Reviewerly/exHarmony huggingface dataset:
Columba: A New Approach to Train an Agent for Autonomous Driving

For autonomous driving in extremely complex scenarios, existing research utilizes deep reinforcement learning or imitation learning to obtain the decision-making capability of agents. However, due to the incomplete information nature of such driving scenarios, existing techniques usually suffer issues such as the incorrect rewards or unstable training which would impact the learning quality seriously. In this paper, we propose a new approach named Columba which trains the agent to learn from expert trajectory data and abnormal trajectory data instead of relying on any manually-set reward functions. In particular, Columba designs a positive and negative feedback regulator to reduce the dangerous or bad states of the car agent at the beginning of training. Further, Columba generates the rewards by coordinating with the discriminator, the random distillation network and the regulator, enhancing the accuracy of rewards. We conduct extensive experiments on the Torcs simulation platform. Experimental results show that the agent trained by Columba outperforms the agents trained by DDPG and GAIL, which are strong baselines in the deep reinforcement learning and the imitation learning, respectively.
A Lattice-based Method for Mining Frequent Weighted Itemsets Using Diffset

Mining Frequent Weighted Itemsets (FWIs) is an important problem in the field of data mining. Itemsets have different importance depending on their values in practical applications. Aiming at the problems of low efficiency in traditional pattern mining, a new method of weighted Node lattice (WN-lattice) in mining FWIs is proposed. This method includes three phases:(1) Building WN-lattice structure solve the high data compression. (2) A Diffset-based strategy for the efficient computation of the supports of inter-transaction patterns is described. Some theorems for pruning redundant itemsets quickly based on these theorems. (3) Frequent Weighted Itemsets Lattice Algorithm (FWIL) is described for directly mining FWIs using WN-lattice. The experimental results show the proposed approach FWIL is more efficient and more those used previously in terms of runtime, especially when run on very large and dense weighed databases.
A Literature Review on H∞ Neural Network Adaptive Control

In this literature survey, the writing reachable for the H∞ adaptive control engineering utilizing neural systems for frameworks whose uncertainty has an ambiguous structure. This engineering merge thoughts from powerful control hypothesis, for example, H∞ control structure, the little addition hypothesis, and L dependability hypothesis with Lyapunov security hypothesis and current hypothetical accomplishments in uncertainty control to build up an adaptive design for frameworks whose vagueness fulfills a neighborhood Lipschitz bound. The strategy enables a control originator to rearrange adaptive tuning method, band limit adaptive control sign, in addition indulgence unequaled vagueness in a solitary plan system. Powerful control configuration limits the impact of uncertainty and nonlinearity to the detriment of diminished execution. The plan outline work is like that utilized in powerful control, yet without giving up execution. The majority of this is adjusted, while giving thoughts of transient execution limits subject to the qualities of two straight frameworks and the adjustment gain. The relevance of neural networks in the control systems, feed forward neural networks direct and indirect adaptive controls, H∞ controller is also reviewed.
Application of Augmented Reality in The Adventure Kelana Application (Travel Agent)

This paper discusses the design of the Kelana Adventure application that applies AR or augmented reality technology. Kelana adventure is a mobile-based travel agency application that uses augmented reality as a superior feature in providing better visualization of tourist objects than various similar applications. This study discusses the methods used in designing applications and their steps in order to obtain a model / prototype that is as close as possible to user needs. From the method used, a mockup of the adventure rover application was formed to provide a visualization of the application layout. The results of the mock up will then become the standard framework for the development of the adventure rover application
PuzzleShuffle: Undesirable Feature Learning for Semantic Shift Detection

When running a machine learning system, it is difficult to guarantee performance when the data distribution is different between training and production operations. Deep neural networks have attained remarkable performance in various tasks when the data distribution is consistent between training and operation phases, but performance significantly drops when they are not. The challenge of detecting Out-of-Distribution (OoD) data from a model that only trained In-Distribution (ID) data is important to ensure the robustness of the system and the model. In this paper, we have experimentally shown that conventional perturbation-based OoD detection methods can accurately detect non-semantic shift with different domain, but have difficulty detecting semantic shift in which objects different from ID are captured. Based on this experiment, we propose a simple and effective augmentation method for detecting semantic shift. The proposed method consists of the following two components: (1) PuzzleShuffle, which deliberately corrupts semantic information by dividing an image into multiple patches and randomly rearranging them to learn the image as OoD data. (2) Adaptive Label Smoothing, which changes labels adaptively according to the patch size in PuzzleShuffle. We show that our proposed method outperforms the conventional augmentation methods in both ID classification performance and OoD detection performance under semantic shift conditions.
Online Object Tracking based on Generative Adversarial Networks

Object Tracking is one of the essential steps in computer vision applications such as human-computer interaction, video surveillance, and augmented reality. Visual object tracking remains one of the most challenging tasks in object tracking due to many variations of conditions. In this paper, we utilize Residual Network ResNet as a feature extractor and address the limitations of object tracking using Generative Adversarial Networks. We describe a generative adversarial network to improve object tracker’s classifier during the online training phase. The network generates adaptive features to augment the positive samples detected by the convolutional layer of the tracker’s model in order to improve the model’s classifier. The network is integrated with Generative Adversarial Networks tracker and the object tracking results are presented.
Optimized Computation Combining Classification and Detection Networks with Distillation

A Convolutional neural network (CNN) has emerged as a widely used approach to computer vision tasks, including object classification and detection tasks. The high requirement for the model to be more computationally efficient on lower information and communication technology (ICT) resource, e.g., mobile terminals can benefit from model distillation. However, most existing distillation methods suffer from a significant accuracy reduction, which requires a large number of pre-training models or doesn't make good use of the more of the network information, e.g., in the middle layers, during the distillation. In this paper, we study how knowledge about traffic signs recognition could be transferred to smaller models by distillation while cutting channels. We present an optimized object detection network, which uses a Region Proposal Network (RPN) weighted loss and hard-soft distribution-wise distillation loss for structural differences between teacher and student networks. We validate the network on multiple real-world datasets, the experiments demonstrate that the classification accuracy can be improved by 9 % with about 16 times parameter reduction while the detection network performance could be increased by 10.6% using an optimized object detection network.
Exploring Autoencoders for Feature Extraction in Multi-Target Classification

Multi-target learning is a prediction task where each example is associated with multiple target variables (outputs) simultaneously. One of the challenges in this research field is related to the high dimensionality of the data and the high number of target variables with dependencies. In such scenarios, it is crucial to extract lower dimensional representations from the original input space, such that these can be provided as input to other multi-target predictors. In this paper, we proposed using Autoencoders as feature extractors in several multi-target classification datasets publicly available. Results were evaluated considering state-of-the-art multi-target classification methods and evaluation measures in the literature. The experiments showed that the neural networks were able to keep the predictive performance even when the extracted features corresponded to a dimension size equivalent to 10% of the original number of features and, in some cases, getting better results than when using the original datasets.
Educating the Next Generation of Cybersecurity Defenders at the University of Cincinnati

As cybersecurity issues become ever more prevalent in today’s society, it is imperative that our computer science and engineering programs train students to work in cybersecurity fields. The University of Cincinnati’s Department Electrical Engineering and Computer Science, part of a NSA Center of Excellence, is developing a new B.S. in Cybersecurity Engineering which will be coordinated with cybersecurity programs in Information Technology and in Political Science. We describe our current cybersecurity certificate progran and our proposed new B.S. in Cybersecurity Engineering, which will meet the criteria to apply for ABET EAC accreditation.
EDUCATIONAL TECHNOLOGY TO FORM THE BASICS OF SOCIAL WORLDVIEW

Based on theoretical and experimental research on the problems of education reform in the context of humanitarization of education, the authors elaborate and present a model of educational technology used to reform the activities in humanitarization of educational process, to create a flexible effective system at secondary school with the focus on teaching natural science disciplines. The theoretical study shows that humanization and humanitarization of education as a holistic system does not have a mechanism to implement social and pedagogical technologies and does not have a driver to realize this educational concept. This fact led to the development of educational technology aimed to form the basics of social worldview. The authors offer possible solutions for the problem by uncovering the pedagogic potential of natural sciences’ disciplines in forming the basic social determinants, namely, social competence and social intelligence of the students of secondary school. The main methods used are: the method of philosophical and subject expansion of natural sciences’ disciplines as a resource to form and develop social intelligence in the context of humanitarization of natural sciences, and statistical analysis to process the quantitative results of the pedagogical experiment. The development of a model of the author's technology is based on ecological approach to the research and design of educational environments. The methods of organizing educational technology that form the foundations of social worldview and paradigm of social thinking in modern conditions of scientific and technical progress are presented. The experimental data show that the organization of educational process of the secondary school students (7–11 forms) according to the model of social and pedagogical technology provides the increase of the level of quality of educational activity that satisfies the educational demand and meets the requirements of Federal Educational Standards of Russian Federation are present in the work. The authors considered the achievements of natural sciences from the point of view of the search for a public ideal. They made recommendations on the application of new scientific approaches, on the introduction of new interpretations, concepts of physical and natural-scientific outlooks. The developed educational technology contributes to the change of thinking paradigm, addressing the problem of authentic understanding of the world. This socio-pedagogical technology contributes to the creation of interdisciplinary interaction and interpenetration of the scientific and humanitarian understanding of the world within the framework of the modern concept of natural sciences.
Analysis of Statistical Characteristics of Ground Lightning Current Amplitude in East China

Ground lightning current amplitude and its features are an important basis for lightning scientific research and design of lightning protection for electric transmission lines. The mean amplitude distribution and cumulative probability distributions of positive and negative flash in East China and its sub-administrative regions including Fujian, Zhejiang, Jiangsu, Anhui, and shanghai, are analyzed using the monitoring data of lightning location system from June to September of the year 2010 to 2019. Based on those work, probability density functions of lightning current amplitude from 2010 to 2019 for each administrative region and East China are shown, which provide the basis to design, rebuild and evaluation of differentiation technology and strategy of lightning protection for electric transmission lines in East China.
Writing using sources for academic purposes—theory, research, and practice, RosemaryWette, New York: Routledge, 2021, ix + 153 pp., 𝒵34.99, Hardback: 𝒵120.00, E‐book: 𝒵24.49 Paperback ISBN: 978‐0‐3671‐7592‐4

International Journal of Applied LinguisticsVolume 32, Issue 1 p. 220-222 BOOK REVIEW Writing using sources for academic purposes—theory, research, and practice, Rosemary Wette, New York: Routledge, 2021, ix + 153 pp., 𝒵34.99, Hardback: 𝒵120.00, E-book: 𝒵24.49 Paperback ISBN: 978-0-3671-7592-4 Xiangmei Yang, Xiangmei Yang College of Foreign Languages, Ocean University of China, Qingdao, ChinaSearch for more papers by this authorYani Xu, Yani Xu College of Foreign Languages, Ocean University of China, Qingdao, China School of Foreign Languages, Jiangsu University of Technology, Changzhou, ChinaSearch for more papers by this authorLianrui Yang, Corresponding Author Lianrui Yang lryang@ouc.edu.cn College of Foreign Languages, Ocean University of China, Qingdao, China Correspondence Lianrui Yang, College of Foreign Languages, Ocean University of China, 238 Songling Road, Laoshan District, Qingdao, 266100, China. Email: lryang@ouc.edu.cnSearch for more papers by this author Xiangmei Yang, Xiangmei Yang College of Foreign Languages, Ocean University of China, Qingdao, ChinaSearch for more papers by this authorYani Xu, Yani Xu College of Foreign Languages, Ocean University of China, Qingdao, China School of Foreign Languages, Jiangsu University of Technology, Changzhou, ChinaSearch for more papers by this authorLianrui Yang, Corresponding Author Lianrui Yang lryang@ouc.edu.cn College of Foreign Languages, Ocean University of China, Qingdao, China Correspondence Lianrui Yang, College of Foreign Languages, Ocean University of China, 238 Songling Road, Laoshan District, Qingdao, 266100, China. Email: lryang@ouc.edu.cnSearch for more papers by this author First published: 15 September 2021 https://doi.org/10.1111/ijal.12385Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume32, Issue1March 2022Pages 220-222 RelatedInformation
A Taxonomy of Defenses against Memory Corruption Attacks

Vulnerabilities caused by memory corruption related bugs are a pervasive threat, continually undermining the security of the whole computing environment. The lack of memory safety mechanisms in indispensable systems programming languages like C or C++ leaves plenty of room for programmer-induced errors which often result in catastrophic security breaches. We analyze the root causes of these vulnerabilities, providing a concise overview of memory corruption bugs and related attacks. Although present in a number of programming languages, we use the C/C++ programming language as a basis for our analyses. We categorize existing defensive mechanisms based on the attack techniques they focused on preventing and give a brief insight into state-of-the-art defenses introduced throughout the years, with a special focus on operating system defenses.
User Local and Global Interest Depth Interaction for Recommendation Algorithm

Using auxiliary information such as social networks or item attributes to improve the performance of recommender systems is a key issue in the research of recommender systems. It plays a very important role in alleviating the interactive sparsity of collaborative filtering methods. In response to this problem, a recommendation algorithm that user local and global interest depth interaction is proposed, which analyzes the user’s implicit preferences from two perspectives: based on the user’s historical behavior records, using a fully connected neural network to extract the user’s local interests; based on the user’s historical behavior items, through the association between entities in the knowledge graph, mines the entities associated with the user’s historical behavior, forming a multi-layer global interest layer to generalize user interests. The user’s local interest vector activates the item entity in the global interest layer to generate the user’s global interest vector. Finally, the deep neural network is used to complete the feature interaction of users’ local interests, global interests and recommended items. The user’s local and global interests accurately describe user preferences and improve the accuracy of click-through rate prediction. Experiments show that the recommendation performance of the algorithm on the two public data sets is better than the comparison baseline and the classic recommendation algorithm.
Hardware in the Loop of an Omnidirectional Vehicle Using Augmented Reality

This article presents the development of a Hardware-in-the-Loop (HIL) system with an augmented reality environment, which allows interaction with an omnidirectional vehicle, the proposed system is designed to develop monitoring and manipulation skills, allowing the user to select or manipulate trajectories from the computer and observe the behavior of the robot in the AR application. The environment has been created using CAD tools and the Unity 3D multiplatform that, combined with MATLAB, exchanges information in real time to execute the simulation of the omnidirectional movement of the vehicle, achieving a high degree of immersion, this technological tool serves as a contribution within the sector educational, providing simulation programs with new, more realistic and intuitive immersion technologies. The experimental tests allow the user to interact with the augmented environment and understand the operation of an omnidirectional vehicle, its structure and behavior when carrying out different trajectories selected by the user and using control algorithms, as well as the application of augmented reality. It demonstrates easy handling and ease of use.
Wheeled Robot Development for Obstruction Avoidance Mechanism

Abstract: Consolidating the current improvement status of clever robots, impediment aversion and programmed following are the focal point of robot travel issues. In light of the improvement status of homegrown clever control frameworks, current innovation, and so on, this paper utilizes Arduino as the centre control framework, joined with an infrared following module. Four modules, for example, ultrasonic impediment aversion module, engine drive module and force module have planned a decent control conspire, accordingly understanding the insightful following and hindrance evasion capacity of the wheeled robot. Keywords: Arduino, Wheel robot, Tracking, Obstacle Avoidance
COBY: COVID-19 Telegram Chatbot by Employing Machine Learning Algorithms

COVID-19 pandemic has been one of the biggest concerns nowadays. People always curious and ask for immediate responses regarding the current situation. The chatbot can be very useful in this kind of situation which allows the system to understand text, which means it can respond appropriately. In order to be able to return the correct responses, the chatbot needs to learn how to classify the text data input from the users. In this paper, we study three different machine learning algorithms to work on text classification problems, namely Naive Bayes, Neural Network, and Support Vector Machine (SVM). An experiment was carried out to study which machine learning algorithms produce the most accurate responses when they are implemented in the Artificial Intelligence (AI) chatbot systems. In order to make sure the tests are consistent and fair, we conducted the experiment on the same dataset, and assessed the accuracy of their respective responses. In addition, we have also successfully implemented each of these algorithms as chatbots on a social media platform, Telegram.
On-Chain Smart Contract Verification over Tendermint

Smart contracts are computer code that runs in blockchain and expresses the rules of an agreement among parties. A bug in their code has major consequences, such as rule violations and security attacks. Smart contracts are immutable and cannot be easily replaced to patch a bug. To overcome these problems, there exist automatic static analyzers that find bugs before smart contracts are installed in blockchain. However, this off-chain verification is optional: programmers are not forced to use it. This paper defines on-chain verification instead, that occurs inside the same blockchain nodes, when the code of smart contracts is installed. It acts as a mandatory entry filter that bans code that does not abide to the verification rules, that are consequently part of the consensus rules of the blockchain. Thus, an improvement in on-chain verification entails a consensus update of the network. This paper provides an implementation of on-chain verification for smart contracts written in the Takamaka subset of Java, running as a Tendermint application. It shows that on-chain verification works, reporting actual experiments.
Brazilian Exchange Rates Forecast via Feature Selection and Artificial Neural Network

The non-linear analysis of redundancy and relevance is made by a filter based on information theory. The Renyi entropy is integrated with a Parzen window and a Cauchy-Schwartz equation, in order to directly estimates the mutual information of the data that is used to choose the most relevant variables and the less relevant yet not redundant ones. The proposed modified filter can be applied to determine the most promising variables that do not have previous knowledge available. Finally, an Artificial Neural Network is used to estimate the Brazilian exchange rate, showing positive and more stable results.