Reviewerly/exHarmony huggingface dataset:
Effects of Data Visualization Choices on Psychophysiological Responses, Judgment, and Audit Quality

ABSTRACT We conduct experiments with Big 4 auditors and business students to investigate psychophysiological responses to Big Data visualizations and the effects of visualization techniques on auditor judgment and audit quality. Results of the first experiment using pupillometry, eye gaze measurements, and automatic facial expression analysis indicate that different visualization techniques produce significant differences in the level of cognitive and emotional arousal. A second experiment investigates whether visualizations that were demonstrated to promote higher and lower levels of arousal have differential effects on auditor judgments and audit quality. In addition, the second experiment investigates whether the reliability of data sources underlying visualizations affect auditors' judgments. Results indicate that visualizations that increase arousal can enhance auditors' ability to recognize disconfirming evidence and incorporate this evidence into their decisions.
A Unified Federated DNNs Framework for Heterogeneous Mobile Devices

Mobile devices can generate a tremendous amount of unique data, and thus, create countless opportunities for deep learning tasks. Due to the concerns of data privacy, it is often impractical to log all the data to a central server for training a satisfactory model. In federated learning, the participating devices can train a shared global model collaboratively while keeping their data locally. However, it is not a trivial task to train the deep neural networks (DNNs) with millions and billions of parameters on resource-constrained mobile devices in a federated manner. We replace each fully connected (FC) layer with two low-rank projection matrices to compact the DNNs model, and establish a global error function to recover the outputs of the compressed DNNs model. Then, we design a communication-efficient federated optimation Algorithm to reduce communication cost further. Considering that the heterogeneous devices may run different models at the same time, we devise three different training patterns to integrate the heterogeneous devices running different models. We conduct extensive experiments on both independently identically distribution (IID) and non-IID data sets. The experimental results demonstrate that the proposed framework can significantly reduce the number of parameters and communication cost while maintaining performance.
Gaussian Process with Graph Convolutional Kernel for Relational Learning

Gaussian Process (GP) offers a principled non-parametric framework for learning stochastic functions. The generalization capability of GPs depends heavily on the kernel function, which implicitly imposes the smoothness assumptions of the data. However, common feature-based kernel functions are inefficient to model the relational data, where the smoothness assumptions implied by the kernels are violated. To model the complex and non-differentiable functions over relational data, we propose a novel Graph Convolutional Kernel, which enables to incorporate relational structures to feature-based kernels to capture the statistical structure of data. To validate the effectiveness of proposed kernel function in modeling relational data, we introduce GP models with Graph Convolutional Kernel in two relational learning settings, i.e., unsupervised settings of link prediction and semi-supervised settings of object classification. The parameters of our GP models are optimized through the scalable variational inducing point method. However, the highly structured likelihood objective requires densely sampling from variational distributions, which is costly and makes its optimization challenging in the unsupervised settings. To tackle this challenge, we propose a Local Neighbor Sampling technique with a provable more efficient computational complexity. Experimental results on real-world datasets demonstrate that our model achieves state-of-the-art performance in two relational learning tasks.
Survey on Enhanced Security Control measures in Cloud Computing systems

Abstract Cloud Computing System is an internet based network of systems that provides a wide range of services to various companies and individuals, creating a digital environment for business progressing for a better world. Cloud Computing delivers a variety of services like remote storage, database management, applications and software, and high power computing performance, all these can be easily attained by subscribing to the cloud service, such that even from a low end PC system, any high end software can be run easily through means of internet. But, providing security to all of these services and stored data of private organizations has always been a challenge. Once a cloud server has been hacked, or an intruder has authorized himself into the server, he can simply sabotage, steal or misuse any sort of information of the client companies stored in within the server’s database. There are many ways to provide security to the different parts of the cloud, some methods are impenetrable, while some are fragile and can be broken through. This paper focusses on the various aspects of the cloud and its security functionalities, and how these security functionalities can be improvised. Some security measures can only protect the cloud by updating and regulating their terms and conditions, and some measures include various authorization methods and regularized access to cloud services.
A new competency ontology for learning environments personalization

Abstract Competency is a central concept for human resource management, training and education. We define a competency as the capacity of a person to display a generic skill with a certain level of performance when applied to one or more knowledge entities. Competencies, and competency referentials grouping competencies, are essential elements for user models, e-Portfolios, adaptive learning, and personalization in Technology-based learning. But to be processed both by humans and by software tools, competencies should be represented in a formal, non-ambiguous model called an ontology. Moreover, this model should use a shared vocabulary to describe the generic skills and the knowledge entities. Defining and linking shared vocabularies is the purpose of ontologies in the semantic web. The goal of our research is to develop a competency ontology for the semantic web to be used as a shared referential in the description of competencies and competency profiles. We analysed five previous competency models and developed COMP2, a new competency ontology that integrates important elements of previous models and the richness of the semantic web vocabulary. COMP2 provides processing capabilities both to humans and computers. Its graphic model is highly readable by humans for design, evaluation and communication purposes. It also translates, together with its data sets, to standard semantic Web code for machine processing. The ontology is composed of five stages that are interlinked with other ontologies in use within the web of linked open data. We will present an example for the use of the ontology for competency-based personalization in learning environments.
Semi-Supervised Deep Learning for Multiplex Networks

Multiplex networks are complex graph structures in which a set of entities are connected to each other via multiple types of relations, each relation representing a distinct layer. Such graphs are used to investigate many complex biological, social, and technological systems. In this work, we present a novel semi-supervised approach for structure-aware representation learning on multiplex networks. Our approach relies on maximizing the mutual information between local node-wise patch representations and label correlated structure-aware global graph representations to model the nodes and cluster structures jointly. Specifically, it leverages a novel cluster-aware, node-contextualized global graph summary generation strategy for effective joint-modeling of node and cluster representations across the layers of a multiplex network. Empirically, we demonstrate that the proposed architecture outperforms state-of-the-art methods in a range of tasks: classification, clustering, visualization, and similarity search on seven real-world multiplex networks for various experiment settings.
Learning Neuro-Symbolic Relational Transition Models for Bilevel Planning

In robotic domains, learning and planning are complicated by continuous state spaces, continuous action spaces, and long task horizons. In this work, we address these challenges with Neuro-Symbolic Relational Transition Models (NSRTs), a novel class of models that are data-efficient to learn, compatible with powerful robotic planning methods, and generalizable over objects. NSRTs have both symbolic and neural components, enabling a bilevel planning scheme where symbolic AI planning in an outer loop guides continuous planning with neural models in an inner loop. Experiments in four robotic planning domains show that NSRTs can be learned very data-efficiently, and then used for fast planning in new tasks that require up to 60 actions and involve many more objects than were seen during training.
Vibration Measurement Using Laser Triangulation for Applications in Wind Turbine Blades

The blades in a wind turbine are currently manufactured with flexible and light materials, which make them more susceptible to the effects of vibrations when the wind speed is high enough, causing fatigue damage, affecting the functionality of its structure and aerodynamic efficiency. This work presents a comparison of the modal vibration parameters, applied to a cantilever beam, determined with two experimental methods—the use of accelerometers and a proposed optical non-contact method—based on the principle of laser triangulation and photogrammetry techniques. This technique uses the geometric symmetry of the equidistant displacements along the z axis of the beam to obtain the amplitude data. Parameters such as natural frequency and modal form are obtained by fitting the data to a nonlinear equation with a solution which is an exponential/harmonic equation. Also, analytically, these parameters are determined, and a comparison is made between the experimental methods. The result shows that the relative error of the first-order natural vibration frequency is below 1%. The proposed method is simple, efficient, reliable, and it is also a method that has not been applied to the test of wind turbine blades, so its implementation as this type of wind turbine component is an area of opportunity for the validation of modal vibration parameters in the wind industry. An analysis of results is presented showing benefits of the proposed method and its limitations.
A k-Nearest Neighbor Medoid-Based Outlier Detection Algorithm

Outlier detection techniques are well known for identifying a small amount of data objects named outliers that are far away from clusters and exist in sparse regions of data space. However, most outlier detection algorithms based on k nearest neighbors are sensitive to parameter k. The outlier detection algorithms based on clustering rely on specific clustering algorithms, and outliers are by-products. To partially circumvent these problems, motivated by the medoid concept of K-medoids clustering algorithms, in this paper, we propose a k-nearest neighbor medoid-based outlier detection method that is easy to implement and can provide competing performances with existing solutions. At the same time, a method to determine the parameter k is proposed in combination with the outlier detection algorithm proposed in this paper. Experiments performed on real datasets demonstrate the efficacy of our method.
Continuous-Discrete Multiple Target Tracking With Out-of-Sequence Measurements

This paper derives the optimal Bayesian processing of an out-of-sequence (OOS) set of measurements in continuous-time for multiple target tracking. We consider a multi-target system modelled in continuous time that is discretised at the time steps when we receive the measurements, which are distributed according to the standard point target model. All information about this system at the sampled time steps is provided by the posterior density on the set of all trajectories. This density can be computed via the continuous-discrete trajectory Poisson multi-Bernoulli mixture (TPMBM) filter. When we receive an OOS measurement, the optimal Bayesian processing performs a retrodiction step that adds trajectory information at the OOS measurement time stamp followed by an update step. After the OOS measurement update, the posterior remains in TPMBM form. We also provide a computationally lighter alternative based on a trajectory Poisson multi-Bernoulli filter. The effectiveness of the two approaches to handle OOS measurements is evaluated via simulations.
Differential Privacy for Tensor-Valued Queries

Private individual information are increasingly exposed through high-dimensional and high-order data, with the wide deployment of learning techniques. These data are typically expressed in form of tensors, but there is no principled way to guarantee privacy for tensor-valued queries. Conventional differential privacy is typically applied to scalar values without a precise definition on the shape of the queried data. Realizing that the conventional mechanisms do not take the data structural information into account, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tensor Variate Gaussian</i> (TVG), a new <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\epsilon,\delta) $ </tex-math></inline-formula> -differential privacy mechanism for tensor-valued queries. We further introduce two mechanisms based on TVG with an improved utility by imposing the unimodal differentially-private noise. With the utility space available, the proposed mechanisms can be instantiated with an optimized utility, and the optimization problem has a closed-form solution scalable to large-scale problems. Finally, we experimentally test our mechanisms on a variety of datasets and models, demonstrating that TVG is superior than other state-of-the-art mechanisms on tensor-valued queries.
Automatic Question Generation and the SmartStart Application

Although content creators, instructors, and students alike see the value in providing interactive courseware to help students learn, they remain a costly solution to create. Platforms that allow the creation and delivery of interactive courseware are only fully utilized by those who can afford the labor cost of building hundreds of formative items just to start. Most of the time this means barriers are too high in either financial cost or extremely long development timelines. Our solution is called SmartStart, a process that works to reduce these barriers to creating courseware by automating basic steps that otherwise require significant manual labor. Automatic question generation (AQG) is one in a series of steps in the SmartStart process that work together to transform textbook content into a courseware learning environment.
Intelligent mechanical systems and its applications on online fraud detection analysis using pattern recognition K-nearest neighbor algorithm for cloud security applications

Banks and financial institutions use systematic business transaction fraud, individual analytics. Distrust analysis definition is based on a customer history of abnormal behaviors that indicate normal activities and fraud, manipulation or application analysis. The challenge of facing the banking and finance sector is in the form of fraudulent transactions. Combined with the explosive development of data and information, it has urged great advances in technology and communication, global consumer awareness, and capabilities. A comprehensive customer engagement strategy of the banking sector should establish opportunities through dynamic consumer estimation. Security is therefore essential for all banks to implement fraud detection methods to minimize their losses. The regulatory sequence based on different methods of machine learning has evolved to detect various fraudulent transactions. The effective way to detect the fraud detection mechanism is far more essential than the standard classification technology. Pattern Recognition K-Nearest Neighbor [PR-KNN] is developed for such a progressive search and non-parametric technique that evaluates the pseudo-nature to find the best solution to the problem. Regression and classification problems of fraudulent transactions mean the spending is reducing the number of false alarms. The detection systems are placed in bank fraud or fraudulent transactions after the transaction are expected to expectation.
unzipFPGA: Enhancing FPGA-based CNN Engines with On-the-Fly Weights Generation

Single computation engines have become a popular design choice for FPGA-based convolutional neural networks (CNNs) enabling the deployment of diverse models without fabric reconfiguration. This flexibility, however, often comes with significantly reduced performance on memory-bound layers and resource underutilisation due to suboptimal mapping of certain layers on the engine's fixed configuration. In this work, we investigate the implications in terms of CNN engine design for a class of models that introduce a pre-convolution stage to decompress the weights at run time. We refer to these approaches as on-the-fly. To minimise the negative impact of limited bandwidth on memory-bound layers, we present a novel hardware component that enables the on-chip on-the-fly generation of weights. We further introduce an input selective processing element (PE) design that balances the load between PEs on suboptimally mapped layers. Finally, we present unzipFPGA, a framework to train on-the-fly models and traverse the design space to select the highest performing CNN engine configuration. Quantitative evaluation shows that unzipFPGA yields an average speedup of 2.14× and 71% over optimised status-quo and pruned CNN engines under constrained bandwidth and up to 3.69× higher performance density over the state-of-the-art FPGA-based CNN accelerators.
Software defect prediction with imbalanced distribution by radius‐synthetic minority over‐sampling technique

Abstract Software defect prediction, which can identify the defect‐prone modules, is an effective technology to ensure the quality of software products. Due to the importance in software maintenance, many learning‐based software defect prediction models are presented in recent years. Actually, the defects usually occupy a very small proportions in software source codes; thus, the imbalanced distributions between defect‐prone modules and non‐defect‐prone modules increase the learning difficulty of the classification task. To address this issue, we present a random over‐sampling mechanism used to generate minority‐class samples from high‐dimensional sampling space to deal with the imbalanced distributions in software defect prediction, in which two constraints are applied to provide a robust way to generate new synthetic samples, that is, scaling the random over‐sampling scope to a reasonable area and distinguishing the majority‐class samples in a critical region. Based on nine open datasets of software projects, we experimentally verify that our presented method is effective on predict the defect‐prone modules, and the effect is superior to the traditional imbalanced processing methods.
Fully dynamic approximation of LIS in polylogarithmic time

We revisit the problem of maintaining the longest increasing subsequence (LIS) of an array under (i) inserting an element, and (ii) deleting an element of an array. In a recent breakthrough, Mitzenmacher and Seddighin [STOC 2020] designed an algorithm that maintains an O((1/є)O(1/є))-approximation of LIS under both operations with worst-case update time Õ(nє), for any constant є>0 (Õ hides factors polynomial in logn, where n is the length of the input). We exponentially improve on their result by designing an algorithm that maintains an (1+є) approximation of LIS under both operations with worst-case update time Õ(є−5). Instead of working with the grid packing technique introduced by Mitzenmacher and Seddighin, we take a different approach building on a new tool that might be of independent interest: LIS sparsification.
Attention-Based Relation Reasoning Network for Video-Text Retrieval

In the field of video-text matching, there are several potential and effective internal relations within a single modal data, which the existing approaches always ignore. In this paper, we propose a novel model named Attention-based Relation Reasoning Network (ARRN), that can robustly learn and reason the word relations of a sentence and temporal relations between video frames. It can jointly capture the local and global characteristics of video and text, thus significantly improves the performance on video-text retrieval. In ARRN, with global-to-local attention strategy, we could attend to important relations of multi-scales, then learn more reasonable local relation features. These features, generated at distinct levels, are powerful and complementary to each other, allowing us to obtain effective video and text representations by very simple fusion. The extensive experiments on two widely-used video-text datasets MSVD and TGIF show that our proposed ARRN approach establishes a substantial improvement.
Digital Ecosystem Development in Customer Relationship Management (CRM) for Hajj Portal Website

The pilgrimage is the fifth pillar of Islam that must be carried out by Muslims around the world who are affordable in term of financial, physical and mental. It takes long preparation to perform well in the pilgrimage that commonly require one or more years in the learning process through books or studies. Actually, technology provide the alternative to learn intimately, which is more accurate and quicker compare to the traditional method. Customer Relationship Management (CRM) is the software package primarily used in business transaction but the concept can be utilized in formulating the digital ecosystem, which provide sustainability and fairness. This study explore the potentiality of digital ecosystem to avoid the conflict between start-up and PPIU in manage and control the pilgrim needs from beginning until finished
Smart Wheelchair- An Implementation of Voice and Android Controlled System

The inspiration driving android and voice automated smart wheelchair venture is to construct a smart wheelchair that helps physically impaired people to locomote from one spot then onto the next. To overcome this disability, a smart voice-controlled fully automated wheelchair is designed for physically disabled, patients, or pregnant women. This smart wheelchair will help them move from one place to another without any problem. Numerous wheelchairs are accessible with various running advancements, yet the expense is high and it isn't much successful. For the most part, designing voice and android control wheelchairs is to conquer a few burdens of the current frameworks. The customer needs to interact with the wheelchair with the help of the application. This framework enables the client to vigorously communicate with the wheelchair at various dimensions of the control (turn left, turn right, proceed, return and stop). This task utilizes a microcontroller circuit and motor drivers to make the development of the wheelchair.