Reviewerly/exHarmony huggingface dataset:
A Pyramid Semi-Autoregressive Transformer with Rich Semantics for Sign Language Production

As a typical sequence to sequence task, sign language production (SLP) aims to automatically translate spoken language sentences into the corresponding sign language sequences. The existing SLP methods can be classified into two categories: autoregressive and non-autoregressive SLP. The autoregressive methods suffer from high latency and error accumulation caused by the long-term dependence between current output and the previous poses. And non-autoregressive methods suffer from repetition and omission during the parallel decoding process. To remedy these issues in SLP, we propose a novel method named Pyramid Semi-Autoregressive Transformer with Rich Semantics (PSAT-RS) in this paper. In PSAT-RS, we first introduce a pyramid Semi-Autoregressive mechanism with dividing target sequence into groups in a coarse-to-fine manner, which globally keeps the autoregressive property while locally generating target frames. Meanwhile, the relaxed masked attention mechanism is adopted to make the decoder not only capture the pose sequences in the previous groups, but also pay attention to the current group. Finally, considering the importance of spatial-temporal information, we also design a Rich Semantics embedding (RS) module to encode the sequential information both on time dimension and spatial displacement into the same high-dimensional space. This significantly improves the coordination of joints motion, making the generated sign language videos more natural. Results of our experiments conducted on RWTH-PHOENIX-Weather-2014T and CSL datasets show that the proposed PSAT-RS is competitive to the state-of-the-art autoregressive and non-autoregressive SLP models, achieving a better trade-off between speed and accuracy.
Targeted Random Projection for Prediction from High-Dimensional Features

We consider the problem of computationally-efficient prediction with high dimensional and highly correlated predictors when accurate variable selection is effectively impossible. Direct application of penalization or Bayesian methods implemented with Markov chain Monte Carlo can be computationally daunting and unstable. A common solution is first stage dimension reduction through screening or projecting the design matrix to a lower dimensional hyper-plane. Screening is highly sensitive to threshold choice, while projections often have poor performance in very high-dimensions. We propose TArgeted Random Projection (TARP) to combine positive aspects of both strategies. TARP uses screening to order the inclusion probabilities of the features in the projection matrix used for dimension reduction, leading to data-informed sparsity. We provide theoretical support for a Bayesian predictive algorithm based on TARP, including statistical and computational complexity guarantees. Examples for simulated and real data applications illustrate gains relative to a variety of competitors.
Research on the Influence of Emoji in Advertising Slogans on Consumers' Purchase Intention

In the Internet era emoji are being used more and more frequently, this study focuses on how the use of emoji in product advertising slogans affects consumers' purchase intentions. We conducted 2 experiments to explore this issue. Experiment 1 found that adding emoji to product advertisements would increase consumers' willingness to buy products, and the effect was mediated by psychological simulation. Based on this, Experiment 2 further found that the position of the emoji in the tagline would have a moderating effect, i.e., the emoji would increase purchase intention only when the emoji was in the middle of the tagline, while the emoji's effect on purchase intention disappeared when the emoji was on both sides (left or right). This study makes some theoretical contributions and practical contributions to how emoji can be used in the consumer market.
Physical education teachers’ attitudes towards the use of the Internet in education

The use of the Internet in the educational process is one of the factors that help to consolidate students’ knowledge and acquire new skills based on the exchange of experiences that take place via email or open group dialogues. The study aimed to identify the attitudes of physical education teachers in Jordan towards the use of the Internet in the teaching–learning process. The descriptive approach was used, in addition to the use of the questionnaire as a tool for the study. The data collection instrument was applied to the study sample of 328 teachers and educators in Jordan in the second academic year 2021–2022. After carrying out the necessary statistical treatment, the study concluded that there is a presence of positive attitudes among teachers towards the use of the Internet in the educational process. In addition, there is a disparity in the use of the Internet in the educational process among physical education teachers in Jordan.&#x0D; Keywords: Educational process, Internet, physical education, teachers;
Disentangled Action Recognition with Knowledge Bases

Action in video usually involves the interaction of human with objects. Action labels are typically composed of various combinations of verbs and nouns, but we may not have training data for all possible combinations. In this paper, we aim to improve the generalization ability of the compositional action recognition model to novel verbs or novel nouns that are unseen during training time, by leveraging the power of knowledge graphs. Previous work utilizes verb-noun compositional action nodes in the knowledge graph, making it inefficient to scale since the number of compositional action nodes grows quadratically with respect to the number of verbs and nouns. To address this issue, we propose our approach: Disentangled Action Recognition with Knowledge-bases (DARK), which leverages the inherent compositionality of actions. DARK trains a factorized model by first extracting disentangled feature representations for verbs and nouns, and then predicting classification weights using relations in external knowledge graphs. The type constraint between verb and noun is extracted from external knowledge bases and finally applied when composing actions. DARK has better scalability in the number of objects and verbs, and achieves state-of-the-art performance on the Charades dataset. We further propose a new benchmark split based on the Epic-kitchen dataset which is an order of magnitude bigger in the numbers of classes and samples, and benchmark various models on this benchmark.
Fun Reading App Berbasis Multimedia Interaktif untuk Meningkatkan Kemampuan Membaca Permulaan

Students with moderate mental-retardation tend to choose learning that is concrete and interesting, as well as learning to read. Learning that is not in accordance with these tendencies has an impact on early reading skills also reduces the level of concentration of students thereby weakening students' opportunities to remember. This research was conducted to produce Fun Reading App, that is an interactive multimedia-based. It is hoped that this product can improve students’ early reading skills with moderate mental-retardation. Product is produced through research and development procedures according to the ADDIE model. The validity testing involves three experts and for the product effectivity test involves 4 students. The research shows: 1) the validity test of the product is in the criteria of valid or usable; and 2) the product effectiveness test with the Gain test is on the N-Gain score with high criteria.
Question Answering in STACK Applying String Similarity

We present a method to evaluate fill-in-the-blank student answers in STACK using a string metric, which is not possible in the current version of STACK. To increase the quality of the evaluation, we use whitelist and blacklist instead of a single teacher answer. The performance of a STACK question equipped with a string metric is quantitatively demonstrated by evaluating its use in mathematics courses.
Updating STACK Potential Response Trees Based on Separated Concerns

The STACK system is a computer aided assessment package for mathematics. STACK questions include full algebraic input with validation. Feedback is provided to students using "potential response trees", written by the teacher for each question. In this research, we are updating STACK potential response tree based on students' concerns in learning induction proofs. We have identified a specific misconception in learning mathematical induction from prior educational research, designed STACK question to test if students exhibit this misconception, and then illustrated how we updated STACK potential response tree based on this misconception. One main goal of separating concerns is to understand students’ responses and evaluate the question itself from an academic prospective and ultimately to improve the questions for future years. In addition, one of the contributions of this study is to improve our general understanding of how to design and use STACK potential response trees.&#x0D;
Clustering Analysis of the Evaluation Data on Students’ Learning under a Blended Environment

This paper explores the establishment of the effectiveness evaluation index system for students’ learning under blended learning environment, so as to provide some theoretical reference and guidance for the evaluation of the effect of students’ learning in blended environment. In current studies, there are few quantitative analyses on the application cases or teaching practice, and there is also a lack of evaluation studies on the students as the main body of the learning process. Therefore, this paper carries out a study on the clustering analysis of the evaluation data on students’ learning in blended environment, displaying the learning effect satisfaction model for students under blended learning environment, and providing the idea of constructing a learning effect evaluation model accordingly. This paper characterizes the learning effect of students at each stage in the form of sets and formulates evaluation index system. The analytic hierarchy process-grey clustering evaluation of students’ learning effect in blended environment is carried out. On the basis of analytic hierarchy process, the evaluation index weight is determined and the evaluation grade is fixed based on the grey clustering analysis. The experimental results verify the effectiveness of the evaluation model.
Impact of Knowledge Sharing on Innovative Work Behaviour: Evidence from Private Universities of Bangladesh

The study aims to investigate the impact of knowledge sharing (KS) on employees’ innovative work behavior. To conduct this research, 90 respondents from 4 private universities of the Sylhet region of Bangladesh were surveyed conveniently through a structured questionnaire; then the responses were analyzed by conducting correlation and regression analysis using SPSS software to test the hypotheses. Correlation analysis reveals that the two dimensions of knowledge sharing i.e., knowledge donation, and knowledge collection has a significant and positive relationship with the innovative work behavior of faculty members, besides regression analysis shows that the two dimensions of knowledge sharing depicted in the model explain about 34 percent of innovative work behavior among the academics of private universities. It has also been found that knowledge collection (KC) has comparatively more influence on employee’s innovative work behavior (IWB) than knowledge donation (KD). Therefore, findings of this study have significant implications to the university authority to ensure sustainable competitive advantage over rivals through academics’ innovative work behavior by cultivating a knowledge sharing culture among their institutions.
Online Adaptation for Implicit Object Tracking and Shape Reconstruction in the Wild

Tracking and reconstructing 3D objects from cluttered scenes are the key components for computer vision, robotics and autonomous driving systems. While recent progress in implicit function has shown encouraging results on high-quality 3D shape reconstruction, it is still very challenging to generalize to cluttered and partially observable LiDAR data. In this paper, we propose to leverage the continuity in video data. We introduce a novel and unified framework which utilizes a neural implicit function to simultaneously track and reconstruct 3D objects in the wild. Our approach adapts the DeepSDF model (i.e., an instantiation of the implicit function) in the video online, iteratively improving the shape reconstruction while in return improving the tracking, and vice versa. We experiment with both Waymo and KITTI datasets and show significant improvements over state-of-the-art methods for both tracking and shape reconstruction tasks. Our project page is at https://jianglongye.com/implicit-tracking .
General 2-path Problem

In this paper, some preliminaries about signal flow graph, linear time-invariant system on F(z) and computational complexity are first introduced in detail. In order to synthesize the necessary and sufficient condition on F(z) for a general 2-path problem, the sufficient condition on F(z) or R and necessary conditions on F(z) for a general 2-path problem are secondly analyzed respectively. Moreover, an equivalent sufficient and necessary condition on R whether there exists a general 2-path is deduced in detail. Finally, the computational complexity of the algorithm for this equivalent sufficient and necessary condition is introduced so that it means that the general 2-path problem is a P problem.
Pengaruh Penggunaan Media Kartu Huruf Bergambar Terhadap Kemampuan Membaca Permulaan Pada Siswa Kelas 1 SDN 067093 Cinta Damai

The purpose of this study is to determine the effect of flash card on early reading skills of first grade students at SDN 067093 Cinta Damai in academic year of 2021/2022. This research is Pre-Experimental Design. The research was conducted in October - November 2022. The sample used was 22 students which was total of the population. The data collection techniques used in this research were test, documentation and observation. The results of this study indicate that there was an effect of flash card on early reading skills of first grade students at SDN 067093 Cinta Damai. It can be proven from the results of the analysis by using t-test, showing that t count &gt; t table , 10,864 &gt; 1.411, then Ho is rejected and Ha is accepted. It can be conclude that there is an effect of flash card on early reading ability of first grade students at SDN 067093 Cinta Damai.
Universal Domain Adaptive Object Detector

Universal domain adaptive object detection (UniDAOD)is more challenging than domain adaptive object detection (DAOD) since the label space of the source domain may not be the same as that of the target and the scale of objects in the universal scenarios can vary dramatically (i.e, category shift and scale shift). To this end, we propose US-DAF, namely Universal Scale-Aware Domain Adaptive Faster RCNN with Multi-Label Learning, to reduce the negative transfer effect during training while maximizing transferability as well as discriminability in both domains under a variety of scales. Specifically, our method is implemented by two modules: 1) We facilitate the feature alignment of common classes and suppress the interference of private classes by designing a Filter Mechanism module to overcome the negative transfer caused by category shift. 2) We fill the blank of scale-aware adaptation in object detection by introducing a new Multi-Label Scale-Aware Adapter to perform individual alignment between the corresponding scale for two domains. Experiments show that US-DAF achieves state-of-the-art results on three scenarios (i.e, Open-Set, Partial-Set, and Closed-Set) and yields 7.1% and 5.9% relative improvement on benchmark datasets Clipart1k and Watercolor in particular.
Distance Matters in Human-Object Interaction Detection

Human-Object Interaction (HOI) detection has received considerable attention in the context of scene understanding. Despite the growing progress on benchmarks, we realize that existing methods often perform unsatisfactorily on distant interactions, where the leading causes are two-fold: 1) Distant interactions are by nature more difficult to recognize than close ones. A natural scene often involves multiple humans and objects with intricate spatial relations, making the interaction recognition for distant human-object largely affected by complex visual context. 2) Insufficient number of distant interactions in benchmark datasets results in under-fitting on these instances. To address these problems, in this paper, we propose a novel two-stage method for better handling distant interactions in HOI detection. One essential component in our method is a novel Far Near Distance Attention module. It enables information propagation between humans and objects, whereby the spatial distance is skillfully taken into consideration. Besides, we devise a novel Distance-Aware loss function which leads the model to focus more on distant yet rare interactions. We conduct extensive experiments on two challenging datasets - HICO-DET and V-COCO. The results demonstrate that the proposed method can surpass existing approaches by a large margin, resulting in new state-of-the-art performance.
Proposed Modeling of Online Exam-Proctoring: The Case of University of Education (VNU-UED)

Digital transformation in education proposed many changes in term of the formation and paradigm of teaching, assessment and evaluation in all aspects. The ability to integrate online platforms, tools, and technology solutions to increase interactivity and personalization etc. considers new opportunities and challenges for the application of online (remote) assessment and supervision platforms.&#x0D; As a phenomenon of online learning mode the online proctored examinations (OPE) have been considered one of the foremost online proctoring options that share the features of a real examination (paperless) in higher education institutions (HEIs) with a proctor observing examinations remotely.&#x0D; In teaching practice today, with online learning tendency the online proctoring (OP) interest rises for some time and accelerated by COVID-19 pandemic. Many of these online learning programs also include online assessment (online exams) that raise some of the issues and challenges associated with plagiarism and academic integrity in general. One of the ways to deal with some of these challenges is the adoption of online proctoring tools for online assessment.
Educational Liberalization, Innovation, and Economic Growth

Innovation is recognized as a necessary factor of industrialization and growth in developing countries but is costly, risky, and path-dependent, and even now ground-breaking innovation is highly concentrated in a few rich countries, linked with specific forms of university science and research capacity, and amongst a small number of firms. More importantly, education has been structured in a way that will make it relevant to the country's drive to learn the English language as the means to have access to improved technology for greater industrialization. The cross-country discussion on education policies indicates two main points. First, while the Philippines is more open to foreign participation than its Latin American counterparts, the other Asian countries seem to be more aggressive in reducing the foreign limitations. Second, these other Asian countries, except the Philippines, had a more aggressive education program as seen by the percentage of government education expenditures to GDP.
O REFLEXO DA PANDEMIA NO AUMENTO DE INCIDENTES DE SEGURANÇA DA INFORMAÇÃO

This article is the result of a research that seeks to show information security incidents around the globe during the years 2020 and 2021, in order to show which were the main Information Security incidents that occurred during the COVID-19 pandemic. 19, and how the event of this pandemic may have influenced these incidents. The methodology used is a bibliographical, descriptive, exploratory and pure research, using several case reports on the subject. The results show a significant increase in the occurrence of cybercrimes and invasions, conditioned to the hasty and unplanned implementation of remote work solutions by companies and the general lack of information about the new coronavirus that allowed exploitation by cybercriminals; certain types of cybercrimes seeing an increase of up to 667%, as shown by the technology company Barracuda Networks.
Content Analysis of Vocational High School Websites in the City of Padang

&lt;div class="WordSection1"&gt;&lt;p&gt;&lt;em&gt;A good school website is not only seen from the design side but&lt;/em&gt;&lt;em&gt;,&lt;/em&gt;&lt;em&gt; what is more important&lt;/em&gt;&lt;em&gt;,&lt;/em&gt;&lt;em&gt; from the content available whether it can provide useful information for the school community and other parties who need information about the school. So far, there are no standard guidelines and rules for creating website content for Vocational High Schools (SMK). Therefore, this study aims to investigate the content quality of SMK websites in the city of Padang. This research is &lt;/em&gt;&lt;em&gt;qualitative&lt;/em&gt;&lt;em&gt;, namely to analyze the availability of content criteria on the website of Vocational High Schools (SMK) in West Sumatra Province. The methods used in this research are content analysis and surveys. Researchers took samples from SMK in Padang City with 27 schools in this study. This research resulted in investigative data that the SMK websites in Padang City mostly used official domains. However, the average criteria for SMK websites in Riau Province were still standard. Still, content that links to the school's business and industrial world only gets a score of 33%, indicating the school &lt;/em&gt;&lt;em&gt;still needs to update content regularly&lt;/em&gt;&lt;em&gt;.&lt;/em&gt;&lt;/p&gt;&lt;/div&gt;
Short Text Semantic Similarity Measurement Approach Based on Semantic Network

Estimating the semantic similarity between short texts plays an increasingly prominent role in many fields related to text mining and natural language processing applications, especially with the large increase in the volume of textual data that is produced daily. Traditional approaches for calculating the degree of similarity between two texts, based on the words they share, do not perform well with short texts because two similar texts may be written in different terms by employing synonyms. As a result, short texts should be semantically compared. In this paper, a semantic similarity measurement method between texts is presented which combines knowledge-based and corpus-based semantic information to build a semantic network that represents the relationship between the compared texts and extracts the degree of similarity between them. Representing a text as a semantic network is the best knowledge representation that comes close to the human mind's understanding of the texts, where the semantic network reflects the sentence's semantic, syntactical, and structural knowledge. The network representation is a visual representation of knowledge objects, their qualities, and their relationships. WordNet lexical database has been used as a knowledge-based source while the GloVe pre-trained word embedding vectors have been used as a corpus-based source. The proposed method was tested using three different datasets, DSCS, SICK, and MOHLER datasets. A good result has been obtained in terms of RMSE and MAE.
Normal Self-injective Hyperrings

In this paper normal self-injective hyperrings are introduced and studied. Some new relations between this concept and essential hyperideal, dense hyperideal, and divisible hyperring are studied.