Reviewerly/exHarmony huggingface dataset:
Multi-cloud Services Configuration Based on Risk Optimization

Nowadays risk analysis becomes critical in the Cloud Computing domain due to the increasing number of threats affecting applications running on cloud infrastructures. Multi-cloud environments allow connecting and migrating services from multiple cloud providers to manage risks. This paper addresses the question of how to model and configure multi-cloud services that can adapt to changes in user preferences and threats on individual and composite services. We propose an approach that combines Product Line (PL) and Machine Learning (ML) techniques to model and timely find optimal configurations of large adaptive systems such as multi-cloud services. A three-layer variability modeling on domain, user preferences, and adaptation constraints is proposed to configure multi-cloud solutions. ML regression algorithms are used to quantify the risk of resulting configurations by analyzing how a service was affected by incremental threats over time. An experimental evaluation on a real life electronic identification and trust multi-cloud service shows the applicability of the proposed approach to predict the risk for alternative re-configurations on autonomous and decentralized services that continuously change their availability and provision attributes.
PENINGKATAN HASIL BELAJAR PESERTA DIDIK DENGAN MENGGUNAKAN MEDIA TEKA-TEKI SILANG PADA MATA PELAJARAN IPS DI KELAS III SDN 50 BULU’ DATU KOTA PALOPO

The main problem in this study is the lack of learning motivation of students in the implementation of learning in social studies subjects in class III SDN 50 Bulu 'Datu, which amounts to 23 students. Sources of data in this study were obtained directly from students and teachers of class III SDN Bulu’ Datu. Data collection techniques are carried out using observation, tests, interviews, and documentation. Data processing and analysis techniques used are analysis quantitative data and qualitative data. The results showed that: with the application of the crossword puzzle media the students' learning outcomes each cycle had increased. Based on student learning outcomes test of class III SDN Bulu’ Datu conducted in two cycles shows that the learning outcomes of students have increased, from cycle 1 the average value is 88 with a percentage of 100% completeness, while cycle II has an average score of 93 with a percentage of 100% and has met the KKM which is 70. Thus the results of this study can be concluded that, using the medium of crossword puzzles on social studies subjects can improve student learning outcomes.
A time-optimal path planning method for AUV docking under geometrical constraints

Challenges in ocean environment bring complexities for AUV docking, including ocean currents, obstacles and geometrical constraints. This paper proposed an evolutionary- based method, to optimize the docking path. First, the ocean environment and constraints are analysed and modelled. Next, the control points are designed to satisfy the model constraints. Then, the adaptive law and mutation operator are introduced in Particle Swarm Optimization (PSO), to achieve the global time- optimization. Finally, the proposed approach is evaluated via Monte-Carlo trials, which demonstrates a significant improvement with respect to the state-of-the-art approaches.
Performance evaluation and comparison of default and small private key rainbow digital signature scheme for IoT devices

With the advent of quantum computers, the security of cryptographic systems based on the problem of the discrete logarithm (PLD) will be compromised. The Internet of Things (IoT) security will be directly affected because it is largely dependent on theses cryptographic techniques. Systems of Multivariate Quadratic Equations (MQ) will become more relevant in cryptography due to the resistance against attacks from these computers. The Rainbow signature scheme is a well known post-quantum algorithm based on MQ. This paper presents a comparison between the default version of Rainbow with a modified one, applied to IoT devices.
An interview with Shafi Goldwasser

research-article Share on An interview with Shafi GoldwasserProviding Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio MicaliOctober 2019 Pages 53–99https://doi.org/10.1145/3335741.3335745Published:04 October 2019Publication History 0citation42DownloadsMetricsTotal Citations0Total Downloads42Last 12 Months4Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
The Algebra of Hidden Relations as a Means of Modeling Statistical Databases

An analogue of relational algebra is considered that is used to model databases when attribute values are not completely visible to the user but only up to a certain equivalence relation on domains. This case is important for so-called statistical databases, when it is necessary to provide and comprehensively analyze the summarized information on data stored in a database, but the user does not have access to personal data in the database. Similarly to the relational approach, this case introduces the concept of a hidden relationship and a system of operations with hidden relationships. Some correlations between these operations are shown and the query language is defined.
Long‐term tracking with fast scale estimation and efficient re‐detection

In long-term tracking applications, occlusion and scale variation are common attributes which cause performance degradation. Existing solutions use heavy calculation to deal with these problems, without considering the real-time implementation. Therefore, the authors propose a novel long-term tracker with fast scale estimation and efficient re-detection scheme to maintain real-time speed and favourable accuracy. Specifically, the authors integrate a distance metric method into correlation filter-based tracker to realise fast translation calculation and scale estimation. In addition, the authors advocate a keypoint-matching based confidence indicator to verify the tracking result and activate the re-detection module when the occlusion happens. The authors test our approach on challenging sequences with scale variation and occlusion. Experiments demonstrate that our proposed tracker procures preferable effect than state-of-the-art methods in the aspect of both speed and accuracy.
Radiometric Quality Assessment of Multitemporal Optical Images in High Latitudes Considering Seasonal Influence

This paper presents an assessment method for radiometric quality of multitemporal optical remote sensing images in high latitudes. In high latitudes, some indicators of radiometric quality assessment are greatly affected by seasonal factors, specifically the ice and snow. The quality of images among the multitemporal series cannot be well distinguished because of the high reflectivity of ice and snow. The proposed method includes two main parts: (1) The multitemporal optical images are firstly divided as two groups automatically based on the calculated ice-snow index, one with snow and ice and the other group without ice and snow. (2) Evaluations are derived for the two groups of images based on selected different references, which are determined according to the indicator changing tendency among the series of multitemporal images. Furthermore, a weighted integration method is introduced to calculate the quality indicators for each image from its composite bands. The proposed method is tested in the assessment of a series of Landsat-5 multitemporal images. By comparing with the traditional methods of image quality assessment that did not consider the seasonal influence, the method derives assessment result that was closer to the real quality status of the images. And therefore, it can provide consistent evaluation results among the multitemporal images.
Machine Learning for the Identification and Classification of Key Phrases from Clinical Documents in Spanish

The key phrases play a very important role because they allow us to characterize the content of a text in a short way and even answer questions related to it. Due to the above, the extraction and classification of these words are a competent problem in different areas of knowledge such as Information Retrieval, Natural Language Processing, among others. This research presents a proposed solution for the identification and classification of key phrases through automatic learning algorithms, in electronic documents related to health topics written in Spanish. According to the experimental results, the proposed algorithm achieves 94% of correctly classified key phrases and 72% of precision for the identification phase.
A Fuzzy Linearity-based Approach for Contour Salience Points Selection

Shape analysis is an essential task for object recognition in images. In this paper, a new approach for detecting shape salience points based on fuzzy properties of its contour is proposed. First, the notion of "curvacity" is introduced for describing to which degree a contour segment is curved; this property is modeled on the basis of the linearity, a fuzzy property introduced in [1]. Second, since salience points are usually around sharp curves, this paper proposes to locate such points in places with high membeship degree to curvacity and, more specificaly, in local extrema of this property. For this analysis, a multiscale approach is applied in order to find points which keep its saliency at several scales; this multiscale analysis increases the robustness of the proposal, avoiding noisy points. Finally, we will show the goodness of our proposal by analyzing the contour saliency in a set of shapes with different characteristics.
Specializing K Nearest Neighbor for Content Based Segmentation of News Article by Graph Similarity Metric

This research is concerned with the graph based KNN version as a text segmentation tool. The text segmentation is mapped into a binary classification where each paragraph pair is classified into boundary or continuance, and the graph is known as the visualized text representations. In this research, we encode the paragraph pairs which are generated from full texts into graphs, define the similarity between graphs, and modify the KNN algorithm by replacing the existing similarity metric by the proposed one for the text segmentation task. The proposed version is empirically validated as the better one in segmenting news articles. It needs to classify an entire text into its corresponding domain before carrying out the text segmentation.
PENGARUH PENGGUNAAN METODE SUGESTOPEDIA TERHADAP KETERAMPILAN MENULIS CERPEN SISWA KELAS X IPA SMA NEGERI 1 KECAMATAN LAREH SAGO HALABAN KABUPATEN LIMA PULUH KOTA

The Impact of Suggestopedia Methods on the Skill of Writing Short Stories of Students of Class X SMA Negeri 1 Kecamatan Lareh Sago Halaban Sub-District of Lima Puluh Kota.Writing skills one of the active productive language skills.the following method is one of the learning methods that can provide opportunities for success writing writing achievement. This study aims to 1) describe the skills of writing short stories students without using Sugestopedia method of class X students SMA Negeri 1 Kecamatan Lareh Sago Halaban District Lima Puluh Kota; 2) describe the skills of writing short stories using Sugestopedia method of class X students of SMA Negeri 1 Lareh Sago Halaban District, Lima Puluh Kota Regency; 3) to know the influence of Sugestopedia method on the skill of writing short stories of students of grade X SMA Negeri 1 Kecamatan Lareh Sago Halaban District, Lima Puluh Kota District. This research type is quantitative research by using experiment method. In this study used two classes, namely experimental class and control class. Experimental research is an investigation designed in such a way that the phenomenon or event can be disseminated from the influence of other influences. This is evidenced by the change in student scores, which in the control class the mean / mean () is 71.25. After applied Sugestopedia method in learning process in experimental class obtained the change of student learning result with average value () 80,5. Based on t test result(21,31> 1,67) , it is concluded that there is a significant difference between the result of short story writing skill in experiment class using Sugestopedia method and without using suggestion method in control class. This can be seen from the result of comparison of tcount with ttable (21,31> 1,67), because tcount is bigger than ttable then Ha (alternative hypothesis) accepted, it means there is influence of Sugestopedia method in class X SMA Negeri 1 Kecamatan Lareh Sago Halaban District Lima Puluh Kota. Kata kunci: Pengaruh metode Sugestopedia, menulis,cerpen, siswa
Comparisons of Various ELM Based Multi-View Clustering Methods for WDSNs

Unsupervised or semi-supervised learning algorithms are essential for solving practical clustering problems due to the real world data are mostly unlabeled or partially labeled. Some common challenges such as poor clustering accuracy and slow learning speed are still need to be solved. Recently, various extreme learning machine (ELM) based methods have been found in clustering due to its extremely fast learning speed and excellent approximation ability. In this paper, some brief introductions of general single-viewed (SV) and multi-viewed (MV) clustering algorithms are firstly included and then the investigation on combining ELM and MV algorithms are emphasized. Furthermore, combination of ELM and SV methods to solve MV clustering problems is attempted and satisfactory simulation results are obtained. Experiments are performed to compare the clustering performance in terms of effectiveness and efficiency among various of algorithms on a set of real world wireless distributed sensor networks (WDSNs) data. It is shown that the ELM based clustering algorithms achieve better clustering accuracy with less time consumption than conventional methods. Our attempts of the combining ELM and SV algorithms achieve comparable accuracy to ELM based MV algorithms but with even better time efficiency.
A Cutting-Edge Unified and Stable Rule Design Pattern

Often, changing market dynamics require business applications to quickly and efficiently adapt to the needs of the ensuing business environment. Business Rules excel in delivering software solutions that are implicitly adaptable to changing business requirements; thus they can prove to be an effective tool to provide necessary flexibility and control for rapidly deploying changes across a wide array of business operations. When a proper design is employed, business rules provide a robust and capable way of enabling enterprise software that adapts to changing business needs. In other words, business rules find varied applications and ways of use, for example, managing a pending problem, using it as production rules and for facilitating collaboration between various systems, etc. However, despite a plethora of tools and technologies available, most organizations still find it difficult to define or model workable business rules explicitly. Furthermore, from a macroscopic point of view, rules are important and inseparable artifacts in governing a software application to make it comply with the system goals. In this paper, the current ways to manage business rules along with their pitfalls are discussed, and therefore, a new approach for developing rule-based business architectures is proposed. The proposed approach allows for both managing and reusing business rules.
Adaptive H Kalman filtering for stochastic systems with nonlinear uncertainties

In this chapter, an adaptive H∞ Kalman filtering approach is discussed with an application to a typical problem of noncooperative maneuvering target tracking. First, we give the problem of H∞ state estimation for dynamic systems with uncertainties. With discussion of the designing conservativeness of the usual H∞ filter (H∞F), we present the adaptive H∞ filtering approach, formed as the switched H∞ Kalman filter (SH∞F) and the optimal-switched H∞ Kalman filter (OSH∞F). SH∞F uses a dual-mode filtering structure, being error-tolerant to uncertainties including inner modeling error and outer disturbance, so that the excessive loss of filtering optimality, called conservativeness of the traditional single-mode H∞F, can be improved remarkably. Furthermore, OSH∞F optimizes the switching mechanism of the SH∞F by minimizing a unified performance evaluation function which we named optimality-robustness cost function. With introduction of an auxiliary parameter space, OSH∞F can optimize the conservativeness of the traditional H∞ filter, achieving the worst-case optimal estimation and showing a unified form of H∞ filtering. An example of maneuvering target tracking in space has illustrated that the adaptive H∞ filters can achieve much better tracking performance than many typical filters.
Access Control Models for Smart Environments

The Internet of Things (IoT) is the extension of the internet to our daily life where common objects, from toothbrush to watches, collect information and interact with their environments with no or little human intervention. These different smart objects interact directly with their physical environments. They collect and transfer sensitive and private data from various users. This puts security and privacy issues at the forefront: the ability to manage the digital identity of millions of people and billions of devices is fundamental for success. As most of the information contained in IoT environment may be personal or sensitive data, there is a requirement to support anonymity and restrain access to information. This paper will focus on access control and authentication mechanisms as well as supporting the cryptography algorithms in constrained devices.
Compatibility-Based Evidential Reasoning

In this article, we utilize an Extension Principle for set-to-point functions to calculate fuzzy-valued belief and plausibility for a fuzzy hypothesis from a fuzzy belief structure based on decomposing the fuzzy hypothesis into its α-cuts and inferring the belief and the plausibility of each α-cut from the belief structure. Aggregating belief and plausibility measures calculated for each α-cut yields fuzzy-valued belief and plausibility measures. Moreover, we extend this method to calculating belief, plausibility, and normative measures by decomposing both the fuzzy hypothesis and the belief structure into their α-cuts and then calculating such measures for each α-cut of the hypothesis from each α-cut of the belief structure and aggregating them into fuzzy-valued uncertainty measures. We demonstrate through a numerical example that our new method yields results that are commensurate with the intuition that hypotheses that have more similarity with the focal elements of a belief structure are more strongly supported by that belief structure.
An Object Tracking Algorithm Based on Motion-Tuned Continuous Wavelet Transform

A continuous wavelet transform (CWT) based object tracking algorithm is proposed. Spatio-temporal motion-tuned wavelet is used to extract motion parameters like velocity, orientation, position and scale. CWT is used to define three energy densities which are used as estimators. Sequential optimization of parameters are done in a frame-by-frame manner which allows the algorithm to track moving objects. The problem of setting initial scale parameter is improved by a new functional relation between radius of the target and velocity using a third degree polynomial constructed from 2D-Chebyshev polynomials. Experimental results show that the new functional relation gives reasonable initial scale parameter without any analysis of huge amount of previous data and the revised algorithm tracks the object in a better way.
Meta_LASH Tree: Bagging at Meta Level Using LASSO Regression Hoeffding Tree for Streaming Data

Building predictive model for streaming data is a major challenge as it involves high speed and huge amount of data stream which is impossible to store and process the entire data. Since the distribution of streaming data changes over time, the traditional static model is not suitable for streaming data. Nowadays most of the streaming data solutions have been centered around ensembles, which integrates predictive responses from multiple homogeneous or heterogeneous base learning algorithms. In this paper a novel, memory efficient and practically useful Ensemble Bagging at Meta level Framework denoted as Meta_LASH Tree is proposed which comprises of two phases. In the first phase a memory efficient base learner named as LASSO Regression Hoeffding Tree (LASH Tree) is constructed, which incorporates Hoeffding Tree and LASSO Regression, that produces better predictions and better insights than using both the models separately. This hybrid model is highly interpretable and can have an insights of both linear and non linear relationship of the data. In the second phase, the predictive responses from the previously constructed LASH Tree are collected using Ensemble Bagging approach, and the dominant base learner is selected by the Meta Learner. The proposed frame work is designed in such a way to reduce the memory usage and overfitting issue of the existing algorithms. It is also designed to enhance the prediction accuracy.
Modified Architecture for Detection of Moving Objects

Object detection is one key aspect in most computer vision applications. The most common technique used for moving article identification is backdrop subtraction. Designing a robust algorithm for background subtraction has to overcome many obstacles primarily in form of illumination variations, poor video quality leading to noise, dynamic background, etc. This paper proposes a modified architecture using background subtraction and frame difference technique for the identification of moving articles. The proposed technique helps to adapt to sudden transformations in the background image. The results exhibit that the suggested method is simple, efficient and real-time with better operational speed and more accuracy. The entire design is implemented on Zed board manufactured by Xilinix consisting of Zynq 7000 FPGA using Vivado 2018.1.
Saliency Map Extraction in Human Crowd RGB Data

Saliency map in human crowded scene is a prediction of regions which attracts human visual attention. Humans have an ability to analyze the context of visual scene and focus their attention to salient regions in the crowd scene. In this work, we propose a novel convolutional neural network based method for saliency prediction. Unlike classical works on crowd scene using hand-crafted face features, our model extracts deep features using convolutional layers from image classification model and learns the global context using large receptive convolutional layers. Self-attention mechanism is applied to detect the dependency between elements of feature maps. This model over-performed state-of-the-art methods on the saliency in human crowd Eyecrowd dataset.
A Note on Artificial Intelligence and Statistics

Now that data science receives a lot of attention, the three disciplines of data analysis, databases, and sciences are discussed with respect to the roles they play. In several discussions, I observed misunderstandings of Artificial Intelligence. Hence, it might be the right time to give a personal view of AI and the part of machine learning therein. Since the relation between machine learning and statistics is so close that sometimes the boundaries are blurred, explicit pointers to statistical research are made. Although not at all complete, the references are intended to support further interdisciplinary understanding of the fields.