Reviewerly/exHarmony huggingface dataset:
Research on infrared sequence image denoising based on multi-frame averaging and improved bilateral filtering

Infrared imaging systems have been widely used in military and civil fields. However, the degradation of imaging quality is constantly affected by stripe noises. The traditional mean filtering, median filtering, Gaussian filtering, Wiener filtering and other algorithms have dependence on different noise images, and the image edge is blurred by denoising. The popular bilateral filter takes a lot of calculation due to a two-dimension way and floating point spatial proximity factor. In this paper, an infrared sequence image denoising method based on multi-frame averaging and improved bilateral filtering is introduced. An improved bilateral filter with an integer spatial proximity factor is designed, which is realized by one dimension filtering in horizontal and vertical directions. First, basal stripe in each frame image is removed by two-point correction, and the improved bilateral filter is used to smooth the noise and protect the edge of the image at the same time. Second, random noise is further removed by averaging multi-frame with a set of 25 images. The experimental results show that the proposed denoising method can effectively remove the noise and better maintain the edge structural information of the image.
Facial Expression Recognition and Beauty Health Management Based on Image Feature Analysis

Face recognition can convey a kind of intuitive and rich information to people. With the current Internet security problems becoming more and more prominent today, in order to better effectively guarantee the security of such digital images during Internet transmission, one of the most direct and effective solutions is to recognize the characteristics of such digital information on facial expressions. However, the customer service system of the beauty and health club itself is a customer service software system based on customer experience and marketing management as its core functions. This article discusses the main software design and system realization of the management platform of the beauty and health club customer information service system in the entire operation process from the application development trend of information software. Based on the theoretical research and application development of the current corporate customer business service management system of the beauty and health club, the basic concepts and operating methods of current corporate customers, business service information management, and business service customer management, as well as the corresponding corporate information customer management. The management technology and other issues are first briefly summarized in theory, and then based on the specific business development characteristics of the current beauty and health clubs, the main application functions and specific market requirements of the service company’s corporate customer service management system are analyzed in detail and introduced the related functions of using the software.
Exploiting auto-encoders for explaining black-box classifiers

Recent years have witnessed the rise of accurate but obscure classification models that hide the logic of their internal decision processes. In this paper, we present a framework to locally explain any type of black-box classifiers working on any data type through a rule-based model. In the literature already exists local explanation approaches able to accomplish this task. However, they suffer from a significant limitation that implies representing data as a binary vectors and constraining the local surrogate model to be trained on synthetic instances that are not representative of the real world. We overcome these deficiencies by using autoencoder-based approaches. The proposed framework first allows to generate synthetic instances in the latent feature space and learn a latent decision tree classifier. After that, it selects and decodes the synthetic instances respecting local decision rules. Independently from the data type under analysis, such synthetic instances belonging to different classes can unveil the reasons for the classification. Also, depending on the data type, they can be exploited to provide the most useful kind of explanation. Experiments show that the proposed framework advances the state-of-the-art towards a comprehensive and widely usable approach that is able to successfully guarantee various properties besides interpretability.
Pelatihan Pembuatan Google Form Sebagai Media Pembelajaran Jarak Jauh

This community service was carried out in October 2020 at SMP Negeri 5 Langsa, as a form of dedication by the service team in answering problems that often arise in distance learning, especially in the city of Langsa, the selection of SMP Negeri 5 Langsa as a forum for service this time is due to its very close location. with a dedicated campus which is also a priority in education development, especially in the city of Langsa. All teachers participated in this community service by sharing fields of study at SMP Negeri 5 Langsa. The steps taken by the first service team are conducting initial studies and field surveys, in order to analyze the problems that occur and then seek solutions so that the problems that occur can be minimized. The second stage was the implementation of the activity, to determine the success in this stage of the activity the servant gave a questionnaire using google form related to participant opinions regarding the benefits, advantages or disadvantages of using google form as a medium for giving online assignments. The third stage analyzes whether the implementation of the service is carried out effectively or not. The final results of the average assessment of the participants showed that 91.74% of participants agreed that the use of Google Forms as an effective online medium and of the 11 indicators given each showed that more than 85% of participants gave positive responses .
A Fault Localization Approach Based on BiRNN and Multi-Dimensional Features

Software fault localization is notoriously tedious and time-consuming. Developed rapidly, machine learning techniques have been adopted for fault localization by researchers. Most existing approaches use the test coverage information as feature input to the learning model, ignoring the limited ability of the single-dimensional features. The effectiveness of fault localization is not greatly improved. To overcome the limitation, we propose a fault localization approach based on Bidirectional Recurrent Neural Networks (BiRNNs) and multi-dimensional features. Our approach collects suspiciousness-based, text similarity-based and fault-proneness-based features from the traditional fault localization areas and software metrics. To evaluate our approach, the experiments have been studied on the real-fault benchmark Defects4J and seeded fault program NanoXML. The experimental results show that our approach effectively improves fault localization accuracy.
An Assistant Teaching System of English Creative Writing Based on Semantic Mining

To enhance students’ English writing ability, English creative writing has become a common course offered by colleges and universities. English reference is an indispensable tool for creative English writing. How to choose appropriate English references is an important guarantee to complete good work. Therefore, this paper proposes a Deep Semantic Mining-based Recommendation (DSMR) algorithm for English writing reference selection and recommendation to assist the completion of high-quality English creative writing works. The model can extract user features and document attributes more accurately by deeply mining semantic information of literature content and user needs, so as to achieve more accurate recommendations. First, the Bidirectional Encoder Representation from Transformers (BERT) pretraining model is adopted to process literature content and user requirement documents. Through in-depth mining of user characteristics and literature attributes, the problems of data sparsity and cold start of items are effectively alleviated. Then, the forward long short-term memory (LSTM) network was used to focus on the changes in user preferences over time, resulting in more accurate recommendations. The experimental results show that the use of heterogeneous information can significantly improve the recommendation performance, and the additional use of user attribute information can also improve the recommendation performance. Compared with other benchmark models, the recommendation quality of this model is greatly improved.
Linguistic and semantic layers for emergency plans

Plans for emergency response are complex collaborations in which actors take roles and responsibilities. They are generally long textual documents containing practical instructions, in natural language, for hazard responses. A more rigorous structured-text would be useful for a twofold audience. From one side, it can be useful for quickly understanding the plan and on the other side it can be used to improve the modelling phase and delivering an automatic emergency-support system. This paper proposes an approach, conceived for humans, for converting a free-form plan document into a structured version of the same document. The approach is based on a linguistic and semantic analysis that are strictly correlated and materialize in a metamodel. It contains the essential elements of an emergency plan, and it aids in interpreting the input document also reducing inconsistencies, redundancies, and ambiguities.
DiBB

DiBB (for Distributing Black-Box) is a meta-algorithm and framework that addresses the decades-old scalability issue of Black-Box Optimization (BBO), including Evolutionary Computation. Algorithmically, it does so by creating out-of-the-box a Partially Separable (PS) version of any existing black-box algorithm. This is done by leveraging expert knowledge about the task at hand to define blocks of parameters expected to have significant correlation, such as weights entering a same neuron/layer in a neuroevolution application. DiBB distributes the computation to a set of machines without further customization, while still retaining the advanced features of the underlying BBO algorithm, such as scale invariance and step-size adaptation, which are typically lost in recent distributed ES implementations. This is achieved by instantiating a separate instance of the underlying base algorithm for each block, running on a dedicated machine, with DiBB handling communication and constructing complete individuals for evaluation on the original task. DiBB's performance scales constantly with the number of parameter-blocks defined, which should allow for unprecedented applications on large clusters. Our reference implementation (Python, on GitHub and PyPI) demonstrates a 5x speed-up on COCO/BBOB using our new PS-CMA-ES. We also showcase a neuroevolution application (11 590 weights) on the PyBullet Walker2D with our new PS-LM-MA-ES.
Association features of smote and rose for drug addiction relapse risk

Drug addiction is a major problem in many countries, with rehabilitation and treatment clinics playing a critical role in aiding drug addicts’ recovery. Thus, the issue requires an effective automated system that can predict the likelihood of relapse in addicts. The system uses a dataset to train and test a machine learning algorithm for the automatic classification of drug patients. Nonetheless, the problem in training a machine learning classifier includes imbalanced classes, which can increase problems of overfitting and hinder generalization performance. The study proposed an association feature rule to combine the two most common over-sampling techniques: Synthetic Minority Over-Sampling Technique (SMOTE) and Random Over-Sampling Technique (ROSE) to balance the number of samples between classes, extending the problem feature space. Accordingly, the Random Forest algorithm is employed to classify new instances. The cross-experiments results on the Validi drug relapse dataset showed that the proposed combination approach outperforms the popular over-sampling and under-sampling approaches, indicating that the selected set of association features help the relapse classification tasks.
Validating new Automated Computer Vision workflows to traditional Automated Machine Learning

This paper presents some experiments to validate the design of an Automated Computer Vision (AutoCV) library for applications in scientific image understanding. AutoCV attempts to define a search space of algorithms used in common image analysis workflows and then uses a fitness function to automatically select individual algorithmic workflows for a given problem. The final goal is a semi-automated system that can assist researchers in finding specific computer vision algorithms that work for their specific research questions. As an example of this method the researchers have built the SEE-Insight tool which uses genetic algorithms to search for image analysis workflows. This tool has been used to implement an image segmentation workflow (SEE-Segment) and is being updated and modified to work with other image analysis workflows such as anchor point detection and counting. This work is motivated by analogous work being done in Automated Machine Learning (AutoML). As a way to validate the approach, this paper uses the SEE-Insight tool to recreate an AutoML solution (called SEE-Classify) and compares results to an existing AutoML solution (TPOT). As expected the existing AutoML tool worked better than the prototype SEE-Classify tool. However, the goal of this work was to learn from these well-established tools and possibly identify one of them that could be modified as a mature replacement for the core SEE-Insight search algorithm. Although this drop-in replacement was not found, reproducing the AutoML experiments in the SEE-Insight framework provided quite a few insights into best practices for moving forward with this research.
Cybersecurity and Research are not a Dichotomy

Cybersecurity and research do not have to be opposed to each other. With increasing cyberattacks, it is more important than ever for cybersecurity and research to corporate. The authors describe how Research Liaisons and Information Assurance: Michigan Medicine (IA:MM) collaborate at Michigan Medicine, an academic medical center subject to strict HIPAA controls and frequent risk assessments. IA:MM provides its own Liaison to work with the Research Liaisons to better understand security process and guide researchers through the process. IA:MM has developed formal risk decision processes and informal engagements with the CISO to provide risk-based cybersecurity instead of controls-based. This collaboration has helped develop mitigating procedures for researchers when standard controls are not feasible.
PEARC ’22: Practice and Experience in Advanced Research Computing Proceedings

The growing prevalence of online hate speech is concerning, given the massive growth of online platforms. Hate speech is defined as language that attacks, humiliates, or incites violence against specific groups. According to research, there is a link between online hate speech and real-world crimes, as well as victims’ deteriorating mental health. To combat the online prevalence of abusive speech, hate speech detection models based on machine learning and natural language processing are being developed to automatically detect the toxicity of online content. However, current models tend to mislabel African American English (AAE) text as hate speech at a significantly higher rate than texts written in Standard American English (SAE). To confirm the existence of systematic racism within these models, I evaluate a logical regression model and a BERT model. Then, I determine the efficacy of the bias reduction method for the BERT model and the correlation between model performance and reduced bias.
Spectral-spatial outlier filter for image matching

Feature-based image matching is often contaminated by some mismatches due to the limited representation of descriptors. Existing two-stage mismatch removal filters usually select some seed points first and then remove outliers according to the consistency in neighborhoods. However, the filter's performance is directly influenced by the selection of effective seed points. In this paper, we design an elegant Spectral-spatial Outlier Filter (SOF) to harvest high-accuracy image matching. Specifically, we first calculate eigenvectors of Laplacian matrix from the joint image graph as feature descriptors in the spectral domain to select more reasonable seed points, and then these points are fed into the local a fine verification in the spatial domain in the second stage to effectively remove outliers. Experimental results on challenging datasets demonstrate that the proposed filter further improves the precision of image matching, and steadily outperforms other state-of-the-art methods.
A Knowledge-Oriented Recommendation System for Machine Learning Algorithm Finding and Data Processing

With the development of the Big Data, data analysis technology has been actively developed, and now it is used in various subject fields. More and more non-computer professional researchers use machine learning algorithms in their work. Unfortunately, datasets can be messy and knowledge cannot be directly extracted, which is why they need preprocessing. Because of the diversity of the algorithms, it is difficult for researchers to find the most suitable algorithm. Most of them choose algorithms through their intuition. The result is often unsatisfactory. Therefore, this article proposes a recommendation system for data processing. This system consists of an ontology subsystem and an estimation subsystem. Ontology technology is used to represent machine learning algorithm taxonomy, and information-theoretic based criteria are used to form recommendations. This system helps users to apply data processing algorithms without specific knowledge from the data science field.
Classification and Machine Learning

The manual classification of a large amount of textual materials are very costly in time and personnel. For this reason, a lot of research has been devoted to the problem of automatic classification and work on the subject dates from 1960. A lot of text classification software has appeared. For some tasks, automatic classifiers perform almost as well as humans, but for others, the gap is still large. These systems are directly related to machine learning. It aims to achieve tasks normally affordable only by humans. There are generally two types of learning: learning “by heart,” which consists of storing information as is, and learning generalization, where we learn from examples. In this chapter, the authors address the classification concept in detail and how to solve different classification problems using different machine learning techniques.
Web Usage Pattern Detection Using Cohesive Markov Model With Apriori Algorithm

Web server maintains the essential user log files, recording every request to it. Web log is a record of events which includes all the user details from the time the web visitor initiated the session to the end of the session. The web usage pattern discovery to identify different states of the user access behavior on web. The design of web recommender system using a context-aware Cohesive Markov Model and Apriori clustering is proposed. The prediction rate of proposed algorithm is higher than conventional Markov model.
Precision measurement method based on optimized combination of pixel-binning cameras

Traditional binocular stereo vision measurement system based on high resolution area array camera has many defects, such as limited acquisition speed, insufficient sensitivity under high frame rate and overlarge data volume. To overcome these defects, this paper proposes a precision vision measurement method which is based on the optimized combination of pixelbinning cameras. This method transforms the traditional area array camera into an equivalent narrow area array (ENAA) camera through single-dimensional binning. Then, multiple ENAA cameras are used to achieve precise 3D measurement of target points through reasonable combination. However, single-dimensional binning will reduce the image resolution in one direction, while the image resolution in another direction (maximum image resolution) remains unchanged. Therefore, this paper studies the typical spatial layouts of two even three ENAA cameras and analysis their measurement errors. Finally, an optimal spatial layout of three ENAA cameras was obtained, and it can reach the same level of measurement accuracy as the binocular stereo vision measurement system based on area array camera. Comparative experiments verify the effectiveness and measurement accuracy of the above method, which can reduce the data amount to 3/8 and increase the acquisition rate by 4 times while ensuring the measurement accuracy.
Knowledge Generation Using Sentiment Classification Involving Machine Learning on E-Commerce

Sentiment analysis manages the computational treatment of conclusion, notion, and content subjectivity. In this article, three sentiment classes such as positive, negative and neutral emotions have been demonstrated by appropriate features from raw unstructured data followed by data preprocessing steps. Applying best in class social analytics methodology to examine the sentiments embedded with purchaser remarks, encourages both producer and individual customers. Machine learning methods such as Naïve Bayes, maximum entropy classification, Deep Neural Networks were used upon the data, extracted from some websites such as Samsung and Apple for sentiment classification. In the online business arena, the application of sentiment classification explores a great opportunity. The subsidy of such an investigation is that associations can apply the proposed social examination framework to exploit the entire social information on the web and therefore improve their proper blueprint promoting strategies corresponding business.
A Unified Framework for Joint Moving Object Detection and Tracking in the Sky and Underwater

The ability to detect and locate the moving object in a video is a fundamental procedure in applications of computer vision. However, these tracking methods still face some challenges, and are contradictory among different tasks. In this paper, a unified framework for joint moving object detection and tracking in the sky and underwater is proposed. This framework meets the requirements of two real applications: (i) tracking unmanned aerial vehicle (UAV) in the sky; and (ii) tracking unmanned underwater vehicle (UUV) in water. It consists of three key steps: (i) moving object detection by pixel classification; (ii) data association by blob detection; and (iii) object tracking by efficient convolution operator. Finally, analysis on the accuracy of the proposed framework is provided. Experimental results on real-world datasets and object tracking benchmark (OTB) demonstrate the advantage of the tracking method compared with some state-of-the-art trackers, in terms of accuracy and robustness. In addition, to the best of the authors’ knowledge, there is no previously published work for joint moving target detection and tracking in the sky and underwater.