Reviewerly/exHarmony huggingface dataset:
Vocational Behavior Estimation Algorithm of Vocational Students in the Context of Big Data With Data Visualization Models

This article investigates and analyzes the actual career situation of students “offline” and “online” with the help of network data resources or mobile APP. Based on the object-oriented data model, key technologies such as level of detail technology and multi-resolution texture model are used to solve the problem of 3D visualization, and the rendering speed of 3D scenes can be accelerated. The existing problems are convenient to give positive guidance to the professional behavior of students in higher vocational colleges, and provide a basis for the school's education and life management of students. The experiment results shown the efficiency has been promoted up to 7.35%. The comparison analysis has proven the effectiveness of the model.
How Automate Requirements Engineering System Effects and Support Requirement Engineering

One of the most important responsibilities in software development is gathering requirements. As a result, it means that it must be collected with great care because it is a crucial factor in the software development life cycle (SDLC). Software engineers benefit from requirement engineering because it helps them better comprehend the problem they’re working on. In this case, an automated requirement engineering tool is used to improve better communication and association amid client investors and condition engineers to validate requirements more thoroughly. This paper used a orderly literature review strategy to examine numerous articles available on how programmed necessities engineering affects and supports requirements validation virtually in real-time. The computerized requirements engineering support is provided to assist the requirements engineer and client-stakeholders in effectively communicating and collaborating in real-time to accept their necessities, even however they are not in the similar geographic location. This allows the requirement engineer to write accurate and full test requests and test cases earlier presenting them to all stakeholders for approval. My suggested software partially helps RE by collecting requirements, eliciting them, documenting them, changing or adding requirements, scheduling interviews, and improving communication. The impact of automated requirements engineering software on RE and how automated systems support Requirement Engineering were examined in this study article (RE).
Differences Between Supplier and Customer Experiences of Marketing Mix in the Construction Industry

Suppliers often contend they “know” best when it comes to what the customer wants. Yet, despite using advanced models, such as the 7P (product, place, price, promotion, people, process, and physical evidence) marketing mix, companies can fail to meet their service objectives because customers perceive services from their own perspective. This difference in perspective presents a knowledge gap that requires it to be filled in an objective way. This research study elucidates how differences between supplier perception and customer perception of delivery are objectively identified. In this study, the target population consisted of the managers of the construction companies in Kuwait and their customers as individual property owners of residential, commercial, and industrial buildings. Discrete questionnaires consisting of the 7P marketing mix constructs were created specifically for the supplier and customer. Using a five-point Likert scale, data from 210 supplier staff and 210 customers were collected from the construction industry. The structural equation modeling (SEM) established the beta coefficients of latent variables reflecting the perceptions of both the supplier and customer. Both models were tested for internal consistency reliability using Cronbach’s alpha, and convergent validity was established based on the standardized factor loading and average variance extracted (AVE). In addition, discriminant validity was established using AVE and correlations. The differences between the standardized coefficients of supplier and customer coefficients were then tested against their pooled variance. The results show that suppliers tend to have a higher perception of their delivery on some constructs, while customers maintained a higher perspective on other constructs, that is, suppliers perceived that their product, place, price, and process were adequate, while customer data indicated otherwise. However, it was promising that the constructs of promotion, physical evidence, and people were more favorable among customer perceptions than supplier expectations. Interestingly, customers perceived that the people factor behind the construction industry was excellent. The findings recommend that suppliers scale their perceptions to be closer to the reality perceived by the customers. The study concludes that this approach of evaluating supplier–customer perceptions is highly beneficial to the supplier.
Carina – A Corpus of Aligned German Read Speech Including Annotations

This paper presents the semi-automatically created Corpus of Aligned Read Speech Including Annotations (CARInA), a speech corpus based on the German Spoken Wikipedia Corpus (GSWC). CARInA tokenizes, consolidates and organizes the vast, but rather unstructured material contained in GSWC. The contents are grouped by annotation completeness, and extended by canonic, morphosyntactic and prosodic annotations. The annotations are provided in BPF and TextGrid format. It contains 194 hours of speech material from 327 speakers, of which 124 hours are fully phonetically aligned and 30 hours are fully aligned at all annotation levels. CARInA is freely available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , designed to grow and improve over time, and suitable for large-scale speech analyses or machine learning tasks as illustrated by two examples shown in this paper.
Improving Reference-Based Image Colorization For Line Arts Via Feature Aggregation And Contrastive Learning

The tremendous semantic discrepancy between the line art drawings without texture and the reference pictures containing rich color challenges current image-to-image translation models. Previous works attempt to establish cross-domain correspondence. However, they fail to capture more detailed features. A Reference-based Line art Translation Network (RLTN) is introduced with a Multi-level Feature Aggregation Module (MFAM) to improve the performance. The MFAM concentrates on more meaningful information for feature matching by utilizing the Multi-stream High Frequency Block (MHFB) and the Pixel-wise Correlation Block (PCB). We also employ the Channel-level Attention Block (CAB) and the Spatial-level Attention Block (SAB) for a better fusion of features. Moreover, a Style-based Contrastive Loss (SCL) is proposed to maintain the style similarity between the synthesized images and the reference examples. Experiments conducted on three datasets demonstrate the effectiveness of our model in producing more pleasing visual effects compared with state-of-the-art approaches.
A web-based application for generating statistical tables

Statistical tables associated with named probability distributions and their families, such as the standard normal, Student’s t, and chi-square tables, among others, have been utilized for years and are still widely used today, especially for mathematics and statistics education. While such tables can be found in many statistics textbooks and even on the internet, various shortcomings exist regarding the process of obtaining and distributing tables, as well as the content and appearance of some. In this article, a description is provided of a free, web-based Shiny application created by the author to address these shortcomings and which enables users to generate common statistical tables that are simple and quick to make, customizable, and that users may download and distribute without fear of committing copyright infringement.
Denoising-Oriented Deep Hierarchical Reinforcement Learning for Next-Basket Recommendation<sup>⋆</sup>

Next basket recommendation aims to provide users a basket of items on the next visit by considering the sequence of their historical baskets. However, since a user’s purchase interests vary over time, historical baskets often contain many irrelevant items to his/her next choices. Therefore, it is necessary to denoise the sequence of historical baskets and reserve the indeed relevant items to enhance the recommendation performance. In this work, we propose a Hierarchical Reinforcement Learning framework for next Basket recommendation, named HRL4Ba, which learns the personalized inter-basket and intra-basket contexts of the user for dynamic denoising. Specifically, the high-level and the low-level agent in the denoising module perform hierarchical decisions, i.e., revise baskets and remove items; the recommendation module serves as the environment to give feedback to agents and recommends the next basket. Extensive experiments on two e-commerce datasets show the HRL4Ba outperforms existing state-of-the-art methods, and our ablation studies further show the effectiveness of each component in HRL4Ba.
Uncertainty Estimation with a VAE-Classifier Hybrid Model

We propose a hybrid model that combines a generative unit and a discriminative classifier to quantify uncertainty in a classification task. The representation learning capability in the VAE module allows our method to learn more useful and generalizable features and outperform other purely discriminative classifiers when training labels are limited. With proper statistical treatment, the probabilistic encoder in our VAE module offers a convenient mechanism to express uncertainty for out-of-distribution (OOD) data. As a result, our method gives better calibrated uncertainty prediction. We demonstrate the effectiveness of our method on MNIST and a challenging medical image dataset for skin lesion diagnosis.
Equal Loss: A Simple Loss Function for Noise Robust Learning

Training accurate deep neural networks in the presence of noisy labels is an important task. Though a number of approaches have been proposed for learning with noisy labels, many open issues remain. In this paper, we show that DNN learning with Cross Entropy is not robust to label noise and exhibits imbalance between the gradient of clean and noisy samples. We propose a new loss function, Equal Loss (EL), boosting DNN with a relaxed target probability and balanced gradient density. Both theoretical analysis and experiments on a range of benchmarks and real-world datasets show that EL outperforms state-of-the-art methods.
Energy Alignment for Bias Rectification in Class Incremental Learning

In class incremental learning (CIL), models are expected to be able to learn new categories continuously. However, the standard DNNs suffer from catastrophic forgetting. Recent studies show class imbalance is an essential factor that causes catastrophic forgetting in CIL. In this paper, from the perspective of energy-based model, we demonstrate that the free energies of categories are aligned with the label distribution theoretically, thus the energies of different classes are expected to be close to each other when aiming for "balanced" performance. However, we discover a severe energy-bias phenomenon in the models trained in CIL. To eliminate the bias, we propose a simple and effective method named Energy Alignment by merely adding the calculated shift scalars onto the output logits, which does not require to (i) modify the network architectures, (ii) intervene the standard learning paradigm. Experimental results show that energy alignment can achieve good performance on several CIL benchmarks.
Partial Consistency for Stabilizing Undiscounted Reinforcement Learning

Undiscounted return is an important setup in reinforcement learning (RL) and characterizes many real-world problems. However, optimizing an undiscounted return often causes training instability. The causes of this instability problem have not been analyzed in-depth by existing studies. In this article, this problem is analyzed from the perspective of value estimation. The analysis result indicates that the instability originates from transient traps that are caused by inconsistently selected actions. However, selecting one consistent action in the same state limits exploration. For balancing exploration effectiveness and training stability, a novel sampling method called last-visit sampling (LVS) is proposed to ensure that a part of actions is selected consistently in the same state. The LVS method decomposes the state-action value into two parts, i.e., the last-visit (LV) value and the revisit value. The decomposition ensures that the LV value is determined by consistently selected actions. We prove that the LVS method can eliminate transient traps while preserving optimality. Also, we empirically show that the method can stabilize the training processes of five typical tasks, including vision-based navigation and manipulation tasks.
Relative Viewpoint Estimation Based on Structured 3d Representation Alignment

Relative viewpoint estimation is a fundamental problem in various image processing applications. Traditional estimation approaches can fail if sufficient appearance overlap is not observed between two images. Recent advances in 3D representation learning from images have made it possible to exploit the underlying 3D structure. In this paper, we propose a relative viewpoint estimation method using an end-to-end trainable network that learns structured 3D representations. In the proposed method, an independent coordinate system is set for each image in order to construct a structured 3D representation. This makes it possible to estimate the relative viewpoint by aligning those representations through coordinate transformations. Experimental results on the ShapeNet, Pix3D, and Thingi10K datasets demonstrated that the proposed method achieves accurate estimation even if there is not sufficient observable appearance overlap between the images.
Enhance Rnnlms with Hierarchical Multi-Task Learning for ASR

It is known that neural language models (NLMs) can implicitly learn certain linguistic information from text. While generally NLMs only use word feature input, the success of factored NLMs has indicated a benefit of using additional linguistic feature inputs for language modeling. On the other hand, multi-task learning (MTL) has shown positive effects on the generalization performance of various natural language processing (NLP) tasks, including language modeling. However, how to best share information among related tasks in MTL remains to be addressed. In this current work, we propose a hierarchical multi-task learning (HMTL) approach to incorporate linguistic knowledge into recurrent neural network language models (RNNLM), instead of using linguistic features as word factors. Specifically, we consider the auxiliary tasks of chunking, part of speech tagging, and named entity recognition, and supervise the learning of these auxiliary tasks in a hierarchical way. Our proposed method has the potential of helping language models learn knowledge of linguistic hierarchy from the auxiliary tasks, and improve the performance of RNNLMs on automatic speech recognition (ASR). We have evaluated our proposed HMTL method on WSJ and AMI speech recognition tasks. Our experiment results demonstrate the effectiveness of the proposed approach.
The Effectiveness of Learning Management System (LMS) as a Learner Self-Service-based Platform in Community Learning Center (CLC)

This study aims to test the effectiveness of the learning management system (LMS) as a platform based on learner self-service at the Community Learning Center (CLC). This study uses a quantitative approach with one group pretest-posttest. Respondents were 90 learners in PKBM Setia Mandiri with quota sampling technique. Data were collected in scores and analyzed using descriptive and inferential statistical analysis. The results showed that the learning management system (LMS) as a learner self-service-based platform effectively increased learner mastery, especially during distance learning. This finding is reinforced by the learner scores showing a pretest score of 48.49 compared to the posttest result of 78.41, with a significance (sig.) of 0.000 <0.05. Thus, a learning management system (LMS) can be an innovative learning platform that helps learners understand the material and form a learning society.
Find The Way Back: Invertible Kernel Estimator For Blind Image Super-Resolution

We address the task of zero-shot blind image super-resolution, where it aims to recover the high-resolution details from the low-resolution input image under a challenging problem setting of having no external training data, no prior assumption on the downsampling kernel, and no pre-training components used for estimating the downsampling kernel. While existing zero-shot blind super-resolution works follow the strategy of firstly estimating the downsampling kernel via cross-scale recurrence and then learning the non-blind upsampling model, we in turn propose a carefully-designed invertible network for modeling both the downsampling and upsampling operations at once. Specifically, the invertible property enables the use of cross-scale recurrence across more scales and thus further benefits the overall model training. We conduct extensive experiments to demonstrate our proposed method’s superior performance over several baselines and its effectiveness in handling the images downsampled by nonlinear kernels.
Distributed Label Dequantized Gaussian Process Latent Variable Model for Multi-View Data Integration

In this paper, we present a novel method for multi-view data analysis, distributed label dequantized Gaussian process latent variable model (DLDGP). DLDGP can integrate multi-view data and class information into a common latent space. In the previous multiview methods, the dimension of label features transformed from the class information is much smaller than those of the other modalities, which causes a dimensionality-limitation problem in the latent space. DLDGP extends the dimension of the label features by a distributed label dequantization scheme. Additionally, DLDGP calculates correlation between different classes by encoding class information into distributed features. DLDGP can correctly capture the relationship between multi-view data and obtain the latent features with high expression ability. Experimental results show the effectiveness of our method by using the open dataset.
Automatic Image and Video Captioning Production using Deep Learning

Deep Learning-based methodologies have a lot of promise for applications that try to automatically produce captions or explanations for photos and video frames. In the field of imaging science, Captioning images and videos is regarded to be a difficult intellectual task. People's images and videos automatically generate captions (or descriptions). with disabilities is one of the application domains, from a range of visual impairments; automatic metadata generation for photos; picture and video search engine indexing; a variety of general-purpose robot vision systems; and so forth. Each of these application domains has its own set of requirements. has a large and beneficial impact on a variety of other task-specific applications This isn't designed to be a complete look of picture captioning; rather, it's a quick rundown of deep learning-based image and video captioning techniques. The algorithmic overlap between picture and video captioning, as well as audio, is the topic of this study.
High-Fidelity Portrait Editing Via Exploring Differentiable Guided Sketches from the Latent Space

This paper studies the task of sketch-guided high-fidelity portrait editing. Advanced unconditional generators, such as StyleGAN, can generate a high-quality portrait image with great diversity. In previous researches, StyleGAN has successfully been utilized for color-guided image editing through latent vector optimization. Nonetheless, passing sketch information to the generating model directly is nontrivial. To this end, we present an algorithm that addresses the problem of well controlling the generation process via differentiable guided sketches from latent space. Specifically, we re-purpose the classic operator – eXtended difference-of-Gaussians (XDoG) that derives differentiable sketches from images. We also propose a multi-scale sketch loss assisted with which can finally guide the model follow the guidance sketch to generate. Extensive experiments validate the efficacy of our model in sketch-guided editing. We show that the quality of produced images is better than that of competitors.
Private Learning Via Knowledge Transfer with High-Dimensional Targets

Preventing unintentional leakage of information about the training set has high relevance for many machine learning tasks, such as medical image segmentation. While differential privacy (DP) offers mathematically rigorous protection, the high output dimensionality of segmentation tasks prevents the direct application of state-of-the-art algorithms such as Private Aggregation of Teacher Ensembles (PATE). In order to alleviate this problem, we propose to learn dimensionality-reducing transformations to map the prediction target into a bounded lower-dimensional space to reduce the required noise level during the aggregation stage. To this end, we assess the suitability of principal component analysis (PCA) and autoencoders. We conclude that autoencoders are an effective means to reduce the noise in the target variables.
Performance Optimization of 3-DOF Application Scene Based on 360-Degree Panoramic Technology with Depth Information

High-quality 3D scenes often show poor rendering effect and insufficient operational performance in low-end devices. Therefore, how to make better use of 360-degree panoramic technology and replace the traditional pure model scene by visual deception is of great significance to the picture quality improvement and performance optimization of low-end equipment. In this paper, a three-degree-of-freedom (3-DOF) application scenario for indoor simulation is used as an example. Under the premise of 360-degree panoramic technology, depth information is obtained through laser ranging, converted into a two-dimensional form of depth image with the help of spherical coordinates, and combined with a transparent information image, the depth image is compressed using DEFLATE compression algorithm, and finally be stored. By optimizing the data through bilinear sampling and with the help of geometric knowledge, the occluded parts are removed or translucent according to the actual situation when the graphics are rendered, and the occlusion relationship is correctly handled in order to achieve a real integration of the virtual environment with the 3D objects. Through multiple data, it is shown that the method possesses significant performance improvement while ensuring geometric realism.