Reviewerly/exHarmony huggingface dataset:
Multi-view Attribute Weighted Naive Bayes

Naive Bayes (NB) continues to be one of the top 10 data mining algorithms due to its simplicity, efficiency and efficacy. Numerous enhancements have been proposed to weaken its attribute conditional independence assumption. However, all of them only focus on the raw attribute view, which is hard to reflect all the data characteristics in real-world applications. To portray data characteristics more comprehensively, in this study, we construct two label views from the raw attributes and propose a novel model called multi-view attribute weighted naive Bayes (MAWNB). In MAWNB, we first build multiple super-parent one-dependence estimators (SPODEs) as well as random trees (RTs), then we utilize each of them to classify each training instance in turn and use all their predicted class labels to construct two label views. Next, to avoid attribute redundancy, we optimize the weight of each attribute value for each class by minimizing the negative conditional log-likelihood (CLL) in each view. Finally, the estimated class-membership probabilities by three views are fused to predict the class label for each test instance. Extensive experiments show that MAWNB significantly outperforms NB and all the other existing state-of-the-art competitors.
A Method to Extract Rail Flatness Profile and Analysis of Different Noise Sources

This article addresses the problem of the rail flatness assessment that occurs at its final production stage. Flatness control must be measured during the total length of each rail to avoid problems during this rail usage. Only the flat rails can be used in high-performance environments such as high-speed trains. Rails quality assessment must be done to avoid highway security problems or any other type of circumstances that may prevent the normal operation of the systems where these rails will be installed. The rail that does not meet the minimum flatness requirement for the high-performance usage, can be used in standard railways, but if the flatness imperfections are greater than certain tolerances, it must go through a process of restraightening or been discarded. To obtain the rail points where flatness will be measured, we propose a method based on a set of cameras distributed in a range along a section of the rail. The goal is to reduce the impact of the rail vibrations, and then, obtain a synthetic shape that calculates the original rail shape. This shape can be used to measure rail flatness using any other process. The obtained results indicate that this method is robust and accurate.
Interval analysis

Interval analysis (or interval arithmetic), which studies computations done with imprecise data, is an algebra of quantities whose exact values are only known to lie within given intervals. Interval analysis allows one to control the inaccuracies resulting from calculations affected by rounding errors or uncertain data. If a calculation is done using intervals rather than numbers, its result is an interval which is expected to include the exact value. The key entities here are interval numbers, i.e., nonempty intervals representing the upper and lower values, denoted by x‾ and x_, respectively, of an imprecisely known real quantity x. An interval number [x_,x‾], which has the dual nature of a number pair and of a set, can be simply denoted by [x]. Formally,[x]≜{x∈R|x_⩽x⩽x‾}. For interval analysis to be an extension of real analysis, its rules must be consistent with the latter whenever the interval reduces to a real number, i.e., when x_=x‾=x.
Vector Miss Distance Parameter Estimation Based on Direct Position Determination

The vector miss distance measurement (VMDM) plays a key role in the scoring system. In the traditional two-step method, the estimation accuracy of intermediate measurements is limited by the nonstationary characteristics of echoes present in the rendezvous process. To address this problem, an innovative vector miss distance parameter estimation method based on direct position determination (DPD) is proposed in this study. The proposed method can directly estimate vector miss distance parameters with higher accuracy from the echoes received, in which the intermediate measurements need not be estimated in advance. The closed-form expressions of the Cramér-Rao bound (CRB) for vector miss distance parameters are also derived. The simulation results show that the proposed method outperforms the traditional two-step method in terms of the estimation accuracy of vector miss distance parameters. Moreover, the proposed method is more robust to the high noise levels and inconsistency of the receiving channels. The experimental data further validated the efficiency of the proposed method.
Towards a Metacognitive Accuracy Training in Military Education

Over- and under-confidence in uncertain and volatile conditions often encountered by military personnel may lead to suboptimal high impact decitions. We investigated the relation between the metacognition accuracy of cadets‘ performance in a technical IT subject and their observed and measured behaviour characteristics: learning outcomes, knowledge of meta-cognitive strategies, emotional states, time spent for studies, and overall satisfaction with the course and their own efforts. Growth of IT knowledge of a group of 19 cadets who received metacognition training during a three-week long IT course significantly exceeded the results of a control group. Their metacognitive accuracy changed from over-confidence to a moderate under-confidence towards the end of the course, although the relation between the accuracy and the final grade did not differ significantly from random data.
Digital Literacy of Chinese Normal Students: A Literature Review

This article reviews Chinese academic papers from the past 20 years to investigate the digital literacy of normal students of pedagogical faculties. The main body of the article concerns “student literacy, to teacher literacy, then to normal student literacy”; regarding the theme, the context is “from digital literacy, to information literacy, then to digital information teaching ability.” Under the guidance of the Chinese education administration, the digital literacy of Chinese normal students has gradually taken on the Chinese characteristics of being practice-oriented and ability-oriented. The findings of this article are as follows: (1) In terms of research trends, the digital literacy of normal students has increased overall. This has depended on the gradual in-depth understanding of its value to the academic world, which promotes in-depth academic research while also focusing on clear and continuous policy planning. (2) In terms of research themes, the digital literacy of normal students is biased toward practice and training, and there is less research on the development of digital teaching ability in specific subjects. (3) In terms of research methods, qualitative research is still the mainstream method, but increasingly, quantitative research, including surveys, is used. (4) In terms of research results, based on the current survey report data, the digital practice ability of Chinese normal students still needs improvement. The current research has indicated the direction for the future academic community and also proposed requirements for policy researchers and makers.
An Improved Cuckoo Search Algorithm Using Elite Opposition-Based Learning and Golden Sine Operator

The existing cuckoo search (CS) algorithm has the drawbacks of slow convergence speed, low convergence accuracy, and easy to fall into local optimum. An improved cuckoo search algorithm is proposed in this manuscript to overcome the mentioned shortages using elite opposition-based learning and golden sine operator (EOBL-GS-CS). The modifications could be summarized from two aspects. On the one hand, the elite opposition-based learning (EOBL) mechanism is employed to improve the diversity and quality of the population, preventing the algorithm from falling into the local optimum. On the other hand, the golden sine operator accelerates the algorithm’s convergence speed and improves the algorithm's optimization ability. In the verification part, 14 unimodal and multimodal benchmark functions are used to highlight the characteristics of the proposed algorithm. The experimental results show that, compared with the standard CS and other variants, the EOBL-GS-CS has a faster convergence speed, higher solution accuracy, and significantly improved optimization performance.
UiTM Postgraduate Tracking System (UPTrackS) Adoption of Blockchain for Postgraduate Good Goverance Practice Universiti Teknologi Mara (UiTM)

Traditional bureaucracy in the administration of academic process for the public university services delivery is one of the factors that reduce good governance practice. The good governance practice helps to improve the public universities and serve for the best interest of the stake holders. A transformation from the traditional bureaucracy to the digital bureaucracy such as e-governance and digital governance in the universities facilitates a transparent, accountability, effective and efficient processes. The dependencies of the human responsive and paper-based applications for the university postgraduate governance can be minimized by using a digitalized platform ecosystem. A blockchain technology also helps to minimize the clerical works when the verified data information is open, and the data can be shared between the various platforms system. In this paper, we discuss a good governance practice via a digital framework for the postgraduate governance process implemented in the Universiti Teknologi MARA, Malaysia. The blockchain technology is implemented to minimize the data collection and to simplify the verifications process for the important data. The postgraduate governance has employed the decentralized data from the blockchain system to support the system and assures a better understanding of how their students and faculty’s management interact with the institution, as evidenced by the data provided in each module for monitoring educational quality and assessing academic supervision. The feedbacks and the user sharing experience are also collected to measure the effectiveness of the system. From the survey, it was found that the confidence level that the UPTRackS system can provide a good governance is found at 7.9 out of 10. The expectation of the users might be increased if the system be improved to be more user friendly. The layout of the and a few more features will be added to improve the user’s interface. In conclusion, the implementation of UPTrackS system in the postgraduate governance has improves the practice of the good governance in the university.
An Efficient Deep Learning-Based Hybrid Architecture for Hate Speech Detection in Social Media

Social media has become an integral part of life as users are spending a significant amount of time networking online. Two primary reasons for its increasing popularity are ease of access and freedom of speech. People can express themselves without worrying about consequences. Due to lack of restriction, however, cases of cyberbullying and hate speeches are increasing on social media. Twitter and Facebook receive over a million posts daily, and manual filtration of this enormous number is a tedious task. This paper proposes a deep learning-based hybrid architecture (CNN + LSTM) to identify hate speeches by using Stanford's GloVe, which is a pre-trained word embedding. The model has been tested under different parameters and compared with several state-of-the-art models. The proposed framework has outperformed existing models and has also achieved the best accuracy.
Shifting Perceptions—Shifting Realities—Shifting Spheres

This chapter showcases a journey of creative expression using augmented reality (AR) as experiential commentary on socio-economic conditions. The AR journey begins on earth and takes the artist to the moon. On one hand, on the earth, an AR heart is located at a local park during Occupy, an international progressive socio-political movement. The ubiety of the AR art provoked an intimate encounter with a local park patron that illustrates the multiplicity of realities related to technology, environment, poetry and socio-economic circumstances. The chapter expands on a moment when the artwork provokes a dialogue and becomes social theater in Bloomington, a town in Indiana USA. “heARt to heARt”, the AR art is a pointer to a personal cultural history and extends a revolutionary’s portrait into the world which in turn creates a poetic encounter during the socio-economic storm of the Occupy movement. AR art was experienced in cities all over the world from New York to its perilous appearance in Shanghai, China. And on the other hand, on the moon, the “Seed Robots” plan, organize and build a person’s lunar comfort zone in the Moon Lust exhibition that explores global interests and issues pertaining to lunar exploration and habitation.
Effect of Language Mixture on Speaker Verification: An Investigation with Amharic, English, and Mandarin Chinese

Speaker verification (SV) tasks with low-resource language corpora naturally face technical difficulties and often require language mixture processing. In this paper, the LibriSpeech ASR corpus, the AISHELL-I Mandarin Speech corpus, and the Yegna2021 corpus were used for training the x-vector model. The Yegna2021 is a bilingual speech corpus consisting of Amharic and English languages. We designed and collected the Yegna2021 corpus to facilitate SV experimentation. Over 200 native Ethiopian speakers who are bilingual in both languages have participated in the creation of the corpus. To the best of our knowledge, this is the first study of SV systems in Amharic language. This study proposes that improving SV performance degradation, caused by language mismatch between training and testing utterances, requires not only combining two or more languages for training, but also considering the phonetic similarities and differences between languages that impact on obtaining better SV performance. The varied effects of language combinations have been examined on Mandarin Chinese, Amharic, and English languages. In this paper, we investigate the impact of language mismatches between training and testing on SV performance using only the Yegna2021corpus. The experimental results show that a language variability between training and testing utterances significantly degrades SV performance (between 6.5% to 9.0%). The combination of Amharic and Mandarin yields better SV performance than English and Mandarin, achieving an Equal error rate (EER) of 8.3% as compared to 9.8%, with relative performance degradation of 17.1%. To verify these results, we paired Mandarin with data from the LibriSpeech, and the result shows 18.2% relative performance degradation, with an EER of 9.9% for English and Mandarin.
Privacy Protected Product Differentiation Through Smart Contracts Based on Bilateral Negotiations in Peer-to-Peer Transactive Energy Markets

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Neuro-Fuzzy Predictive Approach for Visual Analytics Evaluation of Medical Data

In Visual analytics evaluation field we still do not know what would look like an efficient application. We then aim to develop guidelines (design then development), in order to obtain a coherent information base. This paper introduces a new intelligent approach based on neural network and fuzzy logic techniques allowing an automated visual analytics evaluation. This novel approach executes a Self-Organizing Feature Map (SOFM) neural network model that communicates with human environment using natural language, interacts with users, understands the context of conversations, and detects each sentence meaning. Then, we use fuzzy logic to process participant responses. By this way, an intelligent evaluation procedure is generated. After executing a learning algorithm, our application becomes capable to automatically capture new knowledge and reorganize it in visualization evaluation procedures. This enables an efficient visualizations evaluations taking into account different criteria and measures of visualization characteristics. This feature cancels required limitations imposed on users using other evaluations methods. It allows them to freely evaluate visualizations and discovering each time new criteria evaluations. Moreover this method allows an automatic learning of evaluation procedure following sentence given by users.
A Novel Secure Data Processing Mechanism in IoT Using Deep Learning with Ontology

Security has become the biggest concern in recent technologies like the Internet of things (IoT) as it involves heterogeneous users and resources sharing sensitive data through cloud. These heterogeneous users and service providers have multifarious privacy and security requirements and need a common mechanism to share the same across the heterogeneous environments. Ontology proves to be an efficient means to handle heterogeneous environments for the following reasons: It provides simple means to share domain knowledge among entities, it is simple to reuse domain knowledge, and it facilitates convenient means to manage and manipulate domain entities and interrelationships among entities. The performance of ontologies is determined by their reasoning ability. In IoT, many devices are involved, and hence, multiple ontologies are to be involved. Still, most of the works mainly focus on only single ontology for reasoning. They lack considering reasoning involving multiple ontologies. This article proposed a deep learning method for associating various ontology rule bases and thus learning new inference rules and thereby providing efficient security in IoT applications. To verify the usefulness of the proposed work, it is realized in healthcare application and proves to achieve better security.
Distant Supervised Relation Extraction Based on Sentence-Level Attention with Relation Alignment

Current distant supervised relation extraction algorithms based on neural network deep learning always label a large number of irrelevant sentences as valid data or sentences as wrong relation because of their overly broad assumptions. Although many scholars have proposed some methods to reduce the influence of distant supervision noise and their optimization methods, such as organizing sentences into bag of sentences and introducing attention mechanism, they still cannot obtain a good feature vector of a bag of sentences. In this paper, we propose an attention-weight allocation algorithm for sentences inside a bag of sentences based on relative alignment. Compared with previous methods, it can better capture the similarity of sentences to relative vectors, and better extract relationships in sentences under distant supervision. On the standard dataset, the proposed model has improved by 2.5%, 2.4%, 2.8% and 2% compared with the PCNN_ATT model, P@100, P@200, P@300 and AUC indexes, respectively.
Research on Application of Principal Component Analysis in 3d Video Dimension Reduction

With the development of information technology and multimedia technology, a short video to become a user preferences, due to the long short and large amount of information become the user to be bestowed favor on newly, in order to find out from thousands of video two similar video, traditional approach is to extract the corresponding characteristic in the video, such as color, texture, gray level and other characteristics, and create the corresponding data index, Then put to query to extract the corresponding 3 d video features and compare the features in the database, so as to find the most similar 3 d video, the traditional way of data information is two-dimensional video is used, but in the data information of very large 3 d video efficiency and the effect is greatly reduced, and the results will greatly reduce the efficiency of the traditional data retrieval, Here, the method of principal component analysis without supervision problem is used to reduce the dimension of 3d video information, which can reduce the dimension from 128 to 64, thus greatly improving the efficiency of 3D video retrieval. Experimental results show that the method of principal component analysis for 3d data dimensionality reduction can greatly improve the efficiency of video retrieval.
Unsupervised MR to CT Image Translation Exploiting Multi-scale and Contextual Cues

Image-to-image translation is becoming an important frontier in the field of medical image analysis. Many unsupervised methods have been proposed for MR to CT image translation, but few of them have exploited the generator capability of synthesizing globally and locally coherent images with structure and context indistinguishable from real images. To address this issue, we propose a more powerful discriminator including a well-designed multi-scale architecture and a context loss function together with a structure-based slice selecting training strategy. The proposed discriminator can provide multiple feedbacks with different respective fields between pixel-level and image-level to the generator which encourages it to focus on both global structures and local details in a multi-scale view. Besides, our training strategy selects image slices with similar medical structure and constrain them with a context loss function based on greyscale histogram for more effective adversarial training on context details. We evaluate our proposed model using a series of metrics and experimental results have demonstrated that our approach achieves better results.
CrossBehaAuth: Cross-Scenario Behavioral Biometrics Authentication Using Keystroke Dynamics

Behavioral biometrics has been widely investigated and deployed in real world scenarios for human authentication. However, there has been almost nil attempt to identify behavior patterns in a cross-scenario setting, which is common in practice and needs urgent attention. This paper defines and investigates cross-scenario behavioral biometrics authentication using keystroke dynamics. A novel system called CrossBehaAuth is presented for extending keystroke dynamics-based behavior authentication to new scenarios and extensive problems. We design a temporal-aware learning mechanism based deep neural network for cross-scenario keystroke dynamics authentication. This mechanism selectively learns and encodes temporal information for efficient behavioral pattern transfer in cross-scenario settings. A local Gaussian data augmentation approach is proposed to increase the diversity of behavioral data and therefore, further improve the performance. We evaluate the proposed approach on two publicly available datasets. The extensive experimental results confirm the efficacy of our CrossBehaAuth for cross-scenario keystroke dynamics authentication. Our approach significantly improves the authentication accuracy in cross-scenario settings and even achieves comparable performance on single-scenario authentication tasks. In addition, our approach shows its generalizability and advantages in both single and cross scenario keystroke dynamics authentication.