Reviewerly/exHarmony huggingface dataset:
Helping Students Detecting Cyberbullying Vocabulary in Internet with Web Mining Techniques

This article presents a model for the analysis of data on the Internet, using Web mining, to find knowledge about large amounts of information in cyberspace. To test the proposed method, Web pages on Cyberbullying were analyzed as a case study. The procedure integrates a Web Scraper to locate and download information from the Internet, to recover the vocabulary are used techniques of Natural Language Processing (tokenization, cleaning of words without meaning, frequency of term, inverse frequency of the document, synonyms, stemming methods). To obtain knowledge, a dataset was constructed using semantic ontologies to define the predictive variables of Cyberbullying and supervised learning to define the variable to be predicted. To evaluate the efficiency of the model, algorithms of machine learning, AdaBoost and Neural Network were used. The results reveal a percentage of 97% accuracy in the detection of Cyberbullying vocabulary, which was approved through crossvalidation, achieving a time saving of 581% with parallel processing, compared to sequential processing.
Early Detection Application of Bipolar Disorders Using Backpropagation Algorithm

Mental health is an important aspect in realizing overall health and important to be considered as physical health. Mental disorders are classified as difficult to diagnose due to the similarity of symptoms that can occur. In addition, information about mental disorders is inadequate so that it can be difficult for experts to provide a diagnosis of the disorders experienced by patients. The difficulty of experts in diagnosing is usually caused by the similarity of symptoms in mental disorders, such as in schizophrenia and bipolar disorder. Based on these problems, this research would like to conduct an early detection study of bipolar disorder by using screening questionnaire data from 300 respondents and serve as a knowledge base to be processed using the backpropagation algorithm. Based on all the results of testing the backpropagation algorithm that has been done to find out the results obtained accuracy and the highest results of training, the highest results obtained with the total test data correct or suitable is 249 and the wrong data is 1 of 250 test data. If it is calculated by a formula, the resulting accuracy rate is 99.6%. And it can be concluded broadly that the greatest influence of the accuracy of the backpropagation algorithm is based on momentum. Because in testing momentum the highest accuracy can be produced compared to the results of other analyzes.
GreenFlowing: A Green Way of Reducing Electricity Cost for Cloud Data Center Using Heterogeneous ESDs

In this paper, we propose a scheduling scheme called GreenFlowing to reduce the electricity cost for a cloud data center by leveraging heterogeneous ESDs. In our model, the data center can be powered by intermittent green energy like wind and solar, and the electricity with time-varying prices from the power grid. The energy from different sources can also choose to flow into long-term or short-term ESDs for later use. Note that, the former can sustain energy for a long time but with low charging/discharging rate, while the latter can charge/discharge very fast but with high energy leakage that can sustain energy only for a few hours. By combining them together, the energy cost can further be reduced. However, it is hard to decide when and how much energy from different sources should be used to power the data center directly or charged into different types of ESDs. We formulate our scheduling into a large-scale linear programming (LP) problem, which can be solved using CPLEX. Numerical experiments show that our scheduling can significantly reduce the total electricity cost for a cloud data center.
Selective Segmentation of Piecewise Homogeneous Regions

In this article, a novel method for the interactive segmentation of the object and background which has multiple piecewise homogeneous intensities has been introduced. We have proposed a new energy function based on intensity points selected from multiple homogeneous regions in the object and background. To minimize the derived energy function, we use the calculus of variation method and transformed it to PDE. The derived PDE has been solved using an additive operator splitting (AOS) method. Simulation results validate the correctness and accuracy of the proposed method. The performance is assessed by testing the algorithm on synthetic images and results are compared with state of the art methods using Jaccard’s similarity index.
Prediction of Abalone Age Using Regression-Based Neural Network

Artificial neural networks (ANN) has been widely used to speed up data prediction operations with over thousands of features available. In this paper, we propose a regression-based ANN model with three hidden layers to predict the age of abalones. It is salient to predict abalone age as it helps farmers and sellers to determine the market price of abalones. The economic value of abalone is positively correlated with their respective ages. The age of the abalone can be estimated by measuring the number of layers of shell rings The model was built based on a dataset obtained from the UCI Machine Learning Repository. Before developing and training the model, a pre-processing methodology was applied to the dataset. Parameters tuning, which involves modifications in the number of hidden layers as well as the number of epochs, were done to obtain the best result. The finalised results were analysed and the results show that physical measurements of abalone can predict its respective age with less time consumption. This study has shown a result of low root mean-squared error, obtained from the proposed model in comparison with other methods stated in this study. Finally, the proposed model was validated using test dataset, and the results reveal a lower root-mean-squared error value in contrast to the value obtained during model training.
STDADS: An Efficient Slow Task Detection Algorithm for Deadline Schedulers

The MapReduce programming model was designed and developed for Google File System to efficiently process large-scale distributed data sets. The open source implementation of this Google project was called the Apache Hadoop. Hadoop architecture includes Hadoop MapReduce and Hadoop Distributed File System (HDFS). HDFS supports Hadoop in effectively managing data sets over the cluster and MapReduce programming paradigm helps in the efficient processing of large data sets. MapReduce strategically re-executes a speculative task on some other node to finish the computation quickly, enhancing the overall Quality of Service (QoS). Several mechanisms were suggested over the Hadoop's Default Scheduler to improve the speculative task execution over Hadoop cluster. A large number of strategies were also suggested for scheduling jobs with deadlines. The mechanisms for speculative task execution were not developed for or were not well integrated with Deadline Schedulers. This article presents an improved speculative task detection algorithm, designed specifically for Deadline Scheduler. Our studies suggest the importance of keeping a regular track of node's performance to re-execute the speculative tasks more efficiently. We have successfully improved the QoS offered by Hadoop clusters over the jobs arriving with deadlines in terms of the percentage of successfully completed jobs, the detection time of speculative tasks, the accuracy of correct speculative task detection, and the percentage of incorrectly fagged speculative tasks.
Research and Application of Improved CHAMELEON Algorithm Based on Condensed Hierarchical Clustering Method

CHAMELEON clustering algorithm uses dynamic modeling to find high quality clusters of different shapes, sizes and densities. When the CHAMELEON algorithm constructs the k-nearest neighbor graph G, the k value is difficult to select and has a great influence on the result. Using the multi-level partitioned hMetic algorithm for segmenting large hyper graphs to divide Gk is a coarse partition, which easily leads to uncertainty of results. Aiming at this shortcoming, this paper proposes a new condensed hierarchical clustering method based on AGNES algorithm, and uses this convenient and efficient condensed hierarchical clustering method to replace the traditional hMetic algorithm to divide Gk to generate sub-clusters.
On-Chip CMOS Self-Decoupling Battery Cell System for Security Protection

This article presents an effective on-chip power analysis attack countermeasure based on a new CMOS self-decoupling battery cell system that uses a self-decoupling circuit. The self-decoupling circuit dynamically controls an on-chip virtual power supply point, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V_{\mathrm {ddv}}$ </tex-math></inline-formula> , that can be used to power security-sensitive modules. The circuit automatically decouples an on-chip CMOS battery cell from powering a sensitive module when its voltage level reaches a designed minimum threshold level <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V_{\mathrm {dd-min}}$ </tex-math></inline-formula> and connects it for a very short charging cycle to the chip’s main voltage supply, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V_{\mathrm {dd}}$ </tex-math></inline-formula> . The charging cycles for the experiments presented in this article are less than 10 ns and are designed to support the CMOS battery cell size and the minimum designed threshold voltage level <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V_{\mathrm {dd-min}}$ </tex-math></inline-formula> . Simulation results of test designs implemented in the 45-nm CMOS technology process show that the proposed countermeasure is efficient when used with battery cell sizes that can power the protected cryptographic module for more than ten data operation cycles before recharging. In addition, using the on-chip self-decoupling battery cell system allows for power consumption savings within the protected module of up to 43 % due to the dynamic voltage scaling generated at the virtual power supply point.
ALSR: An Adaptive Label Screening and Relearning Approach for Anomaly Detection

Anomaly detection using KPIs (Key Performance Indicators) is key to AIOps (Artificial Intelligence for IT Operations). Recent anomaly detection approaches have adopted machine learning to detect anomalies on the perspective of individual time points more than events. These approaches do not make effective use of the labels of continuous anomaly intervals, nor do they pay attention to the differences among anomaly points. The detection performance is therefore not high enough. In this paper, we propose an anomaly detection approach named ALSR, which uses a label screening model and a relearning model to analyze and utilize the continuous anomaly intervals of KPIs in finer granularity. The label screening algorithm takes advantage of the continuity of anomaly intervals to remove unnecessary data from the training set, so as to better suit to interval-oriented anomaly detection. The relearning algorithm reclassifies the true/false positive points within range of detected anomalies, thus effectively reduces the number of false positive points. ALSR uses statistical characteristics and time series models for feature extraction, and the feature set is proved to better describe the characteristics of KPIs. We conduct comprehensive experiments on 25 KPIs, and the total F-score of ALSR is 0.965, which outperforms state-of-the-art anomaly detection approaches.
SIMDop: SIMD optimized Bounding Volume Hierarchies for Collision Detection

We present a novel data structure for SIMD optimized simultaneous bounding volume hierarchy (BVH) traversals like they appear for instance in collision detection tasks. In contrast to all previous approaches, we consider both the traversal algorithm and the construction of the BVH. The main idea is to increase the branching factor of the BVH according to the available SIMD registers and parallelize the simultaneous BVH traversal using SIMD operations. This requires a novel BVH construction method because traditional BVHs for collision detection usually are simple binary trees. To do that, we present a new BVH construction method based on a clustering algorithm, Batch Neural Gas, that is able to build efficient n-ary tree structures along with SIMD optimized simultaneous BVH traversal. Our results show that our new data structure outperforms binary trees significantly.
ResFlow: Multi-tasking of Sequentially Pooling Spatiotemporal Features for Action Recognition and Optical Flow Estimation

Since deep-learning-based method has been widely-used and is capable of generating generic model, most existing methods about action recognition use either two-stream structure, considering spatial and temporal features separately, or C3D, costing lots of prices in memory and time. We aim to design a robust system to extract spatiotemporal features with aggregation mechanism to integrate local features in temporal order. In light of this, we propose ResFlow to estimate optical flow and predict action recognition simultaneously. Leveraging the characteristic of optical flow estimation, we extract spatiotemporal feature via an autoencoder. Via a novel Sequentially Pooling Mechanism which literally pool global spatiotemporal feature sequentially, we extract spatiotemporal feature at each time and aggregate these local features into global feature. This design use only RGB images as input with temporal information encoded, pre-trained by optical flow, and sequentially aggregate spatiotemporal features in high efficiency. We evaluate our ability of estimating optical flow on FlyingChairs dataset and show the promising results of action recognition on UCF-101 dataset through a series of experiments.
AliMe KBQA: Question Answering over Structured Knowledge for E-Commerce Customer Service

With the rise of knowledge graph (KG), question answering over knowledge base (KBQA) has attracted increasing attention in recent years. Despite much research has been conducted on this topic, it is still challenging to apply KBQA technology in industry because business knowledge and real-world questions can be rather complicated. In this paper, we present AliMe-KBQA, a bold attempt to apply KBQA in the E-commerce customer service field. To handle real knowledge and questions, we extend the classic “subject-predicate-object (SPO)” structure with property hierarchy, key-value structure and compound value type (CVT), and enhance traditional KBQA with constraints recognition and reasoning ability. We launch AliMe-KBQA in the Marketing Promotion scenario for merchants during the “Double 11” period in 2018 and other such promotional events afterwards. Online results suggest that AliMe-KBQA is not only able to gain better resolution and improve customer satisfaction, but also becomes the preferred knowledge management method by business knowledge staffs since it offers a more convenient and efficient management experience.
A Comparative Analysis of Text Classification Algorithms for Ambiguity Detection in Requirement Engineering Document Using WEKA

The volume of digital documents is increasing day by day and thus the task of automatic categorization of document is very important for information and knowledge discovery. Classification is the most common method for finding the mine rule from the large databases. Ambiguity is the major problem in Requirement Engineering (RE) documents. Our proposed work uses WEKA text classification technique to identify and classify ambiguity in the RE document. The present study uses different algorithms on the ambiguity detection dataset and on the basis of different statistical measures like accuracy, time, and error rate we find suitable algorithms for this purpose. The main aim of this paper is to do a comparative study of various classification techniques and methodologies and a detailed analysis of different statistical parameters that are used in classification algorithms in order to analyze the quality of classification.
Extending Aspect-Oriented Programming for Dynamic User's Activity Detection in Mobile App Analytics

Mobile apps analytics represent a core set in the mobile industry to extract relevant data with the aim of modeling user's behavior. Current solutions to detect in-app user's activity are usually based on a continuous app code modification schema, which implies high development efforts and a clear problem to implement changes without compromising the time to come back to the market or even with dependencies in the user's app updates. In this article, we analyze the suitability of aspect-oriented programming for providing a more efficient way to detect user's activity inside apps, which may lead to obtain user analytics. We propose an innovative approach that relies on an in-app solution based on the embedding of a specific library and a configuration file for setting up the events to be tracked in real time, without additional code changes in the app. Thus, this new schema will reduce the time and effort costs derived from the integration of third party trackers.
Information Filter Occupancy Mapping using Decomposable Radial Kernels

Building occupancy maps of the environment is a fundamental problem for robot autonomy. A common assumption in early work was that the occupancy states of different map elements are independent. Recently, Gaussian Process (GP) techniques were proposed to capture correlation, which is important not only for improved accuracy but also for uncertainty quantification and autonomous exploration based on the predicted occupancy of nearby unexplored areas. Despite these desirable properties, current GP mapping techniques are limited to small maps and slow inference speeds. This paper proposes an information space formulation of the GP mapping problem. If a decomposable radial kernel is evaluated over a latent grid of pseudo-input points, the resulting kernel matrix has a Kronecker-product-of-Toeplitz-matrices structure that allows very efficient representation of the occupancy distribution. We utilize this structure to design an information filter occupancy mapping algorithm with linear time and memory complexity that still permits continuous space observations and predictions.
Sharp Characterization of Optimal Minibatch Size for Stochastic Finite Sum Convex Optimization

The minibatching technique has been extensively adopted to facilitate stochastic first-order methods because of their computational efficiency in parallel computing for large-scale machine learning and data mining. However, the optimal minibatch size determination for accelerated stochastic gradient methods is not completely understood. Actually, there appears trade-off between the iteration complexity and the total computational complexity; that is, the number of iterations (minibatch queries) can be decreased by increasing the minibatch size, but too large minibatch size would result in an unnecessarily large total computational cost. In this study, we give a sharp characterization of the minimax optimal minibatch size to achieve the optimal iteration complexity by providing a reachable lower bound for minimizing finite sum of convex functions and, surprisingly, show that the optimal method with the minimax optimal minibatch size can achieve both of the optimal iteration complexity and the optimal total computational complexity simultaneously. Finally, this feature is verified experimentally.
An Extended Intuitionistic Fuzzy Cognitive Map via Dempster-Shafer Theory

Fuzzy cognitive map has gradually emerged as a powerful paradigm for uncertain knowledge representation and a simulation mechanism that is applicable in dealing with complex artificial reasoning problems.To better model uncertain inference reasoning problems, we propose an extended intuitionistic fuzzy cognitive map via Dempster-Shafer theory.First of all, some new operations on IFSs are introduced from the perspective of Dempster-Shafer theory.Then, the extended intuitionistic fuzzy cognitive map is established via the proposed new operations.Next, we investigate the problem of modeling complex system from multiple decision makers using extended intuitionistic fuzzy cognitive maps and present a method to aggregate a number of maps.Particular emphases are put on defining the augmented connection matrices, determining the importance levels of different extended intuitionistic fuzzy cognitive maps and aggregating them.Finally, the performances of extended intuitionistic fuzzy cognitive maps have been validated through a number of simulations.The simulations indicate that the theory of extended intuitionistic fuzzy cognitive map not only provides much more choices to model complex system but also reduces the computational complexity by comparison with intuitionistic fuzzy cognitive map.
Utilization of Metadata and Data Models to Enhance Machine Learning

Data plays the most significant role to attain efficiency in performing a task using Machine Learning (ML) techniques. Metadata (MD) represents data of data. MD extraction and data attribute selection play a vital role in defining the performance of ML models. The study in this paper focuses on the role of MD, data attributes and data models that define the learning capability of ML to evolve with human-like capability to learn and draw inferences. To evolve with such artificially intelligent autonomous systems, the study in this paper is a preliminary step towards applying ML techniques on textual data for performing syntactic analysis, further to evolve with semantic and behavioral analysis. Based on the rigorous survey study and observations, this paper concludes with the description of the parameters to quantify the performance of ML model which are essential to define the performance characteristics of ML. The increased deployment of ML is observed in the recent Artificial Intelligence arena, and hence the study contributes towards evolving performance parameters in applications that employ ML techniquestextbf.
Solar Tracking with Anti-Tracking Support for Ancillary Service

Solar plants can provide ancillary services during the power overproduction periods of time using solar curtailment by using smart inverters. This method, however, has a high toll on equipment longevity since even during normal operation 37% of unscheduled maintenance repairs were inverter-related, 59% of unscheduled maintenance costs were due to inverters. Therefore, we propose the anti-tracking curtailment that can be implemented by the plants that already have tracking systems installed (nearly 80% of new capacity installed in 2018). Anti-tracking could be realized by means of turning the solar panels away from the sun in a closed-loop control manner. Thus, when the signal from the grid is received, the excess energy is not dissipated by the inverters or the solar panels, but energy generation is curtailed by moving solar panels from sun shine, i.e. performing anti-tracking move. All the infrastructure and technology needed to implement tracking/anti-tracking is already available in the grid system.