Reviewerly/exHarmony huggingface dataset:
Research on the measurement method of crankshaft coaxiality error based on three-dimensional point cloud

Abstract Aiming at the demand of rapid detection for coaxiality of crankshaft journal, a method of visual measurement of coaxiality error based on 3D point cloud is proposed. In this research, a laser scanner is utilized to obtain a 3D point cloud model of the part to be measured, and a circular method of rotary cutting is put forward to obtain equally spaced optimal slices of the journal. The center of the slice is then obtained using the Pratt circle fitting method optimized in this paper. After that, the center of the slice is used to obtain the common reference axis using the least squares method, which finally realizes the measurement of the coaxiality error. The experimental results show that the proposed method of getting the slices is more accurate, the maximum absolute error of the circle fitting method compared with the results of the coordinate measuring machine (CMM) is no more than 4.2 μm, and the maximum absolute error of the coaxiality detection results and the results of the CMM is 5.5 μm, which can satisfy the practical needs of fast and efficient measurement of the coaxiality error of crankshafts.
From Skin to Skeleton: Towards Biomechanically Accurate 3D Digital Humans

Great progress has been made in estimating 3D human pose and shape from images and video by training neural networks to directly regress the parameters of parametric human models like SMPL. However, existing body models have simplified kinematic structures that do not correspond to the true joint locations and articulations in the human skeletal system, limiting their potential use in biomechanics. On the other hand, methods for estimating biomechanically accurate skeletal motion typically rely on complex motion capture systems and expensive optimization methods. What is needed is a parametric 3D human model with a biomechanically accurate skeletal structure that can be easily posed. To that end, we develop SKEL, which re-rigs the SMPL body model with a biomechanics skeleton. To enable this, we need training data of skeletons inside SMPL meshes in diverse poses. We build such a dataset by optimizing biomechanically accurate skeletons inside SMPL meshes from AMASS sequences. We then learn a regressor from SMPL mesh vertices to the optimized joint locations and bone rotations. Finally, we re-parametrize the SMPL mesh with the new kinematic parameters. The resulting SKEL model is animatable like SMPL but with fewer, and biomechanically-realistic, degrees of freedom. We show that SKEL has more biomechanically accurate joint locations than SMPL, and the bones fit inside the body surface better than previous methods. By fitting SKEL to SMPL meshes we are able to "upgrade" existing human pose and shape datasets to include biomechanical parameters. SKEL provides a new tool to enable biomechanics in the wild, while also providing vision and graphics researchers with a better constrained and more realistic model of human articulation. The model, code, and data are available for research at https://skel.is.tue.mpg.de.
INFORMATE: Where Are the Data?

Introduction A recent blog post described a new partnership between Metadata Game Changers and CHORUS aimed at understanding how CHORUS data can help federal agencies, other funders, and other users access and use information from the global research infrastructure to measure this infrastructure and understand connections between research objects.
One-shot Multiple Object Tracking with Robust ID Preservation

Maintaining identity consistency and avoiding ID-switch during tracking is one of the primary focuses of multiple object tracking (MOT). One-shot MOT methods which jointly learn the detection and tracking models in one single network (hence namely, one-shot) have achieved promising results in tracking accuracy and speed. However, their capabilities of maintaining ID consistency are somehow weakened. The reason for this weakened ID consistency is two-fold: (1) the ID features learned by one-shot methods are not discriminative enough due to their heatmap-based single-location representation. (2) severe occlusion in the MOT scene leads to feature ambiguity and high ID-switch. In this paper, we propose a one-shot MOT system with strong ID consistency called PID-MOT (Preserved ID MOT). Specifically, we devise a visibility branch to predict the object occlusion level, and a predicted visibility map will be used in both Feature Refinement Model (FRM) and a visibility-guided two-stage association strategy (VGTAS). FRM is designed to strengthen the location-based features and enrich the identity information. VGTAS is proposed for tackling objects with high and low visibility separately. In addition, we initialize the parameters of our model by training on the recently emerged abundant synthetic MOTSynth dataset from scratch rather than the commonly used COCO dataset for full training. Finally, we carry out our method on the commonly used MOT datasets and the experimental results demonstrate that the proposed PID-MOT achieves especially good performances in ID F1 score (IDF1) and ID-Switch (IDS) compared with other state-of-the-art one-shot trackers, with comparable overall HOTA/MOTA performance. The code is available at https://github.com/Kroery/PIDMOT.
Towards Receiver-Agnostic and Collaborative Radio Frequency Fingerprint Identification

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique, which exploits the hardware characteristics of the RF front-end as device identifiers. The receiver hardware impairments interfere with the feature extraction of transmitter impairments, but their effect and mitigation have not been comprehensively studied. In this paper, we propose a receiver-agnostic RFFI system by employing adversarial training to learn the receiver-independent features. Moreover, when there are multiple receivers, collaborative inference are designed to enhance classification accuracy. Finally, we show how it is possible to leverage fine-tuning for further improvement with fewer collected signals. To validate the approach, we have conducted extensive experimental evaluation by applying the approach to a LoRaWAN case study involving ten LoRa devices and 20 software-defined radio (SDR) receivers. The results show that receiver-agnostic training enables the trained neural network to become robust to changes in receiver characteristics. The collaborative inference improves classification accuracy by up to 20% beyond a single-receiver RFFI system and fine-tuning can bring a 40% improvement for underperforming receivers. The system is further evaluated on a more practical testbed. By making additional use of online augmentation and multi-packet inference, the identification accuracy is improved from 50% to 90% at 10 dB.
BERT based natural language processing for triage of adverse drug reaction reports shows close to human-level performance

Post-marketing reports of suspected adverse drug reactions are important for establishing the safety profile of a medicinal product. However, a high influx of reports poses a challenge for regulatory authorities as a delay in identification of previously unknown adverse drug reactions can potentially be harmful to patients. In this study, we use natural language processing (NLP) to predict whether a report is of serious nature based solely on the free-text fields and adverse event terms in the report, potentially allowing reports mislabelled at time of reporting to be detected and prioritized for assessment. We consider four different NLP models at various levels of complexity, bootstrap their train-validation data split to eliminate random effects in the performance estimates and conduct prospective testing to avoid the risk of data leakage. Using a Swedish BERT based language model, continued language pre-training and final classification training, we achieve close to human-level performance in this task. Model architectures based on less complex technical foundation such as bag-of-words approaches and LSTM neural networks trained with random initiation of weights appear to perform less well, likely due to the lack of robustness that a base of general language training provides.
Enhancing Security in WhatsApp: A System for Detecting Malicious and Inappropriate Content

With the increasing reliance on instant messaging applications like WhatsApp, ensuring user privacy and security has become paramount. This paper argues for the need to enhance the security of the WhatsApp messaging service. An API endpoint and Chrome extension capable of detecting profanity, malicious URLs, and inappropriate images are developed to satisfy this need. In its current stage, not only can malicious URLs circulate freely through WhatsApp without any checks, but it is also easy to send inappropriate images and texts which receiving users should be careful with. We survey existing publicly available services and APIs in the domains of profanity, malicious URLs, and inappropriate images. A novel approach that incorporates the most appropriate models for our purpose in an integrated detection system, based on simple statistical models and output from the third-party APIs, is proposed. We compile two extensive datasets of profane text and malicious URLs and employ them in testing the effectiveness of our proposed model using statistical methods. Additionally, we present a publicly available proof of concept Chrome extension that incorporates our model to provide users with an added layer of protection. Finally, we discuss potential areas for future research and suggest improvements to enhance the effectiveness of the proposed system and the cybersecurity aspect of chatting applications in general.
The effectiveness of Authentic Materials For Reading Comprehension of English for Specific Purposes (ESP) Students

This study aimed to investigate the effectiveness of using authentic materials on ESP students in reading comprehension. The participants of this study were the second semester students. Purposive sampling technique was used in this study. The participants were divided into two groups, the experimental was 30 students and the control group was 30 students. This study used quasi-experimental research. The instrument used in collecting data was multiple choice test. The total items of the test were 20 items. The finding shows that a mean score of 29.87 on the pre-test and 52.07 on the post-test reading through authentic materials can help students become more proficient readers, particularly in areas such as identifying primary ideas, supporting details, factual interactions, topic-related vocabulary, pronouncing references, and word meaning recognition. It shows that the hypothesis alternative (ha) was accepted. As conclusion that using authentic materials has positive effect of the students’ reading comprehension of English for specific purposes (ESP) class at UKI Paulus Makassar.
Fallen Sanctuary: A Higher-Order and Leakage-Resilient Rekeying Scheme

This paper presents a provably secure, higher-order, and leakage-resilient (LR) rekeying scheme named LR Rekeying with Random oracle Repetition (LR4), along with a quantitative security evaluation methodology. Many existing LR primitives are based on a concept of leveled implementation, which still essentially require a leak-free sanctuary (i.e., differential power analysis (DPA)-resistant component(s)) for some parts. In addition, although several LR pseudorandom functions (PRFs) based on only bounded DPA-resistant components have been developed, their validity and effectiveness for rekeying usage still need to be determined. In contrast, LR4 is formally proven under a leakage model that captures the practical goal of side-channel attack (SCA) protection (e.g., masking with a practical order) and assumes no unbounded DPA-resistant sanctuary. This proof suggests that LR4 resists exponential invocations (up to the birthday bound of key size) without using any unbounded leak-free component, which is the first of its kind. Moreover, we present a quantitative SCA success rate evaluation methodology for LR4 that combines the bounded leakage models for LR cryptography and a state-of-the-art information-theoretical SCA evaluation method. We validate its soundness and effectiveness as a DPA countermeasure through a numerical evaluation; that is, the number of secure calls of a symmetric primitive increases exponentially by increasing a security parameter under practical conditions.
Clause complexing in research-article abstracts: Comparing human- and AI-generated texts

Abstract The ability of chatbots to produce plausible, human-like responses raises questions about the extent of their similarity with original texts. Using a modified version of Halliday’s clause-complexing framework, this study compared 50 abstracts of scientific research articles from Nature with generated versions produced by Bard, ChatGPT, and Poe Assistant. None of the chatbots matched the original abstracts in all categories. The only chatbot that came closest was ChatGPT, but differences in the use of finite adverbial clauses and – ing elaborating clauses were detected. Incorporating distinct grammatical features in the algorithms of AI-detection tools is crucially needed to enhance the reliability of their results. A genre-based approach to detecting AI-generated content is recommended.
CONSTRUCTIVE-SYNTHESIZING MODELING OF NATURAL LANGUAGE TEXTS

Means for solving the problem of establishing the natural language texts authorship were developed. Theoretical tools consist of a constructors set was developed on the basis of structural and production modeling. These constructors are presented in this work. Some results of experimental studies based on this approach have been published in previous works by the author, the main results should be published in the next ones.&#x0D; Constructors developed: converter of natural language text into tagged, tagged text into a formal stochastic grammar and the authors style similarity degree establishment of two natural language works based on the coincidence of the corresponding stochastic grammars (their substitution rules).&#x0D; The proposed approach makes it possible to highlight the semantic features of the author's phrases construction, which is a characteristic of his speech. Working with a sentence as a unit of text to analyze its construction will allow you to more accurately determine the author's style in terms of the use of words, their sequences and characteristic language constructions. Allows not to be attached to specific parts of speech, but reveals the general logic of building phrases.
FedSea: Federated Learning via Selective Feature Alignment for Non-IID Multimodal Data

The growing demands for privacy protection challenge the joint training of one model by leveraging multiple datasets. Federated learning (FL) provides a new way to overcome this challenge and has attracted many research interests, which enables multiple parties to collaboratively train a machine learning model without exchanging their local data. Despite some success, the non-independent and identically distributed (non-IID) data distributions in different parties remain challenging and easily damage the performance of FL methods, specifically for the heterogeneous multimodal data. Existing FL studies on non-IID data settings are often dedicated to the label space, neglecting the non-IID issues in feature space, thus limiting their performance when the parties with non-IID multimodal data. This paper proposes a new <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fed</b> erated learning method via <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Se</b> lective feature <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> lignment (FedSea) to align representations across multiple parties in the feature space. FedSea uses a domain adversarial learning framework consisting of an affine-transform-based generator and a gradient-reversal-based client discriminator to perform IID transformation and reduce data source distinguishability, respectively. An attention-based mask module and a feature IID confidence quantification method are introduced to effectively address the diverse feature non-IID levels across multimodal data. Comprehensive experiments are conducted on three widely-used public datasets and one large-scale industrial dataset, showing FedSea has: 1) better performance than state-of-the-art FL methods on both multimodal and single-modal datasets; 2) superior feature alignment ability on non-IID datasets, and 3) good model interpretability.
Utilizing AI and Machine Learning for Human Emotional Analysis through Speech-to-Text Engine Data Conversion

Artificial Intelligence (AI) and Machine Learning (ML) technologies have revolutionized various domains, and one of their fascinating applications is in analyzing human emotions through speech-to-text data conversion. This article delves into the innovative use of AI and ML to process spoken words into text and subsequently evaluate human emotions embedded within the speech. We explore the development of speech-to-text technologies, the essential function of natural language processing (NLP), and the use of deep learning models to understand emotional cues. Our research discoveries illustrate how these technologies are altering fields like mental health, customer service, and market research, granting deeper understanding of human emotional states. By scrutinizing emotions conveyed in spoken language, artificial intelligence (AI) and machine learning (ML).
SnakeFace: a transfer learning based app for snake classification

Introduction: deep learning emerged in 2012 as one of themost important machine learning technologies, reducing image identification error from25% to 5%. This article has two goals: 1) to demonstrate to the general public the ease of building state-of-the-art machine learningmodels without coding expertise; 2) to present a basicmodel adaptable to any biological image identification, such as species identification. Method: We present three test-of-conceptmodels thatshowcase distinct perspectives of the app. Themodels aim at separating images into classes such as genus, species, and subspecies, and the input images can be easily adapted for different cases. We have applied deep learning and transfer learning using TeachableMachine. Results: Our basicmodels demonstrate high accuracy in identifying different speciesbased on images, highlighting the potential for thismethod to be applied in biology. Discussions: the presentedmodels showcase the ease of using machine learning nowadays for image identification. Furthermore, the adaptability of this method to various species and genuses emphasizes its importance in the biological fields, as root for inspiring collaborations with computer science. On our, future collaborations could lead to increasingly accurate and efficientmodels in this arena using well-curated datasets.
Spatial resolution improvement by a super-resolution technique depending on training process in the background-orientated schlieren analyses

The effectiveness of the enhanced deep super-resolution network (EDSR), which is a super-resolution technique based on the deep convolutional neural network, is investigated for the improvement of spatial resolution during a background-orientated schlieren (BOS) analysis, and its suitable training process in the EDSR method is clarified. Consequently, a training dataset consisting of the simple dot patterns leads to a better image quality due to super-resolution because the image captured in the BOS measurement shows the similar dot patterns. When the image is enlarged at a large magnification ratio, it is important to adjust an image size in the training dataset individually for each magnification ratio, thereby obtaining a good estimation accuracy of the pixel displacement in the BOS analysis. A measurement error is improved by 62% compared with that of the Bicubic method, which is a classical spatial resolution improvement technique at the magnification ratio of 8. The present result shows that the EDSR method with the best training conditions provides a reasonable pixel displacement vector field up to the magnification ratio of 8 for the BOS analysis; however, its effectiveness depends on flow structure.
Cloud Computing in Healthcare Industry: A Systematic Literature Review

In today's society, technology has become a dominant critical component in every business, including healthcare. The advantages of maintaining health records electronically have boosted patient care productivity and ease of access and utilization. The creation of cloud-based technology is one of the most recent technological advancements in health care. However, numerous issues and security precautions related to the storage of patient information remotely are sources of concern for many in the health care profession. One must comprehend the benefits and concerns associated with the deployment of cloud computing, as well as the advantages and downsides of this new technology.&#x0D;
Model Based Clustering of Functional Data with Mild Outliers

Abstract We propose a procedure, called CFunHDDC, for clustering functional data with mild outliers which combines two existing clustering methods: the functional high dimensional data clustering (FunHDDC) [1] and the contaminated normal mixture (CNmixt) [3] method for multivariate data. We adapt the FunHDDC approach to data with mild outliers by considering a mixture of multivariate contaminated normal distributions. To fit the functional data in group-specific functional subspaces we extend the parsimonious models considered in FunHDDC, and we estimate the model parameters using an expectation-conditional maximization algorithm (ECM). The performance of the proposed method is illustrated for simulated and real-world functional data, and CFunHDDC outperforms FunHDDC when applied to functional data with outliers.
MODERN MEANS OF VISUALIZATION OF EDUCATIONAL CONTENT

The article reveals the problems of organizing the educational process based on the application of one of the most important principles of didactics — visualization (visual presentation of the material). The potential of practical application of this principle in the education system is most clearly reflected in the quality of training and skills development of students. The paper classifies and analyzes various types of visualization used in the educational process. The authors noted the main psychological and pedagogical features and formulated practical recommendations on the use of visual aids and teaching methods on the example of such important integrative subjects as social studies and economics. This research is of scientific and practical value for the training of future teachers in pedagogical universities of Russia, as well as for beginning teachers of higher and additional education, specializing in teaching subjects from the cycle of socio-economic disciplines.