Reviewerly/exHarmony huggingface dataset:
Using Efficient IoU loss function in PointPillars Network For Detecting 3D Object

Detecting three-dimensional (3D) objects has attracted the growing attention in 3D computer vision research. However, the low precision value is a significant trouble in many applications, like automatic driving, robotics, and medical applications. To solve the low precision problem, we use the 3D EIoU loss as localization loss, which emphasizes on the overlapping degree, central position, and structural shape between two rectangular bounding boxes. Furthermore, we propose an EIoU-NMS to enhance the process of suppressing redundant detecting boxes. By incorporating the 3D EIoU loss and EIoU-NMS into the PointPillars one-stage detectors, the detection performance for 3D point cloud objects is considerably improved. By using the KITTI benchmark, empirical experiments have been conducted to measure the average precision (AP) values for detecting Car, Cyclist, and Pedestrian objects.
A Gentle Approach to Imprecise Probability

I had the good fortune of meeting Teddy Seidenfeld early in my career and learning from him over many years how to approach probability in general, and imprecise probability in particular. I first learned about Teddy's work from Henry Kyburg, Teddy's undergraduate teacher and my Ph.D. supervisor, who introduced me to Teddy's and Larry Wasserman's work on dilation, among other striking contributions that Teddy made while I was a graduate student in the 1990s, many with his longtime collaborators Jay Kadane and Mark Schervish. I cannot overstate the impact Teddy's work has had on me. He will doubtlessly conclude that I have not learned from him well enough, but I am nonetheless grateful for his years of instruction. The field of imprecise probability has matured in no small part because of Teddy's decades of original scholarship and essential contributions to building and sustaining the ISIPTA community. Although the basic idea behind imprecise probability is (at least) 150 years old, a mature mathematical theory has only taken full form in the last 30 years. Interest in imprecise probability during this period has also grown, but many of the ideas that the mature theory serves can be difficult to apprehend to those new to the subject. Although these fundamental ideas are common knowledge in the ISIPTA community, they are expressed, when they are expressed at all, obliquely, over the course of years with students and colleagues. A single essay cannot convey the store of common knowledge from any research community, let alone the ISIPTA community. But, this essay nevertheless is an attempt to guide those familiar with the basic Bayesian framework to appreciate some of the elegant and powerful ideas that underpin the contemporary theory of lower previsions, which is the theory that most people associate with the term 'imprecise probability'.
User Experience Evaluation of Kindteractive Application to Improve User Experience of Kindergarten Interior Introduction

Technological advancements, such as the usage of augmented reality (AR) technology, are increasingly supporting the creation and implementation of more interactive learning media in the field of education. The conditions and scenario of the Covid-19 epidemic are also driving an increase in digital and online schooling. The more diverse the apps generated, the more they must analyze the user experience of the application’s design and operation. The User Experience Questionnaire (UEQ) tool may be used to measure user experience in apps. With 26 questionnaire points, this test has six rating scales: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. The UEQ Quality findings reveal that the Attractiveness scale is 2.04, the Pragmatic Quality scale is 1.68, and the Hedonic Quality scale is 1.17, all of which indicate good assessment results, implying that users prefer the program in terms of user experience. Users have utilized comparable programs based on this data, yet updates are still absent. Attractiveness, Perspicuity, Efficiency, and Stimulation are good scales. Based on the findings of the respondents’ data processing, it was determined that the application’s convenience was adequate in the user’s opinion. According to the statistics, the user loved the program.
A Design of Childhood Stunting Assessment Feature with Agile UX Approach

multidisciplinary collaboration between public health, system engineering, and UX is able to generate a solution in healthcare problem like stunting. The principle of Agile UX gathers requirements to generate an application design. Currently, various website and mobile applications are available to be utilized for stunting research. StuntingDB is one of many web-based applications that specialized in stunting data management. The application required several types of users to operate hierarchically. This research focused on Public Health Officers’ workflow that require a solution to in the form of assessment module for stunting DBMS by using Agile UX principle. The method collaborates agile team and UX team to solve Public Health Officer's difficulty in changing stunting status at pre-saved child patient's profile. Collaboration of both agile team and UX team successfully proposed an application design that system design.
BGCL: Bi-subgraph network based on graph contrastive learning for cold-start QoS prediction

With the advent of Web service technologies, the number of services published on the cloud is increasing rapidly. The quality of service (QoS) becomes a crucial criterion for selecting services from a massive pool of candidates. Collaborative filtering (CF) has become a major way for personalized QoS prediction by leveraging historical interactions between users and services. Due to the increasing number of users and services, CF-based QoS prediction often suffers from data sparsity and cold-start difficulties. Inspired by the advantages of graph contrastive learning in cold-start predictions, we propose BGCL, a bi-subgraph network based on graph contrastive learning to solve the above problems. Firstly, we generate different perspectives of user-neighborhood and service-neighborhood sub-graphs based on sparse user–service bipartite graphs. Next, our model learns user and service embeddings using the graph contrastive learning and graph attention aggregation mechanisms on the generated sub-graphs. Finally, user and service embeddings are fed into a multi-layer perception to predict QoS values. Experimental results show that our model outperforms several existing models in terms of prediction accuracy.
A light-weight data augmentation method for fault localization

Fault localization (FL) is essentially a search over the space of program statements to find suspicious entities that might have caused a program failure. However, the input data is high-dimensional and extremely imbalanced since the real-world programs are large in size and the number of failing test cases is much less than that of passing test cases, which limits the effectiveness and efficiency of existing FL methods. The state-of-the-art FL method (Aeneas) solves the imbalanced and high-dimensional problem but in a complex and time-consuming process. Due to the limited effectiveness of original FL methods and the low efficiency of Aeneas, this paper proposes Lamont, a Light-weight data augmentation method to improve the effectiveness of original FL methods and the efficiency of Aeneas. Lamont uses revised linear discriminant analysis (LDA) to reduce the dimensionality of the original coverage matrix and leverage synthetic minority over-sampling (SMOTE) to generate the synthesized failing tests. The balanced coverage matrix with reduced dimensionality is fed into FL methods to obtain the ranked suspicious list of statements. To evaluate the efficiency and effectiveness, we compare Lamont with six representative FL methods and Aeneas on 458 versions of 10 real-life programs. It can be observed that Lamont outperforms in most cases for Top-K metric and reduces the number of statements that need to be checked from 17.45% to 79.81% compared with the original six FL methods. Furthermore, Lamont saves the time over the state-of-the-art data augmentation method Aeneas from 55.33% to 68.39% with comparable effectiveness. This work conducts a large-scale experimental study to investigate the effectiveness and efficiency of Lamont. Two conclusions can be obtained based on the experimental results. First, it shows that Lamont is more effective than the original FL methods. Second, it shows Lamont is more efficient than Aeneas with similar effectiveness in six FL methods.
The pipeline for the continuous development of artificial intelligence models—Current state of research and practice

Companies struggle to continuously develop and deploy AI models to complex production systems due to AI characteristics while assuring quality. To ease the development process, continuous pipelines for AI have become an active research area where consolidated and in-depth analysis regarding the terminology, triggers, tasks, and challenges is required. This paper includes a Multivocal Literature Review where we consolidated 151 relevant formal and informal sources. In addition, nine-semi structured interviews with participants from academia and industry verified and extended the obtained information. Based on these sources, this paper provides and compares terminologies for DevOps and CI/CD for AI, MLOps, (end-to-end) lifecycle management, and CD4ML. Furthermore, the paper provides an aggregated list of potential triggers for reiterating the pipeline, such as alert systems or schedules. In addition, this work uses a taxonomy creation strategy to present a consolidated pipeline comprising tasks regarding the continuous development of AI. This pipeline consists of four stages: Data Handling, Model Learning, Software Development and System Operations. Moreover, we map challenges regarding pipeline implementation, adaption, and usage for the continuous development of AI to these four stages.
A novel Multi-scale architecture driven by decoupled semantic attention transfer for person image generation

Person image generation is a challenging task aimed to transfer the person of the source image from a source pose to a target pose while preserving its style. In this paper, we proposed a Generative Adversarial Network based on Decoupled Semantic Attention Transfer (DSAT-GAN), focusing on that local semantic representations of different image styles and contents cannot be accurately decoupled and transferred. This architecture employs a novel Multi-scale Semantic Mapping Generation Network (Ms-SMGN), driven by two network modules with different semantic attention mechanism, aiming to accurately align and transfer the representations of local semantics at different spatial scales. Then, a channel-separated convolution is applied in the encoding networks instead of the traditional channel fully-connected operation, which reduces computational complexity while realizing channel semantic decoupling. Moreover, a Gram matrix-based global style loss is introduced to further enhance the consistency of high-level semantic between generated and target images. Experiments on Market-1501 and DeepFashion datasets show that DSAT-GAN has superior performance compared with other recent baselines. Additionally, this architecture can be extended to the data enhancement scenes to significantly improve the accuracy of person Re-identification.
Robust and optimal epsilon-insensitive Kernel-based regression for general noise models

Sparse representation of kernel based regression (KBR) has received considerable attention in recent years. Studies on sparse KBR can be divided into two distinct groups, namely (i) pruning-based methods that remove the training samples with the least training errors and retrain the remaining training samples, and (ii) direct methods that begin with a full-dense solution and delete training data according to objective criteria. Pruning-based methods give rise to a high computation time, while direct methods may lead to non-optimal solutions and thus a poor approximation. In addition, most current KBR models assume that the error distribution is Gaussian. However, observations in many practical applications indicate that the noise models do not satisfy the Gaussian error distribution. In such cases, current KBR models are not optimal. To address the above-mentioned problems, this study proposes a new sparse KBR framework for general noise distributions, including the epsilon-insensitive noise family. Compared with other sparse algorithms, sparsity is directly imposed by epsilon-insensitive convex loss functions derived from the theoretical framework of the Bayesian approach within the scope of regularization networks, and then handles the optimization problem in Lagrangian form. Experiments on artificial and real-life benchmark datasets demonstrate that the proposed epsilon-insensitive KBR models are more effective and efficient than pruning-based approaches.
Design of constrained dynamic path planning algorithms in large-scale 3D point cloud maps for UAVs

The technology of unmanned aerial vehicles (UAVs) has been growing rapidly in recent years. To achieve highly autonomous flight capability, collision-free path generation becomes a key research topic. However, for a large-scale terrain map, an efficient path searching algorithm is always a challenging problem. Based on the Goal-bias Rapidly-exploring Random Tree Star (GB-RRT*) together with the aid of a bidirectional search technique, in this paper, a new constrained path planning algorithm, called Parent Revisited Goal and Parent Bias RRT* (PRGPB-RRT*), is proposed. Three major objectives are considered as the development benchmark: 1). shorter total distance, 2). smoother flight path, and 3). high computational efficiency. To meet different task requirements, a dynamic path planning method is investigated to diminish the high costs due to large-scale environments and keep track of the changing destination by finding local target points. In addition, unknown object avoidance is an essential part of flight path planning. As a consequence, the Fitting-Octree is presented to partition the point clouds into multiple cubes to represent the obstacles precisely. Moreover, the method of grouping point clouds into clusters effectively increases the efficiency of the implementation under a large number of obstacles. Finally, the feasibility of the proposed method is verified by a real-world 3D large-scale point cloud map. Experiments show that the proposed method successfully enhances the path generation efficiency as well as the trajectory smoothness. Moreover, the presented PRGPB-RRT* can be applied to different intelligent UAVs subject to flight kinematics constrain.
EHA-Transformer: Efficient and Haze-Adaptive Transformer for Single Image Dehazing

Deep learning based dehazing structures have achieved significant progress in image haze removal. However, most recent methods mainly focused on the excellent feature extraction and representation capabilities of deep networks, and neglected the contributions of traditional haze-relevant priors to image dehazing. In this paper, we propose a novel dehazing method, named EHA-Transformer, which fully integrates the Transformer with haze-relevant features and enhances the interpretability. Since the haze distributions vary in different regions, the difficulties of local patch dehazing are also different. Based on this, we first propose a haze detector to distinguish regions, which are prone to produce residual haze during dehazing. Then, we introduce a haze-adaptive loss into our dehazing framework to increase the stability of the training process. Our dehazing framework is simple and generic, and can be easily applied to current dehazing models without introducing complexity. Since our EHA-Transformer takes full account of haze related properties, comprehensive experiments compared with state-of-the-arts demonstrate our framework have significant improvements in terms of robustness. We also apply our framework into different backbones, the noticeable improvements of different dehazing backbones illustrate the generalization capability of our framework.
3DCMM: 3D Comprehensive Morphable Models for Accurate Head Completion

3D head completion aims at recovering accurate 3D full-head geometry from 2D face images or 3D face scans. Previous 3D shape reconstruction studies primarily focused on the facial region, but ignored the scalp region. Moreover, as critical foundations in 3D head completion, powerful 3D head morphable models, however, are scarce. In this paper, we construct 3D comprehensive morphable models (3DCMM) of human faces and scalps, and develop a novel 3DCMM-based stepwise 3D full-head creation pipeline: reconstructing face regions firstly, and then completing scalp regions. Firstly, large-scale 3D heads from 2,528 identities were parameterized to construct powerful 3DCMM as our foundations. Then, a 3DCMM-based supervised converting method was presented to predict an accurate scalp region from a facial region and produce full-head geometry. Extensive experiments and comparisons demonstrated that our 3DCMM possesses better quality and descriptive power. Benefiting from this, our model-based 3D head completion method has higher accuracy than model-based fitting method.
A COMPARISON STUDY : THE EFFECT OF NOUNS AND VERBS IN FINDING FEATURE LOCATION

Feature location is a method of mapping between high abstraction of software artifacts into specific areas of source code. A use case scenario is a software artifact that contains many words that express the interaction between the actor and the system. The software is designed based on the story. Using a use case scenario as a query for the location of a feature would be beneficial, but not every word of a use case scenario can indicate the location feature. This study aims to measure the use of all words, only nouns, or nouns and verbs from the use case scenario as a query could help to find the features location. Natural language processing is also used as a preprocessing method to reduce the number of words in both the source code and the queries. The source code was indexed with LDA (Latent Dirichlet Allocation) to produce the topics. The topics proportion among the queries and source code were measure by cosine similarity ranking to identify that the queries similar with source code. A precision and recall were used to measure the successful rate of this method. The results are as follows, the average of precision of noun words was better (precision average 11.2%) than all word usage (precision average 9.9%). The average recall of noun word was better (recall average 4.2%) than all word usage (recall average 3.4%). The most interesting part was the noun words usage could minimize the area of documents by 83% (from 1074 documents into 181 documents) if compared with all words usage. The usage of Verb or nouns words in use case scenario got the best average recall (5.4%).
TileSpMSpV: A Tiled Algorithm for Sparse Matrix-Sparse Vector Multiplication on GPUs

Sparse matrix-sparse vector multiplication (SpMSpV) is an important primitive for graph algorithms and machine learning applications. The sparsity of the input and output vectors makes its floating point efficiency in general lower than sparse matrix-vector multiplication (SpMV) and sparse matrix-matrix multiplication (SpGEMM). Existing parallel SpMSpV methods focused on various row- and column-wise storage formats and merging operations. However, the data locality and sparsity pattern of the input matrix and vector are largely ignored.
Design, Development and Validation of an Educational Methodology Using Immersive Augmented Reality for STEAM Education

The main objective of this study is the design and validation of an educational methodological model based on the use of immersive technological resources (Augmented Reality – AR) to improve learning processes in secondary education science subjects (Biology and Geology). The process was developed based on three main quantitative studies: an exploratory study, a study of performance divided into three cases studies, and an attitudinal study. The information obtained was completed with a fourth qualitative study of the training of teachers who participate in educational technology. This research provides empirical evidence that allows validation of the methodological model developed to explain key concepts and to improve the level of motivation and acceptance of AR technology by students. The proposed model can induce improvements in educational processes in the field of STEAM when used with an immersive AR technological resource and an adapted digital evaluation system. It also demonstrates that teachers require specific training in connection with the creation and the adequate use of AR educational resources, and of digital evaluation systems as well. The results of this study have important implications for the field of education, demonstrating the potential of AR technology to improve learning outcomes and the need for teacher training in its use.
Landslide analysis in the Cisarua District using geographic information system

Disaster Management Agency (BNPB) reported more than 100 landslide cases in the Bogor area from January to April 2018. Topography and high rainfall intensity are predicted as the main factor in the landslide occurrence in Bogor. This study aims to develop the vulnerability map and evaluate its accuracy based on the field's landslide points. The methods proposed by the Directorate of Volcanology and Disaster Mitigation, namely DVMBG 2004 and DVMBG 2005, are used in this study. This study shows that the DVMBG 2004 resulted in the landslide-prone classification with a percentage of 74.94% and 71.92%, in 2014 and 2018, respectively. In addition, DVMBG 2005 result in the classification of hazard dominates the potential for landslides with a percentage of 71.78% and 84.58%, for 2014 and 2018, respectively. The validation results show that the vulnerability map developed by DVMBG 2004 results in 59% valid. Meanwhile, in the DVMBG 2005, all points of occurrence were validated. Hence, the DVMBG 2005 landslide estimation model is the recommended method for making landslide hazard potential maps.
Quantum Correlations and Speed Limit of Central Spin Systems

In this article, we consider single, and two-qubit central spin systems interacting with spin baths and discuss their dynamical properties. We consider the cases of interacting and non-interacting spin baths and investigate the quantum speed limit (QSL) time of evolution. The impact of the size of the spin bath on the quantum speed limit for a single qubit central spin model is analyzed. We estimate the quantum correlations for (non-)interacting two central spin qubits and compare their dynamical behaviour with that of QSL time under various conditions. We show how QSL time could be availed to analyze the dynamics of quantum correlations.