Reviewerly/exHarmony huggingface dataset:
An Clustering-based Ontology Summarization Method with Structural and Semantic Information Integration

As a common technique of constructing ontologies, ontology reuse provides technical support for tasks based on ontologies. However, with data scale's continuous expansion, ontology scale expands continuously, making ontology reuse difficult. To enable ontology engineers to understand and reuse ontologies quickly and accurately, ontology summarization is proposed, which aims to generate an abridged version of the original ontology. Existing summarization methods mainly use ontologies' structural information to extract important information, while hardly uses ontologies' semantic information. To handle this issue, this paper proposed an ontology summarization method which can takes semantic information into account. At first, the proposed method transfers ontologies' concepts into high-dimensional vectors with their semantic information, fuses these concepts' structural information to combine different perspectives' contents, and then uses these vectors to calculate their mutual distances. After that, using clustering algorithm to select important concepts and output central nodes as important concepts. Further, this method defines some related calculating concepts and relation importance measurements to select paths connecting important concepts, and thus get the summarized ontologies. Experiments on real ontologies revealed that the proposed method is of higher quality.
Homomorphic Encryption versus RSA: Cloud Security Performance Analysis

Recently, information has been exchanged easily through internet, thanks to the advancement of communications and computations. Internet users are increasing every year, causing traffic in sending and receiving data to be more congested. Even though traffic on the internet is getting denser, data security must be ensured so that important data can be safe and not stolen by irresponsible parties. Therefore, in this paper, data security in the cloud is implemented using homomorphic encryption which can process data in an encrypted state. The experimental results show that the implementation of homomorphic encryption on dense traffic provides a stable processing speed, where encryption, operation, and decryption with sending 10 consecutive times takes 442 seconds, while encryption, operation, and decryption of 1000 data takes 207 second. This implementation is needed for cloud services that have heavy data traffic and require security.
DecNet-CNN Framework for Channel Estimation

research-article DecNet-CNN Framework for Channel Estimation Share on Authors: Wen Chen Changsha University of Science and Technology, China Changsha University of Science and Technology, ChinaView Profile , Honglin Wu Changsha University of Science and Technology, China Changsha University of Science and Technology, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 102Pages 1–5https://doi.org/10.1145/3469213.3470305Online:18 August 2021Publication History 0citation14DownloadsMetricsTotal Citations0Total Downloads14Last 12 Months14Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Research on the Innovation of Experimental Teaching Mode of Economy and Management under the Novel Coronavirus

research-article Share on Research on the Innovation of Experimental Teaching Mode of Economy and Management under the Novel Coronavirus Author: Jiangmei Lin Guangxi University of Finance and Economics Guangxi University of Finance and EconomicsView Profile Authors Info & Claims IPEC2021: 2021 2nd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2021Pages 486–490https://doi.org/10.1145/3452446.3452568Published:25 April 2021Publication History 0citation16DownloadsMetricsTotal Citations0Total Downloads16Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Publisher SiteGet Access
Study on the Curriculum System of English for Chinese Medicine Culture

The popularity of Chinese medicine all over the world needs TCM experts with good foreign language skills. Teachers at TCM universities should study the curriculum system of English for Chinese Medicine Culture, improve students' English competence of TCM culture and build students' self-confidence of TCM culture. Several aspects of curriculum system such as teaching material and faculty, teaching methods and modes and curriculum assessment are expounded to develop a curriculum system with the features of Chinese medicine in relevant universities, so as to provide reference and basis for the curriculum construction of similar universities and promote the influence of Chinese medicine on the international stage.
Analysis of Research and Trends in Online Review: A Perspective

Contemporarily, plenty of literatures focus on analyzing the online reviews. According to those online comments, researchers are able to capture lots of the useful information, e.g., posters’ opinions, authenticities of the reviews, and the key information. In order to excavate more concise and comprehensive information, we review different research papers from above aspects. Based on their results and methods, the differences are demonstrated between proposed models from the literature. Moreover, suggestions are given for future research directions, which will be guideline for researchers, especially freshman. Besides, this paper also benefits for consumers to make wise decisions and practitioners to promote market efficiency.
Factorization length distribution for affine semigroups IV: a geometric approach to weighted factorization lengths in three-generator numerical semigroups

For numerical semigroups with three generators, we study the asymptotic behavior of weighted factorization lengths, that is, linear functionals of the coefficients in the factorizations of semigroup elements. This work generalizes many previous results, provides more natural and intuitive proofs, and yields a completely explicit error bound.
An Efficient Compression Method For Sign Information Of DCT Coefficients Via Sign Retrieval

Compression of the sign information of discrete cosine transform coefficients is an intractable problem in image compression schemes due to the equiprobable occurrence of the sign bits. To overcome this difficulty, we propose an efficient compression method for such sign information based on phase retrieval, which is a classical signal restoration problem attempting to find the phase information of discrete Fourier transform coefficients from their magnitudes. In our compression strategy, the sign bits of all the AC components in the cosine domain are excluded from a bitstream at the encoder and are complemented at the decoder by solving a sign recovery problem, which we call sign retrieval. The experimental results demonstrate that the proposed method outperforms previous techniques for sign compression in terms of a rate-distortion criterion. Our method implemented in Python language is available from https://github.com/ctsutake/sr.
Offline Imitation Learning for Robotic Control using Contrastive methods

Autonomous robotic applications have a huge scope of application in a variety of sectors including manufacturing, warehousing and personal robotic systems. Training a robotic system to autonomously perform a specified set of tasks is a key element in this. Deep learning has proved to be the most efficient tool to train a robotic system. Current methods for using deep learning for robotics involve, Generative Adversarial Networks that employ two networks, the generator and the discriminator. All of the current algorithms require access to a good simulated environment to train. This is a severe limitation as many tasks do not have access to a good simulator. We propose an offline imitation learning method for autonomous robotic tasks, that involves logging data from the environment in the training phase and evaluating it for performance in the environment. In our proposed method, we additionally use Mean Squared error between generated action and logged action for training the generator network which provides an additional signal for the network to learn. With current trends of increasingly large amounts of data collection, our data-driven approach holds enormous potential to outperform the current methods in terms of efficiency.
Sparse Biterm Topic Model for Short Texts

Extracting meaningful and coherent topics from short texts is an important task for many real world applications. Biterm topic model (BTM) is a popular topic model for short texts by explicitly model word co-occurrence patterns in the corpus level. However, BTM ignores the fact that a topic is usually described by a few words in a given corpus. In other words, the topic word distribution in topic model should be highly sparse. Understanding the sparsity in topic word distribution may get more coherent topics and improve the performance of BTM. In this paper, we propose a sparse biterm topic model (SparseBTM) which combines a spike and slab prior into BTM to explicitly model the topic sparsity. Experiments on two short texts datasets show that our model can get comparable topic coherent scores and higher classification and clustering performance than BTM.
Research on the Visualization Schemes of Writing Speed and Pressure

Copying is an important part of calligraphy learning. At present, the copying method of calligraphy is still limited to the copying of static copybooks, and there is no display expression method that is more conducive to the learner's calligraphy learning. It is difficult for learners to effectively observe the writing pressure and stroke speed in the model characters. Similar to dynamic information, and there is a lack of research on the effect of different display schemes on the efficiency of writing copy learning for this type of dynamic information. A test system is designed based on the digital screen device and the wireless pressure-sensitive pen device. Experiment and explore the visualization program of pen stroke speed and the visualization program of writing pressure in turn. The experimental results prove that different visualization schemes have a significant impact on the tester's imitation learning, no matter whether it is writing pressure or stroke strength. This result can provide some ideas for the visual performance of computer-aided design in the field of calligraphy copying.
A survey: blockchain utilization for securing healthcare system

Nowadays healthcare systems have started to be integrated with Internet of Things (IoT) in order to deliver some benefits in diagnosis and treatment process, such as remote patient monitoring and data usage for analytics and fast treatment. With the rise of IoT healthcare devices, number of electronic health records are increased to a rate that it is estimated to exceed billions in the next few years. Although cloud computing is a practical solution for processing this kind of data, healthcare records contain confidential and sensitive patient data which makes this system very vulnerable to the security and privacy threats, so it needs more investigation. For making this critical information more secure, researchers have come up with a solution of applying blockchain technology in healthcare. In this paper, we review the latest literature of blockchain application in healthcare from the security and privacy perspective. Several existing works have been discussed and a comparative study is done among the published works, along with potential future perspectives.
Quantize YOLOv3-tiny For 5-bit Hardware

As deep neural networks have been performing better and better on various tasks, their number of parameters has been increasing, and the demand for computing power and storage has been increasing. In 2016, Joseph Redmon et al. proposed a one-stage target detection method: You Only Look Once [1], which has been widely used worldwide in the past 5 years. However, such a scale network is not possible to be directly applied on mobile devices nowadays [2]. In order to reduce the network model, reduce energy consumption and improve the computational speed, we consider both network pruning and weight quantization for five types of large target detection datasets, prune the YOLOv3-tiny network to 16 layers and quantize it to 5 bits and successfully run it on FPGA, and the mAP drops about 5%.
Scaling the discrete Fourier transform gate in the quantum frequency processor

We show that the d -dimensional discrete Fourier transform can be implemented by adding RF harmonics to the applied modulation in a quantum frequency processor. Implementing the d = 3 case experimentally, we quantify entanglement and perform full quantum state tomography.
Cryptography and Deep Neural Network: An Art of Hiding Data in Images

"Steganography" is a craft of clouding information inside another ordinary document of comparative or differing types. Concealing information has consistently been vital to computerized crime scene investigation. Beforehand, "Steganography" is connected with "cryptography" &amp; "neural" organizations independently. While, the exploration joins "cryptography", "Steganography" along with "neural" organizations combinely shroud a picture at another holder picture of bigger or similar size. Albeit a cryptographic mode utilized is very basic, yet is successful when tangled with profound "neural" nets. Rest "Steganography" modes include concealing information effectively, however at uniform example which diverts towards less security. This technique targets - the difficulties as well as makes information concealing secure &amp; non-uniform.
Solving Connect 4 Using Artificial Intelligence

Training an AI model for complex board games so that our agent will be reminiscent of the human mind is ostensibly a difficult task. There is a rich history of training AI to master these board games. We have trained our agent using DDQN, Monte Carlo Tree Search and Alpha-beta pruning to the game of Connect 4. Alpha-beta and Monte Carlo tree search are based on the game-tree search approach, while DDQN is trained just by self-play. All the above-mentioned agents were made to play with each other to check their potency. The agent trained with DDQN consistently beat the minimax agent. MCTS also consistently beats minimax, given enough time to choose its move. The match between MCTS and DDQN was a cliffhanger till the final whistle, with MCTS as the winner.
Simple and Automatic Distributed Machine Learning on Ray

In recent years, the pace of innovations in the fields of machine learning (ML) has accelerated, researchers in SysML have created algorithms and systems that parallelize ML training over multiple devices or computational nodes. As ML models become more structurally complex, many systems have struggled to provide all-round performance on a variety of models. Particularly, ML scale-up is usually underestimated in terms of the amount of knowledge and time required to map from an appropriate distribution strategy to the model. Applying parallel training systems to complex models adds nontrivial development overheads in addition to model prototyping, and often results in lower-than-expected performance. This tutorial identifies research and practical pain points in parallel ML training, and discusses latest development of algorithms and systems on addressing these challenges in both usability and performance. In particular, this tutorial presents a new perspective of unifying seemingly different distributed ML training strategies. Based on it, introduces new techniques and system architectures to simplify and automate ML parallelization. This tutorial is built upon the authors' years' of research and industry experience, comprehensive literature survey, and several latest tutorials and papers published by the authors and peer researchers.
Framework to Create Employee Engagement Culture in Saudi Banks

Employee engagement has attracted widespread interest over the past twenty years from the practitioner network and researchers. It is claimed that organizations that focus on growing an engagement culture will result in increasing employee productiveness, the fulfilment of the organizational objectives, and competent employee retention. However, the fundamental issues revolving around the meaning and key antecedents of employee engagement nevertheless require similar research interest [2]. The Saudi Banks are an important component of Saudi Arabia’s financial system. In light of the emerging high volume of business activities, Saudi Banks are keen to inspire worker participation and employee engagement. This behaviour will allow banks to achieve sustainable business development. In response to these issues, the present research offers the possibility to advance the knowledge of organizational culture’s influence on employee engagement. The present research adopts a qualitative approach, and the method used is a grounded theory. The data collection process adopted an issue focused approach. Interviews were conducted with banks managers and their subordinates (male- female). The findings of the present study indicate that the factors that make the greatest contribution to employee engagement were employee satisfaction, achievement recognition, and jobs that were in line with the employees’ competent. Furthermore, the results revealed that the Saudi national culture, which is rooted in Islamic belief, has an influence on employee engagement in Saudi banks. This influence manifested itself as Non-interests bearing transactions, and the female segregation rule. The outcomes of the present research contributes to the existing theory of employee engagement by providing empirical evidence regarding the engagement meaning construct and its distinctiveness from similar, alternative, well-established attitudinal constructs. Furthermore, the present research offers a framework consisting of the themes that emerged from the analysis of the present study, and the proposed interventions to maintain an employee engagement culture. Furthermore, a discussion of the analysis’ limitations and recommendations for future researches will be presented, and a conclusion will be drawn.
Downsizing Heatmap Resolution for real-time 3D Human Pose Estimation

In recent years, 3D human pose estimation based on deep learning is being developed and one of the important elements for these is realtime processing. However, the top-down method which is one of methods for 3D multi-person human pose estimation is not fast enough for real-time processing. In this paper, we propose to downsize the input resolution to the heatmap of RootNet and PoseNet, which are sub-frameworks of 3DMPPE framework. When heatmap resolution becomes smaller, computation cost and processing time decrease, and hence, we studied the trade-off relation between performance and processing time when heatmap resolution is reduced. When the input resolution was reduced from 256 to 128, the processing time in RootNet was decreased by 30%, while the performance was decreased by only 4%.
Toward Creative Problem Solving Agents: Action Discovery through Behavior Babbling

Creative problem solving (CPS) is the process by which an agent discovers unknown information about itself and its environment, allowing it to accomplish a previously impossible goal. We propose a framework for CPS by robots for discovering novel actions via behavior babbling, capable of learning a representation of novel actions at both a symbolic planning level, and a sub-symbolic action controller level. Our framework employs two modes of discovery – a focused incubation method that scopes its search to the actions and entities composing the failed plan, and a defocused incubation method which enables exploration of actions and entities outside of the failed plan. We implemented and tested our framework using a Baxter robot in a 3D physics-based simulation environment, where we ran three proof-of-concept object manipulation scenarios. Results suggest that it is possible to use behavior babbling as a method for the autonomous discovery of flexible and reusable actions.
An Optimization Method of Aircraft Centroid Balance Fuel Supply Strategy Based on Sequential Quadratic Programming

The fuel tank of the aircraft plays a role in supplying fuel during missions. At the same time, the centroid of the aircraft can be adjusted through the fuel tank scheduling strategy, so that the aircraft can perform tasks at the ideal centroid to obtain better flight performance and Safety performance. This paper disassembles the aircraft centroid balance fuel supply strategy problem into multiple sub-problems, uses sequential quadratic programming, improved simulated annealing algorithm, etc. to solve each sub-problem step by step, and finally obtains a better overall solution.
Interpretation of the history of culture in education

The paper discusses the relationship of a particular interpretation of historical and cultural artifacts from the ideological interpreter settings. Systematized basic modern ideology, and built an algorithm considering cultural artifacts, which allows to reach the main most common interpretation.
Expert System for Diagnosing Liver Disease Using Web-Based Bayes Theorem Method Metode

Many people still do not know the risks, prevention, and treatment solutions related to liver disease. Therefore, many Indonesian people are affected by liver diseases such as hepatitis and other liver diseases because they are not aware of the symptoms they are experiencing. Therefore, this expert system is designed to help diagnose the symptoms experienced by people or patients who have liver disease. With this expert system, it can help overcome delays in handling so that it is not severe later. This expert system is created using the Bayes theorem method where in each symptom there is a probability or possibility which then gets the final result in the form of how big the event occurred. This expert system diagnoses the symptoms selected by the patient. After that, get the value of the possibility of a disease suffered by the patient. The results of this study are to produce an expert system for diagnosing liver disease using the website-based Bayes theorem method. This system can help diagnose a patient’s symptoms quickly and is used anywhere.
Performance Analysis of Single Image Fog Expulsion Techniques

Haze removal techniques are widely used in various computer vision applications like object detection, tracking, target recognition, and video surveillance. Therefore, in this paper, the classification of different fog removal techniques is presented. Further, recent dehazing algorithms related to each category are analyzed for the restoration of atmospherically degraded images. However, the performance of the different algorithms is evaluated based on the most commonly used image quality assessment parameters. Hence, different comparison parameters utilized for the evaluation of the performance of the various dehazing algorithms are also discussed. Finally, the qualitative and quantitative comparison of the various state-of-art defogging algorithms and research scope for further improvement is discussed.