Miracle-dz/UniFAR-FTUs huggingface dataset:
Clustering of online learning resources via minimum spanning tree

Purpose The quick growth of web-based and mobile e-learning applications such as massive open online courses have created a large volume of online learning resources. Confronting such a large amount of learning data, it is important to develop effective clustering approaches for user group modeling and intelligent tutoring. The paper aims to discuss these issues. Design/methodology/approach In this paper, a minimum spanning tree based approach is proposed for clustering of online learning resources. The novel clustering approach has two main stages, namely, elimination stage and construction stage. During the elimination stage, the Euclidean distance is adopted as a metrics formula to measure density of learning resources. Resources with quite low densities are identified as outliers and therefore removed. During the construction stage, a minimum spanning tree is built by initializing the centroids according to the degree of freedom of the resources. Online learning resources are subsequently partitioned into clusters by exploiting the structure of minimum spanning tree. Findings Conventional clustering algorithms have a number of shortcomings such that they cannot handle online learning resources effectively. On the one hand, extant partitional clustering methods use a randomly assigned centroid for each cluster, which usually cause the problem of ineffective clustering results. On the other hand, classical density-based clustering methods are very computationally expensive and time-consuming. Experimental results indicate that the algorithm proposed outperforms the traditional clustering algorithms for online learning resources. Originality/value The effectiveness of the proposed algorithms has been validated by using several data sets. Moreover, the proposed clustering algorithm has great potential in e-learning applications. It has been demonstrated how the novel technique can be integrated in various e-learning systems. For example, the clustering technique can classify learners into groups so that homogeneous grouping can improve the effectiveness of learning. Moreover, clustering of online learning resources is valuable to decision making in terms of tutorial strategies and instructional design for intelligent tutoring. Lastly, a number of directions for future research have been identified in the study.
A Novel Energy Harvesting Scheme for Mixed FSO-RF Relaying Systems

In this correspondence paper, we consider the performance of a mixed free-space optical and radio frequency (FSO-RF) relaying system with energy harvesting in the existence of both atmospheric turbulence and pointing errors. More specially, we assume that the direct current component of the FSO signal, which is generally filtered out, can be used for energy harvesting at the relay. We derive the exact closed-form expression for the outage probability of this setup in terms of the bivariate Fox-H function. We also provide the asymptotic analysis to obtain the diversity order. Finally, Monte Carlo simulations are carried out to verify the correctness of our analytical results.
Comprehensive Evaluation and Application of GIS Insulation Condition Part 2: Construction and Application of Comprehensive Evaluation Model Considering Universality and Economic Value

On the basis of obtaining many characteristic information and optimization evaluation indicators for characterizing the internal insulation condition of gas insulated switchgear (GIS), how to effectively classify weights and integrate information on the evaluation indexes, and establish a comprehensive evaluation model for insulation condition considering economic value and universality is another difficult problem to be solved. In this research, the above-mentioned 23 comprehensive evaluation index system, which are optimized to cover the PD hazard, SF6 insulation performance and time, environment and economy, are put forward, the idea of correcting the SF6 decomposition component monitoring data are proposed, and the general mathematical correction formula and method are given. The improved DS evidence fusion theory is used to make information decision fusion. The multi-class information fusion evaluation model of GIS equipment insulation condition is established. The fuzzy level analysis is used as the evaluation method framework to solve the problem of the index weight setting and membership determination of the evaluation indexes. The application of the example shows that the evaluation model and technology established in this research has achieved multiple types of information complementation, and the evaluation results are reasonable and credible.
Honey Encryption : implementation challenges and solutions

Most encryption techniques have one essential problem: they are vulnerable to brute-force attacks. In 2014, a new defence mechanism was introduced against brute-force attack and this mechanism was called Honey Encryption. Honey Encryption has been cited in several articles and it has demonstrated potential to be an additional security method alongside conventional encryption. This thesis will offer an overview of the current situation of Honey Encryption and it starts by explaining and discussing the challenges that have been brought up. These challenges include, among other things, the difficulty of producing the honey messages and identifying user typos from an attack. Some of the solution ideas that have been introduced since the introduction of Honey Encryption are discussed as well. There have been some new types of data that could be used with Honey Encryption, such as genomes and multi-dimensional data such as images and videos. These data types are discussed in connection with the use of Honey Encryption. As a last measure to provide overall understanding of the current situation of HE, this thesis provides an implementation model for Honey Encryption in a theoretical level, with some practical programming parts as well. The model is offered by going through the Honey Encryption scheme step by step. Together, these three steps: challenges, new data types, and an implementation model are meant to give a broad view of the situation of Honey Encryption gathered in one place.
Investigation of nonlinear landing gear behavior and dynamic responses on high performance aircraft

The dynamic responses simulation of aircraft as rigid body considering heave, pitch, and roll motions, coupled onto a tricycle landing gear arrangement is presented. Equation of motion for each landing gear consists of un-sprung mass vertical and longitudinal motions considering strut nonlinear stiffness and damping combined with strut bending flexibility. Initially, the nonlinear dynamic response model is subjected to an input of riding over staggered bump and the responses are compared with linear landing gear model. It is observed that aircraft dynamics and important landing gear events such as vertical, spin-up and spring-back are truly represented with nonlinear stiffness and damping model considering strut bending flexibility. Later, landing response analysis is performed, with the input from nonlinear flight mechanics model for several vertical descent rate cases. The aircraft and landing gear dynamic responses such as displacement, velocity, acceleration, and reaction forces are obtained. The vertical and longitudinal drag forces from the nonlinear dynamic response model is compared with “Book-case method” outlined in landing gear design technical specifications. From the reaction force ratio calculation, it is shown that for lower vertical descent rate case the predicted loads are lesser using nonlinear dynamic response model. The same model for higher vertical descent rate cases predicts higher ratios on vertical reaction for main landing gear and longitudinal reaction for nose landing gear, respectively. The scope for increase in fatigue life for low vertical descent rate landing covering major design spectrum and the concern for static strength and structural integrity consideration for higher vertical descent rate cases are discussed in the context of event monitoring on aircraft in services.
Research on Optimization of Regional Comprehensive Transportation Network Structure Based on Multi-objective Planning

The paper, based on the practical problem of optimizing regional comprehensive transportation network structure, and referring to economics theory, uses multi-objective planning to propose an optimization model based on supply-demand equilibrium, minimizing generalized cost, and green sustainability. Due to more than two objective functions in the model, it is troublesome to convert the model into a single-objective programming problem. Therefore, the model is considered to be converted into a bi-level programming problem. Based on the genetic algorithm, the specific algorithm for solving the model is given and conducted a simulation calculation. The results shows that the proposed method in this paper is verified to solve practical problem.
Advanced bioengineering technologies for preclinical research

ABSTRACT Current in vitro practices must overcome important challenges to compare favorably with human studies. The limited applicability of conventional in vitro assays and strategies can be explained by the fact that standard approaches do not enable recapitulation of the complexity of human tissues and physiological functions. To address this challenge, novel bioengineering tools, techniques and technologies are rapidly emerging to advance current fundamental knowledge and innovate in vitro practices. For example, organs-on-a-chip have recently appeared as a small-scale solution to overcome the transability, financial and ethical concerns associated with animal studies in drug discovery and development. In parallel, biomimetic interfaces are increasingly recapitulating 3D structures with tissue-like dynamic properties to allow in-depth investigation of disease mechanisms. This review aims at highlighting current bioengineering approaches poised to address the shortcomings of conventional in vitro research practices towards the generation of more effective solutions for improving human health. Graphical Abstract
An Efficient Framework for Privacy-Preserving Computations on Encrypted IoT Data

There are two fundamental expectations from cloud-IoT applications using sensitive and personal data: 1) utility and 2) privacy. Due to the complex nature of cloud-IoT ecosystems, there is a growing concern about data utility at the cost of privacy. While the current state-of-the-art encryption schemes protect users’ privacy, they preclude meaningful computations on encrypted data. Thus, the question remains “how can IoT device users benefit from cloud computing without worrying about privacy and security? ” Cloud service providers (CSPs) can leverage fully homomorphic encryption (FHE) schemes to build privacy-preserving services. However, there are challenges in adopting them for cloud-IoT devices. Thus, to foster real-world adoption of FHE-based solutions, we propose a framework called proxy reciphering as a service. We leverage schemes, such as distributed servers, secret sharing, FHE, and chameleon hash functions to tailor a solution that enables long-term privacy-preserving computations for encrypted IoT-device data and is secure even after a device-key compromise. We evaluate the framework by developing a testbed and measuring the latencies with real-world ECG records from TELE ECG database. We also analyze the security properties against major cyber threats. We observe that: 1) the computation and communication latencies are acceptable, and the security gains outweigh the latencies introduced; 2) the throughput of the reciphering proxy servers can be increased with preprocessing; and 3) a key-refresh scheme can limit the postcompromise attack exposure window. We infer that proxy reciphering as a service is a practical, secure, scalable and an easy-to-adopt framework for long-term privacy-preserving cloud computations for cloud-IoT applications.
Validation of the Newly Developed Graphical Inventory of Ethical Leadership (GIEL) Scale: Implications for Administrator Preparation and Business Leaders.

This quantitative pilot-study empirically tested the factor structure and reliability of the newly developed Graphical Inventory of Ethical Leadership (GIEL) scale, a Likert-style scale for quantifying key concepts related to ethical leadership. Correlations and principal components analyses were performed using pre-existing data as self-reports from a sample including 103 graduate students, within a Scholarship for Teaching and Learning project design, in an administrator preparation program at a private university located in a large southwestern state of the U.S. Face and content validity were demonstrated and empirical analyses revealed a three-factor structure for a unidimensional construct of ethical leadership. Internal reliability was above the pre-determined indices of Cronbach’s alpha > . 60 considered acceptable for scales in a pilot study. Potential uses of the GIEL include; as a baseline for setting and monitoring professional development goals, as evidence for recommending certification in administrator preparation programs, and as part of a 360-degree evaluation process.
Compound Asynchronous Exploration and Exploitation

Data efficiency has always been a significant key topic for deep reinforcement learning. The main progress has been on sufficient exploration and effective exploitation. However, the two are often discussed separately. Profit from distributed systems, we propose an asynchronous approach to deep reinforcement learning by combining exploration and exploitation. We apply our framework to off-the-shelf deep reinforcement learning algorithms, and experimental results show that our algorithm is superior in final performance and efficiency.
Multilingual Concept Dictionary

(1) The Chinese Concept Dictionary (CCD) implements Chinese corresponding to the English concepts in the WordNet 1.6 version. The total number of concepts is close to 100,000 (of which the total number of words far exceeds 100,000), including 66025 concepts of nouns, 12127 of verbs, 17915 of adjectives and 3575 of adverbs. The transfer of use rights to a number of research institutes and multinational corporations has promoted the progress of Chinese-English semantic analysis. (2) The Multilingual Concept Dictionary (MCD), based on CCD, Japanese WordNet, Korean WordNet and CoreNet, is built by automatic method and artificial expert checkup. Currently, under the multilingual conceptual dictionary, there are 8,400 Japanese concepts (mainly medium and high-level concepts in language) and 9,700 Korean concepts (also middle and high-level concepts), forming connection information with CCD concepts. Under the framework of WordNet, the basic concepts of East Asian languages (Chinese, Japanese and Korean) are generally described. (3) Please login to download the datafiles.
How “Smart” Are K-Pop Fans: Can the Study of Emotional Intelligence of K-Pop Fans Increase Marketing Potential?

Emotional intelligence has been studied in conjunction with many topics since its conception in 1995; however, there is no research linking the concept to the marketing potential for any specific music genre. Emotional intelligence involves the ability to recognize your emotions as they occur and regulate them accordingly, as well as the ability to infer information about the emotions of those around you. K-pop is a music genre that has benefitted from globalization and business strategies that incorporate social media such as YouTube. Teens are the primary demographic for K-pop marketing, which coincides with the teenage propensity towards celebrity worship that has been used to capture merchandising opportunities. Celebrity worship has been shown to be negatively correlated with emotional intelligence; however, the emotional intelligence level of celebrity worship of K-pop fans, and how they score on emotional intelligence in general has not been studied. Nor has the emotional intelligence level of aspiring K-pop idols been studied to determine if they can handle the pressure of the role. This paper attempts to set framework to show that studies should be conducted to determine whether a focus on the emotional intelligence levels of fans and idols could reveal marketing techniques and business strategies which might lead result in increased profits for K-pop music companies. ARTICLE HISTORY Received 20 March 2019 Revised 24 May 2019 Accepted 15 June 2019
Hierarchical Feature Extraction for Early Alzheimer’s Disease Diagnosis

Mild cognitive impairment (MCI) is the early stage of Alzheimer’s disease (AD). In this paper, we propose a novel voxel-based hierarchical feature extraction (VHFE) method for the early AD diagnosis. First, we parcellate the whole brain into 90 regions of interests (ROIs) based on an automated anatomical labeling (AAL) template. To split the uninformative data, we select the informative voxels in each ROI with a baseline of their values and arrange them into a vector. Then, the first stage features are selected based on the correlation of the voxels between different groups. Next, the brain feature maps of each subject made up of the fetched voxels are fed into a convolutional neural network (CNN) to learn the deeply hidden features. Finally, to validate the effectiveness of the proposed method, we test it with the subset of the AD neuroimaging (ADNI) database. The testing results demonstrate that the proposed method is robust with a promising performance in comparison with the state-of-the-art methods.
Causal Impact Analysis on Android Market

Google play store contains a large repository of apps for android users. Play store has two billion active users that have two million apps to download and use. App developers are competing to get a higher success rate and increase user satisfaction but little information is known to developers for succeeding in the android market. This paper presents a comprehensive analytical study on Google play store apps ratings, installs and reviews. This study focuses on the evaluation of the parameters required for the success of an app in different categories. For this purpose data of 10k apps and its reviews are analyzed using exploratory data analysis. This study focuses on finding a correlation between higher ratings, no of installs, reviews with app info like its category, size, and price. We are also going to analyze user reviews to get useful insights. The evaluation shows that personalization, productivity and games categories are performing very well in the android market both in terms of ratings and installs. Most high rated apps are sized below 40MB and priced below 30$, except game apps that are performing well even if they are bulky. Common customer complaints are functional errors and issues like infrequent updates, excessive ads, limited functionality and high purchase price.
Function of big-data over data streams

Data streams" is defined as class of data produced over "text, audio and video" channel in uninterrupted form. The streams are of unlimited length and may consist of ordered or unordered data. With these features, it is hard to store and process data streams with simple and static strategies. The processing of data stream poses four key challenges to researchers. These are countless length, concept- development, and concept-flow and feature development. Unlimited-length is because the amount of data has no limits. Concept- flow is due to sluggish changes in the theory of stream. Concept-development occurs due to existence of unfamiliar classes in data. Feature- development is due to development new features and deterioration of old features. To carry out any analytics data streams, the translation to knowledgeable form is important. The researcher in past have anticipated different strategies, most of the research is focused on difficulty of unlimited -length and concept-flow. The research work offered in the paper describes a well-organized string based methodology to process "data streams" and manage the challenges of unlimited length, concept-development and concept-flow. Subject areas Data mining, Machine learning.
An Improvised Word Recognition System by Hybridizing CNN and SIFT

This paper mainly focuses on developing an efficient word recognition system by combining the good parameters of the SIFT incorporating it with the CNN structure. Several advancements are made in the Automatic Speech Recognition (ASR) technology that brings to ease the machine to understand the natural language. The main constrain rise is the nature of the input speech signal, which makes it difficult to retain the original information. This can be overcome by hybridizing the (SIFT) Scale Invariant Feature Transform with (CNN) Convolutional Neural Network architecture. The noisy speech signal is initially passed through the pre –processing stage and converted to the spectrogram to extract the feature. The extracted features are now fed to the layers of CNN in order to train the model. At the testing phase the vectors are now cross matched and the maximum close weighted value from the fully connected layers lead to the output. The system performs with an efficiency of 94.78% in non-isolated environment.
Characterization of the physical capacity in children of the Chilean National Program of Cystic Fibrosis Caracterización de la capacidad física en niños del Programa Nacional de Fibrosis Quística de Chile

aDepartment of Physical Therapy, Faculty of Medicine, University of Chile bSchool of Kinesiology, Faculty of Health Sciences, University San Sebastián cSchool of Kinesiology, Faculty of Health and Odontology, University Diego Portales dSchool of Kinesiology, Faculty of Rehabilitation Sciences, University Andres Bello eDepartment of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands fDepartament of Respiratory Diseases, Clínica Santa María gDepartament of Pediatric Pulmonology, Hospital San Borja Arriarán, University of Chile hPediatric Pulmonology Unit, Hospital de Niños Dr. Luis Calvo Mackenna, Santiago, Chile iPediatric Pulmonology Unit, Hospital de Niños Roberto del Río, Santiago, Chile jPediatric Pulmonology Unit, Complejo Asistencial Dr. Sótero del Rio, Santiago, Chile kPediatric Pulmonology Unit, Hospital Dr. Exequiel González Cortés, Santiago, Chile lPediatric Pulmonology Unit, Hospital San Juan de Dios, Santiago, Chile
A Data-Driven Approach to Dynamically Adjust Resource Allocation for Compute Clusters

Nowadays, data-centers are largely under-utilized because resource allocation is based on reservation mechanisms which ignore actual resource utilization. Indeed, it is common to reserve resources for peak demand, which may occur only for a small portion of the application life time. As a consequence, cluster resources often go under-utilized. In this work, we propose a mechanism that improves cluster utilization, thus decreasing the average turnaround time, while preventing application failures due to contention in accessing finite resources such as RAM. Our approach monitors resource utilization and employs a data-driven approach to resource demand forecasting, featuring quantification of uncertainty in the predictions. Using demand forecast and its confidence, our mechanism modulates cluster resources assigned to running applications, and reduces the turnaround time by more than one order of magnitude while keeping application failures under control. Thus, tenants enjoy a responsive system and providers benefit from an efficient cluster utilization.