Reviewerly/exHarmony huggingface dataset:
Model-Driven Elasticity Management with OCCI

Elasticity is considered as a fundamental feature of cloud computing where the system capacity can adjust to the current application workloads by provisioning or de-provisioning computing resources automatically and timely. Many studies have been already conducted to elasticity management systems, however, almost all lack to offer a complete modular solution. In this article, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MoDEMO</small> , a new elasticity management system powering both vertical and horizontal elasticities, both VM and Container virtualization technologies, multiple cloud providers simultaneously, and various elasticity policies. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MoDEMO</small> is characterized by the following features: it represents ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> ) the first system that manages elasticity using Open Cloud Computing Interface (OCCI) model with respect to the OCCI standard specifications, ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i> ) the first unified system which combines the functionalities of the worldwide cloud providers: Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP), and ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iii</i> ) allows a dynamic configuration at runtime during the execution of the application. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MoDEMO</small> permits to timely adapt resource capacity according to the workload intensity and increase application performance without introducing a significant overhead.
Intelligent Fault Inference of Inverters Based on a Three-Layer Bayesian Network

A three-layer Bayesian intelligent fault inference model (BIFIM) for an inverter is established to infer the probable uncertain faults. The topological structure of the BIFIM includes the inverter’s operation conditions for the first layer, the inverter’s faults for the second layer, and the fault symptoms for the third layer, which combines the field technicians’ knowledge and experiences with historical running data. The prior probability table of the root node is acquired by the method of basic probabilities corrected historical operation data. The conditional probability parameter table of the BIFIM is obtained by the improved maximum expectation algorithm. Four kinds of incomplete evidence were reasoned and verified, including simple evidence with obvious support, incomplete evidence information, complex evidence without obvious support, and evidence with information conflict. The proposed strategy can make use of the available evidences to inference the probabilities of faults, indicating different reasoning abilities under the different degree of completeness of evidence, especially demonstrating the same inference result under some incomplete evidence information as under complete evidence information.
Dynamic virtual cluster cloud security using hybrid steganographic image authentication algorithm

Storing data in a third party cloud system causes serious problems on data confidentiality. Generally, encryption techniques provide data confidentiality but with limited functionality, which occurs due to unsupported actions of encryption operation in cloud storage space. Hence, developing a decentralized secure storage system with multiple support functions like encryption, encoding, and forwarding tends to get complicated, when the storage system spreads. This paper aims mainly on hiding image information using specialized steganographic image authentication (SSIA) algorithm in clustered cloud systems. The SSIA algorithm is applied to virtual elastic clusters in a public cloud platform. Here, the SSIA algorithm embeds the image information using blowfish algorithm and genetic operators. Initially, the blowfish symmetric block encryption is applied over the image and then the genetic operator is applied to re-encrypt the image information. The proposed algorithm provides an improved security than conventional blowfish algorithm in a clustered cloud system.
Incorporating Priors with Feature Attribution on Text Classification

Feature attribution methods, proposed recently, help users interpret the predictions of complex models. Our approach integrates feature attributions into the objective function to allow machine learning practitioners to incorporate priors in model building. To demonstrate the effectiveness our technique, we apply it to two tasks: (1) mitigating unintended bias in text classifiers by neutralizing identity terms; (2) improving classifier performance in a scarce data setting by forcing the model to focus on toxic terms. Our approach adds an L2 distance loss between feature attributions and task-specific prior values to the objective. Our experiments show that i) a classifier trained with our technique reduces undesired model biases without a trade off on the original task; ii) incorporating priors helps model performance in scarce data settings.
Hadoop Performance Analysis Model with Deep Data Locality

Background: Hadoop has become the base framework on the big data system via the simple concept that moving computation is cheaper than moving data. Hadoop increases a data locality in the Hadoop Distributed File System (HDFS) to improve the performance of the system. The network traffic among nodes in the big data system is reduced by increasing a data-local on the machine. Traditional research increased the data-local on one of the MapReduce stages to increase the Hadoop performance. However, there is currently no mathematical performance model for the data locality on the Hadoop. Methods: This study made the Hadoop performance analysis model with data locality for analyzing the entire process of MapReduce. In this paper, the data locality concept on the map stage and shuffle stage was explained. Also, this research showed how to apply the Hadoop performance analysis model to increase the performance of the Hadoop system by making the deep data locality. Results: This research proved the deep data locality for increasing performance of Hadoop via three tests, such as, a simulation base test, a cloud test and a physical test. According to the test, the authors improved the Hadoop system by over 34% by using the deep data locality. Conclusions: The deep data locality improved the Hadoop performance by reducing the data movement in HDFS.
Youth and forecasting of sustainable development pillars: An adaptive neuro-fuzzy inference system approach

Sustainable development goals are used as a guidance for strategies development on local, regional and national levels. The importance of including young people in this complex process is recognized in all relevant documents (i.e. Agenda 21), however it is not an easy task to elicit opinions and preferences from the youth. Furthermore, the assessment of the sustainable development goals itself presents a challenge for the noisy data and nonlinear relationships in data. Popular approach is fuzzy set models where expert knowledge is presented with comprehensible rules; however expert knowledge elicitation takes a long time too. Several studies proposed an adaptive neuro-fuzzy inference system approach that combines the fuzzy set theory to model expert knowledge with neural networks for inferring rules and membership functions from data to assess the sustainable development performance. We base our assumptions that ANFIS can be used to predict the importance of sustainable development pillars from the demographic data of young people. For this purpose, we have conducted an online survey on sustainable development goals opinions and importance of young people in Serbia. The sample of 386 respondents has been split into a training sample of 300 instances (to generate membership functions and fuzzy rules) and a testing sample of 86 instances to predict the importance of the three pillars. We have conducted a trace-driven simulation test to validate the results of the proposed ANFIS model. Results of the study provided insights into how the young people in Serbia assess the importance of sustainable development goals. Secondly, the results suggest that ANFIS can be applied to predict values of importance of the three sustainable development pillars with the relative error of Rel Err < 5%. It must be noted that the considered model could be further improved by using training samples with more data.
An Enhanced Lightning Attachment Procedure Optimization Algorithm

To overcome the shortcomings of the lightning attachment procedure optimization (LAPO) algorithm, such as premature convergence and slow convergence speed, an enhanced lightning attachment procedure optimization (ELAPO) algorithm was proposed in this paper. In the downward leader movement, the idea of differential evolution was introduced to speed up population convergence; in the upward leader movement, by superimposing vectors pointing to the average individual, the individual updating mode was modified to change the direction of individual evolution, avoid falling into local optimum, and carry out a more fine local information search; in the performance enhancement stage, opposition-based learning (OBL) was used to replace the worst individuals, improve the convergence rate of population, and increase the global exploration capability. Finally, 16 typical benchmark functions in CEC2005 are used to carry out simulation experiments with LAPO algorithm, four improved algorithms, and ELAPO. Experimental results showed that ELAPO obtained the better convergence velocity and optimization accuracy.
A New Hybrid Ant Colony Optimization Based on Brain Storm Optimization for Feature Selection

Machine learning algorithms are becoming more and more popular in current era. Data preprocessing especially feature selection is helpful for improving the performance of those algorithms. A new powerful feature selection algorithm is proposed. It combines the advantages of ant colony optimization and brain storm optimization which simulates the behavior of human beings. Six classical datasets and five state-of-art algorithms are used to make a comparison with our algorithm on binary classification problems. The results on accuracy, percent rate, recall rate, and F1 measures show that the developed algorithm is more excellent. Besides, it is no more complex than the compared approaches.
A Modification to the Nguyen–Widrow Weight Initialization Method

Weight initialization is important factorMittal, Apeksha forSingh, Amit Prakash determiningChandra, Pravin the speed of training in feedforward neural networks. In this paper, a variable parameter $$\alpha $$ is identified through statistical analysis in Nguyen–Widrow weight initialization method. The value of $$\alpha $$ is varied from 1 to 10 and is tested on nine function approximation tasks. The results are compared for each value of $$\alpha $$ using single-tail t-test. An optimal value of $$\alpha $$ is derived, and a new weight initialization technique is hence proposed.
Stop PropagHate at SemEval-2019 Tasks 5 and 6: Are abusive language classification results reproducible?

This paper summarizes the participation of Stop PropagHate team at SemEval 2019.Our approach is based on replicating one of the most relevant works on the literature, using word embeddings and LSTM.After circumventing some of the problems of the original code, we found poor results when applying it to the HatEval contest (F1=0.45).We think this is due mainly to inconsistencies in the data of this contest.Finally, for the OffensEval the classifier performed well (F1=0.74),proving to have a better performance for offense detection than for hate speech.
An Interactive and Multimodal Virtual Mind Map for Future Workplace

Traditional types of mind maps involve means of visually organizing information. They can be created either using physical tools like paper or post-it notes or through the computer-mediated process. Although their utility is established, mind maps and associated methods usually have several shortcomings with regards to effective and intuitive interaction as well as effective collaboration. Latest developments in virtual reality demonstrate new capabilities of visual and interactive augmentation, and in this paper, we propose a multimodal virtual reality mind map that has the potential to transform the ways in which people interact, communicate, and share information. The shared virtual space allows users to be located virtually in the same meeting room and participate in an immersive experience. Users of the system can create, modify, and group notes in categories and intuitively interact with them. They can create or modify inputs using voice recognition, interact using virtual reality controllers, and then make posts on the virtual mind map. When a brainstorming session is finished, users are able to vote about the content and export it for later usage. A user evaluation with 32 participants assessed the effectiveness of the virtual mind map and its functionality. Results indicate that this technology has the potential to be adopted in practice in the future, but a comparative study needs to be performed to have a more general conclusion.
Image-Based Text Classification using 2D Convolutional Neural Networks

We propose a new approach to text classification in which we consider the input text as an image and apply 2D Convolutional Neural Networks to learn the local and global semantics of the sentences from the variations of the visual patterns of words. Our approach demonstrates that it is possible to get semantically meaningful features from images with text without using optical character recognition and sequential processing pipelines, techniques that traditional natural language processing algorithms require. To validate our approach, we present results for two applications: text classification and dialog modeling. Using a 2D Convolutional Neural Network, we were able to outperform the state-of-art accuracy results for a Chinese text classification task and achieved promising results for seven English text classification tasks. Furthermore, our approach outperformed the memory networks without match types when using out of vocabulary entities from Task 4 of the bAbI dialog dataset.
DIY Assistive Technology for Others

One way to help address larger scale social and environmental challenges is to help individuals consider the social impacts of making alongside the technological aspects. We investigate the experiences of volunteers who 3D print prosthetic devices for children, focusing specifically on the way they discuss their relationship with the device recipients. We show that while the volunteers understand the social aspect of the experience, their focus is on the functionality. We suggest some techniques, methods, and principles from HCI that can enrich the experience for the volunteers and recipients by broadening the focus to include consideration of the complexities of the human and social experience.
THU_NGN at SemEval-2019 Task 3: Dialog Emotion Classification using Attentional LSTM-CNN

With the development of the Internet, dialog systems are widely used in online platforms to provide personalized services for their users.It is important to understand the emotions through conversations to improve the quality of dialog systems.To facilitate the researches on dialog emotion recognition, the SemEval-2019 Task 3 named EmoContext is proposed.This task aims to classify the emotions of user utterance along with two short turns of dialogues into four categories.In this paper, we propose an attentional LSTM-CNN model to participate in this shared task.We use a combination of convolutional neural networks and long-short term neural networks to capture both local and long-distance contextual information in conversations.In addition, we apply attention mechanism to recognize and attend to important words within conversations.Besides, we propose to use ensemble strategies by combing the variants of our model with different pre-trained word embeddings via weighted voting.Our model achieved 0.7542 micro-F1 score in the final test data, ranking 15 th out of 165 teams.
Validation and Optimization of Image Compression Algorithms

In current era, social media had been playing a major role considering fields like education, media, and politics. In such situation, information generated is an exponential form of which major chunk is in form of images. It becomes essential to use images compression techniques to save the storage space and bandwidth required for transmission. Image compression techniques help not only in reducing storage space requirement but improve the transmission bandwidth. In this article, an attempt to validate different compression algorithm for images using neural networks is highlighted. An attempt for optimization of compression algorithm is performed. Image compression algorithms like BTC, DCT, DWT, and SVD are considered, and optimization using the strength of each algorithm is performed. The results of optimization and validation technique show reduction in storage space using a variety of methodologies.
Personalized Recommender Agent for E-Commerce Products Based on Data Mining Techniques

In this article, a recommender agent is designed to meet the increasing demand of consumers for the diversity of products offered by big e-commerce Web sites. Turning visitors of Web sites into customers and clicks made by them into purchases is a challenging task which is achieved through accurate product recommendation. The recommendation algorithm designed in the present work initially design a user-context click matrix that predicts each users’ preference level based on deals offered by the company. The users are clustered on the basis of these preferences and neighborhood formation task is completed using collaborative filtering technique that is based on user-item category matrix. The matrix shows users’ preference for the type of item user is interested in. After the neighborhood formation task, like-minded users are found using various similarity measures. Finally, the products that are clicked by similar users are marked to find association level among these products using association rule mining to generate user-product preference matrix. The proposed work is flexible and can be applied to Web sites that keep track of users’ click-stream behavior. The experimental results clearly justify that the proposed work outperforms the conventional ones.
Lower bounds on the runtime of crossover-based algorithms via decoupling and family graphs

The runtime analysis of evolutionary algorithms using crossover as search operator has recently produced remarkable results indicating benefits and drawbacks of crossover and illustrating its working principles. Virtually all these results are restricted to upper bounds on the running time of the crossover-based algorithms. This work addresses this lack of lower bounds and rigorously bounds the optimization time of simple algorithms using uniform crossover on the search space {0, 1}n from below via two novel techniques called decoupling and family graphs. First, a simple steady-state crossover-based evolutionary algorithm without selection pressure is analyzed and shown that after O(µ log µ) generations, bit positions are sampled almost independently with marginal probabilities corresponding to the fraction of one-bits at the corresponding position in the initial population. Afterwards, a crossover-based algorithm using tournament selection is analyzed by a novel generalization of the family tree technique originally introduced for mutation-only EAs. Using these so-called family graphs, almost tight lower bounds on the optimization time on the OneMax benchmark function are shown.
Performance Analysis of Quantum Based Cloud Radio Access Networks

Quantum computing can provide a solution for many problems that classical communication networks encounter, but the knowledge gap between these two is not bridged yet. Accordingly, this paper has proposed directions, evaluations, and futuristic quantum solutions to cloud-based cellular communications. This paper shows the effect of using quantum entanglement phenomena upon the classical cloud network, particularly using an entanglement theory to decrease the enlarged signaling cost delay that the traditional cloud network faces. Through modeling the latency of both paradigms, this paper promises a delay reduction when adapting a quantum method into the mobile cloud networks. This paper also models the power consumption (PC) and energy efficiency of the traditional and quantum-based cloud networks. This paper also shows, via modeling the PC, that installing a quantum-based paradigm is not a power costly method; rather, it shows identical power and energy efficiency figures with a possibility of improvement.
The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost

Privacy has raised considerable concerns recently, especially with the advent of information explosion and numerous data mining techniques to explore the information inside large volumes of data. In this context, a new distributed learning paradigm termed federated learning becomes prominent recently to tackle the privacy issues in distributed learning, where only learning models will be transmitted from the distributed nodes to servers without revealing users' own data and hence protecting the privacy of users. In this paper, we propose a horizontal federated XGBoost algorithm to solve the federated anomaly detection problem, where the anomaly detection aims to identify abnormalities from extremely unbalanced datasets and can be considered as a special classification problem. Our proposed federated XGBoost algorithm incorporates data aggregation and sparse federated update processes to balance the tradeoff between privacy and learning performance. In particular, we introduce the virtual data sample by aggregating a group of users' data together at a single distributed node. We compute parameters based on these virtual data samples in the local nodes and aggregate the learning model in the central server. In the learning model upgrading process, we focus more on the wrongly classified data before in the virtual sample and hence to generate sparse learning model parameters. By carefully controlling the size of these groups of samples, we can achieve a tradeoff between privacy and learning performance. Our experimental results show the effectiveness of our proposed scheme by comparing with existing state-of-the-arts.
Teacher’s Self-Efficacy: A Systematic Review

The systematic review of self-efficacy has been conducted between the year 2014 to 2018.The review focuses on mainstream teachers and inclusive teachers' self-efficacy of special needs.Articles that correspond to teacher's self-efficacy were collected online and analyzed in detail.A total of 30 articles on self-efficacy have been chosen and analyzed on mainstream teachers and inclusive teachers' self-efficacy of special needs.Overall, the studies were found to have a positive relationship which affect teachers' self-efficacy on pedagogy, experience, classroom and student management, students' involvement, teaching strategies, and classroom instructions.The negative factors that were identified are job stress and job satisfaction which is a major factor in the decline on the level of self-efficacy of the mainstream teachers.Additionally, a side-factor that leads to low self-efficacy for inclusive teachers who taught students with special needs in inclusive classes is the lack of training or skills.Lack of qualitative research method in analyzing teachers' self-efficacy is identified in the review for both categories of teachers.This study has great implications in understanding the importance of self-efficacy as well as the enrichment of teachers' self-efficacy in the future.