Reviewerly/exHarmony huggingface dataset:
ADRN: Attention-based Deep Residual Network for Hyperspectral Image Denoising

Hyperspectral image (HSI) denoising is of crucial importance for many subsequent applications, such as HSI classification and interpretation. In this paper, we propose an attention-based deep residual network to directly learn a mapping from noisy HSI to the clean one. To jointly utilize the spatial-spectral information, the current band and its $K$ adjacent bands are simultaneously exploited as the input. Then, we adopt convolution layer with different filter sizes to fuse the multi-scale feature, and use shortcut connection to incorporate the multi-level information for better noise removal. In addition, the channel attention mechanism is employed to make the network concentrate on the most relevant auxiliary information and features that are beneficial to the denoising process best. To ease the training procedure, we reconstruct the output through a residual mode rather than a straightforward prediction. Experimental results demonstrate that our proposed ADRN scheme outperforms the state-of-the-art methods both in quantitative and visual evaluations.
YANG Schema Selection

This document defines a mechanism to allow clients, using NETCONF orRESTCONF, to configure and select YANG schema for interactions with aserver. This functionality can help servers support clients usingolder revisions of YANG modules even if later revisions contain non-backwards-compatible changes. It can also be used to allow clients toselect between YANG schema defined by different organizations. Thisdraft provides a solution to YANG versioning requirements 3.1 and 3.2.
Memory-Safety Challenge Considered Solved? An Empirical Study with All Rust CVEs

Rust is an emerging programing language that aims at preventing memory-safety bugs without sacrificing much efficiency. The property is very attractive to developers, and many projects start using the language. However, can Rust achieve the memory-safety promise? This paper studies the question by surveying the bug reports collected from two public datasets, Advisory-db and Trophy-cases, which contain all existing CVEs (common vulnerability and exposures) of Rust. We manually analyze each bug and extract their memory-safety issues and culprits. Our results show that buffer overflow and dangling pointers are still the major memory-safety issues in Rust, and most culprits are related to unsafe Rust. Such security issues reveal that the security cost of Rust to support unsafe functions is high. To elaborate, the culprits of buffer overflow bugs in Rust are very similar to those in C/C++, which generally involve both logical errors and arbitrary pointer operations that are allowed only by unsafe Rust. However, the culprits of dangling pointers in Rust have unique patterns, especially those related to the vulnerability of Rust's borrow checker and lifetime checker. Based on these findings, we further suggest two directions to improve the resilience of Rust against dangling pointers, including recommending the best practice of some APIs to program developers, as well as approaches to enhancing the borrow checker and lifetime checker. Our work intends to raise more concerns regarding the memory-safety promise of Rust and facilitates the maturity of the language.
Inverse Feature Learning: Feature learning based on Representation Learning of Error.

This paper proposes inverse feature learning as a novel supervised feature learning technique that learns a set of high-level features for classification based on an error representation approach. The key contribution of this method is to learn the representation of error as high-level features, while current representation learning methods interpret error by loss functions which are obtained as a function of differences between the true labels and the predicted ones. One advantage of such learning method is that the learned features for each class are independent of learned features for other classes; therefore, this method can learn simultaneously meaning that it can learn new classes without retraining. Error representation learning can also help with generalization and reduce the chance of over-fitting by adding a set of impactful features to the original data set which capture the relationships between each instance and different classes through an error generation and analysis process. This method can be particularly effective in data sets, where the instances of each class have diverse feature representations or the ones with imbalanced classes. The experimental results show that the proposed method results in significantly better performance compared to the state-of-the-art classification techniques for several popular data sets. We hope this paper can open a new path to utilize the proposed perspective of error representation learning in different feature learning domains.
WRD: an approach for evaluating the performance of training program in virtual reality

The paper presents a new approach for evaluating the performance of the training program in virtual reality. The training process is described through three main steps: watching, recalling and doing (WRD). Each step is related to the sensor-motoric layer. The watching is related to the sensory layer, which accumulates the visual information from virtual reality. After then, that visual information will source for recalling the previous experiences followed by the decision-making using the cognitive layer. Finally, the decision-making activates the motor layer in form doing. The WRD approach uses the tests for the measuring of motor and sensory layers. The tests describe typical model of reaction for operator and activate motor and sensory layers as in real professional case. The cognitive layer may be measured with EEG. At the same time, the continuous generation of training cases may help to saturate the knowledge base. For this reason, the fuzzy model based on the generation of training cases was developed. The analysis of experimental data showed the decreasing of time delay by 83%. Moreover, it was shown the decreasing of rejection from horizontal and vertical axes in pixels by 62.7% and decreasing of time expected by 57% for motor tracking. The WRD approach may help to evaluate training program with evaluating sensory, cognitive and motor layer without developing prototype training simulator.
Training Effective and Confident Computer Science TAs

This poster presents a one-quarter seminar required of first-time and re-turning teaching assistants in the Computer Science and Engineering department at UC Riverside. The seminar covers departmental expectations, introduces TAs to methods for engaging large and diverse classrooms, and covers pedagogical practices in Computer Science. In addition to coverage of general issues surrounding teaching, the seminar includes many exercises that relate specifically to the teaching of computer science. Returning TAs serve as mentors for first-time TAs and participate in peer-observation and other activities. Initial results show a significant improvement in TA evaluations over the past year since the revamping of this seminar course.Moreover, TAs self-report an increased confidence in their teaching ability, communicating with students as well as motivating students.
Pengaruh Penggunaan KÈ‹bar Terhadap Keterampilan Membaca Bahasa Arab Pada Anak Disleksia

This research aims to describe how dyslexic children pronounce Arabic words in KÈ‹bAr book, to mention the factors that can cause pronunciation change in dyslexic children and to know the effects of KÈ‹bAr towards Arabic reading ability in elementary school students. The method used in this research is experimental with single subject and A-B-A design including baseline 1, intervention, and baseline 2. The subjects in this research are three third grade dyslexic students of elementary school of Sempu, Yogyakarta. The data are gathered with reading test and then the data are being analyzed statistically using descriptive method with visual graph both within and inter condition. The result suggested that there was in increase in students reading ability which was seen from the mean level of the three subjects. The mean in PAV subject increased in baseline 1 from 66.64% to 87.47% in the intervention stage and it further increased to 98.61% in baseline 2. The mean in subject AN also increased from 62.47% in baseline 1 to 80.56% in the invention stage. It further increased to 93% in baseline 2. Thirdly, the mean in subject GAS rose from 54.16% in baseline 1 to 76.4% in intervention stage and further improved to 87.47% in baseline 2. Thus, there were significant increases in all subjects. Subject VAG experience significant increase as much as 24.47% while subject AN increased as much as 30.53%. Similarly, the last subject, GAS showed a significant increased 33.31%. the percentage of overlapping data of the three subjects was 0% which meant that KÈ‹bAr can increase students Arabic reading skill especially in Arabic hijaiyyah words
Size scalable integration of photonic neural networks (Conference Presentation)

Photonic systems are candidates for next generation neural networks, promising to boost energy efficiency and speed via optical vector matrix multiplication. We will introduce the first scalable neural network integration strategy, demonstrating a network for 999 neurons in 0.36 mm^2.
The Inertial Gesture Recognition System

The paper is devoted to the implementation of the gestures recognition system. Existing gesture recognition approaches are considered. Among them the system of inertial type is chosen for research, since the systems of this type are less difficult to implement and require relatively low computational complexity. Thus, the proposed system is based on two components: the hardware module for gesture capturing and the subsystem for the recognition of captured gestures. The hardware component of the system employs MEMS accelerometer and gyroscope for motion acquisition. The prototype of this component is implemented using the STM32F401RE microcontroller and the MEMS sensor MPU9250. The communication between the microcontroller and the MEMS sensor is carried out via the I2C bus. The microcontroller initializes the sensor and acquires the motion data. Then it sends the collected data via the virtual COM port to the PC, where the captured motions are recognized. The system component for the recognition of gestures is implemented as the neural network in Matlab environment. This network consists of four layers: the input layer, the BLSTM layer, the fully-connected layer, and the Softmax layer. The key component of the selected neural network architecture is the BLSTM layer, which (due to its properties) provides some invariance to the length and the shift of the gesture signal in time. The suggested system was tested on three gesture classes: circular motion, tapping and other/unknown gesture. Using the hardware module the training and the testing sets were collected. In course of experiments, it was established that the best training results for the selected network architecture and the collected training set are achieved using the Adam optimizer. The experiment with different number of hidden units in the BLSTM layer showed that the highest recognition rate may be achieved when 100 units are used. Particularly, the suggested system with the specified number of hidden units in BLSTM layer was able to attain the recognition error less than 3.5% (recognition rate of 96.7%) on the test set.
An Accurate Structural Reconstruction in 3D of the Hypostyle Hall at the Ancient Egyptian Temple Complex of Karnak

This paper presents an accurate structural reconstruction in 3D of the Hypostyle Hall at the ancient Egyptian temple complex of Karnak. Numerous reconstructions have already been produced, but they are approximations and seek to show what the Hall might have looked like. Here we seek to obtain a dimensionally accurate reproduction that can provide structural details of the elements that make up the Hall. The data obtained here will subsequently enable a full structural analysis of the Hall to be conducted using suitable calculation methods, as they provide the dimensions of its component elements. That analysis will be based on the dimensions given and the characteristics of the materials.
Predicting Election Outcome from Social Media Data

In this era of technology, enormous Online Social Networking Sites (OSNs) have arisen as a medium of expressing any opinions, thoughts towards anything even support their status against any social or political matter at the same time.Nowadays, people connected to those networks are more likely to prefer to employ themselves utilizing these online platforms to exhibit their standings upon any political organizations participating in the election throughout the whole election period.The aim of this paper is to predict the outcome of the election by engaging the tweets posted on Twitter pertaining to the Australian federal election-2019 held on May 18, 2019.We aggregated two efficacious techniques in order to extract the information from the tweet data to count a virtual vote for each corresponding political group.The original results of the election closely match the findings of our investigation, published by the Australian Electoral Commission.
How can student's attitude impact education supply chain

Since students regard education as a passport to the working force, where and what students study can shape their attitude and ultimately determine their future.This research aimed to fill the gap found in the literature related to educational supply chain management by interviewing four students and utilising the attitude towards objects model in order to answer the research question 'How can student's attitude impact educational supply chain?'.Additionally, the study adopted an interpretive phenomenology analysis to examine their answers and draw the necessary conclusions.Several factors such as recommendations, value for money, what the university offered, entry requirements and the city itself among other factors impacted students' attitude as well as their choice of the host country, university, and degree -thus creating a pull in demand on the university's education supply chain that universities aimed to fulfil in order to gain competitive advantage.Moreover, seasonality in terms of the university's ability to offer several cohorts in addition to the traditional September cohort was found to have a significant impact on the student's attitude and choice, thus also creating a demand-pull on the university's supply chain.
LibGuides: Remote Library Services: Letter from Interlibrary Loan

When you need or prefer to work remotely, there are many ways the Libraries can provide you with expert help and services. Always check the Libraries’ website for the latest information about our hours and services.
Implementation of User Centered Design Method in E-Commerce Book Sales Application

Placement of the end user as a center in the system development process is a philosophy of UCD (User Centered Design).UCD method can help end users to overcome the difficulty of reading and translating the documents that exist in every development.The UCD approach has been supported by a variety of techniques, tools, methods, procedures and processes to help design interactive systems to be more user-centered.In the UCD method the user is at the highest level on all graphical interfaces that are used in conjunction with the entire admin content of the information.The user interaction with the information admin must have the same comfort as his experience using other systems.This writing aims to design an e-commerce application with the UCD method of selling books and providing information relating to books.The methods used for collecting data are interviews and observation of end users, and literature studies.The expected outcome of this design is to be able to provide better services, especially in providing books desired by the buyer.
Can Deep Neural Networks Learn Broad Semantic Concepts of Images?

A lot of researches use DNNs to learn image high-level semantic concepts, like categories, from low-level visual properties. Images have more semantic concepts than categories, like whether two images are complement with each other, serve the same purpose, or occur in the same place or situation, etc. In this work, we do an experimental research to evaluate whether DNNs can learn these broad semantic concepts of images. We perform experiments with POPORO image dataset. Our results show that in overall, DNNs have limited capability in learning above-mentioned broad semantic concepts from image visual features. Within DNN models we tested, Inception models and its variants can learn broad semantic concepts of images better than VGG, ResNet, and DenseNet models. We think one of the main reasons for the pale performance in our experiments is the POPORO dataset used in this work is too small for DNN models. Big image datasets with rich and broad semantic labels and measures is the key for successful research in this area.
PENGARUH PENGGUNAAN MEDIA GAMBAR SERI TERHADAP KETERAMPILAN MENULIS CERITA PENDEK SISWA KELAS III SEKOLAH DASAR

The purpose of this study is to learn the impact of using a series of media images on third grade class of short story writing skills. It is a quantitative experiment with a posttest only-control design type, using an experiment class and one control class as a comparison. The subject of the research is a class III of the South Jakarta elementary school of Guntur years 2018/2019 of as many 50 students. Data collection in this study uses writing test instruments. This study was using a hypothesis test of significance α = 0,05. In hypothesis testing at a level significance α = 0,05 tvalue > ttable (1,770 > 1,684) so H0 refused and H1 accepted, so its proven hypothesis. The average score of short story writing skills using a series of media images higher than using a photo media (85,52 > 82,04). Thus concluded there are using of series of media pictures affects short story writing skills III grade.
XML-based Analysis of Early 20th-Century Russian State Duma Verbatims

The study is devoted to the analysis of the documentation of the State Duma of the Russian Empire (1906–1917), which had legitimately limited the power of the monarch. The methodological basis of the research is the creation of a corpus of texts based on personal alphabetic indexes to the verbatim reports of the I-IV State Duma, the marking up of texts based on the developed XML markup scheme and analysis of changes in the structure of sources based on the analysis of tags distribution. The total volume of the created corpus of texts is 749,793 words. The markup scheme reflects the structure of indexes and includes metadata of the source, personal characteristics of the deputies and tags related to the parliamentary activities of the deputies. This article examines the structure of indexes and its sessional difference based on the matrix representation of data. Analysis of the markup data shows that the structure of personal indexes to the verbatim reports has undergone significant changes during their publication between 1906-1917. Differences in the structure of indexes exist between Dumas and in the structure of documents of a single volume among sessions. Initially, the indexes contained additional information about the election processes, which were neglected in the upcoming documents. The social characteristics of the deputies were also not published in the same form and represented different data sets depending on the Duma, and also differed in completeness and information. The research methodology has shown its effectiveness in studying the dynamics of the source structure, and data extraction for the subsequent studying of activity of deputies on the basis of mathematical methods
Train, Learn, Expand, Repeat

High-quality labeled data is essential to successfully train supervised machine learning models. Although a large amount of unlabeled data is present in the medical domain, labeling poses a major challenge: medical professionals who can expertly label the data are a scarce and expensive resource. Making matters worse, voxel-wise delineation of data (e.g. for segmentation tasks) is tedious and suffers from high inter-rater variance, thus dramatically limiting available training data. We propose a recursive training strategy to perform the task of semantic segmentation given only very few training samples with pixel-level annotations. We expand on this small training set having cheaper image-level annotations using a recursive training strategy. We apply this technique on the segmentation of intracranial hemorrhage (ICH) in CT (computed tomography) scans of the brain, where typically few annotated data is available.
Performance Evaluation of JavaScript Rendering Frameworks

When developing interactive web applications a number of different technologies and frameworks could be used. This thesis is set to evaluate a number of popular frameworks that are using different ...
Communication Processes in a Project Implemented by a Public Institution

Abstract The study aims at defining communication relations between a project team, a project manager and a wider project environment within a public institution. Individual in-depth interviews were chosen as the research method. On the basis of the collected material, five main issues related to the communication processes in the studied project were specified: a hierarchical structure, professional dependence, understanding of one’s mutual duties, relations between members of the project team and communication with the steering committee. On the basis of the findings, a scheme of project management in the studied public institution was identified. Communication and organizational weaknesses have been indicated, which may be eliminated or may not occur if they are spotted early on.
The Network Security Classified Protection System Construction of Meteorological Observation Center of China Meteorological Administration

Meteorological Observation Center of China Meteorological Administration (hereinafter called the “MOC”) is an institution directly under the CMA, was established in November 2002. MOC has developed a lot of systems to carry out the national-level duties. Inevitably, such systems take with information and network security problems. In recent years, the Chinese government has promulgated a series of network security laws and regulations, stipulate that systems developed by government offices and public institutions must carry out information system network security classified protection and grading work, must take steps to evade the risks which conform to requirements of national information system network security classified protection (GB 17859-1999).To make sure the large number of common technical protection equipment and strategies matches the systems has been developed or needs to be built, MOC designed and constructed network security classified protection system with the purpose of establishing a universal security platform architecture, a reasonable division of security domains and a precisely configured protection equipment. According to the software and hardware technical indicators of the national information system network security classified protection secondary standard requirements, the related software and hardware devices are designed and deployed. The Network Security Classified Protection System Construction of MOC fully consider the problem of repeated construction, the continued linear expansion of this system and the sensitive data of policy-level protection.