Reviewerly/exHarmony huggingface dataset:
Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems

Missing data are present in most real world problems and need careful handling to preserve the prediction accuracy and statistical consistency in the downstream analysis. As the gold standard of handling missing data, multiple imputation (MI) methods are proposed to account for the imputation uncertainty and provide proper statistical inference. In this work, we propose Multiple Imputation via Generative Adversarial Network (MI-GAN), a deep learning-based (in specific, a GAN-based) multiple imputation method, that can work under missing at random (MAR) mechanism with theoretical support. MI-GAN leverages recent progress in conditional generative adversarial neural works and shows strong performance matching existing state-of-the-art imputation methods on high-dimensional datasets, in terms of imputation error. In particular, MI-GAN significantly outperforms other imputation methods in the sense of statistical inference and computational speed.
Cognitive Effort in Text Processing and Reading Comprehension in Print and on Tablet: An Eye-Tracking Study

In the present article, we examined the effect of the reading medium and the reading time-frame on text processing, metacognitive monitoring of comprehension, and comprehension outcomes. The eye movements of 116 undergraduates were recorded while they read three texts in print and three texts on a tablet under self-paced reading time or under time pressure. After each text, participants predicted their performance on a subsequent text comprehension test (i.e., an indicator of metacognitive monitoring). Overall, the results showed that participant performed similarly across media in all the reading processes evaluated, regardless of the reading time-frame. The only significant difference indicated that participants fixated more on text titles when reading in print than on the tablet. Although comprehension scores were slightly higher when reading in print, the difference only approached significance. The findings adhere to recent evidence suggesting that the in-print reading superiority is negligible when reading on tablets, as compared to reading on computers.
Improved Binary Adaptive Wind Driven Optimization Algorithm-Based Dimensionality Reduction for Supervised Classification

• An improved Binary Adaptive Wind Driven Optimization algorithm (iBAWDO) is proposed. • Improvements include the crossover technique and Simulated Annealing (SA) algorithm. • iBAWDO is employed for wrapper feature selection in supervised classification. • iBAWDO was highly competitive, based on two popular classifiers, k -NN and SVM. • A Wilcoxon’s nonparametric test affirmed the supremacy of final results at α = 0.05. Typically, Feature Selection (FS) is adopted as a critical preprocessing step in most pattern recognition and data mining tasks. It helps to avoid the acute impact of irrelevant and redundant features on the performance of the classification model under consideration. To tackle this problem, researchers have proposed different methods for selecting the most significant features to improve the overall classification accuracy for a given dataset by extracting relevant information. Realistically, for a given dataset with a large number of features, conventional methods usually struggle to find good solutions. Therefore, in this study, a meta-heuristic algorithm called the Wind Driven Optimization (WDO) is enhanced and then cloned into a binary variant, the improved Binary Adaptive WDO (iBAWDO). The proposed iBAWDO would manage to select the most relevant (near-optimal) features while reducing the computational cost and enhancing (or even maintaining) the final classification accuracy. An evolutionary crossover technique as well as the Simulated Annealing algorithm (SA) are incorporated into the original WDO algorithm to enhance its search ability for feasible regions as well as exploitation within these regions, respectively. To assess the relevance of the features selected, two popular classifiers, k -Nearest Neighbor ( k -NN) and Support Vector Machine (SVM), are adopted as fitness evaluators. The proposed iBAWDO algorithm was validated on 18 multi-scale benchmark datasets against binary versions of 11 well-known meta-heuristic approaches: Binary version of the original WDO (BWDO), Binary Particle Swarm Optimization (BPSO), Binary Bat Algorithm (BBA), Binary Grey Wolf Optimization (BGWO), Binary Whale Optimization Algorithm (BWOA), Binary Grasshopper Optimization Algorithm (BGOA), Binary Sailfish Optimizer (BSFO), Binary Harris Hawks optimization (BHHO), Binary Bird Swarm Algorithm (BBSA), Binary Atom Search Optimization (BASO), and Binary Henry Gas Solubility Optimization (BHGSO). A Wilcoxon’s rank-sum non-parametric test was conducted at a 5% significance level to statistically affirm the high competitiveness of the proposed method. Overall, the experimental results revealed that the proposed method is significantly effective on both small- and large-dimensional datasets.
Research Leadership of the “Priority 2030” Program: Success Factors

The article presents the results of comparative analysis of universities’ activities over the past five years, which won the special grant in the “Research Leadership” track of the “Priority 2030” program. The comparison is carried out between the following groups of universities: 1) all universities of the “Research Leadership” track, 2) the first, second and third groups, selected according to the results of the competition, and 3) group of Project 5-100 participants. The state strategic initiatives in the field of the higher education system over the past 15 years were considered to determine their impact on the development of the considered groups of universities; the indicators that make significant contribution to the results of educational, research, international and financial activities of universities were selected. The aggregation of indicators for university groups has been carried out using the Displaced Ideal method and the comparative analysis for 2016 – 2020. The significant indicators of scientific and research activities of analyzed groups of universities for 2018 – 2020 were considered. Based on the analysis, the most significant factors of success have been identified that ensured the victory of the considered universities in the Priority 2030 competition on the “Research Leadership” track.
Deep absolute phase recovery from single-frequency phase map for handheld 3D measurement

Fringe projection profilometry is an effective technique for handheld three-dimensional shape measurement because of its high resolution and accuracy. However, it is hard to precisely achieve rapid measurement for complex scenarios using traditional fringe analysis methods without additional patterns or camera(s). Recovering the absolute phase from single-frequency fringe patterns of complex scenarios is challenging and still not well-addressed. This paper proposed a learning-based approach to address the unwrapping of one single-frequency phase map. A fully convolutional neural network is introduced to learn the mapping from wrapped phase to fringe-order without additional images under the constraint of the boundary fringe-order. This network is designed as a lightweight one and can operate in real-time for high-resolution phase maps. Experiments on a real large-scale dataset demonstrated that the presented method could unwrap single-frequency phase maps of handheld 3D measurement with motion blur, phase discontinuity, and isolated regions.
CitEnergy : A BERT based model to analyse Citizens’ Energy-Tweets

Micro-blogging social site Twitter has emerged as a rich source of unstructured text information which could be processed and analysed to extract people’s opinions about several topics and events including natural hazards, energy, sports, transportation, elections etc. The present research study adds a novel perspective in this dimension by extracting citizens’ opinions on several electricity-related issues. Recent research studies in this domain have employed Bag-of-Words (BoW) model for the numerical representation of tweets. However, the BoW model suffers from several issues such as incapable of handling the semantic relationship between words, sparse and high dimensional representation. The present research work overcomes all aforementioned shortcomings by integrating popular word-embeddings with deep learning models for the classification tasks. The study harnesses social media data for two classification tasks: Sentiment classification task and Complaints classification task. Firstly, a series of preprocessing steps are applied on tweets extracted from Twitter streaming API. Subsequently, different word-embeddings models are employed to generate a numerical representation of tweets while capturing the semantic relationship among words. Several deep learning-based sentiment classification models are then deployed on top of generated word-embeddings for identifying/classifying citizens’ sentiments from the tweets. Lastly, the tweets associated with negative sentiment class (identified by the sentiment classification model) are further processed and analysed for building the complaints classification model. The complaints classification model prioritize and assign negative sentiment tweets into one of the two target classes depending on the target issue raised in the tweets (Community level or recurring complaint and Individual level complaint). In addition to this, the current study also proposes Bi-directional Encoded Representations for Transformers (BERT) based Sentiment classification and Complaints classification models for achieving the improved classification accuracy. Experimental evaluation of the proposed BERT based models is done by comparing prediction results with several benchmark deep learning models.
Mismatching Removal for Feature-Point Matching Based on Triangular Topology Probability Sampling Consensus

Feature-point matching between two images is a fundamental process in remote-sensing applications, such as image registration. However, mismatching is inevitable, and it needs to be removed. It is difficult for existing methods to remove a high ratio of mismatches. To address this issue, a robust method, called triangular topology probability sampling consensus (TSAC), is proposed, which combines the topology network and resampling methods. The proposed method constructs the triangular topology of the feature points of two images, quantifies the mismatching probability for each point pair, and then weights the probability into the random process of RANSAC by calculating the optimal homography matrix between the two images so that the mismatches can be detected and removed. Compared with the state-of-the-art methods, TSAC has superior performances in accuracy and robustness.
Spammer Detection Approaches in Online Social Network (OSNs): A Survey

In recent years, spammer has imbued every social network platform. The growth in users in social platforms like Instagram, Facebook, Sina Weibo, Twitter, YouTube, etc. has unfolded new approaches for spammers. To get the financial benefit, spammers exploit social network platforms using flooded content shared by the users. In order to protect user information and accounts from spammers, many researchers have proposed defensive mechanisms and detection methods. Over the last few years, researchers develop many spam detection methods which improve the performance of user’s account. Therefore, we are motivated to point out few surveys about various methods by which the spammer can spread and some detection mechanisms to protect users and their account information. The solution for the detection of a spammer is different based on the collected dataset and predictive user analysis. This survey includes (1) Various detection mechanisms including the way of spreading spam. (2) Analysis of user contents based on their account information and posts including a comparative analysis of various methodologies for the detection of spammers. (3) Open issues and challenges in social network spam detection techniques.
Secure Chatroom Application using Advanced Encryption Standard Algorithm

This paper, in all its essence, is a group chat Application, between a variable number of users, who are connected to each other, by virtue of being a member of the same network. A central server acts as a service to accept messages from a user and forward it to all other users, who are participating in the group chat by being connected to the server at that point of time. Given that the server is up and running, new users can join the group chat server at any given time by providing the current local IP address and the port on which the server is running, although, this can only be done if the active members in a group chat does not exceed the specified and declared capacity of the server. If a large number of users is expected, the server can be configured to listen for a higher number of incoming connection requests, which should be carefully determined, since, performing an alteration in the capacity of the server requires the server to be restarted for the new capacity to come into effect. The main aim of this group chat server is that it is a secure group chat server which uses cryptographic algorithms Advances Encryption Standard for encrypting message. When the server is started, a random cipher key is generated for encryption and decryption of messages. This cipher key is the secret key that is confidential within the boundaries of the system. When a message originates from a user, it is encrypted before being sent to the server. The server receives this encrypted message and forwards it to the other users that are currently connected to the group chat server.
Towards highly adaptive Edu-Chatbot

Conversational Agents are widely used in different domains to automate tasks and help to improve user experience. In recent decades, AI systems, thanks to deep learning methods and Natural Language Processing (NLP) approaches, can interact with users, understand their needs, map their preferences and recommend an appropriate action with no human intervention. However, chatbots in the education field have received limited attention. In this work, we use Xatkit, a chatbot development framework, for the definition of our Chatbot and propose an Encoder-Decoder framework for intent recognition. For the encoder, we encode utterances as context representations using bidirectional transformer (CamemBERT). For the decoder, we use an intent classification decoder to detect the student’s intent. Our chatbot will be tested in the field of education to improve and simplify teaching for professors and learning for students as well as reducing faculty burnout and raising the speed of comprehension.
AnalyZr: A Python application for zircon grain image segmentation and shape analysis

Zircon grain shape is traditionally interpreted as a product of the physico-chemical conditions during crystal growth and may be modified during grain transport processes. The analysis of magmatic zircon grain shape has been proposed to inform on crystallization conditions, whereas detrital zircon grain shape has been proposed to complement traditional sediment provenance analysis. Shape parameters can be automatically measured from digital images of zircon mounts; however, this requires extraction of individual grain boundaries for measurement. Existing image segmentation software may require the use of proprietary languages, or knowledge of scripting to develop automated image segmentation routines, and is typically not tailored towards the geosciences. Furthermore, the separation of touching zircon grains in images remains a challenge for existing algorithms. To facilitate zircon grain shape analysis, we present AnalyZr, an open-source graphical Python application designed to segment reflected and transmitted light images of zircons mounted in resin. A new segmentation algorithm is implemented to improve the separation of touching zircon grains. Shape parameters are automatically measured from the segmented images and may be output to a .csv or .mdb file. Two case studies demonstrate the use of the application in resolving geologically relevant information in zircon grains sourced from: i) compositionally and age-distinct granite, diorite, and gabbro samples from across Western Australia, and ii) age-distinct detrital zircons from the Canning Basin, Western Australia.
A Cross-Domain Authentication Scheme Based on Cooperative Blockchains Functioning With Revocation for Medical Consortiums

Regional medical consortium systems facilitates medical information sharing. However, many security issues exposed by the dominant centralized architectures, such as single points of failure, unauthorized operations and illegal access, are increasingly apparent constraints on the security and efficiency of data sharing across domains. Even more, any malicious operation detected, effective measures should be executed promptly for identity tracing. In this paper, we propose a secure and efficient cross-domain authentication scheme based on two cooperative blockchains (BCs) for medical consortium systems. Specifically, an intra-domain BC records any legal users’ registration and authentication information while an inter-domain BC is responsible for writing users’ cross-domain authentication information. In each domain, the general hospital acts as a trusted third service provider to achieve cross-chain interactions. For the entire cross-domain authentication procedure, anonymity mechanism is utilized to enhance security, and to trace malicious users, the improved chameleon hash is used in the intra-domain BC to redact the state of the user, and blacklist merkle tree is extended in the inter-domain BC to protect different domains’ services from illegal accessing. In addition, security analysis and performance evaluation are completely given to prove the superior security features and performance compared with other schemes.
Improving the Communication and Computation Efficiency of Split Learning for IoT Applications

Distributed machine learning systems train neural network models by utilizing the devices and resources in the network. One such system that was recently introduced is split learning. It trains a deep neural network collaboratively between the server and the client without sharing the raw data, ensuring private and secure training. Implementing such systems on the edge devices adds computation and communication overhead, which might not suit many edge devices, especially in IoT systems, where resources are limited. In this paper, we introduce a modified split learning system that includes an autoencoder and an adaptive threshold mechanism. The modified system has less communication and computation overhead compared to the original split learning system. The modified system was deployed on an IoT system and the results proved the advantages of the proposed mechanism. The communication overhead and computation overhead were reduced with negligible performance loss.
Experimental study of path planning problem using EMCOA for a holonomic mobile robot

In this paper, a comparative study of the path planning problem using evolutionary algorithms, in comparison with classical methods such as the A&#x2217; algorithm, is presented for a holonomic mobile robot. The configured navigation system, which consists of the integration of sensors sources, map formatting, global and local path planners, and the base controller, aims to enable the robot to follow the shortest smooth path delicately. Grid-based mapping is used for scoring paths efficiently, allowing the determination of collision-free trajectories from the initial to the target position. This work considers the evolutionary algorithms, the mutated cuckoo optimization algorithm (MCOA) and the genetic algorithm (GA), as a global planner to find the shortest safe path among others. A non-uniform motion coefficient is introduced for MCOA in order to increase the performance of this algorithm. A series of experiments are accomplished and analyzed to confirm the performance of the global planner implemented on a holonomic mobile robot. The results of the experiments show the capacity of the planner framework with respect to the path planning problem under various obstacle layouts.
Computer multimedia assisted language and literature teaching using Heuristic hidden Markov model and statistical language model

Computer technology has been used for decades in secondary education and foreign language preparation. Still, attempts to incorporate technology have presented educators with various challenges due to rapid progress in technology and occasional changes in language education methods. One way of improving student engagement is to provide connectivity throughout the teaching and learning process in a classroom, allowing students to improve their English language skills. The multimedia classroom allows students to interact with various texts, giving them a solid background intasks and material of mainstream college classes. This paper introduces a method to recognise large unconstrained handwritten text vocabulary using Heuristic Hidden Markov Model and Statistical Language model (HHMM-SLM).This provides statistical language models to be applied to enhance our system's performance. Numerous experiments with single and multiple writer data have been conducted. Language models have been used to improve system accuracy. The variable size Lexica is used (between 10,000 and 50,000 words). Further, a lexicon for English with a very precise width has been developed, which makes its contribution. Our approach is comprehensive and compared to other literature approaches to deal with the same issue.
Using refined kano model and decision trees to discover learners’ needs for teaching videos

With the advancement of technology and the spread of the COVID19 epidemic, learning can no longer only be done through face-to-face teaching. Numerous digital learning materials have appeared in large numbers, changing people's learning mode. In the era of information explosion, how to capture the learners' attention to teaching videos and improve learning effectiveness is the common goal of every designer of e-leaning teaching content. Previous researches focused on the analysis of learning effectiveness and satisfaction. Instructional designers only provided design elements with high learning effectiveness or high satisfaction, and lacked in-depth analysis of the learners' perspectives. The opinions of these e-learning users are often the key to the success of online teaching videos. Therefore, this study aims at the design elements that will be used in the teaching film. The operation mode of the piano mechanism will be employed as the content of the teaching film. Based on eight elements including arrow cueing, dynamic arrow cueing, spreading-color cueing, contrary to cueing, font style, color application, anthropomorphic, and audiovisual complementarity, we use Refined Kano Model to analyze learners' needs of categorization of each element, and discover learners' expectations for teaching videos. In addition, this study also conducts in-depth data analysis through decision trees algorithm, and stratification analyses using different variables (such as design expertise, using frequency, and usage experience, etc.) to find out the key design factors that affect learners' learning. Depending on the learner's background, the use of e-learning experience, using frequency, and the length of the learning video, our results could provide for reference when designing teaching videos. Instructional designers can better understand how to effectively use design elements, so that the teaching videos can achieve the best learning effect.