Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive Survey on Cloud Data Mining (CDM) Frameworks and Algorithms

Data mining is used for finding meaningful information out of a vast expanse of data. With the advent of Big Data concept, data mining has come to much more prominence. Discovering knowledge out of a gigantic volume of data efficiently is a major concern as the resources are limited. Cloud computing plays a major role in such a situation. Cloud data mining fuses the applicability of classical data mining with the promises of cloud computing. This allows it to perform knowledge discovery out of huge volumes of data with efficiency. This article presents the existing frameworks, services, platforms, and algorithms for cloud data mining. The frameworks and platforms are compared among each other based on similarity, data mining task support, parallelism, distribution, streaming data processing support, fault tolerance, security, memory types, storage systems, and others. Similarly, the algorithms are grouped on the basis of parallelism type, scalability, streaming data mining support, and types of data managed. We have also provided taxonomies on the basis of data mining techniques such as clustering, classification, and association rule mining. We also have attempted to discuss and identify the major applications of cloud data mining. The various taxonomies for cloud data mining frameworks, platforms, and algorithms have been identified. This article aims at gaining better insight into the present research realm and directing the future research toward efficient cloud data mining in future cloud systems.
DNA Sequencing Technologies

The recent advances in DNA sequencing technology, from first-generation sequencing (FGS) to third-generation sequencing (TGS), have constantly transformed the genome research landscape. Its data throughput is unprecedented and severalfold as compared with past technologies. DNA sequencing technologies generate sequencing data that are big, sparse, and heterogeneous. This results in the rapid development of various data protocols and bioinformatics tools for handling sequencing data. In this review, a historical snapshot of DNA sequencing is taken with an emphasis on data manipulation and tools. The technological history of DNA sequencing is described and reviewed in thorough detail. To manipulate the sequencing data generated, different data protocols are introduced and reviewed. In particular, data compression methods are highlighted and discussed to provide readers a practical perspective in the real-world setting. A large variety of bioinformatics tools are also reviewed to help readers extract the most from their sequencing data in different aspects, such as sequencing quality control, genomic visualization, single-nucleotide variant calling, INDEL calling, structural variation calling, and integrative analysis. Toward the end of the article, we critically discuss the existing DNA sequencing technologies for their pitfalls and potential solutions.
Dynamic Analysis of Lower Extremity Exoskeleton of Rehabilitation Robot

In order to control the exoskeleton of lower limbs accurately, it is necessary to establish an accurate dynamic model. In this paper, the lower extremity exoskeleton is simplified to five-bar mechanism, and the walking mode of human body is divided into single-leg support mode and double-leg support mode. Then, the two models of walking patterns were dynamically modeled and analyzed by the Lagrange method to obtain joint torque. Finally, the multi-body dynamics simulation software ADAMS is used to carry out the dynamic simulation, which verifies the correctness of the theoretical analysis, and provides a theoretical basis for motor selection and control system design of rehabilitation robot.
SNMFNet: an Improved PCANet for Image Classification

PCANet is a deep learning network that uses an orthogonal factorization form (i.e., principal component analysis, PCA) to learn the filter bank. It shows superior performance for image classification task. However, PCA only considers the statistics of the image patches while it does not consider the part-based matrix factorization (i.e. Non-negative Matrix Factorization). In this paper, we proposed to replace the PCA algorithm with the Semi-Non-Negative Matrix Factorization (SNMF) algorithm and to construct the SNMFNet. Specifically, first, we propose a seminon- negative matrix factorization algorithm, which uses the l2 norm regularizer to restrict base vectors to substitute the nonnegative constraint on basis matrix. Second, we introduce the SNMFNet, which uses the nonorthogonal matrix factorization algorithm to replace the orthogonal matrix factorization such as PCANet. Experimental results show that our proposed SNMFNet achieves superior performance to PCANet on several benchmark datasets.
Platform Moving Target Defense Strategy Based on Trusted Dynamic Logical Heterogeneity System

With the advent of the Internet age, the security of network has attracted more and more attention. In this paper, platform moving target defense technology is investigated to enhance network security. Based on platform dynamic migration system, relevant indictors of network security, such as defense benefits and defense effectiveness ratio, are devised to quantitatively analyze the gains and losses of network in the process of platform migration. Trusted Dynamic Logical Heterogeneity System (TALENT) is employed to obtain platform migration time and resource utilization and to calculate relevant indictors. Based on platform dynamic migration system and TALENT, two strategies, active random migration (ARM) and passive random migration (PRM), are proposed to defend against constant attacks. Three containers with different Linux operation systems are generated in the simulation of two platform migration strategies in TALENT. Experimental results show that ARM can get a greater defense gain when external attack is frequent. In comparison, PRM can make a faster response to intrusion detection system and find a more appropriate migration time for the system.
Utilization of 5 Senses as Composition of Network Communication for Format of Future Travelling

Nowadays, using the Communication Technology for travelling is continuously more progressive advance and important that the technology for travelling can incur the interaction between the local people and outsiders, which can change ideas or knowledge easier and faster. Map is one of the communication tools that obviously transforms Analog to Digital in this period. Most people use the map in mobile phone from travelling to find information of such place. If the place substitution format is transformed into Sense of Place from 5 senses of human, which will be travelling by the recognition via sight, taste, smell, hearing, and touch, these abstract recognition will be transformed into material object by using graphics as communication tool. Herein, the researcher had experimented in some area of Bangkok and Taipei to be prototype of travelling to other places in another format through 5 senses, and the result appeared the Area Identity or Social History of such area.
Dadu River Runoff Forecasting via Seq2Seq

Precise runoff forecasting is playing a very important role in flood control and economics dispatch control for hydroplant. This paper investigates the accuracy of standard long short-term memory neural network and sequence to sequence(seq2seq) in prediction of hourly, daily runoff. This paper is divided into five sections; the main contents are as follows: The first section mainly introduces the research status of some machine learning algorithms in runoff forecasting. Section 2 describes the basic principles of Long short-term machine. In the third section, the establishment process of runoff forecasting model is introduced. The basic principle of Sequence to Sequence and the design of seq2seq model in this paper are mainly introduced. The fourth section introduces the actual forecasting effect of applying Sequence to Sequence to Danba hydrological station in the upper reaches of Dadu River. Compared with the ordinary forecasting model, it proves that Sequence to Sequence has better forecasting accuracy and has certain practical application value. The fifth section gives a conclusion of this paper and puts forward the next work plan.
The Effect of Motion on the Perception of Material Appearance

We analyze the effect of motion in the perception of material appearance. First, we create a set of stimuli containing 72 realistic materials, rendered with varying degrees of linear motion blur. Then we launch a large-scale study on Mechanical Turk to rate a given set of perceptual attributes, such as brightness, roughness, or the perceived strength of reflections. Our statistical analysis shows that certain attributes undergo a significant change, varying appearance perception under motion. In addition, we further investigate the perception of brightness, for the particular cases of rubber and plastic materials. We create new stimuli, with ten different luminance levels and seven motion degrees. We launch a new user study to retrieve their perceived brightness. From the users’ judgements, we build two-dimensional maps showing how perceived brightness varies as a function of the luminance and motion of the material.
Large-scale Semantic Integration of Linked Data

A large number of published datasets (or sources) that follow Linked Data principles is currently available and this number grows rapidly. However, the major target of Linked Data, i.e., linking and integration, is not easy to achieve. In general, information integration is difficult, because (a) datasets are produced, kept, or managed by different organizations using different models, schemas, or formats, (b) the same real-world entities or relationships are referred with different URIs or names and in different natural languages,(c) datasets usually contain complementary information, (d) datasets can contain data that are erroneous, out-of-date, or conflicting, (e) datasets even about the same domain may follow different conceptualizations of the domain, (f) everything can change (e.g., schemas, data) as time passes. This article surveys the work that has been done in the area of Linked Data integration, it identifies the main actors and use cases, it analyzes and factorizes the integration process according to various dimensions, and it discusses the methods that are used in each step. Emphasis is given on methods that can be used for integrating several datasets. Based on this analysis, the article concludes with directions that are worth further research.
Malicious Software Detection utilizing Temporal-Graphs

In this work we propose a graph-based model that, utilizing relations between groups of System-calls, distinguishes malicious from benign software samples utilizing a behavioral graph representing their interaction with the operating system. More precisely, given a System-call Dependency Graph (ScDG) that depicts the malware's behavior, we first transform it to a more abstract representation, utilizing the indexing of System-calls to a set of groups of similar functionality, constructing thus a mutation tolerant graph that we call Group Relation Graph (GrG); we pointed out that behavior-based graph representations had not leveraged the aspect of the temporal evolution of the graph. Hence, the novelty of our work is that, preserving the initial representations of GrG graphs, we focus on augmenting the potentials of theses graphs by adding further features that enhance its detection abilities. To that end, we construct periodical instances of the graph that represent its temporal evolution concerning its structural modifications, creating another graph representation that we call Temporal Graphs. In this paper, we present the theoretical background behind our approach, and demonstrate the overall architecture of our proposed detection model alongside with its underlying main principles and its structural key-components.
Convolutional Neural Networks Grouped by Transcription Factors for Predicting Protein-DNA Binding Site

Understanding the specific interactions of transcription factors (TFs) and DNA is essential for comprehending regulatory processes in biological systems. Recently deep learning algorithms have outperformed conventional time-consuming and expensive methods such as ChIP-seq in predicting the sequence specificities of DNA-protein binding. However, because TF binding is a cell-specific behavior, most current deep learning methods build one model for each TF-cell line combination, which leads to problems such as the complexity of maintaining numerous models and the poor prediction performance of some models for cell lines without enough ChIP-seq data. Thus, it is useful to build models with both higher accuracy and wider range of application. We propose a method to build a series of Convolutional Neural Network (CNN) based models grouped by TFs, which are named TF models. Trained with the same database of 554 ChIP-seq data, the proposed TF models outperform DeepBind in the motif discovery task. On one hand, the amount of models has been reduced from 554 to 72, which extend the application scope of each model. On the other hand, TF models achieve higher AUC than Deepbind on 94.2% TF-cell line combinations. Moreover, we demonstrated that TF model achieve an average AUC 0.909 when predict the binding of TFs in cell lines that lack ChIP-seq data.
Stress Experienced by Turkish Nursing Students and Related Factors

Correspondence Author/Sorumlu Yazar: Ferhan Açıkgöz E-mail/E-posta: ferhan14@hotmail.com Received/Geliş Tarihi: 28.12. 2015 Accepted/Kabul Tarihi: 31.03. 2016 DOI: 10.5152/clinexphealthsci.2016. 061 ©Copyright by 2016 Journal of Marmara University Institute of Health Sciences Available online at www.clinexphealthsci. com ©Telif Hakkı 2016 Marmara Üniversitesi Sağlık Bilimleri Enstitüsü Makale metnine www.clinexphealthsci. com web sayfasından ulaşılabilir Abstract Öz
Broadening near-field emission performance enhancement in thermophotovoltaics.

The conventional notion for achieving high efficiency in thermophotovoltaics (TPVs) is to use a monochromatic emission at a photon energy corresponding to the band gap of the cell. Here, we prove theoretically that such a notion is only accurate under idealized conditions, and further show that when non-radiative recombination is taken into account, efficiency improvement can be achieved by broadening the emission spectrum, due to an enhancement in the open-circuit voltage. Broadening the emission spectrum also improves the electrical power density, by increasing the short-circuit current. Hence, broadening the emission spectrum can simultaneously improve the efficiency and power density of practical TPV systems. To illustrate these findings, we focus on surface polariton-mediated near-field TPVs. We propose a versatile design strategy for broadening the emission spectrum via stacking of multiple plasmonic thin film layers. As an example, we consider a realistic ITO/InAs TPV, and predict a conversion efficiency of 50% simultaneously with a power density of nearly 80 W/cm2 at a 1300 K emitter temperature. The performance of our proposed system far exceeds previous works in similar systems using a single plasmonic layer emitter.
Title Segmentation of lecture videos based on text : A methodcombining multiple linguistic features

In multimedia-based e-Learning systems, there are strong needs for segmenting lecture videos into topic units in order to organize the videos for browsing and to provide search capability. Automatic segmentation is highly desired because of the high cost of manual segmentation. While a lot of research has been conducted on topic segmentation of transcribed spoken text, most attempts rely on domain-specific cues and formal presentation format, and require extensive training; none of these features exist in lecture videos with unscripted and spontaneous speech. In addition, lecture videos usually have few scene changes, which implies that the visual information that most video segmentation methods rely on is not available. Furthermore, even when there are scene changes, they do not match with the topic transitions. In this paper, we make use of the transcribed speech text extracted from the audio track of video to segment lecture videos into topics. We review related research and propose a new segmentation approach. Our approach utilizes features such as noun phrases and combines multiple content-based and discourse-based features. Our preliminary results show that the noun phrases are salient features and the combination of multiple features is promising to improve segmentation accuracy.
Optimal Policies of Advanced Sleep Modes for Energy-Efficient 5G networks

We study in this paper optimal control strategy for Advanced Sleep Modes (ASM) in 5G networks. ASM correspond to different levels of sleep modes ranging from deactivation of some components of the base station for several micro-seconds to switching off of almost all of them for one second or more. ASMs are made possible in 5G networks thanks to the definition of so-called lean carrier radio access which allows for configurable signaling periodicities. We model such a system using Markov Decision Processes (MDP) and find optimal sleep policy in terms of a trade-off between saved power consumption versus additional incurred delay for user traffic which has to wait for the network components to be woken-up and serve it. Eventually, for the system not to oscillate between sleep levels, we add a switching component in the cost function and show its impact on the energy reduction versus delay trade-off.
Community detection across multiple social networks based on overlapping users

With the rapid development of Internet technology, online social networks have got fast development and become increasingly popular. Meanwhile, the research works across multiple social networks attract growing attention, among which community detection is quite important for online security problems, such as the user behavior analysis and abnormal community discovery. In this paper, a community detection method is proposed across multiple social networks based on overlapping users. First, the concept of overlapping users is defined, then an algorithm CMN_NMF is designed to discover the stub communities from overlapping users based on the social relevance. After that, we expand each stub community in different social networks by adding the users with strong similarity, and in the end different communities are derived out across networks. At last, experiments are carried out to verify the reasonability of community. Our method shows better performance in real datasets compared with others.
Multi-Robot Deep Reinforcement Learning with Macro-Actions

In many real-world multi-robot tasks, high-quality solutions often require a team of robots to perform asynchronous actions under decentralized control. Multi-agent reinforcement learning methods have difficulty learning decentralized policies because the environment appearing to be non-stationary due to other agents also learning at the same time. In this paper, we address this challenge by proposing a macro-action-based decentralized multi-agent double deep recurrent Q-net (MacDec-MADDRQN) which creates a new double Q-updating rule to train each decentralized Q-net using a centralized Q-net for action selection. A generalized version of MacDec-MADDRQN with two separate training environments, called Parallel-MacDec-MADDRQN, is also presented to cope with the uncertainty in adopting either centralized or decentralized exploration. The advantages and the practical nature of our methods are demonstrated by achieving near-centralized results in simulation experiments and permitting real robots to accomplish a warehouse tool delivery task in an efficient way.
Imaging three-dimensional magnetic systems with x-rays

Recent progress in nanofabrication and additive manufacturing have facilitated the building of nanometer-scale three-dimensional (3D) structures, that promise to lead to an emergence of new functionalities within a number of fields, compared to state-of-the-art two dimensional systems. In magnetism, the move to 3D systems offers the possibility for novel magnetic properties not available in planar systems, as well as enhanced performance, both of which are key for the development of new technological applications. In this review paper we will focus our attention on 3D magnetic systems and how their magnetic configuration can be retrieved using x-ray magnetic nanotomography. We will start with an introduction to magnetic materials, and their relevance to our everyday life, along with the growing impact that they will have in the coming years in, for example, reducing energy consumption. We will then briefly introduce common methods used to study magnetic materials, such as electron holography, neutron and x-ray imaging. In particular, we will focus on x-ray magnetic circular dichroism (XMCD) and how it can be used to image magnetic moment configurations. As a next step we will introduce tomography for 3D imaging, and how it can be adapted to study magnetic materials. Particular attention will be given to explaining the reconstruction algorithms that can be used to retrieve the magnetic moment configuration from the experimental data, as these represent one of the main challenges so far, as well as the different experimental geometries that are available. Recent experimental results will be used as specific examples to guide the reader through each step in order to make sure that the paper will be accessible for those interested in the topic that do not have a specialized background on magnetic imaging. Finally, we will describe the future prospects of such studies, identifying the current challenges facing the field, and how these can be tackled. In particular we will highlight the exciting possibilities offered by the next generation of synchrotron sources which will deliver diffraction limited beams, as well as with the extension of well-established methodologies currently implemented for the study of two-dimensional magnetic materials to achieve higher dimensional investigations.
Improving Generalization by Incorporating Coverage in Natural Language Inference

The task of natural language inference (NLI) is to identify the relation between the given premise and hypothesis. While recent NLI models achieve very high performance on individual datasets, they fail to generalize across similar datasets. This indicates that they are solving NLI datasets instead of the task itself. In order to improve generalization, we propose to extend the input representations with an abstract view of the relation between the hypothesis and the premise, i.e., how well the individual words, or word n-grams, of the hypothesis are covered by the premise. Our experiments show that the use of this information considerably improves generalization across different NLI datasets without requiring any external knowledge or additional data. Finally, we show that using the coverage information is not only beneficial for improving the performance across different datasets of the same task. The resulting generalization improves the performance across datasets that belong to similar but not the same tasks.