Reviewerly/exHarmony huggingface dataset:
Design of Big Data Privacy Framework—A Balancing Act

Technological advancements in the field of Big Data and IoT have led to unprecedented growth in digital data. Data is collected from multiple distributed sources by business organizations, government agencies, and healthcare sectors. Data collected is mined to uncover valuable data, and the insights they provide are used by these organizations for optimized decision making. Data thus amassed may also contain sensitive personal information of individuals that are at risk of disclosure during analytics. Hence, there is a need for a privacy-aware system that enforces sensitive data protection. But such a system constrains the usefulness of data. Study shows that although significant findings do exist for balancing these contradicting objectives, the efficacy and scalability of these solutions continue to challenge the research community, given the volume of Big Data. Assessing the appropriate blend of these objectives for mutual benefit of organizations and customers requires leveraging the benefit of the modern tools and technologies in the Big Data ecosystem. This research study extensively reviews the previous work in the direction of privacy preserved Big Data analytics, and the review is first of its kind in exploring the challenges that have to be overcome in striking a balance between data value, privacy, scalability, and performance.
Code Generation from Supervised Code Embeddings

Code generation, which generates source code from natural language, is beneficial for constructing smarter Integrated Development Environments (IDEs), retrieving code more effectively and so on. Traditional approaches are based on matching similar code snippets, and recently researchers pay more attention to machine learning, especially the encoder-decoder framework. Faced with code generation, most encoder-decoder frameworks suffer from two drawbacks: (a) The length of the code snippet is always much longer than the length of its corresponding natural language, which makes it hard to align them, especially for encoders at word level; (b) Code snippets with the same functionality could be implemented in various ways, even completely different at word level. For drawback (a), we propose a new Supervised Code Embedding (SCE) model to promote the alignment between natural language and code. For drawback (b), with the help of Abstract Syntax Tree (AST), we propose a new distributed representation of code snippets which overcomes this drawback. To evaluate our approaches, we build a variant of the encoder-decoder model to generates code with the help of pre-trained code embedding. We perform experiments on several open source datasets. The experiment results indicate that our approaches are effective and outperform the state-of-the-art.
Wigner Distribution of Accelerated TripartiteW-state

In this contribution, we introduce an analytical form of the Wigner distribution function for any tripartite quantum system. These forms are extended to the accelerated tripartite quantum systems of W-state type. The quantum and classical correlations are predicted by negative/ positive values, respectively of the Wigner function. It is shown that for the non-accelerated W-state, the quantum correlations are predicted at large values of the distribution angles. However, for the accelerated W-state, the quantum correlations are predicted at different intervals of distribution angle and different initial acceleration. Due to the acceleration the quantum correlation decays, where the decay rate increases as the number of the accelerated qubits increases. We show that, the local noisy channels; bit and phase bit channels curb the decoherence of the quantum correlation. The ability of the phase-bit channel to defend the quantum correlation is stronger than that shown for the bit-channel, where it decreases if all the three qubits are accelerated. Moreover, we quantify, the amount of the survival quantum correlation by using the volume of the negativity. Our results show that, the volume of negativity doesn’t vanish even at large acceleration, which means thatW-state keeps some of its quantum correlation during the acceleration process.
Attentive Sequences Recurrent Network for Social Relation Recognition from Video

Recently, social relation analysis receives an increasing amount of attention from text to image data. However, social relation analysis from video is an important problem, which is lacking in the current literature. There are still some challenges: 1) it is hard to learn a satisfactory mapping function from low-level pixels to high-level social relation space; 2) how to efficiently select the most relevant information from noisy and unsegmented video. In this paper, we present an Attentive Sequences Recurrent Network model, called ASRN, to deal with the above challenges. First, in order to explore multiple clues, we design a Multiple Feature Attention (MFA) mechanism to fuse multiple visual features (i.e. image, motion, body, and face). Through this manner, we can generate an appropriate mapping function from low-level video pixels to high-level social relation space. Second, we design a sequence recurrent network based on Global and Local Attention (GLA) mechanism. Specially, an attention mechanism is used in GLA to integrate global feature with local sequence feature to select more relevant sequences for the recognition task. Therefore, the GLA module can better deal with noisy and unsegmented video. At last, extensive experiments on the SRIV dataset demonstrate the performance of our ASRN model.
A Novel Adversarial Inference Framework for Video Prediction with Action Control

The ability of predicting future frames in video sequences, known as video prediction, is an appealing yet challenging task in computer vision. This task requires an in-depth representation of video sequences and a deep understanding of real-word causal rules. Existing approaches often result in blur predictions and lack the ability of action control. To tackle these problems, we propose a framework, called VPGAN, which employs an adversarial inference model and a cycle-consistency loss function to empower the framework to obtain more accurate predictions. In addition, we incorporate a conformal mapping network structure into VPGAN to enable action control for generating desirable future frames. In this way, VPGAN is able to produce fake videos of an object moving along a specific direction. Experimental results show that a combination of VPGAN with some pre-trained image segmentation models outperforms existing stochastic video prediction methods.
Pill Image Classification using Machine Learning

Pill classification and recognition are crucial tasks in preventing the misuse of medication. There is a growing need for automation for these tasks due to its inherent complexity. The Food and Drug Administration (FDA) establishes a unique visual appearance to pills based on their shape, color, texture, and imprint information. We propose an automatic classification system for pill images based on their shape and color. Thus, we use image processing techniques to specify an attribute set used by Support Vector Machines and Multilayer Perceptron classifiers. We carry out experiments on a subset of the NLM PIR dataset, provided by the National Library of Medicine. The results indicate that all classifiers perform accurate predictions, with an average accuracy above 99.3%. This high classification accuracy happens even in the presence of unbalanced classes, with precision and recall average scores above 98%. Ergo, the results corroborate the efficiency of our proposal.
Design of Convolutional Neural Network Based on Tree Fork Module

The convolution kernel in the convolutional layer of the traditional convolutional neural network is single, and the feature extraction of the feature map is insufficient and not deep enough. This paper proposes a network built by a tree fork module. The tree-fork module uses multi-convolution kernels for cross-convolution, which not only increases the complexity of the network, but also facilitates feature selection, improves the generalization ability of the network, and extracts more recessive features in the feature map. The basic architecture of the network is unchanged. The traditional convolution module in the network intermediate convolutional layer is replaced by a tree fork module. By training in several public datasets, the performance of tree-fork convolution network is compared with that of traditional convolution network. The accuracy of the network of the tree-fork module in 101_food, caltech256, GTSRB and cifar10 is 4.1 percentage points, 4.7 percentage points, 1.8 percentage points and 1.9 percentage points higher than the traditional CNN. It can be seen from the experimental results that the network has improved in recognition accuracy performance.
Capturing what human eyes perceive: A visual hierarchy generation approach to emulating saliency-based visual attention for grid-like urban street networks

Visual hierarchy is an important notion in urban imagery research. As the skeletons of cities, urban streets attract more attention from urban residents and street network hierarchies are important references for urban planning and urban studies. However, due to the characteristic of over-regularization, it is often difficult for humans to differentiate visual salience for grid-like street networks, resulting in the hierarchies of grid-like streets yielded by existing methods being prone to cause visual cognitive confusion. Therefore, in this study, we proposed a novel model to quantify the extent to which a street attracts human visual attention through emulating the visual attention mechanism that can capture the focus of relatively significant elements at different levels of perception. Using the natural street (also known as the stroke) as the sensor unit, the comprehensive visual salience (CVS) index combining the geometric competitive factors of natural streets at the local scale and psychological competitive factors of natural streets at the global scale is designed. Finally, the visual salience of the urban natural streets is ranked by these CVS scores and the visual hierarchy is derived by the head/tail breaks scheme. The model was applied to eight typical grid-like street networks and the results show that the performance of visual discrimination on street hierarchies is greatly improved. Our hierarchy generation method could effectively detect visually prominent streets for grid-like street networks and generate the visual hierarchies of grid-like street networks that conform to the hierarchies perceived by human eyes. These results would provide helpful suggestions in practical urban street network applications.
Assessing the Difficulty of Classifying ConceptNet Relations in a Multi-Label Classification Setting

Commonsense knowledge relations are crucial for advanced NLU tasks.We examine the learnability of such relations as represented in CONCEPTNET, taking into account their specific properties, which can make relation classification difficult: a given concept pair can be linked by multiple relation types, and relations can have multi-word arguments of diverse semantic types.We explore a neural open world multi-label classification approach that focuses on the evaluation of classification accuracy for individual relations.Based on an in-depth study of the specific properties of the CONCEPTNET resource, we investigate the impact of different relation representations and model variations.Our analysis reveals that the complexity of argument types and relation ambiguity are the most important challenges to address.We design a customized evaluation method to address the incompleteness of the resource that can be expanded in future work.
Comparison of Multi-Label Classification Algorithms for Code Smell Detection

Code smells in a source code shows the weakness of design or implementation. To detect code smells, several detection tools have been developed. However, these tools generally produce different results, since code smells are subjectively interpreted, informally defined and configured by the developers, domain-dependent and based on opinions and experiences. To cope with these issues, in this paper, we have used machine learning techniques, especially multi-label classification methods, to classify whether the given source code is affected with more than one code smells or not. We have conducted experiments on four code smell datasets and transformed them into two multi-label datasets (one for method level and the other one for class level). Two multi-label classification methods (Classifier Chains and Label Combination) and their ensemble models performed on the converted datasets using five different base classifiers. The results show that, as a base classifier, Random Forest algorithm performs better than Decision Tree, Naive Bayes, Support Vector Machine and Neural Network algorithms.
A Formal Modeling Framework for the Implementation of Gaze Guiding as an Adaptive Computer-Based Job Aid for the Control of Complex Technical Systems

Benjamin Weyersa* , Barbara Frankb & Annette Klugeb a Human-Computer Interaction, University of Trier, Trier, Germanyb Department of Work and Organizational Psychology, Ruhr-University Bochum, Bochum, GermanyBenjamin Weyers is currently Assistant Professor at University of Trier and Head of the Human-Computer Interaction Group. Before, he was PostDoc at the Virtual Reality and Immersive Visualization group at RWTH Aachen University. He received his PhD in 2011 at the University of Duisburg-Essen and joined RWTH in 2013. He is interested in the development and research on interactive analysis methods for abstract and scientific data using immersive systems as well as the integration of VR and AR into the control of technical systems for the support of human users in semi-automated control scenarios. Additionally, he focuses on the development and use of formal methods for the description of interactive systems.Barbara Frank obtained her PhD in 2017 at the Department of Work, Organizational and Business Psychology at Ruhr University Bochum. She holds a Master of Science in Applied Cognitive and Media Science. Her research focused on various methods for complex cognitive skill retention and impact of individual factors on skill retention. Barbara Frank is currently working in the field of HR-IT at ThyssenKrupp, Essen.Annette Kluge is a Full Professor for Work, Organizational and Business Psychology at the Ruhr University Bochum, Germany. She obtained her Diploma in Work and Organizational Psychology at the Technical University Aachen and her doctorate in ergonomics and vocational training at the University of Kassel, Germany in 1994. Her expertise is in Human Factors and Ergonomics, training science, skill acquisition and retention, safety management and organizational learning from errors and organizational forgetting.
Quantum Reactivity: An Indicator of Quantum Correlation

Geometry is often a valuable guide to complex problems in physics. In this paper, we introduce a novel geometric quantity called quantum reactivity (QR) to probe quantum correlations in higher-dimensional quantum systems. Much like quantum discord, QR is not a measure of quantum entanglement but can be useful in quantum information processes where a notion of quantum correlation in higher dimensions is needed. Both quantum discord and QR are extendable to an arbitrarily large number of qubits; however, unlike discord, QR satisfies the invariance under unitary operations. Our approach parallels Schumacher’s singlet state triangle inequality, which used an information geometry-based entropic distance. We use a generalization of information distance to area, volume, and higher-dimensional volumes and then use these to define a quantity that we call QR, which is the familiar ratio of surface area to volume. We examine a spectrum of multipartite states (Werner, W, GHZ, randomly generated density matrices, etc.) and demonstrate that QR can provide an ordering of these quantum states as to their degree of quantum correlation.
Res-embedding for deep learning based click-through rate prediction modeling

Recently, click-through rate (CTR) prediction models have evolved from shallow methods to deep neural networks. Most deep CTR models follow an Embedding&MLP paradigm, that is, first mapping discrete id features, e.g. user visited items, into low dimensional vectors with an embedding module, then learn a multi-layer perception (MLP) to fit the target. In this way, embedding module performs as the representative learning and plays a key role in the model performance. However, in many real-world applications, deep CTR model often suffers from poor generalization performance, which is mostly due to the learning of embedding parameters. In this paper, we model user behavior using an interest delay model, study carefully the embedding mechanism, and obtain two important results: (i) We theoretically prove that small aggregation radius of embedding vectors of items which belongs to a same user interest domain will result in good generalization performance of deep CTR model. (ii) Following our theoretical analysis, we design a new embedding structure named res-embedding. In res-embedding module, embedding vector of each item is the sum of two components: (i) a central embedding vector calculated from an item-based interest graph (ii) a residual embedding vector with its scale to be relatively small. Empirical evaluation on several public datasets demonstrates the effectiveness of the proposed res-embedding structure, which brings significant improvement on the model performance.
Comparative Analysis of Manual Strapping Method (MSM) and Electro-Optical Distance Ranging (EODR) Method of Tank Calibration

Abstract Oil storage tanks are mandated for calibration before putting to use and to be recalibrated as a statutory requirement at every five year interval. Oil tanks could be calibrated by geometrical methods such as Manual Strapping Method (MSM) and Electro-Optical Distance Ranging (EODR) method. This study compares both MSM and EODR in terms of cost incurred, duration of calibration and efficiency. Both methods were found to be efficient as they satisfied 95% minimum efficiency as stated by API MPMS 2.2 standard when compared with the wet method of tank calibration; though the cost of EODR was slightly higher than MSM but this was compensated with higher efficiency and reduced duration/time of calibration.
Blockchain Business and Its Regulation

As the blockchainBlockchain industry becomes larger, a new decentralized financial ecosystemDecentralized financial ecosystem is now developing. New financial instruments, represented by terms like tokensTokens, coinsCoins, and ICOsInitial Coin Offering (ICO) are introduced to finance projects on blockchainBlockchain. BlockchainBlockchain is a technology that makes it possible to assign ownershipOwnership of each piece of data to individuals who create that piece. As Pu and Yano (Blockchain and Crypt Currency: A High-Quality Marketplace for Crypt Data. Springer, 2020) points out, that may be the first step towards creating a high quality marketHigh-quality markets. At the same time, like a lock, blockchainBlockchain is merely a technology that is designed to protect a type of property. Such a technology is of no use unless the society agrees a proper set of rules concerning how to prevent the abuse of the technology, what should be protected and how to protect it. The present study is concerned with this issue.
Improved Area Covering in Dynamic Environments by Nonlinear Model Predictive Path Following Control

The problem of area coverage using unmanned aerial vehicles is becoming increasingly important, e.g. for search-and-rescue missions, exploration, and environmental monitoring. The area coverage control problem spans multiple levels, from path planning to tracking and flight control, which are normally time scale separated. Area coverage in highly dynamic environments requires tight integration of these levels to achieve maximum performance and to avoid collision. We propose to fuse the path following level and the mobile platform control level to achieve better performance. By doing so, dynamic constraints, static and dynamic obstacle avoidance and energy efficiency are taken into account. Specifically, we shift the assignment of the reference path velocity from the planner to the controller which leads to improved performance while satisfying all system constraints and therefore increases safety and reliability. Simulation results for a quadcopter illustrate the performance of the proposed algorithm.
A Tutorial and Survey on Fault Knowledge Graph

Knowledge Graph (KG) is a graph-based data structure that can display the relationship between a large number of semi-structured and unstructured data, and can efficiently and intelligently search for information that users need. KG has been widely used for many fields including finance, medical care, biological, education, journalism, smart search and other industries. With the increase in the application of Knowledge Graphs (KGs) in the field of failure, such as mechanical engineering, trains, power grids, equipment failures, etc. However, the summary of the system of fault KGs is relatively small. Therefore, this article provides a comprehensive tutorial and survey about the recent advances toward the construction of fault KG. Specifically, it will provide an overview of the fault KG and summarize the key techniques for building a KG to guide the construction of the KG in the fault domain. What’s more, it introduces some of the open source tools that can be used to build a KG process, enabling researchers and practitioners to quickly get started in this field. In addition, the article discusses the application of fault KG and the difficulties and challenges in constructing fault KG. Finally, the article looks forward to the future development of KG.
Regularized Instance Embedding for Deep Multi-Instance Learning

In the era of Big Data, multi-instance learning, as a weakly supervised learning framework, has various applications since it is helpful to reduce the cost of the data-labeling process. Due to this weakly supervised setting, learning effective instance representation/embedding is challenging. To address this issue, we propose an instance-embedding regularizer that can boost the performance of both instance- and bag-embedding learning in a unified fashion. Specifically, the crux of the instance-embedding regularizer is to maximize correlation between instance-embedding and underlying instance-label similarities. The embedding-learning framework was implemented using a neural network and optimized in an end-to-end manner using stochastic gradient descent. In experiments, various applications were studied, and the results show that the proposed instance-embedding-regularization method is highly effective, having state-of-the-art performance.
Anita: Intelligent Humanoid Robot with Self-Learning Capability Using Indonesian Language

Research and development on Intelligent humanoid robot for general purpose focuses on speech recognition systems to be able to interact with people naturally. In this research, we would like to propose humanoid robot with the self-learning capability based on Indonesian language and small vocabularies for accepting and giving response from people based on Natural Language Processing (NLP). This kind of robot can be used widely in schools, universities and public services. The humanoid robot should consider the style of questions and conclude the answer through conversation under unreliable automatic speech in noisy environment. In our scenario, robot will detect user's face and accept commands from the user to do an action, where the answer from user will be processed to the Google Translator and the result will be compared with vocabularies defined on the system. We describe how this formulation establish a promising framework by empirical results using subjects randomly. The comparative experiments with samples from user were presented.