Logistics order distribution is an important part of the sales process of finished steel products, and plays a vital role in the overall experience of the sales process and the virtuous cycle of the whole process. In the actual production process, the manual order distribution model maintained for a long time seems to have a richer accumulation of experience and knowledge, but in reality it has become difficult to adapt to the development requirements of a long time dimension. In order to fully consider the lowest logistics cost of sales in a time cycle as well as the higher revenue of the carrier driver, while ensuring the long-term revenue of the enterprise, this paper considers multi-objective constraints, establishes Markov decision model, introduces km algorithm to perform dichotomous graph matching, based on the multi-objective optimization for maximizing driver revenue and minimizing enterprise cost, with the long-term ultimate goal of maximizing total steel commodity transaction volume, and finally Combining the value function and the multi-objective optimization function of multiple attributes to form a complete matching decision of vehicles and goods. Using real business data of steel enterprises as an example, the data is pre-processed and then suitable features are screened for model training and the correctness and usability of the algorithm is verified. The results show that the model can better address the needs of steel companies in order allocation scenarios compared to traditional order allocation methods.
Efficient Contrast Subspace Mining Method for Categorical Data

Mining contrast subspace has recently received attention to identify contrast subspace where a query object is most likely similar to a target class but least likely similar to other class. It has many important applications in various domain such as healthcare, security, finance, and business. Tree-based contrast subspace mining method (TB-CSMiner) has been introduced that is capable to identify contrast subspace of query object in categorical data set. However, the effectiveness of the method has not been evaluated thoroughly. Besides, the efficiency of the method in finding contrast subspace has not yet been examined. Real world data sets more often than not containing large amount of data. It is important to have a method that can identify contrast subspace of query object not only effectively but also efficiently on large data set. This paper uses various classification methods to further evaluate the effectiveness of the TB-CSMiner and assesses the execution speed of the method in mining contrast subspace. TB-CSMiner is experimentally shown to be significantly faster and as effective in identifying contrast subspace of query object on real world categorical data sets.
BAGNet: Branch Attention Guided Decoder for Semantic Segmentation

The effectiveness of encoder-decoder structures for semantic segmentation has been demonstrated. Given the importance of decoders, we propose a branch attention guided decoder for semantic segmentation, called BAGNet. The proposed decoder exploits the multi-scale feature maps generated by the backbone and adaptively selects the important feature information. Attention mechanism and multipath representation are important for semantic segmentation. The existing methods mostly introduce the attention mechanisms into the encoder, while we observe that decoder also need the attention module to select important information when using lateral connections. To solve this problem, we present a Branch Attention Unit(BAU), which applies the channel-wise attention on different branches to reduce the semantic gap and leverage their success in capturing cross-feature interactions and learning diverse representations to select important information in branches. To validate the performance of the proposed BAU and BAGNet, we conduct extensive experiments and achieve consistently improved performances on popular segmentation benchmarks, namely 80.33%, 42.30% and 44.66% mIoUs on Cityscapes, ADE20K and Pascal Context validation sets with ResNet-50 backbone, respectively. Furthermore, we verify their generalization ability using different baseline models.
TSDTest: A Efficient Coverage Guided Two-Stage Testing for Deep Learning Systems

In recent years, Deep Learning systems have been applied to face recognition, autonomous vehicles and other safety-critical fields. Testing Deep Learning systems effectively and adequately is increasingly significant. In this paper, we proposed and implemented TSDTest, a coverage guided two-stage testing framework for deep learning systems. To test more logic for Deep Neuron Network (DNN), TSDTest generates highly diverse test cases with as high neuron coverage as possible during its two stages. Compared with DLFuzz, TSDTest achieved an average 1.75 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> improvement in neuron coverage and 80.3% more adversarial test inputs on MNIST and Fashion-MNIST. And the step dynamical adjustment also effectively reduces <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{2}$</tex> distance and avoids the manual identification of test oracle. The implementation of TSDTest shows its effectiveness and superiority in generating diverse test cases and improving the robustness of DNN.
An Abridged Review of Transfer Learning Technology

Computer vision studies have adapted several deep learning approaches to achieve desired results. One of such approaches is transfer learning, which uses pre-trained models as a starting point for computer vision tasks. A model is trained with particular dataset to recognize certain patterns. There are lots of models namely ResNet, VGG-16, MobileNet, Inception etc. These models are trained on more than a million images from the ImageNet database. By using transfer learning technology a model can be reused in a new problem. Typically, in computer vision, neural networks try to detect edges in the first layer, shapes in the intermediate layer, and task-specific properties in the latter layers. Transfer learning uses the early and central layers, while the subsequent layers are only retrained. Transfer learning performs several beneficial functions, including reducing training time, improving neural network performance, and reducing dataset size. Transfer learning has become a promising and popular deep learning field because of its extensive application possibilities. This survey analyses the existing transfer learning research studies, which can help upcoming researchers to better understand current research level and concepts in transfer learning. A review of 20 transfer learning approaches is presented in this survey.
Text Sentiment Analysis Based on CNN-BiGRU Enhanced Features

In order to solve the problem that traditional neural network models tend to ignore the importance between aspect words and context, and can not fully obtain and utilize the corresponding features in aspect sentiment analysis task.A neural network model of convolutional neural network(CNN) and bidirectional gated recurrent unit(BiGRU) based on attention mechanism to enhance the aspect words features is proposed.The model enhances feature information related to aspect words through CNN and BiGRU.Then The enhanced features are used by BiGRU to obtain global features, and finally the attention mechanism is used to screen out the most relevant features of the global features and get the emotion category of the text.Experimental results show that compared with other baseline models, this model can obtain more relevant aspect features.The accuracy and F1 values are better than other models.
FAER: Fairness-aware Event-participant Recommendation in Event-based Social Networks

An event-based social network (EBSN) is a new type of social network that combines online and offline networks. In recent years, an important task in EBSN recommendation systems has been to design better and more reasonable recommendation algorithms to improve the accuracy of recommendation and enhance user satisfaction. However, the current research seldom considers how to coordinate fairness among individual users and reduce the impact of individual unreasonable feedback in group event recommendation. In addition, while considering the fairness of individuals, the accuracy of recommendation is less improved by fully combining the context key information. To solve these problems, we propose a prefiltering algorithm to filter the candidate event set, a multidimensional context recommendation to provide personalized event recommendations for each user in the group. Finally, we verify the effectiveness of our proposed algorithm in real data sets and find that FAGR is superior to the latest algorithms in terms of global satisfaction, distance satisfaction and user fairness.
Time-shiftable Convolutional Sign Language Recognition Based on Key Frame Extraction

Sign language recognition for the deaf-mute is an important technology in the field of computer vision, which is conducive to promoting communication between hearing person and the deaf-mute. However, the current mainstream methods for sign language recognition, such as 3D Convolutional Neural Networks (3D CNN) and two-stream convolutional neural network, all face problems such as too many parameters or difficulties in optical flow extraction, and there are still many obstacles in the realization of sign language video technology. This paper compares the problems existing in the mainstream sign language recognition methods, analyzes the deaf-mute sign language videos at the word level, and proposes a sign language key frame extraction and recognition method based on the time-shift module. The work of this thesis mainly has two aspects. First, the frame difference method is used to process the sign language video and extract key frames. Second, temporal feature modeling is performed on the extracted sign language keyframes using a time-shifting module. The method is trained and analyzed on a Chinese sign language dataset (SLR), trained with different numbers of frame extraction methods, and compared with mainstream methods in terms of recognition accuracy and loss function.
A Comparison Study of Convolutional Neural Network and Recurrent Neural Network on Image Classification

Image classification is a very important task in the field of computer vision, and it is widely used in daily life. In recent years, deep learning has developed rapidly in the field of image classification. Image classification methods based on deep learning can not only deal with complex images that are difficult to be processed by traditional image classification methods, but also with large-scale image data that are difficult to be dealt with by image classification methods based on machine learning algorithms. The motivation of this paper is to do a comparative analysis of the performance of CNN and RNN on image classification. In this paper, we use the CNN model, the RNN model and the CNN and RNN mixed model, which are commonly used in deep learning, to compare their classification performance on the ISVRC dataset. We learn from the comparison experiments that the CNN model has better accuracy and F1-Score than the other two models for the overall classification results and the classification results of individual categories. The results prove that CNN has better feature extraction ability than RNN for image data, and the further investigation is needed. CNN has become the dominant approach in image classification due to its excellent features such as local connectivity, weight sharing, pooling operation and multilayer structure.
A Sentiment Analysis Classification of Product Reviews through Convolutional Neural Networks (CNN)

With the advancement of technology, the behavior of consumers turned into E-commerce, especially when the pandemic started. Defining the success and credibility of e-commerce is important as it is becoming a trend, and one of the factors that can affect it is product reviews. Product reviews build trust and loyalty, which influence the purchase decision. These product reviews can be in textual form, star rating, and emoji. Star rating is the commonly used product review but causes rating inflation. That is why word-based rating eliminates this problem which gives more accurate reviews. In this modern day, we do not only convey our sentiments in the language we are used to but also in other formats, such as using both text and emoji. Since Convolutional Neural Networks have been drawing attention to their reduced effort in feature definition, it has been gaining popularity in text classification.
PUPose:Real-Time Pose Estimation for Push-up on Device

There is typically a need for physical training and assessment of the physical exercise poses of students in schools or members of fitness centers. However, few studies have been conducted on physical exercise poses, and no relevant dataset is available. We introduce a new dataset built for the particular physical fitness action of push-ups called the push-up dataset. We propose a new real-time and device-friendly pose estimation model named PUPose. The trade-off between accuracy and efficiency in human pose estimation has been challenging. In this paper, some recent studies have extended object detectors to unify person detection and pose estimation. Our PUPose combines top-down and bottom-up approaches to get bounding boxes for multiple individuals and their corresponding poses. We investigate the applicability of methods for extending object detectors to estimate human pose to lightweight pose estimation models. We strengthen the backbone structure and design an efficient neck structure, improving the feature extraction capability of the network. We optimize the loss function so that training becomes more stable and efficient. With the above optimizations, our model achieves a better trade-off between accuracy and efficiency on the push-up dataset. With only 4.02 M parameters, PUPose achieves 94.2% mAP.
VAE-based anomaly detection for embedded computer electronic components

The current maintenance of aerospace equipment generally uses regular maintenance, scheduled maintenance, seasonal maintenance, after-the-fact maintenance, and replacement maintenance. These methods are ill-timed, time-consuming, and wasteful of materials. Monitoring the reliability and healthy operating status of each embedded computer electronic component is essential, and maintenance staff will benefit greatly from a data-driven approach to anomaly detection. It can be altered from "repair afterward" to "repair as necessary" and from " repair regularly" to "repair at any time" to solve the practical problems arising from maintenance. The Variational Autoencoder (VAE), which is based on the component storage aging acceleration data, is used in this paper to model the component's normal operating status and perform anomaly detection. The precision and recall of this anomaly detection method are 0.950 and 0.977. This method evaluates the operating status and reliability of each component, improves the reliability and service life of the computer, and establishes the technological framework for the next generation of computer Prognostics and Health Management (PHM) systems.
The Research on Image Classification Based on Convolutional Neural Network

Convolutional neural network (CNN) performs well in image classification and segmentation, target detection and other applications. Researchers are paying more and more attention to its powerful feature learning and feature expression abilities. Based on CIFAR10 data set, this paper used CNN for image recognition. In the experiment, the training accuracy and testing accuracy of CNN can be improved by increasing the training times and the number of full connection layers of CNN. In this paper, if training 1000 epochs, and use 2 full connections layers. the training accuracy can reach 91, and the testing accuracy can reach 90.5.
Feature extraction in speech recognition using linear predictive coding: An overview

Over the past years, advancements in speech processing have mostly been driven by DSP approaches. The speech interface was designed to convert speech input into a parametric form for further processing (Speech-to-Text) and the resulting text output to speech synthesis (Text-to-Speech). Feature extraction is done by changing the speech waveform into a parametric representation at a relatively low data rate so that it can be processed and analyzed later. There are numerous feature extraction techniques available. This paper presents the overview of Linear Predictive Coding (LPC).
Review on Software Dependability and Exception Handling for the Object-Oriented Design Phase

The issue of dependability and exception handling is very important, and it is best to address the dependability of software at the very early stage or beginning of the development life cycle. The Object-Oriented Design (OOD) phase works as a base. The object-oriented approach is used to represent the problem domain and clearly understand the design. This paper defines dependability and exception handling that must be addressed during the design phase. Dependability can be achieved through the proper placement or management of design characteristics and the establishment of relationships between them and to develop an exception-handling model that provides a distinct and transparent separation between a component's regular activity and its exceptional action.
Concluding Remarks

Transfer learning is extremely important to solve the label scarce situations and non-i.i.d issues in machine learning. In this book, we start from the basics of machine learning to the concepts of transfer learning. Then, based on the three fundamental problems of transfer learning: when to transfer, where to transfer, and how to transfer, we introduced the foundations and modern transfer learning algorithms. The last part of this book is transfer learning code practice in real applications and we hope readers can quickly get familiar with it.