Reviewerly/exHarmony huggingface dataset:
Gaussian low‐pass channel attention convolution network for RF fingerprinting

Abstract Radio frequency (RF) fingerprinting is a challenging and important technique for individual identification of wireless devices. Recent work has applied deep learning‐based classifiers to ADS‐B signals without missing aircraft ID information. However, traditional methods are not very effective in achieving high accuracy for deep learning models to recognize RF signals. In this letter, a Gaussian low‐pass channel attention convolution network, which uses a Gaussian low‐pass channel attention module (GLCAM) to extract fingerprint features with low frequency. Specifically, in GLCAM, a frequency‐convolutional global average pooling module is designed to help the channel attention mechanism learn channel weights in the frequency domain. Experimental results on large‐scale real‐world ADS‐B signal datasets show that the method can achieve an accuracy of 92.08%, which is 6.21% higher than convolutional neural networks.
Use and acceptance of voice assistants among people with aphasia in Germany

Aphasia is an acquired disorder that affects the ability to communicate. The increasing availability of voice assistants (such as Amazon Alexa or Google Assistant) provides new opportunities to support people with aphasia in a variety of tasks, from everyday communication to speech and language therapy exercises. To ensure accessibility and acceptance, it is important to involve people with aphasia in the development process. Using the Unified Theory of Acceptance and Use of Technology (UTAUT2) as a theoretical framework, this study examines their willingness to use voice assistants and explores which potential applications they consider useful for participation in social and cultural contexts. These questions were addressed through a survey. Eight people with aphasia took part in the study. Although the sample size does not allow for statistical analysis, the results provide valuable insights for further research. Most of the participants showed a general interest in using voice assistants, two of them were already users. The presence of physical limitations motivates the use of speech-based technology. Participants who already used voice assistants saw them as a practical support in everyday life, while non-users had lower expectations in this respect. Social influence was found to play an important role. Participants' perceptions of privacy and data security issues varied and do not allow for generalization. Finally, some participants showed a preference for communication support applications (e.g., word finding, sentence formation, grammar support) over applications used for therapy exercises such as word training.
MLNet: a multi-level multimodal named entity recognition architecture

In the field of human-computer interaction, accurate identification of talking objects can help robots to accomplish subsequent tasks such as decision-making or recommendation; therefore, object determination is of great interest as a pre-requisite task. Whether it is named entity recognition (NER) in natural language processing (NLP) work or object detection (OD) task in the computer vision (CV) field, the essence is to achieve object recognition. Currently, multimodal approaches are widely used in basic image recognition and natural language processing tasks. This multimodal architecture can perform entity recognition tasks more accurately, but when faced with short texts and images containing more noise, we find that there is still room for optimization in the image-text-based multimodal named entity recognition (MNER) architecture. In this study, we propose a new multi-level multimodal named entity recognition architecture, which is a network capable of extracting useful visual information for boosting semantic understanding and subsequently improving entity identification efficacy. Specifically, we first performed image and text encoding separately and then built a symmetric neural network architecture based on Transformer for multimodal feature fusion. We utilized a gating mechanism to filter visual information that is significantly related to the textual content, in order to enhance text understanding and achieve semantic disambiguation. Furthermore, we incorporated character-level vector encoding to reduce text noise. Finally, we employed Conditional Random Fields for label classification task. Experiments on the Twitter dataset show that our model works to increase the accuracy of the MNER task.
Feature Selection From MagFace Face Recognition Model With Optimization Algorithms

In recent years, many studies have been carried out in the field of artificial intelligence in the literature with the development of equipment. Face recognition algorithms have an important place among these developments. Among the face recognition algorithms, the most successful ones are usually deep learning approaches. Models such as SphereFace, CosFace, ArcFace, and MagFace are important deep learning models in the literature. Despite their success, deep learning models are often computationally costly. Therefore, advanced methods are needed to reduce the computational load for these models. One of the most valid methods for this is to choose the most valuable one among embedding features for face recognition. Thus, cost can be reduced, and accuracy values can be increased even more. In this study, the most valuable of the 512 embedded features in the MagFace model was tried to be obtained by using PSO, GA, SCA, and DE optimization algorithms. As a result, accuracy values of 99.83%, 98.57%, and 98.65% were reached for 193, 252, and 280 features selected in the LFW, CFP, and AGEDB datasets, respectively.
Infrared image enhancement algorithm based on improved wavelet threshold function and weighted guided filtering

Abstract Aiming at the problems such as the poor visual effect of infrared thermal imaging under low illumination conditions, an enhanced infrared image recognition method based on wavelet decomposition is proposed for this paper. First, according to wavelet decomposition, the infrared image is divided into a low-frequency sub-band (LFS) and high-frequency sub-band (HFS) in each orientation, and HFS in each orientation is denoised using a modified wavelet threshold function to strengthen the denoising effect of the infrared image; Second, the multi-scale Retinex (MSR) algorithm based on weighted guided filtering (WGIF) is used to evaluate the component of illumination in the basic layer of the LFS, and the WGIF-segmented detail layer images are fused with the MSR processed ones to effectively highlight the texture details of the LFS; Finally, the LFS and the HFS are wavelets reconstructed (WR) to obtain the infrared enhanced images. This paper applies the algorithm to a low-resolution (resolution 32x32 pixels) infrared thermal imaging module, and the results of combining subjective and objective evaluation indexes show that the overall property index of the algorithm in this paper is superior to other contrasting algorithms and can effectively improve the infrared image quality.
Wavelet Based Solutions to the Poisson and the Helmholtz Equations on the n-Dimensional Unit Sphere

Abstract We present a method of solving partial differential equations on the n -dimensional unit sphere using methods based on the continuous wavelet transform derived from approximate identities. We give an explicit analytical solution to the Poisson equation and to the Helmholtz equations. For the first one and for some special values of the parameter in the latter one, we derive a closed formula for the generalized Green function.
TwinGAN: Twin Generative Adversarial Network for Chinese Landscape Painting Style Transfer

Recently, style transfers have received considerable attention. However, most of these studies are suitable for Western paintings. In this paper, a deep learning method is proposed to imitate multiple styles of Chinese landscape paintings. Twin generative adversarial network style transfer was proposed based on the characteristics of Chinese landscape ink paintings. SketchGAN and renderGAN were performed respectively using generative models based on the generative adversarial network. SketchGAN involves determining the structure and simplifying the content of an input photo. RenderGAN involves transferring the results of sketchGAN into the final stylized image. Moreover, a loss function was designed to maintain the shape of an input content image. Finally, the proposed TwinGAN was successfully used to imitate five styles of Chinese landscape ink paintings. This study also provides ablation studies and comparisons with previous works. Experimental results exhibit that our algorithm synthesizes Chinese landscape stylized paintings that are higher in quality than those produced by previous algorithms.
Image Stitching Based on Color Difference and KAZE with a Fast Guided Filter

Image stitching is of great importance for multiple fields such as moving object detection and tracking, ground reconnaissance and augmented reality. To ameliorate the stitching effect and alleviate the mismatch rate, an effective image stitching algorithm based on color difference and an improved KAZE with a fast guided filter is proposed. Firstly, the fast guided filter is introduced to reduce the mismatch rate before feature matching. Secondly, the KAZE algorithm based on improved random sample consensus is used for feature matching. Then, the color difference and brightness difference of the overlapping area are calculated to make an overall adjustment to the original images so as to improve the nonuniformity of the splicing result. Finally, the warped images with color difference compensation are fused to obtain the stitched image. The proposed method is evaluated by both visual effect mapping and quantitative values. In addition, the proposed algorithm is compared with other current popular stitching algorithms. The results show that the proposed algorithm is superior to other algorithms in terms of the quantity of feature point pairs, the matching accuracy, the root mean square error and the mean absolute error.
Releasing survey microdata with exact cluster locations and additional privacy safeguards

Abstract Household survey programs around the world publish fine-granular georeferenced microdata to support research on the interdependence of human livelihoods and their surrounding environment. To safeguard the respondents’ privacy, micro-level survey data is usually (pseudo)-anonymized through deletion or perturbation procedures such as obfuscating the true location of data collection. This, however, poses a challenge to emerging approaches that augment survey data with auxiliary information on a local level. Here, we propose an alternative microdata dissemination strategy that leverages the utility of the original microdata with additional privacy safeguards through synthetically generated data using generative models. We back our proposal with experiments using data from the 2011 Costa Rican census and satellite-derived auxiliary information. Our strategy reduces the respondents’ re-identification risk for any number of disclosed attributes by 60–80% even under re-identification attempts.
LaMa: a thematic labelling web application

Qualitative analysis of data is relevant for a variety of domains including empirical research studies and social sciences.While performing qualitative analysis of large textual data sets such as data from interviews, surveys, mailing lists, and code repositories, condensing pieces of data into a set of terms or keywords simplifies analysis, and helps in obtaining useful insight.This condensation of data can be achieved by associating keywords, a.k.a.labels, with text fragments, a.k.a artifacts.It is essential during this type of research to achieve greater accuracy, facilitate collaboration, build consensus, and limit bias.LaMa, short for Labelling Machine, is an opensource web application developed for aiding in thematic analysis of qualitative data.The source code and the documentation of the tool are available at https://github.com/muctadir/lama.In addition to being open-source, LaMa facilitates thematic analysis through features such as artifact based collaborative labelling, consensus building through conflict resolution techniques, grouping of labels into themes, and private installation with complete control over research data.With the help of this tool and flow it enforces, thematic analysis becomes less time consuming and more structured.
A study of lattice reformulations for integer programming

Branch-and-bound for integer optimization typically uses single-variable disjunctions. Enumerative methods for integer optimization with theoretical guarantees use a non-binary search tree with general disjunctions based on lattice structure. These disjunctions are expensive to compute and challenging to implement. Here we compare two lattice reformulations that can be used to heuristically obtain general disjunctions in the original space, we develop a new lattice-based variant, and compare the derived disjunctions computationally with those produced by the algorithm of Lovász and Scarf.
An optimization scheme of partial total homomorphic encryption

In order to further improve the effectiveness of homomorphic cryptography, we optimize Lauter et al. 's partial homomorphic cryptography scheme, and propose a new RLWE-based total homomorphic cryptography (FHE) scheme, which combines a series of existing techniques with the Chinese residual theory, and merges several “plaintext slots” into a ciphertext for homomorphic computation. The extended key transformation technology and modulo transformation technology are used to reduce the noise of ciphertext and enable it to carry out the next homomorphic operation. Compared with the original scheme, the security and effectiveness of the scheme were studied on the premise of ensuring CPA security, and the results showed that the performance of the scheme was improved by N times.
Protein folding quantum circuit quantum circuit for bio material modelling compression

Computational material science aims to simulate substances to understand their physical properties. Bioelectronics is an interdisciplinary field that studies biological material from the conductivity point of view. In case of proteins, the folding is an important feature that directly influences physical and chemical properties. The folding modelling is a hard task. The enormous number of degrees of freedom makes modelling impossible for classical computation due to resource limits. Quantum computations aim to process multidimensional data with logarithmic growth of quantum bits. Quantum operators (gates) form quantum programs, known as circuits that process the input data. In real quantum computers, the gates are noisy and expensive to execute. Thus, it is essential to reduce the number of quantum gates both for the quality of the result and the cost of computations. This work describes an approach to decrease the number of quantum gates based on their mathematical property. The matrix properties form the first optimization technique. In this case, the optimized quantum circuit predicts precisely the same protein folding as the not optimized circuit predicts. This happens because both of the circuits are mathematically equivalent. The removal of weakly-parametrized gates forms the second optimization technique. In such case the optimized quantum circuit calculates the approximate protein folding. The error depends on parameter’s amplitude of the gates. The first technique allows to decrease the circuit depth from 631 to 629 gates while modelling the part of Azurin peptide. The second technique allows to decrease the depth to 314 gates with the threshold parameter value 0.4 radians.
EFL Teaching Material Development for Junior High Schools in Arfak Mountains Regency, West Papua

This Research discusses about teaching attractive materials that are suitable for the students and situation in Junior High Schools in Arfak Mountains Regency. This research aims to identify kinds of teaching attractive materials that suitable to the students and situation in Junior High Schools in Arfak Mountains Regency, and to indicate some types of teaching attractive materials chosen that can be developed by English teachers concerning to availability of the equipment needed in Arfak Mountains Regency. This research used Qualitative method, and the data is collected from observation or interview. The result of this research is found there are only some that might be able to be adopted to use in teaching English in SMP Arfak Mountain Regency. Those are realia, games (board race, Simon says, board jumble race, and hot seat), audio/tapes, and pictures. To develop those types of teaching materials, teachers are expected to be able to create their own teaching materials proposed.
Automatic Real-Estate Image Analysis for Retrieval and Classification

Abstract Nowadays, the real estate sector is also benefiting from the advance of the technology. Digital images accompany announcements advertising for renting or selling a property. Computer vision and image analysis contribute to the understanding of these images towards better consumer experience. In this paper we show how retrieval and description can be used efficiently to isolate cvasi-duplicate images and identify similar examples to the ones that have drawn viewer’s interest. Three problems related to real-estate imaging are identified and solutions are proposed and evaluated on large databases.
Third Person Pronoun and Demonstrative Resolution in Mandarin Chinese and Discourse Prominence

Introduction: The third person pronoun and demonstrative resolution are influenced by multiple factors in Mandarin Chinese. The factors influencing reference choice and the relations between discourse prominence and pronoun resolution are discussed. It suggests discourse prominence is an essential organizational principle in reference resolution. The factors that influence pronoun resolution in Mandarin Chinese might be studied and explained from the perspective of discourse prominence.&#x0D; Methodology: A formal operational scheme with conditional inference recursive partitioning tree and random forest analysis analyzing discourse data collected from a picture-sequence-based narrative elicitation method is provided.&#x0D; Results: The results confirmed the idea that the discourse prominence-lending cues, including thematic role, animacy, grammatic role, topic, referential distance, and mentioned number, influence pronoun resolution in Mandarin Chinese. Animacy and reference distance might be involved in the important variances, and reference distance relates to topic maintenance, discourse dynamicity, and structural attracting.&#x0D; Conclusion: The findings demonstrated that the discourse prominence-lending cues influence pronoun resolution in Mandarin Chinese. The third person pronouns often signal topic maintenance and high discourse prominence, while demonstratives often signal topic shift and low discourse prominence. Moreover, demonstratives often signal focus reinforcement. Therefore, topic and focus are also considered as the two crucial elements affecting pronoun resolution in Mandarin Chinese.
Clustering

Clustering is employed to divide a data set into an appropriate number of groups. Clustering is a form of unsupervised learning, which means a data scientist can bring labelled features of interest into the mining model. Furthermore, after dividing the data set, the data scientist can label each cluster. In business, clustering is used to analyze a customer or product segment that matches a target market. This chapter introduces clustering techniques including k-means, hierarchical clustering, and DBSCAN as well as techniques to indicate the efficiency of the clustering analysis. Data scientists can assess the efficiency of clustering analysis in two ways. Firstly, subjective measurement is where a data scientist consults a domain expert to confirm the efficiency of the cluster analysis, and secondly, data scientists can use objective measurements that test the efficiency of the cluster analysis result based on calculations. This chapter demonstrates cluster analysis adoption with RapidMiner so that readers can follow the process step-by-step.
Classification

In the world of data mining, classification reigns supreme as a popular technique for supervised learning. Its ability to identify patterns in data by dividing it into training sets and utilizing machine learning makes it an essential tool in answering critical questions related to data. For instance, classification can aid businesses in identifying customers with high purchasing potential. One of the standout features of classification is k-nearest neighbors (k-NN), which allows data to be classified according to the training data set. Decision trees are also commonly used to support decision making by producing easily interpretable diagrams. RapidMiner is an outstanding data mining tool that can employ a range of classification techniques, including k-NN, decision trees, and naïve Bayes. In this book, readers can follow a step-by-step guide to using these techniques with RapidMiner to achieve effective data classification.
Software Installation and Introduction to RapidMiner

Licensed and open-source data mining software has made it easier for data scientists to process and analyze large datasets. Python and R are popular open-source tools used for teaching data mining techniques, including data classification and clustering. However, one of the main challenges for domain experts looking to upskill in data science is computer programming. Licensed software such as RapidMiner provides a solution to this issue, as it is designed for data mining processing without requiring computer programming skills. RapidMiner allows data scientists to bring their data sets and mining models into an analysis process that includes all data science mechanisms. It also offers various supporting models for data pre-processing and creates data visualizations during data exploration. After data mining, RapidMiner displays easy-to-interpret results, enabling data scientists to effectively explain the answer to a business question to stakeholders. With its user-friendly interface, RapidMiner is an excellent choice for those without a technical background in programming.
Data Mining for Junior Data Scientists

The availability of off-the-shelf tools for data mining has made it easier to process data. However, in many cases, such software packages are not flexible enough to allow for algorithmic improvements. Therefore, data scientists need to write computer programs to customize the processing methods in tandem with the software packages. This chapter introduces Python, a programming language that provides libraries that support data science work. The content includes Python programming syntax, such as variables, structured programming, decision-making programming, recursive programming, data structure handling, and file handling. Additionally, the chapter introduces Google Colab as a tool for programming experiments. It provides a crucial foundation for data science students who are going to process data using data mining techniques in the next chapter using Python programming. Although data scientists don't need a deep understanding of computer programming, learning computer languages is essential, and this chapter caters to beginners.
Introduction to Data Mining

Data mining is a powerful and increasingly popular tool that uses machine learning to uncover patterns in data and help businesses stay competitive. Data scientists are trained to understand business objectives and select the correct techniques for data exploration and pre-processing. After formulating the business question, data mining methods are chosen and evaluated to determine their ability to fit the data set and answer the query. Results are then reported back to the business owner. Data mining is an essential part of modern business, allowing the organization to keep up with the competition and remain successful. With its growing popularity, the need for data scientists is rapidly increasing.