Reviewerly/exHarmony huggingface dataset:
Deep Cognitive Reasoning Network for Multi-hop Question Answering over Knowledge Graphs

Knowledge Graphs (KGs) provide human knowledge with nodes and edges being entities and relations among them, respectively.Multihop question answering over KGs-which aims to find answer entities of given questions through reasoning paths in KGs-has attracted great attention from both academia and industry recently.However, this task remains challenging, as it requires to accurately identify answers in a large candidate entity set, of which the size grows exponentially with the number of reasoning hops.To tackle this problem, we propose a novel Deep Cognitive Reasoning Network (DCRN), which is inspired by the dual process theory in cognitive science.Specifically, DCRN consists of two phases-the unconscious phase and the conscious phase.The unconscious phase first retrieves informative evidence from candidate entities by leveraging their semantic information.Then, the conscious phase accurately identifies answers by performing sequential reasoning according to the graph structure on the retrieved evidence.Experiments demonstrate that DCRN significantly outperforms state-of-the-art methods on benchmark datasets.
Microorganisms: Integrating Augmented Reality and Gamification in a Learning Tool

Microorganisms is a Year 6 Science subject that primary school students find considerably less attractive because of the enormous facts that require them to have good imaginary skills to understand.Only limited applications are available on smartphones as tools to learn subjects, especially Science, Technology, Engineering and Mathematics (STEM).Since the young generation is very much into current technology, there is a need to develop an application to gain students' interest and improve their understanding of microorganisms.Therefore, a microorganism learning application that combines augmented reality (AR) and gamification called Microorganisms was developed.The Microorganisms prototype includes two modules; learning and training.The learning modules use AR technology that scan marker images to display a digital layer of microorganisms in three dimensions (3D).Meanwhile, the training module is represented through gamification consisting of quiz questions, a timer, and a score.The application was designed using Agile methodology and developed using various software such as Unity, Autodesk 3ds Max, Vuforia, and Firebase.Ten respondents, nine students and one teacher in a primary school, assessed the prototype through experimental testing.The results showed that, on average, the user satisfaction value was 4.6 out of 5. Thus, the Microorganisms application based on AR and gamification can be considered a good learning tool for primary school students to learn about micro-organisms.
Wasserstein Adversarial Regularization for Learning With Label Noise

Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping. We propose a new regularization method, which enables learning robust classifiers in presence of noisy data. To achieve this goal, we propose a new adversarial regularization scheme based on the Wasserstein distance. Using this distance allows taking into account specific relations between classes by leveraging the geometric properties of the labels space. Our Wasserstein Adversarial Regularization (WAR) encodes a selective regularization, which promotes smoothness of the classifier between some classes, while preserving sufficient complexity of the decision boundary between others. We first discuss how and why adversarial regularization can be used in the context of noise and then show the effectiveness of our method on five datasets corrupted with noisy labels: in both benchmarks and real datasets, WAR outperforms the state-of-the-art competitors.
New Norms of Online Teaching and Learning: Covid-19 Semester Experience for Universiti Malaysia Terengganu Students

The COVID-19 pandemic has affected the national education agenda at all levels of education. New Teaching and Learning (T&amp;L) online norms have been executed except for specific academic programs and subjects only since 18 March 2020 when the Movement Control Order (MCO) was administered and continued to this day. To guarantee students’ continuity of education without online T&amp;L dropout, online face-to-face (Synchronous) or not face-to-face (Asynchronous) is now become the primary approach and method platform with many virtual education applications. Therefore, this research examined students’ readiness to follow online teaching and learning and analysed the impact of online T&amp;L on the national education agenda. This study involved 133 students of Diploma in Fisheries, Universiti Malaysia Terengganu, for the first semester of 2020-21. Data were obtained through a questionnaire using Google Form and presented to students by sharing links to their WhatsApp group in the final week of study. The questionnaire was adapted from several instruments related to various aspects of online T&amp;L during the COVID-19 pandemic. The outcomes revealed that most students were among Gen-Z with digital literacy background. Thus, it was assisting them having a high level of readiness to face online T&amp;L. In terms of the availability of device infrastructure, internet access, e-Learning, and computers, most (80%) have mastered it. Only (20%) have low proficiency due to limited experience using computers and gadgets due to family constraints and their literacy levels. The research conclusion recommends a consecutive enhancement in curriculum structure flexibility, delivery, evaluation; internet accessibility and digital gap, and self-motivation of students entering the era of the self-regulated learner. Transformation demands the strategic cooperation of various parties in educational institutions, government agencies, the private sector, NGOs, and people’s leaders in the interest of the country’s advancing education relevant to the era of global technology-oriented education and digital infrastructure.&#x0D; &#x0D; Received: 4 March 2021 / Accepted: 6 May 2021 / Published: 8 July 2021
Language Processing Model Construction and Simulation Based on Hybrid CNN and LSTM

Deep learning is the latest trend of machine learning and artificial intelligence research. As a new field with rapid development over the past decade, it has attracted more and more researchers' attention. Convolutional Neural Network (CNN) model is one of the most important classical structures in deep learning models, and its performance has been gradually improved in deep learning tasks in recent years. Convolutional neural networks have been widely used in image classification, target detection, semantic segmentation, and natural language processing because they can automatically learn the feature representation of sample data. Firstly, this paper analyzes the model structure of a typical convolutional neural network model to increase the network depth and width in order to improve its performance, analyzes the network structure that further improves the model performance by using the attention mechanism, and then summarizes and analyzes the current special model structure. In order to further improve the text language processing effect, a convolutional neural network model, Hybrid convolutional neural network (CNN), and Long Short-Term Memory (LSTM) based on the fusion of text features and language knowledge are proposed. The text features and language knowledge are integrated into the language processing model, and the accuracy of the text language processing model is improved by parameter optimization. Experimental results on data sets show that the accuracy of the proposed model reaches 93.0%, which is better than the reference model in the literature.
APPLICATIONS AND GAMES FOR THE DEVELOPMENT OF ALGORITHMIC THINKING IN FAVOR OF EXPERIENTIAL LEARNING

Teaching during a pandemic cannot be qualitatively compared to regular traditional school and school attendance in person. Teachers also had to get used to online teaching. It was necessary to research and find such tools and teaching aids that can revive teaching, bring fun and motivation for pupils. In this paper we present some examples of freely available applications suitable for the development of algorithmic thinking. They are also good choices to introduce the basics of programming in primary school during informatics. In their essence, these applications resemble algorithmic games in which the kids compile a sequence of commands from a predefined set of instructions. Although it is not possible to build up a program with any kind of goal, they nevertheless have a clearly defined goal to be achieved. They help to learn the basics of programming in an inconspicuous form of gaming (route planning - the way to the destination/goal/finish, under certain conditions). The paper focuses on 5 applications that can be successfully used in teaching programming for beginners with support for developing their algorithmic thinking: Bee-Bot Emulator, Bit By Bit, LightBot, Run Marco, Scratch Jr.
Optimization of Multi-Intelligent Robot Control System Based on Wireless Communication Network

The robot is a very complex multi-input multioutput nonlinear system. It has time-varying, strong coupling, and nonlinear dynamic characteristics, and its control is very complicated. Due to the inaccuracy of measurement and modeling, coupled with changes in the load and the influence of external disturbances, it is actually impossible to obtain an accurate and complete dynamic model of the robot. We must face the existence of various uncertain factors of the robot. This paper analyzes the real-time communication protocol in the wireless network control system and confirms that the main way to improve the real-time performance of the wireless network control system is to implement the real-time media access control (MAC) protocol. This paper studies robots from the perspective of control and mainly discusses how to use artificial immune algorithms to design robust nonlinear proportion integral derivative (PID) controllers. A nonlinear PID controller is used to replace the classic PID controller. The nonlinear link can be adjusted with the change of the error, so as to achieve the purpose of improving the adaptability and robustness to obtain satisfactory tracking performance. We carried out selective compliance assembly robot arm (SCARA) robot remote control experiment, dual robot following experiment, SCARA and ABB robot collisionless motion planning experiment, and multirobot intelligent collaborative assembly experiment. The experimental results show that the C/S mode remote control system has good practicability and can complete remote tasks; the P2P communication system has good information transmission effects and can realize real-time information sharing between robots; the collision-free motion planning algorithm enables the dual robots to complete obstacle avoidance tasks well in complex operating environments; the functional modules of the system can closely cooperate to complete the tasks in coordination, and the multirobot system has a certain degree of intelligence.
Integrated taxonomy of errors in chat-oriented dialogue systems

This paper proposes a taxonomy of errors in chat-oriented dialogue systems. Previously, two taxonomies were proposed; one is theory-driven and the other data-driven. The former suffers from the fact that dialogue theories for human conversation are often not appropriate for categorizing errors made by chat-oriented dialogue systems. The latter has limitations in that it can only cope with errors of systems for which we have data. This paper integrates these two taxonomies to create a comprehensive taxonomy of errors in chat-oriented dialogue systems. We found that, with our integrated taxonomy, errors can be reliably annotated with a higher Fleiss’ kappa compared with the previously proposed taxonomies.
Blockchain Technology - Current Status and Future Research Opportunities in Various Areas of Healthcare Industry

Purpose: Blockchain technology is one of the emerging Information Communication and Computation (ICCT) underlying technologies of the 21st century with potential applications in primary, secondary, tertiary, and quaternary industry sectors. In this paper, we have identified and analyzed some of the potential fields of the healthcare industry that can get benefit by means of using blockchain technology principles. Based on a systematic review on the development of blockchain technology and its application in healthcare sector to improve the quality of healthcare services, this paper identifies some of the application areas in the healthcare industry including Healthcare Security &amp; Authentication aspects, Clinical Trials &amp; Precision Medicine, Personalizing the Healthcare Services, Healthcare Data Management, Strengthening Public Health Surveillance, e-Healthcare to Customers, Healthcare Administration &amp; Medicine Management, Telehealth &amp; Telemedicine, Managing Medical Imaging, Developing Smart Healthcare System, and Healthcare Information System. The purpose also includes the analysis of the current implementation challenges of blockchain technology in healthcare industry services. Methodology: The study is descriptive and exploratory in nature. The related information is collected from various secondary sources for review. The secondary sources include published literature from various scholarly journals searched through Google scholar by means of identified keywords. Results/Findings: Based on a systematic review, we have identified the current status of the use of blockchain in several areas of healthcare sector, desired status called ideal status, and the research gap of use of blockchain technology in various application areas of the healthcare industry along with identification of various possible research agendas for future research. Originality/Value: It is found that blockchain technology facilitates for the improvement of quality services in the healthcare sector and various research agendas are proposed to carry out further research for patient satisfaction and comfortability. Type of the Paper: Review based research analysis.
An intelligent model to assist people with disabilities in smart cities

Smart cities emergence has allowed a wide variety of technological services to metropolitan areas. These services can improve life quality, minimize environmental impacts, improve health service, improve security, and bear the increasing number of people in the cities. Life quality encompasses many subjects, and accessibility for People with Disabilities (PwD) is one. In this article, smart cities focused on helping PwD are called Assistive Smart Cities (ASCs). In this sense, the article proposes a Model for Assistive Smart Cities called MASC. Related works do not cover geographically broad areas, such as cities and metropolitan regions. Moreover, they are not generic in terms of disabilities and are usually intended only for one type of disability. Given this scenario, the MASC covers large regions and supports various disabilities, such as hearing, visual impairment, and limitation of lower limb movements. Unlike the related works, MASC uses the interactions of PwD to compose histories of contexts offered as services. MASC proposes an ontology-based on ubiquitous accessibility concepts. The model evaluation focused on performance, functionality, and usability. Performance and functionality evaluations were performed using data generated by a context simulator called Siafu and data from the Open Street Maps (OSM) platform. Usability was evaluated using a smart wheelchair prototype. The results of usability show 96% acceptance regarding ease of use and 98% regarding system utility. The results indicate that the model supports massive applications, managing information to generate trails. Besides, MASC provides services for different types of users, namely PwD, healthcare professionals, and public administration.
Detection of fake reviews using NLP &amp;Sentiment Analysis

In this COVID-19 scenario the majority have an interest in on-line searching. So, many folks order the merchandise depends on the previous reviews. These reviews square measure enjoying necessary role in creating purchase choices. however, in these reviews' spammers might manufacture pretend reviews because of such behavior of spammers clients would I mislead and create the incorrect call to beat this drawback we've to spot the actual one who posed reviews over just once and therefore the admin can delete that review supported the customer review info.
Detection of lane line based on Robert operator

As autonomous driving technology becomes more and more popular, its safety is also attracting attention. Regarding the automatic driving of vehicles, the detection of road markings is particularly important. This paper improves the lane edge detection part of the Hough transform lane line detection method. Because the traditional Canny operator edge detection method is good for image processing, but the detection time is long, this paper replaces the Canny algorithm with the Robert operator edge detection method. The sub-edge detection method can improve the detection speed of lane line extraction. In MATLAB, by using multiple edge detection operators to perform edge detection on the same image 100 times, and taking the average of the detection time, it is found that the Robert operator takes a shorter time in the detection process than the Canny operator; Then the Robert operator and Canny operator are respectively fused into the Hough transform lane line detection. After 100 times, the same image is detected, and the running time is statistically averaged for comparison and analysis. The Robert operator is better than the Canny operator. The time taken is reduced by 0.15191 s. The simulation results show that the integration of Robert operator in Hough transform lane line detection improves the real-time performance of lane detection.
Markov Chains for Computer Music Generation

Random generation of music goes back at least to the 1700s with the introduction of Musical Dice Games. More recently, Markov chain models have been used as a way of extracting information from a piece of music and generating new music. We explain this approach and give Python code for using it to first draw out a model of the music and then create new music with that model.
On continuation-passing transformations and expected cost analysis

We define a continuation-passing style (CPS) translation for a typed λ-calculus with probabilistic choice, unbounded recursion, and a tick operator — for modeling cost. The target language is a (non-probabilistic) λ-calculus, enriched with a type of extended positive reals and a fixpoint operator. We then show that applying the CPS transform of an expression M to the continuation λ v . 0 yields the expected cost of M . We also introduce a formal system for higher-order logic, called EHOL, prove it sound, and show it can derive tight upper bounds on the expected cost of classic examples, including Coupon Collector and Random Walk. Moreover, we relate our translation to Kaminski et al.’s ert-calculus, showing that the latter can be recovered by applying our CPS translation to (a generalization of) the classic embedding of imperative programs into λ-calculus. Finally, we prove that the CPS transform of an expression can also be used to compute pre-expectations and to reason about almost sure termination.
Path Planning of Indoor Mobile Robot Based on Improved A* Algorithm

research-article Path Planning of Indoor Mobile Robot Based on Improved A* Algorithm Share on Authors: Fenggang Liu Wuchang University of Technology, China Wuchang University of Technology, ChinaView Profile , Shuai Qiu Wuchang University of Technology, China Wuchang University of Technology, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 106Pages 1–4https://doi.org/10.1145/3469213.3470309Online:18 August 2021Publication History 0citation18DownloadsMetricsTotal Citations0Total Downloads18Last 12 Months18Last 6 weeks1 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
Server Side Method to Detect and Prevent Stored XSS Attack

Cross-Site Scripting (XSS) is one of the most common and dangerous attacks. The user is the target of an XSS attack, but the attacker gains access to the user by exploiting an XSS vulnerability in a web application as Bridge. There are three types of XSS attacks: Reflected, Stored, and Dom-based. This paper focuses on the Stored-XSS attack, which is the most dangerous of the three. In Stored-XSS, the attacker injects a malicious script into the web application and saves it in the website repository. The proposed method in this paper has been suggested to detect and prevent the Stored-XSS. The prevent Stored-XSS Server (PSS) was proposed as a server to test and sanitize the input to web applications before saving it in the database. Any user input must be checked to see if it contains a malicious script, and if so, the input must be sanitized and saved in the database instead of the harmful input. The PSS is tested using a vulnerable open-source web application and succeeds in detection by determining the harmful script within the input and prevent the attack by sterilized the input with an average time of 0.3 seconds.
Virtual extensions improve perception-based instrument alignment using optical see-through devices

Instrument alignment is a common task in various surgical interventions using navigation. The goal of the task is to position and orient an instrument as it has been planned preoperatively. To this end, surgeons rely on patient-specific data visualized on screens alongside preplanned trajectories. The purpose of this manuscript is to investigate the effect of instrument visualization/non visualization on alignment tasks, and to compare it with virtual extensions approach which augments the realistic representation of the instrument with simple 3D objects. 18 volunteers performed six alignment tasks under each of the following conditions: no visualization on the instrument; realistic visualization of the instrument; realistic visualization extended with virtual elements (Virtual extensions). The first condition represents an egocentric-based alignment while the two other conditions additionally make use of exocentric depth estimation to perform the alignment. The device used was a see-through device (Microsoft HoloLens 2). The positions of the head and the instrument were acquired during the experiment. Additionally, the users were asked to fill NASA-TLX and SUS forms for each condition. The results show that instrument visualization is essential for a good alignment using see-through devices. Moreover, virtual extensions helped achieve the best performance compared to the other conditions with medians of 2 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mm</i> and 2° positional and angular error respectively. Furthermore, the virtual extensions decreased the average head velocity while similarly reducing the frustration levels. Therefore, making use of virtual extensions could facilitate alignment tasks in augmented and virtual reality (AR/VR) environments, specifically in AR navigated surgical procedures when using optical see-through devices.