Reviewerly/exHarmony huggingface dataset:
Multimodal Matching Transformer for Live Commenting

Automatic live commenting aims to provide real-time comments on videos for viewers. It encourages users engagement on online video sites, and is also a good benchmark for video-to-text generation. Recent work on this task adopts encoder-decoder models to generate comments. However, these methods do not model the interaction between videos and comments explicitly, so they tend to generate popular comments that are often irrelevant to the videos. In this work, we aim to improve the relevance between live comments and videos by modeling the cross-modal interactions among different modalities. To this end, we propose a multimodal matching transformer to capture the relationships among comments, vision, and audio. The proposed model is based on the transformer framework and can iteratively learn the attention-aware representations for each modality. We evaluate the model on a publicly available live commenting dataset. Experiments show that the multimodal matching transformer model outperforms the state-of-the-art methods.
Upper, Middle and Lower Region Learning for Facial Action Unit Detection

Facial action units (AUs) detection is fundamental to facial expression analysis. As AU occurs only in a small area of the face, region-based learning has been widely recognized useful for AU detection. Most region-based studies focus on a small region where the AU occurs. Focusing on a specific region helps eliminate the influence of identity, but bringing a risk for losing information. It is challenging to find balance. In this study, I propose a simple strategy. I divide the face into three broad regions, upper, middle, and lower region, and group AUs based on where it occurs. I propose a new end-to-end deep learning framework named three regions based attention network (TRA-Net). After extracting the global feature, TRA-Net uses a hard attention module to extract three feature maps, each of which contains only a specific region. Each region-specific feature map is fed to an independent branch. For each branch, three continuous soft attention modules are used to extract higher-level features for final AU detection. In the DISFA dataset, this model achieves the highest F1 scores for the detection of AU1, AU2, and AU4, and produces the highest accuracy in comparison with the state-of-the-art methods.
Types of E-Resources: An Overview

Twenty first century is the century of growing digitalization at a global level. Millions of users exchange information and do communication on the online platform. This cross exchange of digital information leads to massive globalization. It has opened up multiple possibilities for providing quicker reach to information at the global level. In modern ICT, electronic researches are the invaluable mines of information in cyberspace. They are compact form of resources which can be accessed simultaneously from infinite points by a great number of audiences.
Guidelines for writing term papers

An important component of training in higher education institutions of highly qualified specialists is the research work of students, which includes mastering the methods of organizing research work and work on scientific research under the guidance of teachers from the number of scientific and pedagogical staff of graduate departments of public law departments and public departments and international law, English.One of the directions of research work carried out within the educational process is the writing and preparation of coursework, which are the main qualifying scientific works performed by students individually in the course of their studies in higher education.Coursework involves the systematization, consolidation and expansion of theoretical knowledge, familiarization with methods developed by other researchers in the relevant field of science, substantiation of the results of their own research.
Improving the students’ ability to use adjective through bingo game at the second year of SMP Buq’atun Mubarakah Gombara Makassar

The objective of this research was to find out whether or not the bingo game can improve students’ ability to use adjectiveeffectively. The population of this research was the second year of SMP Buq’atun Mubarakah Gombara Makassar with the total 4 0 students. The researcher used total sampling technique in choosing the sample of the research and employed quasi experimental design where VII I.A which consists of 2 0 students will be in the experimental group and VI I I .B which consists of 20 students will be in the controlled group . The instrument of this research was a vocabulary test used in pre andpost test. The result of the data indicated there was a significant difference between students’ post-test in experimental class and controlled class. The mean score of post-test (80.35) in experimental class was greater than the mean score of post-test (73.55) in controlled class and the standard deviation of post-test (8.29) in experimental class was greater than the standard deviation of post-test in controlled class (5.17). From t-test, the researcher found that the value of t-test (3.24) was greater than t-table (2.021) at the level of significance. It means that this is a significant difference between the result of the students’ pre-test and post-test. Based on the finding and discussion of the research, the researcher drew a conclusion that the use of the Bingo Game can improve the students’ a bility to use adjective at the second year of SMP Buq’atun Mubarakah Gombara Makassar
Self-Learned Feature Reconstruction and Offset-Dilated Feature Fusion for Real-Time Semantic Segmentation

Recent approaches for real-time semantic segmentation usually employ the encoder-decoder architecture as the backbone to generate a high-quality segmentation prediction. There has been a lot of research on designing efficient encoding methods. However, enhancing the performance of components in decoder is also crucial for pixel-level recognition. In this paper, we propose a self-learned feature reconstruction (SFR) method and an offset-dilated feature fusion (ODFF) module to improve the prediction reconstruction capability of the decoder. Concretely, SFR can effectively reconstruct the high-resolution feature maps by recombining feature space, in which the space transformation matrix implicitly contained in a convolution layer can selectively highlight features at each position by leveraging the knowledge of label space in a self-learned way. Moreover, ODFF module can effectively fuse multilevel features with multiscale contextual information by feeding the feature maps into designed parallel offset-dilated convolutions, which enhances the feature representation capability of the decoder. Experiments on Cityscapes and CamVid datasets demonstrate the superior performance of our proposed methods embedded in ESPNet.
GPU Implementation of Grid Search based Feature Selection: Using Machine Learning to Predict Hydrocarbons using High Dimensional Datasets

To optimize the exploitation of oil and gas reservoirs both on- and offshore, Biodentfiy has developed a method to predict prospectivity of hydrocarbons before drilling. This method uses microbiological DNA analysis of shallow soil or seabed samples to detect vertical upward microseepage from hydrocarbon accumulations, which change the composition of microbes at the surface. Microbiological DNA analysis of shallow soil or seabed samples results in a high-dimensional dataset, which is interpreted using machine learning. Using the machine learning method Elastic Net, features (microbes) are selected from an existing DNA database to classify new shallow soil or seabed samples. Multiple models, each with a different combination of externally set parameters (called hyperparameters), are trained to improve accuracy, essentially creating a grid of models. The aim of this thesis is to investigate if it is possible to accelerate feature selection on high-dimensional datasets by implementing a parallel design on a GPU to train this grid of models, and to investigate the performance of this GPU implementation. Inspired by an implementation called Shotgun, which is able to improve performance by exploiting parallelism across features when training a single model on a CPU, an implementation, named GPU Shotgun (GPU-SG) was devised, which could exploit parallelism across samples, features, and multiple models in the grid (of combinations of hyperparameters). Depending on the size of the grid and the hardware, using GPU-SG, a speedup of between 0.2 and 5.26 can be reached for sparse datasets (a datasets with lots of 0 values) when compared to standard CPU implementations. When considering dense datasets (a dataset with few 0 values), using GPU-SG, a speedup of between 0.5 and 10 can be achieved. The amount of memory available to store a dataset is lower for GPU's than for a CPU, and currently the design is limited by this, because the design does not allow a dataset that is larger than the memory available. GPU-SG can be used to design improved implementations, which reduce the time when the GPU or CPU is idle to improve performance.
ES-DAS: An Enhanced and Secure Dynamic Auditing Scheme for Data Storage in Cloud Environment

Outsourcing sensitive data on cloud storage is a service in demand. Cloud Storage lessens the burden on the user by discarding the need to possess costly infrastructure setup and its need for continuous maintenance. However, security, privacy, and integrity of the data remain critical due to the absence of control and physical proprietorship over the delicate data by the proprietors. Therefore, it is significant to develop an enhanced data auditing scheme to guarantee data owners that their data is safe and secure in the remotely hosted cloud storage providers. In this paper, an enhanced dynamic auditing scheme for providing integrity assurance of outsourced data is presented. The proposed scheme is based on Elgamal signature on a conic curve and homomorphic function. This scheme incorporates trusted third party auditor for detecting accidental or intentional data modifications to provide integrity assurance to the data owners. With the adoption of ElGamal signatures and homomorphic function, this scheme achieves benefits like privacy preservation, reduced computation, communication and storage cost. The performance of the proposed ES-DAS scheme is evaluated by detailed experimental analysis in comparison with other existing techniques. The results prove to be efficient and secure in terms of computation, communication and storage costs on the system entities.
EFEKTIVITAS METODE FUN AND STIMULATION TECHNIQUE (FAST) TERHADAP KEMAMPUAN MEMBACA ANAK USIA DINI DI TAMAN KANAK-KANAK KARTIKA 1-54 PADANG

This research is based on the fact that a variety of innovative learning methods can be applied in the process of early childhood learning. One interesting method that stimulates children's reading ability is the Fun And Stimultion Technique (FAST) method. This study aims to determine the effectiveness of Fun And Stimultion Technique ( FAST ) method in developing early reading ability of children at kindergarten Kartika 1-54 Padang. This study use quantitative approach in type of quasy experiment. Based on the data analysis, the average of post-test results in the experimental group was 93.5, while the control group with an average of 85. Based on these results, it can be concluded that the average value of Fun And Stimulation Technique ( FAST ) method is higher than the average ability of spelling method in control class.  The FAST method is more effective to develop children’s reading ability in kindergarten Kartika 1-54 Padang.
C-D Ratio in multi-display environments

Research in user interaction with mixed reality environments using multiple displays has become increasingly relevant with the prevalence of mobile devices in everyday life and increased commoditization of large display area technologies using projectors or large displays. Previous work often combines touch-based input with other approaches, such as gesture-based input, to expand the possible interaction space or deal with limitations of other two-dimensional input methods. In contrast to previous methods, we examine the possibilities when the control-display (C-D) ratio is significantly smaller than one and small input movements result in large output movements. To this end one specific multi-display configuration is implemented in the form of a spatial-augmented reality sandbox environment, and used to explore various interaction techniques based on a variety of mobile device touch-based input and optical marker tracking-based finger input. A small pilot study determines the most promising input candidate, which is compared to traditional touch-input based techniques in a user study that tests it for practical relevance. Results and conclusions of the study are presented.
Gender Genetic Algorithm in the Dynamic Optimization Problem

A general approach to optimizing fast processes using a gender genetic algorithm is described. Its difference from the more traditional genetic algorithm it contains division the artificial population into two sexes. Male subpopulations undergo large mutations and more strong selection compared to female individuals from another subset. This separation allows combining the rapid adaptability of the entire population to changes due to the variation of the male subpopulation with fixation of adaptability in the female part. The advantage of the effect of additional individual learning in the form of Boldwin effect in finding optimal solutions is observed in comparison with the usual gender genetic algorithm. As a promising application of the gender genetic algorithm with the Boldwin effect, the dynamics of extinguishing natural fires is pointed.
THE INFLUENCE OF INQUIRY AND DISCOVERY LEARNING METHOD AND CREATIVITY LEVEL IN WRITING SKILLS DESCRIPTION IN NAUTIC TARUNA AND TECHNIQUE IN SURABAYA SHIPPING POLYTECHNIC

Surabaya Shipping Polytechnic emphasizes on certain areas of expertise that Taruna must possess. This is the basis after graduating from shipping polytechnics, cadets must have expertise and skills. The purpose of this study was to study the effect of inquiry, discovery learning, and creativity levels on the ability to write descriptive essays on nautical and technical cadets at Surabaya Shipping Polytechnic. This type of research is research. This research uses quantitative methods using experiments. The location used in this research is Surabaya Shipping Polytechnic. The subjects in this study were the cadets of the Nautika A, Nautika B, Teknika A, and Teknika B. classes. Based on the results of the research and discussion, the following conclusions are obtained: There are those that can be solved looking for description essays in the cadets. learning discovery method. The test results show better investigation methods than the discovery of learning, There is a difference in the ability to write a description essay about cadets who have a high level of creativity with cadets who have a low level of creativity, the test results show better who have a high level of creativity, there are related with learning methods and descriptions of the ability to write essay descriptions, the test results show learning methods and creativity descriptions of the ability to write essay descriptions.
Estimasi Biaya Software FAS (Financing Analysis System) Menggunakan Metode Function Point (Studi Kasus Pada PT BPRS Lantabur Tebuireng)

Software cost estimation is the process of predicting software development efforts. The basic input of software cost estimation is the measurement metric. Projects often experience delays, over-budget, and are not completed due to failure to estimate software development costs. PT BPRS (Bank Perkreditan Rakyat Syariah) Lanatabur Tebuireng determines the estimated cost based on the amount of human resources, features needed, and funds owned. This study explains the estimated costs of the FAS (Financing Analysis System) software at PT BPRS Lantabur Tebuireng using the Function Point method. Function Point is a method of measuring software functionality based on the type of user function that is External Input, External Output, External Inquire, Internal Logic File, and External Interface File as well as technical calculations of software development. The final of the FAS (Financing Analysis System) study cost around IDR 94,797,120.
Comparison of Mammography and Ultrasonography in Evaluation of Breast Masses

Introduction: Breast cancer is the most common cancer and second leading cause of cancer deaths in women. Early detection, efficient and accurate diagnosis can reduce the mortality rate.&#x0D; Objectives: To compare the screening accuracy of mammography (MMG) and ultrasonography (USG) in suspected cases of breast masses.&#x0D; Material and Methods: This cross-sectional analytical study was carried out in the Department of Radiology and Imaging, Combined Military Hospital, Dhaka from June 2016 to July 2017. A total of 100 clinically suspected case of breast masses aged from 20 to 75 years referred for MMG and USG was selected. Each patient underwent USG and MMG followed by a histopathological examination of the biopsy material taken from the lump lesion. Two cases histopathological report was not found. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy of USG and MMG were compared with histopathology as the gold standard. In order to determine the agreement of diagnoses between USG and MMG, consistency analysis was performed using Kappa-statistics.&#x0D; Results: Sensitivity, specificity, PPV, NPV and diagnostic accuracy of USG was 58.8%, 98.4%, 77.8%, 85.0% and 83.6% and for MMG 92.0%, 84.5%, 67.6%, 96.8% and 84.7% respectively. Kappa-statistics shows that the two diagnostic modalities had a test agreement in 39.8% cases to differentiate malignant breast tumour from the benign one (k-value = 0.398, p &gt; 0.05).&#x0D; Conclusion: Two diagnostic modalities USG and MMG had a fair agreement in the differentiation of malignant breast tumour from the benign.&#x0D; Journal of Armed Forces Medical College Bangladesh Vol.13(2) 2017: 22-24
Meteorological data analysis capabilities in the “VEGA constellation” information systems family

Satellite monitoring information systems require joint analysis of heterogeneous data, including meteorological data. To solve these problems, IKI RAS developed a technology for automatic acquisition, storage, presentation and analysis of meteorological data. The archive of meteorological data in IKI RAS is maintained within the Center for Collective Use “IKI-Monitoring“. The data is being continuously updated from a number of sources, primarily with the data obtained by the NCEP model. The data is available for visualization on the map in various ways. Plotting provides time series analysis in comparison to other variables such as vegetation indices. The developed technologies have been applied in development of a number of specialized information systems of satellite monitoring of natural and anthropogenic objects.
Tracking the Ripple Impact of Code Changes through Version Control Systems

Continuous updates and modifications are integral part of software development and maintenance process. With the advancements in technology, geographically distributed teams can contribute to code of a project shared with the help of integrated development environments (IDE) like Visual Studio and version control systems (VCS) like VSTS. In addition to managing shared code repository, VCS helps in keeping track of changes made in the source code. However, sometimes developers may detect the ripple impact of changes made in a specific code file. For this purpose, developers have to obtain the specific version of source code file from VCS and then use IDE to track the references of modified methods. This paper proposes an approach that enables VCSs to maintain a log of files and methods that have been affected by changes in a specific method within a project. This will assist developers in reviewing the impact of a specific change prior to merging the code files into their working directory. The proposed technique can be linked with any VCS like GIT, VSTS or Subversion (SVN) in the form of extensions. The effectiveness of having this capability withing the VCS is evaluated through a survey pertaining relevant questions. Survey result yields that having visibility of the source code impacts within the VCS will ease development and review process.
Convolutional Neural Networks for Sentiment Analysis in Persian Social Media

With the social media engagement on the rise, the resulting data can be used as a rich resource for analyzing and understanding different phenomena around us. A sentiment analysis system employs these data to find the attitude of social media users towards certain entities in a given document. In this paper we propose a sentiment analysis method for Persian text using Convolutional Neural Network (CNN), a feedforward Artificial Neural Network, that categorize sentences into two and five classes (considering their intensity) by applying a layer of convolution over input data through different filters. We evaluated the method on three different datasets of Persian social media texts using Area under Curve metric. The final results show the advantage of using CNN over earlier attempts at developing traditional machine learning methods for Persian texts sentiment classification especially for short texts.
LibGuides: Celebrating Banned Books!: Home

What are Banned Books? Check out this easy to navigate guide that answers all your questions about this important week in September.
A visual analysis framework for dinghy sailing: Towards leveraging recorded training sessions

Nowadays video plays an important role in the coaching of athletes across many different sports. To make more use of the advantages videos can provide for coaching, the Dutch Sailing team is shifting from manually recording short videos towards continuously recording training sessions. This new recording approach provides opportunities and creates challenges at the same time. In this thesis we present a pipeline to address the problems with the stability of the recording and the first steps towards a Visual Analysis Framework, which leverages the available video data. New information is extracted from the video recordings by detecting and tracking the boat and sailors. Moreover, we semi-automatically highlight interesting intervals in time of a recorded training session. These are the first steps towards an extensive Visual Analysis Framework which has the potential to make the analysis of the videos easier and provide the coaches with tools to improve the analysis of the performance of the sailors.
Underwater Image Enhancement Based on Stepwise Estimation of Transmission and Superpixel Segmentation

In order to solve the problem of detail loss and color cast caused by underwater imaging, we propose a novel underwater image enhancement method. We combine a stepwise estimate of transmission with a dark channel prior to obtain more detail from dark areas in underwater images. In addition, we use superpixel segmentation to avoid DCP invalid and white balance to correct color. The experimental results show that our method not only achieves a significant visual improvement, but also has a faster speed than other methods.