Reviewerly/exHarmony huggingface dataset:
Deep Attentive Skip Connection Model and Gaussian Priors based Visual Saliency Prediction

Predicting human visual attention mechanisms using deep learning has recently attracted a great amount of research interest in the computer field. The visual system of humans serves as a filter to guide greater attention to the most attractive and interesting objects for subsequent processing. With the recent widespread use of deep learning algorithms, the problem of predicting saliency has seen a tremendous improvement. In this study, a saliency prediction scheme is proposed that includes an Attentive Convolutional Long Short-Term Memory network (Attentive ConvLSTM) that repeatedly refines saliency features by focusing on key spatial locations. The input picture is first utilised to extract a series of feature maps using VGG16, a dilated convolutional network. After that, to improve predictions Attentive ConvLSTM architecture is proposed in the form of Skip Connection with VGG16 feature map which successively focuses on different spatial locations of a stack of features. Then the predictions are merged with various learned priors to illustrate the tendency of humans to fix the centre region of the image. The success of this proposed Deep Attentive Skip Connection with Gaussian Priors Model (DASC-GP-Model) is evaluated on MIT saliency benchmark dataset and this model achieves 90.97% of accuracy. From the results, it is observed that this proposed network can predict strong saliency values on individuals, things, faces, and other important cues and also it creates virtuous saliency maps.
Implementation and Performance Assessment of Wavelet Prefiltered Platform Tilt Computation Using Low-cost MEMS IMU

MEMS sensors are becoming ubiquitous. Their applications range from navigation solutions in automated driving and personal health monitoring as wearable health devices to biomedical applications. However, their inherent high noise levels limit their use in high-accuracy applications. The scientific community explores many methods to overcome this limitation. Often a trade-off between noise levels, complexity, and bandwidth is encountered. In this paper, MEMS IMU data is denoised using wavelet-based prefiltering. This method retains the high dynamics content of the signal as well. The paper presents the algorithm of Wavelet transform and threshold parameters. We also perform the comparison with traditional methods by defining the key performance indicators (KPIs). Comparison carried out on a composite simulated signal that mimics the real-world signal and original MEMS IMU signal. Further, an actual use case is presented, viz., platform tilt computation, as part of the static alignment process in Inertial Navigation. Through extensive simulations, we establish the effectiveness of denoising sensor data using wavelet packet transform.
Financial Data Privacy Protection Framework Construction Based on Blockchain Technology

In the era of big data, the traditional centralized data processing mode is not conducive to data security, data sharing, the reduction of data transaction cost and the full mining of data value. The unique data structure, distributed storage and consensus mechanism of blockchain technology have unique advantages in ensuring data security and sharing. However, the openness and distributed storage of Blockchain also make data privacy protection become a difficult problem. Specific to the financial field, the disclosure of financial data privacy will not only concern the privacy of users, the reputation of financial industry, but also concern China's financial security. Based on this, this paper takes Blockchain as the underlying technology of financial data application to discuss the privacy protection of financial data from the perspectives of technology and regulation. Finally, this paper constructs a financial data privacy protection framework of “Blockchain+privacy computing technology+regulation” in order to create a good dynamic environment for the security, sharing and privacy protection of financial data.
Diagnosis of power operation and maintenance records based on pre-training model and prompt learning

The operation and maintenance records of power equipment contain abundant historical operation state information of equipment. However, due to the characteristics of multi ambiguity, difficult to segment ambiguity and multi noise, this paper proposes a two-stage model for the text diagnosis of power equipment. First, the large-scale pre-training model is trained based on the massive text, and then the pre-training language model is fine-tuned by the prompt technology for equipment diagnosis. The proposed solution is assessed through experiments and the numerical results demonstrate that the proposed solution can achieve about 20% improvement over the traditional method.
Accurate Object Tracking by Utilizing Diverse Prior Information

Siamese trackers draw continuous attention in the object tracking community due to their proper balance between performance and inference speed. Despite that, it remains unclear how to effectively exploit the target appearance cues and motion cues involved in videos to improve trackers’ performance. To address this problem, we develop a Siamese network with diverse prior information integrated, namely DPINet, by extending two novel blocks to a powerful anchor-free Siamese network. First, we design a channel- and space-aware feature enhancement (CSE) block to highlight target-specific feature weights in two aspects (channel and spatial dimensions). It is devoted to making full use of the target cues in the initial frame by considering them as guidances, in which way target-related representation in feature maps can be improved. It also facilitates the interplay between two input branches. Second, we advance a cross-correlation block with multi-dimensional information fusion (MDI-XCorr). In this block, target motion cues within adjacent frames can be mined and treated as supervisions to refine the response map in the current frame during inference. Hence, both tracking quality and stabilization can be enhanced. Evaluations on four popular benchmarks are conducted, showing that DPINet achieves 0.702 (AUC), 0.474 (EAO), 0.336 (EAO), 0.613 (AO), and 0.527 (AUC) on OTB100, VOT2018, VOT2019, GOT-10k, and LaSOT, respectively.
Synthesis Ensemble Oversampling and Ensemble Tree-Based Machine Learning for Class Imbalance Problem in Breast Cancer Diagnosis

The Wisconsin Breast Cancer Database dataset describes the imbalanced class. The imbalanced class will produce accuracy that only favors the majority class but not the minority class. Several ensemble oversampling methods are SMOTE and Random Over Sampling. Meanwhile, the tree-based machine learning ensemble used is Random Forest, Adaptive Boosting, and eXtreme Gradient Boosting. At the level 1 ensemble stage, one of the ensemble models with the best performance will be selected as input for the level 2 ensemble process. The level 2 ensemble is a boosting ensemble, where the results of the best ensemble model chosen at the level 1 ensemble will be used as the base model for boosting the XGBoost algorithm. The results were tested with 10 Fold Cross Validation of 0.981, Accuracy 0.987, Recall 0.980 and Precision 0.982. The performance of our proposed framework outperforms several recent classification studies in the breast cancer domain.
Exploration and Practice of Project-driven Smart Teaching Model in the Context of Emerging Technologies

In order to cultivate excellent university graduates with strong application ability and innovation spirit, with the background of new engineering construction and engineering certification as the opportunity, taking social demand as the guide and combining the type of application-oriented schools, taking the teaching of Computer Network course of computer-related majors as an example, fully using the project-driven teaching method, the “Internet+” environment With the help of emerging technologies such as cloud computing and big data, we adopt the model of diversified intelligent teaching, comprehensive teacher assessment and diversified student assessment to implement the fundamental task of establishing moral education, cultivate outstanding engineers who meet the needs of society and improve students' creativity and competitiveness in the employment process. The new form of project-driven intelligent teaching system is explored in the context of emerging technologies, with a view to providing reference and learning for the reform and innovation of related teaching in domestic higher education institutions.
College Student Employment Recommendation Algorithm Based on Convolutional Neural Network

The employment of graduates is one of the important evaluation indicators of the quality of running a school. It monitors the employment of graduates in real time, improves employment guidance, improves the quality of employment, and delivers qualified talents to the society. Accelerating school development plays a vital role. The purpose of this paper is to study the research and design of college students' employment recommendation algorithm based on convolutional neural network. The reasons for studying employment recommendation algorithms and their functions and advantages are introduced. Then, the research progress of employment recommendation is expounded, and the current problems and challenges faced by the employment recommendation algorithm are introduced. The processed text data is vectorized using the knowledge distillation-based job corpus model proposed in this paper. Then use the fusion algorithm based on CNN and BILSTM to locate the occupation category on the vectorized data. Finally, the cosine similarity algorithm is used to recommend personalized jobs for college talents. The experimental results show that the algorithm has a recall rate of 97%, which can effectively solve the problems of current college graduates' employment confusion and difficult positioning.
Optimal Design of FIR Filter Based on Improved Artificial Bee Colony Algorithm

The optimal design of FIR filter is very important, and using artificial bee colony algorithm can optimize fir parameters, but it also has the disadvantages of slow convergence speed and easy to fall into local optimum. Therefore, an improved artificial bee colony algorithm is proposed in the paper, which introduced the random disturbance term of chi-square distribution and convergence operator. The improved algorithm is applied to the parameter optimization design of low-pass and band-pass FIR filters, and the design effect is remarkable, especially the filter has small ripple in the passband and stopband, flat amplitude, and good attenuation characteristics.
Machine Learning Based Approach for Breast Cancer Detection

Breast cancer is one of the deadliest diseases faced by many women in the world. This cancer is more common in women than men. Breast cancer is the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> dangerous cancer suffered by women in the world. The mortality rate is very high. 2.3 million women already in the list of diagnosed with breast cancer and death rate is also increased by 685 000 deaths throughout world according to the reports published in 2020 by WHO. Over past 5 years it is observed that a huge number breast cancer affected women are predicted of about 7.8 million. Hence it is a genuine issue for the women and their health. This cancer can’t be cure at final stage. But the cancer normally predicted at 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> or 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> stage by physicians. It is better when recognized it at an early stage. Due to advancement in computational algorithms, there is a possibility of prediction model of breast cancer. This work is mainly focusing on developing prediction of breast cancer using classification algorithms. Among all the classification algorithm used such as K NN, Logistic Regression, Naive Bayes, SVM, Decision Tress and Random Forest. Logistic Regression has outperformed with the 98.74%.
The Application of Information Technology in Discrete Mathematics Teaching

With the continuous development of information technology, our country education informationization enterprise has achieved unprecedented fast development, informationization to education reform and development push function significantly increased. Discrete mathematics is a professional compulsory course for computer and its related majors. Its research objects are consistent with the objects handled by computers, and it plays a very important role in the teaching of computer and its related majors. However, discrete mathematics involves many knowledge points, students are relatively more difficult to learn, learning interest is not high. In order to solve the problems in the teaching process of discrete mathematics, this paper explains the application of information technology in the teaching of discrete mathematics, and designs an online examination system. The system recommends the questions that can attract students to think and answer carefully through the recommendation algorithm, which greatly improves the teaching effect of discrete mathematics and students' learning enthusiasm.
Interactive Teaching Mode Based on Big Data in Blended English Teaching

In the era of big data (BD), with the development of mobile communication networks, a brand-new teaching mode, namely interactive teaching mode, is provided for various industries. This new teaching method can take the advantage of modern digital resources and multimedia tools for information sharing and communication. Therefore, in the current social development situation, there is an urgent need to change educational concepts and carry out reforms and innovations. In order to better adapt to the demand trend of higher requirements for compound talents in the new era, more comprehensive and internationalization, the application of interactive teaching mode in mixed English teaching is very necessary. This article uses QSs and data analysis methods to gain a more comprehensive understanding of the application of interactive teaching mode in blended English teaching and the acceptance of this teaching mode by students. According to the results of the QS, most of the interviewees are able to accept this teaching mode and have a liking attitude, while only a few do not like it.
Learning Maximum Margin Channel Decoders

The problem of learning a channel decoder is considered for two channel models. The first model is an additive noise channel whose noise distribution is unknown and nonparametric. The learner is provided with a fixed codebook and a dataset comprised of independent samples of the noise, and is required to select a precision matrix for a nearest neighbor decoder in terms of the Mahalanobis distance. The second model is a non-linear channel with additive white Gaussian noise and unknown channel transformation. The learner is provided with a fixed codebook and a dataset comprised of independent input-output samples of the channel, and is required to select a matrix for a nearest neighbor decoder with a linear kernel. For both models, the objective of maximizing the margin of the decoder is addressed. Accordingly, for each channel model, a regularized loss minimization problem with a codebook-related regularization term and hinge-like loss function is developed, which is inspired by the support vector machine paradigm for classification problems. Expected generalization error bounds for the error probability loss function are provided for both models, under optimal choice of the regularization parameter. For the additive noise channel, a theoretical guidance for choosing the training signal-to-noise ratio is proposed based on this bound. In addition, for the non-linear channel, a high probability uniform generalization error bound is provided for the hypothesis class. For each channel, a stochastic sub-gradient descent algorithm for solving the regularized loss minimization problem is proposed, and an optimization error bound is stated. The performance of the proposed algorithms is demonstrated through several examples.
Improving Learner's Comprehension Using Entailment-Based Question Generation

In recent decades, artificial intelligence has made significant contributions to education. With the rise of online learning, most tedious tasks have been transferred from human hands to machines and programs. Both human and machine intelligence require good questioning abilities. To improve learning efficiency, asking questions on the reading material is an effective approach. However, the learners' reading comprehension may diminish if the questions are similar to the original sentence. In this study, a question generation method based on textual entailment is proposed. A question is constructed from the inferred text of the original sentence to help readers improve their reading comprehension. As a result, in order to answer the questions, learners must comprehend textual entailment. The proposed system first generates the entailment for the sentence to which the question should be generated, then generates the question for the generated entailed sentence. Furthermore, the findings are evaluated based on lexical and semantic similarity to ensure that the generated questions are semantically consistent.
A Comparison Study of Graph Neural Network and Support Vector Machine

A variety of issues, including classification, link prediction, and graph clustering, have been solved using graph neural network (GNN), an efficient method for handling non-Euclidean structural data. Another effective and reliable mathematical tool for classification and regression applications is support vector machine (SVM). We hope that this paper will help readers gain a better knowledge of the latest developments in graph neural networks and how they are used in a variety of fields. We also describe current research on using support vector machines for prediction and classification problems. Following that, a comparison between SVM and GNN is made, and the results are discussed.
Frequent and High Utility Itemsets Mining Based on Bi-Objective Evolutionary Algorithm with An Improved Mutation Strategy

Frequent and high utility itemsets mining (FHUIM) is one of the important tasks in pattern mining. In order to solve the exponential search space and parameter setting problems that traditional HUIM algorithms encountered, the task of FHUIM was reformulated as a bi-objective problem that can be solved by multi-objective evolutionary algorithms (MOEAs). However, the search efficiency of the MOEAs may become lower when the total distinct items, the number of transactions, and the average length of transactions in the database are larger. To further improve the efficiency of MOEAs for FHUIM, we proposed FHUIM based on bi-objective evolutionary algorithm with an improved mutation strategy (FHUIM-BOEA-IMS). In FHUIM-BOEA-IMS, an improved mutation strategy is proposed to make the items with higher support and utility more likely to be saved in population, by which the FHUIs are more likely to be searched. The results on four popular datasets show that the proposed FHUIM-BOEA-IMS has better performance than the compared baseline in the task of FHUIM in terms of the convergence and final solutions.
Generative Image Steganography Scheme Based on Deep Learning

The popularity of the Internet and the development of multimedia technology have made it easier to store, modify, and share digital multimedia information (text, audio, video, etc). Because multimedia data contains some personal information, if this information is intercepted and stolen by an illegal third party, resulting in the public transmission of the information on the Internet, then the personal contact between the communicating parties will no longer be private. Therefore, how to ensure communication privacy and information security has always been an important issue that is difficult to get rid of in the development of the Internet. This paper studies the generative image steganography scheme based on deep learning, understands the related theories of image steganography and deep learning based on the literature, and then designs the generative image steganography scheme based on deep learning, and the designed scheme is tested, and the test results show that the steganography scheme designed in this paper has better performance in accuracy. Compared with the traditional scheme, the difference between the two is up to 4%.
VLSI and AES based IoT security by Modified Random S- Box Generation

The next phase of the digital revolution is the Internet of Things (IoT), which refers to the scenario in which billions of people and objects are interconnected to permit the flow of enormous volumes of information from many locations. This development is due to the extraordinary expansion in Integrated circuit and sensor technology. IoT’s data transit phases have some security issues with data confidentiality. Along with some benefits and its high usage, some complexity issues arise in form of numerous forms. Thus, a security system is suggested in this research paper that tackles the aforementioned difficulties in order to improve reliable information transmission in a smart IoT scenario with low power consumption, throughout information sending among an IoT system and a cloud server using revised Advance Encryption Standard with random S- Box which is generated by various (nine) irreducible polynomial GF (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> ) in finite field techniques. In the revised S- Box generation, this research aims to optimize the VLSI circuit design of the SubBytes and inverse SubBytes procedures. To achieve this, all of the building blocks in the modified random S-box transformation are done by finite field arithmetic. For further improvement of the area performance, a blended design of modified S-boxes and inverse S-boxes is suggested where on each occasion a different irreducible polynomial is utilized in the finite field of GF (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> ) and this irreducible polynomial and secret key is sent to the receiver. As a result, a random S-Box is formed and thereby the algorithm's security is improved.
Exploring Backlinks Profile in Defining Metrics for Enhancing University Websites Visibility Ranking

Webometrics Ranking focuses on the Open Access Initiative, where the web content impact/visibility category has the greatest weight. As webometrics committee does not publish the exact formula on visibility ranking calculation, authors conducted an in-depth exploration of the factors that affect the visibility assessment. Authors crawl data on the best three university in the world, best three and three lowest visibility ranking in Indonesia using Ahrefs tools. The exploration results found that universities with high DR and UR also have high visibility ranks. The backlinks are distributed quite a lot and consistently in the URL rating of 21–90 (for the best three visibility rank in Indonesia's and world's universities) while the three lowest has a URL Rating distribution of 11–20. On the other hand, most of the referring domains that refer to Indonesian universities are carried out by the press channels of Tirto and Tribunnews.