Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN E-BOOK BAHASA INGGRIS UNTUK SISWA SMA

The aims of this study to: Create an e-book on the subject of English XI grade at Vocational High School Number 3 Pagar Alam City which is feasible to improve student achievement. In this study uses a development design (Research and Development). The subjects of this study are students of Science Class XI of Senior High School Number 3 Pagar City. The data collection techniques used in the study were (1) Assessment sheets for product validation and to describe student responses. (2) The results of student tests to analyze the increase in student achievement. The data analysis used mean and t-test. The instrument of this study uses a Linkert scale. The conclusions of this research are; (1) The development of this English E-Book is feasible to improve student achievement in learning English according to the validation of material experts and the validation of media experts obtained an average score is 3.85 with “Very Eligible” criteria. (2) The development of this English e-book can improve student learning outcomes based on the results of a wide-scale trial trial of class XI MIPA students of SMA Negeri 3 Pagar Alam in English subjects obtained based on the results of a wide- scale trial trial of class XI MIPA students of SMA Negeri 3 Pagar Alam in English subjects, the results obtained from the study it can be concluded that the developed English ebook can significantly improve student achievement in English subjects
Learning image embeddings without labels

Can we automatically learn discriminative embedding features from images when human-annotated labels are absent? The problem of unsupervised embedded learning remains a significant and open challenge in image and vision community. A joint online deep embedded clustering and hard samples mining framework are proposed to improve the representation ability of embedded learning. In addition, to enhance the discriminability of feature representations, a structure-level pair-based loss is introduced to take full advantage of structure correlation between all the mined hard samples. Finally, the quantitative results of exhaustive experiments on three benchmarks show that our proposed method performs better than existing state-of-the-art methods.
Hubness-aware User Identity Linkage

Nowadays, it is common for one natural person to join multiple social networks to enjoy different types of services. User identity linkage (UIL), which aims to link identical identities across different social platforms, has attracted increasing research interests recently. Most existing approaches focus on the sophisticated architecture engineering of the linkage model but ignore the challenge of hubness in the post-processing nearest neighbor search phase. Hubness appears as some identities in a social platform, called hubs, being extra-ordinary close to the identities in the other platform, which will degrade the alignment performance. Different from existing heuristic methods, in this paper we propose a hubness-aware user identity linkage model HAUIL to smoothly learn hubless linkage signals. A carefully-designed objective function is presented to explicitly mitigate the hubness information from the pre-learned linkage guidance. HAUIL can be easily adapted to most existing UIL models. Empirically, we evaluate HAUIL over multiple publicly available datasets, and the experimental results demonstrate its superiority.
Do We Fix it or Burn it Down? Towards Practicable Critique at CSCW

CSCW has a rich interdisciplinary and methodological history, and our work focuses on designing and building technologies for collaboration and community as well as evaluating and critiquing these technologies. At the intersection of these interdisciplinary perspectives comes a tension playing out in formal and informal venues: is CSCW’s role to fix and improve existing technologies, or is it to start over and build anew? In this panel, we address this question with an eye towards enabling practicable critique within CSCW, and help navigate this tension that arises in our interdisciplinary community. Our panelists reflect methodological diversity in CSCW and positional differences on these questions. We look forward to a lively, spirited discussion between panelists that build on three provocations and engage the community on this important and critical issue.
A lightweight model for multi-traffic object detection based on deep learning under complex traffic conditions

ABSTRACTThe object detection is extremely important in autonomous driving environment awareness. Besides vehicle and pedestrian detection, traffic signs and lights are important objects. The paper presents how to achieve precise results in multi-traffic object detection while minimizing the model size. A deep learning network YOLOv5s-Ghost-SE-DW is proposed based on the YOLOv5s. The proposed network can detect all traffic objects including traffic signs and lights. First, the convolution layer is replaced by Ghost module to reduce the parameter and model size. Second, in order to improve accuracy and real-time performance, the attention mechanism SELayer is embedded to fuse more spatial features. Third, the DW convolution is used to extract features and further reduce the parameter number. The effect of different modules on the whole network is verified by ablation experiments. The YOLOv5s-Ghost-SE-DW yields a model size of 5.22MB while achieving 15.58 FPS real-time performance on CPU. The FPS increases by 27.5%.
Assess the Effectiveness of Planned Teaching on Assisting Self-care Activities of Patients with Plaster of Paris Casts of Lower Extremities among Caregivers

Aim of the Study: To assess the effectiveness of planned teaching on assisting self-care activities of patients with plaster of Paris casts of lower extremities among caregivers.&#x0D; Study Design: The design that will be used in the study is One Group Pretest Posttest research design. &#x0D; Methodology: A study used a pre-test post-test design with no control group and a pre-experimental design. The impact of planned teaching on assisting self-care activities of patients with plaster of Paris casts of the lower extremities among caregivers will be assessed using the purposive sampling technique. In this study Evaluatory research approach will be used. Selected 100 caregivers of patients with plaster of Paris cast of lower extremities will be assessed primarily for knowledge in assisting self-care activities by structured questionnaires and then planned teaching will be given to the selected sample developed by researcher as intervention.&#x0D; Expected Results: To assess the effectiveness of planned teaching on assisting self-care activities of patients with plaster of Paris casts of lower extremities among caregivers. In this study the planned teaching will be useful in improving the caregivers knowledge in assisting selfcare activities.&#x0D; Conclusion: The conclusion will be drawn from the results and will be published in per review journal.
A Mitsuba-based Study on Trade-offs Between Projection and Reflection Based Systems in Structured-Light 3D Imaging

We present a first study on the trade-offs between projection and reflection based 3D imaging systems under diffusive and specular reflection mixtures. Experiments are conducted in our projection/reflection simulator framework using the Mitsuba2 ren- derer.
Inferring Users’ Social Roles with a Multi-Level Graph Neural Network Model

Users of social networks have a variety of social statuses and roles. For example, the users of Weibo include celebrities, government officials, and social organizations. At the same time, these users may be senior managers, middle managers, or workers in companies. Previous studies on this topic have mainly focused on using the categorical, textual and topological data of a social network to predict users’ social statuses and roles. However, this cannot fully reflect the overall characteristics of users’ social statuses and roles in a social network. In this paper, we consider what social network structures reflect users’ social statuses and roles since social networks are designed to connect people. Taking an Enron email dataset as an example, we analyzed a preprocessing mechanism used for social network datasets that can extract users’ dynamic behavior features. We further designed a novel social network representation learning algorithm in order to infer users’ social statuses and roles in social networks through the use of an attention and gate mechanism on users’ neighbors. The extensive experimental results gained from four publicly available datasets indicate that our solution achieves an average accuracy improvement of 2% compared with GraphSAGE-Mean, which is the best applicable inductive representation learning method.
Capturing Contentiousness

Recent initiatives by cultural heritage institutions in addressing outdated and offensive language used in their collections demonstrate the need for further understanding into when terms are problematic or contentious. This paper presents an annotated dataset of 2,715 unique samples of terms in context, drawn from a historical newspaper archive, collating 21,800 annotations of contentiousness from expert and crowd workers. We describe the contents of the corpus by analysing inter-rater agreement and differences between experts and crowd workers. In addition, we demonstrate the potential of the corpus for automated detection of contentiousness. We show that a simple classifier applied to the embedding representation of a target word provides a better than baseline performance in predicting contentiousness. We find that the term itself and the context play a role in whether a term is considered contentious.
Military Supply Chain Management and Blockchain Development

Blockchain has become a powerful technology and when it comes to supply chain management, blockchain has a lot to offer which could contribute to its development and make the supply chain more effective. The same benefit could be also gained when blockchain is incorporated in the Military Supply Chain Management (MSCM). Theaim of this paper is to develop and integrate blockchain in the MSCM. The developed MSCM is focusing on three (3) main blockchain components, which are transparency, integrity and secure communication. The methodology to develop the MSCM blockchain similar to UnicalCoin. The findings show that incorporating blockchain into the MSCM enables transparency, integrity and secure communication. Thus, blockchain may reduce fraud, improve communication between parties and made end-to-end tracking transparency in MSCM. Future work is to embed a smart contract feature to automate some processes in MSCM.
Sampling-based Reachability Analysis: A Random Set Theory Approach with Adversarial Sampling

Reachability analysis is at the core of many applications, from neural network verification, to safe trajectory planning of uncertain systems. However, this problem is notoriously challenging, and current approaches tend to be either too restrictive, too slow, too conservative, or approximate and therefore lack guarantees. In this paper, we propose a simple yet effective sampling-based approach to perform reachability analysis for arbitrary dynamical systems. Our key novel idea consists of using random set theory to give a rigorous interpretation of our method, and prove that it returns sets which are guaranteed to converge to the convex hull of the true reachable sets. Additionally, we leverage recent work on robust deep learning and propose a new adversarial sampling approach to robustify our algorithm and accelerate its convergence. We demonstrate that our method is faster and less conservative than prior work, present results for approximate reachability analysis of neural networks and robust trajectory optimization of high-dimensional uncertain nonlinear systems, and discuss future applications.
A Practical Sampling-based Motion Planning Method for Autonomous Driving in Unstructured Environments

Motion planning is a fundamental technique for autonomous driving that guides the ego vehicle to the destination. Demands of driving safety and real-time calculation make it tend to adopt a layered strategy in the motion planning. This paper proposes an efficient method for the reference trajectory generation at a global level in unstructured driving environments. First, sampling-based path construction and optimization are applied to incrementally search for a feasible spatial reference path. Then, a trapezoidal speed profile, further smoothed by a cubic B-spline, is carefully designed along the path considering speed and acceleration limits. Finally, a simplified tracking method is implemented along the speed-path-coupled reference to obtain a feasible control sequence and further smooth the trajectory. Simulation results illustrate the effectiveness of the proposed method that it can efficiently obtain a collision-free trajectory, including waypoints information as well as speed and steering inputs profiles.
ANALISIS BAHAN AJAR BAHASA INGGRIS DI SMB DHAMMA PANNA TEMANGGUNG

This research objectives was to identify and dscribe the quality of English Textboooks. The companion boook entitled English Book Elementary School. Specially, it’s seen from: a) content feasibility; b) language eligibility; c) feasibility of serving; d) Graphic feasibility; and e) the level of readability of the discourse in teaching in teacing materials or text books. This research is evaluative research, the method used in this resarch is mixed methods research. The sbjects of studied were three books. The results showed that the content feasibility, very good category was in KLS 2 and KLS 3, while gor good category in KLS 1. Language eligibility standards, KLS 2 and KLS 3were good categories, KLS 1 had enough categories. In terms of presentation based on the language content, the categories are very good, namely KLS 2 and KLS 3, while for KLS 1 it gets good categoris. In terms of graphics, the categories are very good with the order of KLS 1, KLS 2 and the last is KLS 3 with good categories. The measurement of discourse readability shows that the three English books used in SMB Dhamma Panna Temanggung are not accordance with the class levels taught, especiallly at KLS 1 and KLS 2, while KLS 3 books is better.
Toward Sarcasm Detection in Reviews—A Dual Parametric Approach with Emojis and Ratings

Detection of sarcasm is very crucial in today’s world where social media become a major platform of expressing emotions. Sarcastic statements are the statements where the sentiment polarity and contextual meaning are completely contrary. It affects the efficiency and accuracy of present sentiment analysis systems (SAS). Most of the currently available sarcasm detection models such as vector space models, CNN, RNN, etc. consider the raw review text in order to determine the sentiment which ignores the presence of negation, lexical ambiguity, and irony created by general facts. Due to this, detecting sarcasm may not be very accurate. To improve the accuracy of sarcasm detection and to better understand the context, the model proposed integrates the ratings, reviews, and emojis. The performance of the proposed system is evaluated using annotator-agreement methods with the metrics such as F1 score, Precision, Recall, and Accuracy. The performance shows that integrating more features enhances the accuracy by a considerable margin as compared to previously defined methodologies.
Reconstruction of Missing Data in Satellite Imagery Using SN-GANs

In the field of remote sensing satellite imagery, malfunctions in the available raw data are prominent. Especially in Short-Wave Infrared (SWIR) detectors used in satellite imaging cameras, which suffer from dropouts in pixel and line direction in raw data. With the recent development in generative adversarial networks and its vast application in inpainting the missing data, the possibility to predict and fill in the missing data accurately with contextual attention has become prevalent. This paper presents SN-GANs (SN-generative adversarial networks) which is two-staged architecture, and it is based on the concept of feed forward neural networks with contextual attention layers. While reconstructing the corrupted part of the images, the model takes surrounding pixels into consideration. Moreover, this architecture is adept enough to fill in the multiple lines and pixel dropouts efficiently even in super-resolution satellite images. The available traditional methods fail to address the loss of data that incurs, while inpainting a 16-bit raw image because they are effective enough for 8-bit RGB images. SN-GANs have effectively resolved this issue with a lossless image inpainting method for 16-bit satellite images as it retains the features of non-corrupted data. The performance of the model is evaluated using similarity metrics like structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR) and mean-squared error (MSE).
Automatic Detection of Display Defects for Smart Meters based on Deep Learning

The smart meter is an essential part of an intelligent grid system. Defects in the LCD screen the smart meters affect their use. Therefore, detection of LCD screen defects of smart meters is of great significance for management and use of smart electricity meters. At present, detection methods are mainly realized by manual detection and automatic detection based on machine vision. However, performance of these two methods is not satisfactory. The fault detection task of a smart meter LCD screen can be divided into two parts: smart meter LCD localization and LCD fault detection. Therefore, this paper proposes a twostage system based on deep learning, which combines YOLOv5 with ResNet34. YOLOv5 is used for smart meter LCD localization and the classification network based on ResNet34 for LCD fault detection. We have constructed an LCD screen localization dataset and an LCD screen defect detection dataset to train and test our model. As a result, our model achieves a defect detection accuracy of 98.9% on the dataset proposed in this paper and can accurately detect the common defects of an LCD screen.
The power of summarization in graph mining and learning

Our ability to generate, collect, and archive data related to everyday activities, such as interacting on social media, browsing the web, and monitoring well-being, is rapidly increasing. Getting the most benefit from this large-scale data requires analysis of patterns it contains, which is computationally intensive or even intractable. Summarization techniques produce compact data representations (summaries) that enable faster processing by complex algorithms and queries. This talk will cover summarization of interconnected data (graphs) [3], which can represent a variety of natural processes (e.g., friendships, communication). I will present an overview of my group's work on bridging the gap between research on summarized network representations and real-world problems. Examples include summarization of massive knowledge graphs for refinement [2] and on-device querying [4], summarization of graph streams for persistent activity detection [1], and summarization within graph neural networks for fast, interpretable classification [5]. I will conclude with open challenges and opportunities for future research.
Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language

In this work, we propose a unified framework, called Visual Reasoning with Differ-entiable Physics (VRDP), that can jointly learn visual concepts and infer physics models of objects and their interactions from videos and language. This is achieved by seamlessly integrating three components: a visual perception module, a concept learner, and a differentiable physics engine. The visual perception module parses each video frame into object-centric trajectories and represents them as latent scene representations. The concept learner grounds visual concepts (e.g., color, shape, and material) from these object-centric representations based on the language, thus providing prior knowledge for the physics engine. The differentiable physics model, implemented as an impulse-based differentiable rigid-body simulator, performs differentiable physical simulation based on the grounded concepts to infer physical properties, such as mass, restitution, and velocity, by fitting the simulated trajectories into the video observations. Consequently, these learned concepts and physical models can explain what we have seen and imagine what is about to happen in future and counterfactual scenarios. Integrating differentiable physics into the dynamic reasoning framework offers several appealing benefits. More accurate dynamics prediction in learned physics models enables state-of-the-art performance on both synthetic and real-world benchmarks while still maintaining high transparency and interpretability; most notably, VRDP improves the accuracy of predictive and counterfactual questions by 4.5% and 11.5% compared to its best counterpart. VRDP is also highly data-efficient: physical parameters can be optimized from very few videos, and even a single video can be sufficient. Finally, with all physical parameters inferred, VRDP can quickly learn new concepts from a few examples.
Design of Decentralized Application for Telemedicine Image Record System with Smart Contract on Ethereum

This paper discusses the implementation of smart contracts on the Ethereum blockchain system for telemedicine data storage.Telemedicine is one of the currently developing digital technologies in the health and medical sectors.Telemedicine can be more efficient when seeking treatment because patients do not need to see a doctor face to face.When using blockchain technology, the stored data becomes more transparent for each node in the blockchain network but has verification on every transaction which takes time and gas costs.However, telemedicine has several risks and problems, one of which is long data storage process time because there must be a verification process first to ensure data security.Another problem faced is the issue of the gas fee of the blockchain telemedicine system which is billed in every data storage transaction.In this study, a blockchain system was introduced for managing and securing databases on telemedicine.The implementation of this blockchain system was carried out on a website page that can add data to and retrieve data from the blockchain system.The results of this study showed that blockchain was successfully implemented to store telemedicine data with Ethereum.The analysis in this paper refers to the set and gets functions.The set function is used to send data to the blockchain, and the get function is used to retrieve data from the blockchain.From testing, the Get function has a much faster execution time than the Set function because the Get function does not require verification to retrieve its data.In the iterations carried out-namely 1, 10, and 100-the longest time on average was at 100 iterations when compared to the other iterations.In the tests carried out, the more characters that were stored, the more gas costs must be paid.In the tests, the percentage increase in costs was 0.34% per character.