Reviewerly/exHarmony huggingface dataset:
Design and Development of Optimized Scheduling Algorithm for Software As A Service Based Applications in Secure Cloud Environment

In layman terms, cloud computing can be described as sharing of computing services on internet. In this paper, a brief introduction to cloud computing, its features, applications, and its limitations has been discussed. As, cloud computing involves execution of multiple activities simultaneously, management is required for the smooth functioning of applications in a secured manner. Different load balancing techniques for the efficient resource provisioning of applications have also been discussed. The Paper concludes with a description of a framework for the development of optimized scheduling algorithm for Software as a Service based applications in secure cloud environment.
The tactical distributed ledger: an enabling technology for deploying resilient decentralized systems

This work develops a principled approach for coordinating decentralized systems that can exchange information, issue and service requests, and perform outcome-focused actions together. It harnesses the resiliency and robustness offered by blockchain frameworks to enable sustained activity in the presence a diverse set of failure modes, operating constraints, and changing task requirements. We accomplish this through the Tactical Distributed Ledger, a distributed computer that can accommodate a wide range of sensors, agents, vehicles, and devices to create a decentralized system. We have used this framework to deploy a multi-participant system that combines sensing, vehicle mobility, and situational awareness to perform coordinated activities in a simulated city environment. The key mechanism that enables this interaction is a robust auction framework implemented on top of a smart contract blockchain system. This paper details the design, components, and features of the auction system.
The Comparative Analysis of Single-Objective and Multi-objective Evolutionary Feature Selection Methods

This research presents a comparative analysis of single-objective and multi-objective evolutionary feature selection methods over interpretable models. The question taken in this research is to investigate the role of aforementioned techniques for feature selection on classification model’s interpretability as well as accuracy. Since, feature selection is a non-deterministic polynomial-time hardness (NP-hard) problem therefore exhaustively searching for all the possible feature sets is not computationally feasible. Evolutionary algorithms provide a very powerful searching mechanism that is utilized for candidate feature generation in a reasonable time frame. Single-objective (SO) algorithms are generally geared towards finding a subset of candidate feature set which achieves highest evaluation score e.g. classification accuracy. On the other hand, multi-objective (MO) methods are relatively more comprehensive than their counterparts. MO feature selection algorithms can simultaneously optimize two or more objectives such as classification accuracy of a final feature set along with the cardinality of the feature set. In this research, we have selected two representative feature selection algorithms from both the groups. Decision tree and a rule-based classifiers are used for the performance evaluation in terms of interpretability i.e. model size, and predictive accuracy. This research is undertaken to investigate application of SO and MO feature selection methods on small to medium sized classification datasets. The experimental results on 3 interpretable classification models indicate that the relative differences between the two set of models on small datasets may not be much pronounced, yet for the medium-sized datasets MO models provide a promising alternative. Although multi-objective techniques resulted in smaller feature subsets in general, but overall the difference between both the single-objective and the multi-objective feature subset selection techniques, investigated in this study, is not statistical significant.
Towards path‐based semantic dissimilarity estimation for scene representation using bottleneck analysis

In natural images, it remains challenging to estimate dissimilarities between image elements for scene representation due to gradual variations of illuminations, textures or clutters. To tackle this problem, we utilise a path‐based bottleneck analysis method that captures the semantic information between image elements to measure the dissimilarity. By integrating both the spatial continuity and feature consistency into the understanding of the semantic information, we detect the bottlenecks on the proposed double‐S path to define the bottleneck distance, which demonstrates a favourable capability of grouping image elements that follow a similar pattern and separating different ones. In the experiments, the method is proved to be robust to noises and invariant to changing illumination and arbitrary scales in natural images. Tests on some challenging datasets validate the advantage of applying the path‐based bottleneck distance in image ranking and salient object detection.
Modernization of Environmental Education with the Use of Project-Based Learning, Outdoor Education, and Mobile Learning Supported by Information and Communication Technology

The paper proposes an innovative model of environmental education which combines mobile learning with student projects, outdoor education, and various information and communication technologies (ICTs), mainly the geographic information system and the global navigation satellite system. The aim of the model is to achieve modernization and efficiency of environmental education in the secondary education. Model design is based on the use of project-based learning, where students address the real problems working on projects, outdoor education moves part of the educational process to the exterior, and mobile learning allows the effective use of students’ mobile phones (smartphones). The global environmental problems were chosen as a main topic since they link abovementioned pedagogical principles and ICT and allow students a direct contact with the environmental problems occurring often in the immediate vicinity of the school or residence. The model gives students the opportunity for cooperation and confrontation of their ideas, planning their own activities, mapping, and self-learning but mainly active participation in solving environmental problems at a local level. Through the effective combination of different pedagogical approaches, the model has a strong potential to develop a wide range of additional knowledge, skills, and attitudes and increase student motivation to learn.
Open Problems in Wavelet Theory

We present a collection of easily stated open problems in wavelet theory and we survey the current status of answering them. This includes a problem of Larson ((2007) Unitary systems and Wavelet sets. In: Wavelet analysis and applications. Applied and Numerical Harmonic Analysis. Birkhäuser, Basel, pp 143–171) on minimally supported frequency wavelets. We show that it has an affirmative answer for MRA wavelets.
Groundwater Softening Materials Based on Natural Raw Materials

The studies on water softening using sorbents based on modified sunflower husk and buckwheat are given. It is shown that these crop wastes can be raw materials for sorption materials for softening groundwater. The sorption capacity of the materials obtained under static and dynamic conditions for hardness salts was studied, a regeneration solution was selected, regeneration was carried out. The time of the protective action of the filter and the achievement of its full dynamic capacity by the method of GOST and the author's method are determined.
Nonlinear encryption system based on the fractional Fourier operators and truncation operations

Abstract In this paper, the fractional Fourier operators, the random phase masks and the nonlinear operations of amplitude and phase truncations are utilized to encrypt and decrypt images. We use the following fractional Fourier operators in the image encryption-decryption system: the fractional Fourier transform, the fractional traslation and the fractional correlation. The proposed encryption system uses nonlinear operations, such as phase encoding and truncation operations, in order to increase the security of the encrypted image. The encryption-decryption system has the following security keys: one fractional order of the fractional Fourier transform, two random phase masks and two pseudorandom code images. When all the proper security keys are used in the decryption system, the obtained decrypted image is a replica of the image to encrypt.
Sequence theory for classification in multi-label ATR classification tasks

This work examines the scenario of ATR classification in multi-label settings by using the framework of a classification sequence. Classification tasks are often composed of a sequence of identification tasks that together, generate an overall classification. For instance, objects may be sorted and classified as one particular target type and then those targets are further identified. Rather than passing all objects through each classifier, a sequence of classifiers may be used to identify objects without the need to process data through each classifier. Such sequences exist for two-label outcomes (such as target and non-target) and have been called: Believe the Negative, Believe the Positive, and Believe the Extremes. In each of these sequences, the first classification system is able to identify objects such that only a portion of objects must be passed to the second system for identification. However, to extend these sequences to k-labels, a new definition of the ordering on the labels must be generated in order to incorporate all k-labels into the classification sequence. In this work, we develop the mathematical structures that exist for a k-label classification sequence, provides formula for both the optimal performance and operational cost of these sequences, and examines the performance of such sequences under a variety of operating conditions. Conceptually, we will begin and demonstrate these results with a 3-label ATR system. In conclusion, this work will demonstrate the utility of using a sequence to fuse information in a multi-label classification task.
Self-training for Cell Segmentation and Counting

Learning semantic segmentation and object counting often need a large amount of training data while manual labeling is expensive. The goal of this paper is to train such networks on a small set of annotations. We propose an Expectation Maximization(EM)-like self-training method that first trains a model on a small amount of labeled data and adds additional unlabeled data with the model's own predictions as labels. We find that the methods of thresholding used to generate pseudo-labels are critical and that only one of the methods proposed here can slightly improve the model's performance on semantic segmentation. However, we also show that the induced value changes in the prediction map helped to isolate cells that we use in a new counting algorithm.
Analysis of Digital Currency Investment

Digital currency has long been a hot and controversial topic in finance. Blockchain technology seems like having a promising application in finance, but there are both positive attitude and negative attitude towards digital currency. The meaning and status of digital currency are introduced in the paper. And the definition and characteristics of digital currency are given then. The investment of digital currency is analyzed according to the characteristics of digital currency. At last, based on the above analysis, this paper point out that the price of digital currency is higher than its value or its cost in 2017; digital currency is now not a safe-haven asset but a financial product with high risk and high income.
A Sparse-denoising Auto-encoder with Generalization Ability on Sub-network

The original image data usually has high dimension and need to be solved by extracting the main features. Due to the design of the auto-encoder, we can apply it to obtain the main features from data if constraining the dimensions of the hidden layer to be smaller than the dimension of the input layer. The sparse auto-encoder has strong dimensionality reduction ability, but its hidden layer has low generalization ability. If the auto-encoder has both strong dimensionality reduction ability and strong generalization ability, it can be applied to more datasets. Therefore, this paper proposed a new auto-encoder for high-dimension data. By adding noise to the network, the new auto-encoder increases the robustness of the sub-network and improves the generalization ability of sub-learning and improves the generalization of the entire network at last. Experiments show that our method is better than the other sparse auto-encoder and the denoising auto-encoder, and it can learn the main features better and can be widely applied to the image classification and image recognition.
Critical Systems for Smart Cities: Towards Certifying Software

Critical systems require high reliability and are present in many applications. Standard techniques of software engineering are not enough to ensure the absence of unacceptable failures and/or that critical requirements are fulfilled. Verifying and certifying systems for Smart Cities is one of the challenges that still require some effort. Smart Cities models may be seen as Cyber-Physical Systems and they may be formalized as Finite State Machines. We discuss how to reason over these models as Finite State Machines formalized in a logical background from which it is possible to provide certified software for the Smart Cities domain.
Mixed Virtuality in the Communication of Cultural Heritage

ICTs are today frequently used in the communication of cultural heritage. Among these Mixed Virtuality offers a visualization system on smart devices where, depending on the needs of dissemination, the reconstructions of objects and spaces can be viewed in Virtual Reality or Augmented Reality mode. As a case study, an application is proposed in the archaeological area of the Acropolis of Volterra.
STRATEGIES OF OPTIMAL IMPLEMENTATION OF PRINCIPLES OF TOLERANCE IN CONTEMPORARY SCIENTIFIC DISCOURSE

The article analyzes the implementation of the principles of tolerance in the modern world. The lack of a single definition of this concept makes the researchers clarify the definition of tolerance in terms of its importance as a virtue. The directivity of tolerance may be different, but it is important that it does not go beyond universal human values that protect the sound development of the society and individuals. This leads to the necessity of a scientific substantiation of the tolerance limits. The development of critical thinking contributes to the fact that the individual is really able to be imbued with the diversity of cultures and ways of life within the framework of universal values, not exceeding the limits of tolerance. The formation of this type of thinking contributes to increasing the empathy of the individual, flexible perception of different types of being, adequate decision-making on different problem situations, which contributes to the ability to determine the limits of tolerant attitudes towards the Other.
On privacy-protected outsourced processing for mobile multimedia

Mobile devices are usually computationally restricted in carrying out complicated processing algorithms on its multimedia document. While cloud computing provides resources for customers, a major concern is the customer data's privacy and security. The information-theoretic secure (ITS) approach has some advantages over the homomorphic encryption approach. ITS is faster and requires much shorter encryption. In this paper, we proposed a framework based on the ITS paradigm using the linear secret sharing scheme for image and audio processing tasks on mobile devices. Our experiments were carried out on the Amazon EC2 commercial cloud platform with better performance than existing ones.
A rapid convergent genetic algorithm for NP-hard problems

This paper proposes a novel solution for the Traveling Salesman Problem, a NP (non-deterministic polynomial-time) hardness problem. The algorithm presented in this paper offers an innovative solution by combining the qualities of a Nearest Neighbor (NN) greedy algorithm and the Genetic Algorithm (GA), by overcoming their weaknesses. The paper analyses the algorithm features/improvements and presents this implementation on a FPGA based target board. The experimental results of the algorithm, tested in software (Matlab) and implemented on a portable hardware (FPGA for GA, Raspberry Pi 3 for NN) shows a significant improvement: a shorter route, compared to NN , a shorter running time (less generations) compared to traditional GA , and reaching the optimal minimum (validated by Matlab). In real time, the algorithm runs on a handheld console, which can also act as a server, through a custom Android client application.
Towards High-Resolution Multi-Stage Security Games

In recent years, we have seen a large number of cyber-incidents, which demonstrated how difficult it is to prevent cyber-breaches when facing determined and sophisticated attackers. In light of this, it is clear that defenders need to look beyond the first lines of defense and invest not only into prevention, but also into limiting the impact of cyber-breaches. Thus, an effective cyber-defense must combine proactive defense, which aims to block anticipated attacks, with reactive defense, which responds to and mitigates perceived attacks (e.g., isolating and shutting down compromised components). However, planning defensive actions in anticipation of and in response to strategic attacks is a challenging problem. Prior work has introduced a number of game-theoretic security models for planning defensive actions, such as Stackelberg security games, but these models do not address the overarching problem of proactive and reactive defenses in sufficient detail. To bridge this gap, we introduce a modeling approach for building high-resolution multi-stage security games. We describe several approaches for modeling proactive and reactive defenses, consider key modeling choices and challenges, and discuss finding optimal defense policies. With our study, we aim to lay conceptual foundations for developing realistic models of cyber-security that researchers and practitioners can use for effective cyber-defense.
Formal Concept Analysis for Partner Selection in Collaborative Simulation Training

In this paper, we propose to apply formal concept analysis (FCA) technique to partner selection for joint simulator training. Simulator training is an important tool for preparation of seafarers in a classroom. However, the costs to acquire simulator centers is high. That is why the educational institutions and centers can only afford limited number of simulators. Though the price for simulator classes are quite high, it still more advantageous to train sailors in class than offshore. New cloud-based technologies allow to connect simulators situated in different places, including cross-country communication. This makes it possible to carry out joint training of different simulator types. In the study, we elaborate the approach of formal concept analysis is used to facilitate partner selection from the pool of potential collaborating institutions.
Pengaruh Strategi Language Experience Approach (LEA) terhadap Kemampuan Menulis Teks Cerita Pendek

RINGKASAN Penelitian ini bertujuan untuk mendeskripsikan pengaruh strategi Language Experience Approach (LEA) terhadap kemampuan menulis teks cerita pendek meliputi, penggambaran tokoh dan penokohan, perangkaian plot atau alur, penguraian nilai kehidupan atau amanat. Jenis penelitian yang digunakan adalah kuantitatif eksperimen one group pretest-posttest. Populasi dan sampel siswa kelas IX A SMP Negeri 1 Pademawu. Instrumen berupa tes dan rubrik penilaian. Data penelitian ini berupa hasil skor siswa dalam melakukan pretes dan postes menulis teks cerita pendek. Hasil penelitian ada siginifikansi pada kemampuan penggambaran tokoh dan penokohan, perangakain plot atau alur, penguraian nilai kehidupan atau amanat, dan kemampuan menulis teks cerita pendek secara keseluruhan. Kata Kunci: strategi Language Experience Approach (LEA), kemampuan menulis, teks cerita pendek ABSTRACT: The aim of this research is to describe the influence of Language Experience Approach (LEA) strategy to the ability to write short story texts including character's portrayal and characterization, plot or plot sequences, and deciphering life values or message. The type of research used is a quantitative experimental research by using one group pretest-posttest. The population and sample are the students of IX A class at Junior High School 1 Pademawu. The instruments are in the form of tests and assessment rubrics. The research data is in the form of student scores in doing the pretest and posttest in writing short story texts. The results of the research shows that there is a significance of the ability to describe characters portrayal and characterization, plot or plot sequences, deciphering of life values or message, and the ability to write short story texts as a whole. Keywords: Language Experience Approach (LEA) strategy writing ability, short story text Normal 0 false false false IN X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable{mso-style-name:Table Normal;mso-tstyle-rowband-size:0;mso-tstyle-colband-size:0;mso-style-noshow:yes;mso-style-priority:99;mso-style-qformat:yes;mso-style-parent:;mso-padding-alt:0cm 5.4pt 0cm 5.4pt;mso-para-margin-top:0cm;mso-para-margin-right:0cm;mso-para-margin-bottom:10.0pt;mso-para-margin-left:0cm;line-height:115%;mso-pagination:widow-orphan;font-size:11.0pt;font-family:Calibri,sans-serif;mso-ascii-font-family:Calibri;mso-ascii-theme-font:minor-latin;mso-fareast-font-family:Times New Roman;mso-fareast-theme-font:minor-fareast;mso-hansi-font-family:Calibri;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Times New Roman;mso-bidi-theme-font:minor-bidi;}
Auto-generating training data for network application classification

We present and evaluate the idea of auto-generating training data for network application classification using a rule-based expert system on two-dimensions of the feature space. That training data is then used to learn classification of network applications using other dimensions of the feature space. The rule-based expert system uses transport layer port number conventions (source port, destination port) from the Internet Assigned Numbers Authority (IANA) to classify applications to create the labeled training data. A classifier can then be trained on other network ow features using this auto-generated training data. We evaluate this approach to network application classification and report our findings. We explore the use of the following classifiers: K-nearest neighbors, decision trees, and random forests. Lastly, our approach uses data solely at the ow-level (in NetFlow v5 records) thereby limiting the volume of data that must be collected and/or stored.
Analysis on Variables Affecting Youth Stress with Bayesian Networks

Although Bayesian Networks have fascinated researchers in many fields, they have been hardly used in educational domain due to their complexity. In this paper, we adopt Bayesian Networks to discover which variables are in charge of youth stress and how much the variables influence. In order to learn a Bayesian Network and to choose the relevant variables, Korean Children Panel Survey (KCPS) data are used and Markov Blanket is adopted. From the analysis, “aggressiveness”, “depression”, “worry about studying”, “parent’s expectation of study”, and “interest in school study” are extracted as the relevant variables to youth stress, therefore caregivers should pay attention to these variables to relieve youth stress. This paper shows Bayesian Networks are quite effective in finding the causal variables and their effects in educational domain.