Reviewerly/exHarmony huggingface dataset:
Study on web pillar failure mechanism during auger mining and its associated risk assessment

Abstract To safely and efficiently recover trapped coal under final endwalls in open cut mines, theoretical analysis and numerical calculation were used to study the stability of web pillar during auger mining. The partial ordered set (poset) evaluation model was used to develop a risk assessment methodology, and auger mining at Pingshuo Antaibao open cut coal mine was used as a field example for validation. Based on the catastrophe theory, the failure criterion of web pillar was established. From the limit equilibrium theory, the maximum allowable plastic yield zone width and minimum web pillar width were proposed under various Factor of Safety (FoS) thresholds. This in turn provides a new method for web pillar design. Based on the poset theory and combining with the risk evaluation and proposed hazard levels, input data were standardized and weighted. Subsequently, the comparison matrix, HASSE matrix and HASSE diagram were established. The study shows that: when the width of the plastic zone of web pillar exceeds 88% of the total width, web pillar may be unstable. Based on the calculation formula for the required width of web pillar, the required pillar width was 4.93 m and it was deemed as “mostly stable”. This was consistent with the field condition on site. Such that this method was validated.
Optimized Modulation and Coding for Dual Modulated QR Codes

We present optimized modulation and coding for the recently introduced dual modulated QR (DMQR) codes that extend traditional QR codes to carry additional secondary data in the orientation of elliptical dots that replace black modules in the barcode images. By dynamically adjusting the dot size, we realize gains in embedding strength for both the intensity modulation and the orientation modulation that carry the primary and secondary data, respectively. Furthermore, we develop a model for the coding channel for the secondary data that enables soft-decoding via 5G NR (new radio) codes already supported by mobile devices. The performance gains for the proposed optimized designs are characterized via theoretical analysis, simulations, and actual experiments using smartphone devices. The theoretical analysis and simulations inform our design choices for the modulation and coding, and the experiments characterize the overall improvement in performance for the optimized design over the prior unoptimized designs. Importantly, the optimized designs significantly increase usability of DMQR codes with commonly used QR code beautification that cannibalizes a portion of the barcode image area for the insertion of a logo or image. In experiments with a capture distance of 15 inches, the optimized designs increase the decoding success rates between 10% and 32% for the secondary data while also providing gains for primary data decoding at larger capture distances. When used with beautification in typical settings, the secondary message is decoded with a high success rate for the proposed optimized designs, whereas it invariably fails for the prior unoptimized designs.
Random Projector: Efficient Deep Image Prior

Deep image prior (DIP) has shown great promise in tackling a range of image restoration problems. However, its optimization is extremely sluggish, which inevitability hinders its practical usage when there are hard time constraints. To mitigate this issue, we propose a more compact and efficient model, dubbed random projector (RP), and freeze the convolutional layers of the neural network to prevent slow learning. We further make use of an explicit prior—total variation— to regularize the reconstructed natural images and promote pleasure-looking images. We evaluate our proposed method on different image restoration tasks such as image denoising and image inpainting, and conduct comparisons with DIP and its prevalent variants. The experimental results suggest that our proposed random projector achieves competitive restoration quality in terms of PSNR while it significantly reduces the optimization (OPT) time.
A CARS of Novels with Imbalanced Sample Treatment

ABSTRACTWidely spread, recommender systems might face some challengessuch as overspecialization and lack of diversity. In this paper, wepropose a book context-aware recommender system (CARS) thatuses individual characteristics as model features and active searchas a pre-filtering context method in an attempt to increase user’snewness perception and diversity. To achieve this goal, we revisedliterary critic essays to create five binary base-questions able toseparate and aggregate novels through subjective concepts.We alsoconducted a data collection to form a dataset around 50 selectedbooks, evaluated by the public using these questions. Going further,we developed two recommender systems (RS) using differentstrategies to handle imbalanced samples (SELC and SMOTE) andcompare their performance to conclude that SELC generates betterrecommendations on an inner performance.
IRIS: Extração, Organização e Classificação de Conteúdos do Projeto Pedagógico de Curso do Técnico em Automação Industrial

ABSTRACTTraditional search systems on the World Wide Web haverevolutionized searching for information, almost wholly replacingphysical encyclopedias. However, these systems must developnew search methods that make the process more efficient andcomprehensive for students. In technical courses, it is common forstudents to struggle with searching for quality content on theinternet that serves both as a bibliographic base for the disciplinesand as reference material to deepen their knowledge. TheIntelligent Research Interactive System (IRIS) automates searchesfor contents of the fields in the Industrial Automation TechnicalCourse to contribute to an effective improvement in the quality oftheir studies and reduce the time spent in processes that are notlinked to the education activities.
Comparação de Técnicas para Representação Vetorial de Imagens com Redes Neurais para Aplicações de Recuperação de Produtos do Varejo

ABSTRACTProduct retrieval from images has multiple applications rangingfrom providing information and recommentations for customersin supermarkets to automatic invoice generation in smart stores.However, this task present important challenges such as the largenumber of products, the scarcity of images of items, differencesbetween real and iconic images of the products, and the constantchanges in the portfolio due to the addition or removal of products.Hence, this work investigates ways of generating vector representationsof images using deep neural networks such that theserepresentations can be used for product retrieval even in face ofthese challenges. Experimental analysis evaluated the effect thatnetwork architecture, data augmentation techniques and objectivefunctions used during training have on representation quality. Thebest configuration was achieved by fine-tuning a VGG-16 modelin the task of classifying products using a mix of Randaugmentand Augmix data augmentations and a hierarchical triplet loss as aregularization function. The representations built using this modelled to a top-1 accuracy of 80,38% and top-5 accuracy of 92.62% inthe Grocery Products dataset.
Algoritmo de Treinamento para uma Rede SLFN com Projeção Aleatória e Margem Larga Learning Algorithm for an SLFN Network with Random Projection and Large Margin

ABSTRACTThis work presents a large margin learning algorithm for singlehidden layer feedforward networks (SLFNs) with random weightsfor the hidden neurons, called RP-IMA. This algorithm, applied tobinary classification problems, proposes randomly assignedweightsto the hidden layer and the use of an incremental margin algorithmto calculate the weights of the output neuron of the SLFN. Theresults showed that the proposed algorithm is capable to obtaina large separation margin in the feature space and has its performanceless sensitive to variations in the network architecture, whencompared to Extreme Learning Machines.
Peak-First CTC: Reducing the Peak Latency of CTC Models by Applying Peak-First Regularization

The CTC model has been widely applied to many application scenarios because of its simple structure, excellent performance, and fast inference speed. There are many peaks in the probability distribution predicted by the CTC models, and each peak represents a non-blank token. The recognition latency of CTC models can be reduced by encouraging the model to predict peaks earlier. Existing methods to reduce latency require modifying the transition relationship between tokens in the forward-backward algorithm, and the gradient calculation. Some of these methods even depend on the forced alignment results provided by other pretrained models. The above methods are complex to implement. To reduce the peak latency, we propose a simple and novel method named peak-first regularization, which utilizes a frame-wise knowledge distillation function to force the probability distribution of the CTC model to shift left along the time axis instead of directly modifying the calculation process of CTC loss and gradients. All the experiments are conducted on a Chinese Mandarin dataset AISHELL-1. We have verified the effectiveness of the proposed regularization on both streaming and non-streaming CTC models respectively. The results show that the proposed method can reduce the average peak latency by about 100 to 200 milliseconds with almost no degradation of recognition accuracy.
Efficient Siamese Network for UAV Tracking

In this work, we propose an efficient Siamese-based tracker (ESTrack) for aerial visual object tracking using dual global correlation and accurate center localization. The dual correlation module embeds task-specific global similarity information for target classification and state estimation. The center localization highlights the classification score where the target exists, which improves the accuracy of the classification and reduces complex hyperparameter tuning. Extensive experiments on five UAV benchmarks show that our ESTrack-50 performs favorably against many state-of-the-art Siamese- based trackers with a speed of 110 fps. Meanwhile, ESTrack-18 and ESTrack-AO achieve 180 fps with comparable performance to most UAV-based trackers.
Spammer Detection on Short Video Applications: A new Challenge and Baselines

Users can interact with the advertisements and share their impressions through the review system on short video applications. However, spammers may post false or malicious comments to mislead normal users due to profit-driven reasons, damaging the community’s positive atmosphere. In this paper, we introduce a new challenge of spammer detection on short video applications, where the multi-modal information of videos and reviews plays a more critical role than the spam relation graph. Then we propose SPAM-3, a novel baseline to detect SPAM reviews with Multi-Modal representation using attentive heterogeneous graph convolution. Our approach balances multi-modal representation fusion and graph relation extraction, enabling fine-grained interaction and generating discriminative features for the spammer classification task. We demonstrate SPAM-3 via experiments and analysis.
KU Leuven Research Data Management Competence Centre (RDM-CC)

The RDM-CC is a virtual collaborative partnership of experts embedded in different central services at KU Leuven (ICTS, Research Coordination Office (DOC), KU Leuven Libraries, and LIBIS).They coordinate the rollout of various projects with regards to Research Data Management services and tools, with focus on:Their mission is to support high quality research at KU Leuven Association by enabling
Klasifikasi Sistem dan Hubungan sebagai Inti dari Sistem

The system is a collection of objects that have relationships with each other. The components in this system must be integrated with each other. As a result, the most important factor in integrating existing components in a system is the relationship. The purpose of this study is to examine the system's classification and relationships as the core of the system. This study uses a qualitative approach with library research and is included in the qualitative research category. Sources of research data were obtained from related books and journals as well. Data collection techniques include collecting literature data or library materials related to the discussion in the research. The results of this study indicate that there are eight system classifications, and relationships are the core of the system.&#x0D; Keywords: System Classification, System Core Relations, Systems Approach
Gerenciamento de Dívida Técnica no Desenvolvimento Ágil: resultados preliminares de um survey

ResumoAgile methods provide gains in software development, however,they become vulnerable to the presence of TechnicalDebt (TD). Thus, this study has the general objective of understandinghow TD management is currently being carriedout in practice in agile projects. As a research method, asurvey is being applied in different organizations in the areaof technology. Among the preliminary results, the causesthat are associated with the emergence of a technical debtand which tools are currently used to automate managementactivities were identified. At the end, it is expected to builda briefing with this evidence and share it widely among professionalsand companies that work in the research region.
Pengelompokan pembagian zakat dengan menggunakan metode clustering k-means

Zakat is a worship that involves property. Zakat is also included in the fourth pillar of Islam which has the aim of purifying the assets of every Muslim by setting aside a portion of his wealth, if it has reached the time and the amount is given to those who are entitled to receive it. The collection and distribution of zakat is usually handled by the Amil Zakat Agency (BAZ) in every region of Indonesia, one of which is in Pekanbaru. In accordance with the regulations, there are two stages in providing assistance to mustahik, namely conducting interviews and field observations, then determining the nominal amount of assistance given to the Mustahik category of recipients of zakat 1, zakat 2, and zakat 3. Problems that are often encountered in determining potential recipients assistance is a way of selecting Mustahik which still uses the manual method, so that it often causes problems such as the length of the selection process and the occurrence of miscalculations so that the results of Mustahik's selection become inaccurate. For that, it is necessary to create analysis that can process data into information. Data mining is a process for processing data into information using statistical techniques, AI, and machine learning. There are many methods in data mining. In this study using the k-means clustering and for testinguse Davies Bouldin Index. based on testing using the davies bouldin index (DBI) cluster 4 is the best cluster with a value of 0.671, where the lower the value, the better the cluster.
Research, promotion, and practice of open science at the Bennett Institute

The Bennett Institute for Applied Data Science (formerly The DataLab) at the University of Oxford operates as an interdisciplinary team of software developers, clinicians, and traditional academic researchers that pool skills and knowledge to produce academic papers, live, interactive, data-driven tools and services, and practical policy insights on how data can be used to improve lives.Open science principles are at the core of the work at the Bennett Institute as they combine best practice from the software development and academic community.This presentation covers the various ways in which the Bennett Institute aims to incorporate open science principles throughout their own research, promote best practice through advocacy and policy work, and innovate new ways to openly and transparently conduct research through the creation of better tools and software.The Bennett Institute wishes to inspire other researchers to manage better open science practice within their own research groups.
Registered Reports: A challenging open science practice that offers great opportunity to all disciplines

Study objectives: Registered Reports (RRs) are an open science practice where researchers submit the introduction and methods section of a manuscript for peer-review prior to data collection/analysis.Following acceptance of this Stage 1 submission, researchers collect/analyze data and, providing the study is conducted as per the accepted Stage 1 submission, the full manuscript is guaranteed to be published irrespective of the results.RRs commit journals to publish papers based on the importance of the research questions and the quality of the methods, rather than the results, reducing publication bias.RRs have many advantages, but they are also one of the more complicated and challenging open science practices to implement in research.This presentation provides practical guidance on how to approach publishing according to this article format using an example from the author's work, and gives an early career perspective on the advantages and challenges of RRs. Methods and material:The two-step publication process of RRs is explained, aiming to highlight the possibilities, along with the challenges, of RRs by using published work on attachment and self-harm in adolescents as a case study.Data used for this case study were pre-existing and originate from a large-scale (N=1913) general population study of mental health and development in Flemish adolescents, called "SIGMA".Results: Advantages of RRs include enhanced reproducibility and transparency in science, and reduced likelihood of publication bias, p-hacking, and HARKing (i.e.hypothesizing after the results are known and presenting the hypothesis as a priori) across all disciplines.Concerns include the time commitment for RRs relative to "traditional" article formats and power analysis for statistically rare phenomena.Potential solutions to these concerns include advance planning and two-step power calculations. Conclusion:Implementing open science practices, such as publishing in the RR article format, as an early career researcher is feasible and offers great opportunity, but advance planning is crucial.RRs reward best practices, create incentives for direct replication, and increase accuracy and credibility of published results.Therefore, these offer opportunities for a higher degree of reproducibility and transparency in research across all disciplines.
The Analysis of Maxim in Student and Lecturer Conversations through WhatsApp Messenger

The presence of instant messaging applications and its practice in daily life makes it easier for us to communicate without space and time limitations. This study aims to describe the adherence to politeness principles in WhatsApp messages sent by students to lecturers during thesis guidance. The method used is the descriptive method. The collecting data is in the form of utterances in WhatsApp messages which show adherence to the principle of linguistic politeness. The data collection technique used is the documentation. Data analysis steps (1) collecting WhatsApp messages, (2) reading messages, (3) identifying maxims, (4) classifying maxims, (5) analysing maxims, and (6) conclusions. The results of the study show that speech between speakers and speech partners (lecturers and students) fulfils the rules/principles of maxims.
Investigating Hospitality Student’s Acceptance in Online Learning Platform: Utilising UTAUT Model

This conceptual paper aims to investigate the relationship between hospitality students' acceptance of using online learning platforms during the pandemic of COVID-19 by utilizing the UTAUT Model.The spreading of COVID-19 has altered the landscape of world education, including the higher education system in Malaysia.All traditional classes need to be switched to online learning activities to ensure the spreading of this virus can be retained.Additionally, this enormous online movement has also allowed many educators to reconsider the purpose of education and their relationships with students.Furthermore, limited studies have investigated the challenges surrounding student acceptance and adoption of such systems and their consequences for teaching and learning, particularly among students in the hospitality and tourism fields.UTAUT is the most adopted model in investigating user acceptance of online technology, which in this conceptual paper focuses on hospitality students' online learning.The UTAUT model was adopted and modified to fit the proposed research framework by incorporating all dimensions acting as the variable.Five theoretical propositions are suggested in the literature review.This study is added to the literature on digital technology and application adoption in hospitality students' online learning environments.
Blockchain in Food Supply Chain: Benefits, Challenges and Review

Abstract: Blockchain technology has the potential to revolutionize the food supply chain by increasing transparency and traceability. With blockchain, every step of the food supply chain can be recorded and verified, allowing for greater accountability and trust between suppliers, manufacturers, distributors, retailers, and consumers. This study inclines towards the study of blockchain technology in use with food supply chain management. It also mentions the advantages and barriers to use this technology. To better comprehend the development of pertinent study and shed light on the benefits, issues, and difficulties with blockchain technology, there has been a review of the literature and an logical assessment of the supply chain exploration based on blockchain. Several intermediaries, trust issues, and subpar performance are all factors in traditional supply chain operations. Supply chain operations may be disrupted utilizing blockchain technology to enhance performance, disperse governance, and automate procedures. From the standpoint of literature analysis, this study contributes to our understanding of blockchain applications in supply chain management and offers a roadmap for these applications.
Social Capital and Social Media Antecedents’ Influence on Sustainable Indigenous Tourism of the Mah Meri, in Carey Island, Malaysia

This paper presents the significant role of social capital and social media on sustainable indigenous tourism of the Mah Meri, in Carey Island, Malaysia.This study employs a quantitative approach that forms the use of social media in indigenous tourism development.This study also integrates social capital theory, especially bridging, bonding, and linking.The gap in Social Capital Theory is addressed in relation to social media utilization towards sustainable tourism.The quantitative approach is used in this study to gather and analyze data towards a regression model development.The result of the study gave significant contribution from both theoretical and practical perspectives.The antecedents of bridging, linking, and bonding, adaptation, participation, and sharing are found to be significant in predicting the sustainable indigenous tourism.It is hoped that the findings of this study will contribute to the existing literature on how social capital and social media can contribute towards sustainable practice of tourism by the indigenous communities.