Reviewerly/exHarmony huggingface dataset:
Training teaching quality evaluation system in colleges and universities based on BP neural network

The quality of practical teaching in colleges and universities is an important evaluation index of the level of colleges and universities, but the quality of practical teaching in colleges and universities is related to a variety of factors, and the law of change is very complex. To reduce the error rate of teaching evaluation system in colleges and universities, expand the scope of actual teaching evaluation, and ensure the practical application effect of the system, this paper carries out the research of teaching quality evaluation system in colleges and universities based on the BP neural network. Firstly, the system requirements are analyzed, and the requirement structure framework is briefly described for the application requirements and annotations of the system. Then through the establishment of the BP neural network to evaluate the instruction cluster, a circular system execution process is created. Finally, in the main program, access the multi-level evaluation function module, divide the application pressure of the system, divide and verify the results of practical teaching quality, and complete the software design. The system test results show that the error rate of the final system evaluation is controlled below 2. 5% for the evaluation of the four classes, which shows that the system can control the quality of teaching evaluation in each cycle, improve the pertinence of teaching quality evaluation, and improve the final effect of teaching evaluation.
Construction and application of credit card fraud crime prediction model based on SVM algorithm

In recent years, with the rapid development of the Internet, telecommunications and financial industry, credit card fraud as a non-traditional crime cases surge. People's dependence on credit card payments and mobile payments has been further deepened, which also provides a fertile soil for credit card fraud. This paper uses SVM Algorithm to build a credit card fraud prediction model, and verifies the prediction performance through data experiments, and finds that it has a high accuracy for identifying credit card fraud. If the model is applied to real life, it can avoid the loss caused by credit card fraud in time, which has a broad development prospect.
Application and study of virtual reality in the diagnosis system of peritoneal effusion

The virtual reality-based pneumoperitoneum diagnosis system is an important part of the simulation of laparotomy, and the simulation of pneumoperitoneum is the key to virtual puncture teaching. This paper conducts a test study on the frame rate, the rendering time per frame, the number of triangles and fixed points to be drawn and other related factors during the simulation of pneumoperitoneum. The paper compares the topographic water resources in the standard repository provided by unity and the water resources in the OBI particle system, and addresses the problems and shortcomings of the topographic water in the standard repository, which cannot flow due to gravity and has poor simulation quality, and the OBI particle system, which has low rendering frame rate and long rendering time for simulating water, and proposes a Shader Graph water rendering method based on the Shader Graph, and then simulates the peritoneal fluid through programming, improved rendering frame rate, reduced the number of unnecessary drawn triangles and vertices, and accelerated rendering time.
Research on text similarity: study case in project management

With the increasing investment of China Telecom in technical projects, the number and scope of project applications are larger than before, which brings more difficulties to the examination and evaluation, and also increases the difficulty of management. The repeated declaration and repeated approval of projects occur from time to time. This paper is mainly based on the semantic similarity calculation method of project contents, which can find similar projects conveniently, efficiently and accurately, reduce the redundant construction of enterprise scientific research projects, improve the utilization efficiency of enterprise scientific research funds and further improve the scientific research management level of enterprises.
MaskBEV: mask of accurate 2D object to optimize multi-view 3D object detection

As a key part of autonomous driving technology, 3D object detection in Bird-Eye-View(BEV) view is designed to accurately detect road conditions based on multiple sensor inputs. However, current 3D detection methods are usually biased towards the acquisition of depth information. There is a strong correlation between 2D object detection and 3D object detection in BEV, especially the latest 2D detection has high robustness and accuracy in complex environments. To the end, this paper proposes a 3D object detection algorithm with object feature enhancement , which reasonably uses mask generated by 2D object detection as prior information and facilitates object extraction in BEV. In particular, in order to amplify the difference between the target and the background, we first designed the Data Preparation Module(DPM) to convert the 2D detection results into a binary target mask which completely separates the target from the background. Then, an Object Enhancement Module (OEM) is designed to enhance the contrast and texture details between the object and background. Finally, we propose Channel Interaction Module(CIM) considering the correlation between multi-channel features, which fully allocates the weights between channels. In this way, MaskBEV-Tiny scores 34.0% mAP and 41.1% NDS on the nuScenes val set which surpasses BEVDet-Tiny by a largin of 2.9% mAP and 2.0% NDS.
Tangible Experience Design Toolkit: Simplifying Cross Device Interaction Design

&lt;p&gt;Cross-device environments are attracting attention from designers working on interactive experiences, as there is an increased desire to create advanced and sophisticated narratives. As these experiences increase in popularity, they use increasingly complex embedded sensor-based environments. These environments deem to be challenging for inexperienced creators as they require knowledge of networked systems. This paper explores the initial design of a toolkit that provides novice designers with an easy to use authoring and development environment for designing cross-device experiences. The toolkit uses Tangible User Interfaces in order to make cross-device development easier to understand for inexperienced designers and provide them with a robust support framework for expanding into developing more complex experiences. The toolkit is tested with a group of potential users. The conclusions from the testing, along with potential future steps, are presented in this paper. &lt;/p&gt;
The cleaning method of duplicate big data based on association rule mining algorithm

In order to overcome the problems of low cleaning efficiency and serious memory consumption in traditional large data cleaning methods, this paper proposes a new cleaning method of repeated big data based on association rule mining algorithm. This method uses association rule mining algorithm to obtain the frequent itemsets of repeated big data after repeated cycle calculation. At the same time, the output mode of the algorithm is optimised in parallel, and the Hadoop interface is modified to change the reading mode of MapReduce. The first frequent itemset is used to clean the repeated big data. The experimental results show that the proposed method can effectively reduce the execution time and memory consumption, and the shortest cleaning time is only 1.28 min, indicating the feasibility of the proposed method.
ModEx: a general purpose computer model exploration system

We present a general purpose visual analysis system that can be used for exploring parameters of a variety of computer models. Our proposed system offers key components of a visual parameter analysis framework including parameter sampling, deriving output summaries, and an exploration interface. It also provides an API for rapid development of parameter space exploration solutions as well as the flexibility to support custom workflows for different application domains. We evaluate the effectiveness of our system by demonstrating it in three domains: data mining, machine learning and specific application in bioinformatics.
Research on transfer effect of ink style based on ChipGAN

Generated Adversarial Network has been widely used in the field of image style transfer. Among them, ChipGAN is specifically aimed at the study of Chinese ink painting style transfer. In previous studies, it has demonstrated its excellent transfer effect at the visual level. In this paper, using CycleGAN and ChipGAN model, we adjust the parameters by controlling the variable method, quantitatively compare the image style transfer of the two models on the basis of visual effect comparison, and prove the effectiveness of ChipGAN in ink style transfer and the complexity in training.
Adaptive differential private in deep learning

With the continuous development of artificial intelligence, it embodies important value in more and more scenarios, such as speech recognition, recommendation systems, computer vision, etc. The deep learning techniques behind them are built on a large amount of data to learn and extract features from different data. The deep learning techniques behind them are built on a large amount of data, learning from different data and extracting features. To protect data security, differential privacy is a good mechanism which is independent of the background knowledge possessed by the attacker and shows excellent results in privacy protection. In this paper, based on differential privacy and combined with knowledge in deep learning optimizer, We propose Adam-DP to improve the learning efficiency by adapting the learning rate and noise, and the adaptive noise also reduces the impact of noise on the model accuracy to some extent. The experiments show that the method has higher accuracy and learning rate compared with the DPSGD method.
Camouflage object segmentation based on feature extraction and mixed attention

In recent years, the technology of camouflage semantic segmentation using machine learning and computer vision has been widely concerned as one of the emerging research hotspots in the field of artificial intelligence. There are several methods have been developed for camouflage object segmentation, which still can be improved to solve the existing problems such as incomplete segmentation, edge dispersion, and too many parameters. This paper proposes a lightweight camouflage segmentation model called ConvNeXt Orientation Aggregation net (COA-net). In this paper, the camouflage segmentation model PFnet is selected as the basic network, and the feature extraction ability is enhanced by using the lightweight backbone network ConvNeXt. Futhermore, a plug-and-play mixed attention mechanism, called CDSEnet, is proposed, which enhances the ability to identify effective features and interference information. The experimental results show that the proposed method can achieve better segmentation performance compared with the original model. The experimental results show the proposed method can achieve the improvement of the index structure measure Sα, the adaptive measure Eφ, the weighted measure Fβ and the average absolute error M by 3.3%, 2.7%, 6.3% and 20.1% respectively and the subjective quality is better as well compared with the original method.
Improving active learning on imbalanced datasets by features mixing

When resources are limited, active learning seeks to optimize the dataset labeling process by choosing the most valuable unlabeled samples through sampling strategy. The majority of active learning techniques in use today are intended for balanced datasets. Machine learning, however, frequently deals with unbalanced datasets in the actual world. We think that unbalanced datasets make active learning more difficult. Here, we suggest an innovative active learning strategy for unbalanced datasets called the imbalanced feature-mixing K-center method (IFMK). In order to lessen the imbalance in the unlabeled samples, it develops interpolation between the representation of unlabeled samples by learning the more pertinent generic properties of the class. We employ a regularization term to enhance the proximity and diversity of unlabeled and labeled samples during sample querying. Results from two datasets with a wide range of imbalance variables demonstrate the superiority of our strategy over more current active learning techniques. On a dataset with more imbalance, these improvements are more noticeable.
Fuzzy cognitive maps learning algorithm based on modified covariance matrix adaptation evolution strategy

Fuzzy cognitive maps (FCMs) are an efficient soft computing tool commonly used for modeling and analyzing complex systems, and have significant advantages in knowledge representation and fast reasoning. At present, many researchers have devoted themselves to solving the learning problem of FCMs. However, most of the existing learning algorithms are prone to local convergence and are not suitable for large-scale FCMs learning problems. Moreover, the learning problem of FCMs is often a complex non-convex optimization problem, which is easy to cause the final learning result of the algorithm to be only a local optimal solution. To address these limitations, this paper proposes a novel algorithm for learning FCMs by combining multimodal optimization and modified covariance matrix adaptation evolution strategy (CMA-ES), termed as MCMA-ES. First, the learning problem of FCMs is modeled as a multimodal optimization problem (MMOP), and then a multimodal optimization algorithm is proposed by combining the detect multimodal clustering (DMC) and CMA-ES, and the cubic chaos factor is combined to enhance the capability of local search. Finally the proposed algorithm is combined with the decomposition strategy to learn FCMs. Experiments are carried out on 5 benchmark datasets, and MCMA-ES is also applied to 15 groups of large-scale gene regulatory system reconstruction problems. The experimental results show that MCMA-ES has high learning generalization and accuracy.
Web service recommendation based on knowledge graph enhanced

Mashup is an information system development project based on Web services. It can build new business processes by mixing data, resources and APIs from different sites or applications, and meet new user needs. However, in recent years, the number of Web API is increasing quickly, and it is difficult for developers and users to find suitable Mashups, which influences the development and functional efficiency of information systems. Therefore, designing an accurate and efficient Web service recommendation method to improve the functional characteristics of information systems has become a research hotspot. At present, many algorithms, like matrix factorization and collaborative filtering, often have serious cold start and sparse problems. In order to alleviate the above problems and capture as much information as possible, we proposed a multi-modal knowledge graph enhancement recommendation method (MKE), which combines structured information and unstructured information of Mashup and Web API. Firstly, the feature information is mined from the text description of API and Mashup to assist the construction of the service recommendation system. Then, the knowledge graph is constructed by using the related attributes and relationships of Mashup and API, as well as the entity and relationship of Mashup and API, and the text features. The low-dimensional embedded Trans model provides Top-K recommendation results for the specified Mashup description. Finally, experiments are carried out on the latest ProgrammableWeb dataset, and the experimental results show that the recommendation performance of this method is better than that of RippleNet, NCF, CKE, KGCN.
DSENet: a deep sub-ensembles convolutional neural network for robust semantic segmentation

Deep neural networks are widely used at computer vision while the hidden layers form a black-box and make the networks uncertain. Quantifying the uncertainty in neural networks is a big challenge. Although the Bayesian neural networks can construct model for black-box problems by distributing learning weight, these networks are computationally expensive compared to standard neural networks. In this paper, we propose a novel encoder-decoder semantic segmentation network to reduce the uncertainty of the network. In addition, we innovatively introduce information entropy into semantic segmentation, which intuitively reflects the level of uncertainty in and out of the domain. The result shows the network has a good segmentation effect (79.4% mIOU in CamVid), and makes the network robust by increasing the difference between in-domain and out-of-domain information recognition.
A generic framework for cloud-native network function

With the introduction of Cloud-native thinking and container technology, the industry trend is to transform network functions into cloud-native implementation with container bearing, that is, CNF (Cloud-native Network Function). Existing CNF research focuses on both CNF collaboration with CISM systems and CNF performance enhancements. This paper focuses on the implementation of CNF itself, and proposes a general CNF framework that can be assembled on demand to provide different network services with good scalability.
Adversarial attacks in deep neural networks: an explanation through multi-level interactions and the use of Shapley value

The field of adversarial robustness has gained significant attention in recent years due to the advancements in deep learning and the increasing complexity of tasks performed by deep neural networks. Despite this progress, the underlying mechanism behind the performance of these networks in the face of adversarial attacks remains poorly understood. The existing explanations are largely based on intuition. This study provides a comprehensive explanation of various adversarial attacks by examining the multi-level interactions between input variables in deep neural networks. Utilizing the Shapley value, we believe that these attacks primarily target high-level interactions to mislead the network. The study’s effectiveness is demonstrated through application to re-identification models. Additionally, possible reasons for minor variations in results are discussed.
An attention-enhanced neural network with distillation training for barcode detection

Barcodes have played an essential role in our daily life. Localizing, or detecting them in real scenes in a fast and robust way has many practical applications. Recently, some deep learning-based methods have shown great potential in object detection. However, because barcodes are placed at any angle, vertical bounding boxes cannot sufficiently capture accurate orientation and scale information. In this paper, we propose a barcode detector that performs dense prediction to accurately locate the position of pixels belonging to the barcode region. For better detection performance, we design a spatial attention module to integrate global information adaptively, which can be easily plugged into the prediction backbone. Meanwhile, we employ the knowledge distillation training strategy to train a small student network with the help of a heavy teacher network. Extensive experiment results demonstrate that our method can perform real-time speed on CPU environments and locate barcodes in images with complex scenes.
A One Stop Solution Platform for various Services: Helping Tools

Abstract: The service-based economy supports too much of the global economy for us to ignore its importance. Any tool or service's main goal is to help the user by solving a problem or simplifying a certain procedure. Numerous services are available to address problems ranging from extremely small to artificial intelligence levels. Getting the precise information or completing a certain activity is an essential component of any business. The key resource and initial step in locating any problem's answer is the internet. Using the internet is a common method to locate services that meet a certain need. To put it mildly, there are many different services and service providers on the internet. There are often alternatives to problems, as well as theories or techniques that may be used. But sorting through all the available alternatives to pick the best one is surely a difficult undertaking. It might be quite difficult to select—or seek for—the best tool. Finding a few answers does not always indicate that they are the best option for the issue at hand. Additionally, the services are not personalised. Every issue or circumstance is distinct. As the name implies, helping.tools is a one-stop shop for assistance when choosing a tool, service, or service provider. It is a platform that offers a variety of tools and services in one place. Users can choose the services they need from a number of categories and explore the platform easily. The services' different types range a variety of industries, including daily utility, blockchain, artificial intelligence, and other fields. This is accomplished by gathering a variety of products and services with enhanced functionality under one roof and making them accessible according to various sectors. Helping.tools has something for everyone. With a user-friendly interfacethe platform ensures that users get the best possible services at an single stop. In short, Helping.tools is a one-stop solution for anyone looking to access quality services tailored to their specific needs.
A survey on correlation and its structural analysis method for failure factors in cloud service

Failure correlation is a necessary condition for source trace and violations determination in accountability of cloud services. This paper surveys the failure correlation. Most studies were involved in the correlation between services violation and failure factors only. However, an understanding of the correlation between the factors, the integrality of the factors is ambiguous and even few involve the correlation patterns of specific violations. Above all, the taxonomy of the failure factors, the methods of structure representation and analysis, some problems of recent research and suggestions are proposed in our paper.