Reviewerly/exHarmony huggingface dataset:
Cross Copy Network for Dialogue Generation

In the past few years, audiences from different fields witness the achievements of sequence-to-sequence models (e.g., LSTM+attention, Pointer Generator Networks, and Transformer) to enhance dialogue content generation. While content fluency and accuracy often serve as the major indicators for model training, dialogue logics, carrying critical information for some particular domains, are often ignored. Take customer service and court debate dialogue as examples, compatible logics can be observed across different dialogue instances, and this information can provide vital evidence for utterance generation. In this paper, we propose a novel network architecture - Cross Copy Networks(CCN) to explore the current dialog context and similar dialogue instances' logical structure simultaneously. Experiments with two tasks, court debate and customer service content generation, proved that the proposed algorithm is superior to existing state-of-art content generation models.
WITHDRAWN: A subset methodology to image classification based on multi feature extraction

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Administration of Virtual Data Processing Center over OpenFlow

This paper researches the building principles and administration of virtual data processing centers based on hyper-converged systems over OpenFlow.We provide the implementation features of such virtual centers on the basis of software-defined networking that is managed by a dedicated controller (a server).We suggest the graph administration model of hyper-converged system resources compliant with required performance on the one hand and economic requirements on the other.Based on the proposed model, the implementation of a greedy control algorithm for the virtual data processing center over OpenFlow was examined.This algorithm assigns the requests to physical resources by using of dedicated server software.The advantages of such hyper-converged system model on performance issues were outlined, e.g., multi-threaded routing and security, elimination of the majority of current threats.We summarize the possibilities of transition to network infrastructure in these virtual data processing centers.Such infrastructure is focused on data and usage of blockchain technology providing high reliability and content protection.
A research on students’ readiness for Education 4.0 program

Nowadays, in Vietnam, the government tends to have a great concern to the 4th Industrial Revolution, and Education 4.0 is also a matter to be paid attention to. In order to implement Education 4.0, besides technology and social, people are an important factor involved in the development of the education 4.0. The purpose of the study is to find out the factors affecting student readiness for education 4.0. The proposed model consists of 8 independent factors: Internet self-efficacy, Online communication self-efficacy, Self-regulation, Creativity, Self-discovery, Upgrade, Collaboration, Attitude and 1 dependent factor namely Readiness. Based on 361 samples, this quantitative research was conducted: reliability testing, exploratory factor analysis, correlation coefficients and linear regression. The result shows that there are four key factors affecting readiness including Self-regulation, Creativity, Upgrade and Attitude. The result of t-test shows that the group of students who had experienced online learning had higher readiness for the education 4.0 than the others.
Investigation of Clinical Utility of Radiological Technologist’s Reading Report as a Second Opinion for Medical Doctor Reading of Digital Mammogram

We investigated the clinical utility of a radiological technologist's (RT)'s reports (RRs) as a second opinion by the free-response receiver operating characteristic (FROC) observer study that compared the performance of medical doctors' (MDs') reading of digital mammogram with and without consulting the RR.One hundred women (39 malignant, 61 benign or normal) who underwent diagnostic mammography were selected from among 1674 routine clinical images classified by the degree of difficulty and categories for inclusion in the FROC study. The first FROC study performed by three RTs (RT 1-3) was conducted to collect the data for RR utilized in the second FROC study. The second FROC study was performed by five MDs, and the statistical significance of MDs' performances with and without reference to the RR was investigated by figure of merit (FOM).The FOM values of three RTs obtained in the first FROC study were 0.529, 0.576, and 0.539, respectively. In the second FROC study, RT 2 had the highest FOM, RT 1 the lowest false positives/case, and RT 3 the highest sensitivity. The average FOM values in the second FROC study for the five MDs with/without reference to the RR were as follows: RT 2's RR was 0.534/0.588 (p=0.003), RT 1's RR was 0.500/0.545 (p=0.099), and RT 3's RR was 0.569/0.592 (p=0.324).We concluded that the MDs' performance of reading mammogram was statistically improved by consulting the RR when the RT's reading skill was high.
KONSEP PENGENDALI LAMPU PENERANGAN RUMAH DARI JARAK JAUH MENGGUNAKAN GELOMBANG FREKUENSI

The Advancement of Science and Technology (Science and Technology) has been widely felt by the general public. Many people enjoy facilities or tools that can simplify and speed up their work. The advancement of science and technology covers various fields, including electronics, automotive machinery, medicine and others. Progress in the electronics sector is growing very rapidly. For example, now many electronic devices are used by the public, such as radio, television, fans and others. The advancement in the electronics field is increasingly sophisticated and modern. For example television, in ancient times we had to press the buttons that were attached to the television, to move the television waves, adjust the volume, and use other adjustment buttons. But now no longer, we can control or regulate the functions that exist on the television from where we sit using the remote control. In modern times like today remote control devices need to be developed so that we can control things from where we sit or from whatever we want, so that human work becomes easier and faster.
How much progress have we made in neural network training? A New Evaluation Protocol for Benchmarking Optimizers

Many optimizers have been proposed for training deep neural networks, and they often have multiple hyperparameters, which make it tricky to benchmark their performance. In this work, we propose a new benchmarking protocol to evaluate both end-to-end efficiency (training a model from scratch without knowing the best hyperparameter) and data-addition training efficiency (the previously selected hyperparameters are used for periodically re-training the model with newly collected data). For end-to-end efficiency, unlike previous work that assumes random hyperparameter tuning, which over-emphasizes the tuning time, we propose to evaluate with a bandit hyperparameter tuning strategy. A human study is conducted to show that our evaluation protocol matches human tuning behavior better than the random search. For data-addition training, we propose a new protocol for assessing the hyperparameter sensitivity to data shift. We then apply the proposed benchmarking framework to 7 optimizers and various tasks, including computer vision, natural language processing, reinforcement learning, and graph mining. Our results show that there is no clear winner across all the tasks.
PERUBAHAN FORCE NIKEL TITANIUM CLOSED COIL SPRING DAN ELASTIC CHAIN TERHADAP PERUBAHAN TEMPERATUR

Introduction: Fixed orthodontic appliances which made of nickel titanium closed coil spring dan elastic chain is the most commonly used in dentistry. There are various factors that able to change the force of orthodontic appliances such as temperature, pH, and time. The purpose of this research is to find out the influence of temperature toward the force changes in nickel titanium closed coil spring and elastic chain. Method: The method that used in this study was an experimental research with 36 research samples. The samples were attached on an acrylic board and soaked with different temperatures. The force was measured with force gauge NK-500 and noted within 0 hour, 24 hours, 72 hours, 168 hours and 240 hours. Result: The result of this research was statistically done by using Kruskal Wallis (α= 0,05). The temperature changes in each treatment that set based on different length of time showed that there was a reduction of force both in elastic chain and nickel titanium closed coil spring. In line with the research result, the reduction force in nickel titanium closed coil spring was higher than in elastic chain. Conclusion: Temperature changes can alter the force in nickel titanium closed coil spring and elastic chain.
Pendeteksian Kendaraan dengan Menggunakan Metode Running Average Background Substraction dan Morfologi Citra

Traffic conditions on the highway at this time has started to be crowded. To find out if there is traffic jam or can not be seen by counting the number of vehicles passing through the area. However, it is impossible for security forces to count the number of vehicles manually. This requires a method that can be used and is calculated from the number of vehicles. In this study, the running average background substraction method and morphological operations were used to detect vehicles and use the center of the object (centroid) to calculate the number of vehicles. The sample used is a traffic video in the Bekasi area. From the research results, there were 37 vehicles detected in real conditions and stated as vehicles in the application and there were 7 vehicles detected in real conditions but not stated in the application. The next stage is an evaluation by calculating the value of precision, recall and accuracy. In this study, the precision value obtained was 84.09%, the recall value was 94.87% and the accuracy rate was 80.43%.
한국 대학생들의 영어 인터넷 자료 사용에 관한 연구

The Journal of Studies in Language 35.1, 053-067. This study aims to explore the characteristics of usage of English Internet materials used by Korean EFL university students. Five hundred seventy-six Korean university students participated in this study. The results showed that Korean university students used English online videos or YouTube the most. The students used English internet materials for leisure activities more than for information search and learning activities. While there were no gender differences in using English internet materials, statistically significant differences were found between the academic years and proficiency groups. Based on the results, pedagogical implications were suggested for English teaching and learning. (Geumgang University · Chosun University)
Similarity Analysis of Self-Supervised Speech Representations

Self-supervised speech representation learning has recently been a prosperous research topic. Many algorithms have been proposed for learning useful representations from large-scale unlabeled data, and their applications to a wide range of speech tasks have also been investigated. However, there has been little research focusing on understanding the properties of existing approaches. In this work, we aim to provide a comparative study of some of the most representative self-supervised algorithms. Specifically, we quantify the similarities between different self-supervised representations using existing similarity measures. We also design probing tasks to study the correlation between the models' pre-training loss and the amount of specific speech information contained in their learned representations. In addition to showing how various self-supervised models behave differently given the same input, our study also finds that the training objective has a higher impact on representation similarity than architectural choices such as building blocks (RNN/Transformer/CNN) and directionality (uni/bidirectional). Our results also suggest that there exists a strong correlation between pre-training loss and downstream performance for some self-supervised algorithms.
THE DEPARTMENTAL SCHOOL HEADMASTER´S COMPETENCE IMPORTANT FOR ITS EFFECTIVE MANAGEMENT AND LEADERSHIP

The competences of school principals of departmental schools are currently insufficiently monitored.The aim of the paper is to define the most important competencies of the school principal of the departmental school, which he needs for effective management of the school.The research methodology is based on a questionnaire survey conducted between 95 students of the 1st and 2nd year of the School Management study program in the academic year 2019/2020 and focused on the analysis of the competences of the school principals of departmental schools important for proper school management and leadership.At the same time, semi-structured interviews were conducted with 30 students on specific examples of effective management gained in management practices in departmental schools.Research question: Which competencies are most important for effective management of departmental school and leadership?The authors' research results support the assumption that the most important competencies of the school principals of the departmental school include communication skills, general overview, ability to delegate tasks to others, self-management, cooperation and the right choice of co-workers and should be systematically implemented in their further education.
Mutual-Supervised Feature Modulation Network for Occluded Pedestrian Detection

State-of-the-art pedestrian detectors have achieved significant progress on non-occluded pedestrians, yet they are still struggling under heavy occlusions. The recent occlusion handling strategy of popular two-stage approaches is to build a two-branch architecture with the help of additional visible body annotations. Nonetheless, these methods still have some weaknesses. Either the two branches are trained independently with only score-level fusion, which cannot guarantee the detectors to learn robust enough pedestrian features. Or the attention mechanisms are exploited to only emphasize on the visible body features. However, the visible body features of heavily occluded pedestrians are concentrated on a relatively small area, which will easily cause missing detections. To address the above issues, we propose in this paper a novel Mutual-Supervised Feature Modulation (MSFM) network, to better handle occluded pedestrian detection. The key MSFM module in our network calculates the similarity loss of full body boxes and visible body boxes corresponding to the same pedestrian so that the full-body detector could learn more complete and robust pedestrian features with the assist of contextual features from the occluding parts. To facilitate the MSFM module, we also propose a novel two-branch architecture, consisting of a standard full body detection branch and an extra visible body classification branch. These two branches are trained in a mutual-supervised way with full body annotations and visible body annotations, respectively. To verify the effectiveness of our proposed method, extensive experiments are conducted on two challenging pedestrian datasets: Caltech and CityPersons, and our approach achieves superior performance compared to other state-of-the-art methods on both datasets, especially in heavy occlusion case.
Heraldic Lion on Post Stamps

Heraldry as a science of the coats of arms and philately - a collector’s love of collecting sand studying stamps: they are inseparable. The artistic expression brings them closer.This is confirmed by the postage stamps issued by the Republic of Macedonia. Specifically: PE Macedonian Post, under the heading Heraldry of Macedonia issued postal stationery with face value of 10; 36 and 9; 36 denars; including First Day Envelope. The postage stamps were printed on 19 June 2002 and 23 June 2003. The motif of the heraldic lion on the stamps was taken from various armorials.
Personalized 3D Breast Cancer Models with Automatic Image Segmentation and Registration

Breast conservative surgery (BCS) combined with radiotherapy has become the treatment of choice for the majority of women suffering from breast cancer.Every year approximately 2.1million new cases arise worldwide, but fortunately the 10-year survival rate now exceeds 80% mostly due to the early detection of non-palpable breast cancer.The accurate localization of the tumor is of utmost importance to decrease the re-excision rate and necessity of a second surgery, and also pivotal to the minimization of unaesthetic outcomes caused by these interventions.Nevertheless, 7 out of 10 patients will need invasive pre-operative localization and women will live long lives with the dire consequences of cancer treatments.Currently, it is up to the physicians to correlate multimodal 2-dimensial (2D) sectional images to the 3D space for surgical planning.The challenge is now focused on the technology to create an alternative non-invasive tumor location procedure that can be ethical and fit to the clinical set.This paper describes an automatic image segmentation and registration algorithm that fuses 3D optical scans of the breast surface, with interior radiological data for tumor characterization, to build a personalized digital breast model that can potentially be used as a non-invasive digital pre-operative localization technique, improving tumor visualization and surgery planning.
Analysis of Ascon, DryGASCON, and Shamash Permutations

Ascon, DryGASCON, and Shamash are submissions to NIST's lightweight cryptography standardization process and have similar designs. We analyze these algorithms against subspace trails, truncated differentials, and differential-linear distinguishers. We provide probability one 4-round subspace trails for DryGASCON-256, 3-round subspace trails for \DryGASCON-128, and 2-round subspace trails for \Shamash permutations. Moreover, we provide the first 3.5-round truncated differential and 5-round differential-linear distinguisher for DryGASCON-128. Finally, we improve the data and time complexity of the 4 and 5-round differential-linear attacks on Ascon.
MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous Driving

Extrinsic perturbation always exists in multiple sensors. In this paper, we focus on the extrinsic uncertainty in multi-LiDAR systems for 3D object detection. We first analyze the influence of extrinsic perturbation on geometric tasks with two basic examples. To minimize the detrimental effect of extrinsic perturbation, we propagate an uncertainty prior on each point of input point clouds, and use this information to boost an approach for 3D geometric tasks. Then we extend our findings to propose a multi-LiDAR 3D object detector called MLOD. MLOD is a two-stage network where the multi-LiDAR information is fused through various schemes in stage one, and the extrinsic perturbation is handled in stage two. We conduct extensive experiments on a real-world dataset, and demonstrate both the accuracy and robustness improvement of MLOD. The code, data and supplementary materials are available at: https://ram-lab.com/file/site/mlod
FRAGMENT SELECTION METHOD FOR BINDING MULTI-CHANNEL IMAGES

The method of selecting reference fragments for coordinate matching of images from different observation channels, including images from different spectral ranges, is considered. The proposed method is based on calculating the Fourier spectrum in the local vicinity of fragment points and analyzing the anisotropy of the spectrum. Image fragments with the maximum summary anisotropy value are assigned as reference fragments. The procedure is performed automatically and does not require entering threshold values. A reference fragment is detected by searching for the extremum of the target function. It is shown that the error of image coordinate binding depends on the total value of the anisotropy of reference fragments.
Peer Review: Readers in the Making of Scholarly Knowledge

Driven by a constant process of specialization, the extension of judging instances to readers may appear as a reallocation of expertise, ewpowering ing a growing number of people in the name of distributed knowledge.In an ongoing context of revelations of massive scientific fraud, which often implicates editorial processes and journals themselves, the derelic- tion inherent to judging instances prior to publication has transformed themass of readers into a vital resource for unearthing error and fraud. Thus, peerdom shall be reshaped, as lay readers have now full access to a large part of the scientific literature and have become valued audiences as quantified end users of published articles
Multi-User Classroom Environment in Virtual Reality : Creating and Implementing new Features

A research group from Lulea University of Technology in Skelleftea is working to create a virtual reality classroom to be used for educational purposes in mining education. In this classroom, a tea ...
Verifying the Causes of Adversarial Examples

The robustness of neural networks is challenged by adversarial examples that contain almost imperceptible perturbations to inputs, which mislead a classifier to incorrect outputs in high confidence. Limited by the extreme difficulty in examining a high-dimensional image space thoroughly, research on explaining and justifying the causes of adversarial examples falls behind studies on attacks and defenses. In this paper, we present a collection of potential causes of adversarial examples and verify (or partially verify) them through carefully-designed controlled experiments. The major causes of adversarial examples include model linearity, one-sum constraint, and geometry of the categories. To control the effect of those causes, multiple techniques are applied such as $L_2$ normalization, replacement of loss functions, construction of reference datasets, and novel models using multi-layer perceptron probabilistic neural networks (MLP-PNN) and density estimation (DE). Our experiment results show that geometric factors tend to be more direct causes and statistical factors magnify the phenomenon, especially for assigning high prediction confidence. We believe this paper will inspire more studies to rigorously investigate the root causes of adversarial examples, which in turn provide useful guidance on designing more robust models.