Reviewerly/exHarmony huggingface dataset:
Anchor-free instance segmentation and fast performance evaluation of Anammox granular sludge

<p indent="0mm">The method of measuring the biochemical reaction equivalence of Anammox granular sludge is accurate but time-consuming, and the method based on image color modeling is fast but inaccurate enough. Therefore, the study proposes a new model combining the visual features of granular sludge (such as color, size, and roughness) for performance evaluation based on the instance segmentation task, which can guarantee high accuracy while performing a fast evaluation. A sampling scheme is first designed to collect the granular sludge images, and then image annotation is performed to obtain the granular sludge image dataset. This study proposes an improved BlendMask anchor-free instance segmentation method, which combines deformable convolution on the backbone network to extract small target information, adopts an adaptive sample selection strategy to balance samples on the head network, and redesigns the modules of postprocessing, loss function, and data augmentation to quickly and accurately extract the granular sludge individuals in image scenes. Then, the association model of visual features and performance is designed to obtain the visual features of granular sludge and discriminate the performance class of the input image. The proposed method can obtain a faster and more accurate evaluation of the performance of granular sludge. Finally, the effectiveness of the proposed method is verified. Compared with the previous methods, the improved BlendMask and the performance evaluation model have improved the accuracy of granular sludge image segmentation and performance evaluation by 3.93% and 1.49%, respectively, achieving the most advanced performance.
CCMT 2022 Translation Quality Estimation Task

This paper presents the method used by Huawei Translation Services Center (HW-TSC) in the quality estimation (QE) task: sentence-level post-editing effort estimation in the 18th China Conference on Machine Translation (CCMT) 2022. This method is based on a predictor-estimator model. The predictor is an XLM-RoBERTa model pre-trained on a large-scale parallel corpus and extracts features from the source language text and machine-translated text. The estimator is a fully connected layer that is used to regress the post-editing distance scores using the extracted features. In the experiment, it is found that pre-training the predictor with the semantic textual similarity (STS) task in the parallel corpus and using augmented training data constructed by different machine translation (MT) engines can improve the prediction effect of the Human-targeted Translation Edit Rate (HTER) in both Chinese-English and English-Chinese tasks.
JPEG Column: 90th JPEG Meeting

The 90th JPEG meeting was held online from 18 to 22 January 2021. This meeting was distinguished by very relevant activities, notably the new JPEG AI standardization project planning, and the analysis of the Call for Evidence on JPEG Pleno Point Cloud Coding.
Image Enhancement of 3-D SAR via U-Net Framework

Image resolution is the key point for the 3-D synthetic aperture radar (SAR) application, especially in small-scale scene observation. The traditional filter-based image enhancement algorithms used for 3-D SAR may suffer from quality degeneration in case of parameter mismatch. This paper proposes a robust and efficient convolutional neural network (CNN) based U-net framework for 3-D SAR image enhancement. The U-net extracts image features in down sampling and up sampling, which is realized by max pooling and deconvolution layers. We use the mean square error(MSE) as the loss function to estimate the difference between the predicted images and the label, while Adam optimizer updates parameters to achieve the global minimum MSE. Both simulation and measured data verify the effectiveness of the network. The results demonstrate that the U-net outperform some traditional filter-based algorithms.
Application of Accounting Information System for Cash Receipts and Disbursements to Improve Internal Control

This research uses descriptive qualitative method with primary and secondary data sources. Sources of data obtained through interviews, observation and documentation. The results of the study stated that the application of accounting information systems at SMA Muhammadiyah 3 reinforcement had used a system designed by the school itself, this system was called SIA SMAMUGA. The cash receipt system at SMA Muhammadiyah 3 Reinforcement comes from cash receipts and transfers. The procedure for implementing the cash receipt system has been going well, the staff just needs to log in through the application and then input data according to the receipt. The cash disbursement system at SMA Muhammadiyah 3 Tulangan comes from cash disbursements, transfers and checks. The procedure for implementing the cash disbursement system has been going well, the staff just needs to log in through the application and then input the data according to the receipt, this makes it easier to check the data that has been inputted and stored in the system. This system facilitates financial work, especially in cash disbursement. The element of internal control in the application of the cash receipts accounting information system was found to have several weaknesses including the presence of other empty sections so that there were still holding concurrent positions, namely the treasurer who doubled as cash receipts administration staff. The element of control in the application of the accounting information system for cash receipts and disbursements also has weaknesses, namely lack of supervision from the principal.
Recognition of Red Light Running Behavior of Electric Bicycles in Monitoring Video

In order to accurately and efficiently identify the behavior of running red light by electric bicycles, this paper proposed an intelligent recognition model combining target detection and tracking, cross-road analysis and red light running decision. This model takes video frame sequence as input, firstly, electric bicycles and traffic lights are detected by YOLO network, and the bicycles are tracked in real time by DeepSort algorithm to get its trajectory. Then, LSTM is used to analysis the trajectory and distinguish whether there is behavior of crossing road. Finally, make decision of illegal behavior by integrating the state of signal light and the behavior of crossing road. In YOLO, MobileNet is introduced to replace the original CSPDarknet53 backbone, which greatly simplifies the network structure and improves detection efficiency. Dropout is introduced into LSTM to improve detection accuracy. Experimental results show that the optimized model achieves balance between accuracy and efficiency, can detect in real time on the experimental data set, and the final accuracy of recognition reach 94.9 %.
Conceptions of place in the information practices of the Mahamewnawa Asapuwa Temple community

The findings of a study of the information practices of devotees and monks associated with a Buddhist temple are used to examine the way place is understood, and to evaluate the strengths and weaknesses of the conceptualisations presented in the literature. An insider approach facilitated the collection of data through interviews of monks and devotees, observations and analysis of websites and social media platforms produced by the temple community. Thematic analysis was used to analyse interview transcripts, the researcher’s field notes and online content. Place can be physical, modified by time, symbolic, created through the affordances of technology and organisational. Three categories of information practices emerged, being cultural, everyday life and organisational information practices. Existing conceptualisations of place in information research, including information grounds, information landscapes and space of flows were insufficient to cover the ways that place was expressed in this study. Understandings of place and associated information practices are tied to cultural knowledge and beliefs. The outsider researcher may only make sense of data through the use of metaphor or analogy. Further analytical and empirical work is essential to develop guidelines for establishing appropriate metaphors.
Investigating Continued Pretraining for Zero-Shot Cross-Lingual Spoken Language Understanding

Spoken Language Understanding (SLU) in task-oriented dialogue systems involves both intent classification (IC) and slot filling (SF) tasks. The de facto method for zero-shot cross-lingual SLU consists of fine-tuning a pretrained multilingual model on English labeled data before evaluating the model on unseen languages. However, recent studies show that adding a second pretraining stage (continued pretraining) can improve performance in certain settings. This paper investigates the effectiveness of continued pretraining on unlabeled spoken language data for zero-shot cross-lingual SLU. We demonstrate that this relatively simple approach benefits either SF and IC task across 8 target languages, especially the ones written in Latin script. We also find that discrepancy between languages used during pretraining and fine-tuning may introduce training instability, which can be alleviated through code-switching.1
Research on the Application of “Chinese Story” in College English Teaching from the Perspective of Multiliteracies

The continuous development of educational technology makes it extremely urgent to cultivate students' multi-literacy ability.Under the background of international public opinion, spreading Chinese voice, carrying forward Chinese traditional culture and telling "Chinese story" are becoming increasingly necessary qualities for English learners.Based on the "Chinese story" as the breakthrough point, under the guidance of multiliteracies, this paper aims to explore the pluralistic college English teaching and to further improve and perfect the design of college English teaching.Finally, the basic procedures of college English teaching design based on the cultivation of multiliteracies and the integration of "Chinese story" are put forward in line with the development of the times.
Calibration of Finger Pointing Direction Considering Finger Flection Using Mobile Robot Equipped with RGB-D Camera

With the aging of Japan’s population, there is a growing need for daily life support for the elderly. In addition, from the perspective of reducing the burden on medical care workers, automation of household chores by life support robots has been expected. The authors have focused on garbage cleaning, which is one of the most burdensome household chores. In this paper, the authors propose a method to identify garbage by user’s finger pointing because it is too difficult to identify what is trash with current machine learning technology. The proposed method calibrates the finger pointing direction using point cloud processing and image processing with a skeletal detection library. Using the proposed method, a life support robot can identify an object on the tabletop even if the user’s finger isn’t completely straight Elderly people may be difficult to do so. As a result of pilot experiments, it is confirmed that the proposed method was able to identify the target object with high accuracy.
Image Rectification of Industrial Equipment Nameplate Based on Progressive Probabilistic Hough Transform

In this paper, we put forward an industrial nameplate picture correction method based on Progressive Probabilistic Hough Transform. Our method can effectively correct the image tilt caused by the wrong shooting direction. Even the oblique images taken from a long distance have certain effects. We also introduce the Mining Equipment Nameplate Dataset. The frame of the industrial nameplate is quadrilateral. The two sides of the nameplate border in the photo will cross each other after being extended. This result is caused by the tilt of the shooting angle. Our method firstly grays the picture. Then binarizes the image and Gaussian smoothing filter. We use the Progressive Probabilistic Hough Transform to locate the two longest line segments in the picture. The four endpoints of the two line segments are the four endpoints of the quadrilateral. Finally, the correct picture is obtained by perspective transformation. Our method makes the nameplate text more visible, and the detection method is fast and effective. The pictures obtained by experiments are clearer and easier to observe. In the second half of the article, we list some experimental results. Our method can well handle the requirements in actual production.
Improving Low-Resource Question Answering with Cross-Lingual Data Augmentation Strategies

We investigate cross-lingual data augmentation to improve question answering (QA) systems in low-resource settings. Specifically, we propose 2 strategies in fine-tuning a multilingual pretrained language model (PLM) with cross-lingual QA data. The first is the combined approach, a strategy that simply combines QA data in 2 different languages, that is, a low-resource language as the intended fine-tuning target, and a higher-resource language for augmentation, to be used simultaneously during fine-tuning. Our second strategy is 2-step fine-tuning, where we first fine-tune the multilingual model with a QA data from the augmentation language, then fine-tune the resulted model with the data of the intended target language. Our experiments showed that the 2-step fine-tuning strategy boosts the final QA model in the target language when compared to vanilla one-step fine-tuning using only the target language as training data. Our experiments are mainly focused on Indonesian as the target language, using the FacQA dataset, while we use various English QA datasets for augmentation. We further show that our approach is extensible to QA in other languages. All codes and data used are available at: https://github.com/ryanpram/2step/finetuning/xlda
LANDCHAIN: A Blockchain-Based Lightweight Land Administration System for Bangladesh

In a developing country like Bangladesh, the land is seen as a valuable asset. Therefore, investing in land assets appears to be the best way to put people’s money to utilize. However, purchasing land is time-consuming due to the need for a sequence of communications among buyers, sellers, and many government agencies to obtain the required documentations for approval. In addition, most land administration operations in Bangladesh are paper-based, making them more difficult to manage and secure. This paper presents LANDCHAIN, a blockchain-based land administration system that automates the land administration process while maintaining security and transparency. Instead of a typical database, the system is built on the blockchain, ensuring that data is tamper-proof. Our proposed approach is lightweight, making it simple to deploy and to scale automatically. This proposed model is tested on the Ethereum Virtual Machine (EVM) and is shown to be more reliable, secure, efficient, and easier to scale than that of a typical database management system. Thus, the LANDCHAIN solves the existing issues of land administration while also assisting the government in optimizing the processes.
A study of the teacher-student interaction in a flipped classroom of college oral English based on mobile learning

In order to solve the problem of low analysis accuracy in the traditional method of interactive effect analysis of English spoken in classrooms in colleges and universities, a mobile learning-based method of the interactive effect analysis of spoken English is proposed. By analysing the characteristics of teacher-student interaction in a flipped classroom, a mobile learning app was constructed. To obtain the relevant information about the teacher-student interaction effect, an evaluation system of the teacher-student interaction in a college English-flipped classroom based on mobile learning was established. Combined with the fuzzy comprehensive evaluation method and multi-level gray evaluation method, the interactive effect of flipped classroom in college English-speaking was analysed according to the evaluation results. The experimental results show that the accuracy of the proposed method is up to about 99%, and the analysis time is about 4 s.
Research on the Reform of Physics Classroom Teaching in Five-Year System Higher Vocational Education

With the continuous progress of society, people pay more and more attention to education in the new era.Physics is a difficult subject, with certain complexity and systematicity, involving more theorems and formulas.With the advent of the information age, various kinds of multimedia-assisted instruction have gradually emerged and become powerful tools for people to learn.In the process of the new curriculum reform, we should pay attention to the effectiveness of teaching methods.Teachers should think about what students need and like from the standpoint of students, cultivate their computational thinking and understanding ability, adopt living teaching strategies, strengthen subject penetration, and constantly improve the effectiveness of Physics teaching in higher vocational colleges.
Distortion Controlled Secure Reversible Data Hiding in H.264 Videos

Reversible data hiding in H.264 videos aims to keep the quality of recovered video resulting after extracting the information to be of the same quality as that of the original cover video. Most reversible data embedding algorithms in H.264 video streams are based on the Intra-prediction mode. Embedding decision is made only based on the loss occurring to IPM blocks. IPM-based embedding decision results in distortion getting drifted across multiple blocks reducing the quality of the reconstructed video. This work exploits the IPCM mode in intra-prediction to embed secret information with the constraints of minimizing distortion and maximizing embedded capacity. This work proposes a IPCM frame selection for embedding secret information. The selection is done in a way to minimize the distortion cost. The proposed solution increases the embedding capacity by adaptive control of the IPCM block generation rate. Security is provided for the embedded content by encrypting them before embedding. Comparison is done in terms of distortion introduced in video and the embedding capacity achieved. The proposed solution is able to achieve at least 6.3% higher embedding capacity, 8.9% higher Peak Signal-to-Noise Ratio (PSNR) and 4.66% lower MSE compared to existing works.
Content-Aware Client Selection for Federated Learning in Wireless Networks

Federated learning (FL) is a framework to collaboratively train a deep learning model without sharing the raw data of distributed clients. Due to the limited communication resources in wireless networks, only a subset of clients are allowed to communicate their model updates with the central server in each communication round. Existing works selected the client based on the wireless channel states or the size of local dataset, while ignoring the local model status, i.e., the content of the local updates. In this paper, we first provide a comprehensive convergence analysis to investigate the impact of the client selection strategy for FL in wireless networks. Based on the theoretical analysis, we propose a novel client selection approach, referred to as the content-aware client selection (CACS). The proposed CACS strategy selects the client subset based on both the wireless channel states and the content of model updates from the clients. Empirical experiments shall verify the analysis and demonstrate that the CACS strategy outperforms the state-of-the-art methods in terms of both the convergence speed and learning performance of FL in wireless networks.
Digital Education Platform

Today there are different understandings of what is considered part of a digital platform, what is on it and what is available through it. A digital integrated implementation of the following functions is required, regardless of how we define their relationship with the platform: support for group communication with the ability to record the process; accounting for the roles of the student, teacher, administrator, parent; automatic generation of digital journals and report cards; formation of the history of individual work and recording of educational events of the student, teacher, class; including it in the big data of education; designing an individual path for students to achieve educational goals with a choice of those posted on the platform; selection and implementation of tasks offered by the teacher; placement the goals, assignments, feedback and assessment tools, other learning materials; accommodating student learning requests and suggestions from teachers, organizations, and educational programs that go beyond the boundaries of one school; recording learning outcomes; displaying and forecasting on the basis of big data of the educational process with varying level of detail.
What are the best practices for online student engagement among Hispanic-Serving Institutions? A narrative review

&#x0D; &#x0D; &#x0D; The recent coronavirus disease-19 (COVID-19) pandemic has led many post-secondary education institutions to transition their in-person courses to a completely virtual format. Concerns have been raised about these institutions’ readiness to create a student-centered online learning environment. Historically, rates of student attrition and lack of student engagement within the online environment are higher among courses delivered virtually when compared with those delivered in-person. This adds to the concern that graduation rates among first-time, full- time Hispanic undergraduate students are lower than their White and Asian counterparts. Faculty and students often differ in their views regarding which pedagogical strategies improve student success in online environments. Theories addressing student engagement exist, but do not adequately address students of color or faculty teaching at Hispanic Serving Institutions (HSIs). The purpose of this review is to explore how post-secondary education instructors within HSIs, specifically, can increase learner engagement and improve course retention rates particularly at a time when many courses within post-secondary institutions are being delivered online. Recommendations based on available data will also be provided.&#x0D; &#x0D; &#x0D;
Análise dos fatores associados para a internação hospitalar de vítimas de COVID-19 Relacionado ao Trabalho

Objective: To recognize the characteristics related to hospitalizations due to COVID-19 infection acquired in the environment or in the work process, such as: occupation, age group and sex and to verify if they are related to hospitalization. Method: data from the cases reported in the year 2020 of the population hospitalized for Work-Related COVID-19 were analyzed, obtained from the SINAN Notifiable Diseases Information System and tabulated through Tabwin. Results: There is a predominance of females (64.7%) over males (35.3%). Furthermore, advanced age is established as a preponderant factor, especially the age group of 40-49 years (70.58%), highlighting cases in the 5th decade of life. Among the occupations, the ones that most demanded hospitalization were: Nursing Technician (23.52%), Pharmacist (11.76%), Nurse (11.76%) and Radiology and Imaging Technician (11.76%). . Conclusion: Data analysis demonstrates a definition of a group of greater vulnerability to hospitalization for COVID-19, represented by female professionals, aged 40-49 years, nursing technicians.
THALER v COMPTROLLER GENERAL OF PATENTS

Abstract H1 Patents — Patent applications — Identification of inventor — Artificial intelligence — Whether the inventor could be a machine — Entitlement to apply — Entitlement to grant — Appeal to Court of Appeal
SQL Edge to Cloud

When I joined Microsoft in 1993, we had just released SQL Server 4.2 on Windows NT (the predecessor to Windows Server). We had previously only offered SQL Server on OS/2 systems, and our code was still based on the original Sybase server. These are the days of small to medium servers, and we as a product were trying to find our place in the industry.