Reviewerly/exHarmony huggingface dataset:
An improved RANSAC algorithm for detection, extraction, and enhancement of noisy indoor house type image lines

For noisy indoor house type images obtained by infrared scanning and other methods, they usually need to be further optimized before they can be used. This paper proposes an improved RANSAC algorithm based on the conventional RANSAC algorithm,and combines the least squares method to detect,extract and enhance lines of noisy indoor house type image.The binarization of the image is completed by threshold judgment division to avoid the excessive dispersion of pixels, which leads to the deviation of the subsequent RANSAC extraction model.Then, a recursive-based desalination method is proposed to desalinate the image, desalt the noise and improve the accuracy.When detecting a straight line, a sequential recording method is proposed to record and process the multi-segment line segments that are in the same straight line but not connected to ensure that there will be no omission or error. Finally, the extraction of multiple straight lines and line segments is realized by cyclically removing interior points, so as to complete the straight line detection and extraction enhancement process of the entire noisy indoor house type image. The actual results show that the method has high accuracy and timeliness, and can make the line detection and extraction completion rate reach about 90%, and can still maintain a very low error rate even in a heavily noisy environment.
Data enhancement analysis for deep-based image classification

Previous works have shown that the effectiveness and reliability of CNN used in computer vision is closely dependent on whether the dataset is adequate. In this paper, we illustrated how data augmentation, a technique which expands original datasets through transformations, could help improve CNN models’ performance and prevent overfitting through several experiments. After importing two extremely small datasets found on Kaggle, we carried out experiments by comparing the performance of models employing and not employing data augmentation techniques, while keeping track of their training accuracy, validation accuracy and their final test accuracy respectively. Eventually, noticeable improvement could be witnessed when employing appropriate data augmentation techniques on small datasets in our experiments, as such methods could generate a more comprehensive and sufficient dataset to help models learn the key features.
Anomaly detection based on multi-source heterogeneous data fusion

In recent years, with the development of the Internet of things, the data collection methods are more and more abundant and the data structure is more complex. Anomaly detection by analyzing multi-source data has become a research hotspot. Multi source data is robust in data accuracy, but there are still some problems, such as difficulty in effective fusion and feature extraction. This paper proposes an anomaly detection method based on multi-source heterogeneous data fusion from the perspective of sequence. The extracted subsequences are mapped into the feature subspace, and then a unified fusion feature space matrix based on multi view is constructed. Finally, an anomaly scoring method based on property attribute fusion is proposed. Anomaly detection is carried out based on fuzzy clustering. The accuracy of the algorithm is verified by many experiments.
Research on the parking planning algorithm based on DDPG and TD3

With the continuous growth of private transportation, parking has become a great difficulty in densely populated areas. This paper is committed to realizing vehicle autonomous parking under controlled and limited external complex conditions. Considering the continuity of state and action space, TD3 and DDPG algorithms based on actor critical architecture are adopted. The paper uses two algorithms to train the agent in simulated parking environment. The simulation results show that the DDPG algorithm based on DRL can achieve high-precision parking path planning.
Research on the application of cloud computing technology in computer laboratory construction and management

With the continuous development of cloud computing technology, the application of cloud computing technology in all walks of life has become a trend. Combined with the current situation of university computer laboratory construction, this paper introduces the application scheme of cloud computing technology in computer laboratory construction and management from the perspective of cloud computing, virtualization and other technologies. Compared with the traditional laboratory construction scheme, the laboratory system based on cloud computing can effectively reduce the laboratory construction and management cost, improve the management efficiency and resource utilization, and achieve the sharing of experimental resources. Students can experiment on demand anytime and anywhere.
Label-only membership inference attack based on the transferability of adversarial sample

Machine learning (ML) has been widely used in various scenes, such as face recognition, automatic driving and image recognition and other fields. However, in these applications, attackers usually exploit various vulnerabilities in machine learning models to mine private information, including model training information and private data information. The attacks against machine learning training data mainly include membership inference attack (MIA). The attacker of membership inference attack will judge whether the data sample is in the training data set of the target model given the data sample and the target model. Most of the existing member inference attacks are based on confidence attacks. Researchers found that if the confidence returned is hidden, they can effectively defend against these attacks. Therefore, the latest research proposed label-only attack, but this attack requires hundreds of queries for a sample, and abnormal query accesses are easy to be found by the target model. In this paper, we propose a member inference attack based on the transferability of adversarial samples. The adversarial samples generated under different models of the same task have certain mobility characteristics, so as to use the shadow model to generate adversarial samples for attack and reduce the attack cost. The evaluation shows that under the restriction of fewer queries, only the label output will still reveal private information, and the performance of the member inference attack based on the transferability of adversarial samples is better than that based on confidence in most cases.
COVID-19 pandemic prediction using machine learning methods

In this paper, we aim to predict the cases of covid-19 pandemic according to linear regression model and random forest model. We decide to try to predict the virus using the daily high and low temperatures because it is one of the biggest factors that can affect the spread and death of the virus.we decide to use days_num,vaccine_days,and ma_temp_high as features.Cases and deaths as labels. We find that that the virus surely has some relationship with temperature. If the theory turns out to be true, in the future, adjusting control efforts based on temperature could greatly improve efficiency and save money. Reduce ineffective expenditures and improve the quality of prevention and control.
Falling detection based on deep learning and video classifier

Nowadays, falls are a common problem for older adults, which leads to injuries and decreased quality of life. According to the Chinese Center for Disease Control and Prevention, falls have become the leading cause of injuries to people over 65. Therefore, how to detect falls quickly becomes very important. In this paper, we assess the performance of five different video classification algorithms, namely, Efficient Convolutional Network for Online Video Understanding (ECO), C3D network, Temporal Segment Network (TSN), NeXtVLAD, Convolutional two-stream Network. Finally, we find that ECO and TSN algorithms perform better, and their accuracy is around 95%. However, ECO has a shorter running time and a faster response, so the ECO algorithm performs best in detecting falls.
AGC: automated machine learning for garbage classification on mobile devices

To protect the environment, governments introduced many policies about garbage recycling and garbage classification. However, the different garbage classification policies between different areas bring many inconveniences for people who come from other areas. In the meantime, the development of garbage classification application requires developers to have lots of experience about object classification. In this work, we employed Automated Machine Learning for the garbage classification application, which is called AGC method. The AGC method uses Google AutoML service to train the garbage classification model with Baidu AI studio garbage dataset. During train the model, we set the data augmentation and normalization to increase the precision of the model. To compare model effects, the model trained by AutoML was compared with MobileNet. At the end, the model was deployed on an iPhone to test the model. Our method achieves 0.92 in precision, which is much higher than MobileNet, and the application correctly recognized the real-word garbage image with 0.87 confidence.
Drug quality re-inspection based on YOLO deep learning

The drug quality re-inspector is an application of machine vision artificial intelligence technology to drug quality reinspection. In this paper, the author proposes a lightweight deep convolutional neural network machine learning model using a custom-trained YOLO machine learning algorithm to detect the integrity of pharmaceuticals. More importantly, it uses artificial neural networks (ANNs) to model complex relationships in the data. A color drug image segmentation method using a combination of HSV model and color distance is used to address the case of uniformly colored drug attachment in drug images. After testing, the accuracy rate was significantly improved compared to the traditional manual rechecking, eventually reaching 86%.
A scoring algorithm based on retrieval term expansion

Information retrieval is the foundation of content-driven applications, and the quality of the search results directly affects whether users can quickly obtain the required information. At present, vertical search engines for specific fields meet the needs of users to obtain specific information to some extents, but full-text search engines based on text matching cannot search from the perspective of semantics, so that search results strongly dependent on the selection of search terms. Aiming at the above problems, a scoring and sorting algorithm based on search term expansion is proposed. This method combines the research contents of search term expansion based on relational ontology and semantic similarity calculation based on word vector, discusses the search strategy for learning data library, and proposes a reasonable expansion method for search terms in vertical search engines. The Dscore ranking algorithm of search results proposes a new optimization strategy for the ranking algorithm of search results, realizing a search engine more suitable for library applications.
Unsupervised confident co-promoting: refinery for pseudo labels on unsupervised person re-identification

Unsupervised domain adaptation (UDA) person re-identification (re-ID) aims to apply useful knowledge learned on labeled source domain datasets to unlabeled target domain datasets. Most successful UDA re-ID methods combine clustering to generate pseudo labels for feature learning and further fine-tuning on the target domain in an alternating manner. However, the interaction of the two steps is offline, which may make the noisy pseudo labels greatly hinder the classification and retrieval ability of the whole model. In order to purify these noisy pseudo labels, in this paper, a framework called Unsupervised Confident Co-promoting (UCC) is proposed. Specially, two peer teach-student co-training models are adopted simultaneously to online refine the noisy pseudo labels with the offline clustering algorithm and supervise each other during iterations. More significantly, we introduce a confidence strategy that greatly improves the confidence of generating pseudo labels in the case of multi-network collaboratively guided learning. The combination of the above two allows our final method to greatly improve noisy pseudo labels on re-ID task, achieve a huge performance boost and generalize to more deep learning domains. Moreover, our method achieves a significant improvement in common evaluation indicators in the four most common re-ID experiments compared to the state-of-the-art (SOTA) methods, and even on some results is comparable to the supervised learning method.
Multi-scale weighted image enhancement algorithm based on HSV space

Aiming at the problems of low-illuminance image brightness, low contrast, and lack of detailed information, a multi-scale weighted image enhancement algorithm based on HSV space is proposed. The algorithm first converts the image from the RGB space to the HSV space; then uses the information that can characterize the image characteristics in the H and S channels of the HSV space as weights to enhance the V channel; finally, the guided filtering is used to enhance the details of the image to obtain a strong contrast, Images with sharp edges and details. To verify the superiority of the algorithm, a comparative experiment was carried out, and the enhancement algorithm was objectively compared through three indicators of En (Entropy), EME (Enhancement by Entropy), and AG (Average Gradient). The test results show that the indicators of low-illuminance images have been significantly enhanced, from the original 6.32, 15.53, and 2.87 to 8.35, 75.89, and 6.58 respectively, and compared with the existing methods, the contrast and detail information of the restored results Verifies the effectiveness of the proposed method.
Overview of mobile communication network architecture based on federated learning

Combine federated learning with the mobile network a good opportunity for the introduction of AI technology research and application in the mobile communication network.This article proposes a kind of the mobile network architecture based on FL to promote the integration of FL technologies with mobile communication network and to meet the joint modelling needs of users, operators, and industries. And points out some key technologies of the mobile network to illustrate how the mobile network to support FL working. Meanwhile, proposes some use cases to illustrate what possible application scenarios and values, if the FL technology is introduced into the mobile communication network.
An efficient federated learning optimization algorithm on non-IID data

Recently, federated learning has received a lot of attention. It is a novel distributed machine learning scheme that enables a large number of edge computing devices to collaboratively learn and train models without leaking local data. Federated learning can break down data barriers, effectively solve the problem of data island, and is committed to ensuring user privacy and data security, and has received extensive attention from industry and academia. However, in practical industrial scenarios, federated learning needs to consider more effects of heterogeneity than traditional machine learning. Aiming at the problems of high communication cost and heterogeneous client data in federated learning, this paper proposes a federated learning training method based on knowledge distillation, which realizes the sample-level information exchange between the server and the client, and guides each client to train local personality. The model is optimized to reduce the impact of heterogeneous data on the client side, and the distillation loss is used to ensure the stability of the model performance. Theoretical analysis and experimental results show that the federated learning algorithm proposed in this paper can ensure the data privacy and security of participants. And in the scenario of Non-IID data, the algorithm can improve the training efficiency and reduce the system communication cost on the premise of ensuring the accuracy of the model, and has the feasibility of practical industrial application.
Malaria cell classification with residual neural network

Malaria is a transmittable disease caused by the parasites which belong to the Plasmodium family which is spread by the bite of the female mosquito. In the past, they were detected by trained microscopists who analyze microscopic blood smear images. However in recent years, a series of deep learning methods had been appeared. In this passage, we use ResNet with different parameters to classify Malaria cell images. We preprocess a dataset with more than 27,000 Malaria cell images from more than 350 patients. On this dataset, we train a convolutional neural network which outputs the classification accuracy and loss. By using transfer learning, we find the best network architecture in which we can classify the cells with the accuracy of 94.93%.
Similarity activation map for co-salient object detection

Co-salient object detection aims to detect the common objects within a group of relevant images, to which the spatial similarity contributes a lot. Existing methods utilize the inner product to compute the pixel-wise correlations, imitating the tracking methods. We present a novel yet effective module (Similarity Activation Module, SAM) to generate the similarity activation maps as the spatial modulator. The similarity activation maps are learned to highlight the common objects across the multiple images while suppressing other objects and the background. Moreover, we propose the Edge Extraction Module (EEM) and Feature Fusion Module (FFM) which can be easily applied to any existing methods without requiring architectural changes. Extensive experiments on different co-salient detection datasets demonstrate that our method (SimiNet) achieves state-of-the-art performance under various evaluation metrics.
MACR: Multi-information Augmented Conversational Recommender

Conversational recommender systems (CRS) aim to provide high-quality recommendations through fewer multi-turn conversations. However, because short conversation histories lack sufficient item information, CRSs not only struggle to make accurate recommendations but also lack diversity in the generated responses. Existing CRSs mainly alleviate these problems by introducing external information (e.g., reviews) while ignoring information inside the conversations (e.g., potential category preferences in user utterances). Besides, item introduction is a kind of external information that is more objective and contains more entities than reviews. Therefore, we propose a Multi-information Augmented Conversational Recommender (MACR), which improves the performance of recommendation and response generation by mining the underlying category preferences in users’ utterances and incorporating item introductions. Specifically, we enhance the category associations among entities by constructing a knowledge graph DBMG with category nodes, extracting and encoding the item categories that match the user preferences into the user representation. For item introductions, we extract the entities in them and fuse them into the conversation using an introduction-attentive encoder–decoder. Extensive experiments on the dataset REDIAL show that our MACR significantly outperforms previous state-of-the-art approaches. The source code will be available at https://github.com/zcy-cqut/MACR.
Interactive and Immersive Auralization

Abstract Real-time auralization is essential in virtual reality (VR), gaming, and architecture to enable an immersive audio-visual experience. The audio rendering must be congruent with visual feedback and respond with minimal delay to interactive events and user motion. The wave nature of sound poses critical challenges for plausible and immersive rendering and leads to enormous computational costs. These costs have only increased as virtual scenes have progressed away from enclosures toward complex, city-scale scenes that mix indoor and outdoor areas. However, hard real-time constraints must be obeyed while supporting numerous dynamic sound sources, frequently within a tightly limited computational budget. In this chapter, we provide a general overview of VR auralization systems and approaches that allow them to meet such stringent requirements. We focus on the mathematical foundation, perceptual considerations, and application-specific design requirements of practical systems today, and the future challenges that remain.
Spatial Audio Mixing in Virtual Reality

Abstract The development of Virtual Reality (VR) systems and multimodal simulations presents possibilities in spatial-music mixing, be it in virtual spaces, for ensembles and orchestral compositions or for surround sound in film and music. Traditionally, user interfaces for mixing music have employed the channel-strip metaphor for controlling volume, panning and other audio effects that are aspects that also have grown into the culture of mixing music spatially. Simulated rooms and two-dimensional panning systems are simply implemented on computer screens to facilitate the placement of sound sources within space. In this chapter, we present design aspects for mixing in VR, investigating already existing virtual music mixing products and creating a framework from which a virtual spatial-music mixing tool can be implemented. Finally, the tool will be tested against a similar computer version to examine whether or not the sensory benefits and palpable spatial proportions of a VE can improve the process of mixing 3D sound.
Video Visualization Technology and Its Application in Health Statistics Teaching for College Students

In view of the present situation of “learning difficulty” in health statistics, this paper proposes a video visualization technology based on the convolutional neural network, which updates parameters by calculating the gradient of loss function to obtain accurate or nearly accurate loss function. Taking the students from 2014 to 2017 in a university in Henan as the research object, this paper analyzes the video visualization technology and its application effect on the teaching of college students’ health statistics from the aspects of students’ course awareness, learning behavior, communication between teachers and students, knowledge mastery, and course satisfaction. The results show that the external model load difference between each explicit variable and latent variable is statistically significant. Learning behavior and communication between teachers and students have a direct impact on the mastery of knowledge, and the degree of influence from high to low is as follows: learning behavior and communication between teachers and students. The teaching effect model of health statistics based on video visualization technology of the convolutional neural network has certain practicability.
Acceleration of software execution time for operations involving sequences or matrices

The article describes three methods for lowering program runtime that are solutions to computer science Olympiad problems involving sequences or matrices. The first method relies on the representation of some sequences as matrices, after which the program for calculating the sequence's members will have asymptotics equivalent to the exponentiation algorithm's time complexity and be O(log (n)). The second strategy is to improve the existing code in order to significantly shorten program runtime. For scientists who create code for scientific inquiries and deal with matrix multiplication operations, understanding this approach is crucial. The author's challenge is presented and solved using the third strategy, which is based on minimizing temporal complexity by looking for regularities.