Reviewerly/exHarmony huggingface dataset:
Research on the innovation of teaching mode of vocational and technical education

Vocational and technical education group is the main training object of new high-quality talents in our school, and teaching is the basic link of Vocational and technical education. The current teaching mode of Vocational and technical education has the disadvantages of emphasizing theory and neglecting practice. Therefore, it is necessary to discuss the teaching mode of Vocational and technical education in our school. Based on the current situation of the teaching mode of Vocational and technical education in our school, this paper puts forward the teaching and assessment mode of “combining theory with practice” and “combining thinking cultivation with practice”.
A Fast and Generalized Broad-Phase Collision Detection Method Based on KD-Tree Spatial Subdivision and Sweep-and-Prune

Various graphics applications use multibody collision detection, a critical technology in computer graphics, system simulations, and virtual reality. In these simulation environments, broad-phase collision detection, as part of collision detection, plays a critical role in ensuring that rejecting disjoint objects and collision detection is accelerated. Few existing methods implement collision detection of millions of objects in a general-purpose environment on the CPU. This paper proposes a broad-phase collision detection algorithm based on KD-Tree spatial subdivision and sweep-and-prune, which optimizes and accelerates broad-phase collision detection using a pre-sorting and temporal inference solution. Our method enables broad-phase collision detection for coherent and non-coherent settings for uniformly and non-uniformly sized objects respectively. Based on our proposed solution is tested in the context of complex scenarios and compared with other solutions available in the literature and in the industry. The experimental results show that our approach has a 1X to 2X performance improvement in virtual environments with up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1024 \times 10^{3}$ </tex-math></inline-formula> objects, reaching the fastest collision detection speed of 119.45 milliseconds per frame in the test environment.
Mapping singly-linked rules to linear temporal logic formulas

Business services are provided by enacting interrelated business processes. Service providers must ensure enactments comply with policies, regulations, and business rules, including rules with quantitative time constraints. Enforcing such rules at design-time may be too restrictive, so effective service provisioning includes expressing rules in a formal specification language and detecting violations of these rules at runtime. Many specification languages do not include quantitative time constraints; for languages with such constraints, it is often unknown if they have runtime monitors whose auxiliary data storage is of bounded size. In this paper, we formulate a technical model of services, a logic language with quantitative time constraints for specifying rules, and develop techniques for automatically generating monitors to detect rule violations. This approach involves two steps, translating: (1) rules to formulas in linear temporal logic (LTL) on finite traces, and (2) LTL formulas to finite state machines. Since algorithms exist for step (2), we focus on step (1), i.e., mapping rules to equivalent LTL formulas. We present and establish the correctness of two translation techniques for “singly-linked” rules. We also compare the size of formulas produced by these techniques with a method of translation derived from Kamp’s Theorem, showing an improvement from hyper-exponential to exponential size.
Traffic Participant Classification Method Based on Decision Tree for Point Cloud Data Detected by Event Camera

Abstract Event camera has the advantage of accurately identifying moving targets while being insensitive to stationary targets, which makes up for the lack of traditional video streaming camera and has a wide range of applications in the field of traffic flow detection. In this paper, a traffic participant classification method based on a decision tree for point cloud data acquired by an event camera in a roadside installation scenario is proposed. For traffic identification, 5 basic features to describe the geometrical and quantitative characteristics, and 8 Gaussian projection features to describe the point distribution characteristics are extracted and analyzed. Furthermore, the CART decision tree is used to identify four kinds of traffic participants, including the large vehicle, the compact vehicle, the non-motor vehicle, and the pedestrian. By modulating parameters of the maximum layer and feature weighting values, the proposed method can reach equilibrium in generalization ability and accuracy. Experimental results show that the proposed method demonstrates high accuracy of 97.49% in target participant classification.
A Guided Neural Network Approach to Predict Early Readmission of Diabetic Patients

Diabetes is a major chronic health problem affecting millions globally. Effective diabetes management can reduce the risk of hospital readmission and the associated financial losses for both the healthcare system and insurance companies. Hospital readmission is a high-priority healthcare quality measure that reflects the inadequacies in the healthcare system that also increase healthcare costs and negatively influence hospitals’ reputation. Predicting readmissions in the early stages prompts great attention to patients with a high risk of readmission. There has been some attempt in applying machine learning predictive models such as ensemble learning with Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Artificial Neural Networks (ANN) to correctly identify if the readmission can happen within 30 days (< 30 days) or it may never happen or happens after 30 days ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ge 30$ </tex-math></inline-formula> days). We are proposing a new method that is applied to ANN to guide it through its gradient descent optimizers by realizing consistent vs inconsistent data in every batch. Our results show that there are up to 1.5% improvement in classification accuracies in both 2-class and 3-class variations of the experimented benchmark dataset when using the guided optimizer to train the ANN as opposed to the standard optimizer. Guided ANN is also able to achieve better error convergence than standard ANN.
Computing Longest Lyndon Subsequences and Longest Common Lyndon Subsequences

Abstract Given a string T of length n whose characters are drawn from an ordered alphabet of size $$\sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>σ</mml:mi> </mml:math> , its longest Lyndon subsequence is a maximum-length subsequence of T that is a Lyndon word. We propose algorithms for finding such a subsequence in $$\mathop {}\mathopen {}\mathcal {O}\mathopen {}(n^3)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mrow /> <mml:mrow /> <mml:mi>O</mml:mi> <mml:mrow /> </mml:mrow> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>n</mml:mi> <mml:mn>3</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> time with $$\mathop {}\mathopen {}\mathcal {O}\mathopen {}(n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow /> <mml:mrow /> <mml:mi>O</mml:mi> <mml:mrow /> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> space, or online in $$\mathop {}\mathopen {}\mathcal {O}\mathopen {}(n^3)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mrow /> <mml:mrow /> <mml:mi>O</mml:mi> <mml:mrow /> </mml:mrow> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>n</mml:mi> <mml:mn>3</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> space and time. Our first result can be extended to find the longest common Lyndon subsequence of two strings of length at most n in $$\mathop {}\mathopen {}\mathcal {O}\mathopen {}(n^4 \sigma )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mrow /> <mml:mrow /> <mml:mi>O</mml:mi> <mml:mrow /> </mml:mrow> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>n</mml:mi> <mml:mn>4</mml:mn> </mml:msup> <mml:mi>σ</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> time using $$\mathop {}\mathopen {}\mathcal {O}\mathopen {}(n^2)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mrow /> <mml:mrow /> <mml:mi>O</mml:mi> <mml:mrow /> </mml:mrow> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>n</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> space.
Gürcü Dijital Kelime Dağarcığındaki İngilizce Kökenli Kelime ve Deyimlerin Dilsizleştirme Eğilimi

Verb forms that appear in Georgian with English cause many problems. The vulgar and inappropriate forms of these new forms have an impact on the language. Although these words have Georgian equivalents, there are examples where the use of English words distorts the language. There is no need to derive verbs from English in the Georgian language. On the other hand, the prevalence of such rudeness in language also creates some problems. Language becomes incomprehensible with social networks. The aim of the article is to group, classify and analyze Georgian spellings of computer-related language uses that are very common in Georgian.
Innovative cybernetic approach in learning process

The study is based on the concept of shaping students' self-organisation in the digital learning environment of a technical university. The recommendations for creating an effective learning system can be used in the creation of effective education systems. They will be created through the use of digital technologies in the educational process of the university to create a culture of self-organization of students. According to these provisions, the author of the work should identify the characteristics of educational activities and ensure their effective implementation during the formation of the culture of students of a technical university. Scientific research on students' self-organization culture formation will be carried out considering the specifics of students' interaction and information environment of the university. The direction of the project approach in pedagogy is "pedagogical planning". The authors have identified the stages of pedagogical planning necessary for successful implementation of the project methodology and the problem under the study. They are necessary in the formation process of university students' self-organization culture. To conduct the research, it is necessary to determine the way in which the ability to create a culture of self-organization in the digital educational environment of a technical university can be developed. The authors have set a goal to provide students with the opportunity to realize their creative potential, to form self-organization. To enable students to design projects independently, the authors suggest that they show their autonomy in designing. They plan means to organize an effective learning process and ensure its effectiveness, which in modern conditions is possible through the introduction of digital technologies in the educational process of the university to create self-organization of students. The article describes technological methods of students' self-organization culture formation in the conditions of digital educational environment of a technical university, namely, project technologies, which are based on the relationship of a teacher and a group.
Vibration Distribution Measurement of Car Door and Engine Head Using OPPA Vibration Distribution Analyzer

In order to address the issue of vibration, it is crucial to accurately measure the vibration distribution. The authors previously developed the one-pitch phase analysis (OPPA) method, which allows for rapidly capturing the three-dimensional shape of a flat object. By integrating this method into a system, an OPPA vibration distribution measurement system was created, utilizing a line light source consisting of LEDs or optical fibers and also a high-speed camera to measure the vibrations of three-dimensional objects without physical contact. To further extend the application of the OPPA method to larger objects, such as cars, in this paper, a new system is introduced using a commercially available projector using a liquid crystal display (LCD) instead of a liner light source and a glass grating. This new system, which employs an ultra-short throw projector, is highly sensitive in displacement measurements and provides a wide-area analysis. These kinds of projectors produce noises at the frequency of the cooling fan and the refresh rate of the LCD. However, in this study, these noise sources were also examined. The capabilities of the new system are demonstrated through its application to the measurement of vibrations in a car door and an engine head. The measurement system and examples of its application are presented.
Automatic feature selection for performing Unit 2 of vault in wheel gymnastics

We propose a framework to analyze the relationship between the movement features of a wheel gymnast around the mounting phase of Unit 2 of the vault event and execution (E-score) deductions from a machine-learning perspective. We first developed an automation system from a video of a wheel gymnast performing a tuck-front somersault to extract the four frames highlighting its Unit 2 performance of the vault event, such as take-off, pike-mount, the starting point of time on the wheel, and final position before the thrust. We implemented this automation using recurrent all-pairs field transforms (RAFT) and XMem, i.e., deep network architectures respectively for optical flow estimation and video object segmentation. We then used a markerless pose-estimation system called OpenPose to acquire the coordinates of the gymnast's body joints, such as shoulders, hips, and knees then calculate the joint angles at the extracted video frames. Finally, we constructed a regression model to estimate the E-score deductions during Unit 2 on the basis of the joint angles using an ensemble learning algorithm called Random Forests, with which we could automatically select a small number of features with the nonzero values of feature importances. By applying our framework of markerless motion analysis to videos of male wheel gymnasts performing the vault, we achieved precise estimation of the E-score deductions during Unit 2 with a determination coefficient of 0.79. We found the two movement features of particular importance for them to avoid significant deductions: time on the wheel and angles of knees at the pike-mount position. The selected features well reflected the maturity of the gymnast's skills related to the motions of riding the wheel, easily noticeable to the judges, and their branching conditions were almost consistent with the general vault regulations.
A Quantitative Model for the Assessment of Ethics Risks in Information Technology

The management of sensitive and personal data in the healthcare sector must guarantee the widest respect of patients' fundamental rights. However, some quantitative evaluation framework for assessing the level of ethical compliance of a technology to the most important ethical principles is still missing. In this work, we first provide a model to quantitatively assess constitutive ethics, i.e., the intrinsic ethical compliance of a technology. Secondly, we propose a method for quantitatively assessing circumstantial ethics risks of a technology, when used in some specific context. Our ethics risk assessment model is based on the evaluation of the compliance of the technology to a defined set of controls about some ethical principles and about the robustness of the technological infrastructure underneath. Then, we validate our model by applying it to some recent healthrelated blockchain frameworks, and we compare a qualitative ethical assessment with the quantitative assessment made with the proposed model for constitutive ethics compliance. Through our assessment, we identify some technical choices that achieve the highest ethical scores, such as using a permissioned blockchain, off-chain storage, and encryption of data. Finally, we observe that the principles of privacy and data governance turn out to be the most satisfied ethical principles, contrary to fairness.
A Dual-Branch Network for Few-Shot Vehicle Re-Identification With Enhanced Global and Local Features

Traditional vehicle Re-Identification (Re-ID) methods mainly rely on large-size training samples to achieve better results. However, obtaining abundant training samples is challenging for applications of these methods to few-shot vehicle Re-ID in real-world traffic scenes. To address this challenge, we propose a dual-branch network (DB-Net) for few-shot vehicle Re-ID with enhanced global and local features. Our proposed method reduces the dependence of vehicle Re-ID network on a large number of training samples by improving its feature expression ability and feature quality, rather than synthesizing vehicle images in traditional methods. The proposed DB-Net, composed of global and local branches, extracts global appearance features and local detail features of the vehicle. A global feature optimization module in the DB-Net mines and maintains more global semantic information using convolution and concat operations. And a feature screening module selects the most relevant local features based on Mutual Information and Shell Sorting to enhance local features. Additionally, a local attention module assigns adaptive weights to enhance salient local regions. Our approach is evaluated on a new dataset (Veri-FS) with small sample sizes and poor illumination conditions. It outperforms state-of-the-art methods on VehicleID, VeRi-776, and Veri-FS datasets, demonstrating its effectiveness in few-shot vehicle Re-ID.
COVID Down Under: where did Australia's pandemic apps go wrong?

Governments and businesses worldwide deployed a variety of technological measures to help prevent and track the spread of COVID-19. In Australia, these applications contained usability, accessibility, and security flaws that hindered their effectiveness and adoption. Australia, like most countries, has transitioned to treating COVID as endemic. However it is yet to absorb lessons from the technological issues with its approach to the pandemic. In this short paper we a) provide a systematization of the most notable events; b) identify and review different failure modes of these applications; and c) develop recommendations for developing apps in the face of future crises. Our work focuses on a single country. However, Australia's issues are particularly instructive as they highlight surprisingly pitfalls that countries should address in the face of a future pandemic.
WITHDRAWN: Visual interpretation for contextualized word representation

The transformer model has achieved great performance in natural language processing (NLP) tasks because it is an effective tool for contextualized word representation. However, the structure is complex and the classification process of the model is difficult to understand. When users have little understanding of the internal behavior of their models, it is difficult to explain the misclassification cases and quickly find a way to improve their models. Because a model usually contains a huge number of complex parameters, it is also difficult to solve this problem through simple parameters or mathematical analysis. Therefore, we developed a visual analysis tool to help users understand their models in detail, such as the impact of input data on their models and the behavior of each layer. Our tool focuses on interpreting models’ decision-making processes in NLP tasks. Our tool provides a complete set of processes to help users deeply understand the details of each model step, analyze the input data properties, and interact directly with their models. Using this visual tool, users can formulate and verify their hypotheses.
CoGAN: Cooperatively trained conditional and unconditional GAN for person image generation

Abstract Person image generation aims to synthesize realistic person images that follow the same distribution as the given dataset. Previous attempts can be generally categorized into two classes: conditional GAN and unconditional GAN. The former usually uses pose information as condition to make pose transfer using GAN. The generated person have the same identity as the source person. The latter generates person images from scratch, and the real person images are only used as references for the discriminator. While conditional GAN is widely studied, unconditional GAN is also worth exploring because it can synthesize person image with new identity, which is a useful manner of data augmentation. These two types of generating methods have their different advantages and disadvantages, and sometimes they are complementary. This paper proposes a CoGAN to cooperatively train two types of GANs in an end‐to‐end framework. The two GANs serve different purposes, and can learn from each other during the cooperative learning procedure. The experimental results on public datasets show that the proposed CoGAN improves the performance of both baseline methods, and achieves competitive results compared with state‐of‐the‐art methods.
Robust object tracking via ensembling semantic‐aware network and redetection

Abstract Most Siamese‐based trackers use classification and regression to determine the target bounding box, which can be formulated as a linear matching process of the template and search region. However, this only takes into account the similarity of features while ignoring the semantic object information, resulting in some cases in which the regression box with the highest classification score is not accurate. To address the lack of semantic information, an object tracking approach based on an ensemble semantic‐aware network and redetection (ESART) is proposed. Furthermore, a DarkNet53 network with transfer learning is used as our semantic‐aware model to adapt the detection task for extracting semantic information. In addition, a semantic tag redetection method to re‐evaluate the bounding box and overcome inaccurate scaling issues is proposed. Extensive experiments based on OTB2015, UAV123, UAV20L, and GOT‐10k show that our tracker is superior to other state‐of‐the‐art trackers. It is noteworthy that our semantic‐aware ensemble method can be embedded into any tracker for classification and regression task.