Reviewerly/exHarmony huggingface dataset:
IMPLEMENTATION OF JAVANESE SPEECH AS A FORM OF STRENGTHENING LOCAL WISDOM FOR MIDDLE SCHOOL TEACHERS IN NGARGOYOSO DISTRICT, KARANGANYAR REGENCY

&lt;p&gt;Javanese speech is a way of someone with special skills in Javanese literature and language who is able to bring events in Javanese society, such as weddings (temanten), death (kesripahan), meetings (pepanggihan), banquets (pasamuan), recitation (pengaosan), stage, and so on. Partner problems experienced, namely (1) there are still Javanese language teachers who have not mastered Javanese speech techniques according to the correct rules; (2) Junior high school Javanese language teachers in Ngargoyoso District have not all mastered Javanese language and literature in Javanese speeches; (3) Not all middle school Javanese language teachers in Ngargoyoso District know and understand paramasastra (phonology, morphology, semantics, syntax), discourse, and Javanese pragmatics; (4) Not all middle school Javanese language teachers in Ngargoyoso Subdistrict master Hamicara (Public Speaking); (5) Not all middle school Javanese language teachers in Ngargoyoso Subdistrict have confidence in giving Javanese speeches. The implementation method used is by using the practice/practice method, listening and elearning where the activity planning is expected to answer Javanese speech skills for junior high school teachers in Ngargoyoso District, Karanganyar Regency, Central Java. Plans for implementing program activities include (1) making video tutorials on examples of Javanese speeches as e-learning materials; (2) Conducting courses/training in Javanese speech by presenting experts; (3) Provide assistance in training in Javanese speech until proficient; and (4) To evaluate gradually. The planned outputs are (1) Publication of service in journals with ISSN or proceedings of seminars with ISBN; (2) Publication of service activities in print or online mass media; and (3) Publication of service in the form of activity videos.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords&lt;/strong&gt;: Javanese speech, local wisdom, middle school, Ngargoyoso, Karanganyar.&lt;/p&gt;
Metaverse and the Web 3: a systematic mapping

The Metaverse and the Web 3 are two emerging technologies whose adoption is growing lately. More enterprises are investing in them to keep up with the market. Therefore, it is important to have a general and better understanding of Metaverse and the Web 3 also known as the decentralized web. This paper describes the relationship between these two technologies through a systematic mapping methodology, aiming to contribute to a clearer understanding of the Metaverse and Web 3.
Mixed gamification with virtual tools modify poor school performance

&lt;span lang="EN-US"&gt;Currently, the use of gamifiers as teaching tools in virtuality is present due to the digital education that is developed in Latin America. The objective was to determine the effects of two methodologies: i) Mixed gamification; and ii) Usefulness of virtual teaching tools, in the cognitive processes of poor school performance. The methodology was experimental, quantitative. The evaluation was carried out on 150 Spanish-speaking schoolchildren, grouped into three comparison groups (8.5 years of age ±0.4). They underwent 40 learning sessions with the D-S-F methodology (dynamics, strategy, feedback). The validated instruments were three performance tests on the areas of mathematics, science and communication. Significant results were obtained in cognitive processes from the activities designed with mixed gamification, although the effects were not decisive in the area of communication. It is concluded that gamification reduces low performance to raise cognitive processes for logical reasoning, cognitive deduction in mathematics, skills to generate hypotheses in science; and those of reading and oral expression. It is suggested to develop experiments with variables that include a digital evaluation of text writing, to avoid the limitations presented in this research&lt;/span&gt;&lt;span lang="EN-US"&gt;.&lt;/span&gt;
Multi-task Learning for Recommendation over Heterogeneous Information Network (Extended abstract)

Heterogeneous Information Network based Recommender Systems (HIN-based RS) can model the complex interactions between different objects in RS. However, existing models assume HIN is invariable and merely use HIN as a data source for assisting recommendation. In this paper, we summarize our multi-task learning framework MTRec for recommendation over HIN. MTRec relies on the self-attention mechanism to learn the semantics of meta-paths in HIN and jointly optimizes the tasks of both recommendation and link prediction. Using a Bayesian task weight learner, MTRec is able to achieve the balance of two tasks during optimization automatically. Moreover, MTRec provides good interpretability of recommendation through a “translation” mechanism which is used to model the three-way interactions among users, items and the meta-paths connecting them. Experimental results demonstrate the effectiveness and the robustness of MTRec over state-of-the-art models.
On Whose Shoulders?

Building on the concept of translaboration and on situated learning approaches in translator education, we propose an investigation of the collaborative sphere of a large multilingual simulated translation project designed to foster students’ comprehensive development. We apply our interaction typology based on Dialogue Act Analysis, as well as on Social Network Analysis to project email and forum communication.
Learning node representation via Motif Coarsening

Motifs, as fundamental units of the graph, play a significant role in modeling complex systems in a variety of domains, including social networks, as well as biology and neuroscience. Motif preservation is a widely studied problem that provides new avenues for structure preservation. This paper is dedicated to exploring the significance of motifs with various patterns and effectively incorporating different motif patterns into node-level graph representation learning. We propose a novel node Representation learning framework via Motif Coarsening (RMC), which effectively incorporates different granularity structural information into node representation learning. RMC consists of two parallel components, the node representation learning aggregator and the motif-based node representation learning aggregator. In the node representation learning process, RMC directly encodes lower-order structures into node representation by a one-layer graph convolution network. For the motif-based node representation learning process, we propose a Motif Coarsening strategy for incorporating motif structure into the graph representation learning process. Furthermore, the MotifRe-Weighting strategy is proposed to biased convert motif representation into motif-based node representation. We verify the effectiveness of RMC by several node-related tasks on a series of widely used real-world datasets. Experimental results demonstrate that our proposed framework delivers superior promising representation performance to existing benchmarks. Ablation experiments proved that RMC has potential as an auxiliary framework, which indicates the excellent quality of Motif Coarsening and MotifRe-Weighting strategies over existing benchmarks from several evaluation metrics, involving mean classification accuracy, Micro-F1, and Macro-F1.
A Versatility-Performance Balanced Hardware Architecture for Scene Text Detection

Detecting and extracting textual information from natural scene images needs Scene Text Detection (STD) algorithms. Fully Convolutional Neural Networks (FCNs) are usually utilized as the backbone model to extract features in these instance segmentation based STD algorithms. FCNs naturally come with high computational complexity. Furthermore, to keep up with the growing variety of models, flexible architectures are needed. In order to accelerate various STD algorithms efficiently, a versatility-performance balanced hardware architecture is proposed, together with a simple but efficient way of configuration. This architecture is able to compute different FCN models without hardware redesign. The optimization is focused on hardware with finely designed computing modules, while the versatility of different network reconfigurations is achieved by microcodes instead of a strenuously designed compiler. Multiple parallel techniques at different levels and several complexity-reduction methods are explored to speed up the FCN computation. Results from implementation show that, given the same tasks, the proposed system achieves a better throughput compared with the studied GPU. Particularly, our system reduces the comprehensive Operation Expense (OpEx) at GPU by 46%, while the power efficiency is enhanced by 32%. This work has been deployed in commercial applications and provided stable consumer text detection services.
W-Net: Convolutional neural network for segmenting remote sensing images by dual path semantics

In the latest research progress, deep neural networks have been revolutionized by frameworks to extract image features more accurately. In this study, we focus on an attention model that can be useful in deep neural networks and propose a simple but strong feature extraction deep network architecture, W-Net. The architecture of our W-Net network has two mutually independent path structures, and it is designed with the following advantages. (1) There are two independent effective paths in our proposed network structure, and the two paths capture more contextual information from different scales in different ways. (2) The two paths acquire different feature images, and in the upsampling approach, we use bilinear interpolation thus reducing the feature map distortion phenomenon and integrating the different images processed. (3) The feature image processing is at a bottleneck, and a hierarchical attention module is constructed at the bottleneck by reclassifying after the channel attention module and the spatial attention module, resulting in more efficient and accurate processing of feature images. During the experiment, we also tested iSAID, a massively high spatial resolution remote sensing image dataset, with further experimental data comparison to demonstrate the generality of our method for remote sensor image segmentation.
Comparative analysis of pretrained deep neural networks for anomalies detection

Robust anomaly detection algorithm is critical to a significant number of applications. Deep Neural Network can resolve this problem with the good accuracy. This paper demonstrates the ability of solving anomalies detection using pretrained convolutional neural networks. Three pretrained neural networks were used to solve this problem - SqueezeNet, GoogleNet and Resnet18. The contribution of this paper consists in a comparative analysis of performances of these deep neural networks. The result was compared against the following metrics: accuracy, recall, precision and the F1 Score. The neural networks were tested against two different public available datasets, showing different type of anomalies.
A cross-chain rating system: bridging EVM-based blockchains with Chainbridge

Rating systems are services collecting users' opinions about any kind of item, such as movies, locations, or hobbies, typically characterized by centralized governance making them susceptible to censorship or rating manipulation. This paper leverages blockchain technology to build a decentralized system to secure the ratings and operations performed by the users. We assume a scenario where many rating systems coexist and are hosted by different blockchains, and these blockchains are connected with one that could handle monetary-like operations common to the rating systems. The paper describes an experiment with Chainbridge, a tool to perform transfers across two Ethereum-like blockchains, applied to a cross-chain rating system prototype involving two blockchains: one hosting the ratings and one handling the payments. The results include the costs in units of gas and USD, i.e. the blockchain fees, and the latency time to perform a transfer across two test networks, Goerli and Mumbai.
Visual Representation and Layout Optimization for Comparison of Dynamic Graph

The comparison between temporal graph data and the representation of the results are very helpful for in-depth understanding of the difference between the two data. However, limited by display technology, it is more difficult to visually analyze the comparison of temporal graph data. Based on the analysis of the temporal characteristics of the graph data, this paper summarizes and reorganizes the data that needs to be compared, and efficiently displays the difference in the time sequence of the graph data by means of graphic embedding. This paper introduces the data processing process, visual element design and layout optimization algorithm in detail, and verifies it on real data. Finally, the evaluation experiment proves that the proposed method is more efficient in comparison of temporal graph data.
Augmenting Legacy Networks for Flexible Inference

On intelligent vehicles, Deep Neural Networks (DNNs) may run on devices whose computational load varies over time. Within the context of variable network architectures, that can be used to constrain the inference latency for real-time deployment with varying system resources, we introduce LeAF (Legacy Augmentation for Flexible inference), a novel paradigm to augment a pre-trained DNN with trainable, shallow execution paths that can run in place of the legacy ones. While preserving the legacy DNN weights, LeAF allows changing the DNN architecture with minimal overhead to effectively adapt to different system performance targets. LeAF-ResNet-50 has less than 14% storage overhead over the legacy DNN; its accuracy varies from the legacy 76.1% to 70.15% (up to 5% better than Slimmable [1] with a latency that is 37% better than OFA [2] on an A100 GPU with batch size 256). Our analysis shows the importance of considering not only the target device, but also the batch size and the temporal dynamic of the DNN configuration to optimize the performances of variable architecture DNNs, LeAF in particular.
M2F2-Net: Multi-Modal Feature Fusion for Unstructured Off-Road Freespace Detection

Freespace detection is an important part of autonomous driving technology. Compared with structured on-road scenes, unstructured off-road scenes face more challenges. Multi-modal fusion method is a viable solution to these challenges. But existing fusion methods do not fully utilize the multi-modal features. In this paper, we propose an effective multi-modal network named M2F2-Net for freespace detection in unstructured off-road scenes. We propose a multi-modal feature fusion strategy named Multi-modal Cross Fusion (MCF). MCF module is simple but effective in fusing the features of RGB images and surface normal maps. Meanwhile, a multi-modal segmentation decoder module is designed to decouple the segmentation of two modalities, and it further helps the features of both modalities to be fully utilized. In order to solve the problem that the road edge is difficult to extract in the unstructured scenes, we also propose an edge segmentation decoder module. Extensive experiments show that our approach can lead to significant improvements, which brings 6.1% F1 and 10.8% IoU improvements. Our code will be available at https://github.com/yhl1010/M2F2-Net.
Hardware-Aware Latency Pruning for Real-Time 3D Object Detection

3D Object detection is a fundamental task in vision-based autonomous driving. Deep learning perception models achieve an outstanding performance at the expense of continuously increasing resource needs and, as such, increasing training costs. As inference time is still a priority, developers usually adopt a training pipeline where they first start using a compact architecture that yields a good trade-off between accuracy and latency. This architecture is usually found either by searching manually or by using neural architecture search approaches. Then, train the model and use light optimization techniques such as quantization to boost the model’s performance. In contrast, in this paper, we advocate for starting on a much larger model and then applying aggressive optimization to adapt the model to the resource-constraints. Our results on large-scale settings for 3D object detection demonstrate the benefits of initially focusing on maximizing the model’s accuracy and then achieving the latency requirements using network pruning.
Beyond the Standard Model with HAWC

The High Altitude Water Cherenkov (HAWC) Gamma-ray Observatory, located in the mountains of Mexico, has been performing an unbiased survey of the Northern sky at energies above 300 GeV since becoming fully operational in 2015.HAWC's wide field-of-view enables indirect searches for TeV-scale dark matter from diverse targets including galaxy clusters, dwarf spheroidal galaxies, the Milky Way galactic halo and the Sun.Beyond dark matter, sensitivity to transient bursts of gamma-rays provides a window into the early universe through searches for evaporating primordial black holes and its high energy reach enables searches for violations of the Lorentz symmetry.I will present an overview of beyond-the-Standard-Model searches with HAWC and present some of the world's strongest constraints on these processes at the TeV scale.
Problems and Countermeasures in the Cultivation of Students’ Innovation Skills in Vocational Colleges from the Perspective of Skills Competition

The cultivation of students’ innovation skills in vocational colleges is of paramount importance in preparing them to excel in the everchanging and competitive world. However, there are several challenges that impede the development of students’ innovation skills. This paper addresses these challenges from the perspective of skills competition and proposes practical countermeasures. These suggestions include incorporating skills competition into the curriculum, provision of teacher training on innovation skills, encouraging industry partnerships, and fostering a culture of innovation within vocational colleges. Through the implementation of these measures, an environment that nurtures students’ practical abilities, creativity, and innovation skills can be created. This research contributes to the existing body of knowledge on innovation education in vocational colleges and offers practical strategies for enhancing the cultivation of students’ innovation ability.
Problems of the Formation of the FGOS in the Fourth Generation

Purpose. The strategic goal of Russia at present is the development of Russian higher education. Since the interested subjects of this process are all members of Russian society: from the state to students, various approaches are being developed in the higher education system to form a new concept for the formation of the fourth generation of the Federal State Educational Standards of Higher Education. The purpose of the article is to describe various approaches to the formation of new educational standards and the problems that developers face. The research methodology of the article includes the analytical, synthetic method, system analysis method. In the article, the author makes the following conclusions: 1) the process of modernization of the national system of higher education in our country continues; 2) one of the concepts of the new FSES HE of the fourth generation, which is debatable, is presented; 3) some problems that arise in the development of a new standard are revealed. Scientific and practical significance. This study reveals some problems and different approaches to the formation of the concept of the new Federal State Educational Standard of Higher Education, which is the basic document for the development and formation of modern graduates competitive in the labor market.
Wavelength Multiplexed Photonic Edge Computing in the Output Stationary Frame

We propose a photonic edge computing architecture based on WDM, broadband modulation, and output-stationary integration. Using this scheme, we demonstrate 98.8%-accurate DNN inference over an 86-km deployed fiber link with 3 THz optical bandwidth.
Super-resolution image projection using a diffractive optical decoder

We report a diffractive super-resolution image display framework consisting of a digital encoder and an all-optical decoder to synthesize/project high-resolution images at an output plane using a low-resolution input light-modulator.
Evaluation of a compact butterfly-style photonic-electronic neural chip on complicated deep learning tasks

We deploy a compact butterfly-style photonic-electronic neural chip on ResNet-20 and achieve &gt; 85% measured accuracy on the CIFAR-10 dataset with only 3-bit weight programming resolutions, showing its practicality in implementing complicated deep learning tasks.
Quantum-aided feature selection model – A quantum machine learning approach

The accuracy of information retrieval systems is measured by the relevancy of retrieved results as per the user’s query. Relevant results are presented by performing various methods viz. indexing and crawling and the output of these processes is the retrieved results that have to pass through the ranking process which is the central goal of information retrieval systems. The ranking is carried out through the classification or clustering of processed results which can include redundant and noisy features. The accuracy of classification or clusters for the ranking process can be maximized by removing noisy and duplicate features through the feature selection method. Although feature selection is an expensive computational process, after many decades, quantum computation tools are in use for many algorithms to implement realistic problems, particularly in the standard of Quantum Annealing. This paper focuses to prospect the standard of quantum computing in order to increase the quality of information classes through the feature selection method. The persuasiveness of the quantum approach is comparable to the classical process that focused on the reliability of quantum methodology from different perspectives.
The Effect of Immersive Reality on Science Learning: A Meta-Analysis

The study aims to investigate the effects of immersive reality applications on students' science achievement. For this purpose, 1,323 articles reporting the effects of immersive reality applications on students' science achievement were examined. The following databases were searched for articles: National Thesis Center, Google scholar, Scopus, ProQuest, SAGE Journals Online, Tylor &amp; Francis, ScienceDirect, and Turkish academic database. 32 effect size values from 23 studies that fit the including criteria determined before the study were included in the meta-analysis. The meta-analysis revealed that the immersive reality applications have a statistically significant effect (Hedge's g = 0.89) on science achievement based on the random effect model. In addition, distribution of the effect sizes was heterogeneous (Q = 300.86, p = 0.000). Therefore, the effects of immersive reality application on achievement within different subgroups were also examined. The subgroups were defined by (i) grade levels in which the studies conducted, (ii) academic discipline in which the study took place, (iii) immersive reality application types, and (iv) location where the study was conducted (in or out of Turkey). Statistical significance between group effect was only observed between the groups defined by the place of study conducted (in vs out of Turkey).