Reviewerly/exHarmony huggingface dataset:
Task Scheduling in Heterogeneous Cloud Environment—A Survey

Pradhan, Roshni Satapathy, Suresh ChandraCloud computing environment provides various computing services along with provision of resources in pay per use basis. Task scheduling and resource scheduling in cloud environment mostly take the help of Infrastructure as a Service (IaaS). In the current computing era, allocation of task to a machine is one of the most important issues. It is a NP–Hard problem. Many task scheduling algorithms are available to resolve such problems in a heterogeneous cloud environment. In this paper various task scheduling methods are compared and described. The performance of the different scheduling techniques are measure with the help of metrics like makespan, cloud utilization, etc.
Wavelet Transform Domain Methods for Resolution Enhancement of Satellite Images

In today’s world, many applications require satellite images, but resolution is the major weakness of these images. Many researchers have initiated to determine the resolution problem by using transform domain methods like Stationary Wavelet Transform (SWT), Discrete Wavelet Transform (DWT), SWT&DWT and proposed method which integrates DWT, SWT & Integer Wavelet Transform (IWT) that is implemented to overcome the resolution problem of mentioned images. In all these methods, the low resolution (LR) image is broken down into four sub-band images namely Low-Low (LL), Low-High (LH), High-Low (HL) and High-High (HH). The Bi-Cubic Interpolation factor 2 is used on high-frequency sub-band images for resizing the sub-band images. Resolutions of images are enhanced by applying Inverse Transform. For comparison of transform domain methods, the satellite images- LANDSAT8, LANDSAT7 and LANDSAT5 are used in this paper. Objective of the proposed method is to integrate the features of IWT, DWT and SWT in order to improve the resolution. The results demonstrate the supremacy of the suggested method. The Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Time in seconds (s) provide improved result by using the proposed technique.
Disparity Map Estimation Using Combined Cost Measure

Disparity map generation is necessary in many 3D applications. This paper focuses on disparity map estimation using local based stereo matching. Stereo correspondence algorithms are categorized as global method and local method. Global based methods are computationally expensive. This paper proposes an improved stereo matching algorithm by using local method which combines cost computation of gradient information and histogram of oriented gradient. The results obtained shows better quality disparity map.
The Impact of Industry 4.0 on The Marketing Competitiveness of Enterprises, The Scope For Millennial’s and Gen Z: A Study on Dubai (UAE)

The purpose of this exploration is to exhibit a pathway of Industrial Revolution 4.0. Our intent is to give a short view of IR 4.0, The difficulties in present environment, and display how we need to structure and execute future evolving business associations. Various specialists have referenced that, actualizing IR 4.0 is a reaction to the present difficulties in quick evolving conditions. To improve adaptability, lessen expenses and offer modified products, organizations must overhaul their creation forms properly. Introducing the new ideology of "IR 4.0", will give a far-reaching definition and clarify the exploration technique, after which the analysis will display a few views about difficulties and barriers of IR 4.0, also displaying the significand advantages to this evolving world. Before we work more on piecing ideas to form summaries we can reach to a determination and future research opportunities.
Movie Genres Classification using Collaborative Filtering

In this paper, we present an approach for classifying movie genres based on user-ratings. Our approach is based on collaborative filtering (CF), a common technique used in recommendation systems, where the similarity between movies based on user-ratings, is used to predict the genres of movies. The results of conducted experiments show that our genres classification approach outperforms many existing approaches, by achieving an F1-score of 0.70, and a hit-rate of 94%.
LIKC: A liberty of encryption and decryption through imploration from K-cloud servers

The technological advancement in the field of IoT and cloud computing along with the use of handheld gadgets generate huge amount of data. To process and share these data with fine-grained access control while maintaining confidentiality using cloud servers is a challenging task. Individuals as well as the organizations apprehend to use public cloud to share their private data due to security issues. Ciphertext-policy-attribute-based-encryption(CPABE) achieves one-to-many encryption with data confidentiality and fine-grained access control. Due to the computational intensive operations of encryption and decryption the performance of CPABE as a solution in resource-constraint environment is not encouraging enough. Some of the existing models use the resources of cloud servers to carry out outsourced computation either with compromised security or with communication-overhead. To reduce the computational time of encryption and decryption we propose a scheme LIKC which incorporates the outsourcing mechanism using a proxy server. With the imploration the computation time get reduced without compromising the security. It also supports attribute-revocation to support flexible data sharing. The proposed model is implemented using python charm crypto. The output reveals that LIKC is able to produce promising results in comparison to the data sharing models for this study.
Human tracking control system using Kinect sensors on wheelchair based on Arduino

Many people with disabilities who do not get the opportunity to work or the same rights as non-disabled people give encouragement to conduct research to help persons with disabilities. Currently there are several types of research and development on mind wave wheelchairs, but the developed wheelchair has limitations for blind users. Most of the wheelchairs require the user to see the surrounding area in order to move the wheelchair. Therefore, it is not optimal for disabled people who are also visually impaired. To help blind people with disabilities, a wheelchair system with human tracking control capabilities need to be developed. In this study, a prototype of a wheelchair system was developed using Kinect Xbox 360 sensor to read motion data which will then be processed on a mini PC before being sent to an Arduino pro micro based main control to drive the DC motor and track the wheelchair. The prototype has been tested by conducting start and stop maneuver. The results show that the wheelchair prototype has an average success rate for conducting a start maneuver of 87.50% and the average success rate for being able to stop after carrying out a maneuver of 85.83%. In the future the wheelchair is expected to be used by persons with multiple disabilities, namely those who are blind who cannot use an electric wheelchair with mind wave control to help with their daily mobility.
Employing Unsharp Masking for Contrast Enhancement in Reversible Data Hiding

Recently, reversible data hiding (RDH) with contrast enhancement has received substantial interest in order to address the need of applications that deal with poorly illuminated images. The algorithms that have been proposed for this purpose focus on embedding the data in way that improves the global contrast of the image; basically, through manipulating the image intensity histogram. In this paper, we address the issue of improving the local contrast and the sharpness of the image by adapting and formulating the popular unsharp masking technique in a way that makes it suitable for RDH. The new formulation was employed in one of the RDH algorithms and the experimental results showed its capability in improving the sharpness of the image.
Feature Significance in Wide Neural Networks

Wide neural networks were recently proposed as a less costly alternative to deep neural networks. In this paper, we analyze the properties of wide neural networks regarding feature selection and their significance We compared the random selection ofweights in the hidden layer to the selection based on radial basis functions. Wide neural networks were also compared with fully connected cascade networks. Feature significance was introduced as a measure to compare various feature selection techniques. Another performance measure introduced in this paper - incremental feature significance - determines the level of improvement that results from selecting only some features, which were added to the existing features, rather than replacing one set of features with another. In both cases, we can also estimate the number of features saved by replacing the original features with the selected ones for which recognition levels improve. This approach can be applied to wide networks that use different feature selection methods than those that are analyzed in this paper; like a k-nearest neighbor, an autoencoder etc.
A Hybrid Machine Learning Approach for Improving Mortality Risk Prediction on Imbalanced Data

The efficiency of Machine Learning (ML) models has widely been acknowledged in the healthcare area. However, the quality of the underlying medical data is a major challenge when applying ML in medical decision making. In particular, the imbalanced class distribution problem causes the ML model to be biased towards the majority class. Furthermore, the accuracy will be biased, too, which produces the Accuracy Paradox. In this paper, we identify an optimal ML model for predicting mortality risk for Intensive Care Units (ICU) patients. We comprehensively assess an approach that leverages the efficiency of ML ensemble learning (in particular, Gradient Boosting Decision Tree) and clustering-based data sampling to handle the imbalanced data problem that this model faces. We comprehensively compare different competitors (in terms of ML models as well as clustering methods) on a big real-world ICU dataset achieving a maximum area under the curve value of 0.956.
Progress Management Method for Software Development Project-based Learning Using Automated Teaching Assistants

In recent years, project-based learning (PBL) in university education has been emphasized for distributed software development courses. Therefore, by assuming distributed development, this research targets PBL and proposes a progress management method for solving project delays of individual students while maintaining the independence of distributed development. Typically, a teaching assistant (TA) provides support to students they recognize as facing issues in class. We propose a progress management method that uses an automated TA (TA-BOT) to solve students' development progress delays by detecting development delays and advising them accordingly in a timely manner. In the proposed method, the timing for advising a student is determined by the degree of his/her delay. We conducted experiments using several student projects, assessed the importance and validity of the advice contents provided by TA-BOT, and verified the effectiveness of the proposed method in some cases.
Implicit and Explicit Words of Wisdom in Aeschylus<b>and in</b><b><i>Prometheus Bound</i>:</b>A Laconically Generalizing Titan and a Densely Lavish Poet

Abstract Prometheus Bound is a disputed play in the Aeschylean corpus. For some time now the impact of this short description seems to be gradually unraveling the renowned reputation this play used to enjoy. What was in the past the grandiose work of an eminent master, is now regarded by a rising number of scholars as a rather simplistic composition by an anonymous author. Yet, even though the disputed play could not have been composed by Aeschylus, and is indeed nothing like Aeschylus in the summit of his art, as we know him in the main through the fully extant dramas of the last fifteen or so years of his career, Pr . is not devoid of genuine dramatic value. In the present study I focus on the generalizations in the plays in the Aeschylean corpus. I attempt to show that even though the author of Pr . and Aeschylus are clearly different in how they exploit generalizations, this does not – ipso facto – imply that the anonymous former is incompetent in this respect, while the famous latter is most skillful. They are two different playwrights with two different, yet both very special, approaches in handling generalizations.
Stacked lossless deconvolutional network for remote sensing image restoration

Satellite image restoration from its degraded observation has a significant impact on the remote sensing industry. There are many potential applications that can directly benefit from this technique. A convolutional neural network (CNN) has been recently explored in image restoration and achieved remarkable performance. However, most deep CNN architectures in the literature do not properly handle the inherent trade-off between localization accuracy and the use of global context, which is vital for satellite images covering a ultrabroad area. We present a stacked lossless deconvolutional network (SLDN) for remote sensing image restoration. We fully exploit global context information while guaranteeing the recovery of fine details. Specifically, we design a lossless pooling by reformulating the pixel shuffle operator and incorporate it with a shallow deconvolutional network. The resulting lossless deconvolution blocks are stacked one by one to enlarge the receptive fields without any information loss. We further propose an attentive skip connection and progressive learning scheme to improve gradient flows throughout the SLDN. The SLDN can reconstruct high-quality satellite images without noticeable artifacts. An extensive ablation study is also provided to show that all the components proposed are useful for remote sensing image restoration. Experimental comparisons on various restoration tasks, including super-resolution, denoising, and compression artifact reduction, demonstrate the superiority of the proposed method over state-of-the-art methods both qualitatively and quantitatively.
Blind Modulation Classification under Non-Gaussian Noise via Radio Frequency Analytics

Blind modulation classification has emerged as a promising technology in many military and civilian applications, such as cognitive radios, satellite systems, etc. However, it is very challenging to support this blind mechanism within non-Gaussian noise environments, which recently have been identified in a variety of electromagnetic communication networks. Also, start-of-the-art classification methods are mainly based on neural networks or deep learning, which inevitably induces heavy computation loads and thus cannot proactively learn from wireless data in real time. To address the challenges, this paper introduces a series of low- computation radio frequency analytics, including generalized cyclic spectrum (GCS), principal component analysis (PCA), and support vector machine (SVM), which enables the blind modulation classification under non-Gaussian noise. First, based on raw sensory signals and the designed bounded nonlinear function, GCS is extracted as the radio frequency feature to facilitate discrimination of modulation schemes. This GCS can also effectively suppress the burstiness impact of non-Gaussian noise. Then, PCA method is adopted to optimally reduce the dimensionality of GCS features, and a simple and efficient SVM classifier is employed to identify the exact modulation of received signals. Both Monte Carlo simulations and real- data experiments confirm that the proposed design outperforms existing solutions with higher classification accuracy and robustness, i.e., at least 13\% improvement of recognition accuracy in very low (-2 dB) generalized signal-to-noise ratio scenario.
Unsupervised Machine Learning for Card Payment Fraud Detection

Credit card fraud is one of the most common cybercrimes experienced by consumers today. Machine learning approaches are increasingly used to improve the accuracy of fraud detection systems. However, most of the approaches proposed so far have been based on supervised models, i.e., models trained with labelled historical fraudulent transactions, thus limiting the ability of the approach to recognise unknown fraud patterns. In this paper, we propose an unsupervised fraud detection system for card payments transactions. The unsupervised approach learns the characteristics of normal transactions and then identify anomalies as potential frauds. We introduce the challenges on modelling card payment transactions and discuss how to select the best features. Our approach can reduce the equal error rate (EER) significantly over previous approaches (from $$11.2\%$$ to $$8.55\% ERR$$), for a real-world transaction dataset.
Survey of Sentiment Analysis of Political Content on Twitter

Social media usage has seen a dramatic rise in the recent years. With the use of just 140 characters on Twitter, people can voice their opinion on any subject. Various techniques have come up to identify the sentiment of these tweets so as to reach appropriate conclusions. Sentiment analysis of tweets related to politics is theorized to be able to identify public sentiment toward candidates and predict election results. The methodologies have been broadly classified into lexicon-based approaches and machine learning-based approaches. Algorithms like Naive Bayes (NB), support vector machine (SVM) and neural networks are used in machine learning-based approaches. Owing to their greater flexibility, they are more useful than lexicon-based approaches when it comes to political tweet analysis. A survey of all these approaches reveals that SVM provides accuracy over 70%, making it the most efficient algorithm for political sentiment analysis of tweets.
Human Skeleton Detection, Modeling and Gesture Imitation Learning for a Social Purpose

Gesture recognition is topical in computer science and aims at interpreting human gestures via mathematical algorithms.Among the numerous applications are physical rehabilitation and imitation games.In this work, we suggest performing human gesture recognition within the context of a serious imitation game, which would aim at improving social interactions with teenagers with autism spectrum disorders.We use an artificial intelligence algorithm to detect the skeleton of the participant, then model the human pose space and describe an imitation learning method using a Gaussian Mixture Model in the Riemannian manifold.
Leveraging Neural Caption Translation with Visually Grounded Paraphrase Augmentation

Since a concept can be represented by different vocabularies, styles, and levels of detail, a translation task resembles a many-to-many mapping task from a distribution of sentences in the source language into a distribution of sentences in the target language. This viewpoint, however, is not fully implemented in current neural machine translation (NMT), which is one-to-one sentence mapping. In this study, we represent the distribution itself as multiple paraphrase sentences, which will enrich the model context understanding and trigger it to produce numerous hypotheses. We use a visually grounded paraphrase (VGP), which uses images as a constraint of the concept in paraphrasing, to guarantee that the created paraphrases are within the intended distribution. In this way, our method can also be considered as incorporating image information into NMT without using the image itself. We implement this idea by crowdsourcing a paraphrasing corpus that realizes VGP and construct neural paraphrasing that behaves as expert models in a NMT. Our experimental results reveal that our proposed VGP augmentation strategies showed improvement against a vanilla NMT baseline.
Method and Dataset Mining in Scientific Papers

Literature analysis facilitates researchers better understanding the development of science and technology. The conventional literature analysis focuses on the topics, authors, abstracts, keywords, references, etc., and rarely pays attention to the content of papers. In the field of machine learning, the involved methods (M) and datasets (D) are key information in papers. The extraction and mining of M and D are useful for discipline analysis and algorithm recommendation. In this paper, we propose a novel entity recognition model, called MDER, and constructe datasets from the papers of the PAKDD conferences (2009-2019). Some preliminary experiments are conducted to assess the extraction performance and the mining results are visualized.
Multi-agent Reinforcement Learning in Spatial Domain Tasks using Inter Subtask Empowerment Rewards

In the complex multi-agent tasks, various agents must cooperate to distribute relevant subtasks among each other to achieve joint task objectives. An agent's choice of the relevant subtask changes over time with the changes in the task environment state. Multi-agent Hierarchical Reinforcement Learning (MAHRL) provides an approach for learning to select the subtasks in response to the environment states, by using the joint task rewards to train various agents. When the joint task involves complex inter-agent dependencies, only a subset of agents might be capable of reaching the rewarding task states while other agents take precursory or intermediate roles. The delayed task reward might not be sufficient in such tasks to learn the coordinating policies for various agents. In this paper, we introduce a novel approach of MAHRL called Inter-Subtask Empowerment based Multi-agent Options (ISEMO) in which an Inter-Subtask Empowerment Reward (ISER) is given to an agent which enables the precondition(s) of other agents' subtasks. ISER is given in addition to the domain task reward in order to improve the inter-agent coordination. ISEMO also incorporates options model that can learn parameterized subtask termination functions and relax the limitations posed by hand-crafted termination conditions. Experiments in a spatial Search and Rescue domain show that ISEMO can learn the subtask selection policies of various agents grounded in the inter-dependencies among the agents, as well as learn the subtask termination conditions, and perform better than the standard MAHRL technique.
A Conceptual Framework for Cloud-based Collaborative Online Course Provision

A cloud-based collaborative environment for online course provision between universities can facilitate a cost-effective, efficient and enriched education enabling good student experience. However, there are a number of challenges and issues that should be taken into consideration prior to adopting the collaborative environment. They relate to management and administration, cost, collaborative Virtual Learning Environment, ownership, development of courses and teaching resources, as well as culture. To address these challenges a conceptual framework is proposed for this environment which highlights key issues and associated relationships between them. The framework contains five main elements, including Quality, Legal, Security, Operation and Education. The framework illustrates the relationship between the elements. A prototype is also discussed which was designed to test part of the framework.