Reviewerly/exHarmony huggingface dataset:
SUSTAINABLE HIGHER EDUCATION LEADERSHIP: A CONCEPTUAL APPROACH FROM THE FUNCTIONALIST PARADIGM FOR HIGHER INSTITUTIONS OF LEARNING

Propose of the study: This paper intends to explore the concept of sustainable higher education leadership and to develop a propose conceptual approach of leadership for higher institutions of learning from the perspective of the functionalist paradigm. &#x0D; The Methodology: The paper critically analyzed the concept of sustainable higher education leadership, explore the functionalist paradigm, and their relevance in leading higher institutions of learning. The paper have methodologically selected the three main concepts of the paradigm and link them with higher institutions of learning and work out a plan and modalities on how they could be applied in practice.&#x0D; Main Findings: The main findings and the contribution of the paper were identified in the two main sections. The first, explore the definitions and features of various functionalist paradigm and its dimensions within the cycle of leadership. While the second part of the paper synthesized the theories and discuss their relevance with sustainable leadership in higher education and systematically come up with a framework/model that is practically applicable within the context of higher educational institutions of learning.&#x0D; Applications of this study: This research can be applicable to the higher institutions of learning including Universities, Polytechnics, Colleges as well as Moto-technics. &#x0D; Novelty/Originality of this study: This research paper has critically analyzed the functionalist paradigm with a specific focus on leadership and systematically come up with a framework/model on how such can be applied in the leadership of higher educational institutions of learning.
Protecting Personally Identifiable Information (PII) in Critical Infrastructure Data Using Differential Privacy

In critical infrastructure (CI) sectors such as emergency management or healthcare, researchers can analyze and detect useful patterns in data and help emergency management personnel efficaciously allocate limited resources or detect epidemiology spread patterns. However, all of this data contains personally identifiable information (PII) that needs to be safeguarded for legal and ethical reasons. Traditional techniques for safeguarding, such as anonymization, have shown to be ineffective. Differential privacy is a technique that supports individual privacy while allowing the analysis of datasets for societal benefit. This paper motivates the use of differential privacy to answer a wide range of queries about CI data containing PII with better privacy guarantees than is possible with traditional techniques. Moreover, it introduces a new technique based on Multiple-attribute Workload Partitioning, which does not depend on the nature of the underlying dataset and provides better protection for privacy than current differential privacy approaches.
Searching the great metadata timeline

Purpose The purpose of this paper is to make a systematic review of the library metadata development history listing out the most significant landmarks and influencing events from Thomas Bodley's rules to the latest BIBFRAME architecture, compare their significance and suitability in the modern-day Web environment. Design/methodology/approach Four time divisions were identified, namely pre-1900 era, 1900–1950, post-1950 to pre-Web era and post-Web era based on pre-set information available to the authors regarding catalogue rules. Under these four divisions, relevant information sources regarding the purpose of the study were identified; various metadata standards released at different times were consulted. Findings Library catalogue standards have undergone transitive changes from one form to another primarily influenced by the changing work environment and different forms of resource availability in libraries. Modern-day metadata standards are influenced by the opportunities provided by the World Wide Web towards libraries and work as a suitable base for data organisation at par with Semantic Web standards. Research limitations/implications Information organisation processes have gone towards a more data-centric approach than earlier document-centric nature in current Semantic Web environment. Libraries had to make a move in this process, and modern-day guidelines in this regard bring the possibility of large-scale discovery services through curated information resources. Originality/value The study discovers relationships between key events in the course of development of metadata standards and provides suggestions and predictions regarding it's future developments.
Concentric Circle Detection Method Based on Minimum Enveloping Circle and Ellipse Fitting

At present, the commonly used algorithm for detecting circles cannot be directly applied in the detection of concentric circles, there are few researches on the special multi - circle detection problems such as concentric circles. In this paper, the color concentric circles are transformed into gray scale images without color space conversion. The minimum envelopment circle is adopted to locate the target area quickly and accurately. Then, combined with the least square ellipse fitting method, each concentric circle is detected and the position of the center of the circle is marked. Finally, the experiment proves that the algorithm in this paper improves the detection efficiency and detection effect compared with the classical algorithm.
Tracing the Points in Search Space in Plant Biology Genetics Algorithm Optimization

A very recent meta-heuristic optimizer is by inspiration from plant biology where the Mendel law of heredity is implemented through multi-species in two generations. Plant biology-inspired optimizer named as Mendelian Evolutionary Optimization Algorithm (METO), which has several advantages outperforming the state-of-the-art optimizers. It is highly capable of finding the best solution for multimodal problems with global optimal solution and computationally fast. METO not only performs well over the problems with around thirty variables but also performs well on the very high-dimensional problems such as hundred variables. Besides the literature introducing the characteristics of the METO, this chapter investigates the way METO explores the search space of the problem by exchanging the gene’s information between the multi-species. Each plan in a species represents by double strands DNA. Here, we will observe how METO covers the search space and avoid being stuck in a local minimum and moves toward the global solution. In this chapter, we investigate the behavior of the operators of METO such as flipper, pollination, self- and cross-breeding, and epimutation.
UGAN: Unified Generative Adversarial Networks for Multidirectional Text Style Transfer

Recently, text style transfer has become a very hot research topic in the field of natural language processing. However, the conventional text style transfer is unidirectional, and it is not possible to obtain a model with multidirectional transformations through training once. To address this limitation, we propose a new task called multidirectional text style transfer. It aims to use a single model to transfer the underlying style of text among multiple style attributes and keep its main content unchanged. In this paper, we propose Unified Generative Adversarial Networks (UGAN), a practical approach that combines target vector and generative adversarial techniques to perform multidirectional text style transfer. Our model allows simultaneous training of multi-attribute data on a single network. Such unified structure makes our model more efficient and flexible than existing approaches. We demonstrate the superiority of our approach on three benchmark datasets. Experimental results show that our method not only outperforms other baselines, but also reduces training time by an average of 13%.
Hardware and Software Medical Complex Monitoring of Vital Signs of the Patient

The paper proposes a method of organizing a structural diagram of a hardware-software medical complex, in which the main attention was paid to: a method for constructing the architecture of a biomedical system for recording and processing physiological signals, which increases the functionality of the complex and its manufacturability; a mathematical model of the distribution and physical representation of data to solve the problem of identifying parallel processes the technique provides functions of registration, storage, processing, analysis of a number of physiological signals: pulse, body temperature, electrocardiograms; high reliability and immunity to interference; the development of an original circuitry solution in order to reduce the amount of telemetric data and the large amount of information transmitted, as well as increase the speed of wireless data transfer. Monitoring vital signs with the help of a hardware-software medical complex improves the quality of life of the patient during the examination and allows you to quantify the response of the patient's cardiovascular system to stress tests, establish the effectiveness of the stimulation regimen and therapy in patients with implanted pacemakers, and many other pathologies.
Off-Topic Essay Detection Using C-BGRU Siamese

Automatic essay evaluation (AEE) and also called automatic essay scoring have become necessary with the rise of online learning and evaluation platforms and several works have been carried out on automatic essay evaluation. Since inputs to such systems come from learners, the system needs a mechanism to validate inputs before evaluation. In this paper, we have proposed a way to validate user responses using an off-topic essay detection method that is used in automatic essay evaluation. In the proposed C-BGRU Siamese architecture, the convolution layer learns and captures the contextual features of the word n-gram from the embedding vectors of the word, and a variant of the RNN so-called bidirectional-gated recurrent unit (BGRU) is used to access both previous and subsequent contextual representations. The experiment was conducted on eight datasets that were based on Kaggle for the task of AEE experimental results show that our proposed method achieves significantly higher accuracy compared to the baseline methods.
Signs your cyber security is doomed to fail

Cybercrime represents big money for cyber criminals. Unfortunately, that will likely equate to significant operational and financial losses for unprepared businesses. While most enterprises have come to terms with the fact that a future security incident is inevitable, many still struggle to translate this into the right security architecture and mindset. One of the reasons is that too many organisations buy into some persistent misconceptions regarding cyber security. They also make some basic errors in their attitudes towards the threats and how their organisations should deal with them. Marcus Chung of BoldCloud Looks at seven of the most common problems that could indicate your business is at risk.
Adaptive learning rates for support vector machines working on data with low intrinsic dimension

We derive improved regression and classification rates for support vector machines using Gaussian kernels under the assumption that the data has some low-dimensional intrinsic structure that is described by the box-counting dimension. Under some standard regularity assumptions for regression and classification, we prove learning rates, in which the dimension of the ambient space is replaced by the box-counting dimension of the support of the data generating distribution. In the regression case, our rates are in some cases minimax optimal up to logarithmic factors, whereas in the classification case our rates are minimax optimal up to logarithmic factors in a certain range of our assumptions and otherwise of the form of the best known rates. Furthermore, we show that a training validation approach for choosing the hyperparameters of a SVM in a data dependent way achieves the same rates adaptively, that is, without any knowledge on the data generating distribution.
Cancer Detection in Breast Histopathology with Convolution Neural Network Based Approach

Breast cancer is one of most common causes of mortality in women. However, few limitations, e.g., similar structure statistics in inter-class and textural variations in intra-class images make the breast histology analysis a challenging process. In this paper, the multi-class breast cancer classification is carried out with deep convolution neural network (CNN) based transfer learning approach. To explore the feasibility of transfer learning in breast histology, pre-trained deep CNN model is inherited and simultaneously a multi-scale feature concatenation strategy is used. Moreover, incorporating with stain normalization and channel color modification strategies the designed model can be effectively trained. The experiments on publicly available multi-class ICIAR 2018 breast dataset corroborated the efficiency of ou method. The designed approach outperforms the existing methods by achieving 94.3% and 97.5% accuracy on 4-class and 2-class histology image recognition respectively.
Combination subspace graph learning for cross-modal retrieval

In this paper, a novel supervised cross-modal retrieval method, combination subspace graph learning (CSGL), is proposed, which primarily concentrates on the cross retrieval between images and texts, i.e., image retrieving texts (I2T) and text retrieving images (T2I). To project multimodal data from a low-level feature space into a latent common subspace, most classical methods would learn the projective matrix separately for each mode, ignoring the consistency between different modes. Graph regularization is added to our objective function to preserve the structure of the original data in the projective space. Furthermore, to avoid the suboptimal solution during optimization, we use the collaborative learning strategy to obtain the projective matrix directly, which unites all the modes for better projection. Generally, the CSGL method takes the advantage of the semantic information and the original distribution of the image and text to obtain a more discriminative projection, which is learned in combination, rather than learning individually. Experimental results on three benchmark datasets, Wikipedia, Pascal Sentence, and INRIA-Websearch, show that the proposed method outperforms the state-of-the-art methods.
Privacy-preserving mixed set operations

Privacy-preserving set operations have important practical and theoretical significance and have always been a research focus of secure multiparty computation. Most existing studies focus on single set operations which cannot express many complicated set operations. However, the studies on mixed set operations are highly limited. The available protocols for mixed set operations can only be implemented through a threshold fully homomorphic encryption scheme. Since mixed set operations are necessary in practice, it is necessary to design an efficient and practical secure computation protocol for mixed set operations. In this paper, we propose a new protocol for mixed set operations that is based on a new encoding method and the threshold ElGamal cryptosystem. Through an efficiency analysis, it is proved that the efficiency of the protocol that is proposed in this paper are significantly higher than those of the available protocols.
BSENet: A Data-Driven Spatio-Temporal Representation Learning for Base Station Embedding

Base station (BS) plays a critical role in the wireless network.There has been some research on exploring the spatio-temporal information of BS in different fields.However, these works lack re-usability, which require the new researcher to re-do the work of representing the spatio-temporal information.To solve this problem, we propose a neural network model based on autoencoder and representation learning called BSENet to learn embedding of BS based on raw data.The embedding contains spatio-temporal information of BS.In this way, other fields that are related to BS can make use of spatio-temporal information with BSENet embeddings.Besides the spatial information, BSENet can maintain the independence of BS.Moreover, we use bi-directional LSTM to get temporal information and propose a time dropout method to improve the generalization ability.We propose a soft threshold to consider all spatial relations.In addition, we introduce weight to enhance compatibility.We treat the missing states as the inputs to deal with the missing values.The results of clustering show that BSENet embedding is better than other embeddings.In experiments of mobile traffic prediction, BSENet embedding helps a temporal model to improve the Mean Squared Error (MSE) from 0.01463 to 0.01334, which is similar to 0.01302 of a spatio-temporal model.At the same time, the training time increases a little but is still 5.0× faster than spatio-temporal model.
Blockchain Based Smart Park

Based on a practical park planning project, this paper analyzes the requirements of cleaning management, puts forward a blockchain based scheme of smart park cleaning. It proposes the service model of cleaning blockchain, and designs the system framework, DAPP modules, smart contracts and process flow of the cleaning blockchain system. The approach is also applicable for the application of blockchain technology in smart park public services, housekeeping, device maintenance and other fields.
A New Type-II Fuzzy System for Flexible-Joint Robot Arm Control

In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) based on the Interval Gaussian Type-II Fuzzy sets in the antecedent part and Gaussian Type-I Fuzzy sets as coefficients of a linear combination of the input variables in the consequent part is presented. The capability of the proposed control method to function approximation and dynamical system identification is investigated. An adaptive learning rate based on the Backpropagation method with guaranteed convergence is employed for parameter learning. Finally, the proposed method is applied to control a flexible-joint robot arm. The simulation results show the robustness and effectiveness of the proposed control method. The proposed control method is also compared with the conventional ANFIS method.
Immersive Learning in Real VR

Immersion is an essential factor for successful learning. Immersive learning environments put learners directly into experiences and events related to the learning content. Virtual reality (VR) experiences give us new opportunities and ways for enabling immersive learning processes. In this chapter, we review different learning environments that use (real) VR as a tool to teach in different learning settings and discuss the state-of-the-art design and evaluation techniques and analyze the benefits and limitations of the different methods.
Heterogeneous Track-to-Track Fusion in 2D Using Sonar and Radar Sensors

In our previous work we considered heterogeneous track-to-track fusion (T2TF) in 3D using an infrared search and track (IRST) sensor and an air moving target indicator (AMTI) radar. In this paper we consider the 2D counterpart, heterogeneous T2TF in 2D using a passive sonar and an active radar. The target is assumed to move with nearly constant velocity (NCV) motion in 2D. The sonar sensor measures bearing of the target whereas the radar measures range and azimuth of the target. For the sonar and radar trackers, we use range-parametrized modified polar coordinates (RP-MPC) and Cartesian state vector, respectively. Both trackers use the cubature Kalman filter (CKF) as the tracker filter. Local trackers send their information matrices and the corresponding information state estimates to the fusion center (FC). The FC performs T2TF using the information filter (IF). Monte Carlo simulations are used to evaluate the accuracy of the proposed IF based T2TF relative to that of centralized fusion.
Gradient Boosted Trees with Extrapolation

Gradient boosted decision tree algorithms only make it possible to interpolate data. Therefore, the prediction quality degrades if one of the features, such as time, lies outside the boundaries of training data set. It is hard to eliminate these types of features because they can affect other features indirectly. Our new quick and robust extrapolation algorithm, ExtraBoost, for binary classification and regression problems can handle these dependencies and perform functional analyses, e.g., derivative analysis. In this article we describe the mathematical background of ExtraBoost, perform a running time analysis of the learning and inference stages in terms of big-O notation and evaluate the Tensorflow GPU-friendly implementation of the algorithm. Due to the lack of public binary classification data sets with indirect time dependencies, we propose a method of generating such data sets as well as an example of a data set based on data from a commercial search engine.
Recommending Research Article Based on User Queries Using Latent Dirichlet Allocation

The researchers need to access a large number of articles so as to keep abreast with recent developments in their respective research areas. Finding a relevant article from a huge collection of information, therefore, becomes a huge challenge. Recommender systems play a vital role in assisting researchers to filter correlated information from large volume articles. In this work, we have used the topic modeling approach, latent Dirichlet allocation (LDA), for recommending research articles based on user queries. We have collected reference articles of a query given by the user from the Wikipedia site. The proposed algorithm comprises four steps: 1. LDA first iteration to find the top N words. 2. LDA second iteration elicits weighted topic distribution. 3. Pointwise mutual information (PMI) obtains the correlation between the top N words and the weighted topic distribution words. 4. Reference articles are collected for top correlated words, and the top-ranked articles are recommended to the user by applying collaborative filtering algorithm. The two-pass LDA helps to avoid the ambiguities. The proposed algorithm performance is evaluated using the parameters of precision and recall. As compared to the results obtained from the existing recommender systems, our work demonstrates that the LDA-based algorithm gives a powerful recommender framework.
Hybrid Genetic Based Algorithm for CNN Ultra Compression

The increasing deployment of the Convolutional Neural Networks (CNNs) in computer vision algorithms is because of the high accuracy of CNNs in image recognition jobs which makes CNNs of dominant use, but the huge number of parameters inside the CNN puts hurdles against the ease of deployment of the CNN in embedded systems, IoT chips and even mobile devices. Huge number of parameters means excessive computations and storage requirements which makes it infeasible to use in hardware chips with limited resources. In this paper a new hybrid algorithm for CNNs compression is proposed. This new algorithm combines pruning, quantization and compression techniques which are used to reduce the huge size of different CNNs while maintaining a similar accuracy percentage. The proposed algorithm is to combine the use of genetic algorithms (GA) to conduct the selection criteria of pruning of convolutional layers only and applying conventional pruning to the fully connected layers, Finally applying the quantization and compression on the resulted neural network weights.