Reviewerly/exHarmony huggingface dataset:
Research on the construction and practice of Java curriculum group based on EIP-CDIO mode

At present, engineering education emphasizes the development of students' skills and the cultivation of their professional quality. The society has a high demand for students' technical ability and professional quality, but there are many problems in the process of practical teaching. Faced with the demand of the current software development employment market, many colleges and universities regard Java-related courses as the core curriculum system of computer specialty. The system has many contents and strong relevance. If there is no comprehensive teaching design and reasonable practice cases, we can not really achieve the goal of training applied talents. Due to the complexity of Java course group, it is difficult for students to really understand it. To improve the teaching effect of the course, the construction and practice method of Java course group based on EIP-CDIO mode is studied. The basic information of students is collected and recorded in the teaching system in the form of data. The construction and practice of Java course group are carried out, and the effect of construction and practice is comprehensively analyzed. The final results of Java class (1) and class (2) are compared to further determine the effect of construction and practice of Java course group. The results show that the academic performance of students in Class 1 is significantly higher than that in Class 2, and the construction and practice of Java course group is better. It shows that under the EIP-CDIO mode, the construction and practice of Java course group has achieved remarkable results, which is of great significance to the development of software development.
Cloud computing resource scheduling based on ant colony optimization and simulated annealing algorithm

In order to improve the utilization of cloud computing resources, this paper studies cloud computing resource scheduling based on improved Ant Colony Optimization Algorithm (ACO) and Simulated annealing algorithm (SA). We apply ACO and SA to schedule the large-scale cloud computing resource data. This paper uses SA to prevent ACO falling into the local optimal solution, and accelerate the convergence speed of ACO. The experimental results show that the proposed method is more effective compared with the already existed approaches. Our proposed method can improve the resource utilization.
Research on information recommendation method of online education platform based on user preference model

At present, there are few personalized information recommendation services in online education platforms. The resources received by users are basically the same, and there is no personalized recommendation based on user preferences. How to accurately locate relevant resources from massive resources according to the needs of learners, and provide personalized online education services for them, has become a current research hotspot. Although the research on recommendation algorithms in other fields has achieved good results, there are relatively few applications of online course resource recommendation systems. This paper improves the collaborative filtering recommendation algorithm. On the basis of the traditional algorithm, the dual-attribute scoring matrix is used for attribute division and the BP neural network is used for scoring prediction. This improvement is to fill the scoring matrix and solve the problem of the recommendation quality degradation caused by "cold start" and too sparse scoring data in traditional algorithms. Finally, the effectiveness of the improved algorithm is proved by experiments.
Analysis of e-commerce users' preference behavior based on k-means algorithm

In the process of continuous development of e-commerce, to better meet the needs of users and tap the consumption potential of users, personalized recommendation systems have emerged on various e-commerce platforms. Although the clustering algorithm is suitable for solving the user segmentation problem, the traditional K-means algorithm has some shortcomings, such as the quality of the initial center point determined randomly is not high, and there is no definite criterion to select the value of K. Therefore, this paper proposes an optimized K-means algorithm, which uses the value of effectiveness index CH to determine the value of K, and combines with particle swarm algorithm to solve the initial center point. Experiments show that the optimization algorithm CH-PSO-K-means algorithm proposed in this paper has improved the DB index, accuracy rate, and error square index. The comprehensive performance of the clustering effect has been significantly improved, which can effectively solve the problem of e-commerce user segmentation with large data and multiple characteristics. The optimization algorithm not only makes up for the shortcomings of the K-means algorithm, but also improves the maintenance strategy of individual e-commerce users, which is conducive to enterprise cost control and profit improvement.
Path planning design of robots based on improved particle swarm optimization algorithm and artificial potential field method

Robot path planning is to find the best path for robot movement considering the interference of surrounding obstacles. In this paper, the feasible region is rasterized and the robot path planning problem is transformed into abstract space that can be dealt with. In order to improve the computational efficiency, the improved particle swarm optimization algorithm combined with artificial potential field method was used to find the best moving path in the grid environment. Bessel curve is used for final smoothing operation to solve the problem of multiple path breaks in grid environment and obtain the final moving path. An example shows that this method can overcome the shortcomings of traditional algorithms and intelligent algorithms in robot path planning, and has strong search and convergence ability.
Realization of knowledge service and management system based on data mining technology

Digital mining technology is a network information processing technology based on the perspective of big data analysis, and its important value in the construction of library knowledge service management system is prominent. This paper starts with the realization of the knowledge service and management system of university library under the digital mining technology, as well as the business flow and composition of the knowledge service and management system, and discusses how to improve its performance design and safety design, and realize the optimization service and management decision-making function of the system, to meet the needs of users for knowledge acquisition in the digital age, and to meet the greatest needs of teaching and research construction in colleges and universities.
Robot path planning and trajectory optimization based on Sparrow search algorithm

This paper presents an intelligent optimization algorithm applied to the two-dimensional path planning of a mobile robot and optimizes its trajectory. The method is based on the sparrows' behavior of finding food and avoiding predators. The method is implemented in a raster map and uses the Sparrow search algorithm for preliminary path planning to find a passable optimal path, and in order to solve the shortcomings of many folds and not being smooth enough at the folds, the use of B spline curve to smooth the path to solving the shortcomings of many fold points and not being smooth enough at the fold.
Attacking neural machine translations via hybrid attention learning

Abstract Deep-learning based natural language processing (NLP) models are proven vulnerable to adversarial attacks. However, there is currently insufficient research that studies attacks to neural machine translations (NMTs) and examines the robustness of deep-learning based NMTs. In this paper, we aim to fill this critical research gap. When generating word-level adversarial examples in NLP attacks, there is a conventional trade-off in existing methods between the attacking performance and the amount of perturbations. Although some literature has studied such a trade-off and successfully generated adversarial examples with a reasonable amount of perturbations, it is still challenging to generate highly successful translation attacks while concealing the changes to the texts. To this end, we propose a novel Hybrid Attentive Attack method to locate language-specific and sequence-focused words, and make semantic-aware substitutions to attack NMTs. We evaluate the effectiveness of our attack strategy by attacking three high-performing translation models. The experimental results show that our method achieves the highest attacking performance compared with other existing attacking strategies.
Detection system for correctly wearing mask based on YOLO v5 algorithm

With the spread of the new crown epidemic and the increasing number of confirmed cases, wearing correct masks is an effective means of keeping the virus at bay. Using artificial intelligence technology to determine whether masks are being worn correctly is a possible solution. By training and learning from a certain amount of image data, target detection of masks can be achieved. RCNN or FAST-RCNN is widely used in the related field of research, although the method has good accuracy and robustness. However, there are some disadvantages such as low efficiency and long training time. In addition to this, most of the current work only stops detecting whether or not a mask is worn, but does not consider whether or not the mask is worn correctly. Therefore, this paper proposes a mask detection model based on the YOLO v5 algorithm, which uses a self-made training set to detect whether a pedestrian is wearing a mask and whether he or she is wearing it correctly. The experimental validation shows that the detection accuracy is higher, and the robustness is almost the same based on the original data set, while the practicality is greatly improved.
Application of classifier based on STDPM algorithm

In the current era of big data, semi-supervised learning algorithms are widely used. The self-training algorithm is one of the commonly used semi-supervised algorithm frameworks. During the iteration process of the self-training algorithm, the classification performance of the base classifier has a significant influence on the final classifier trained by the model. To study the effect of the base classifier on the performance of the STDPM algorithm, we use KNN, decision tree, and SVM base classifier to conduct experiments on four medical images. The experimental results show that on the four data sets diabetes, heart, hepatitis and Ilpd, the three classifiers KNN (K=1), SVM and decision tree have little effect on the STDPM algorithm. When K=3, the algorithm STDPM performs poorly using the KNN base classifier.
Classification of tumor from computed tomography images: A brain-inspired multisource transfer learning under probability distribution adaptation

Preoperative diagnosis of gastric cancer and primary gastric lymphoma is challenging and has important clinical significance. Inspired by the inductive reasoning learning of the human brain, transfer learning can improve diagnosis performance of target task by utilizing the knowledge learned from the other domains (source domain). However, most studies focus on single-source transfer learning and may lead to model performance degradation when a large domain shift exists between the single-source domain and target domain. By simulating the multi-modal information learning and transfer mechanism of human brain, this study designed a multisource transfer learning feature extraction and classification framework, which can enhance the prediction performance of the target model by using multisource medical data (domain). First, this manuscript designs a feature extraction network that takes the maximum mean difference based on the Wasserstein distance as an adaptive measure of probability distribution and extracts the domain-specific invariant representations between source and target domain data. Then, aiming at the random generation of parameters bringing uncertainties to prediction accuracy and generalization ability of extreme learning machine network, the 1-norm regularization is used to implement sparse constraints of the output weight matrix and improve the robustness of the model. Finally, some experiments are carried out on the data of two medical centers. The experimental results show that the area under curves (AUCs) of the method are 0.958 and 0.929 in the two validation cohorts, respectively. The method in this manuscript can provide doctors with a better diagnostic reference, which has certain practical significance.
SMpeaks: a semi-supervised clustering algorithm based on density peaks

Clustering by fast search and find of Density Peaks (referred to as DP) was introduced by Alex Rodriguez and Alessandro Laio. DP algorithm is based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively large distance from points with higher densities. This algorithm can discover clusters regardless of their shapes and the dimensions of the space containing them. However, it cannot effectively detect clusters with different sizes and densities of arbitrary shapes, especially the same cluster with multiple peaks. Moreover, the DP algorithm needs to select the centers of the clusters by using a decision graph manually. Despite a highly improved performance in semi-supervised clustering, to address this problem, we propose a semi-supervised framework for DP, namely SMpeaks, by integrating pairwise must-link and cannot-link constraints to guide the clustering procedure. We tested the SMpeaks algorithm on complex data sets having clusters with arbitrary shapes, different sizes, and densities. The experimental results have demonstrated that this algorithm is more effective in finding clusters of complex shapes and different densities than DP.
Visual design and implementation of campus epidemic monitoring system based on Internet of Things

In order to reduce the aggregation of personnel and realize the intelligent prevention and control of campus epidemics, a campus epidemic monitoring system based on the Internet of Things has been designed and implemented, including a population flow data acquisition module, a network epidemic data acquisition module, and a campus Internet of Things and data visualization module, which can quickly establish the Internet of Things platform and realize the synchronous monitoring of local and cloud data. The system uses the STM32F429 as the main control chip, with the HC-SR501 human infrared sensing module and the LoRa node module. The bidirectional detection algorithm is used to collect real-time human flow data throughout the campus, and the Python network crawler is used to obtain real-time network epidemic data. The campus Internet of Things, based on LoRa wireless communication technology, is built to achieve data transmission with network servers. For the data obtained through the software driver cleaning and stored in the database for human-computer interaction interface calls. After testing the system function, the results show that the data collected by the front end can be displayed on the LCD terminal and the cloud HTML terminal. The collected data can be intuitively displayed on the terminal interface through the Internet of Things transmission, which provides effective human flow data and epidemic data for campus teachers and students.
Research on indoor anti-theft system based on YOLOv5

With the continuous development of society, indoor monitoring is increasingly widely used. At present, video surveillance mainly uses artificial real-time monitoring or post-monitoring to check indoor conditions and people. This paper describes the components and working principle of YOLOv5, and uses YOLOv5 algorithm to conduct network simulation and training for indoor common targets. Through the indoor photography head to collect the scene, using YOLOv5 algorithm to process the original photos, when there is an abnormal person in the image, the system will issue an alarm to remind the owner to deal with it in time. The experiment shows that the system can respond the indoor situation economically, quickly and effectively and meet the demand of indoor anti-theft.
Clustering analysis on network public opinion in Shunyi District of Beijing based on LDA algorithm

With increasing growth of the Internet and rapid development of search engine technology, people can transmit or acquire all kinds of information that happens all the time on a variety of social media and network platforms. The network information management departments of local governments attach great importance to these online public opinions which have a certain impact on the society. The crawling of network public opinion information in Shunyi District of Beijing from Weibo is taken as an example and analyzed in this Paper for final focuses on livelihood concerns and providing reference for the situation awareness of public opinion in Shunyi District by clustering analysis on public opinion with the LDA text clustering algorithm and determination of topic characteristics of each category of public opinions.
Towards adaptive unknown authentication for universal domain adaptation by classifier paradox

Universal domain adaptation (UniDA) is a general unsupervised domain adaptation setting, which addresses both domain and label shifts in adaptation. Its main challenge lies in how to identify target samples in unshared or unknown classes. Previous methods commonly strive to depict sample "confidence" along with a threshold for rejecting unknowns, and align feature distributions of shared classes across domains. However, it is still hard to pre-specify a "confidence" criterion and threshold which are adaptive to different tasks, and a mis-prediction of unknowns further incurs mis-alignment of features in shared classes. In this paper, we propose a new UniDA method with adaptive Unknown Authentication by Classifier Paradox (UACP), considering that samples with paradoxical predictions are probably unknowns belonging to none of the source classes. In UACP, a composite classifier is jointly designed with two types of predictors. That is, a multi-class (MC) predictor classifies samples to one of the multiple source classes, while a binary one-vs-all predictor further verifies the prediction by MC predictor. Samples with verification failure or paradox are identified as unknowns. Further, instead of feature alignment for shared classes, implicit domain alignment is conducted in output space such that samples across domains share the same decision boundary, though with feature discrepancy. Empirical results validate UACP under both open-set and universal UDA settings.
Why is this language complex? Cherry-pick the optimal set of features in multilingual treebanks

Abstract This paper investigates linguistic complexity across natural languages from a corpus-based perspective and relies on the assumptions of linguistic profiling as a methodological framework. We focus in particular on the domain of syntactic complexity and analyze the distribution of a set of features taken as proxies of complexity phenomena at sentence level, which were extracted from 63 treebanks annotated according to the Universal Dependencies formalism. This dataset guarantees that the features considered are modeling the same linguistic phenomena in different treebanks, allowing reliable comparison among languages. We show that our approach is able to identify tendencies of structural proximity between languages not necessarily in line with typologically-supported classification, thus shedding light on new corpus-based findings.
A systematic security analysis of EMV protocol

EMV is the leading and widely used international standard for payment with smart cards. The EMV specification defines a highly configurable toolkit for payment protocols, which allows different combinations of card authentication, cardholder authentication and transaction authorization. Due to its complexity and its flexibility, it is difficult to comprehensively analyze the security of EMV standard, yet it is critical to obtain practical security guarantees for EMV. In this paper, we present the first systematic and formal treatment of EMV protocol. We introduce a three-party security model, covering all known kinds of combinations and providing reasonably strong security notions. Furthermore, via a modular approach, we prove that the EMV protocol with reasonable improvement can achieve our desired security. We also identify various known attacks on EMV protocol in our security model.
Understanding Counterfactual Generation using Maximum Mean Discrepancy

With the dramatic development of deep learning in the past decade, interpretability has been one of the most important challenges that often prevents neural networks from being applied to fields such as finance. Among many existing explainable analyses, counterfactual generation has become widely used for understanding neural networks and making tailored recommendations. However, few studies are devoted to providing quantitative measures for evaluating counterfactuals. In this paper, we propose a quantitative approach based on maximum mean discrepancy (MMD). We employ several existing counterfactual methods to demonstrate this proposed method on the MNIST image data set and two tabular financial data sets, Lending Club (LCD) and Give Me Some Credit (GMC). The results demonstrate the potential usefulness as well as the simplicity of the proposed method.
Adversarial Fraud Generation for Improved Detection

Generative Adversarial Networks (GANs) are known for their ability to learn data distribution and hence exist as a suitable alternative to handle class imbalance through oversampling. However, it still fails to capture the diversity of the minority class owing to their limited representation, for example, frauds in our study. Particularly the fraudulent patterns closer to the class boundary get missed by the model. This paper proposes using GANs to simulate fraud transaction patterns conditioned on genuine transactions, thereby enabling the model to learn a translation function between both spaces. Further to synthesize fraudulent samples from the class boundary, we trained GANs using losses inspired by data poisoning attack literature and discussed their efficacy in improving fraud detection classifier performance. The efficacy of our proposed framework is demonstrated through experimental results on the publicly available European Credit-Card Dataset and CIS Fraud Dataset.
Stryker Polaris Spectra USB Converter

Biomedical Safety & Standards 52(20):p 156, November 15, 2022. | DOI: 10.1097/01.BMSAS.0000896864.63589.c0
Demonstrating Dynamic Toolchains for Machine Control

Humans are increasingly able to work side-by-side with desktop-scale digital fabrication machines. However, much of the software for controlling these machines does not support live, interactive exploration of their capabilities. We present Dynamic Toolchains, an extensible development framework for building parametric machine control interfaces from reusable modules. Toolchains are built and run in a live environment, removing the repetitive import and export bottleneck between software programs. This enables humans to easily explore how they can use machine precision to manipulate physical materials and achieve unique aesthetic outcomes. In this demonstration, we build a toolchain for computer-controlled watercolor painting and show how it facilitates rapid iteration on brush stroke patterns.