Reviewerly/exHarmony huggingface dataset:
Knowledge Distillation with Metric Learning for Medical Dialogue Generation

In recent years, the research of the medical dialogue system has attracted much attention. Considering that in the dialogue system, queries with similar meanings tend to have similar replies. In the medical field, this phenomenon is even more prevalent. For queries of the same class, their corresponding replies typically have similar meanings and can be classified into the same category. Having observed that, we propose to improve the neural sequence-to-sequence (Seq2Seq) based medical dialogue system by utilizing this internal relationship of category information between queries and replies. In our model, we first cluster similar queries into the same category according to their query vectors obtained from the encoder. Then we put forward the indirect and direct distillation learning approach to transfer the category information and category center distance from the queries to the replies. In the indirect distillation process, we employ metric learning to learn better representations of replies, in which replies of corresponding queries in the same category are closely grouped together, whereas those with different categories are far apart. In the direct distillation, to transfer the inter-class relationship, we minimize the Kullback-Leibler (KL) divergence between the category center distance distribution of queries and replies. A large number of experimental results on medical datasets have proved that our method is superior to the most advanced one.
The cost of immortality: A Time To Live for smart contracts

Smart contracts, scripts at the heart of blockchain-based applications, are meant to be available forever once deployed. However, this property has a price. The amount of space required to store new contracts keeps increasing. This increase impacts each participating node's performance and makes it inconvenient for low-end devices to participate in the network. Among all contracts deployed in the blockchain, a vast majority will lead to little if any usage. We demonstrate that, in the course of one year, 70 % of deployed contracts lead to no use. Unfortunately, unused contracts keep occupying space on the blockchain. To tackle this issue, we propose a new protocol to identify and delete unused contracts. Through simulation, based on Ethereum historical data, we show that deletion of smart contracts after an inactivity period of 90 days could lead to a 66 % reduction in the number of contracts stored over a year.
Fuzzy Adaptive Focal Loss for Imbalanced Datasets

Artificial intelligence has remarkable effectiveness throughout many domains, and deep neural networks (DNNs) have been shown to outperform other artificial intelligence mechanisms. DNNs imitate how a human brain learns and eliminate the time-consuming process of feature engineering. It can learn implicit information from datasets. However, adequate performance requires large amounts of training data, and real-world applications are often characterized by class imbalance, which results in a bias toward majority classes. In this paper, we use a fuzzy adjustment mechanism to dynamically tune the focal loss hyperparameter based on the three factors: class size, focal loss, and focal loss change. Experimental results on the CIFAR-10 dataset attest to the effectiveness of the proposed method.
Temporal face feature progression with cycle GAN

Abstract The aging process creates significant changes in the appearances of people’s faces. When compared to other causes of variation in face imaging, aging-related variation has specific distinct properties. Facial Aging variations, for example, is unique for each person; it occurs gradually and is significantly influenced by other characteristics including health, gender, and life-style. As a result, the proposed effort will use Generative Adversarial Networks to address these critical concerns (GANs). Generative Adversarial Networks (GAN’s) is made up of a generator and a discriminator network. The generator model generates images that a discriminator model analyses to determine if they are real or fake. This paper provides a Temporal Face Feature Progressive framework with Cycle GAN, which maintains the initial appearance and identity in the elderly aspect of their facial structure. To address aging concerns, our goal is to transform an initial age category image into a targeted age with age progression. We show that our temporal face features progressive cycle GAN learns and transfers facial traits from the source group to the targeted group by training various images. The IMDB-WIKI Face dataset has been used to obtain the results for the same.
Automatic Detection of Morse with Wideband Receiver

Morse communication is widely used in the field of land, water and air transportation and military communication, and especially plays a great role in military communication as a necessary means. Morse signals have noise interference inevitably in the process of HF channel transmission. Because of the noise has greatly harmed to human body, it increases the Labor intensity of copy personnel, especially for that long time engaged in HF Morse copy work. To solve the problem that receiving of Morse signals requires manual operation, this paper presents a solution to automatically detect Morse signals with a wideband receiver. The solution features equipment miniaturization, enables Morse signals to be received in full frequency and time domain and gets rid of manual work. It also allows real-time access to the situation of HF Morse signals.
A Graph Analysis Method to Improve Peer Grading Accuracy for Blended Teaching Courses

Peer grading is a tool widely used by instructors to provide fast reviews for homework that consists of open-ended questions. As the grades obtained from peer grading are not as accurate as those provided by the instructors, many methods have been proposed to improve the accuracy of peer grading. However, the current methods mainly focus on the scenario of online teaching, and they lose effectiveness in blended teaching courses because the mandatory of task and affinity among students may make the students perform irresponsibly in the grading task. This paper proposes a method based on graph analysis to improve the accuracy of peer grading. The peer grading system is modeled as a bipartite graph. In the graph, three interdependent metrics are defined to measure the dutifulness of the grader, the reliability of the rating and the true score of the submission. The stable values of the metrics are computed in an iterative way to obtain the peer grading results. Experiments demonstrate the proposed method is effective in blended teaching settings, and outperforms the current methods. Compared to the baseline of the mean value method, the proposed method decreases the root mean square error by 2.31% in the worst case and 30.72% in the best case on real-world data. It is robust to irresponsible graders, where the root mean square error keeps small even when the proportion of irresponsible graders increases to 30%.
Virtual Reality Applications as an Innovative Educational Practice in Adult Education

Virtual reality educational applications are supporting educational systems to provide better and more realistic training. Military training systems have incorporated such applications in their training programs having positive results in terms of training outcomes, safety, and cost efficiency. The aim of the chapter is to present the benefits of using virtual reality applications in connection to the modern learning theories. More specifically, the chapter summarizes the concepts of experiential, active and constructive learning theories conjoining them with the results of a research conducted to the Hellenic Air Force training pilots concerning their virtual reality training via flight simulators. As derived from the research, virtual reality educational applications are very helpful as far as it concerns acquiring new knowledge, developing skills, with predominant flexibility in decision making and more effective task prioritization and changing trainees' attitudes at the level of self-confidence, understanding, and self-reflection.
Towards the Development of Segment Level Speech Overlap Detection Using Convolutional Neural Network

There are various factors that affects the performance of a speech processing system and one of these is the presence of overlapped speech. It occurs when a certain segment contains more than one speaker, thus altering the information of a segment. This study is part of an ongoing research that aims to detect overlapped speech on segment level. This paper presents the performance of two variations of convolutional neural network (CNN) models for frame level overlapped speech detection using Augmented Multi-party Interaction (AMI) ES2002a corpus. The dataset is split into 70&#x0025; for training and 30&#x0025; for testing. In the experiments performed, the fluctuation of the loss and accuracy during the modified CNN model development is not as stable as the conventional CNN model but it outperformed the conventional CNN model with an accuracy of 87.02&#x0025; and 86.18&#x0025; respectively. For future works, other speech features for segment-wise detection of overlapped speech will be explored.
EDUCOLOGICAL ASPECT OF THE DEVELOPMENT OF THE INTELLECTUAL POTENTIAL OF SOCIETY: HE QUALITY OF HIGHER EDUCATION

The article is devoted to the problem of forming the intellectual potential of society due to the system of higher education and its quality. The author considers the factors, mechanisms, levels and stages of formation of the intellectual potential of society, as well as the relationship between the concepts of intellectual potential of society and intellectual culture, intellectual capital and scientific capital. Particular attention is paid to the role of university education in the process of forming the intellectual potential of society and ensuring the quality of education. The author analyzes the effectiveness of teaching an integrated, interdisciplinary course “Educology” for the development of intellectual potential of future professionals in education, emphasizes its theoretical and practical importance for understanding education in its dynamic development and promoting knowledge about achievements and contradictions in theory and practice of modern educational systems. The contribution of studying the course “Educology” in the study of the concept of “intellectual potential” as the ability of the social system to realize its potential, as well as the characteristics of its influence as a factor in creating a competitive society. The term “intellectual culture” is directly related to the concept of “intellectual potential”. It begins to be considered from the personality (microlevel), and then pass to another level (mesolevel), where the interaction of a set of individuals, i.e. groups. At this mesolevel, the intellectual potential of the team, institution, region, industry is already being formed. Ascending to the macro level, we determine the intellectual potential of the state, nation and society. If we talk about the intellectual potential of society, firm, enterprise, educated person, then it can be considered as available to society, enterprises, organizations stock of knowledge and information and its implementation through all components of “self-growth of value” and income by owners of knowledge and information». Intellectual potential is often understood as a set of opportunities to achieve certain goals through the systematic formation and use of intellectual resources for the analysis of economic and financial problems, development of creative ways to solve them, reasonable choice and implementation of management decisions. In a broad sense, the intellectual potential (of man, society) is a set of intellectual qualities (intellectual resources) that can be used by the system to solve problems of self-preservation and development. In a narrow sense, intellectual potential is identified either with the intellectual potential of man and the conditions of its reproduction, or with intellectual capital, in particular with its structural (organizational) component, which includes organizational and managerial experience, knowledge, skills, intellectual capital, corporate culture and others.
Non-Parallel Text Style Transfer using Self-Attentional Discriminator as Supervisor

Non-parallel text style transfer aims to rephrase a sentence with another style while reserving its content relying on non-parallel data. Most existing methods can be divided into two groups: 1) separating content and style of the input text and 2) directly modeling the style transfer process. To the best of our knowledge, all these existing works lack fine-grained supervisory signals during training, which leads to difficulty in achieving a good balance between content preservation and style satisfaction. However, the study on supervisors which could provide fine-grained supervisory signals for training transfer models has received relatively less attention. Thus, we propose a self-attentional discriminator and a training strategy for training an attentional transfer model by leveraging the fine-grained supervisory signals from the proposed discriminator. Specifically, our discriminator provides token-wise style/content weights by performing self-attention between the sentence vector and the token embeddings, which forms a shortcut in back-propagation leading to more accurate gradients. The style/content weights also pose a better content alignment constraint and improve the interpretability of the training procedure by identifying stylized tokens. The training of our transfer model is end-to-end via Gumbel-Softmax with the pre-trained discriminator. Experiments on two datasets with automatic and human evaluations as well as theoretical and empirical analysis demonstrate the effectiveness of our method. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Leadership’s Policy in Pandemic School Era: New Student Admission, Zonation System, and Digital Problem

This study discusses the leadership of school principals in the context of new student admissions by zonation system, and tracking digital problems in the pandemic era. This study aims to analyze how technology responds to policies related to new student admissions in the Covid-19 era. A qualitative approach is used. researcher as an active instrument. Data related to leadership policies, intake of new students, and digital problems were obtained through interviews, observations, and documentation with data flow chart analysis by Miles and Huberman. The informants are the principal, deputy principal, student affairs, administration, and school operators. The findings show that the acceptance of new students at MAN 2 Jambi City takes into account the number of teachers, and facilities according to the on-line PPDB technical instructions. The on-line 2020/2021 PPDB acceptance is intended to apply 2 channels, namely the "general route and achievement (zonation system)" connected to the databases of the ministry of social affairs, the ministry of education, and the ministry of religious affairs to avoid fraud. Research also proves that the digital problem in this madrasah does not occur. It is recommended that the on-line PPDB implementation be carried out through a zonation system.
Past, Present and Future of Quantum Computing: A Systematic Study

Quantum Computing is relatively new and it's kind of a special type of computing that uses the laws of quantum physics. In classical computing, we have some limitations and we can overcome those with the help of Quantum Computing as it uses qubits, but we need to keep those qubits at low temperature. Quantum Computing uses the probabilistic nature of electrons. The power of a quantum computer increases exponentially with the number of qubits linked tougher. Quantum computers are very difficult to make but there are a huge number of calculations that can be done easily with the use of a quantum computer. Quantum computers are way better and faster than classical computers. So, we can say that quantum computers rather than quantum computing will be used in the near future to replace classical computing.
Performance Evaluation of Savitzky Golay Filter Method that Implement within Key Generation

In this paper, we propose a key generation system involving multiple users and received signal strength (RSS) as a key extraction source. In the pre-processing stage, the savitzky golay filter (SGF) method is used which is expected to increase the correlation of the RSS value between the user and the access point so as to reduce the bit mismatch. While at the quantization stage, multi-bit quantization is used, namely the zhang method which aims to increase the speed of the generated key. From the test results, it can be seen that the SGF method is able to increase the correlation value up to 29.3% in the LOS &#x2013; Static (LS) measurement scenario. Meanwhile, for testing the Zhang quantization method, the smallest key disagreement rate (KDR) value is 11.3% and the highest key generation rate (KGR) is 12 bit/s also in the LS scenario. Overall, it can be concluded that the key generation system built is able to generate keys that are used to secure the information of several users. The highest number of keys generated is able to reach 46 keys.
Multi-angle Development Analysis for Automatic Modulation Classification Technology

Modulation recognition is an important part of signal perception and a key step towards intelligent perception. With the continuous development of communication technology, it has already entered the era of automatic modulation classification (AMC) from manual modulation recognition. Aiming at the signal modulation recognition technology, this article summarizes the value of modulation recognition technology in military applications and the development process of the technology. According to the modulation recognition method, It is mainly divided into the recognition method based on the likelihood function and the recognition method based on feature extraction, and the two categories are divided in detail. Explains how deep learning is applied to modulation recognition technology and what advantages it has. The recognition rate of various methods under different signal-to-noise ratio(SNR) conditions, as well as the advantages and disadvantages of various methods are summarized. The new challenges that may be encountered for modulation recognition technology are analyzed, and the future development of the modulation recognition is prospected.
Practice and Exploration of Higher Mathematics Teaching Mode Supported by Wechat Public Platform

Mobile learning can help people to acquire knowledge with the help of mobile devices and electronic devices at any time and any place, which is a major change for human beings to acquire information and master knowledge. The traditional teaching method is mainly teaching by teachers, supplemented by mass practice by students. And the teaching method led by teachers is easy to make students lose interest and initiative in learning and can only passively accept knowledge, and the classroom effect is unsatisfactory. This paper combines wechat with teaching. The main functions of wechat include platform function, communication function, social function, etc. These functions can be fully applied to teaching, and the full combination of the two can achieve a good learning effect.
EBANet: Efficient Boundary-Aware Network for RGB-D Semantic Segmentation

Semantic segmentation is widely used in robot perception and can be used for various subsequent tasks. Depth information has been proven to be a useful clue in the semantic segmentation of RGB-D images for providing a geometric counterpart to the RGB representation. At the same time, considering the importance of object boundaries in the robot’s perception process, it is very necessary to add attention to the boundaries of the objects in the semantic segmentation model. In this paper, we propose Efficient Boundary-Aware Network (EBANet) which relies on both RGB and depth images as input. We design a boundary attention branch to extract more boundary features of objects in the scene and generate boundary labels for supervision by a Canny edge detector. We also adopt a hybrid loss function fusing Cross-Entropy (CE) and structural similarity (SSIM) loss to guide the network to learn the transformation between the input image and the ground truth at the pixel and patch level. We evaluate our proposed EBANet on the common RGB-D dataset NYUv2 and show that we reach the state-of-the-art performance.
Geo-Registration and Real-Time Correction of Multiple Fisheye Videos for Wide-Area Surveillance

Abstract: The Distributed Video Surveillance System (DVSS) can monitor a wide area without blind spots, but it is often affected by the viewpoint discontinuity and space information inconsistency of multiple videos in the area. This paper proposed a novel multiple fisheye video geo-registration and real-time correction algorithm for wide-area surveillance. Firstly, by extending the line photogrammetry theory under central projection to spherical projection, a fisheye video geo-registration model is established and estimated using the orthogonal parallel lines on the ground of monitoring area. Secondly, all fisheye videos are corrected in real time through photogrammetry ortho-rectification technology and Lookup Table calculation using the geo-registration parameters. Thirdly, according to the unified spatial coordinates and the observation range determined by current viewport, the corrected fisheye videos are adaptively selected and mosaiced into a distortion-free panoramic video, which is automatically mapped to the monitoring area as a texture for 3D visualization. In this step, viewport dependent panoramic video generation strategy is adopted to balance the bandwidth limitation of fisheye video transmission and the requirements of 3D rendering calculation, so as to realize the fast 3D visualization of monitoring area mapped by multiple fisheye videos. A parking lot of about 0.4 square kilometers along the expressway is selected as the test area, which is monitored by 9 fisheye cameras. The experimental result shows the line re-projection error in fisheye video under geo-registration parameters is about 0.5 pixel, and the overall efficiency including fisheye video ortho-rectification, mosaicing, mapping and 3D rendering is not less than 30 PFS. Hence, proposed algorithm can well organize multiple fisheye cameras for wide-area surveillance and is very valuable for applications, such as continuous target detecting and tracking in large scene.
Application of the automatic selection and configuration of clustering algorithms method for the Apache Spark framework

This article proposes the MASSCAH method realization for Apache Spark clustering algorithms selection and configuration. Optimization of one of the clustering quality measures is used to configure the algorithm. In the course of this study, additional clustering quality measures were implemented that are not included in the Apache Spark framework, since at the moment only the silhouette criterion is available in the framework.
On One Method of Constructing a Decision Rule in Pattern Recognition

The general problem of pattern recognition is to establish the theory and principles of building systems that share complex situations and events into classes. In this case, usually the number of possible situations in the classes is great and they are almost impossible to be remembered. The method of estimates' calculating contributes to a variety of scoring functions' building, which in turn, allows us to formulate and solve a wide class of multivariate data processing. In recent years more and more widely distributed methods of pattern recognition, in which the primary step is to calculate the balance of information and consideration of evidence. Based on the latest evidence being implemented, all further procedures until the distinction of classes or assign any object to one of the classes.