Reviewerly/exHarmony huggingface dataset:
Non-Binary Robust Universal Variable Length Codes

The famous Fibonacci series defined by F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> = 0, F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> = 1 and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> =F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i-1</sub> + F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i-2</sub> , for i ≥ 2, has contributed to several applications in Data Compression. Quite a few encodings have been suggested that are based on the properties of the Fibonacci sequence, and can be used as alternatives to fixed length codes. Interest in the Fibonacci sequence has shifted also to other adjacent areas, such as compressed matching[1], in which a pattern P is to be located in a text T, which is assumed to be given in some compressed form. The advantage of using Fibonacci codes in this context is the 11 separator, that acts as a border between adjacent codewords. Compressed matching is not limited to text files only, and in [3] the Fibonacci code was adapted to tools in image compression. Codes of order d are related to d-ary trees and there are several motivations for using such trees with d > 2. A d-ary tree is of height [log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> |Σ|], which may improve the processing time for larger d, e.g., in higher order Wavelet trees, where instead of storing binary bitmaps in every internal node, one rather stores sequences over the alphabet {1,...,d}. Ferragina et al.[2] show how to handle rank and select of such sequences, that improve both time and space complexities. A Fibonacci Wavelet tree, in which pruning was applied for additional savings, was defined in [4]. This data structure has then been generalized to higher-order of another kind, namely by the use of higher order Fibonacci Codes in which each element of the underlying sequence is the sum of the k preceding ones, for k ≥ 2. The corresponding binary code has the property that there is no occurrence of a string of m consecutive 1s. We extend the binary Fibonacci code to d-ary codes, with d ≥ 2. This is motivated by future technological developments in which the basic unit of storage will not be just a 2-valued bit, but possibly an element that is able to distinguish between d different values. The proposed codes are prefix-free, complete and more robust than Huffman codes. Experimental results illustrate that the compression efficiency of non-binary Fibonacci codes are very close to the savings achieved by the corresponding non-binary Huffman coding of the same order.
A Formal Technique for Composing Cloud Services

&#x0D; &#x0D; &#x0D; Recent cloud search engines lack a formal method in their service composition mechanisms to automatically build composite services realizing user requirements. This paper prescribes behavior composition framework as a formal tools for the search engines. The framework automatically synthesizes a controller that delegates the service operations requested by a cloud user to the proper available cloud services whose operations satisfy the request. Since most cloud search engines support semantic and ontology to discover similar service operations, the paper extends the framework to be more adaptable with such search engines through the use of resource reasoning. Several experiments are provided to demonstrate how the extended framework outperforms the original one in terms of realizing users’ requirements.&#x0D; &#x0D; &#x0D; &#x0D;
On Robust Deep Neural Decoders

The design of neural-based decoders is well understood for classical point to point channels such as the Additive White Gaussian Noise (AWGN) channel, the Binary Symmetric Channel (BSC) and the Binary Erasure Channel (BEC). For such channels, an optimal training noise distribution allows the neural decoder to generalize to other channel parameters unseen during training, i.e., other values of Signal to Noise Ratios (SNR), crossover probabilities or erasure probabilities. However, we show in this work that, for other families of channels such as the Binary Asymmetric Channel (BAC), the training and validation mismatch can be detrimental to the performances of the learned decoder. We investigate more robust constructions of neural decoders based on Domain Adaptation (DA) techniques.
Input representation in recurrent neural networks dynamics.

Reservoir computing is a popular approach to design recurrent neural networks, due to its training simplicity and its approximation performance. The recurrent part of these networks is not trained (e.g. via gradient descent), making them appealing for analytical studies, raising the interest of a vast community of researcher spanning from dynamical systems to neuroscience. It emerges that, even in the simple linear case, the working principle of these networks is not fully understood and the applied research is usually driven by heuristics. A novel analysis of the dynamics of such networks is proposed, which allows one to express the state evolution using the controllability matrix. Such a matrix encodes salient characteristics of the network dynamics: in particular, its rank can be used as an input-indepedent measure of the memory of the network. Using the proposed approach, it is possible to compare different architectures and explain why a cyclic topology achieves favourable results.
A High Efficient Cascade Coder with Predictor Blending Method for Lossless Audio Compression

Based on the idea of RLS-LMS cascade of predictors as in MPEG-4 lossless audio coder we developed our own improved cascade of predictors with backward adaptation and new predictor blending method. In carried out experiment comparing the results with other known lossless audio coders, our method obtained the best efficiency.
Implementation of Voice over Internet Protocol (VoIP) using softphone applications based on Session Initiation Protocol (SIP)

Voice over Internet Protocol (VoIP) is a technology that can pass voice, video, and data over an Internet Protocol (IP) network. This study aims to implement VoIP using softphone applications based on Session Initiation Protocol (SIP) at the Said Na’um Education Foundation and to recognize both the voice quality resulting from the communication made using VoIP as well as the bandwidth required for voice quality that will be used appropriately. This study used Trixbox CE as the VoIP server while the client used two softphone applications installed on the client device for testing, 3CX for computers and Zoiper for the smartphone. The bandwidth system was set to 64, 128, and 256 Kbps and the voice quality was measured in terms of two tests, Quality of Service (QoS) and Mean Opinion Score (MOS). Quality was measured twice a day, in the morning and in the evening, and the results were compared to learn about its network traffic. The results of this study show that implementation of VoIP using softphone applications based on SIP works well in terms of the results of QoS and MOS, and a minimum bandwidth for the communication on VoIP was obtained at 64 Kbps.
SENIOR HIGH SCHOOL STUDENTS’ PERCEPTIONS IN THE USE OF ELECTRONIC MEDIA IN NON-FORMAL EDUCATION CLASSROOM

Communication and information technology are currently developing so fast. This development certainly has an impact on non-formal education students. Non-formal education students usually use conventional learning models. However, with the development of technology, non-formal education students are also familiar with social media, students tend to use smart applications on their cellphones and laptops to surf the internet. Learning methods for students in non-formal education are part of the teaching and learning process, a strategy so that students can understand the material delivered by the tutor. The use of learning methods supported by appropriate and appropriate media will facilitate students to achieve their competence. As the objective of this research is to quantitatively analyze the advantages and disadvantages of using e-learning which was chosen as an effective learning method for non-formal education students. The study was conducted with a survey of observation, identification, filling students' perceptions of the teaching method of tutors, learning variables, evaluation of learning outcomes, electronic media used. Students have the perception that learning to use e-learning is more beneficial because of freedom of choice of place, freedom of time, ease of access but the drawbacks are objective assessment and high quota costs.
THE WATER CYCLE – OBSERVATIONS AND RECOMMENDATIONS FROM TEACHING PROCESS

In the national project IT Academy – education for the 21st century realized in Slovakia, more than 60 activities were prepared for secondary school teachers and students. One of those activities is the activity “Water Cycle” which combines theoretical and practical knowledge in this area by using an inquiry-based cycle. The presented paper informs about experiences from 35 teachers who performed this activity by more than 600 students at K-7 education level. After the lesson, teachers answered a questionnaire about the learning and teaching process, time management, problematic areas, students´ perception and participation in the activity. All these answers were analyzed and evaluated. According to teachers’ answers, reading comprehension is one of the students´ weaknesses because students are impatient and inattentive. Teachers also agreed that students need to improve their practical skills by performing chemical experiments. Similarly, practical activity was appreciated by students for whom it was not only exciting but also educational. Acknowledgement:This article was created in the framework of the National project „IT Academy – Education for the 21st Century“, which is supported by the European Social Fund and the European Regional Development Fund in the framework of the Operational Programme Human Resources, ITMS code of the project: 312011F057.
CoProtect: Collaborative Management of Cryptographic Keys for Data Security in Cloud Systems

Cryptography key management system plays a very central role in the cloud data security.Nonetheless, a great part of the current commercial solutions rely on cloud providers that hold both the encrypted data and the related private master key of their served customers in their secure key vaults, having a de-facto total control on their customer digital assets.Conversely, entrusting customer companies for key holding can be dangerous as witnessed by many cases of key loss or theft.In this work we present CoProtect, a novel architecture to protect the cryptography keys in cloud systems that leverage on the cooperation between the cloud provider and the customer company.With such trust model, we present the proposed data management strategy, the key generation and the crypto procedures, and a proof of concept.
Run Data Run! Re-Distributing Data via Piggybacking for Geo-Distributed Data Analytics

Effectively analyzing geo-distributed datasets is emerging as a major demand in a cloud-edge system. Previous researches mainly focus on offloading proper data analytical tasks from hot or weak edges to the datacenter (DC) to minimize the response time for current jobs. Since several datasets would be accessed multiple times, we argue that re-distributing data along with task offloading would benefit the forthcoming jobs as well as improve the overall performance, although it may increase the completion time of current job. In order to minimize the overall completion time for a sequence of jobs as well as guarantee the current job response time and WAN usage, we formulate ε-bounded geo-distributed data-driven task scheduling problem under the consideration of heterogeneity. Afterwards, we design an online data-driven task scheduling schema runData, which offloads proper tasks and related data via piggybacking to a DC based on delicate calculated probabilities. Through our rigorous theoretical analysis, runData can be proved concentrated on its optimum with high probability. We implement runData based on Spark. Both testbed and simulation results show that runData re-distributes proper data via piggybacking and achieves up to 37% improvement on average response time compared with state-of-the-art task scheduling schemas.
Training Future Primary School Teachers in the Context of Developing Constructive Skills in Younger Pupils

Constructive skills help younger pupils to perceive and evaluate the harmony in life, nature, art, understand and create beauty in the surrounding reality and be creative. Therefore, it is crucial to train primary school teachers to develop constructive skills in younger pupils to reinforce their creative development. The paper aims to theoretically justify and experimentally verify the content, forms and methods of effective training of future primary school teachers for developing constructive skills in younger pupils. The students were divided into two groups, namely control group (CG –132 respondents) and experimental group (EG – 137 respondents). It presents the author’s educational and methodical complex, which reveals the content of such training and the use of effective forms for organizing the educational process in higher education institutions. It analyzes the levels of readiness of future teachers to develop constructive skills in younger pupils based on questionnaires, interviews, expert assessment, self-assessment, grading of teachers’ personal and professionally essential qualities, tests, observations and methods for assessing creative thinking and motivation towards learning in younger pupils. The results obtained from the formative stage of the pedagogical experiment in the experimental group (EG) show that a mean value of a low level of future primary school teachers’ readiness to develop constructive skills in younger pupils has decreased by 30.6%; a mean value of a sufficient level has increased by 14.9%; a mean value of a high level has decreased by 15.6%. At the same time, the corresponding values in the control group (CG) have not changed significantly. The results of the experimental work prove the expediency of introducing the author’s model in the training of future primary school teachers.
Comprehensive Study on Task Scheduling Strategies in Multicloud Environment

The task scheduling in multicloud computing environment is complicated task.The cloud computing has increase tremendous popularity both in academia and business because of its on demand service over internet to the various customer.The task scheduling problem is N-P (Non deterministic polynomial) completeness problem.Task scheduling in cloud computing is a best-known problem that has been paid attention.This is again more challenging, specially for multicloud computing environment.This survey paper presents the Shortest Job First algorithm, Round Robin algorithm and Genetic Algorithm for task scheduling in multicloud computing.The round robin algorithm provides the fair allocation and square allocation of resource to the task.It provides the accurate result in finding solutions to large scale optimization problems, by Genetic algorithms such as task scheduling and it is also helpful.A good task and resource scheduling mechanism must satisfy the QoS requirement of the user and at the same time make an efficient utilization of resources.This survey paper presents an algorithm which tries to achieve application high availability, and minimum makespan, minimum response time and completion time.
Predicting Impact of Odia Newspaper Articles on Public Opinion

News articles regarding a political party, a political issue, a business house, or a particular product can be judged as the feeling of writers over a time. News article can also be treated as a form of public opinion as by reading those news articles; the public opinion is influenced relating to a particular topic. So that news article can be analyzed to extract the sentiment or for opinion mining by using different supervised learning models. This paper proposes the methods to extract opinion content from Odia news article available in portal and e-paper sites using supervised learning models. A total of 500 related news articles are collected from different newspapers manually. Out of that, 350 news articles are used from training the classifier and 150 news articles are used for testing the classifier. Those collected news articles are preprocessed and then vectorized with respect to tf–idf score which is calculated using unigram and bigram representation of data at document level passed to SVM, and the results are analyzed by calculating the accuracy and F1 score.
Machine Learning Approach on Apache Spark for Credit Card Fraud Detection

In the digital era, industries such as banking and financial organizations are facing different challenges with transaction-related activities.One of the significant challenges in financial organizations is Credit card fraud.In order to identify credit card fraud activities.In this paper, we employ an integrated hybrid approach using Apache Spark.The proposed hybrid approach is the integration of K-Means and C5.0 decision tree with an adaptive method, which is examined through Hadoop and Spark.Using K-Means, we find the closest clusters, and with the rules of a decision tree, each normal and fraud instance in the dataset is classified.This model is evaluated on one million synthetic datasets and achieved a good classification rate.We present our model with detailed experimental results and comparison with other models.This model is suitable for computationally complex datasets, and it can be applied to various fields for anomaly detection on big data.
Object contour tracking via adaptive data-driven kernel

Abstract We present a novel approach to non-rigid object tracking in this paper by deriving an adaptive data-driven kernel. In contrast with conventional kernel-based trackers which suffer from the constancy of kernel shape as well as scale and orientation selection problem when the tracking targets are changing in size, the adaptive kernel can robustly achieve the adaptation to target variation and act toward the actual target contour simultaneously with the mean shift iterations. Level set technique is novelly introduced to the mean shift sample space to both cope with insufficient low-level information and implement the adaptive kernel evolution and update. Since the active contour model is designed to drive the kernel constantly to the direction that maximizes the appearance similarity, this adaptive kernel can continually seize the target shape to give a better estimation bias and produce accurate shift of the mean. Finally, accurate target region can successfully avoid the performance loss stemmed from pollution of background pixels hiding inside the kernel and qualify the samples fed the next time step. Experimental results on a numer of challenging sequences validate the effectiveness of the technique.
Forward and Backward Random LSBs Steganography Against Visual Attacks

Steganography is the field of science that deals with how to hide secret data (text, image, audio,etc.)into another media objects as a cover or carrier in a way that no one can notice that there is hidden data.There are many techniques for applying steganography.The most popular technique is Least Significant Bit (LSB).This technique, however, is easy to be attacked and hidden data can be retrieved in the case of sequential-based hiding.This research introduces a new randomized Forward-and-Backward LSB algorithm for image steganography differs from other algorithms in the way of hiding secret data.We developed an algorithm based on two indicators: one for determining the cover bytes and the other for specifying the cover bytes capacity.Random Forward-and-Backward selection of cover bytes makes the proposed algorithm robust against visual attacks.Moreover, the number of bits to be hidden using the proposed algorithm is not fixed; hence increasing the capacity of the cover bytes (payload).The proposed algorithm was tested, evaluated and compared with existing algorithms.Our proposed algorithm achieved better results than other methods with respect to steganography aspects: imperceptibility, capacity (i.e., payload) and robustness (resistance to attacks); especially against visual attack.
VIDEO CASES AS A TOOL OF DEVELOPING PRE-SERVICE TEACHERS' SKILL TO FORMULATE A PEDAGOGICAL PROBLEM

I. Yarmakeev, T. Pimenova, A. Abdrafikova, N. Valiakhmetova, R. AkhmadullinaKazan Federal University (RUSSIAN FEDERATION)
ARSIP DAN RETENSI ANALISIS ISI JADWAL RETENSI ARSIP KEMENTERIAN KETENAGAKERJAAN

This paper aims to find out how many records in the Ministry of Manpower's Records Retention Schedule (JRA) that are to be appraised as archival records or in regular cases having permanent value. By using content analysis method, the results of this study state that there are 177 records series (26.62%) of Ministry of Manpower's Retention Schedule which are stated as permanent. The theoretical implications are against archival theory in general which states that the number of archival records in an organization are roughly 1% to 10%. This high percentage can be attributed to Derrida's ‘archive fever’ concept that both records managers and archivists are afraid of losing memory from the Ministry of Manpower's archival records. This effort to give meaning is temporary because it departs from latest findings and is a limitation of quantitative content analysis methods. It is a must to develop further research among others by using phenomenological method.
Analyzing the Relationship of Ethical Work Climate, Job Satisfaction, Organizational Commitment and Job Performance in Tabanan University, Bali

The primary aim of this research is to identify determinants of job performance and attitudinal mechanism process, which are considered as consequence factors for the ethical work climate in organization.The purpose of this quantitative study was to better understand the relationship of perceived ethical work climate on the organizational commitment, job satisfaction, and job performance among lecturers in institutions of higher education.Among 61 lecturers were taken as respondents who have to participate in this study.All of hypotheses proposed in this study are accepted, with linear relationship among variables as general findings.Both of individual attitudes (satisfaction and commitment) founded to be exactly different with previous empirical findings when they related with ethical work climate and job performance.Ethical work climate found as better predictor for job satisfaction, while the effect of organizational commitment on job performance is greater than job satisfaction.Future research study is recommended to explore how perceived ethical climates may differ in another form of organizations, in order to clarify the concepts of ethical climate with both attitudinal and behavioral outcomes.
Deep Temporal Analysis of Twitter Bots

Automated accounts which are otherwise known as bots are rampant in most of the popular online social networks. Similar to email spam, these social media bots are used for spreading information with the goal of propaganda or advertisements for profit. Due to the impact they pose on influencing the user communities, understanding the bot behaviour is important. In this paper, we employ deep neural network analysis on temporal data of bot accounts and have identified the role of temporal activity in bot detection. The bidirectional LSTM network is used for studying the temporal patterns of Twitter bots and its behavioural pattern. The ability of the model to distinguish the tweeting rate and frequency of bot accounts from the genuine accounts has led to a good classification rate.