Reviewerly/exHarmony huggingface dataset:
A Study on the Reestablishment of Competency-Based Major Curriculum Accreditation System: The Case of H University

Objectives This study was conducted to redefine the competency-based major curriculum accreditation system of H University and has the following purposes. First, H University's competency-based major curriculum accreditation( 1st stage redesign, 2nd stage outcome) indicators were reestablished, and second, H University's competency- based major curriculum accreditation system (system including indicators) was specified.&#x0D; Methods First, in order to set the direction of H University's accreditation evaluation system and development base, we analyzed previous studies and other university cases and drew a draft based on them. Next, we asked internal and external experts for advice on the draft derived, and repeatedly revised by advice. Finally, content validity (CVR) was secured based on the minimum content validity rate used in the Delphi technique (Lawshe, 1975; re-citation; Kim Suran, Lee Sunkyung, 2021).&#x0D; Results H University's competency-based major curriculum first-stage accreditation index was reestablished into 3 evaluation areas, 8 evaluation elements, and 11 detailed items for the purpose of self-checking with an emphasis on “competency-based major curriculum redesign and plan.” and, second-stage accreditation indicators were derived into 3 evaluation areas, 5 evaluation factors, and 7 detailed items for the purpose of self-checking with an emphasis on the operation and outcome of the competency-based major curriculum. Second, specific measures were established for the overall annual system of continuous improvement of the quality management of major curriculum, that is, recognition criteria, accreditation procedures, and follow-up management, including accreditation indicators.&#x0D; Conclusions This study is meaningful in that it was re-established by considering the educational direction of H University, reflecting repeated reviews and advice by many internal experts, and to overcome the limitations of a temporary fragmentary certification system.
O papel do design emocional em capas de livros: revisão de estudos

This article aims to identify studies that make it possible to understand which aspects of the cover design can influence the reader in the process of choosing a physical book through the emotional design, using as a method the Systematic Literature Review conducted in the databases CAPES and Google Scholar.As a result, it was possible to identify which visually attractive covers have greater market appeal and sales potential, generating curiosity and instigating the reader to get to know the story.Among the identified aspects, the colors proved to be fundamental elements for the creation of context of the story, influencing the reader to choose a book by its cover, however, no articles were found that related the influence of book covers on the reader with the Emotional Design.
The Unbearable Lightness of Morph Classification

In light of the recent push for the creation and unification of large morphologically annotated resources, there is a call for (preferably language-independent, low-resource) methods of morph classification. This paper reports on a pilot experiment on morph classification of the Czech language. We have performed two experiments - root morph recognition and complete morph classification. By exploiting simple quantitative methods and - in some cases - available Czech morphological resources, we have achieved morph-level precision of respectively 96.7% and 88.3%.
Generalized Integrated Interleaved Codes for High-Density DRAMs

As the density of dynamic random-access memories (DRAMs) keeps increasing, which results in higher error rates, the conventional single error correction and double error detection codes are no longer sufficient. Generalized integrated interleaved (GII) codes based on Reed-Solomon (RS) codes are among the best error-correcting codes for high-density DRAMs due to their hyper-speed decoding and good correction capability. However, the very short codeword length required by DRAMs leads to miscorrections that substantially degrade the error-correcting performance of GII-RS codes if untreated. Previous miscorrection mitigation schemes for longer GII-BCH codes lead to additional code rate loss when applied to very short GII-RS codes and hence affect the cost of DRAMs. This paper presents new miscorrection mitigation schemes with improved code rates. A small number of parity bits are allocated in an optimized manner and decoding trials are carried out to close the performance gap. Moreover, low-latency hardware implementation architectures have been developed for the proposed GII-RS decoder. For the example code considered for DRAMs, the proposed decoder reduces the worst-case latency by 45% with small area overhead while keeping the same average latency and critical path compared to the best possible alternative design.
A Multi-level Synthesis Strategy for Online Handwritten Chemical Equation Recognition

Handwritten chemical equation recognition is an appealing task, but its development is hampered by the lack of publicly available datasets. To this end, we propose a multi-level synthesis strategy to synthesize the corresponding handwritten equations from LaTeX expressions and regard the chemical equation recognition as an image-to-markup task. In particular, our approach first decomposes the LaTeX expression into a symbol layout tree (SLT) and obtains different multi-level components in stages by traversing the SLT. Then, online isolated symbols are placed in appropriate locations consistent with handwritten habits through a baseline-based layout strategy. Furthermore, expression patterns are enhanced at the local, component, and global levels to increase the diversity of synthesized data. It is worth noting that our synthesis strategy is theoretically applicable to any LaTeX-based expression. We also collected a real dataset containing 1595 handwritten chemical equations, and the experimental results confirm that our proposed method can effectively improve the performance of handwritten chemical equation recognition systems. The dataset we generated will be released.
Neural Texture Synthesis with Guided Correspondence

Markov random fields (MRFs) are the cornerstone of classical approaches to example-based texture synthesis. Yet, it is not fully valued in the deep learning era. This pa-per aims to re-promote the combination of MRFs and neural networks, i.e., the CNNMRF model, for texture synthesis, with two key observations made. We first propose to compute the Guided Correspondence Distance in the nearest neighbor search, based on which a Guided Correspondence loss is defined to measure the similarity of the output texture to the example. Experiments show that our approach sur-passes existing neural approaches in uncontrolled and con-trolled texture synthesis. More importantly, the Guided Cor-respondence loss can function as a general textural loss in, e.g., training generative networks for real-time controlled synthesis and inversion-based single-image editing. In con-trast, existing textural losses, such as the Sliced Wasserstein loss, cannot work on these challenging tasks.
Data Allocation for Approximate Gradient Coding in Edge Networks

To leverage the computing power in an edge network, one can divide a machine learning task into several subtasks and assign the subtasks to several computing devices to complete. Under master-worker architecture, the master divides and distributes the data to several workers. In each iteration, the master asks the workers to compute some function of the local data stored in the workers. For example, in gradient-based learning, this function can be the partial gradient function. Since the workers have different computing resources, the speed of the distributed learning is hindered by some workers with long latency, called the stragglers. Gradient coding solves the problem of stragglers by allowing the master to recover the desired feedback information in the presence of s stragglers. If the total number of stragglers is n, the master can just wait for the n−s fastest workers. In this paper we consider the problem of data allocation so that the gradient vector can be approximated obtained by the master node with small error. A block repetition scheme is proved to be the optimal data allocation scheme if we want to minimize the average recovery error.
Task planning of Robotic Grasping for Deep-Container Environment Based on Dynamic Space Constraints RRTC Algorithm

Multi-axis collaborative manipulators encounter challenges such as slow planning speed, low efficiency, and poor path quality while conducting grasping operations. This paper proposes the Dynamic Space Constraints RRTC motion planning algorithm that considers the grasping pose in the deep-container picking problem and introduces a novel method to optimize the collision-free grasping pose in compliance with environmental constraints. The paper also proposes a prior graph path generation algorithm based on the end-effector’s sphere space constraint that compresses the body’s search space to improve efficiency. Avoiding complicated non-convex constraint modeling required for obstacle avoidance can be achieved by modeling nonconvex sets with an obstacle-free state as the union of a finite number of convex regions. Based on the prior path, the algorithm generates a dynamic constraint manifold and uses a bidirectional search tree that employs a sliding constraint manifold for traction. The algorithm includes an adaptive step length mechanism dynamically based on the obstacle distance for higher efficiency and accuracy. Experimental results demonstrate that the Dynamic Space Constraints RRTC algorithm plans an optimized path that satisfies the constraint condition compared to traditional algorithms in terms of computation time and efficiency.
Balancing Real-world Interaction and VR Immersion with AI Vision Robotic Arm

With the widespread adoption of Virtual Reality (VR) technology, VR devices can be more realistic and make the tasks they perform feel real to the user. It is this sense of realism that makes more and more people are using VR headsets. While using VR devices, the need to interact with the real world will also exist. For example, when people are using a VR headset to study and work, they have a physiological need (thirst, hunger, etc.), and need to interact with reality. But the closed nature of the VR headset causes this interaction to be very disruptive to the immersion of the user's current task. To address this issue, we propose an interaction method that combines an AI vision algorithm and a robotic arm to preserve immersion during task performance in VR. This method addresses the challenge of interacting with the real world while using closed VR devices. Our experiments show that our method outperforms see-through and mapping methods, allowing users to quickly re-immerse themselves after interruptions and reducing interruptions during real-world task performance. Future research will focus on improving this method without sensitization and expanding the range of real-world operations that can be performed.
PENGEMBANGAN MEDIA MEMBACA KARPACA UNTUK MENINGKATKAN KEMAMPUAN MEMBACA SISWA KELAS 1 SEKOLAH DASAR

This research was conducted according to the background, namely the discontinuity between the government's prohibition on reading tests for prospective new students in grade 1 SD and the existing competence test at the grade 1 SD level, there was no media or starting reading material at the grade 1 SD level, learning had not been completed. letter recognition at the kindergarten level due to the Covid-19 pandemic. This research aims to produce reading media that is valid, appropriate and can improve the reading ability of 1st grade students. The development research model used is the Borg and Gall model which has been simplified into 7 research steps namely 1) research and initial information gathering, 2) planning, 3) initial product format development, 4) validation, 5) product revision, 6) trial small scale product, 7) final product revision. Based on the results of the material expert's assessment, a percentage score of 89% was obtained in the valid category. The percentage by media experts is 93% with a very valid category. The feasibility of the media is shown by a teacher user review questionnaire of 92% with a very decent category. The attractiveness of the media is shown by the results of the student response questionnaire of 100% in the very interesting category. An increase in reading ability is shown by a comparison of the results of the pre-test score of 48.52 and the acquisition of the post-test score of 84.80. The results of the gain test (N-Gain) show an index of 0.7 which is included in the high category, so that KARPACA reading media can improve the reading ability of 1st grade students.
MonoHuman: Animatable Human Neural Field from Monocular Video

Animating virtual avatars with free-view control is crucial for various applications like virtual reality and digital entertainment. Previous studies have attempted to utilize the representation power of the neural radiance field (NeRF) to reconstruct the human body from monocular videos. Recent works propose to graft a deformation network into the NeRF to further model the dynamics of the human neural field for animating vivid human motions. However, such pipelines either rely on pose-dependent representations or fall short of motion coherency due to frameindependent optimization, making it difficult to generalize to unseen pose sequences realistically. In this paper, we propose a novel framework MonoHuman, which robustly renders view-consistent and high-fidelity avatars under arbitrary novel poses. Our key insight is to model the deformation field with bi-directional constraints and explicitly leverage the off-the-peg keyframe information to reason the feature correlations for coherent results. Specifically, we first propose a Shared Bidirectional Deformation module, which creates a pose-independent generalizable deformation field by disentangling backward and forward deformation correspondences into shared skeletal motion weight and separate non-rigid motions. Then, we devise a Forward Correspondence Search module, which queries the correspondence feature of keyframes to guide the rendering network. The rendered results are thus multi-view consistent with high fidelity, even under challenging novel pose settings. Extensive experiments demonstrate the superiority of our proposed MonoHuman over state-of-the-art methods.
Wide-Angle Rectification via Content-Aware Conformal Mapping

Despite the proliferation of ultra wide-angle lenses on smartphone cameras, such lenses often come with severe image distortion (e.g. curved linear structure, unnaturally skewed faces). Most existing rectification methods adopt a global warping transformation to undistort the input wideangle image, yet their performances are not entirely satisfactory, leaving many unwanted residue distortions uncorrected or at the sacrifice of the intended wide FoV (field- of-view). This paper proposes a new method to tackle these challenges. Specifically, we derive a locally-adaptive polardomain conformal mapping to rectify a wide-angle image. Parameters of the mapping are found automatically by analyzing image contents via deep neural networks. Experiments on a large number of photos have confirmed the superior performance of the proposed method compared with all available previous methods.
An Approach of Multi-Comment Emotional Perception Based On K-Means Clustering towards Online Government Service System

The service quality of online government services is related to the efficiency of government affairs. To cluster the sentiment of comments based on the quantification of user comments. Proposed methods and mechanisms for judging the direction of emotions and visualizing data. In this paper, we further enrich multidimensional information extraction and promote system intelligence by using K-means clustering algorithm based on comments quantification. It provides a quantitative basis and model for the government platform to implement web-assisted services and provides strong data support for subsequent sensing tasks. We will promote the development of platforms and improve the control capabilities of platforms.
Recommendation Algorithm Combining Weighted Average and Trust Degree

Most of the traditional rating forecasts only rely on the user’s rating of the project, and there is no integration of user’s social relationship. Therefore, this paper proposes a recommendation algorithm combining weighted draw and trust, which combines explicit features such as users’ ratings of projects with implicit features such as the trust relationship under the premise of considering users’ social relations. In this way, users’ preferences can be obtained more accurately, and the problem of cold start of users caused by sparse data can be alleviated. In addition, the trust degree proposed in the article is the synthesis of multiple relationships, which is manifested in the subjective trust of users to trusted users and the influence of trusted users among all users. Through experimental analysis, it is superior to other baseline models in three commonly used data.
GEOCHEMICAL CHARACTERISTICS OF THE SALTYCHIAN GRANITES (WESTERN AZOV AREA) ACCORDING TO THE ICP-MS RESEARCH RESULTS

The ICP-MS research results of the interesting from a metallogenic point of view Saltychian granites of the Azov geoblock western part are presented. Compared to the surrounding granitoids, the Saltychian granites can be enriched in Nb and Ta. It is assumed that vein pegmatites and pegmatoid granites with minerals of rare metals (Nb, Ta, REE, Y) are genetically related to them. For the first time, the results of ICP-MS analysis for the determination of the full REE spectrum as well as elements, such as Hf, Ta, Sc, etc., are presented. The main impurity elements in rocks and minerals from them (apatite, ilmenite, biotite, titanite, orthite) were determined in seven samples. Such specific features of the Saltychian granites as the presence of orthite and an increased amount of titanite, are highlighted. These are the main concentrator minerals of REE, Th, U, and Y. Based on the results of our research and the materials of the predecessors, detailed comparative characteristics of orthite and titanite in various rocks are provided. A significant part of REE and Y is concentrated in apatite and titanite, and Nb, Ta, Zr, V — in ilmenite and biotite. The geochemical features of these minerals are considered by comparing the author's results and literature data. Possible reasons for some discrepancies are analyzed. The research results presented in the article confirm the current ideas postulating the genetic connection of Saltychian granites and pegmatites of the Azov with rare metal mineralization (Nb, Ta, REE, Y).
DegAE: A New Pretraining Paradigm for Low-Level Vision

Self-supervised pretraining has achieved remarkable success in high-level vision, but its application in low-level vision remains ambiguous and not well-established. What is the primitive intention of pretraining? What is the core problem of pretraining in low-level vision? In this paper, we aim to answer these essential questions and establish a new pretraining scheme for low-level vision. Specifically, we examine previous pretraining methods in both high-level and low-level vision, and categorize current low-level vision tasks into two groups based on the difficulty of data acqui-sition: low-cost and high-cost tasks. Existing literature has mainly focused on pretraining for low-cost tasks, where the observed performance improvement is often limited. However, we argue that pretraining is more significant for high-cost tasks, where data acquisition is more challenging. To learn a general low-level vision representation that can improve the performance of various tasks, we propose a new pretraining paradigm called degradation autoencoder (De-gAE). DegAE follows the philosophy of designing pretext task for self-supervised pretraining and is elaborately tai-lored to low-level vision. With DegAE pretraining, SwinIR achieves a 6.88dB performance gain on image dehaze task, while Uformer obtains 3.22dB and 0.54dB improvement on dehaze and derain tasks, respectively.
Research on Acquisition and Processing Method for Extravehicular Panoramic Images of Manned Spacecraft

Compared to the discrete image recording method during the assembly process of manned spacecraft, panoramic imaging technology has the advantages of high resolution and wide viewing angle. However, the application of traditional panoramic image acquisition and processing methods in the image recording during the assembly process of manned spacecraft can lead to issues such as low coverage of extravehicular panoramic images and insufficient image registration accuracy. According to the structural characteristics of manned spacecraft, this paper proposes a method for acquiring and processing extravehicular panoramic images of manned spacecraft, designs a layout scheme of extravehicular panoramic images, develops a control-by-wire panoramic pan-tilt-zoom(PTZ) system, and proposes an image registration algorithm based on perceptual hash matching point purification. The perceptual hash matching point purification method proposed in this paper calculates the similarity of small area images around two matching points based on hash fingerprints, and removes matching point pairs that are less than the set threshold. The method proposed in this paper meets the remote acquisition requirements of extravehicular manned spacecraft above 15 meters, ensuring complete coverage of extravehicular panoramic images, and improving the quality of panoramic images.
Deep Combination of Radar With Optical Data for Gesture Recognition: Role of Attention in Fusion Architectures

Multimodal time series classification is an important aspect of human gesture recognition, in which limitations of individual sensors can be overcome by combining data from multiple modalities. In a deep learning pipeline, the attention mechanism further allows for a selective, contextual concentration on relevant features. However, while the standard attention mechanism is an effective tool when working with Natural Language Processing (NLP), it is not ideal when working with temporally- or spatially-sparse multi-modal data. In this paper, we present a novel attention mechanism, Multi-Modal Attention Preconditioning (MMAP). We first demonstrate that MMAP outperforms regular attention for the task of classification of modalities involving temporal and spatial sparsity and secondly investigate the impact of attention in the fusion of radar and optical data for gesture recognition via three specific modalities: dense spatiotemporal optical data, spatially sparse/temporally dense kinematic data, and sparse spatiotemporal radar data. We explore the effect of attention on early, intermediate, and late fusion architectures and compare eight different pipelines in terms of accuracy and their ability to preserve detection accuracy when modalities are missing. Results highlight fundamental differences between late and intermediate attention mechanisms in respect to the fusion of radar and optical data.