Reviewerly/exHarmony huggingface dataset:
Learner Characteristics in a Chinese Mathematical Intelligent Tutoring System

The present study aims to explore the effectiveness and learner characteristics of Chinese Mathematical intelligent tutoring system in multiple mathematics subjects. Data were collected from primary school, secondary school, and college. The students’ interactions were recorded in database as log files. The data were coded to fit the format of Datashop, which is a data repository and web application for researchers analyzing learning sequence of data. A learning curve visualizes changes in student learning state over interactions on different mathematics subjects. The results of learning characteristics of students were discussed. Furthermore, the study compared students’ learning patterns in three different score groups (high, medium, and low). The results found that students with lower pre-test score received more prompting in their interaction comparing students with higher pre-test score. More learning patterns were discussed.
Application of Image Recognition Based on Deep Learning Model

Traditional image recognition is mainly based on shallow neural networks, mainly including artificial networks, but these shallow neural networks have limited feature extraction capabilities. This paper can speed up the operation efficiency of the model, eliminate over-fitting, and improve the recognition accuracy by adding batch normalization layers to the neural network. The experiments are based on the MNIST data set, and have fully verified the recognition ability depth model proposed in this article.
Extraction of Frequent Itemset Using Hybrid Algorithm for Large Transactional Data

Association rule mining is one of the significant strategies for bigdata to extract the knowledge from data. This Knowledge acts as a considerable part in the decision-making process. Especially for the business insights, So, this paper proposed a new Hybrid Ant Colony and African Buffalo Model which helps to discover the frequent item sets within less execution time and high accuracy. Moreover, Fitness function of this model is utilized to compute the support count of each item to attain the high accuracy rate. Finally, the strength of the association rules is measured using the key metrics support and confidence.
Remote Sensing Image Dataset Expansion Based on Generative Adversarial Networks with Modified Shuffle Attention

With the development of science and technology, neural networks, as an effective tool in image processing, play an important role in gradual remote-sensing image-processing. However, the training of neural networks requires a large sample database. Therefore, expanding datasets with limited samples has gradually become a research hotspot. The emergence of the generative adversarial network (GAN) provides new ideas for data expansion. Traditional GANs either require a large number of input data, or lack detail in the pictures generated. In this paper, we modify a shuffle attention network and introduce it into GAN to generate higher quality pictures with limited inputs. In addition, we improved the existing resize method and proposed an equal stretch resize method to solve the problem of image distortion caused by different input sizes. In the experiment, we also embed the newly proposed coordinate attention (CA) module into the backbone network as a control test. Qualitative indexes and six quantitative evaluation indexes were used to evaluate the experimental results, which show that, compared with other GANs used for picture generation, the modified Shuffle Attention GAN proposed in this paper can generate more refined and high-quality diversified aircraft pictures with more detailed features of the object under limited datasets.
AI based Examination Assessment Mark Management System

Calculation of exam scores and management of these scores is a crucial process in every examination. But these works have always been a tedious process, including calculation of scores for each question, then the calculation of total marks for each student and then updating the marks for each student. In spite of the modern technologies and new techniques around us, these chores are still done in traditional methods to avoid errors as the tasks considered are crucial. Hence, we focus to reduce the time spent in all these tasks and the human intervention in the entire process by using modern techniques and still provide the same quality in work done. We propose a system using Image Processing based edge detection and contour detection techniques along with Deep Learning based Image classification technique to automate the entire process. We perform two tasks: (1). Automated table detection from the students' answer scripts using image processing (2). Automated mark detection and the total calculation from the students' answer scripts using deep learning techniques. We use Convolutional Neural Networks (CNN) trained and tested on handwritten digit dataset obtained with a high accuracy rate to do the task of mark detection from the answer sheet. We have done our evaluations using the answer sheet used in our college and the system works well for table recognition for the given pictures.
Representation of Conflicts in Non-Functional Requirement using Conceptual Graphs

In software engineering process, the conflict of non-functional requirements (NFR) is one of major problems during analysis and design phase. Since NFR is difficult to be understood and may be interfered, conflicted, or contradicted with other NFRs. In addition, many of NFRs relate to the same quality attribute which leads to the requirement conflict problem. This paper presents a knowledge representation method for detecting the conflicts in NFRs using the conceptual graphs. The proposed method enhances the understanding of NFR. Subsequently, the requirement conflicts between NFRs model can be detected using reasoning process with conceptual graph. The case study of billing system in telecommunication company is discussed for a feasibility and an effectiveness of the proposed method.
Research on Recommendation Algorithm Based on Dynamic Collaborative Filtering and Convolutional Neural Network

In recent years, with the rapid development of network technology, more and more information is generated, and how people can find the interested information quickly and accurately in the huge amount of information has become a problem. And the generation of recommendation systems based on recommendation algorithms has well solved this problem. Traditional movie recommendation systems only recommend for collaborative filtering of users or items, and do not fully consider in personalized recommendation, cold start problem and data sparsity and information expiration problem. In this paper, we use MovieLens dataset, based on dynamic collaborative filtering and convolutional neural network algorithm, use text network as regression model, extract user features and movie features, and multiply them to get movie rating scheme, and fuse time features into rating prediction model, which effectively solves personalized recommendation, cold start problem and information expiration problem under the premise of excellent recommendation accuracy.
Implementation of Action Maps and Interaction Prototypes as a Strategy to Improve Information Architecture and Interface Design in an Academic Management Platform

Organizations have found a great ally in information technologies to achieve success in their management processes. For universities, for example, information systems and platforms are essential to support the academic and administrative management of their institutional mission. These platforms, whether proprietary or licensed, serve institutions not only as a data repository but also as a scenario for managing and systematizing processes and procedures. The Pascual Bravo University Institution in Medellín, Colombia, has articulated its academic management processes through the University Academic Information and Control System or SICAU (in its Spanish acronym) platform, a proprietary development that perhaps has not achieved a coherent integration between information architecture and interface design. That is, the information experience specified in the backend (data architecture) is not integrated or reflected in the experience of the different users in the frontend (interface design). The objective of this case study is to propose the design of the interface level interaction (graphic elements and feedback) for the SICAU platform in the teaching module at the Pascual Bravo University Institution. It is concluded that the analysis on action maps allows to identify gaps on the information architecture. The interaction analysis model proposed in this research facilitated the design of interactions with the purpose of improving the user experience (UX) and attributes in the platform’s backend.
Comparative Study of Computational Techniques for Smartphone Based Human Activity Recognition

Human activity recognition (HAR) has been popular because of its diverse applications in the field of health care, geriatrics care, the security of women and children, and many more. With the advancement in technology, the traditional sensors are replaced by smartphones. The mobile inbuild accelerometer detects the orientation or acceleration, and the gyroscope detects the angular rotational velocity. In this study, computational techniques-based comparative analysis has been carried out on publicly available dataset on human activity recognition using smartphone dataset. Traditional and contemporary computational techniques (support vector machine, decision tree, random forest, multi-layer perceptron, CNN, LSTM, and CNN-LSTM) for HAR are explored in this study to compare each model’s accuracy to classify a particular human activity. Support vector machine outperforms in most of the activity recognition tasks.
Green Power Electricity Generation Devices Using Blockchain-Based Accent

Abstract Power storage systems offer numerous attractive characteristics of contemporary smart grids such as improved system stability, efficient demand response, and lower bills. Uncoordinated loading of ESUs, though is exhausting and can lead to an end of the power system. This paper uses blockchain and intelligent agreements to create a decentralised loading system with no need for a centralised load coordinator. The first ESU tokens can be used for authenticating itself anonymously in the blockchain. The ESU will give a request to charge the clever agreement address for the blockchain. This includes the state-of-charge (SoC), time to comprehensive chariots (TCC) and the quantity of charge required. This smart contract will then run the charge coordination process self-running, to postpone stresses for lower priority ESUs on potential time slots. ESUs with the top priorities are paid on the current slot. Each ESU should ensure the accurate measurement of charging schedules. Finally, our study indicates that performance costs can be appropriate in terms of gas usage while allowing de-central charge coordination, while increasing transparency, trustworthiness, and privacy conservation. We applied the projected gadget for the Ethereum test bed blockchain.
Determining the Close Price of Bitcoin Using Regression Based on Blockchain Information

Bitcoin is a worldwide virtual currency. It is globally used as a financial asset and as a currency factor for buying and selling products and services in exchange of fractions or numbers of Bitcoins. Bitcoin is not owned by a lone authority or organization but rather is decentralized. Bitcoins can be sold, bought, or traded on platforms called “Bitcoin exchanges.” Exchanges permit individuals to buy/trade/sell Bitcoins using an array of currencies using a P2P (i.e., peer-to-peer) network. This paper presents regression models for determining close price of Bitcoin in Bitstamp exchange based on characteristics of the cryptocurrency and blockchain information. Two live datasets have been acquired that provide real-time data consisting of various attributes of Bitcoin price and blockchain information and the two datasets have been combined to prepare final dataset for the investigation. Times-series model of the live data has been developed. The motive of this research is to provide an insight on prediction of Bitcoin close price. The underlying catalyst for implementing machine learning techniques is to meticulously forecast time-series data. Machine learning techniques have better demonstrated to outplay nonlinear techniques including neural network-based algorithms. The ultimate goal is to contribute an observation into the applications of various predictive models that can be used to predict Bitcoin prices.
A New Kind of Extension

The field of virtual reality research is relatively young and quickly evolving. Proposals on what virtual reality is and how the mind deals with it are continually being refined. In this chapter, we outline a view according to which virtual reality is a tool enabling a unique kind of extension: it extends not only the space in which participants can act, but also their sense of corporeality and more generally the sense of who they are. The illusory transformations in virtual reality may lead, at least temporarily, to changes in participants’ attitudes and behaviors, and sometimes such changes may continue in real life situations. Participants may or may not be aware of such changes. The power of virtual reality is in evoking such responses, despite limitations of current virtual reality technologyVirtual reality virtual reality technology.
Reviewing Trust Issues in Cloud Computing

Abstract Cloud computing is a technique to minimize the use of hardware and software infrastructure digitally for the dynamic delivery of services. There are numerous components of current cloud computing models: end-user computers, networks of contacts, access management systems, and infrastructures of applications. Different organizations of cloud computing are providing various policies for handling tasks of cloud vendors and consumers. During task migration, whenever data is transferred over the cloud from one node to another; then the sink node should be trustworthy. It becomes a challenging task to create reliable and trustworthy cloud platforms. As the base from the concept of cloud computing, many authors proposed data privacy, security problems, security challenges, trust models, trust issues, challenges of trust management In this paper, we highlight some of the trust issues faced by both cloud vendors and cloud customers while using cloud services.
Machine-Human Interaction: A Paradigm Shift?

The abstract should summarize the contents of the paper in short terms, i.e. 150–250 words. In this article, I perform a preliminary exploration on hypothesised the paradigm shift from human-initiated interaction to machine-initiated interaction. There are consequences and structural issues that need to be highlighted when considering such an important topic beyond the academic setting and ontological issues. While continuing to develop the theoretical work related to evaluating this shift, I use this as an opportunity to ask a classic “so what” question. What matters when machines are the ones trying to initiate interactions with humans? Specifically, what happens to parts of humanity that are often excluded from issues related to the deployment of AI in society? How can such groups, which are often marginalised react and to machines that act autonomously? To do this, I chose Black Twitter as the main case study of this article to explore if and how marginalisation can occur when machines initiate interactions with humans. Recently, Twitter has become the ground of much experimentation with AI deployed by its operators. However, it is also the experiment ground for third-party “bots” that interact with humans, often, without the latter being aware that of the interaction undertaken is with a machine. The article is part of a larger study investigating if there are significant differences between the way machines and members of Black Twitter interact with one another.
ARSCC: An Assisted Reading System for Contemporary Chinese Based on Multi-aspect Analysis

With the on-going Chinese fever all over the world, there is an increasing number of scholars studying methods and tools for assisting Chinese learning. This paper discusses the construction of an Assisted Reading System for Contemporary Chinese based on multi-aspect analysis (ARSCC). First of all, short essays about Chinese culture are selected as reading materials according to preliminary statistics. Secondly, a multi-aspect analysis of these essays is conducted in three stages: word segmentation and POS tagging, sense of content words and usage of function words annotation, and syntactic analysis. Finally, the above analysis results are displayed hierarchically and systematically. The system provides second-language learners with multi-aspect assisting information to improve their analysis-oriented reading comprehension ability. It also provides a platform for teaching Chinese as a foreign language.
Improving Pedestrian Detection Methods by Architecture and Hyperparameter Modification of Deep Neural Networks

Now analysis of real-time data streams from vehicle’s sensors and cameras by deep neural networks (DNNs) is a very big challenge, especially in the context autonomous driving, unobtrusive health and elderly care for monitoring camera-to-person and inter-person distances. On the basis of the current pedestrian localization methods the several minimal improvements were proposed and investigated by usage of various activation functions (like Swish and GELU) and changes of the underlying neural network (the number of layers and channels in them). The most effective combination of the mentioned measures was found that leads to the smaller DNNs without detriment to their performance. These proposed improvements are considered as very promising for the potential usage of the investigated DNNs in Edge Computing applications on the computing units with the limited resources.
Synthetic Media and Information Warfare: Assessing Potential Threats

As the information environment is evolving, so are its manipulative uses. Hence, this chapter will focus on so-called synthetic media, produced by employing techniques such as Deep Learning. This class of media includes, e.g., deepfakes, Extended Reality applications, and (some) virtual influencers. To assess emergent threats, this chapter will first discuss the nature of Information Warfare and its connections with changes in the information ecosystem, before moving to the main relevant types of synthetic media, discussing their potential Information Warfare uses and weighing their pros and cons, subsequently evaluating the likelihood of nefarious use by Russia and other aggressive states.
ProMo variable/equation Ontology-based systemmodelling

The description of materials in the context of thermodynamics is an essential part of the chemical engineer’s models. The representation of thermodynamic relations is the objective of this study. By making use of ProMo variable/equation builder, we construct all the thermodynamics quantities based on the configuration space of contact geometry. The configuration space builds on the quantities internal energy (U), entropy (S), Volume (V) and mass species (n) adding the equations of state being the derivatives yielding the temperature, the negative pressure and the chemical potential. ProMo expert domain provides the facilities to construct variable/equation sets from the conserved basic physical quantities, namely the ones defined above. It follows the strict rule of defining new variables as a function of existing variables. This ProMo building regulation is an excellent match to the contact geometry’s fundamental approach to define thermodynamics. It generates a consistent set of variables and equations and provides a formal description that builds on pure mathematical terms. This mathematical representation also facilitates and simplifies automatic multiscale simulation modelling. The use of well-defined variables and equations for the base ontologies system domain, facilitate an integrating factor for the application domain both for chemical engineers as well as the material multidisciplinary modelling community.
What Does It Cost to Deploy an XAI System: A Case Study in Legacy Systems

Enterprise Resource Planning (ERP) software is used by businesses and extended via customisation. Automated custom code analysis and migration is a critical issue at ERP release upgrade times. Despite research advances, automated code analysis and transformation require a huge amount of manual work related to parser adaptation, rule extension and post-processing. These operations become unmanageable if the frequency of updates increases from yearly to monthly intervals. This article describes how the process of custom code analysis to custom code transformation can be automated in an explainable way. We develop an aggregate taxonomy for explainability and analyse the requirements based on roles. We explain in which steps on the new code migration process machine learning is used. Further, we analyse additional effort needed to make the new way of code migration explainable to different stakeholders.
On Weil sums, conjectures of Helleseth, and Niho exponents

Let F be a finite field, μ be a fixed additive character and s be an integer coprime to |F×|. For any a∈F, the corresponding Weil sum is defined to be WF,s(a)=∑x∈Fμ(xs−ax). The Weil spectrum counts distinct values of the Weil sum as a runs through the invertible elements in the finite field. The value of these sums and the size of the Weil spectrum are of particular interest, as they link problems in coding and information theory to other areas of math such as number theory and arithmetic geometry. In the setting of Niho exponents, we examine the Weil sum, its bounds and its spectrum. As a consequence, we give a new proof to the Vanishing Conjecture of Helleseth (1971) on the presence of zero in the Weil spectrum in the case of Niho exponents. We also state a conjecture for when the Weil spectrum contains at least five elements, and prove it for a certain class of Weil sums.