Reviewerly/exHarmony huggingface dataset:
Cost-Effective Resource Provisioning for Real-Time Workflow in Cloud

In the era of big data, mining and analysis of the enormous amount of data has been widely used to support decision-making. This complex process including huge-volume data collecting, storage, transmission, and analysis could be modeled as workflow. Meanwhile, cloud environment provides sufficient computing and storage resources for big data management and analytics. Due to the clouds providing the pay-as-you-go pricing scheme, executing a workflow in clouds should pay for the provisioned resources. Thus, cost-effective resource provisioning for workflow in clouds is still a critical challenge. Also, the responses of the complex data management process are usually required to be real-time. Therefore, deadline is the most crucial constraint for workflow execution. In order to address the challenge of cost-effective resource provisioning while meeting the real-time requirements of workflow execution, a resource provisioning strategy based on dynamic programming is proposed to achieve cost-effectiveness of workflow execution in clouds and a critical-path based workflow partition algorithm is presented to guarantee that the workflow can be completed before deadline. Our approach is evaluated by simulation experiments with real-time workflows of different sizes and different structures. The results demonstrate that our algorithm outperforms the existing classical algorithms.
Mobile Learning System of Ideological and Political Education in Universities Based on Android

This paper introduces the research background and key technologies of this project, and chooses Android mobile operating system as the platform of project design and implementation. Through the study and analysis of the theory of mobile learning, it puts forward the demand analysis of the mobile learning system of ideological and political education in universities. Through the research of mobile development technology and mobile learning system, this paper expounds the overall functional framework design of Android client, and explains the design of five functional modules and their sub functions. From the overall point of view, the communication protocol, data storage and server-side design of the system are analyzed. The communication protocol, data storage and plug-in secondary development of the server are mainly described. The test results show that the system can run stably in practical application, which has perfect functions and it is easy to be operated. The scheme meets the friendly interaction of mobile application, and meets the requirements of mobile learning system in practical learning.
Evaluating Reading Habit among Junior Secondary School Students in Ekiti State in Nigeria

Abstract The study evaluated reading habit among students in Ekiti State Secondary Schools. The study specifically examined the frequency and time of reading among Junior Secondary School students in Ekiti State, the extent students read, how teachersâ€™ attitudes affect their students reading habits, and ways reading habit can be improved. The descriptive research design of the survey type was adopted in this study. The population for the study consisted all of the Junior Secondary School Students in public secondary schools in Ekiti \central senatorial district. The size of the sample was 200 students drawn from ten public secondary schools using multistage sampling technique. A self-structured questionnaire tagged Reading Habit Questionnaire (RHQ) was used to collect relevant data for the study. The responses obtained were collated and analyzed using descriptive statistics. The findings revealed that most of the students rarely read as majority of them spend less than 2 hours daily on reading. They settle down to read only when given assignments or when examination was near. Based on the findings, it is therefore recommended among others that parents should teach their children to start reading from an early age and should provide reading materials for them. Also, it was recommended that teachers should engage students in fervent reading and writing activities. Keywords: Reading, Habit, Library, Descriptive, Revealed Students Teachers
Detection of Hate Speech in Hinglish Language

As mobile phones and Internet become more and more popular, the number of social media users in India continues to go up. Majority of Indian social media users use Hinglish as their medium of communication. The Hinglish language is a mixture of Hindi words (typed in English) and English words. However, with increasing numbers, there is also an increase in the amount of hate-filled messages, posts, and comments put up on social media platforms. Hate speech is usually done to target an individual or group of individuals on the basis of caste, community, ethnicity, religion, gender, or any other discriminating factor. It can have negative impacts on the individuals facing it and consequently on the society as well. As the amount in which such kind of content is generated is huge, it becomes necessary to automatically detect hate speech so that preventive measures can be taken to control it. Although there has been quite a lot of research on hate speech detection in English texts, not much work can be found on hate speech detection in Hinglish language. This paper presents an approach of detecting hate speech in Hinglish texts using long short-term memory (LSTM), which works on word embeddings generated by gensim’s word2vec model.
Fundamental Limit on Network Size Scaling of Oscillatory Neural Networks due to PrMnO3 based Oscillator Phase Noise

Oscillatory Neural Networks (ONNs) have been reported to solve graphical constraint optimization and pattern recognition problems in a time efficient manner [1], [2]. In particular, the vertex coloring problem can be solved using a capacitively coupled network of relaxation oscillators of the same frequency $(f_{i})$ (Fig. 1) [1]. The critical information for the accuracy is governed by the permutation (ordering on phase circle) of the oscillator settling phases. Compact device level oscillators have been proposed to enable densely packed arrays for large network sizes [3]–[6]. These are based on physics like insulator to metal transitions (IMT), volatile conductive filament and Joule heating. Hence, they are prone to phase variability during operation. The impact of phase variability in relaxation oscillators on the network solutions for ONNs has not yet been demonstrated. Here, we study the cycle to cycle variability (C2CV) in transient Joule heating based PrMnO 3 (PMO) device [4]. Further, we demonstrate the effect of C2CV on the temporal distribution of the settling phases, i.e. the phase noise, in coupled oscillator networks. We show that the presence of phase noise in a finite output solution space (360°) puts a fundamental limit on the maximum size of the graphical problem for which the permutation of the output phases can be read-off reliably (Fig. 2).
SOAP: Semantic outliers automatic preprocessing

Genetic Programming (GP) is an evolutionary algorithm for the automatic generation of symbolic models expressed as syntax trees. GP has been successfully applied in many domain, but most research in this area has not considered the presence of outliers in the training set. Outliers make supervised learning problems difficult, and sometimes impossible, to solve. For instance, robust regression methods cannot handle more than 50% of outlier contamination, referred to as their breakdown point. This paper studies problems where outlier contamination is high, reaching up to 90% contamination levels, extreme cases that can appear in some domains. This work shows, for the first time, that a random population of GP individuals can detect outliers in the output variable. From this property, a new filtering algorithm is proposed called Semantic Outlier Automatic Preprocessing (SOAP), which can be used with any learning algorithm to differentiate between inliers and outliers. Since the method uses a GP population, the algorithm can be carried out for free in a GP symbolic regression system. The approach is the only method that can perform such an automatic cleaning of a dataset without incurring an exponential cost as the percentage of outliers in the dataset increases.
Cache Characterization of Workloads in a Microservice Environment

The advent of Microservice based architecture has lead to the obsolescence of the tedious way of building and deploying applications in a monolithic way. The number of organizations taking advantage of cloud computation and microservice architecture is steadily increasing. From the cloud providers perspective, the purpose of cloud computation is to leverage economies of scale by co-locating several workloads dynamically on the same powerful many-core servers. In this paper, we attempt to characterize cache usage and its impact on performance when multiple workloads are collocated on multicore servers.
Pathological Detection Using HMM Speech Recognition-Based Amazigh Digits

In the last decade, the automatic speech pathology detection systems based on voice production theory are evolving up to date. Overall, there have not been much speech technology research studies for persons regarding voice disorders which center on Amazigh language. This research project focuses on the build of an automatic speech recognition system based on Sphinx-4 that permits to detect the differences between normal and pathological voices based on the produced speech. The performance in our system was measured using the combinations of different Hidden Markov Models and Gaussian mixture distributions. Results show that the maximum accuracy with the normal voices is greater than the maximum accuracy obtained from the pathological speaker.
Chaotic signal denoising algorithm based on sparse decomposition*

Denoising of chaotic signal is a challenge work due to its wide-band and noise-like characteristics. The algorithm should make the denoised signal have a high signal to noise ratio and retain the chaotic characteristics. We propose a denoising method of chaotic signals based on sparse decomposition and K-singular value decomposition (K-SVD) optimization. The observed signal is divided into segments and decomposed sparsely. The over-complete atomic library is constructed according to the differential equation of chaotic signals. The orthogonal matching pursuit algorithm is used to search the optimal matching atom. The atoms and coefficients are further processed to obtain the globally optimal atoms and coefficients by K-SVD. The simulation results show that the denoised signals have a higher signal to noise ratio and better preserve the chaotic characteristics.
Prediction of Horizontal Displacement of Foundation Pit Based on NAR Dynamic Neural Network

Abstract Absrtact. NAR dynamic neural network has the function of feedback and memory, which can be effectively used in time series data modeling. Taking the actual horizontal displacement of a foundation pit in Qingdao as the experimental data, the time series prediction model of the horizontal displacement of the foundation pit is established by NAR dynamic neural network. The model is applied to predict the horizontal displacement of foundation pit, and compared with grey prediction, support vector machine and BP neural network. The results show that the prediction accuracy of NAR dynamic neural network model is high. It has strong stability and can be effectively applied to the prediction of foundation pit displacement.
Digital Literacy of Future Preschool Teachers

The basics of digital literacy begin to form at an early age, and as they grow older, digital literacy must continue to evolve, adapting to the rapidly changing digital world. The first (both in importance and in time) after the family cognitive social institution for most people is the educational system, or rather, the system of preschool education. The problem of increasing the digital literacy of preschool teachers, their willingness to use information technology in educational activities is clearly relevant. Studies have shown an insufficiently high level of digital literacy of future teachers. The goal of this work is to strengthen the digital literacy component in preparing future preschool educators. The authors of the article conducted an experimental study on the introduction of a new program of study of the discipline Information Technology at the Elabuga Institute of Kazan (Volga) Federal University. This program includes the following modules: Interactive Didactic Games, Animation Basics, Programming Basics and Network Technologies. Based on an analysis of the design work for each module, as well as a survey of 68 respondents of different age categories who took part in the study, an increase in the level of digital literacy of future teachers in all indicators, an increase in the number of people wishing to use information technology in their professional activities, as well as a positive attitude of future teachers to the proposed program.
An <i>in silico</i> rat liver atlas

Numerous hepatic function, disease and pharmacological experiments are performed on rat livers. Many of these experiments rely on an accurate understanding of the rat liver anatomy. In this short paper, we present an in silico rat liver atlas which is constructed from the micro-CT images of explanted rat livers. The atlas consists of the parametric mesh for four liver lobes and a paracaval portion. 1D and 3D cubic Hermite mesh are used to represent the rat liver vessels and lobes, respectively. We discuss potential applications that can be performed from the in silico atlas.
An Optimized Quantization Constraints Set for Image Restoration and its GPU Implementation

This paper presents a novel optimized quantization constraint set, acting as an add-on to existing DCT-based image restoration algorithms. The constraint set is created based on generalized Gaussian distribution which is more accurate than the commonly used uniform, Gaussian or Laplacian distributions when modeling DCT coefficients. More importantly, the proposed constraint set is optimized for individual input images and thus it is able to enhance image quality significantly in terms of signal-to-noise ratio. Experimental results indicate that the signal-to-noise ratio is improved by at least 6.78% on top of the existing state-of-the-art methods, with a corresponding expense of only 0.38% in processing time. The proposed algorithm has also been implemented in GPU, and the processing speed increases further by 20 times over that of CPU implementation. This makes the algorithm well suited for fast image retrieval in security and quality monitoring system.
IoT Forensics Pitfalls for Privacy and a Model for Providing Safeguards

Internet of Things (IoT) is a new domain which is designed to make human lives more comfortable and convenient. Conversely, it has also raised issues of security and privacy as a data-driven could-based ecosystem. IoT technology raises challenges for digital forensics investigators when working on cybercrime or digital intrusions. Among these challenges, protacting consumer privacy when conducting investigations is important and difficult. Currently, literature has shown that there is not a single agreed upon IoT-forensic standard model that can protect privacy of 3rd party consumers. This research will address this limitation by developing a privacy-aware IoT forensics model. In this paper, by mapping the ISO/IEC 27037 standard to the three layered reference architecture of IoT, the authors discuss the breach of privacy that may happen when investigating an IoT environment.
Direct Text Classifier for Thematic Arabic Discourse Documents

Maintaining the topical coherence while writing a discourse is a major challenge confronting novice and non-novice writers alike. This challenge is even more intense with Arabic discourse because of the complex morphology and the widespread of synonyms in Arabic language. In this research, we present a direct classification of Arabic discourse document while writing. This prescriptive proposed framework consists of the following stages: data collection, pre-processing, construction of Language Model (LM), topics identification, topics classification, and topic notification. To prove and demonstrate our proposed framework, we designed a system and applied it on a corpus of 2800 Arabic discourse documents synthesized into four predefined topics related to: Culture, Economy, Sport, and Religion. System performance was analysed, in terms of accuracy, recall, precision, and F-measure. The results demonstrated that the proposed topic modeling-based decision framework is able to classify topics while writing a discourse with accuracy of 91.0%.
A Befitting Image Data Crawling and Annotating System with CNN Based Transfer Learning

With the Growth of Big Data technology, Artificial Intelligence technology, especially Deep Learning, is growing rapidly. In Deep Learning technology that like Convolutional Neural Network (CNN), collecting and training a sizeable befitting training data has a significant impact on technology maturity and completeness. But it is difficult for a person to collect a sizeable training data directly, and there are some problems in collecting it automatically with an ordinary web image crawler. For example, the name of a car is often obtained from a specific place name. When these keywords are crawled, the crawler retrieves only the text metadata of the image and gets the image. As a result, the crawler collects all images of different but has the same text. Also, even if the image of the car has been collected, only some of the car's interior or only a portion of the data that is not suitable for learning is also collected. In this paper, we present a method to classify and collect only data that is suitable for learning among crawled images using deep learning networks. Also, we offer a method to automatically generate training data by detecting objects and making object coordinates necessary for CNN learning at the same time.
A synergy Thompson sampling hyper‐heuristic for the feature selection problem

Summary To classify high‐dimensional data, feature selection plays a key role to eliminate irrelevant attributes and enhance the classification accuracy and efficiency. Since feature selection is an NP‐Hard problem, many heuristics and metaheuristics have been used to tackle in practice this problem. In this article, we propose a novel approach that consists in a probabilistic selection hyper‐heuristic called the synergy Thompson sampling hyper‐heuristic. The Thompson sampling selection strategy is a probabilistic reinforcement learning mechanism to assess the behavior of the low‐level heuristics, and to predict which one will be more efficient at each point during the search process. The proposed hyper‐heuristic is combined with a 1 nearest neighbor classifier from the Weka framework. It aims to find the best subset of features that maximizes the classification accuracy rate. Experimental results show a good performance in favor of the proposed method when comparing with other existing approaches.
Artificial Intelligence Based Risk Management in Global Software Development: A Proposed Architecture to Reduce Risk by Using Time, Budget and Resources Constraints

In today’s competitive world software organizations are moving towards global software development environment where experts in teams from different geographical locations and different cultures are working on their roles and responsibilities to deliver a quality and cost-effective product. These teams are distributed in nature and work on same set of goals and objectives. They are undertaking their assignments to get multiple advantages like global exposure, new software world to explore, learning environment, etc. This change is having a significant effect on marketing and distribution as well as on the way software products are perceived, designed, developed, tested, and delivered to customers. Software experts in Global Software Development environment are also facing many challenges like as well. Moreover, risk is a big challenge out of other challenges but not many researchers addressed risk related to Time, Budget and Resources. Developing software projects to address business needs and requirements is so exceedingly complex and troublesome that it is common for software projects to overrun budgets and exceed scheduled completion dates. Business managers need the whole development process streamlined and more manageable, with projected cost and timings and for better decision making. The utilization of artificial intelligence techniques to manage risk is helpful when taking care of and assessing unstructured information because of the self-learning and self-healing nature of artificial intelligence-based algorithms. In this paper an Artificial Intelligence based architecture has been proposed that reduces risk by using Time, Budget and Resource constraints that will help decision maker to take decisions and get optimal solutions.
Aerial-ground vehicle path coordination algorithms for monitoring applications

This study concerns a multi-UAV path planning problem in concert with a known ground vehicle path. The environment is given as a grid map, where each cell is either a target, an obstacle or contains nothing (neutral). Respectively, each cell has a reward that is either positive, negative, or zero. The team of UAVs has to visit as many targets as possible under a given time span or distance constraint, to maximize the collected reward. More formally, this problem is a generalization of the Orienteering Problem (OP) and is NP-Hard. In addition, the inclusion of obstacle avoidance and area coverage introduces additional complications that the current literature has not readily addressed. We propose using a greedy heuristic based on the A* algorithm, which involves three stages (selection, insertion, and post-processing) to solve this problem. A large scale problem instance is generated and the results are presented for different variations of our proposed algorithm. For large problems with thousand of nodes, our algorithm was able to provide a feasible solution to the proposed problem within few minutes of computation time on a standard laptop.
Cross-Industry Use of Blockchain Technology and Opportunities for the Future

Blockchain and artificial intelligence are two disruptive technologies in the today's world; Blockchain can connect data storage and data users from multiple domains, and different kinds of analytics applications run on top of the data in artificial intelligence techniques. So that Blockchain technology provide the excellent backbone for the development of artificial intelligence algorithms, it is useful to secure the data input from multiple data storage locations and to have whatever applications running on top of the data in a Blockchain visible to everyone. The concept of Blockchain technology and artificial intelligence techniques together develop interesting uses cases and nice applications. When paired with Blockchains, AI is better understood by humans, operates more efficiently, and the blockchains in general more efficiently. AI and Blockchain together support B2B environment and getting better outcomes.