Reviewerly/exHarmony huggingface dataset:
Network models and sensor layers to design adaptive learning using educational mapping

Abstract A network modelling approach to educational mapping leads to a scalable computational model that supports adaptive learning, intelligent tutors, intelligent teaching assistants, and data-driven continuous improvement. Current educational mapping processes are generally applied at a level of resolution that is too coarse to support adaptive learning and learning analytics systems at scale. This paper proposes a network modelling approach to structure extremely fine-grained statements of learning ability called Micro-outcomes , and a method to design sensors for inferring a learner’s knowledge state. These sensors take the form of high-resolution assessments and trackers that collect digital analytics. The sensors are linked to Micro-outcomes as part of the network model, enabling inference and pathway analysis. One example demonstrates the modelling approach applied to two community college subjects in College Algebra and Introductory Accounting. Application examples showcase how this modelling approach provides the design foundation for an intelligent tutoring system and intelligent teaching assistant system deployed at Arapahoe Community College and Quinsigamond Community College. A second example demonstrates the modelling approach deployed in an undergraduate aerospace engineering subject at the Massachusetts Institute of Technology to support course planning and teaching improvement.
The robot consciousness based on empirical knowledge

Abstract How to make robot conscious is an important goal of artificial intelligence. Despite the emergence of some very creative ideas, no convincing way to realize consciousness on a machine has been proposed so far. For example, the integrated information theory of consciousness proposes that consciousness can exist in any place that can reasonably process information, whether brain or machine. It points out that a physical system must satisfy two basic conditions to cause the emergence of consciousness: it must have rich information, and it must be highly integrated. However, the theory does not give an idea of how to realize consciousness on a machine. In this paper, we propose the robot consciousness based on empirical knowledge. We believe that the empirical knowledge of robots is an important basis for robot consciousness, any cognitive experience of robot can lead to the generation of consciousness. We firstly propose a formal framework for describing robot empirical knowledge; then we discuss the robot consciousness based on its own empirical knowledge; finally, we propose a cost-oriented evolutionary method of robot consciousness.
Universal Competences in Modern Education

Educational objectives can be represented as the following chain containing conceptual algorithms: universal skills - soft skills - hard skills.&#x0D; Under the universal competences (universal skills) is meant the possession of effective algorithms for solving informational in nature tasks facing a person who relies on the highest cultural values. Universal competencies allow you to form a picture of the world. The status of “universal” competencies implies that these competencies are equally important in professional, social and personal life.&#x0D; On the basis of universal competencies, narrowly specialized competencies (hard skills) and “personal qualities” - soft skills should be formed.
Optimizing generalized kernels of polygons

Let $$\mathcal {O}$$ be a set of k orientations in the plane, and let P be a simple polygon in the plane. Given two points p, q inside P, we say that p $$\mathcal {O}$$ -sees q if there is an $$\mathcal {O}$$ -staircase contained in P that connects p and q. The $$\mathcal {O}$$ -Kernel of the polygon P, denoted by $$\mathcal {O}$$ - $$\mathrm{Kernel }(P)$$ , is the subset of points of P which $$\mathcal {O}$$ -see all the other points in P. This work initiates the study of the computation and maintenance of $$\mathcal {O}$$ - $$\mathrm{Kernel }(P)$$ as we rotate the set $$\mathcal {O}$$ by an angle $$\theta $$ , denoted by $$\mathcal {O}$$ - $$\mathrm{Kernel }_{\theta }(P)$$ . In particular, we consider the case when the set $$\mathcal {O}$$ is formed by either one or two orthogonal orientations, $$\mathcal {O}=\{0^\circ \}$$ or $$\mathcal {O}=\{0^\circ ,90^\circ \}$$ . For these cases and P being a simple polygon, we design efficient algorithms for computing the $$\mathcal {O}$$ - $$\mathrm{Kernel }_{\theta }(P)$$ while $$\theta $$ varies in $$[-\frac{\pi }{2},\frac{\pi }{2})$$ , obtaining: (i) the intervals of angle $$\theta $$ where $$\mathcal {O}$$ - $$\mathrm{Kernel }_{\theta }(P)$$ is not empty, (ii) a value of angle $$\theta $$ where $$\mathcal {O}$$ - $$\mathrm{Kernel }_{\theta }(P)$$ optimizes area or perimeter. Further, we show how the algorithms can be improved when P is a simple orthogonal polygon. In addition, our results are extended to the case of a set $$\mathcal {O}=\{\alpha _1,\dots ,\alpha _k\}$$ .
Practice Teaching Method of Electronic Commerce Major Based on Data Mining Algorithm

Abstract With the rapid development of network, communication, and information technology, e-commerce has received widespread attention as a new business model. E-commerce has become a popular term, and e-commerce has become a popular mainstream in universities. However, as a comprehensive and free subject of art and science penetration, e-commerce puts forward more comprehensive requirements for teaching content, process and methods. Based on the above background, the purpose of this article is to study the professional practice teaching methods of e-commerce based on data mining algorithms. This article first analyzes the privacy issues caused by decentralized data mining under the condition of rapid development of e-commerce. How to build a more accurate model to complete data mining tasks without revealing personal privacy, mainly discussing data mining privacy protection algorithms. Next, through literary research method, questionnaire survey method, case research method, and interview method, explore the practical teaching method of e-commerce professional based on data mining algorithm. The role is to study the problems and solutions that blended learning needs to solve in specific applications. The experimental results show that, compared with the previous guidance method, the new teaching method can improve students’ initiative, enthusiasm and creativity by about 10% in the learning process.
bleiglas: An R package for interpolation and visualisation of spatiotemporal data with 3D tessellation

Schmid et al., (2021). bleiglas: An R package for interpolation and visualisation of spatiotemporal data with 3D tessellation. Journal of Open Source Software, 6(60), 3092, https://doi.org/10.21105/joss.03092
A Kind of Chicken Swarm Algorithm Based on Elite Reverse Learning

In view of the drawbacks of chicken swarm algorithm, such as the fall into local optimization and slow optimizing speed, an improved strategy is proposed in this paper. The convergence speed and optimization accuracy of the algorithm are improved by elite reverse learning strategy. Adaptive Cauchy mutation strategy is applied to avoid the algorithm falling into local optimal in the late iteration, that is, elite reverse learning chicken algorithm with adaptive Cauchy mutation. The optimization performance of the improved algorithm is verified by the test function, and the numerical analysis of the simulation experiment shows that the improved algorithm has more powerful optimization capability than other improved algorithms.
A Novel Kernel-based Extreme Learning Machine with Incremental Hidden Layer Nodes

Extreme learning machine (ELM) is widely used in various fields because of its advantages such as short training time and good generalization performance. The input weights and bias of hidden layer of traditional ELM are generated randomly, and the number of hidden layer nodes is determined by artificial experience. Only by adjusting parameters manually can an appropriate network structure be found. This training method is complex and time-consuming, which increases the workload of workers. To solve this problem, the incremental extreme learning machine (I-ELM) is used to determine the appropriate number of hidden layer nodes and construct a compact network structure in this paper. At the same time, a new hidden layer activation function STR is proposed, which avoids the disadvantages of incomplete output information of hidden layer due to uneven distribution of sample data. The proposed algorithm is evaluated by public data sets and applied to the classification of superheat degree (SD) in aluminum electrolysis industry. The experimental results show that STR activation function has a good learning speed, and the proposed algorithm is superior to the existing SD identification algorithm in terms of accuracy and robustness.
Layered Relative Entropy Policy Search

Many reinforcement learning problems are hierarchical in nature. Exploiting this property can ease the learning process. In this paper, a hierarchical policy search method based on clustering is presented. We use a hierarchical policy which is composed of a high-level gating policy and a set of low-level sub-policies. Depending on the observed state, the gating policy chooses one of the sub-policies for action selection. The gating policy and each sub-policy is provided with a learning method to update their parameters. If a task is truly hierarchical, its state–action space must have meaningful clusters. We cluster the observed samples and use each cluster to update one sub-policy. This way, each sub-policy is adapted to some portion of the state–action space. Using the updated sub-policy probability density functions and observed samples, the gating policy is updated. We evaluate our method on three multimodal tasks as well as a simulated and real robotic manipulation task. Our experiments show that our method can discover versatile sub-policies for multimodal tasks and the manipulation task. Moreover, we point out the errors in some of the equations of Hierarchical Relative Entropy Policy Search paper and provide the necessary corrections.
Optimization and Improvement of Association Rule Mining using Genetic Algorithm and Fuzzy Logic

In Association rule mining, association rules grows exponentially with the size of the frequent itemset. With so many association rules one is interested in the optimized rules to make decisions. With huge attribute’s domain, the rules generated in the association rule mining are also huge. In this paper, the main focus is on how to optimize the association rules using Genetic Algorithm and How to improve the working of Association Rule Mining using Fuzzy Logic. With explosion of data, optimization of the results is as important as producing the results. Genetic Algorithm perform global search to find attribute interaction than the greedy rule induction algorithm often used in Data Mining. To show the use of fuzzy logic and Genetic Algorithm in Association Rule Mining, a system to predict the chance of having heart disease is considered in this paper.
One Port Impedance Quantization For a Class of Annihilation Operator Linear Quantum Systems

This paper provides a procedure for building a one port impedance quantization involving annihilation operators only for a class of linear quantum systems having a positive real impedance transfer function matrix. Based on the positive real properties of these quantum systems, it is shown that it is possible to use the Brune algorithm in order to find an electrical circuit that can physically implement these quantum systems. This theory, illustrated for one-port circuits may be useful for the implementation of superconducting microwave circuits used in quantum filters found in the field of quantum computing.
Wheeled Mobile Robot Trajectory Planning Using Evolutionary Techniques

This paper proposes two multi-objective trajectory planning optimization algorithms, namely, Multi-objective differential evolution (MODE) and Elitist Non-dominated sorting genetic algorithm (NSGA-II). They are applied for a differential drive wheels mobile robot (DDWMR). A cubic NURBS curve is utilized to constitute the mobile robot's path. The objective functions considered are travel time, traveled length, and actuators efforts. All objective functions are to be minimized. The constraints considered are mobile robot's kinematic limits, obstacle avoidance, and dynamic limits. Two stationary and five moving obstacles are present around the robot. Numerical simulation results are examined and compared.
Comparative Analysis of Methods for Detecting Fraudulent Transactions

This article is focused on the comparative analysis of machine learning models used to identify fraudulent transactions. During the research, models of three different algorithms were considered, and their optimization for the task was performed. The accuracy of the models was compared, the advantages and disadvantages of each model were identified, recommendations for their use were given, and conclusions were drawn. The experiment was conducted to evaluate the effectiveness of various machine learning models in transaction classification problems.
A Study on the Next Generation Security Control Model for Cyber Threat Detection in the Internet of Things (IoT) Environment

Recently, information leakage accidents have been continuously occurring due to cyberattacks, and internal information leakage has also been occurring additionally. In this situation, many hacking accidents and DDoS attacks related to IoT are reported, and cyber threat detection field is expanding. Therefore, in this study, the trend related to the commercialization and generalization of IoT technology and the degree of standardization of IoT have been analyzed. Based on the reality of IoT analyzed through this process, research and analysis on what points are required in IoT security control was conducted, and then IoT security control strategy was presented. In this strategy, the IoT environment was divided into IoT device, IoT network/communication, and IoT service/platform in line with the basic strategic framework of 'Pre-response-accident response-post-response', and the strategic direction of security control was established suitable for each of them.
Vision-driven NMPC for Autonomous Aerial Navigation in Subterranean Environments

This work establishes a novel robocentric Non-linear Model Predictive Control (NMPC) framework for fast fully autonomous navigation of quadrotors in featureless dark tunnel environments. Additionally, this work leverages the processing of a single camera to generate direction commands along the tunnel axis, while regulating the platform’s altitude. The extracted visual dynamics are coupled in the sequel with the NMPC problem, structured around the Proximal Averaged Newton-type method for Optimal Control (PANOC), which is a fast numerical optimization method that is not sensitive to ill conditioning and is suitable for embedded NMPC implementations. Multiple fully realistic simulation results demonstrate the effectiveness of the proposed method in challenging environments.
Control principle and error estimation for inverse trajectory method under locating error with optimization

Accurate trajectory control of wheeled robot is an unavoidable problem in path planning, especially in the environment of large positioning error. In this paper, by designing the reverse trajectory and using martingale method, the accurate execution of the target trajectory is realized, and the error estimation is given. Finally, through numerical simulation, it is shown that when the observation error is 0.5 units, the average trajectory error is about 0.1 units, and the probability of error exceeding 0.347 units is less than 2%.
Emerging Markets Redefined: Comprehensive Measurement and Future Prospects

The twenty-first century saw the emerging market countries rise as a cluster, becoming a major force driving the global economic growth and playing an increasingly important role in global governance. The existing definition of emerging market countries is vague and quite one-sided, far from capturing the reality. Based on historical experiences and actual circumstances of developing countries and the basic theories of development economics, this article builds a comprehensive index system comprising the five dimensions of nation-state size, institutional environment, economic growth, socio-economic structure, and development impetus to study 30 countries selected from among the 183 countries (regions) around the world. This provides a new analysis framework and theoretical support for in-depth study of the emerging markets.
Smartphone User Identification and Authentication Based on Raw Accelerometer Walking Activity Data Using Convolutional Neural Networks

The biometrical characteristics of the human being such as fingerprints, eye (iris and retina), DNA, palm veins, and face recognition are competent to distinguish uniquely. Similarly, human activity recognition also carries research challenges to furnish an alternative way to identify and authenticate the human beings. The activity recognition contains a two-step process: sensing the raw data and extracting the features and classification. The experimental data used with this paper contains a single accelerometer embedded smartphone for sensing the walking patterns of users. We have intentionally excluded handicraft feature extracting techniques. The deep learning approach is used rather than classical machine learning algorithms. The automatic effective feature learning characteristics of deep learning reduce the barrier to be a domain expert. We have proposed a novel CNN model for user identification based on their activity patterns. The publically available user identification from the walking activity dataset on UCI repository has been used as the experimental dataset. Our model achieved 99.88% accuracy while user recognition based on walking patterns. This paper also includes a comparison study on our proposed model with classical machine learning algorithms listed as AdaBoost, decision tree, GaussianNB, linear discriminant, logistic regression, quadratic discriminant, and random forest. The recognition performance of random forest with 95.78% accuracy became close to our model. But, our model is more efficient than the random forest in case of recognition time consumption and accuracy.
The Transformer Neural Network Architecture for Part-of-Speech Tagging

Part-of-speech tagging (POS tagging) is one of the most important tasks in natural language processing. This process implies determining part of speech and assigning an appropriate tag for each word in given sentence. The resulting tag sequence can be used as is and as a part of more complicated tasks, such as dependency and constituency parsing. This task belongs to sequence-to-sequence tasks and multilayer bidirectional LSTM networks are commonly used for POS tagging. Such networks are rather slow in terms of training and processing large amounts of information due to sequential computation of each timestamp from the input sequence. This paper is focused on developing an accurate model for POS tagging that uses the original Transformer neural network architecture.
User-centric framework to facilitate trust worthy cloud service provider selection based on fuzzy inference system

The widespread adoption of cloud computing by several companies across diverse verticals of different sizes has led to an exponential growth of Cloud Service Providers (CSP). Multiple CSPs offer homogeneous services with a vast array of options and different pricing policies, making the suitable service selection process complex. Our proposed model simplifies the IaaS selection process that can be used by all users including clients from the non-IT background. In the first phase, requirements are gathered using a simple questionnaire and are mapped with the compute services among different alternatives.In the second phase, we have implemented the Sugeno Fuzzy inference system to rank the service providers based on the QoS attributes to ascertain the appropriate selection. In the third phase, we have applied the cost model to identify the optimal CSP. This framework is validated by applying it for a gaming application use case and it has outperformed the online tools thus making it an exemplary model.
Is Human Walking a Network Medicine Problem? An Analysis Using Symbolic Regression Models with Genetic Programming

Human walking is typically assessed using a sensor placed on the lower back or the hip. Such analyses often ignore that the arms, legs, and body trunk movements all have significant roles during walking; in other words, these body nodes with accelerometers form a body sensor network (BSN). BSN refers to a network of wearable sensors or devices on the human body that collects physiological signals. Our study proposes that human locomotion could be considered as a network of well-connected nodes.While hypothesizing that accelerometer data can model this BSN, we collected accelerometer signals from six body areas from ten healthy participants performing a cognitive task. Machine learning based on genetic programming was used to produce a collection of non-linear symbolic models of human locomotion.With implications in precision medicine, our primary finding was that our BSN models fit the data from the lower back's accelerometer and describe subject-specific data the best compared to all other models. Across subjects, models were less effective due to the diversity of human sizes.A BSN relationship between all six body nodes has been shown to describe the subject-specific data, which indicates that the network-medicine relationship between these nodes is essential in adequately describing human walking. Our gait analyses can be used for several clinical applications such as medical diagnostics as well as creating a baseline for healthy walking with and without a cognitive load.
A Novel vision based embedded framework system to detect and track dynamic vehicles

Abstract A vehicle tracking system is a smart device installed in vehicles to detect and track the moving vehicle. In the emerging world the number of vehicles on road has been increasing globally. Intelligent transport system has been explored better to make an efficient transport system and also for traffic arrangements. Hence there is a need for a system that tracks the vehicle and provide assistance to the driver. The study aims at developing a novel embedded driver assistance framework that could analyze the dynamics of vehicle in the front and rear surround views. In the proposed study, to detect and track the moving vehicles Gaussian Mixture Model (GMM) and Kalman filter is implemented thereby critical safety to the vehicles is enabled by calculating the distance along with the directions. The developed prototype contains 4 cameras each equipped with an embedded processor. In order for the system to run at a near real time pace, optimized vision-based techniques are used that detect vehicles that are near-by. The only modality used for sensing are the camera sensors which analyze the surroundings of the ego or the host vehicle. Object detection by GMM technique proves to be more accurate and reliable in light traffic conditions and during day time whereas during night time the detection technique is not that accurate. Hence the developed prototype includes ultrasonic sensors that calculates the distance between the objects by sound wave. Further the developed detection system is validated by analyzing Receiver Operating Characteristics. Thus vision based embedded computing framework proves to be more efficient method for tracking objects.