Reviewerly/exHarmony huggingface dataset:
Rule Combinations

I discuss my assumptions about individual morphological rules, and then explain the ways in which rules combine. Rule composition, the default mode of rule combination, models many canonical patterns, but also models certain deviations from the canonical morphotactic criteria. Holistic rule combination, a second mode of combination, accounts for deviations from the compositional content criterion, in which a rule combination realizes more than the sum of the content that those rules realize individually. Rule aggregation, a third mode of combination, accounts for deviations from the stem operand criterion, in which a rule operates not on a stem, but on the affix introduced by the rule with which it combines. Counterpotentiation, a fourth mode of combination, accounts for deviations from the intermediate well-formedness criterion, in which the result of applying one rule is ill-formed unless its application is followed by that of another particular rule. I outline the elaboration of these ideas in the ensuing chapters.
Resource-Aware Capsule Network

Capsule Networks (CapsNets) are a generation of image classifiers with proven advantages over Convolutional Neural Networks (CNNs). Better robustness to affine transformation and overlapping image detection are some of the benefits associated with CapsNets. However, CapsNets cannot be classified as a resource-efficient deep learning architecture due to the high number of Primary Capsules (PCs). In addition, CapsNets’ training and testing are slow and resource hungry. In this chapter, we propose two methods to reduce PCs to make CapsNet resource-efficient. In our first approach, we introduce Light and Enhanced Capsule Network (LE-CapsNet). In LE-CapsNet we modify the CapsNet architecture by introducing the Primary Capsule Generator (PCG) module. We further compress this network by optimizing the feature extraction and introduce LE-CapsNet-T as a tiny variant of the network. Using 3.8M weights, LE-CapsNet obtains 77.21% accuracy for the CIFAR-10 dataset while performing inference 4x faster. In our second approach, we investigate the possibility of pruning PCs in CapsNet. We show that a pruned version of CapsNet performs up to 9.90x faster than the conventional architecture by removing 95% of Capsules without loss of accuracy. Also, our pruned architecture saves on more than 95.36% of floating-point operations in the dynamic routing stage of the architecture. Moreover, we provide insight into why some datasets benefit significantly from pruning while others fall behind.
On the Diagnosis and Development of Creativity Based on the Ontology of the Game

The subject of the study is to understand the phenomenon of creativity, and the purpose is to provide recommendations for its diagnosis and development of creative abilities. Creativity is currently one of the most sought-after skills according to the forecasts of analysts of the World Economic Forum. The methodological basis of the research is the theoretical and practical results known in science in the psychology of creativity and the story-game concept of reality. Particular attention is paid to the identification in the works on the definition and diagnosis of creativity of their ontological connection with the story-game paradigm, which also intersects meaningfully with studies on situational semantics. A comparative and critical analysis of the basic concepts in these approaches is carried out. The main conclusions of the study are as follows. When teaching any subject, it is useful to diagnose the type of thinking of the audience, periodically offer various tasks that require little time, but develop analytical and creative thinking at the same time, requiring the use of broad categories, synthetics of perception of the environment and a high level of cognitive flexibility, the ability to understand and form metaphors, free association of meanings, interpretations of any text, picture, phenomenon; that is, in fact, it is teaching a creative and playful attitude to various problems. The story-game approach, both in training and in any field of activity, makes it possible to optimally maintain a balance between following the patterns – the "rules of the games" of nature and society and original, creative behavior.
Optimizing blockchain technology using a data sharing model

The improvement of different data-sharing technology has increasingly permeated many industries as technology continues to improve. As a result, for the value of the data to be realized, data sharing and security are essential. However, a fundamental data sharing mechanism is difficult to check for electronic data usage traces. Furthermore, data providers' unwillingness to provide their data is a challenge. Taking use of the dispersed ledger, smart contract, data trust, and traceability aspects of blockchain technology. This research presents a data-sharing model based on blockchain technology optimizing to overcome the challenges in terms of security and control, of conventional centralized data sharing and management, enabling safe access to the data as a result. Moreover, the research assesses the prototype's usefulness and security. Additionally, this paper suggests a method for using blockchain technology to optimize the efficiency of data sharing. This study showed that data sharing via the blockchain technology paradigm proposed in this work is feasible, secure, controllable, and efficient. This was demonstrated in a novel way employing blockchain technology.
Living in the era of codes: a reflection on China’s health code system

This article offers a critical analysis of China's health code system, a data-powered pandemic control and contact tracing system that supposedly subjects all individuals in the country to its panopticon control, a surveillance system that monitors and categorises the Chinese population into the healthy (green), the dubious (yellow), and the unhealthy (red). The article highlights the pretence of surveillance as care and the digital divide that normalises discrimination against the elderly and other digitally left-behind population. It also illustrates how, from policy making and technological design to user engagement, the health code system is implemented, optimised, and used in everyday life to meet the needs of the vulnerable population. The health code is better taken as a medium of adaptable and communicative process that can reset the relation between the system and the lifeworld. It is the process of interchange between the system and the lifeworld that deserves our critical attention.
Quantum circuit fidelity estimation using machine learning

The computational power of real-world quantum computers is limited by errors. When using quantum computers to perform algorithms which cannot be efficiently simulated classically, it is important to quantify the accuracy with which the computation has been performed. In this work we introduce a machine-learning-based technique to estimate the fidelity between the state produced by a noisy quantum circuit and the target state corresponding to ideal noise-free computation. Our machine learning model is trained in a supervised manner, using smaller or simpler circuits for which the fidelity can be estimated using other techniques like direct fidelity estimation and quantum state tomography. We demonstrate that, for simulated random quantum circuits with a realistic noise model, the trained model can predict the fidelities of more complicated circuits for which such methods are infeasible. In particular, we show the trained model may make predictions for circuits with higher degrees of entanglement than were available in the training set, and that the model may make predictions for non-Clifford circuits even when the training set included only Clifford-reducible circuits. This empirical demonstration suggests classical machine learning may be useful for making predictions about beyond-classical quantum circuits for some non-trivial problems.
GEO-Accumulation Index for Heavy Metals in Groundwater: a Study of Bompai and Sharada Industrial Estates, Kano Metropolis, Nigeria

The level of concentration of heavy metal in soil is detrimental to groundwater quality in Sharada and Bompai industrial areas. A total of 40 sampling points were selected from both two areas. The digested samples were analysed for As, Cd, Cr, Cu, Co, Ni and Pb using Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-OES) machine. Geographic Information Systems (GIS) was used to model and present the spatiotemporal changes of the pollution sources and factors affecting the levels of pollution. Pollution in water and potential risks introduced by heavy metal accumulation were assessed using Geo-accumulation index. Results indicated that Cadmium and Arsenic are the most vulnerable around Bompai with mean of 0.2-0.4/mg/l and 1.3-1.6 mg/l respectively, Geo-accumulation index mapping showed that Arsenic, Cadmium and Copper to have high accumulation index ranging from strong to very strong (0.99-1.48), (0.01-0.22) and (0.27-0.33) respectively, Cobalt to have moderate contamination in Sharada (wet season) with (-0.39-0.12) while lead and Chromium has the least contamination index (uncontaminated). GIS modelling and mapping indicated that all the heavy metals were found in the groundwater of the two areas but among them Cd is more concentrated. South-western part of the study area confronts the most serious heavy metal pollution, hence the need for groundwater treatment before drinking to avoid dangers associated with heavy metals.
Research on Application Testing Method Based on Test Script

This project provides a research on application testing method based on test script, especially relating to the technical field of big data. The method includes: according to a preset interface document, obtaining the environment information, field information and field standard information corresponding to the field information of the tested application; Filling the environmental information, field information and field standard information corresponding to the field information into a preset test script template to obtain a test script; Running the test script to perform the application test. This project can automatically generate test scripts based on the current interface documents, thus improving the speed and accuracy of generating test scripts, and further improving the efficiency of the overall application test.
Improved Local Path Planning for Mobile Robot Using Modified Dynamic Window Approach

The tracking path needs to be planned in advance when the mobile robot is in a highly restricted area. In this paper, a modified dynamic window approach (MDWA)-based local path planning method is proposed to guarantee the moving security. First, a strategy for constructing sub-target points online is designed to resolve the non-optimal problem in fixed sub-target point planning by selecting the least cost point on the original path as a sub-target point. Moreover, the path planning evaluation function of the MDWA method are constructed to avoid falling into local minimum or leading to large deviation of the original path distance. Then, end-curve splicing method is used to make the mobile robot return to the original path smoothly. Finally, extensive experiments are performed to verify the effectiveness of the proposed MDWA algorithm.
Collision Avoidance Pathfinding of Multiple AGVs Considering Motion Uncertainties

The multiple automatic guided vehicles (AGVs) pathfinding methods that do not consider the uncertainties of the future motion state may lead to large-scale congestion. To address this problem, this paper proposes an improved conflict-based search (CBS) algorithm with a load-information map. Firstly, the grid map with load information and the evaluation criteria of collision-free pathfinding is constructed. Then, an improved CBS algorithm is designed under a set of constraints on the motion states. This is achieved by performing a search on the constrained conflict tree between individual robots using load-information-map at the high level while exploring efficient single-robot searches with modified penalty function at the low level. Finally, the proposed improved CBS algorithm is tested through simulation and experiment. The results show that it not only finds a collision-free path for multiple AGVs under the motion uncertainties but also obtains a comprehensive optimal solution that considers the evaluation criteria and map load simultaneously.
The study of quantum speed limit time in a quantum critical environment

Quantum theory sets a bound on the speed of quantum evolution. The shortest time needed for a quantm system to evolve from an initial state to the target state is known as the quantum speed limit time. The study of this time in open and closed quantum systems has been the subject of much work in quantum information theory. Quantum speed limit time is inversely related to the evolution rate of a quantum process. This relation is such that the speed of evolution decreases with increasing quantum speed limit time and vice versa. In this work we study the QSL time for the case in which a qubit interacts with a quantum critical environment. We choose the environment to be an Ising spin chain in a transverse field. It is observed that for the quantum speed limit time has a direct relation with quantum coherence of the initial state of the system. We will also study the effect of perturbation coupling on quantum speed limit time. It is observed that the quantum speed limit time decreases with increasing the coupling parameter. It is also observed that the quantum speed limit time decreases with increasing the number of spin in spin chain.
ADVICE PLANNING SYSTEM PADA DINAS PEKERJAAN UMUM DAN PENATAAN RUANG TATA KOTA PANGKALPINANG BERBASIS ANDROID

Advice Planning is a recommendation regarding the explanation of land designation for development or building in an area so that it is in accordance with its land designation based on the urban spatial pattern plan as regulated in the Pangkalpinang City Regional Regulation Number 1 of 2012 concerning Regional Spatial Planning (RTRW). This Advice Planning is also an initial requirement that must exist in managing the development permit process in Pangkalpinang City. Submission of Advice Planning can be done on behalf of an individual or on behalf of a company. The submission process is also done manually by visiting the Pangkalpinang City Public Works and Spatial Planning Office to collect the requirements form. Then come back again to complete the file submitted by the applicant to the concerned agency and after completing the existing documents and requirements the applicant must wait a few days until the Advice Planning is issued. Therefore, to improve the quality of public services with the concept of e-Government and facilitate the community in the process of submitting Advice Planning, an Android-based Advice Planning service application is needed. So that public services will become easier and shorten the time. The results of the research are in the form of an android-based application which is expected to facilitate applicants in submitting Advice Planning to the Pangkalpinang City Government without being hindered by time and place.
Cohand VR: Towards A Shareable Immersive Experience via Wearable Gesture Interface between VR Audiences and External Audiences

Head-mounted displays (HMDs) increase immersion in virtual world, however which limits VR audiences’ awareness of the external audiences that co-located in the same physical environment. In this paper, we superimposing the virtual world onto physical environment and propose a shareable VR experience between VR audiences and external audiences via a wearable interface, thereby enabling external audiences to observe and interact with the virtual objects by using hand gestures. Our system allows external audiences to explore the virtual scenario spatially and to generate asymmetric communication with VR audiences in three different modes. Research suggests that these design strategies could be adapted for future shareable VR experience.
Multi-classification of Human Action Based on ResNet

With the development of machine learning and other related technologies, more and more excellent research on human action recognition and classification has been proposed, which significantly promotes the application of this technology in the actual situation. This paper mainly focuses on the characteristics of ResNet18, ResNet50, ResNet101, and ResNet152 in 15 human action recognition and classification tasks respectively. First, adjust and enhance the sample images of all training sets and test sets, and adjust the overall parameters according to the characteristics of the input image, so that all images can be normalized and high quality can be guaranteed at the same time; Then use the above four ResNet models to study and test with an epoch of 200, and explore the characteristics of the four models in this project by comparing the accuracy, loss, confusion matrix graphic, F1-score, and the number of epoch experienced in reaching the steady state of accuracy. The results show that in the overall effect, with the increase of the number of layers of the ResNet models, the accuracy changes in a positive correlation, and the epoch number experienced by the accuracy reaching the steady state changes in a negative correlation.
Credit Card Fraud Detection Using Feature Fusion-based Machine Learning Model

Credit card fraud is a very common means of financial fraud at present. In order to reduce social and personal economic property losses, this study aims to propose a model to predict credit card fraud, so as to detect possible fraudulent transactions in the transaction process. In this paper, the data set to simulate fraudulent transactions be used, and construct the model after processing the characteristic data and selecting the features. In the model construction part, four algorithms are used as the trained classifiers, which are K Nearest Neighbor algorithm, Bgging algorithm, Logistic Regression algorithm and Gaussian Bayesian algorithm. First, the four algorithms are used to train the data set respectively, and then the parameters of each classifier are adjusted respectively, so that each individual classifier achieves the optimal performance. On this basis, a new model is constructed by using the method of model fusion. The classifier constructed by K Nearest Neighbor algorithm, Bagging algorithm and Gaussian Bayesian algorithm is used as the primary learner, and the classifier constructed by logistic regression algorithm is used as the secondary classifier to build a fused model as a whole. The final experimental results will give the classification performance of the five models, including individual models. The results show that the performance of the model after integrating the four individual models is better than that of the single model, which can provide accurate feedback for credit card holders on whether there is fraud.
Evaluating Low Embodiment Multi-User Virtual Reality Learning Environments in Remote Art Education

The COVID-19 lockdown forced education to make a shift to Emergency Remote Learning (ERL) model, raising multiple issues, such as the higher level of distraction, absence, and scheduling difficulty. To improve the level of student engagement, a low condiment Multi-User Virtual Reality Learning Environment (MUVRLE) with high accessibility was built and assessed to evaluate its usability and effectiveness. A low embodiment MUVRLE was used for maximal accessibility and was compared with the traditional remote learning environment. 100 college students were invited to the experiment. A MUVRLE system based on Mozilla Hubs was designed to create a multi-user virtual classroom based on a course of 19th art history. Students participated in 2 questionnaire studies after they finished the experiment. The evaluation of System Usability Scale (SUS) was conducted in comparing the actual system usability of the MUVRLE model and traditional remote model. The outcome suggested that low embodiment MUVRLE learners had a better performance. Additionally, the low embodiment MUVRLE model had better usability than traditional remote learning in art education.
Comparative Analysis of Incomplete Business Data Clustering

Incomplete values can significantly reduce the accuracy and usability of missing data. In particular, in analyzing commercial data sets, missing values often lead to the dilemma of data selection. It means that a common way to deal with missing data is to delete the sample that contains the missing attribute. However, this can lead to biased and invalidated conclusions, as some data are too critical to be omitted. Therefore, we should use some method to fill the data set rather than delete the data with missing values. The filling of missing data is divided into supervised learning and unsupervised learning. This paper compares six benchmark business datasets by adopting several different data imputation methods and supplementing the missing data with a clustering approach (unsupervised learning). The results are guided to dealing with incomplete business data.
Study on properties and composition of ancient glass based on PCA and gradient lifting tree model

In order to help archaeologists better study ancient glass, uncover the mystery of the early Silk Road, and also better protect these cultural heritage.In this paper, the properties and composition of ancient glass are studied.All types of glass samples were analyzed by PCA principal component analysis and weighted summation, and the type characteristic equation was obtained. After calculating the candidate points iteratively in MATLAB, the optimal critical eigenvalue was set to 5, and the type judgment model was established.According to get the type of the characteristic equation, the selection is related components as high potassium and the composition of class division, choose negative correlation components as lead, barium and the composition of class division, through the PCA principal component analysis of two types of glass data are reduced to 3 d in the class we USES Kmeans clustering algorithm for two types of glass, For the sensitivity analysis of subclass partitioning, we used SOBOL global sensitivity analysis method, and obtained that principal component 1 would have the greatest impact on the model output.
SENTIMENT CLASSIFICATION OF E-COMMERCE REVIEWS BASED ON BERT-CNN

Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) were used to handle natural language tasks in early days, but the Transformer model changed that. The Bidirectional Encoder Representations from Transformers (BERT) model is another optimization based on the Transformer model, which directly makes the performance of the NLP model reach an unprecedented height. In order to distinguish the emotion classification model with the best processing effect in the field of e-commerce reviews, the BERT model is fine-tuned based on the mobile e-commerce review data, and then input to another deep learning models(such as CNN,RNN) as embedding. Finally, we compare the training effect of several current deep learning models, such as BERT, BERT-RNN and BERT-CNN. Experimental results show that the BERT-CNN model performs best in the binary classification of e-commerce review text sentiment.
Tree-based models

This chapter aims to introduce several machine learning models based on a tree structure called the decision tree, which are widely believed to be among the most popular methods for both classification and regression tasks. The basic structure and concepts as well as the tree-growing algorithms of a single decision tree are first introduced. As a single decision tree is prone to over-fitting, ensemble models consisting of a certain number of decision trees are developed. Random forest-based on bagging and gradient boosting decision trees based on boosting will be introduced as the representatives of ensemble models on decision trees.