Reviewerly/exHarmony huggingface dataset:
Assets Predictive Maintenance Using Convolutional Neural Networks

Predictive Maintenance (PdM) performs maintenance based on the asset's health status indicators. Sensors can measure an unusual pattern of these indicators, such as an increased motor's vibration level or higher energy consumption, and, in most cases, failures are preceded by an unusual pattern of these measurements. Convolutional Neural Network (CNN) is a Machine Learning technique capable of extracting data representation. This paper presents a CNN framework to tackle assets predictive maintenance problem and a method to transform 1-dimensional (1-D) data into an image-like representation (2-D). A data transformation step is very important to make the use of CNN feasible. To evaluate the proposed framework two datasets were obtained from fans, with distinct electrical pattern, from a building at Western University. The data was preprocessed, transformed in a image-like representation and fed to a tuned classifier. The results presented by the CNN-PdM framework showed that the combination of CNN with the proposed data transformation method outperformed traditional machine learning techniques (Random Forest, Support Vector Machine, and Multi-Layer Perceptron). The model created by the CNN-PdM framework achieved accuracy rates as high as 98% for one of the datasets and 95% for the other.
Forest optimization algorithm‐based feature selection using classifier ensemble

Abstract Features selection is the process of choosing the relevant subset of features from the high‐dimensional dataset to enhance the performance of the classifier. Much research has been carried out in the present world for the process of feature selection. Algorithms such as Naïve Bayes (NB), decision tree, and genetic algorithm are applied to the high‐dimensional dataset to select the relevant features and also to increase the computational speed. The proposed model presents a solution for selection of features using ensemble classifier algorithms. The proposed algorithm is the combination of minimum redundancy and maximum relevance (mRMR) and forest optimization algorithm (FOA). Ensemble‐based algorithms such as support vector machine (SVM), K‐nearest neighbor (KNN), and NB is further used to enhance the performance of the classifier algorithm. The mRMR‐FOA is used to select the relevant features from the various datasets and 21% to 24% improvement is recorded in the feature selection. The ensemble classifier algorithms further improves the performance of the algorithm and provides accuracy of 96%.
Fake Image Detection Using DCT and Local Binary Pattern

The following topics are dealt with: learning (artificial intelligence); image classification; feature extraction; convolutional neural nets; image segmentation; medical image processing; neural nets; diseases; video signal processing; face recognition.
What is decidable about gradual types?

Programmers can use gradual types to migrate programs to have more precise type annotations and thereby improve their readability, efficiency, and safety. Such migration requires an exploration of the migration space and can benefit from tool support, as shown in previous work. Our goal is to provide a foundation for better tool support by settling decidability questions about migration with gradual types. We present three algorithms and a hardness result for deciding key properties and we explain how they can be useful during an exploration. In particular, we show how to decide whether the migration space is finite, whether it has a top element, and whether it is a singleton. We also show that deciding whether it has a maximal element is NP-hard. Our implementation of our algorithms worked as expected on a suite of microbenchmarks.
A Game Analytics Model to Identify Player Profiles in Singleplayer Games

Data Science is a multidisciplinary area related to systems, methods, and processes to extract knowledge from a high volume of data. In this context, we use the term game analytics to designate the science of online analysis and metrics of games. Research works in this area have been focusing on the use of player behavior data to increase revenue and avoid users leaving the game too early. To help this behavior analysis, we created a classification method based on Richard Bartle's player types model, mixed with the definition of Casual and Hardcore players, resulting in eight archetypes: Casual and Hardcore Killers; Casual and Hardcore Achievers; Casual and Hardcore Socializers; and Casual and Hardcore Explorers. We used the first four types from this new model in a singleplayer shoot'em up game, which gathers players' behavior attributes during each match. The right profile is chosen using K-means and Decision Tree algorithms, based on data from previous gameplay sessions. This whole method was tested using two new questionnaires to match the player's profile evaluation with the game's final profile, revealing accuracy between 75% and 80%.
A Feature Integrated Saliency Estimation Model for Omnidirectional Immersive Images

Omnidirectional, or 360°, cameras are able to capture the surrounding space, thus providing an immersive experience when the acquired data is viewed using head mounted displays. Such an immersive experience inherently generates an illusion of being in a virtual environment. The popularity of 360° media has been growing in recent years. However, due to the large amount of data, processing and transmission pose several challenges. To this aim, efforts are being devoted to the identification of regions that can be used for compressing 360° images while guaranteeing the immersive feeling. In this contribution, we present a saliency estimation model that considers the spherical properties of the images. The proposed approach first divides the 360° image into multiple patches that replicate the positions (viewports) looked at by a subject while viewing a 360° image using a head mounted display. Next, a set of low-level features able to depict various properties of an image scene is extracted from each patch. The extracted features are combined to estimate the 360° saliency map. Finally, bias induced during image exploration and illumination variation is fine-tuned for estimating the final saliency map. The proposed method is evaluated using a benchmark 360° image dataset and is compared with two baselines and eight state-of-the-art approaches for saliency estimation. The obtained results show that the proposed model outperforms existing saliency estimation models.
Building your kingdom Imitation Learning for a Custom Gameplay Using Unity ML-agents

Imitating human-like behavior in action games is a challenging task in machine learning research, with several methods being employed to solve that problem. As individual players often have characteristic styles when playing the game, this method attempts to find these behaviors which makes it unique. In this paper, we consider learning human-like behavior via Unity ML-Agents Toolkit without being explicitly given a reward function, and learning to perform the task by observing expert's demonstration. The use of imitation learning makes every player imprints his behaviour on the clone(Agent).Therefore, each agent will play differently because they are following the main player's(Teacher) patterns. Our work shows there is significant reduction in computational time and the amount of training data needed by using imitation learning on top reinforcement learning.
On the Floquet analysis of commutative periodic Lindbladians in finite dimension

We consider the Markovian Master Equation over matrix algebra $\mathbb{M}_d$, governed by periodic Lindbladian $L_t$ in standard (Kossakowski-Lindblad-Gorini-Sudarshan) form. It is shown that under simplifying assumption of commutativity, i.e. if $L_t L_{t'} = L_{t'}L_t$ for any moments of time $t,t'\in\mathbb{R}_+$, the Floquet normal form of resulting completely positive dynamical map is not guaranteed to be given by simultaneously globally Markovian maps. In fact, the periodic part of the solution is even shown to be necessarily non-Markovian. Two examples in algebra $\mathbb{M}_2$ are explicitly calculated: a periodically modulated random qubit dynamics, being a generalization of pure decoherence scheme, and a classically perturbed two-level system, coupled to reservoir via standard ladder operators.
Efficient and Secure Key Management for Conditional Access Systems

In Digital TV service, subscribers get the access of channels of their interest. In conditional access systems (CAS), Control Word is used to deliver the multimedia content to legitimate subscribers securely. In order to update the Control Word frequently, a large number of messages are exchanged in the conventional key distribution systems. This article analyzes the strengths and weaknesses of the existing key distribution protocols for CAS and presents an efficient key distribution protocol for CAS based on Elliptic Curve Cryptography. The proposed protocol minimizes the storage cost and communication cost of subscribers. In addition to this, the proposed protocol facilitates the unrestricted channel package composition and minimum communication cost on rekeying. The proposed protocol also provides perfect forward and backward secrecy. As a result, the proposed protocol is well suited for Pay-TV systems.
Classification of Bengali Questions Towards a Factoid Question Answering System

This paper demonstrates question classification for Bengali language questions which is the first step towards developing a closed domain based Bengali factoid question answering system. Question classification is an influential step towards a question answering system on which the performance of the whole system depends. We have used machine learning algorithms Stochastic Gradient Descent(SGD), Decision Tree(DT), Support Vector Machine(SVM) and Naive Bayes(NB) for the question classification process where Support Vector Machine(SVM) with linear kernel performs the best providing the accuracy 90.6% over five coarse-grained categories.
Eliminating the noise from quantum computing hardware

Quantum EngineeringVolume 2, Issue 1 e28 RESEARCH HIGHLIGHT Eliminating the noise from quantum computing hardware Keren Li, Corresponding Author Keren Li likr@pcl.ac.cn orcid.org/0000-0001-5559-0421 Center for Quantum Computing, Peng Cheng Laboratory, Shenzhen, China Keren Li, Center for Quantum Computing, Peng Cheng Laboratory, Shenzhen 518055, China. Email: likr@pcl.ac.cnSearch for more papers by this author Keren Li, Corresponding Author Keren Li likr@pcl.ac.cn orcid.org/0000-0001-5559-0421 Center for Quantum Computing, Peng Cheng Laboratory, Shenzhen, China Keren Li, Center for Quantum Computing, Peng Cheng Laboratory, Shenzhen 518055, China. Email: likr@pcl.ac.cnSearch for more papers by this author First published: 11 December 2019 https://doi.org/10.1002/que2.28Citations: 5Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume2, Issue1March 2020e28 RelatedInformation
A Study on the Performance of Supervised Algorithms for Classification in Sentiment Analysis

Sentiment Analysis has gained a lot of popularity in the last few years. It is the processes of identifying the underlying sentiment in a comment, review or in a document. Sentiment of a document or sentence generally falls under three categories namely positive, neutral or negative. This paper has surveyed literature relating to supervised algorithms for classification in sentiment analysis. The contribution of the paper includes the extensive survey and classification of literature related to sentiment analysis based on classification algorithm used and application area. We have then performed a comparative study of five popular supervised classification algorithms namely Naive Bayes, Random Forest, Support Vector Machine (SVM), Decision Tree and K Nearest Neighbors (KNN) and evaluated the performance of each of these algorithms in sentiment analysis. The study has been performed on two different data sets to nullify the bias that might happen in the comparative study when focusing on a single data set. This paper has also proposed a novel method with advanced preprocessing(AP) which offers improved performance of about 8% over all the methods taken into study in the paper.
Study on Path Planning of Intelligent Mower Based on UWB Location

With the progress of intelligent robot technology and the increase of urban greening area, the intelligent mower has also been greatly developed. However, most intelligent mowers do not have the ability to locate outdoors accurately, so the random path is mainly used for mowing. This makes mowing efficiency low. In this paper, the UWB(Ultra Wideband) location is used to solve the problem that intelligent mower cannot accurately locate outdoors. UWB location system consists of location base station and location label. In the experiment, the intelligent mower can be located accurately to the Ami level, which provides the basis for path planning. In the aspect of path planning, this paper mainly takes the global static known environment as the research background. In this paper, the map construction of intelligent mower operation, the point-to-point optimal path algorithm, and the whole area coverage algorithm are studied. The improved Boustrophedon cell decomposition is proposed, which makes the mower more efficient.
Fusion of Convolutional Neural Network and Statistical Features for Texture classification

Texture is a fundamental characteristic of many types of images, especially those with significant rotation, scale illumination, and viewpoint change. Texture image classification is one of the challenging problems that have various applications such as remote sensing, material recognition, and computer-aided medical diagnosis, etc. Various Computer vision techniques have been used. More recently, Deep learning architectures demonstrated impressive results. This paper aims to investigate combining two feature extraction methods: Handcrafted-based and CNN-based in a two-stream neural network architecture. We believe that Statistical features could enhance the performance of the CNN architecture, especially in the case of small datasets. To test our approach we used two challenging datasets, the Describable Textures Dataset (DTD) and Flicker Material Database (FMD). Results showed that our two-stream neural network which has an image as a first stream and a statistical feature vector as a second stream achieve better results than a Convolutional neural network achieved with just the RGB image as input. The Xception network [9] combined with SIFT-FV demonstrated an accuracy superiority for both datasets.
Exploring Perspective Dependency in a Shared Body with Virtual Supernumerary Robotic Arms

With advancements in robotics, systems featuring wearable robotic arms teleoperated by a third party are appearing. An important aspect of these systems is the visual feedback provided to the third party operator. This can be achieved by placing a wearable camera on the robotic arm's "host", but such a setup makes the visual feedback dependent on the movements of the main body. Here we reproduced this view dependency in VR using a shared body. The "host" shares their virtual body with the virtual Supernumerary Robotic Arms of the teleoperator. In our research, the two users perform two tasks: (i) a "synchronization task" to improve their joint action performance and (ii) a "building task" where they worked together to build a tower. In a user study, we evaluated the embodiment, workload, and performance of the teleoperator through the "building task" with three different view dependency modes. We present some early outcomes as trends that might give directions for the future investigations.
Accounting Information Systems: The Scope of Blockchain Accounting

Distributed Ledger Technology—of which Blockchain is an example—is revolutionizing different sectors, creating new challenges and new opportunities. In this paper, we will investigate the impact of this technology on Accounting and Accounting Information Systems (AIS). The adoption of a Distributed Ledger Accounting presents extremely interesting characteristics, eliminating or redefining the role of entities external to the company, such as Banks, Insurance Companies, Certified Public Accountants and Auditors. Furthermore, we will try to outline the impact of this technology on AIS by hypothesising possible paths of development.
Efficient Image‐Based Simulation of Flow and Transport in Heterogeneous Porous Media: Application of Curvelet Transforms

Abstract Flow, transport, reaction, adsorption, and deformation (FTRAD) are phenomena that occur in a wide variety of heterogeneous porous media and have been studied for decades. Models of heterogeneous porous formations involve approximations, simplifications, and even unjustified assumptions. With advances in instrumentation, obtaining highly resolved images of such media has become feasible, opening the way to direct image‐based simulation of the FTRAD. Such simulations require, however, intensive computations, limiting their utility. We propose a novel methodology whereby (i) the image is taken to the curvelet space, (ii) all the curvelet coefficients that are smaller than a threshold are set to 0, and (iii) the image is reconstructed. By carrying out image‐based simulations of diffusion and fluid flow in two distinct types of heterogeneous porous media, we demonstrate that simulations in the reconstructed images yield as accurate results as those in the original ones while speeding up the computation by 2 orders of magnitude or better. Thus, the methodology represents a significant step toward the ultimate goal of making direct image‐based simulation of the FTRAD in heterogeneous porous media a standard practice.
Neural language models for the multilingual, transcultural, and multimodal Semantic Web

A vision of a truly multilingual Semantic Web has found strong support with the Linguistic Linked Open Data community. Standards, such as OntoLex-Lemon, highlight the importance of explicit linguistic modeling in relation to ontologies and knowledge graphs. Nevertheless, there is room for improveme nt in terms of automation, usability, and interoperability. Neural Language Models have achieved several breakthroughs and successes considerably beyond Natural Language Processing (NLP) tasks and recently also in terms of multimodal representations. Several paths naturally open up to port these successes to the Semantic Web, from automatically translating linguistic information associated with structured knowledge resources to multimodal question-answering with machine translation. Language is also an important vehicle for culture, an aspect that deserves considerably more attention. Building on existing approaches, this article envisions joint forces between Neural Language Models and Semantic Web technologies for multilingual, transcultural, and multimodal information access and presents open challenges and opportunities in this direction.
User Recommendation in Content Curation Platforms

We propose a personalized user recommendation framework for content curation platforms that models preferences for both users and the items they engage with simultaneously. In this way, user preferences for specific item types (e.g., fantasy novels) can be balanced with user specialties (e.g., reviewing novels with strong female protagonists). In particular, the proposed model has three unique characteristics: (i) it simultaneously learns both user-item and user-user preferences through a multi-aspect autoencoder model; (ii) it fuses the latent representations of user preferences on users and items to construct shared factors through an adversarial framework; and (iii) it incorporates an attention layer to produce weighted aggregations of different latent representations, leading to improved personalized recommendation of users and items. Through experiments against state-of-the-art models, we find the proposed framework leads to a 18.43% (Goodreads) and 6.14% (Spotify) improvement in top-k user recommendation.
Galois Hulls of Cyclic Codes over Finite Fields

The Galois hull of linear code is defined to be the intersection of the code and its Galois dual. In this paper, we investigate the Galois hulls of cyclic codes over Fqr. For any integer s≤r, we present some sufficient and necessary conditions that cyclic codes have l-dimensional s-Galois hull. Moreover, we prove that a cyclic code C has l-dimensional s-Galois hull iff C has l-dimensional (r-s)-Galois hull. In particular, we also present the sufficient and necessary condition for cyclic codes with 1-dimensional Galois hulls and the relationship between cyclic codes with 1-dimensional Galois hulls and cyclic codes with Galois complementary duals. Some optimal cyclic codes with Galois hulls are obtained. Finally, we explicitly construct a class of cyclic codes with 1-Galois linear complementary dual over Fq3.
LS-Tree: Model Interpretation When the Data Are Linguistic

We study the problem of interpreting trained classification models in the setting of linguistic data sets. Leveraging a parse tree, we propose to assign least-squares-based importance scores to each word of an instance by exploiting syntactic constituency structure. We establish an axiomatic characterization of these importance scores by relating them to the Banzhaf value in coalitional game theory. Based on these importance scores, we develop a principled method for detecting and quantifying interactions between words in a sentence. We demonstrate that the proposed method can aid in interpretability and diagnostics for several widely-used language models.
A Comparative Study of Load Balancing Algorithms in a Cloud Environment

Providing computing resources on Cloud is an on demand, systematic way to deliver computing services like databases, storage services, computing servers, networking services, software’s, etc. The Cloud resource service provider ensures resource availability via the Internet with the help of web-based applications and tools. Or in other words, it provides a simple way to take advantage of the Computing Services available on Cloud via the internet. As we know, there is a rapid expansion in the number of users accessing the Cloud Computing Services which may lead to the scarcity of the resources. Further, leading to a state of deadlock. The deadlock condition arises when a group of users shares the same resources at the same time. So to avoid the deadlock condition, we should implement Load Balancing on the virtual machines. This paper aims to deliver the concept of Load Balancing, how it improves the performance of the system, as well as it describes a comparative study of Algorithms to balance the Load with the help of Cloud Analyst Simulation Tool.