Reviewerly/exHarmony huggingface dataset:
Deep Learning-Aided Automatic Contouring of Clinical Target Volumes for Radiotherapy in Breast Cancer After Modified Radical Mastectomy

Purpose: The aim of this study is to develop a practicable automatic clinical target volume (CTV) delineation method for radiotherapy of breast cancer after modified radical mastectomy. Methods: Unlike breast conserving surgery, the radiotherapy CTV for modified radical mastectomy involves several regions, including CTV in the chest wall ( CTV cw ), supra- and infra-clavicular region ( CTV sc ), and internal mammary lymphatic region ( CTV im ). For accurate and efficient segmentation of the CTVs in radiotherapy of breast cancer after modified radical mastectomy, a multi-scale convolutional neural network with an orientation attention mechanism is proposed to capture the corresponding features in different perception fields. A channel-specific local Dice loss, alongside several data augmentation methods, is also designed specifically to stabilize the model training and improve the generalization performance of the model. The segmentation performance is quantitatively evaluated by statistical metrics and qualitatively evaluated by clinicians in terms of consistency and time efficiency. Results: The proposed method is trained and evaluated on the self-collected dataset, which contains 110 computed tomography scans from patients with breast cancer who underwent modified mastectomy. The experimental results show that the proposed segmentation method achieved superior performance in terms of Dice similarity coefficient (DSC), Hausdorff distance (HD) and Average symmetric surface distance (ASSD) compared with baseline approaches. Conclusion: Both quantitative and qualitative evaluation results demonstrated that the specifically designed method is practical and effective in automatic contouring of CTVs for radiotherapy of breast cancer after modified radical mastectomy. Clinicians can significantly save time on manual delineation while obtaining contouring results with high consistency by employing this method.
Key Point Matching with Transformers

Key point analysis (KPA) has been proposed as a way to summarize arguments with shortsized pieces of text termed key points.This work aims at describing a solution for the Track 1 of the KPA 2021 shared task, analyzing different methodologies for the specific problem of key point matching, which consists in finding a reasonable mapping from arguments to key points.The analysis will focus on transformer based architectures, experimentally investigating the effectiveness of variants specifically tailored to the task.
PENERAPAN SISTEM INFORMASI MANAJEMEN TERHADAP BUDIDAYA IKAN GURAME DAN IKAN HIAS DESA SAMBIROBYONG

This study aims to determine the understanding of how to cultivate carp and ornamental fish with tarpaulin pond media, to determine marketing strategies for gouramy and ornamental fish, to determine the sales of gouramy and ornamental fish, and to know how to apply a management information system to carp cultivation. and ornamental fish. Cultivation of gouramy and ornamental fish is a business that is considered to have a fairly large level of profit. By using the right cultivation system, starting from capital, making ponds and stocking capacity, determining seeds, maintenance, determining selling prices, and harvesting times, the results will be maximized. The focus of this research is, How is the application of management information systems to SMEs in the cultivation of gouramy and ornamental fish in Sambirobyong village. This research use desciptive qualitative approach. This data collection technique is done by interviewing the informants in the field. And the research results obtained are the process of making ponds with tarpaulin media is an effective and efficient way compared to concrete or cast pools, the provision of water and seeds is adjusted to the width of the pool, marketing and sales strategies are also good, namely by using a website management information system.
The Effect of Corporate Social Responsibility Activities on Accrual Quality, Real Earnings Managements: Focused on Listed Companies in the Hotel, Restaurant and Leisure Industry

Recently, corporate social responsibility (CSR) has been recognized as an important strategic element in corporate management. In this study, in order to find out the effect of CSR activities on accounting transparency, accounting transparency was analyzed using the accrual quality (AQ) and real earnings management (REM) as substitute values. The results of the study are summarized as follows. First, it was found that CSR activities and the AQ acted in opposite directions. The more REM activities are, the lower the AQ has been shown, which is likely to be used as a short-term and temporary means for companies to increase corporate value in the short-term, temporarily improve financial performance, or improve corporate reputation. Second, the hypothesis that CSR activities will reduce the possibility of actual profit adjustment has been partially verified, but the possibility of adjustment between cash and discretionary expenses is likely to be small. Third, the signs of the factors used as control variables were different from those of the existing studies focused on the manufacturing industry.
Electronic Hovercraft

The small autonomous vehicles of the future will have to navigate close to obstacles in highly unpredictable environments. Risky tasks of this kind may require novel sensors and control methods that differ from conventional approaches. Recent ethological findings have shown that complex navigation tasks such as obstacle avoidance and speed control are performed by flying insects on the basis of optic flow (OF) cues, although insects' compound eyes have a very poor spatial resolution. The implementation of an optic flow-based autopilot on a fully autonomous hovercraft. Tests were performed on this small (878-gram) innovative robotic platform in straight and tapered corridors lined with natural panoramas. A bilateral OF regulator controls the robot's forward speed (up to 0.8m/s), while a unilateral OF regulator controls the robot's clearance from the two walls. A micro-gyrometer and a tiny magnetic compass ensure that the hovercraft travels forward in the corridor without yawing. The lateral OFs are measured by two minimalist eyes mounted sideways opposite to each other. For the first time, the hovercraft was found to be capable of adjusting both its forward speed and its clearance from the walls, in both straight and tapered corridors, without requiring any distance or speed measurements, that is, without any need for on-board rangefinders or tachometers.
A Two-Stage Approach to Important Area Detection in Gathering Place Using a Novel Multi-Input Attention Network

An important area in a gathering place is a region attracting the constant attention of people and has evident visual features, such as a flexible stage or an open-air show. Finding such areas can help security supervisors locate the abnormal regions automatically. The existing related methods lack an efficient means to find important area candidates from a scene and have failed to judge whether or not a candidate attracts people's attention. To realize the detection of an important area, this study proposes a two-stage method with a novel multi-input attention network (MAN). The first stage, called important area candidate generation, aims to generate candidate important areas with an image-processing algorithm (i.e., K-means++, image dilation, median filtering, and the RLSA algorithm). The candidate areas can be selected automatically for further analysis. The second stage, called important area candidate classification, aims to detect an important area from candidates with MAN. In particular, MAN is designed as a multi-input network structure, which fuses global and local image features to judge whether or not an area attracts people's attention. To enhance the representation of candidate areas, two modules (i.e., channel attention and spatial attention modules) are proposed on the basis of the attention mechanism. These modules are mainly based on multi-layer perceptron and pooling operation to reconstruct the image feature and provide considerably efficient representation. This study also contributes to a new dataset called gathering place important area detection for testing the proposed two-stage method. Lastly, experimental results show that the proposed method has good performance and can correctly detect an important area.
Information systems flexibility and robustness of manufacturing firms in Nigeria

This study examined the nexus between Information systems flexibility and Robustness of manufacturing firms in South-South, Nigeria. Nine objectives and hypotheses were postulated to examine the relationship between the dimensions (IT flexibility, Process flexibility and Data flexibility) and the measures (Persistence, Structural stability and Superposition). A structured questionnaire was prepared, while Google forms was used to gather the data from the 210 respondents. Structural Equation Modelling (SEM) with the aid of Smart PLS 3.3.3, was used to examine the relationship between the dimensions of Information systems flexibility and the measures of Robustness. A total of 185 responses were collected and used for the analysis. The results show that all three dimensions of Information systems flexibility improved Robustness. The study recommends amongst others, that Management of manufacturing firms should adopt flexible information systems through strengthening of networks, relationships with stakeholders and the emphasis on information technology. This will advance the organisations options and provide the firm with various advantages in terms of information access and knowledge sharing needed for their agility within the business environment.
Digital Print Synthesis Based on Image Processing and Interactive Technology

Abstract Fire is a common disaster, which causes major threats and losses to human life and property. Countries around the world have been committed to the study of the mechanism and internal mechanism of fires, with the goal of preventing fires from occurring and minimizing the losses caused by fires. Among the many methods, fire detection technology is an effective method to prevent and reduce the occurrence of fire. This article focuses on the research of the fire detection system based on artificial intelligence technology, improves the accuracy of the fire detection system by introducing artificial intelligence technology into the fire detection system, and uses experiments to verify the error rate of the artificial intelligence technology fire detection system. The experimental results show that the system’s detection of fire is not very different from the actual situation, and the error rate is within 10%. Then compared with the traditional detection system, the detection performance is relatively high, and the error rate can be reduced by one time.
Sustainable Anomaly Detection in Surveillance System

As the need for security is ever-increasing, more surveillance systems are being deployed for domestic and organizational security. Traditional surveillance systems have a CCTV camera in place, which works as either recording equipment or monitoring system. The former does not provide any prevention from damage, and the latter suffers from human errors. This paper proposes to overcome the mentioned overheads by automating the process of anomaly detection. It aimed to classify surveillance videos as normal or abnormal using two approaches. First, a CNN Long Short-Term Memory Network (CNN-LSTM) and second, a pre-trained CNN network for obtaining features of the videos followed by an LSTM network for classification of the video footage. Analysis using the CNN-LSTM and Xception-LSTM model has been carried out on the UCF-Crime dataset. Upon comparing results, it was found that the CNN-LSTM model works better than the Transfer Learning model by approximately 20% better accuracy. The final real-time system uses Raspberry Pi as the microprocessor. The CNN-LSTM model file uploaded onto the Pi has a minimal size of 5.8 MB, which is far smaller than the state-of-the-art model size. This paper also highlights the input specifications which result in an inefficient transfer learning model as a reference for future works.
Semantic Based Weighted Web Session Clustering Using Adapted K-Means and Hierarchical Agglomerative Algorithms

The WWW has a big number of pages and URLs that supply the user with a great amount of content. In an intensifying epoch of information, analysing users browsing behaviour is a significant affair. Web usage mining techniques are applied to the web server log to analyse the user behaviour. Identification of user sessions is one of the key and demanding tasks in the pre-processing stage of web usage mining. This paper emphasizes on two important fallouts with the approaches used in the existing session identification methods such as Time based and Referrer based sessionization. The first is dealing with comparing of current request’s referrer field with the URL of previous request. The second is dealing with session creation, new sessions are created or comes in to one session due to threshold value of page stay time and session time. So, authors developed enhanced semantic distance based session identification algorithm that tackles above mentioned issues of traditional session identification methods. The enhanced semantic based method has an accuracy of 84 percent, which is higher than the Time based and Time-Referrer based session identification approaches. The authors also used adapted K-Means and Hierarchical Agglomerative clustering algorithms to improve the prediction of user browsing patterns. Clusters were found using a weighted dissimilarity matrix, which is calculated using two key parameters: page weight and session weight. The Dunn Index and Davies-Bouldin Index are then used to evaluate the clusters. Experimental results shows that more pure and accurate session clusters are formed when adapted clustering algorithms are applied on the weighted sessions rather than the session obtained from traditional sessionization algorithms. Accuracy of the semantic session cluster is higher compared with the cluster of sessions obtained using traditional sessionization.
Regional Development: New Challenges for Engineering Education (SYNERGY-2021 Conference Results Review)

The article summarizes the results of the plenary session of the international network conference “Regional development: new challenges for engineering education – SYNERGY-2021”, held at Kazan National Research Technological University from October 19 to 20, 2021. The forum which brought together representatives of universities and industrial enterprises of Russia and abroad was devoted to the issue of training engineers for the petrochemical industry. Among the participants were representatives of international societies for engineering education, ten national research universities and seven supporting universities of PJSC Gazprom, state authorities and industrial enterprises of Tatarstan. It was possible to observe the work of the plenary session in real time via the Internet in all the supporting universities of Gazprom. The event was organized by the Ministry of Science and Higher Education of the Russian Federation, the International Society for Engineering Pedagogy (IGIP), the Association of Engineering Education of Russia (AIOR), as well as the Ministry of Industry and Trade of the Republic of Tatarstan and Kazan National Research Technological University. Gazprom PJSC became the general sponsor. In total, the conference gathered more than 450 participants (380 online and 85 in person) from 40 universities in Russia, the USA, Great Britain, Germany, Portugal, Finland, Poland, Kazakhstan, Belarus, Armenia, Latvia, and Estonia. Representatives of 7 industrial enterprises spoke, 77 reports were made.
Influences of Hygiene Practices, Awareness and Involvement Levels on Environmental Hygiene among UMT Students

Industrial development plays the most important role in the degradation of the global environment. In line with this issue, most universities in the world have introduced environmental management to increase awareness among young generation towards the environment. Therefore, this study is employed to examine the influences of hygiene practices, awareness and involvement levels towards environmental hygiene among UMT students. The design of this study is quantitative using cross-sectional data collected from students at residential colleges in Universiti Malaysia Terengganu. A total of 120 respondents participated but only data from 100 students could be used for further analysis after the data screening process. The result of this study showed that the level of awareness of UMT students on environmental issues could be categorized as moderate. Besides that, this study found that the level of awareness had significant positive influences on environmental hygiene. Meanwhile, hygiene practices and the level of involvement did not have significant influences on environmental hygiene. In addition, using the findings of this study, we suggest that the stakeholders of UMT increase the hygiene practice and involvement among young generation to increase the level of awareness regarding environmental hygiene.
Your reserves may be best estimate, but are they valid?

Abstract This paper outlines frameworks to use for reserving validation and gives the reader an overview of current techniques being employed. In the experience of the authors, many companies lack an embedded reserve validation framework and reserve validation can appear piecemeal and unstructured. The paper outlines a case study demonstrating how successful machine learning techniques will become and then goes on to discuss the implications of machine learning to the future of reserving departments, processes, data and validation techniques. Reserving validation can take many forms, from simple checks to full independent reviews to add value to the reserving process, enhance governance and increase confidence in and reliability in results. This paper discusses covers common weaknesses and their solutions and suggestions of a framework in which to apply validation tools. The impacts of the COVID-19 pandemic on reserving validation is also covered as are early warning indicators and the topic of IFRS 17 from the standpoint of reserving validation. The paper looks at the future for reserving validation and discusses the data challenges that need overcoming on the path to embedded reserving process validation.
A Survey of Crowd Counting CNN-based

Abstract Benefiting from the powerful feature representation ability of deep learning, Convolutional Neural Network (CNN) provides a better solution to estimate accurately the number of people in a crowded scene, but it still faces many problems that need to be solved urgently. It is one of the key and difficult points in the field to reduce the complexity of the network and to improve the real-time performance of the network, so as to improve the accuracy of crowd counting. Firstly, this paper introduces the research background and application of crowd counting. Secondly, it focuses on the commonly used counting model, loss function, and dataset and evaluation method. Then compare the performance structure, advantages and disadvantages of different algorithms horizontally on several published datasets. Finally, it summarizes the shortcomings of the existing crowd counting, put forward to the future research direction of crowd counting.
PELATIHAN BAHASA INGGRIS DAN PENGENALAN TEHNOLOGI INFORMASI BAGI REMAJA BENGKONG KOLAM BATAM

This service activity is an activity in the form of training on English Conversation and Introduction to Information Technology for Youth in RT 02 RW 09, Bengkong Sadai Batam which is held at the Gedung Pertemuan RW09 Bengkong.The training held is a multi-disciplinary training or combines several study programs, especially English and Computer Study Programs. The training materials have been prepared in such a way that they are able to at least provide some solutions to the problems of the younger generation that are happening in the current millennial era. These problems include, among other things, teenagers in these neighborhoods do not master English and lack information technology. This training is carried out starting in November 2021 and ending in December 2021 with as many as 5 meetings held regularly. From this training, it is hoped that the training participants will get refreshed knowledge about the application of English and Introduction to Information Technology
A Better Multiway Attention Framework for Fine-Tuning

Powerful pre-training models have been paid widespread attention. However, little attention has been devoted to solve downstream natural language understanding (NLU) tasks in fine-tuning stage. In this paper, we propose a novel architecture named multiway attention framework (MA) in fine-tuning stage. Which utilizes a concatenated feature of the first and the last BERT-style model (e.g., BERT, ALBERT) layers, and a mean-pooling feature of the last BERT-style model layer as input. Then it applies four various attention mechanisms on the input features to learn a sentence embedding in phrase-level and semantic-level. Moreover, it aggregates the output of multiway attention, and sends this result to self-attention to learn the best combination scheme of multiway attention for target task. Experimental results on GLUE, SQuAD and RACE benchmark datasets show that our approach can obtain significant performance improvement.
Using G-Suite Docs to Improve Students’ Writing Ability at SMA Negeri 1 Batuan, Sumenep

The aim of this research were to describe the implementation of Using G - Suite Docs to Improve Students’ Writing Ability at SMA Negeri 1 Batuan, Sumenep and to find out the achievement of student’s writing ability in Using G - Suite at SMA Negeri 1 Batuan, Sumenep. The approach used in this research was classroom action research. Data collection tools in this study were observation sheets of learning management through G-Suite Docs media, student activity observation sheets and tests. The objects of this study were 25 students of class XII IPA 1 SMA Negeri 1 Batuan even semester of the 2020/2021 school year. The research showed that the result of implementing of using G-Suite Docs showed that the students were more enthusiastic in writing discussion text and all students could respond to their group work by commenting on the process in writing. Moreover, the students achievement from the initial conditions up to second cycle. The average of initial condition was 66.6 changed to be 79.64 or increased 80% with the result of completeness reaching 92%.&#x0D; &#x0D; Keywords: G - Suite Docs, Writing ability, Discussion Text
Analysis of the Transformation of College English Teaching Model Based on Online Class

This article analyzes the psychological and technical difficulties faced by teachers during the transformation of the Internet, and proposes solutions, and carry out online teaching activities actively by using online course platforms at all levels. College English teachers can fully ensure the teaching progress and quality through flipped classroom models such as tutoring, students’ online self-study, teacher-student online interaction, and after-class consolidation. The construction of online courses has promoted the transformation of teachers’ roles and the improvement of capabilities of applying new media technology, and has created a mature teaching team, which has had a positive impact on the transformation of college English classrooms.