Reviewerly/exHarmony huggingface dataset:
Research on Language Learning and Cross-Cultural Function Based on Mobile Digital Platform

From the perspective of linguistics and cross-cultural communication, this paper analyzes three theoretical problems about confusion and anxiety in cross-cultural communication and language learning, namely pragmatic reasoning, cultural hypothesis, and the degree of confusion and anxiety. With the increasing use of digital mobile platform, more and more teachers begin to pay attention to applying the highly interactive course app based on this platform to the actual teaching process. Therefore, cross-cultural communicators and language learners must constantly expand language and cultural knowledge, enhance signal sensitivity and desire for communication and learning, and improve their intercultural adaptation skills. Language teachers should also pay enough attention to confusion and anxiety, and help students control and reduce confusion and anxiety when necessary.
Research on the Application of Cloud Computing College English Teaching Assistant Platform

This paper discusses the application of cloud computing in College English courses. As a tool in the Internet age, cloud provides a broad space for the innovative development of English education and promotes the construction and development of English education at all levels. At present, there are some deficiencies in using rhyme as a tool in English teaching. It is not enough to build, develop and utilize cloud computing platforms that support English learning. Use cloud to define benefits. In the design process, we should fully consider the existing knowledge base, the right to physical and intellectual development, and behavior acquisition. During the implementation of the plan, a complete English curriculum system will be established to adapt group learning and autonomous learning to students’ learning habits. In the process of evaluation, the method of combining diversified evaluation and quantitative evaluation is adopted, and scientific and effective teaching feedback is obtained. In this context, we attach importance to the application and construction of English teaching platform, constantly study the different types and aspects of English teaching application, and study English teaching platform. Actively develop discussion platforms and other platforms.
Research on System Design of Educational Curriculum Construction Based on Big Data Platform

With the rapid development of online education, the new mode of carrying Internet+ on educational resources breaks through the time and space constraints of the traditional education industry, and solves the unequal distribution of educational resources caused by regional differences and other factors to a certain extent. Therefore, an educational intelligence platform based on big data is designed. Its overall structure includes physical layer, virtual resource layer, logic layer, presentation layer, application layer, network layer and user layer; The big data center module in the platform collects all business data through networks, sensors and other devices, and saves the business data in massive data storage devices; The software design part uses the multi feature fusion acquisition algorithm to collect student data, and completes the early warning of student performance through the performance early warning algorithm based on correlation analysis technology. The basic idea of the website is to provide teaching and learning materials and career planning services based on online video teaching platform. And on this basis, develop multiple plates, guided by interest, combined with Holland's career interest test, so that contemporary college students have a clearer understanding of their future career, so as to carry out targeted learning and planning.
Research on English Teaching Ability Evaluation Algorithm Based on Big Data Fuzzy k-means Clustering

This paper studies the evaluation algorithm of English teaching ability based on big data fuzzy k-means clustering, which is that the improvement of the evaluation method of students’ learning effect of English teaching ability evaluation can stimulate more students to improve their learning interest and motivation, promote students to consciously use scientific learning strategies for English learning and cultivate good learning habits. Moreover, the improvement of evaluation methods can enable more students to learn to evaluate themselves positively and optimistically, enjoy the learning process and develop good character. This paper presents an evaluation algorithm of the English educational capacity based on large data fuzzy k-means clustering. K means the most basic and most used method in classical analysis. Method to find classification centres and classification centres from classification data. His main advantage is simple and fast. When the results of the group are dense, the difference between groups is important, and the effect is the best. The algorithm has a relative scalability and an effect on a large scale of data. Using this method to evaluate English instructions, it has better information and analysis capabilities and improves the precision of teaching and application of instructions.
Using Whale Optimization Algorithm and Haze Level Information in a Model-Based Image Dehazing Algorithm

Single image dehazing has been a challenge in the field of image restoration and computer vision. Many model-based and non-model-based dehazing methods have been reported. This study focuses on a model-based algorithm. A popular model-based method is dark channel prior (DCP) which has attracted a lot of attention because of its simplicity and effectiveness. In DCP-based methods, the model parameters should be appropriately estimated for better performance. Previously, we found that appropriate scaling factors of model parameters helped dehazing performance and proposed an improved DCP (IDCP) method that uses heuristic scaling factors for the model parameters (atmospheric light and initial transmittance). With the IDCP, this paper presents an approach to find optimal scaling factors using the whale optimization algorithm (WOA) and haze level information. The WOA uses ground truth images as a reference in a fitness function to search the optimal scaling factors in the IDCP. The IDCP with the WOA was termed IDCP/WOA. It was observed that the performance of IDCP/WOA was significantly affected by hazy ground truth images. Thus, according to the haze level information, a hazy image discriminator was developed to exclude hazy ground truth images from the dataset used in the IDCP/WOA. To avoid using ground truth images in the application stage, hazy image clustering was presented to group hazy images and their corresponding optimal scaling factors obtained by the IDCP/WOA. Then, the average scaling factors for each haze level were found. The resulting dehazing algorithm was called optimized IDCP (OIDCP). Three datasets commonly used in the image dehazing field, the RESIDE, O-HAZE, and KeDeMa datasets, were used to justify the proposed OIDCP. Then a comparison was made between the OIDCP and five recent haze removal methods. On the RESIDE dataset, the OIDCP achieved a PSNR of 26.23 dB, which was better than IDCP by 0.81 dB, DCP by 8.03 dB, RRO by 5.28, AOD by 5.6 dB, and GCAN by 1.27 dB. On the O-HAZE dataset, the OIDCP had a PSNR of 19.53 dB, which was better than IDCP by 0.06 dB, DCP by 4.39 dB, RRO by 0.97 dB, AOD by 1.41 dB, and GCAN by 0.34 dB. On the KeDeMa dataset, the OIDCP obtained the best overall performance and gave dehazed images with stable visual quality. This suggests that the results of this study may benefit model-based dehazing algorithms.
Special Issue on Ensemble Learning and/or Explainability

This article will summarize the works published in a Special Issue of Algorithms, entitled “Ensemble Learning and/or Explainability”(https://www [...]
Variability of Energy Resources at a Diverse Scale

Analysing and characterizing the swing in solar insolation across space and time require a technique known as spatio-temporal and hyper-temporal variability analysis. Multiple measurements of a quantity provide different values. Spatial variability. The range measures spatial variability. Identifying regional fluctuations in solar insolation and their impacts on energy production may be helpful in specialized energy management systems. It's the study of unpredictability in science. In this portion, we looked at the techniques a researcher uses to examine a topic, including variability and reasoning. The researcher must know research and analytical methodologies. Researchers must know how to undertake variability analysis and employ research techniques. They must also know which techniques or processes are significant, what they mean and why. Researchers must grasp the assumptions behind certain approaches and the criteria for determining whether certain techniques and procedures are acceptable.
Sentiment Classification for Amazon Fine Foods Reviews Using Pyspark

The mechanical study of people’s opinions, sentiments, attitudes, and emotions as they are expressed through communication is known as sentiment analysis or opinion mining. This one has recently been among the most popular analytical themes in text mining and Natural Language Processing. Its widespread popularity is due to two factors: The first factor is opinions are fundamental to practically all human endeavors and are significant determinants of a person’s behavior. It has a wide range of applications. Second, it resolves several challenging analysis problems that were never even attempted. We employed Big Data Framework to solve the issue because traditional solutions cannot handle the exponential growth of Amazon’s food data. In this study, we investigate various approaches to sentiment analysis for large datasets of Amazon Fine Food reviews using the Apache Spark data processing engine and utilizing MLlib, the machine learning package for Apache Spark, three techniques—Linear SVC, Logistic Regression, and Naive Bayes—with more than 80% accuracy rates. We find that linear SVC outperforms NB and logistic regression. Through empirical analysis, we find that sentiment analysis through a big data framework is more efficient in computation time compared to sentiment analysis without the map-reduce framework.
Development of the HySpex hyperspectral drill core scanner: case study on exploration core from the Au-rich LaRonde-Penna volcanogenic massive sulfide deposit, Quebec, Canada

HySpex is presenting an integrated solution for hyperspectral drill core imaging. The system’s mineral mapping capabilities are presented in close cooperation with renowned academic and industrial partners through the Center to Advance the Science of Exploration to Reclamation in Mining (CASERM) led by the Colorado School of Mines and Virginia Tech. Utilizing HySpex cameras covering the spectral range between 400 and 2500 nm, the system is capable of scanning full core boxes in seconds. Using Prediktera’s new Breeze-GEO Software, real-time mineral mapping of the highest quality is achieved. Apart from different interactive qualitative and quantitative data analysis tools offered by Breeze, the platform includes the publicly available USGS Material Identification and Classification Algorithm (MICA) for mineral identification, as well as the Minimum Wavelength Mapping (MWL) algorithm. The scanner’s capabilities are demonstrated using drill cores from the LaRonde-Penna deposit. The deposit is located within the Archean Abitibi greenstone belt of Ontario and Quebec, Canada, which is home to numerous Volcanogenic Massive Sulfide (VMS) deposits. LaRonde-Penna contains an endowment of 71 Mt of ore grading on average 3.9 g/t Au and economic grades of Zn, Cu and Pd. Because the deposits have been studied extensively over the past decades, cores from the deposit open up a unique opportunity for research and development.
A Reinforcement Learning-based Sequence Generation Algorithm for Password Guessing

Human-generated passwords are naturally rich in structure, making them vulnerable to guessing attacks. Recently, people have employed strong deep neural networks to model and generate human-generated passwords, showing its great potential for efficient password guessing. However, the research on password guessing decoding algorithms, which is the other essential part of the sequence generation task, remains barely explored. In this paper, we provide an in-depth analysis of the two most widely used classes of sequence decoding algorithms, i.e., Beam Search and Sampling, and find out that a dynamically adjusted Temperature Sampling could be the most suitable one for massive password generation. However, it takes a lot of knowledge and practice even for a human expert to control the temperature parameters appropriately. Thus, we provide a Reinforcement learning-based Dynamic Temperature Sampling algorithm for massive password generation. We first train a Transformer-based password language model in an auto-regressive fashion. Next, we generate the passwords using Temperature Sampling in a batched manner. A neural Q-network is trained to adjust the temperature parameter automatically for each generation batch. A lower temperature value at the start batches allows the most common passwords to be generated rapidly. Then the temperature is gradually increased to generate more non-repetitive long-tail passwords. Experimental results demonstrate that our proposed method far outperforms baseline methods in terms of both generation speed and hit rate.
Privacy-preserving Collaborative Learning with Scalable Image Transformation and Autoencoder

Collaborative learning in which local clients jointly train a deep learning model by sharing parameters to the central- ized server has gained great popularity. However, recent works have shown that local private data can be leaked to the server by gradient sharing. In this paper, a privacy-preserving collaborative learning scheme is proposed to defend against gradient-based reconstruction attacks. The sensitive training images are firstly permutated by transformation with scalable block sizes. Then, features of permutated images are extracted by a classification- compliant autoencoder for meaningful representation of high- dimensional images and facilitating classification. The model accuracy constraint is incorporated in the training process to maintain decent classification accuracy. Experimental results demonstrate that the proposed scheme can achieve high privacy preservation with minimal impact on model accuracy.
Mining and Predicting Users Clickstream Patterns from Noisy Interleaving Clicks

With the recent advancement in technology and a vast amount of information available, research in pattern mining has started to attract more attention. Specifically, various techniques have been developed for clickstream mining, which is a specific type of sequential pattern mining, to discover the underlying patterns from the Internet user clickstream. Due to the complexity of clickstream patterns, many of the existing works applied sequential pattern algorithms to generate an exponential candidate space of patterns with respect to patterns letters. Further, those patterns were generated in a noiseless environment. To address this problem, we focus on a nonoverlapping clickstream pattern mining task with noisy interleaving clicks between the clickstream patterns letters. Additionally, we are interested in labeling the extracted patterns in the user browsing history. A modified suffix tree is proposed to extract those patterns with the exact occurrence in the user noisy database. Following this, we model the user browsing behavior via a Hidden Markov Model (HMM) to capture the dependencies between the extracted patterns and then predict the future clickstream patterns. Experimental results on both real-life and synthetic datasets show that our proposed algorithms outperform the state-of-the-art benchmarks in efficiency and prediction accuracy.
Incentive Mechanism Design For Federated Learning in Multi-access Edge Computing

Federated learning (FL) is a type of distributed machine learning in which mobile users can train data locally and send the results to the FL server to update the global model. However, the implementation of FL may be prevented by the self-fish nature of mobile users, as they need to contribute considerable data and computing resources for participating in the FL process. Therefore, it is of importance to design the incentive mechanism to motivate the users to join the FL. In this work, with explicit consideration of the impact of wireless transmission, we design an incentive scheme to facilitate the FL process by investigating interactions between the multi-access edge computing (MEC) server and mobile users in a MEC-based FL system. By using a two-stage Stackelberg game model, we explore the transmission power allocation of the users and reward policy of the MEC server, and then analyze the Stackelberg equilibrium. The simulation results show that our model is effective for different parameter settings and the utility of the MEC server can be increased significantly compared to the baseline.
A Simplified Machine Learning Approach to Classifying Individual Websites

We quantify the classification accuracy of Neural Networks (NNs) to specific websites using only the packet size and difference in inter-packet arrival time, which are easily observable via passive attackers in the network. Our flow classification work with NNs is unique in that we do not classify traffic by application type. Rather, we observe the accuracy of various NNs classifying specific web sites using HTTP traffic over TCP. We test a diverse set of neural network structures including a fully connected network (FCN), a convolutional neural network (CNN), a long short-term memory network (LSTM), and an autoencoder network (AE). We found that CNNs consistently had the highest accuracy, typically 80-90% when using 20 million packets as training data. We suspect that individual websites generate unique traffic patterns which are discoverable using NN techniques. Our work has important privacy implications. In particular, our work supports that both packet sizes and inter-packet timing must be randomized to obtain strong web browsing privacy. Many privacy preserving techniques, such as VPNs, will require additional enhancements.
DWT in P4: Periodicity Detection in the Data Plane

This paper presents a P4 implementation of the (1-D) Discrete Wavelet Transform (DWT) method. As a mathe-matical tool for analyzing signals such as packet-level traces, the DWT divides a given signal into different frequency components and analyzes each component with a resolution matched to its scale. We develop an efficient online algorithm that circumvents various limitations of existing P4-programmable data plane devices and performs the DWT decomposition entirely in the data plane. Our evaluation of a hardware implementation (i.e., Netronome NFP-4000 SmartNIC) of the algorithm shows that it results in only minimal throughput overhead (less than 1% for average-sized packets) and operates within constraints imposed by the limited available data plane resources. As an application, we use our lightweight P4 implementation of the DWT and describe a novel threshold-based approach for detecting periodic behavior in a signal in real-time, at line rate in the data plane (40 Gbps). We illustrate our approach with different examples of synthetic and real-world packet-level traffic traces that exhibit periodic patterns of either benign or malicious origins.
Sparsification and Optimization for Energy-Efficient Federated Learning in Wireless Edge Networks

Federated Learning (FL), as an effective decentral-ized approach, has attracted considerable attention in privacy-preserving applications for wireless edge networks. In practice, edge devices are typically limited by energy, memory, and computation capabilities. In addition, the communications be-tween the central server and edge devices are with constrained resources, e.g., power or bandwidth. In this paper, we propose a joint sparsification and optimization scheme to reduce the energy consumption in local training and data transmission. On the one hand, we introduce sparsification, leading to a large number of zero weights in sparse neural networks, to alleviate devices' computational burden and mitigate the data volume to be uploaded. To handle the non-smoothness incurred by sparsification, we develop an enhanced stochastic gradient descent algorithm to improve the learning performance. On the other hand, we optimize power, bandwidth, and learning parameters to avoid communication congestion and enable an energy-efficient transmission between the central server and edge devices. By collaboratively deploying the above two components, the numerical results show that the overall energy consumption in FL can be significantly reduced, compared to benchmark FL with fully-connected neural networks.
Storage-aware Joint User Scheduling and Spectrum Allocation for Federated Learning

Massive data drives the development of machine learning (ML) for a long time. However, at present, data is starting to hinder ML's development. The first reason is that the privacy of data is increasingly valued by the public. Therefore, Federated Learning (FL) has emerged, which realizes model training through distributed computing and centralized aggregation. Second, due to the popularity of FL, edge devices need to store all data, which may quickly occupy the entire storage space of edge devices, resulting in fatal errors. To address these challenges, we proposed a storage-aware joint user scheduling and spectrum allocation algorithm, named FedSUS, to reduce the storage stress of each device and guarantee traditional FL metrics, i.e., learning accuracy and training latency. First, a probabilistic framework is adopted for user scheduling. Second, we introduce a data influence evaluation method to FL and analyze its convergence. Based on this, two problems are formulated to tradeoff the storage resource, the influence of data, and the learning latency and to minimize the transmission latency, respectively. Then, the closed-form results to the above problems are both developed. Finally, FedSUS is validated by using a popular convolutional neural network (CNN) and datasets (CIFAR-10). And numerical results demonstrate that our algorithm can effectively reduce the local data size while keeping (even improving) the learning accuracy as compared with baseline.
Communication-Efficient Secure Aggregation for Federated Learning

Secure aggregation is a privacy-aware protocol for model aggregation in federated learning. A major challenge of conventional secure aggregation protocols is their large communication overhead. Towards addressing this challenge, in this work we propose the first gradient sparsification framework for communication-efficient secure aggregation, which allows aggregation of sparsified local gradients from a large number of users, without revealing the individual local gradient parameters in the clear. We provide the theoretical performance guarantees of the proposed framework in terms of the communication efficiency, resilience to user dropouts, and model convergence. We further evaluate the performance of our framework through large-scale experiments in a distributed network with up to 100 users, and demonstrate a significant reduction in the communication over-head compared to conventional secure aggregation benchmarks.