Reviewerly/exHarmony huggingface dataset:
Pelatihan Penyusunan Laporan Keuangan bagi Guru SMK Akuntansi di Jakarta Barat: Pendekatan e-learning

Financial Reporting based on IT with Digital-based learning management workshop is the existence of new knowledge that can be applied by teachers during the learning process with their students, especially making questions or quizzes using Google Classroom and Google Forms. The subjects of this workshop are the teachers of the Senior High School – Majoring Accounting (SMK). A digital-based learning workshop for 5 days, namely accounting cycle and financial reporting preparation using ACCURATE including Certification Online learning workshops that have been organized by the Master of Accounting provide new understanding and skills to SMK-Accounting teachers in using ACCURATE as an one accounting software for prepare financial report. The drawback of this activity is the limited facilities and infrastructure available owned by teachers to support digital- based learning, as there are still few teachers who have laptops or computers to support learning and internet signal instability. Contribution of this activity is to support schools to encourage the ability of educators to carry out certification as a skill proved as financial report preparer.
A qualitative method for investigating design

This chapter describes our qualitative study of designers and design practice. It situates the study in the broader field of empirical studies of intellectual property, and it describes in detail the methodology and benefits of a qualitative interview study of designers and design practice to shed light on some of the persistent puzzles in design law. The chapter focuses on four lines of inquiry: defining “design” and “design practice” and from within the profession; exploring the various inputs to design practice and the process of “problem solving” designers pursue; understanding what “integrated” form and function mean to designers; and explaining the features of “successful” or “excellent” design as a professional standard.
Research on a Knowledge Graph Embedding Method Based on Improved Convolutional Neural Networks for Hydraulic Engineering

In response to the shortcomings of existing knowledge graph embedding strategies, such as weak feature interaction and latent knowledge representation, a unique hydraulic knowledge graph embedding method is suggested. The proposed method incorporates spatial position features into the entity-relation embedding process, thereby enhancing the representation capability of latent knowledge. Furthermore, it utilizes a multi-layer convolutional neural network to fuse features at different levels, effectively capturing more abundant semantic information. Additionally, the method employs multi-scale dilated convolution kernels to capture rich explicit interaction features across different scales of space. In this study, the effectiveness of the proposed model was validated on the link prediction task. Experimental results demonstrated that, compared to the ConvE model, the proposed model achieved a significant improvement of 14.8% in terms of mean reciprocal rank (MRR) on public datasets. Additionally, the suggested model outperformed the ConvR model on the hydraulic dataset, leading to a 10.1% increase in MRR. The results indicate that the proposed approach exhibits good applicability and performance in the task of hydraulic knowledge graph complementation. This suggests that the method has the potential to offer significant assistance for knowledge discovery and application research in the field of hydraulics.
RELIABLE TRANSMISSION OF MEDICAL IMAGES

Transmission of large volumes of image data via limited spectrum/ bandwidth is a challenge, therefore data needs to be compressed at the transmission end and expanded at the receiver.This compression and expansion may lead to distortion which will hinder the smooth operation of the system.With wireless communication the situation becomes even worse due to noise while traversing.Random bit error or packet losses introduced by the channel may corrupt the critical information.Reliable Transmission refers to error free transmission.In this w o r k an effort has been made to study the factors influencing reliable transmission of medical images via wireless networks and possible solutions to overcome the challenges.Reliability of data transmission over wireless channels can be increased by using channel coding before transmission.1/2 TC and 3/4 LDPC can be used in case of lower compression ratio and 1/3 TC and 2/3 LDPC codes can be used in case the image is compressed at higher CR.
On the Correspondence between Human Vision and Convolutional Neural Networks: A Visual Quality Assessment Perspective

Deep features of convolutional neural networks (CNNs), designed for high-level computer vision tasks such as object detection, have been shown to be also effective for image quality assessment (IQA). This motivates further investigations to understand why CNNs are good IQA estimators and, in a broader sense, whether a correspondence exists between CNN's processing stages and the hierarchical mechanism of the human visual system (HVS). In this paper, we stimulate CNNs with a new family of maximally-regular textures to investigate if higher areas of the visual cortex can be approximated within the CNN layers. The results show interesting correspondence between CNNs and the visual cortex, suggesting that these frameworks might be able to serve as suitable baseline candidates for developing a new generation of quality metrics that better replicate the complex stages of the HVS. Developing such IQA models needs in-depth research into the mechanism of the HVS and designing CNNs with better quality-aware feature encoding. As an initial step toward this goal, we established new criteria for improving the performance of pre-trained networks in quality assessment applications by leveraging texture sensitivity. The outcomes illustrate that our feature map weighting and neuron selection criteria could improve the IQA task.
Localization of Just Noticeable Difference for Image Compression

The just noticeable difference (JND) is the minimal difference between stimuli that can be detected by a person. The picture-wise just noticeable difference (PJND) for a given reference image and a compression algorithm represents the minimal level of compression that causes noticeable differences in the reconstruction. These differences can only be observed in some specific regions within the image, dubbed as JND-critical regions. Identifying these regions can improve the development of image compression algorithms. Due to the fact that visual perception varies among individuals, determining the PJND values and JND-critical regions for a target population of consumers requires subjective assessment experiments involving a sufficiently large number of observers. In this paper, we propose a novel framework for conducting such experiments using crowdsourcing. By applying this framework, we created a novel PJND dataset, KonJND++, consisting of 300 source images, compressed versions thereof under JPEG or BPG compression, and an average of 43 ratings of PJND and 129 self-reported locations of JND-critical regions for each source image. Our experiments demonstrate the effectiveness and reliability of our proposed framework, which is easy to be adapted for collecting a large-scale dataset. The source code and dataset are available at https://github.com/angchen-dev/LocJND.
Evaluation of point cloud features for no-reference visual quality assessment

The development and widespread adoption of immersive XR applications has led to a renewed interest in representations that are capable of reproducing real-world objects and scenes with high fidelity. Among such representations, point clouds have attracted the interest of industry and academia alike, and new compression solutions have been developed to facilitate their adoption in mainstream applications. To ensure the best quality of experience for the end-user in limited bandwidth scenarios, new full-reference objective quality metrics have been proposed, promoting features designed specifically for point cloud contents. However, the performance of such features to predict the quality of point cloud contents when the reference is not available is largely unexplored. In this paper, we evaluate the performance of features commonly used to model point cloud distortions in a no-reference framework. The obtained features are integrated into a quality value through a support vector regression model. Results demonstrate the potential of full-reference features for no-reference assessment.
Tactile and Visual Virtual Reality Attention Distraction From Pain in Cold Pressor Test

The goal of this study was to test the efficacy of a tactile attention distraction from pain and compare its effectiveness with a virtual reality (VR) distraction on an analogous task.VR is considered to be the gold standard for attention distraction, but it cannot be used in certain clinical cases or for particular medical procedures.A repeatedmeasures experimental study was carried out with 42 participants using tactile and VR variants of an n-back task and a cold pressor test for pain.The independent variable was the distraction type (tactile, VR, or no-distraction) and the dependent variable was pain tolerance (i.e., time participants kept their hand in cold water).The results showed that both tactile and VR games effectively increased pain tolerance compared to the control condition.Effect sizes for both interventions were similar.However, the effect was observed only for female participants.
Physics of Complex Photonic Media and Metamaterials: feature issue introduction

We introduce the Optical Materials Express feature issue on the Physics of Complex Photonic Media and Metamaterials. This issue comprises a collection of nine manuscripts on the development, characterization, control, and applications of complex photonic media and metamaterials, including but not limited to metagratings, chiral metamirrors, diamond nanopillars, rotating metamaterials, and networks of random lasers.
ARTIFICIAL INTELLIGENCE MODEL FOR LEUKEMIA DETECTION

Not only is leukaemia lethal in nature, but it is also highly expensive to treat.However, early detection of leukaemia can save lives and money for those affected, particularly youngsters, among whom leukaemia is a common cancer type.In this research, we describe a supervised machine learning model that reliably predicts the likelihood of early-stage leukaemia based solely on symptoms.Furthermore, feature selection is performed on the dataset to demonstrate the power of individual features and improve the performance of classification models.We are applying two machine learning algorithms, nave bayes classifier and support vector machine.Cross-validation is a strategy for testing ML models that involves training numerous ML models on subsets of the available input data and then evaluating them on the complementary subset.To detect overfitting, use cross-validation.The SVM algorithm based on performance achieved the highest accuracy, precision, recall, and F-measure.
Single Human Parsing Based on Visual Attention and Feature Enhancement

Human parsing is one of the basic tasks in the field of computer vision. It aims at assigning pixel-level semantic labels to each human body part. Single human parsing requires further associating semantic parts with each instance. Aiming at the problem that it is difficult to distinguish the body parts with similar local features, this paper proposes a single human parsing method based on the visual attention mechanism. The proposed algorithm integrates advanced semantic features, global context information, and edge information to obtain accurate results of single human parsing resolution. The proposed algorithm is validated on standard look into part (LIP) dataset, and the results prove the effectiveness of the proposed algorithm.
Quantum interference of identical photons from remote GaAs quantum dots

Efficient generation and detection of coherent single photons are key to advances in photonic quantum technologies such as quantum computation, quantum simulation, and quantum communication. Among many quantum emitters, semiconductor quantum dots are promising due to their deterministic and high-rate single-photon emission and the possibility of integration into nanostructures. However, poor quantum coherence between single photons created by independent emitters poses a major roadblock. Here, we present near-unity two-photon interference visibilities from two separate GaAs quantum dots. This high visibility (~93%) is achieved under rigorous conditions: there is no Purcell enhancement, no temporal post-selection, no narrow spectral filtering, nor frequency stabilization. One key component is the heterostructure, an n-i-p diode using material of excellent quality. The quantum dot charge is locked via Coulomb blockade; within a charging plateau, the exact emission frequency can be tuned via the bias applied to the gate; the charge noise is very low. A second key component is the quantum dot itself: the relatively large size confers multiple benefits. Our results suggest that GaAs quantum dots represent a versatile choice for generating identical photons from multiple emitters.
Artificial Climate Prototype for IoT-Based Vines

Abstract This climate manipulation prototype is a tool that helps monitor vine condition and automatically carries out the treatment. The design of this climate manipulation prototype can also make it easier to monitor and care for plants remotely. The plant state can be controlled with the android application which can display the condition of the plants in real-time. This type of research is research &amp; development and uses a waterfall development model, which has stages: analysis, design, coding, and testing. Based on the results, a prototype of climate manipulation for IoT-based vines was produced. After designing the prototype, climate manipulation conducts quality testing based on functionality suitability testing. Testing aspects of functionality suitability using the black box testing method, carried out by two system experts where the test results are worth 1 with a very decent category and have met the functionality aspect.
Transductive semantic knowledge graph propagation for zero-shot learning

The domain shift is a very common phenomenon in Zero-shot Learning (ZSL), because the data distribution between the source and target domain might not match well in real scenarios. This paper focuses on dealing with the domain drift problem in ZSL based on the semantic knowledge graph propagation. Our method consists of visual feature extraction (VFE) module, semantic feature extraction module (SFE) and feature mapping module (FM). In VFE module, the high-level visual features are extracted under Convolutional Neural Network (CNN) framework, where unseen samples participate in model training and stay away from seen ones under the premise of distinguishability. SFE module relies on the Graph Convolutional Network (GCN) framework and focuses on the transmission of semantic embeddings. A modified message aggregation and transformation strategy is proposed to effectively relieve the information smoothing phenomenon caused by the increase of GCN layers. In the recognition stage, AutoEncoder framework achieves a two-way visual and semantic interaction to further minimize the domain drift phenomenon. Simulation results on ImageNet dataset have demonstrated the performance of the proposed method.
Content moderation in times of war: testing state and self-regulation, contract and human rights law in search of optimal solutions

Abstract The full-scale invasion of Ukraine and crimes against humanity accompanying it have been fuelled by the mass spread of fakes and hatred incitements, forcing the largest online platforms to review and strengthen their content moderation policies. However, the approaches taken by platforms have not been perfect, and some of them could even exacerbate the situation. All in all, this is another evidence of the need to develop mechanisms being able to cope with the challenges to online speech and safety caused by dramatic social events. Basic approaches to address content moderation issues developed by now are self-(co-) and state regulation, on the one hand, and contract and human rights law, on the other. However, neither of them taken separately can ensure the needed level of protection of human rights online. Thus, in this article the ways to combine and improve these approaches are proposed. On the one hand, there is a need to dwell on private law mechanisms allowing to ensure the protection of human rights by virtue of judgements in private disputes. On the other hand, state regulation should be improved by international instruments allowing to provide for a uniform approach to regulation at a global scale.
Spark based Parallel Frequent Pattern Rules for Social Media Data Analytics

The number of users on social media are increasing, thus the data produced is also increasing tremendously. Social media data mining and analysis can produce a lot of hidden information, which can be helpful in decision-making. Prediction of the co-occurring words with confidence can provide deep insights of social media. The paper presents an applied process to mine social media dataset to retrieve frequent patterns (or rules) in cost effective time. The retrieved patterns can be useful in making decisions related to social media. The experiment is performed on three social media datasets and various rules are analyzed by varying the values of threshold (minimum support and minimum confidence). Experiments are also performed for both Frequent Pattern (FP) Growth and Parallel FP (PFP) Growth using the same datasets. The parallel computation is achieved with the help of a scalable Apache Spark environment. Execution time for both FP-Growth and PFP-Growth on the same datasets is also described. While performing experiments it is found that FP-Growth of SPMF requires preprocessing to convert item-sets into transactional databases. The pre-processing time is required only once, as a result the time required to generate rules is less. Whereas, the PFP-Growth does not require preprocessing on the dataset to generate rules. This saves time to directly generate the association rules using PFP-Growth.
Disentangling User Conversations with Voice Assistants for Online Shopping

Conversation disentanglement aims to identify and group utterances from a conversation into separate threads. Existing methods primarily focus on disentangling multi-party conversations with three or more speakers, explicitly or implicitly incorporating speaker-related feature signals to disentangle. Most existing models require a large amount of human annotated data for model training, and often focus on pairwise relations between utterances, not accounting much for the conversational context. In this work, we propose a multi-task learning approach with a contrastive learning objective, DiSC, to disentangle conversations between two speakers -- a user and a virtual speech assistant, for a novel domain of e-commerce. We analyze multiple ways and granularities to define conversation "threads''. DiSC jointly learns the relation between pairs of utterances, as well as between utterances and their respective thread context. We train and evaluate our models on multiple multi-threaded conversation datasets that were automatically created, without any human labeling effort. Experimental results on public datasets as well as real-world shopping conversations from a commercial speech assistant show that DiSC outperforms state-of-the-art baselines by at least 3%, across both automatic and human evaluation metrics. We also demonstrate how DiSC improves downstream dialog response generation in the shopping domain.
Retrieval-Enhanced Generative Model for Large-Scale Knowledge Graph Completion

The task of knowledge graph completion (KGC) is of great importance. To achieve scalability when dealing with large-scale knowledge graphs, recent works formulate KGC as a sequence-to-sequence process, where the incomplete triplet (input) and the missing entity (output) are both verbalized as text sequences. However, inference with these methods relies solely on the model parameters for implicit reasoning and neglects the use of KG itself, which limits the performance since the model lacks the capacity to memorize a vast number of triplets. To tackle this issue, we introduce ReSKGC, a Retrieval-enhanced Seq2seq KGC model, which selects semantically relevant triplets from the KG and uses them as evidence to guide output generation with explicit reasoning. Our method has demonstrated state-of-the-art performance on benchmark datasets Wikidata5M and WikiKG90Mv2, which contain about 5M and 90M entities, respectively.
Development of Learning Application for College Students with Special Needs using Universal Design for Learning

This study aims to analyse the learning needs of college students with special needs, develop a Universal Design for Learning based learning application named AJAR MBK, and assess the efficiency of the AJAR MBK application.It used a research and development model adapted from the ADDIE (analysis, design, development, implementation, and evaluation) development model.Our analysis results showed the learning needs of college students with special needs, which were used in the AJAR MBK learning application development.Further, the AJAR MBK's feasibility test results showed 4.56 and 4.67 scores from the experts in learning materials and media, respectively.Both feasibility scores were classified as very feasible.Meanwhile, the average feasibility score from the students with special needs was 4.6, categorized as very feasible.Our last analysis results showed that the developed learning media was effective to be used as presented by students' average final score in the education for children with spectrum autism course that increased by 15.4% from their pre-test score.The higher post-test score than the pre-test score confirmed that the AJAR MBK is effective to be used by students with special needs in the education for children with spectrum autism course
The Effects of Digital/Online Written Feedback on Paper-based Test for College Students

In recent years, online teaching has become more and more popular, but it also brings many challenges to teachers and students.Currently, the lack of feedback is one of the biggest challenges in online teaching.This study provides students with a new method of living feedback through digital written feedback on paper-based test.Research shows that this method can effectively improve students' learning strategies and academic achievement. An Overview of the Effects of Digital Written Feedback on Paper-Based Test for College Students in China Motivation
Analysis of hot spots of business environment research based on CiteSpace

Business environment is a soft power for national development and a major support for building a new system of open economy.In order to explore the evolution and prospect of business environment research, a total of 813 relevant documents from SCI, CSSCI and CSCD three core journals from January 2003 to June 2022 were selected as the research objects, and CiteSpace 6.1.R2 was used to conduct textual statistics and visual analysis of this topic for the hotspot research of business environment.