Reviewerly/exHarmony huggingface dataset:
Interdisziplinäre Annotation von Entitätenreferenzen

This contribution presents the results of our interdisciplinary engagement with entities.We developed interdisciplinary guidelines for a reliable and semantically valid annotation of entity references, and a methodological workflow for their semi-automatic identification within large amounts of textual data.From the perspective of four different disciplines within the Humanities and Social Sciences, we discuss challenges related to the application of a generic workflow to heterogeneous text corpora, and present possible solutions.We conclude that the interdisciplinary collaboration enhances the overall methodological stringency and fosters conceptual reflections within each participating discipline.
DAENet: Making Strong Anonymity Scale in a Fully Decentralized Network

Traditional anonymous networks (e.g., Tor) are vulnerable to traffic analysis attacks that monitor the whole network traffic to determine which users are communicating. To preserve user anonymity against traffic analysis attacks, the emerging mix networks mess up the order of packets through a set of centralized and explicit shuffling nodes. However, this centralized design of mix networks is insecure against targeted DoS attacks that can completely block these shuffling nodes. In this article, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DAENet</i> , an efficient mix network that resists both targeted DoS attacks and traffic analysis attacks with a new abstraction called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stealthy Peer-to-Peer (P2P) Network</i> . The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stealthy P2P network</i> effectively hides the shuffling nodes used in a routing path into the whole network, such that adversaries cannot distinguish specific shuffling nodes and conduct targeted DoS attacks to block these nodes. In addition, to handle traffic analysis attacks, we leverage the confidentiality and integrity protection of Intel SGX to ensure trustworthy packet shuffles at each distributed host and use multiple routing paths to prevent adversaries from tracking and revealing user identities. We show that our system is scalable with moderate latency (2.2s) when running in a cluster of 10,000 participants and is robust in the case of machine failures, making it an attractive new design for decentralized anonymous communication. DAENet ’s code is released on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hku-systems/DAENet</uri> .
Clustering Spatial Temporal Distribution of Fishing Vessel Based lOn VMS Data Using K-Means

Management of sustainable marine resources is a national and global problem, and fisheries management has a complex issue, more research is need with a more comprehensive approach. Through the Ministry of Marine Affairs and Fisheries, the Government of Indonesia has made the Vessel Monitoring System (VMS). VMS data contains the position, movement, and activity of the fishing vessels utilized in this research. Data mining techniques and machine learning are using, and this study consists of three steps: i) Finding the number of optimum clusters by the Elbow Method, ii) Conducting clustering with the K-Means algorithm with the optimum k-value that has set, iii) Analyze the distribution of VMS data spatially and temporally. Overall, the optimum number of clusters obtained is 7 with the results of the compactness of the cluster members the percentage is 90.7%, spatially the distribution of VMS data in the Fisheries Management Area WPPNRI-711 is uneven and temporally very volatile. The results of this study can provide information about the intensity and location of fishing activity and prevent overfishing.
Outdoor inverse rendering from a single image using multiview self-supervision

In this paper we show how to perform scene-level inverse rendering to recover shape, reflectance and lighting from a single, uncontrolled image using a fully convolutional neural network. The network takes an RGB image as input, regresses albedo, shadow and normal maps from which we infer least squares optimal spherical harmonic lighting coefficients. Our network is trained using large uncontrolled multiview and timelapse image collections without ground truth. By incorporating a differentiable renderer, our network can learn from self-supervision. Since the problem is ill-posed we introduce additional supervision. Our key insight is to perform offline multiview stereo (MVS) on images containing rich illumination variation. From the MVS pose and depth maps, we can cross project between overlapping views such that Siamese training can be used to ensure consistent estimation of photometric invariants. MVS depth also provides direct coarse supervision for normal map estimation. We believe this is the first attempt to use MVS supervision for learning inverse rendering. In addition, we learn a statistical natural illumination prior. We evaluate performance on inverse rendering, normal map estimation and intrinsic image decomposition benchmarks.
KORELASI KETERAMPILAN MEMBACA PEMAHAMAN CERPEN DENGAN KETERAMPILAN MENULIS TEKS ULASAN CERPEN SISWA KELAS VIII SMP NEGERI 1 PADANG

ABSTRACT The purpose of this study consists of three things as follows. First, it describes the level of reading skills in understanding the short stories of students of class VIII SMP Negeri 1 Padang. Secondly, it describes the level of short story writing skills of students of VIII SMP Negeri 1 Padang. Third, describe the relationship between reading skills understanding short stories and short story writing skills of students of VIII SMP Negeri 1 Padang. The variables of this research are the short story comprehension reading skills as the X variable and the short story writing text writing skills as the Y variable. The research data were the scores of the short story reading comprehension test skills of the eighth grade students of SMP Negeri 1 Padang and the scores of the results of the short story writing skills text review of the eighth grade students of SMP Negeri 1 Padang. The results of this study consisted of three things as follows. First, the reading skills of the short story comprehension of Grade VIII students of SMP Negeri 1 Padang are in the Qualification More Than Enough with an average count of 67.01. Second, the short story writing skills of the eighth grade students of SMP Negeri 1 Padang in short story writing are in the Fair qualifications with an average count of 62.15. Third, based on the results of the t-test, an alternative hypothesis was accepted at the 95% confidence level. Kata Kunci: Korelasi, Membaca Pemahaman Cerpen, Menulis Teks Ulasan Cerpen
Optimizing Convolutional Neural Network Accelerator on Low-Cost FPGA

Convolutional neural network (CNN) is one of the most promising algorithms that outweighs other traditional methods in terms of accuracy in classification tasks. However, several CNNs, such as VGG, demand a huge computation in convolutional layers. Many accelerators implemented on powerful FPGAs have been introduced to address the problems. In this paper, we present a VGG-based accelerator which is optimized for a low-cost FPGA. In order to optimize the FPGA resource of logic element and memory, we propose a dedicated input buffer that maximizes the data reuse. In addition, we design a low resource processing engine with the optimal number of Multiply Accumulate (MAC) units. In the experiments, we use VGG16 model for inference to evaluate the performance of our accelerator and achieve a throughput of 38.8[Formula: see text]GOPS at a clock speed of 150[Formula: see text]MHz on Intel Cyclone V SX SoC. The experimental results show that our design is better than previous works in terms of resource efficiency.
Security Assessment of Intelligent Distribution Transformer Terminal Unit Based on RBF-SVM

Aiming at the main security problems faced by the intelligent distribution transformer terminal Unit in the station area, this paper proposes a security situation assessment method for the intelligent distribution transformer terminal deployed in the distribution station area. By extracting key system indicators that affect terminal security, a nonlinear support vector machine(SVM) classifier based on Gaussian kernel(RBF) function is constructed, and k-fold cross validation and grid search are used to determine the optimal parameters C and g of the classifier, establishing an intelligent configuration.then Simulating and training the normalized system index data.the results show that this method can effectively identify and classify the abnormal working indexes of the terminal, so as to realize the security situation assessment, which has certain practical value.
Development of a Solar Tracker Using Servo Motor and Light Dependent Resistor for Electrical Boats

Solar power is one of the natural sources available in most countries. This gives a definite advantage to the tropical countries that experience hot weather to use solar energy to generate electrical power supply. However, the generation of electricity using solar irradiation is costly and it can produce its maximum capability only at peak hours which is 12 pm to 3 pm in a day. In order to overcome this disadvantage, researcher has developed a mechanism called the solar tracker. The solar tracker will track or trace the position of the sun from the morning until evening and manoeuver accordingly to get maximum exposure. By doing so, the solar panel can obtain its maximum capability in the morning and evening although the time is not in peak hour. A solar tracker can also increase the solar output from 5% to 20% depending on the tracker and weather. This project extends the development of the solar tracker by building a portable solar tracker by using a servo motor and a light-dependent resistor (LDR). The average voltage, current, and power output between this solar tracker and a fixed solar panel show more than 50% improvement. The percentage of the output between these two panels has been calculated to show the rate of improvement.
PENGARUH MODEL DISCOVERY LEARNING BERBANTUAN MEDIA AUDIOVISUAL TERHADAP KETERAMPILAN MENULIS TEKS FABEL SISWA KELAS VII UPT SMP NEGERI 1 KOTO XI TARUSAN

ABSTRACT The purpose of this study is three. First, describing the skill of writing fable texts of VII grade students of UPT SMP Negeri 1 Koto XI Tarusan before using discovery learning models assisted by audiovisual media. Second, describing the skill of writing fable texts of VII grade students of UPT SMP Negeri 1 Koto XI Tarusan after using discovery learning models assisted by audiovisual media. Third, describing the influence of discovery learning models assisted by audiovisual media on fable text writing skills of seventh grade students of UPT SMP Negeri 1 Koto XI Tarusan. This type of research is quantitative research with experimental methods. The design of this study was one group pretest and posttest design. The population in this study were 167 students. The sample in this study were 28 students who were determined using purposive sampling technique. The variable of this research is fable text writing skills before and after using the discovery learning learning model assisted by audiovisual media of VII grade students of UPT SMP Negeri 1 Koto XI Tarusan. The instrument of this research is the performance test. The results of this study are three, namely as follows. First, fable text writing skills before using the discovery learning model assisted by audiovisual media of VII grade students of UPT SMP 1 Koto XI Tarusan are more than Sufficient (LdC) qualifications with an average of 72.62. Second, fable text writing skills after using the discovery learning model assisted by audiovisual media in seventh grade students of UPT SMP 1 Koto XI Tarusan are in a Good qualification (B) with an average of 81.25. Third, based on the t-test, the alternative hypothesis (H_1) is received at a significant level of 95% and the degree of freedom (dk) = n-1 because of tcount&gt; t table, which is 2.73&gt; 1.70. Kata Kunci: pengaruh, model discovery learning, keterampilan menulis teks fabel.
A Visual Based Robot Trajectory Teaching Method for Traditional Chinese Medical Moxibustion Therapy

At present, Chinese traditional physiotherapy is more and more trusted and valued by people, among which moxibustion is a very effective treatment. In order to improve the efficiency of human-computer interaction and help physiotherapists to use robots to replace human power, this paper proposes a visual based robot arm trajectory teaching method. The physiotherapy staff only needs to use an optical marker to demonstrate the therapeutic trajectory on the patient's back, and the robotic arm can pick up the expected trajectory and repeat the same work. We use LK optical flow algorithm to obtain marker's motion trajectory, and then smoothen the trajectory through SG filter, which is more executable for robot arm. Finally, we design an experiment and simulate it on MATLAB to obtain the accuracy of the trajectory and the results is impressive.
RETRACTED: Research on English Translation based on Computer Technology

Abstract With the rapid development of science and technology, computer technology has been applied to all walks of life, including English translation. Through computer technology translation, we can greatly improve the efficiency of translation production, which has an unprecedented impact on the language service industry. At the same time, with the rapid development of global economy, the economy and society put forward higher requirements for the translation industry, which requires us to constantly improve the quality and speed of translation. Therefore, in order to better English translation, computer science and technology has brought a significant basis for it. However, due to the limitations of computer language translation technology, we must adopt post editing method to adjust the machine translation, which will improve the accuracy of translation, such as computer aided technology (hereinafter referred to as CAT) software. First, this paper introduces the main role of computer-aided translation technology. Then, this paper analyzes the important principles of computer-aided translation software. Finally, some suggestions are put forward.
Quantifying Challenges in the Application of Graph Representation Learning

Graph Representation Learning has experienced significant progress as a means to extract structural information in a meaningful way for subsequent learning tasks. Current approaches including shallow embeddings and Graph Neural Networks have mostly been tested with node classification and link prediction tasks. In this work, we provide an application oriented perspective to a set of popular embedding approaches and evaluate their representational power with respect to real-world graph properties. We implement an extensive empirical data-driven framework to challenge existing norms regarding the expressive power of embedding approaches in graphs with varying patterns along with a theoretical analysis of the limitations we discovered in this process. Our results suggest that "one-to-fit-all" GRL approaches are hard to define in real-world scenarios and as new methods are being introduced they should be explicit about their ability to capture graph properties and their applicability in datasets with non-trivial structural differences.
A No-Reference Super Resolution for Satellite Image Quality Enhancement for KOMPSAT-3

Recently, a deep learning based super-resolution (SR) technology has been applied to satellite images to improve spatial resolution and sharpness, and to increase extractable information. In this paper, we propose a no-reference single image super-resolution method that improves the image quality by doubling spatial resolution of Korea Multi-Purpose Satellite-3 (K3), achieving a ground sampling distance (GSD) of 0.7 m. When training SR networks, the proposed method generates low-resolution (LR) images by applying the degradation model to K3 images and creates enhanced high-resolution images (HRe) by applying the top and bottom hat transformation to the original high-resolution (HRo) images. As a result of applying SR to the original K3 image, it was possible to obtain an image with improved quality. Additionally, as a result of testing the Baotou area used for satellite image quality evaluation, it was confirmed that the resolution is similar to the spatial resolution of Korea Multi-Purpose Satellite-3A (K3A), which is a GSD of 0.55 m.
Intensity normalization of prostate MRIs using conditional generative adversarial networks for cancer detection

Magnetic Resonance Imaging (MRI) is increasingly used to localize prostate cancer, but the subtle features of cancer vs. normal tissue renders the interpretation of MRI challenging. Computational approaches have been proposed to detect prostate cancer, yet variation in intensity distribution across different scanners, and even on the same scanner, poses significant challenges to image analysis via computational tools, such as deep learning. In this study, we developed a conditional generative adversarial network (GAN) to normalize intensity distributions on prostate MRI. We used three methods to evaluate our GAN-normalization. First, we qualitatively compared the intensity of GAN-normalized images to the intensity distributions of statistically normalized images. Second, we visually examined the GAN-normalized images to ensure the appearance of the prostate and other structures were preserved. Finally, we quantitatively evaluated the performance of deep learning holistically nested edge detection (HED) networks to identify prostate cancer on MRI when using raw, statistically normalized, and GAN-normalized images. We found the detection network trained on GAN-normalized images achieved similar accuracy and area under the curve (AUC) scores when compared to the detection networks trained on raw and statistically normalized images. Conditional GANs may hence be an effective tool for normalizing intensity distribution on MRI and can be utilized to train downstream deep learning tasks.
Improved U-net for Zebra-crossing Image Segmentation

A safe crossing system is a prerequisite for improving the mobility of the visually impaired. For the blind, it is important to know exactly where the zebra crossings are. Zebra-crossing detection by machine vision can be a good solution to this problem. In this paper, we propose a model for fast and stable segmentation of crosswalks from captured images. For the blind, it is important to know exactly what area ahead is a zebra crossing. A common feature of all zebra crossings is the periodic appearance of white stripes on a black road. In this paper, we proposed a model for fast and stable segmentation of crosswalks from captured images. The model is improved based on U-net and consists of three steps. First, the input image is subsampled using ResNet-34's convolutional neural network to extract image features. Second, dilated convolution is used to increase the receptive field of feature points without decreasing the feature map resolution. Finally, the abstract features are restored to the original image size through the original up-sampling network of U-net with the complementary information of the skip connection.
Quantum Entanglement Based Sentence Similarity Computation

Sentence representation is one of the foundations of natural language processing. A quantum entanglement-based approach is provided to determine the sentence similarity. The sentence embedding based on quantum computation considers the semantic structure of a sentence, tensor product and attentive mechanism. Main components of modifiers of a complete sentence are obtained by using the attentive mechanism combining with quantum entanglement of two consecutive notional words. The semantic features and syntactic structures of sentences are extracted by the tensor product of two consecutive notional words without any big-corpus or prior knowledge. Experimental results implemented on 17 datasets with an excellent effect show that taking the quantum entanglement into the sentence embedding yields improvements on mining semantic relations and syntactic structures. These results shed new light on sentence representation with limited resources, and represent a new step towards the quantum entanglement-based application in natural language processing.
Foreign language electronic educational resource development on the basis of blended learning in the process of informatization in higher education

The increasing interest to the development and implementation of electronic educational resources in higher education is caused by a number of factors of the modern higher education system. The rapid process of informatization, reduction of face-to-face academic hours and increase of students’ independent work hours demands adaptation of learning methods and forms from a teacher to the new pedagogical reality.In this regard, the problem of methodological support improvement in the educational process is becoming urgent. The aim of the paper is to consider the features of informatization in education process at Nosov Magnitogorsk State Technical University; to underline the stages of electronic educational resources designing that is developed with the basicon principles of blended learning; to formulate the main advantages of electronic educational resources, as well as the disadvantages of its using and tasks that are solved while using electronic educational resources.The research methods were the analysis of didactic and methodological literature on the research problem, systematization and generalization in summing up the results that are relevant for the scientifically based approach formation on the electronic technologies introduction into the highereducation system.The study describes the five stages of the education informatization process, which resulted in the formation of teachers’ information and communication competence. It is shown that Informatization of education is a long-term and continuous process that requires systematic and concerned actions both from the University administration and its teachers
Fitness coach: design and implementation of a smart mirror based on automatic image recognition and action model comparison

The smart mirror designed and developed in this study aims to help people who cannot receive professional guidance in completing the standardisation and correction of a series of actions, such as bodybuilding, yoga, and dance. Techniques such as the establishment and recognition of the kinect human motion model, 3D stereoscopic imaging, postural correction based on OpenCV, and the use of voice prompts have been applied to create this smart mirror. The final product is a physical smart mirror that displays corrective images, evaluates the user's actions, and provides helpful suggestions through voice prompts when a user performs actions while facing the mirror.