Reviewerly/exHarmony huggingface dataset:
Democratizing Quality-Based Machine Learning Development through Extended Feature Models

Abstract ML systems have become an essential tool for experts of many domains, data scientists and researchers, allowing them to find answers to many complex business questions starting from raw datasets. Nevertheless, the development of ML systems able to satisfy the stakeholders’ needs requires an appropriate amount of knowledge about the ML domain. Over the years, several solutions have been proposed to automate the development of ML systems. However, an approach taking into account the new quality concerns needed by ML systems (like fairness, interpretability, privacy, and others) is still missing. In this paper, we propose a new engineering approach for the quality-based development of ML systems by realizing a workflow formalized as a Software Product Line through Extended Feature Models to generate an ML System satisfying the required quality constraints. The proposed approach leverages an experimental environment that applies all the settings to enhance a given Quality Attribute, and selects the best one. The experimental environment is general and can be used for future quality methods’ evaluations. Finally, we demonstrate the usefulness of our approach in the context of multi-class classification problem and fairness quality attribute.
Protecting Encrypted Virtual Machines from Nested Page Fault Controlled Channel

AMD Secure Encrypted Virtualization (SEV) assumes the hypervisor (HV) is untrusted and introduces hardware memory encryption support for virtual machines (VMs). Previous studies have proposed various attacks against encrypted VMs by exploiting SEV security flaws such as unencrypted VMCB and lack of memory integrity. Most of these flaws have been solved by the subsequent releases of SEV with Encrypted State (SEV-ES) and SEV with Secure Nested Paging (SEV-SNP). However, the latest SEV-SNP cannot stop the malicious HV tampering with critical flags in the nested page table (NPT). So SEV-SNP is still vulnerable to the nested page fault (NPF) controlled channel attack, which is a commonly shared step of most attacks against SEV. Existing works on SEV also cannot defend against NPF controlled channel. In this paper, we first analyze the root cause of NPF controlled channel. Then we propose a software-based approach to protect encrypted VMs from NPF controlled channel. We introduce a virtualization security module (VSM) as a software TCB to deprivilege the HV by modifing the HV to access critical resources indirectly through interfaces managed by VSM. To prevent the untrusted HV from compromising the VSM-based protection, we extend the nested kernel architecture to the virtualization layer to provide isolation for VSM at the same privilege level. A prototype of this approach is implemented based on KVM. The experiments show that the approach can protect encrypted VMs from NPF controlled channel with 1.21% average runtime overhead and 1.47% average I/O overhead.
Aerial tracking based on vision transformer

In recent years, object tracking depends upon deep networks or complex architectures made great progress in accuracy, but less attention has been paid to tracking speed. On edge platforms with limited resources are used on UAVs, trackers are difficult to meet the requirements of real-time processing. To address the above problems, we proposed UAV object tracking based on vision transformer. Firstly, a lightweight neural network ResNet18 is used for feature extraction. Secondly, a transformer encoder is used to fully enhance fused features of the template features and the search features to improve feature expression, then, the enhanced features output from encoder are used for target prediction. Finally, further weighted fusion at decision level is used to obtain final tracking result, and a multi-supervised strategy is used. The proposed method achieves better results on three challenging large-scale tracking datasets, UAV123, LaSOT, and TrackingNet, reaching the success score(AUC) of 66.82, 64.26, and 81.2, respectively. In addition, our model tested speed on UAV123's test dataset, running at 116.2fps on NVIDIA 1660TI and 20.2fps on NVIDIA Jetson Xavier NX, which basically meets UAV's demand for real-time performance.
Research on dynamic obstacle avoidance technology of mobile robot

In order to realize the effective dynamic obstacle avoidance of the robot when the indoor topographic map is unknown, this paper uses hardware such as lidar, panoramic camera, and software codes such as deep learning to build a mobile robot that meets dynamic obstacle avoidance. First, the robot uses lidar to obtain information about the surrounding environment of the robot, and uses the firefly algorithm to improve the traditional G-Mapping algorithm to draw a more accurate grid map to obtain the global optimal path. At the same time, the hyperbolic panoramic camera 360⁰is used to extract the information of the surrounding environment of the moving robot and transfer it to the PC, and then usetheYoloV5 deep learning framework combined with ShufflenetV2 to integrate the DWA algorithm to avoid obstacles in real time for the robot. Experiments show that the accuracy and speed of real-time obstacle avoidance of the robot are improved to a certain extent under this method.
Named entity recognition of military equipment based on BERT-BILSTM-CRF model

Named entity recognition of military equipment is an important task in the construction of knowledge graph in the military domain. It is a key technical means to improve the intelligence degree of military intelligence information retrieval, intelligence analysis, command and decision. There are many problems in the task of named entity recognition in the field of military equipment, such as fuzzy entity boundary, complex grammar structure and many professional words, which directly lead to the loss of accuracy in named entity recognition. Aiming at the above challenges, this paper proposes a BERT- BILSTM -CRF neural network model based on type labeling and part-of-speech labeling of entities in the field of military equipment. BERT's pre-trained language model fully considers the correlation between words when constructing word vectors, which is used to supplement the semantic relations embedded in words, and can solve the fuzzy problem in name entity recognition. BILSTM layer is used to carry out bidirectional semantic coding, which can solve the long-distance dependence problem. Finally, the output of BERT-BILSTM layer is decoded by CRF layer, and the optimal tag sequence is obtained. The experimental results show that compared with CRF model and BILSTM-CRF model, the F1 value of the proposed model is increased by 10%.Compared with the BILSTM-Attention-CRF model, the F value of this model increased by 10.48%, and the recall rate increased by 15.02%.Compared with the BERT-IDCNN-CRF model, the F value of this model is increased by 0.62%, and the recall rate is increased by 4.55%.
About this title - Geological Mapping of Our World and Others

From planetary surfaces to crystals and their boundaries, maps are fundamental tools for developing geological knowledge. This book spans not only these scales but also the history of earth science – exploring techniques and applications. It celebrates geological mapping in developing knowledge of Earth and planetary evolution and processes.
Classical mechanics

Subject Astronomy and Astrophysics Computational Physics Collection: Oxford Scholarship Online
CS-IntroVAE: Cauchy-Schwarz Divergence-Based Introspective Variational Autoencoder

Although generative models are still being developed, image reconstruction and generation tasks have evolved dramatically. Since the most popular generative models still have some limitations, it is still challenging. For example, while generative adversarial network (GAN) produces clear images, it is hard to train. The hybrid VAE-GAN incorporates the benefits of both, although it is computationally intensive and prone to drawbacks such as overfitting and gradient disappearance. A novel generative model called the Cauchy-Schwarz Divergence-based Introspective Variational Autoencoder (CS-IntroVAE) is based for this challenge. Extensive experiments show that our model has good performance on both tasks by employing mixed Gaussian distributions as prior distributions and Cauchy-Schwarz divergence as a measure of the distance between prior and posterior distributions. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/CoderSnack/CS-IntroVAE</uri> .
Glowing dyes could store data at low cost

Current optical and magnetic data storage technologies can store an impressive amount of information, but the data typically last less than 2 decades. Researchers have now stored data in tiny dots of glowing dye molecules as a low-cost, potentially longer-lasting storage option ( ACS Cent. Sci. 2021, DOI: 10.1021/acscentsci.1c00728 ). George M. Whitesides of Harvard University and colleagues used an inkjet printer to deposit droplets of fluorescent dye solutions on an epoxy surface. Each droplet encodes a text character using a mixture of up to seven dye colors. The presence or absence of each color represents a one or zero. The researchers use a fluorescence microscope to decipher the individual colors and read the data. Compared with molecular data storage systems that require synthesizing and sequencing DNA, the dye-based system is “simple and low cost,” says Whitesides, who is also a board member at the data storage solution start-up Datacule.
Supplemental Material: Fluid-fluxed melting in the Himalayan orogenic belt: Implications for the initiation of E-W extension in southern Tibet

&lt;p&gt;Supplemental Text S1: Analytical Methods. Figure S1: Cathodoluminescence (CL) showing the texture, spot, and respective age of zircon U-Pb dating for the leucogranites from the Cona and Everest regions along the Himalayan orogenic belt. Figure S2: U-Pb Concordia diagrams showing the crystallization ages of the representative leucogranites derived from fluid-absent melting (Group A) from the Cona and Everest regions along the Himalayan orogenic belt. Figure S3: Sr-Nd isotope systematics of the two types of leucogranites from the Malashan, Cona, and Everest regions along the Himalayan orogenic belt. Table S1: Summary of SHRIMP zircon U-Pb isotope data for the leucogranite in the Himalayan orogenic belt. Table S2: Summary of MC-ICP-MS zircon U-Pb isotope data for the leucogranite in the Himalayan orogenic belt. Table S3: Major and trace element compositions, as well as Sr-Nd isotope compositions of the leucogranite in the Himalayan orogenic belt.&lt;/p&gt;
Gribanov Readings — 2022 as a Platform for Discussing the History and Development of Book Culture in Russia

The All-Russian scientific and practical conference with international participation “Gribanov Readings — 2022” was held on December 14—16, 2022 at the National Library of the Republic of Sakha (Yakutia). The article discusses the problems of the book culture of the regions of Russia; this theme was in the focus of attention of conference participants and was the subject of scientific discussion at the plenary session and in the course of the sections: “National and local history bibliography: history, status and new horizons”, “Reading infrastructure: research, exchange of experience”, “Historical and cultural studies to preserve the cultural heritage of the indigenous peoples of the Russian North, Siberia and the Far East” and “Digital transformation of libraries”. Within the framework of the conference, there were organized the round table “Modern Library Education: exchange of opinions of practitioners and theorists, managers and performers, teachers and students, readers and librarians” and the First Regional Festival of Scientific Research Cinema “Science Cinema”. The conference aroused great interest in the professional and scientific community. More than 130 specialists took part in it — theorists and practitioners of library and information activities, experts in the book industry, philosophers, historians, ethnographers, cultural scientists, art historians, employees of archives and museums from 30 regions of Russia, as well as from Belarus, Kazakhstan and Mongolia. The conference was aimed at identifying the main vectors of the history and current trends in the development of book culture in the regions of Russia, assessing the role of books and reading, bibliography, modern book publishing, discussing the key issues of the domestic book market, finding ways to digitally transform libraries and preserve the best traditions of the development of the book industry.
Lagrangian and Hamiltonian formalism for classical fields

Subject Astronomy and Astrophysics Computational Physics Collection: Oxford Scholarship Online
User Satisfaction Analysis of Paylater Services Using K-Means Algorithm in Campus

In the 4.0-based digital era, the use of e-commerce is increasing. The convenience provided to e-commerce users is increasingly being considered by companies engaged in e-commerce. Paylater is a fairly new payment method among Indonesian e-commerce, so research is needed to improve the service and satisfaction of e-commerce users, especially those using the paylater payment method. The purpose of this study is to analyze user satisfaction with paylater services using the k-means algorithm on campuses in region 3 Cirebon. This research is also to find out the benefits of paylater used by students. This research is a type of quantitative research using the k-means algorithm to determine the classification of paylater user satisfaction in several e-commerce applications at several universities in region 3 Cirebon which is then clustered. The results of the study show that Cirebon students in the Campus 3 area are satisfied with services from companies or online shops that have paylater payment facilities
Penerapan Algoritma Apostolico Giancarlo Pada Pencarian Fasilitas Umum di Provinsi Aceh Berbasis Android

Public Facilities are facilities built by the government for the benefit of the general public. The problem that is often faced in Aceh Province is the difficulty of knowing the location of Public Facilities in Aceh Province due to the difficulty of the signal, many Public Facilities are not yet registered and mapped on Google Maps. Mobile Phone is one of the fastest growing technologies. Mobile Phone was originally used to send letters and make calls, but has now evolved into a smartphone that has the function of searching for a location. Apostolico Giancrlo works by comparing each index of keyword characters with the String character being examined. The search for strings in the Apostolico Giancarlo algorithm starts with pre-processing keywords. Pre-processing of keywords. This pre-processing is done to look at the index number of strings that are checked for each keyword character
Understanding Food Planning Strategies of Food Insecure Populations: Implications for Food-Agentic Technologies

To identify technological opportunities to better support nutrition security and equality among those living in low-socioeconomic situations, we conducted 33 semi-structured interviews and seven in-home visits of lower- to middle-income households from a mid-sized city in northern Indiana. Inspired by assets-based approaches to public health, we investigated technology's role in supporting how participants selected and purchased food, planned meals, and worked through logistical barriers to obtain food. Technology helped participants identify sales and coupons, search for recipes and health-related insights to address diet and health concerns, and share information. We contribute design implications (e.g., amplifying optimization behaviors and social engagement, leveraging substitutions) in support of food agency. We further contribute three emergent archetypes to convey central shopping tendencies (i.e., inventory shoppers, menu planners, and adaptive shoppers) and identify corresponding design implications. We situate our results into nutrition decision-making and education, social psychology, food consumer studies, and HCI literature.
AN UNSUPERVISED FEATURE SELECTION BASED ON CROW SEARCH ALGORITHM AND GREY WOLF OPTIMIZATION ALGORITHM FOR DNA-MICROARRAY DATA

High-dimensional data encounter the curse of dimensionality in classification tasks, particularly in DNAmicroarray technology, due to a lack of statistical power, complicating biomarker discovery.DNAmicroarray technology expresses gene profiles, aids in the identification of diseases, and provides drugs to treat patients.Feature selection successfully reduces execution time and increases classification accuracy in preprocessing data by discarding irrelevant and redundant features for which technology has large dimensions and few samples in classification tasks.Most of the research on feature selection problems is considered in supervised feature selection, which is known as the label class in the classification process, but some of them cannot access the label class.Therefore, this study proposed an unsupervised filter-based feature selection algorithm based on a proposed algorithm based on CSA and GWO with a hybrid technique called UHCSAGWO.In the details of our proposed algorithm, we proposed three crucial mechanisms to find the significant feature subsets without using any learning model in the process of evaluating them, such as a pool for finding the top candidate features, a local search, and a new fitness function.To remove the irrelevant and redundant features in the local search step based on a pool to store top features and obtain a significant subset of the selected features.Then, to evaluate the subset of the selected feature by utilizing a new fitness function that is examined by relevant, redundant information is discarded without any classifiers.The performance of the proposed algorithm was compared to that of state-of-the-art feature selection algorithms using different classifiers on the meta-heuristic algorithms.The experimental results prove that the proposed algorithms can significantly reduce computational time and achieve better performance than others in classification error rate and other well-known criteria, such as precision and recall.
Penerapan Teknik Single Linkage Dalam Klasterisasi Mahasiswa Berdasarkan Indikator Pembidangan Skripsi

Pelita Nusantara Medan STMIK Computer Informatics study program provides several fields of science that students can choose in completing their final thesis in the form of a thesis. During this time in its implementation, the determination of the field of this thesis was chosen by the student who will submit the thesis title as one of the requirements to obtain a bachelor's degree at STMIK Pelita Nusantara Medan without any direction from the campus either from the academic department or the head of the study program. Therefore, not a few students who ultimately choose a field of science that is not in accordance with their abilities. Based on the existing problems regarding the development of this thesis, a student clustering system was made based on the indicators of thesis development by applying one of the data mining methods, namely clustering, where the technique used was the Single Linkage Technique. Single Linkage Technique is a way to group objects or individuals based on criteria similarity size or closest distance between objects (minimum distances). Thus, the results of clustering (clustering) that are designed can help the academic STMIK Pelita Nusantara Medan, especially the Chairperson of the Computer Informatics Study Program in directing students in the determination of the field of thesis so that students choose the field of thesis in accordance with their abilities
Handheld Tools Unleashed: Mixed-Initiative Physical Sketching with a Robotic Printer

Personal fabrication has mostly focused on handheld tools as embodied extensions of the user, and machines like laser cutters and 3D printers automating parts of the process without intervention. Although interactive digital fabrication has been explored as a middle ground, existing systems have a fixed allocation of user intervention vs. machine autonomy, limiting flexibility, creativity, and improvisation. We explore a new class of devices that combine the desirable properties of a handheld tool and an autonomous fabrication robot, offering a continuum from manual and assisted to autonomous fabrication, with seamless mode transitions. We exemplify the concept of mixed-initiative physical sketching with a working robotic printer that can be handheld for free-hand sketching, can provide interactive assistance during sketching, or move about for computer-generated sketches. We present interaction techniques to seamlessly transition between modes, and sketching techniques benefitting from these transitions to, e.g., extend (upscale, repeat) or revisit (refine, color) sketches. Our evaluation with seven sketchers illustrates that RoboSketch successfully leverages each mode’s strengths, and that mixed-initiative physical sketching makes computer-supported sketching more flexible.
Teaching schoolchildren in the basics of artificial intelligence technologies in conditions of additional education

The article is devoted to the description of methodological approaches to teaching schoolchildren the basics of artificial intelligence based on the technology of organizing project and educational and research activities to achieve meta-subject results of education, the formation of systemic representations and the experience of their application in real life. The experience of organizing courses within the framework of additional education for students of 10–11th grades of secondary school on the use of neural networks in solving practice-oriented problems is presented. The block structure of the content of the courses is given, which is based on the phased implementation of the idea of the future project. A detailed description of the content of each block is given with a demonstration of a presentation of complex theoretical material that is accessible to the understanding of schoolchildren. In particular, recommendations are given for the study of sequential and recurrent models of artificial neural networks, fully connected and convolutional layers, object detectors, as well as an algorithm for training neural networks by backpropagation of errors. Data acquisition tools for training artificial neural networks are described. An algorithm for writing a program for training a neural network to recognize handwritten digits of the MNIST set is characterized. Examples of projects for the use of neural networks, carried out by students together with the teacher as part of the study of the proposed courses, are given. The article is addressed to teachers of additional education, informatics teachers, teachers and students of universities — everyone who is interested in the use of artificial neural networks in solving practical problems and methods of teaching schoolchildren in project activity.
Shape-Adaptive Ternary-Gaussian Model: Modeling Pointing Uncertainty for Moving Targets of Arbitrary Shapes

This paper presents a Shape-Adaptive Ternary-Gaussian model for describing endpoint uncertainty when pointing at moving targets of arbitrary shapes. The basic idea of the model is to combine the uncertainty related to the target shape with the uncertainty caused by the target motion. First, we proposed a model to predict endpoint distribution on static targets based on a Dual-Space Decomposition (DUDE) algorithm. Then, we linearly combined a 2D Ternary-Gaussian model with the newly proposed DUDE-based model to make the 2D Ternary-Gaussian model adaptable to moving targets with random shapes. To verify the performance of our model, we compared it with the original 2D Ternary-Gaussian model and a recent proposed Inscribed Circle model in predicting endpoint distribution. The results show that the proposed model outperformed the two baseline models while maintaining good robustness across different shapes and moving speeds.