Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence and Cognitive Science Case Grammar on Solving Chinese Arithmetic Word Problems in Elementary Schools

Artificial intelligence and cognitive science technologies are combined to develop a problem-solving system of Chinese arithmetic words in elementary schools. Students can input an arithmetic word problem or update the existed problem into the system. The system automatically transforms the problems with a semantic network based on the cognitive science case grammar. In this way, the student can understand the meaning of the arithmetic word problems more clearly by the semantic network. For finding the answer, the keywords of the question part spread in the semantic network based on the spreading activation theory to find the correct answers. Students can change the question part, or add, delete, update sentences of the problem during the interaction with the system. In this way, students understand the correct meaning and knowledge of the arithmetic word problems and achieve the purpose of self-learning. For obtaining the correct label used in the case grammar for understanding the role of each Chinese word, the convolutional neural network (CNN, an artificial intelligent neural network model) is used to learn more than 600 Chinese arithmetic word problems (addition and subtraction) during the construction of the system. Experiment results showed that the system had achieved a high performance (accuracy&#x003E;90%). This is an innovative technology by combining artificial intelligence and cognitive science case grammar to solve Chinese arithmetic word problems. It is also attractive and practical for online learning of other courses.
Recognition Technology with Machine Learning into Swallow Ecological Education

In recent years, artificial intelligence (AI) technology has become the hottest industry trend due to the improvement of computing capability for computer hardware. AI refers to making machines have the same logical thinking and behavior patterns as humans, including learning, perception, reasoning, self-correction, and so on. At present, many industries, such as marketing, finance, transportation, agriculture, and medical care, have integrated AI applications. In this paper, we intend to apply recognition technology to ecological education of swallows. The computer is able to recognize the swallow&#x2019;s behavior and record it, instead of only using the nest-cam. When students observe the swallows, they are informed of relevant knowledge immediately for learning, without the teacher&#x2019;s explanation. This way of learning through technology is easy to attract the attention of students and is more effective.
Prediction of College Students' Autonomous Learning Ability Based on Markov Chain Prediction Algorithm

Markov model prediction is to establish a random time series model for prediction by using probability. It regards time series as a random process and determines the state change trend by studying the initial probability of different states of things and the transition probability between states to predict the future of things. Through the establishment of a Markov chain prediction model of college students' autonomous learning ability, this study is carried out to predict and error analysis. It includes a questionnaire survey of college students' autonomous learning, initializing the survey data to calculate the key step of the algorithm-state probability transition matrix. After the model is established by using this matrix, the relevant data are brought in to predict the future state. In the error analysis of the results, the average relative error is 6%, which meets the requirements and proves the reliability of the algorithm. After that, the actual data is predicted by using MATLAB, and the ideal results are obtained.
Design of Drone Remote-Control System using Human Motion Recognition

At present, the most common and low-cost method to control the drone is to use a mobile phone with an APP. The operator takes his mobile phone screen as a human-machine interface, and at the same time controls and receives the images returned by the drone. The control mode can be divided into two symmetry modes according to how the operator views and knows the location of the drone: exocentric viewing and egocentric viewing mode. In the exocentric viewing mode, the operator directly visually confirms the position of the drone, while in the egocentric viewing mode, the drone is equipped with a camera and transmits the flight image to the operator in real-time. Since the drone cannot be far away from the operator in the exocentric viewing mode, the relative egocentric mode allows the operator to feel immersed in the environment to control the drone more accurately. Therefore, by adapting the egocentric viewing and using human motion recognition, an unmanned vehicle remote-control system is designed. The low-speed, small-scaled, nature-stability UAVs are ideally suited for the proposed system. The system was designed with a PID controller to improve the UAV's handling. The experimental results verified the feasibility of the UVA control system and it could stabilize the posture of the drone.
V4α: A New Method for CNNs Inspired by Trichromacy Perception

Despite the incredible evolution in computer vision recognition with CNNs, a full comprehension inside of neural network layers is still a challenging task due to their complex structure, several different methods used, and several hyperparameters to adjust. With these uncountable variables, every detail could highly impact the performance of visual recognition algorithms. This work aims to investigate a small feature that has not been deeply investigated in the scientific community, the chromatic influence on network models. Trichromacy perception is a pervasive part of our visual experience and influences our interpretation of the world, however, few works investigate the color effects on deep neural networks. We proposed the V4α method, inspired by human brain perception of the color stimuli and by conducting an empirical evaluation. Our outcome shows that a simple adjustment on the network structure could reach an improvement of more than 4% on the efficiency for classification tasks also with decreased computational expenses in 10%. This method was applied at the AlexNet but could be extended to any other complex network. Interestingly, we could percept a certain similarity with the human anatomy comparing our method and how colors are processed in the human brain.
Research on Controllability Improvement based on Theoretical Analysis of Stability of Front-wheel Drive Wheelchair

In this study, we evaluated the effect of the parameter of interest on the dynamic characteristics of the electric wheelchair thorough the automatic running experiment excluding the influence of human maneuvering and the mathematical stability analysis with the aim of investigating the cause of the low straightness of the front-drive electric wheelchair. Furthermore, from the experimental results, it was clarified that it is possible to effectively improve the straightness of the front-wheel drive wheelchair by using a control method that cancels the influence of centrifugal force.
Visual Angular Haze Detection Using SSIM

In this work, we present an approach to calculate the haze of a Polymer Dispersed Liquid Crystal (PDLC) with respect to the viewing angle of the object using the Structural Similarity Index Measure (SSIM) method. The method shown in this project is developed based on a limited but authentic image dataset provided by an international glass company. This method calculates the measure of haze, dividing the input image into the same size regions and extracts the ones where the image does not have haze, to use those as a reference against the frames with haze. Then, based on the SSIM components, the system produces a haze grid and calculates two features, the scope of linear regression and the mean of the grid. These features are used to obtain a value that represents the amount of haze in the glass. The final model is approximated using a quadratic regression with MSE = 5.388.
Conclusion

The publication deals with the research and development of a contactless system for measurement and evaluation of the selected feature of the machined surfaces. According to the existing structural solutions we designed and constructed the contactless laser profilometer intended for measurements and evaluation of surface roughness.
Laser Profilometer Design

Based on existing solutions and available literature three main parts of the contactless system have been designed, i.e. a mechanical part, an optical part, and a control part. The mechanical part comprises a supporting frame consisting of components with vertical adjustment of a measuring head position and a programmable slide of the sample in the X and the Y axes using a stepper motor.
Current State of the Art

A number of adequate descriptions of 3D optical topographic methods are available at present [1–6 ]. 3D optical metrology represents a branch that has undergone significant development in the last decades especially owing to the development of computer technology, optical detectors, and light sources.
A Blink Detection Algorithm Based on Image Processing and Convolutional Neural Networks

Blink detection is an important task for human-computer interaction and behavior analysis. Although there is previous research regarding drowsiness detection, computer vision syndrome, and computer access by disabled patients, these have certain limitations for their algorithm’s accuracy due to a wide range of acquisition. Particularly, head movements, scene conditions, and the number of people in a frame present the main limiting factors. This paper proposes a low latency algorithm based on image processing and a convolutional neural network (CNN). The first technique is used to simplify the amount of computational cost by reducing the input data of the CNN. Then, the CNN is used to classify whether a specific frame is in an ‘open’ or ‘closed’ eye state. As this proposal was tested in a development board, limited CPU specifications and a reduced image database were considered for the CNN architecture and its training. The algorithm was tested using a CSI camera and a Jetson Nano 4 GB development board, obtaining a 99.5% accuracy for blink detection.
A Practical View on Training Neural Networks in the Edge

In recent years, the topic of embedded machine learning has become very popular in AI research. With the help of various compression techniques such as pruning, quantization and others compression techniques, it became possible to run neural networks on embedded devices. These techniques have opened up a whole new application area for machine learning. They range from smart products such as voice assistants to smart sensors that are needed in robotics. Despite the achievements in embedded machine learning, efficient algorithms for training neural networks in constrained domains are still lacking. Training on embedded devices will open up further fields of applications. Efficient training algorithms would enable federated learning on embedded devices, in which the data remains where it was collected, or retraining of neural networks in different domains. In this paper, we summarize techniques that make training on embedded devices possible. We first describe the need and requirements for such algorithms. Then we examine existing techniques that address training in resource-constrained environments as well as techniques that are also suitable for training on embedded devices, such as incremental learning. At the end, we also discuss which problems and open questions still need to be solved in these areas.
Perceptual Evaluation of Educational Robots’ Consequential Sounds

This study reasons that the sounds robots produce consequentially bring up the noise level in the classroom that might not benefit learning, and the perception of such sounds can impact the acceptability of the robot. Therefore, the consequential sounds of six different educational robots were studied on affective sound quality, and perception related to specific quality attributes of the robot producing the sound. The treatment was organized in a form of a live listening test, and sound impressions were collected by using a survey with nine five-point bipolar Likert items. Additionally, pupils were asked to rank the robots based on their respective sounds and appearance separately. As a result, observable differences were noted. mBot was rated most positively, followed by Thymio, and the worst ratings were attributed to the Jimu robot, probably due to the interaction of its caterpillar treads with the wooden surface. On the other hand, Jimu had the best ranking on behalf of its appearance. Cumulatively, based solely on the sounds, most pupils think that robots are safe, somewhat precise, robust, and working properly. However, the sounds are perceived as somewhat bothering, unpleasant and unusual and could distract pupils while learning. The full paper of this study can be retrieved from the project’s website ( https://www.zesoi.fer.hr/_download/repository/full_Perceptual_Evaluation_of_Educational_Robots_Consequential_Sounds.pdf ).
Perspectives on Virtual Reality in Higher Education for Robotics and Related Engineering Disciplines

Industrial engineering education has a strong focus on and affinity towards technology. While Virtual Reality hardware and applications advance and learning behaviour changes, it is particularly interesting to determine the possible use of Virtual Reality for teaching engineering subjects, for example fundamentals of robotics. This paper presents a study which examines the possible use of Virtual Reality learning environments at higher learning institutions. The study shows perspectives of students and lecturers and identifies opportunities and challenges for the use of Virtual Reality in industrial engineering education. The results of the indicated study show that the participants have a positive attitude towards Virtual Reality and strong motivation for in class use. The study results also suggest, that Virtual Reality content creation should be included in engineering curricula.
Playing Tic-Tac-Toe with a Lightweight Robot

This article presents an interdisciplinary approach to developing a robot demonstrator, combining the research fields of robot force/torque control, image processing, artificial intelligence, robot programming, and human-robot cooperation. In this regard, we chose to implement the game tic-tac-toe, also known as noughts and crosses or Xs and Os with a robot manipulator playing against a human. For this purpose, we observed with a camera and exploited them using image processing. Thereafter, artificial intelligence planned the robot move, the execution of which required the application of force control while drawing on the whiteboard. The presented robot system is appropriate for teaching students in different subject areas and is also scalable with respect to the complexity factor. In addition to the education of students, the resulting demonstrator presents an attractive scenario to inspire pupils and students to pursue a career in robotics.
Representing Cybersecurity Risk

AbstractA picture is said to be worth a thousand words. The same can be said of a trend line. More precisely if less pithily, a trend line reveals how a function is changing in both magnitude and direction. In mathematical terms, a trend line graphically reveals the relationship between the independent and dependent variables of a function. If the independent variable happens to be a specific threat scenario parameter or feature, e.g., a risk factor for information compromise, and the dependent variable is a component of risk, risk-relevant information is conveyed at-a-glance.
The role of civil society in a time of pandemic

A large number of studies have been conducted on the information behaviour and needs of vulnerable groups, especially victims of family violence during (quasi)emergencies and the role of NGOs in providing assistance to their users. Still, in the Republic of Croatia, it is an unexplored area. The lack of research was the reason for conducting a broader study in the Republic of Croatia that would enable a deeper understanding of the described topic. The aim of this study is to fill the gap on the role of NGOs in providing the necessary information to their users in order to identify the best ways and communication channels through which victims who have experienced family violence can access the necessary information in everyday life. The purpose of this study is to investigate the problems encountered by employees of NGOs in accessing and using information from the immediate environment during COVID-19, especially relevant information for preserving and protecting the rights of persons exposed to family violence and potentially other vulnerable groups. Efforts are being made to investigate the experiences and knowledge of NGO professionals regarding the violent victimisation of their users and COVID-19 as a potential criminogenic factor in the occurrence of family violence. A qualitative focus group method was used in the research. Focus groups were conducted from September 2020 to December 2021 in five different cities among NGO employees dealing with the protection of victims of family violence. Given the lack of research on the topic, the research results contribute to the theoretical and practical reflection on the role of NGOs in providing the necessary information to its users at the national and international levels. In the scientific sense, the conducted research opens the way for further research and increases awareness of this topic in the community.
A Multi-Node Removal Scheme Based on Community Partitioning in Large Scale Infrastructure Networks

Node removal plays a vital role in the reliability of large scale infrastructure networks, which has attracted intensive research interest. Unfortunately, most of them focus on single node removal. Multi-node removal based on MBA (Module based Adaptive) is a promising research vector with great improvement in efficiency. However, MBA-based node removal typically needs to consider community structures of the target network during the entire node removal process, which brings significant loss in network reliability and causes computational overhead. In light of this, we propose a novel scheme based on MBA to implement multi-node removal, which adopts the Multilevel community partitioning algorithm to overcome the above limitations. We evaluate our scheme on two artificial synthetic networks and four real-world networks with comparison to four widely used node removal schemes, and analyze the non-negligible superiority of our scheme over all benchmarks. Especially in the US Powergrid, our scheme can always achieve robustness around 10 times that of the MBA scheme [1] when deleting the same percentage of nodes. To the best of our knowledge, we are the first to achieve an MBA-based node removal scheme that only needs to execute the community partitioning algorithm once during the entire node deletion process.
Sentiment Analysis of Chinese Complex Long Sentences Based on Reinforcement Learning

Sentiment Analysis is a hot topic of Natural Language Processing (NLP). There have been relatively good solutions to general sentiment analysis problems, but in the face of the complex long sentences and the change of sentence sentiments such as transition and progression, it is very difficult to effectively learn the sentiment changes of long sentences based on deep learning methods. So we propose an approach named Reinforcement Learning for Long Sentence Classification (RL4LSC), a method based on reinforcement learning, to automatically identify the sentiment transitions of sentences and output the local sentiment tendency of each specific location and give the intensity of the sentiment tendency. Experiment results show that RL4LSC not only outperforms the state-of-the-art methods, BiLSTM and TextCNN, but also outputs the sentiment change of each word in a sentence through Reinforcement Learning.
Neural Reranking-Based Collaborative Filtering by Leveraging Listwise Relative Ranking Information

Reranking is a critical task used to refine the initial collaborative filtering (CF) recommendation by incorporating information from different viewpoints, such as the extra item side-information and user profile. In this article, a neural reranking-based CF (NRCF) model is proposed to leverage composite viewpoints from the basic CF model and user preference. More precisely, the predictive implicit user preference is first constructed from the initial top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> items. The implicit user preference is then aggregated with the explicit user embedding to enrich the user intent representation. Moreover, the traditional listwise loss functions for reranking optimization are suboptimal, due to the fact that they neglect the relative ranking information (ReinRank) between the unobserved and positive items. To address this issue, a novel listwise loss function that leverages relative ranking information, referred to as ReinRank, is proposed for reranking optimization. ReinRank assigns different values to the unobserved items, according to their relative ranking distances between the positive items. Extensive experiments are performed on three public benchmarks and different CF models, in order to demonstrate the effectiveness of NRCF and ReinRank.
Artificial Intelligence Technologies for Business Applications

This chapter introduces key artificial intelligence technologies and related business applications. The following important topics are covered: business expert systems, robotic process automation, fuzzy logic, interactive decision support systems, case-based reasoning, procedural content generation, voice chatbots, genetic algorithms, and hybrid AI systems. These AI technologies will also be referred to in different following chapters. Two cases are provided in this chapter, including IBM and Hyperscience.
Quantum Optimal Control for Pauli Operators Based on Spin-1/2 System

In this paper, based on spin-1/2 system, the optimal simulation of three Pauli logic operators is carried out by using quantum optimal control theory. Under Pauli z spin-presentation, the results show that the Pauli operators achieve the expected target quantum state with high fidelity. When the control pulse is applied on x-axis, the number of iterations required to optimize Pauli x operator to achieve the target state is the least, and the ones required to optimize Pauli z operator is the most. In addition, the comparison shows that when fidelity reach 0.9999, the population of quantum final state can reach ideal theoretical expectation. Besides, the optimization fidelity of Hadamard gate can also reaches 0.9999 based on spin-1/2 system. Finally, the study on phase evolution of quantum states shows that the phase difference between the initial and final quantum states of optimized Pauli x and Pauli z logic operator is π/2.
Machine Learning for Business Applications

This chapter introduces machine learning techniques and its applications in business. It covers three basic types of machine learning and various algorithms, including linear and multiple regression, polynomial and logistic regression, decision tree, neural networks, deep learning, recurrent neural network, genetic algorithms, support vector machine, Naive Bayes algorithm, and the Bayesian network. A case study on Microsoft is provided to illustrate AI research.