Reviewerly/exHarmony huggingface dataset:
Wild, Wild Theft: Identity Crimes in the Digital Frontier

Identity theft is a pervasive and expensive problem. Not only does the crime incur a significant financial and mental cost on the victim, but also exhibits a financial toll on the organizations that are swept up in the crime. Yet, while there is some research on the offender part of the identity theft problem, much less research has been devoted to identity theft victims and almost no research has examined victim attempts at remediation. This article examines how the use of a nonprofit victim service organization, the Identity Theft Resource Center (ITRC), affects the experiences of identity crime victims with respect to a variety of problems. Findings reveal that victims incur many problems related to identity theft, that many problems linger over time, and that some services are useful in helping them manage the crime’s after-effects.
Nonlinear Deformation Synthesis via Sparse Principal Geodesic Analysis

Abstract This paper introduces the construction of a low‐dimensional nonlinear space capturing the variability of a non‐rigid shape from a data set of example poses. The core of the approach is a Sparse Principal Geodesic Analysis (SPGA) on the Riemannian manifold of discrete shells, in which a pose of a non‐rigid shape is a point. The SPGA is invariant to rigid body motions of the poses and supports large deformation. Since the Riemannian metric measures the membrane and bending distortions of the shells, the sparsity term forces the modes to describe largely decoupled and localized deformations. This property facilitates the analysis of articulated shapes. The modes often represent characteristic articulations of the shape and usually come with a decomposing of the spanned subspace into low‐dimensional widely decoupled subspaces. For example, for human models, one expects distinct, localized modes for the bending of elbow or knee whereas some more modes are required to represent shoulder articulation. The decoupling property can be used to construct useful starting points for the computation of the nonlinear deformations via a superposition of shape submanifolds resulting from the decoupling. In a preprocessing stage, samples of the individual subspaces are computed, and, in an online phase, these are interpolated multilinearly. This accelerates the construction of nonlinear deformations and makes the method applicable for interactive applications. The method is compared to alternative approaches and the benefits are demonstrated on different kinds of input data.
Matrix Factorization for Recommendation System

Recommendation systems (RS) aim at prediction of user preferences for a given set of items. Conventionally, RS uses collaborative filtering, content-based filtering, or hybrid of both the approaches for generating recommendation lists. Each of these approaches suffers from one or the other problems such as cold-start, sparsity, scalability, processing streaming data, and low latency. Furthermore, with high frequency of data updates, the user preferences change too, that demands for latest recommendation list for each user based on recent activity, capturing the concept shift and eliminating the stale item preferences. Hence, we propose a solution approach based on matrix factorization, that is robust, handles sparse and streaming data. The user preferences are represented in a matrix form and are decomposed into smaller matrices for ease of interpretation and information retrieval. Four matrix factorization techniques, namely NMF, NMFALS, CUR, and SVD, have been used for empirical analysis, and it is found that SVD outperforms NMF, NMFALS, and CUR in terms of time and recommendation accuracy.
An Interactive Pedagogy in Mobile Context for Augmenting Early Childhood Numeric Literacy and Quantifying Skills

Mobile learning techniques have brought the potential of scaffolding information in real-world context that allows learners to interact more actively with their learning content. In this study, we present an interactive learning approach that allows learners to interact with their learning content in real-world context. With learning environment and tasks aligned to the learning content and outcomes, we have developed a mobile application for preschool children to experience their own learning environment using an interactive learning approach and mobile technologies Quick Response codes for learning counting and quantifying skills. The learning content is delivered to learners based on an interactive learning path and the assessment of the learners’ progress to further learning. We evaluate the learning approach in a group of 34 preschool children between 3 and 5 years old, divided into intervention and control group. The intervention group were engaged in numeric learning tasks using the interactive learning approach in mobile context. The control group were engaged in learning activities according to the preschool existing program’s pedagogical curriculum. Pre and post-test learning progress assessment related to the mathematical domain indicate that children from the experimental group performed better than the comparison group after four-month intervention period.
Dynamic Bayesian Adjustment of Dwell Time for Faster Eye Typing

Eye typing is a hands-free method of human computer interaction, which is especially useful for people with upper limb disabilities. Users select a desired key by gazing at it in an image of a keyboard for a fixed dwell time. There is a tradeoff in selecting the dwell time; shorter dwell times lead to errors due to unintentional selections, while longer dwell times lead to a slow input speed. We propose to speed up eye typing while maintaining low error by dynamically adjusting the dwell time for each letter based on the past input history. More likely letters are assigned shorter dwell times. Our method is based on a probabilistic generative model of gaze, which enables us to assign dwell times using a principled model that requires only a few free parameters. We evaluate our model on both able-bodied subjects and subjects with a spinal cord injury (SCI). Compared to the standard dwell time method, we find consistent increases in typing speed in both cases. e.g., 41.8% faster typing for able-bodied subjects on a transcription task and 49.5% faster typing for SCI subjects in a chatbot task. We observed more inter-subject variability for SCI subjects.
Deep learning for very short term solar irradiation forecasting

The use of renewable energy sources (RES) has increased significantly in recent years, in particular, photovoltaic energy which is one of the RES most used for electricity production. Indeed, the world has experienced the installation of a huge number of photovoltaic systems, autonomous or connected to the electricity distribution grid. However, the improvisational nature of solar energy negatively influences the stability and reliability of the electricity grid. One of the best solutions to stabilize and secure the operation of the network is to forecast energy production and to promote the integration of photovoltaic energy on a large scale. In this context, this work aims to develop appropriate forecasting models in the forecasting of photovoltaic energy production. For this reason, we have tested machine learning and deep learning techniques to predict time series data for solar irradiation. For this, we used data from preprocessing, training and testing, three types of error metrics are used to evaluate the models. Recurrent neuron network (RNN) as a machine learning model, on the other hand LSTM and GRU as deep learning models have been discussed from the mathematical and practical simulation using the Anaconda and python environment with their math libraries.
Evolutionary Multiobjective Optimization of a System-Level Motor Drive Design

The use of optimization algorithms to design motor drive components is increasingly common. To account for component interactions, complex system-level models with many input parameters and constraints are needed, along with advanced optimization techniques. This article explores the system-level optimization of a motor drive design, using advanced evolutionary multiobjective optimization (EMO) algorithms. Practical aspects of their application to a motor drive design optimization are discussed, considering various modelling, search space definition, performance space mapping, and constraints handling techniques. Further, for illustration purposes, a motor drive design optimization case study is performed, and visualization plots for the design variables and constrained performances are proposed to aid analysis of the optimization results. With the increasing availability and capability of modern computing, this article shows the significant advantages of optimization-based designs with EMO algorithms as compared to traditional design approaches, in terms of flexibility and engineering time.
Deep Learning Based Automatic Modulation Classification in the Case of Carrier Phase Shift

Various tools and methods have been used in the problem of automatic modulation classification (AMC) with success, such as maximum likelihood estimation (MLE), K-nearest neighbor (KNN), genetic programming or deep learning. In this paper, we consider another perspective: efficiency. We proposed a novel polar coordinate approach in deep learning to mitigate the adverse effects of carrier phase shift without using it in the training of deep learning networks. Thus, we can reduce the amount of training data and the amount of training time.
Key Factors Affecting Blockchain Adoption in Organizations

Blockchain is a decentralized network with a distributed ledger designed to store data permanently, thus offering immutability, trust, transparency, and security. It is a disruptive technological innovation beneficial to many industries. Even with such useful features, blockchain is still finding its roots in the industry. Blockchain adoption has been slower than expected. Hence, understanding the critical reasons for such delayed adoption becomes a very crucial issue. In this paper, we attempt to answer a straightforward research question “How to sell blockchain to a C-level executive?” We believe that this is the fundamental research question that needs to be answered to understand what decision-makers think of blockchain. To answer this question, we adopted a non-participant form of netnography research methodology, and we did a targeted search of social media platforms (LinkedIn, Facebook, and Twitter) to find opinions shared by experts on this topic. We gathered 98 comments posted on social platforms as secondary data for our study. Data screening was conducted to determine the relevant comments, meaning that some comments (or posts) were not related to the research question, and were omitted from the study. We selected 44 comments and categorized all the comments to identify five key factors that influence blockchain adoption in organizations, and these factors are cost, trust, awareness, efficiency, and storytelling. This research work will contribute to the body of knowledge because it will provide an insightful message to the organization towards blockchain adoption as well as give the industries an unbiased direction to build their blockchain strategy.
One Day Ahead Prediction of PV Power Plant for Energy Management System Using Neural Network

One of the aims of this paper is to reduce electricity bills and provide added value to its users, besides producing environmentally friendly and emissions-free electricity. Therefore, the power generated from solar panels must be maximized to achieve the target expected by the user. In this study, the author means to predict power output using Elman Neural Network (ENN) and Feed Forward Neural Network (FFNN). The Levenberg-Marquardt algorithm is used for the learning process as an activation function of the time series of PV power production, and the inputs used in Neural Networks (NN) are several meteorological variables. Some error values from the simulation results will be evaluated to estimate the accuracy of the forecasting method, for energy management systems and those with the smallest MSE error values will be selected as a reference in planning the establishment of solar panel power plants in the future. The results show that FFNN provides the best MSE, for all cases an example in case II days 3 and 4 are 0.01471% and 0.00097% respectively.
A multidimensional intuitionistic fuzzy InterCriteria analysis in the restaurant

The intuitionistic fuzzy InterCriteria analysis (ICrA) is a new method for correlation analysis, which is based on the concepts of index matrices (IMs) and intuitionistic fuzzy sets (IFSs), aiming at detecting of the dependencies between pairs of rating criteria in both clear and uncertain environments. In the present paper, which is an extension of [39], our aim is to extend ICrA to multidimensional ICrA (n-D ICrA) under intuitionistic fuzzy environment for situations where the evaluations of the objects against multidimensional criteria are completely unknown and to show its efficiency through an application in identifying correlations between pairs of criteria when referred to actual data gathered through estimates of a restaurant’s kitchen staff over a three-year period in Bulgaria. We also present a comparative analysis of the correlations between the evaluated criteria of the kitchen staff, on the basis the application of the correlation methods of ICrA, Pearson (PCA), Spearman (SCA) and Kendall (KCA). The four-correlation analysis yielded very similar correlation coefficients, but only the ICrA can be applied to intuitionistic fuzzy evaluations. It is observed that considerable divergence of the ICrA results from those obtained by the other classical correlation analyzes, is only found when the input data contains mistakes.
Analysis of an Enhanced Dual RSA Algorithm Using Pell’s Equation to Hide Public Key Exponent and a Fake Modulus to Avoid Factorization Attack

Public key cryptography generates two distinct keys: one for encryption and another separate but related key for decryption. There exists only one private key for every public key to decipher the message. A variant of RSA, called the Dual RSA, has two different key pairs having separate private and public key exponents. Security of the RSA is compromised using mathematical attacks, by the factorization of 'n'. This paper proposed an enhanced approach to Dual RSA with the help of Pell's equation and a fake modulus key. The algorithm eliminates the distribution of 'n', whose factors compromise the RSA algorithm. Also, the solutions to Pell's equation are shared instead of the public key components. A comparative analysis is carried out with respect to RSA, Dual RSA and the proposed algorithm based on the complexity of each step of the algorithm. It is observed that the proposed system provides more security to the public key exponents and the system modulus 'n', than RSA and Dual RSA.
Person re-identification based on multi-scale constraint network

Combining features of different scales to learn a more discriminative model is an essential solution for person re-identification (Re-ID) tasks. Most existing multi-scale methods are based on the fusion of features from different scales, which cannot exploit information throughly at each scale and cause gradient chaos in optimizing. To address this problem, in this paper we propose an end-to-end multi-scale constraint network(MSCN) to capture detailed information from multiple scales which can independently train each scale and integrate the features of each scale for prediction. In order to retain more information at different scales, we uniformly divide the feature maps into several parts, and vary the number of parts in different scales, then concatenate all the parts in each scale as the entire feature for training. We use both classification loss and metric loss to optimize the network from different aspects. Extensive experiments on three datasets demonstrate that our method achieves very competitive performance. Especially on the CUHK03 dataset, our approach achieves the state-of-the-art results outperforming the current best method by 2.4%/2.0% in Rank-1/mAP.
Discussion of: “Nonparametric regression using deep neural networks with ReLU activation function”

First we would like to congratulate Professor Johannes Schmidt-Hieber for his excellent paper, which shows the surprising result that deep neural networks can achieve good rates of convergence even in case of nonsmooth activation functions.In the following we divide our discussion into three parts:1.The importance of compository assumptions.2. The necessity of the sparsity of the networks.3. The theoretical difference between ReLU and sigmoidal functions.
Awareness of Cybersecurity: Implications for Learning for Future Citizens

In real life contexts, predicting how technology will actually be used is often difficult. This can be a problem in the context of cybercrime: attempts to steal our identities, our money, or simply to frustrate our work by infecting our computers, rely on exceptions, derogations, people not paying attention, or not understanding what is at stake. There are various ways for the criminal mind to operate, but our concern is that future users need to develop more awareness of cybersecurity, meaning not only knowing the risks of using technology, but also how to handle threats that may happen in cyberspace. We will report two qualitative studies that show that cybersecurity awareness is about evolving resilience in a collaborative practice, and therefore cybersecurity awareness is collaborative. This has implications for Technology Enhanced Learning for future citizens.
Interactive Sculpting of Digital Faces Using an Anatomical Modeling Paradigm

Abstract Digitally sculpting 3D human faces is a very challenging task. It typically requires either 1) highly‐skilled artists using complex software packages for high quality results, or 2) highly‐constrained simple interfaces for consumer‐level avatar creation, such as in game engines. We propose a novel interactive method for the creation of digital faces that is simple and intuitive to use, even for novice users, while consistently producing plausible 3D face geometry, and allowing editing freedom beyond traditional video game avatar creation. At the core of our system lies a specialized anatomical local face model (ALM), which is constructed from a dataset of several hundred 3D face scans. User edits are propagated to constraints for an optimization of our data‐driven ALM model, ensuring the resulting face remains plausible even for simple edits like clicking and dragging surface points. We show how several natural interaction methods can be implemented in our framework, including direct control of the surface, indirect control of semantic features like age, ethnicity, gender, and BMI, as well as indirect control through manipulating the underlying bony structures. The result is a simple new method for creating digital human faces, for artists and novice users alike. Our method is attractive for low‐budget VFX and animation productions, and our anatomical modeling paradigm can complement traditional game engine avatar design packages.
Division of crustal units in China using grid-based clustering and a zircon U–Pb geochronology database

Zircon U–Pb dating is one of the most effective and widely used methods due to the widespread existence of zircon as an accessory mineral in rocks and its high closure temperature. Throughout history, each region of the earth has experienced its own crustal evolution. Using zircon U–Pb geochronology data, researchers can reveal the crustal growth history and regional discrimination. In this study, a large spatial analysis was performed across China, involving more than 120,000 data records taken from the zircon U–Pb geochronology database. By applying the Gaussian multipeak fitting method, nine age peaks corresponding to crustal growth events on the continent of China were identified, which centered at 45, 132, 249, 441, 815, 1236, 1908, 2505, and 2933 Ma. Using a grid-based clustering algorithm, grids with similar crustal growth event combinations were gathered and their features extracted. Based on the time series of the crustal growth events, the gathered grid clusters were further integrated into larger crustal units. We used various grid sizes in our algorithm, including 0.325 ° × 0.325 °, 0.4 ° × 0.4 °, 0.6 ° × 0.6 °, 0.8 ° × 0.8 °, 1.0 ° × 1.0 °, 1.2 ° × 1.2 °, and 1.4 ° × 1.4 °. Although the shape and number of the crustal units varied depending on grid size, six large entities were clearly identified and were best visualized using the 0.8 ° × 0.8 °, 1.0 ° × 1.0 °, 1.2 ° × 1.2 °, and 1.4 ° × 1.4 ° grids. These six entities include the Tibet-Sichuan-Yunnan, North Xinjiang, Northeast, Gansu-Qinghai, North China, and South China units. Furthermore, while there were some variations, the crustal unit results found herein were in agreement with tectonic unit research, displaying a new potential avenue of research.
Attestation of Trusted and Reliable Service Function Chains in the ETSI-NFV Framework

The new generation of digital services are natively conceived as an ordered set of Virtual Network Functions, deployed across boundaries and organizations. In this context, security threats, variable network conditions, computational and memory capabilities and software vulnerabilities may significantly weaken the whole service chain, thus making very difficult to combat the newest kinds of attacks. It is thus extremely important to conceive a flexible (and standard-compliant) framework able to attest the trustworthiness and the reliability of each single function of a Service Function Chain. At the time of this writing, and to the best of authors knowledge, the scientific literature addressed all of these problems almost separately. To bridge this gap, this paper proposes a novel methodology, properly tailored within the ETSI-NFV framework. From one side, Software-Defined Controllers continuously monitor the properties and the performance indicators taken from networking domains of each single Virtual Network Function available in the architecture. From another side, a high-level orchestrator combines, on demand, the suitable Virtual Network Functions into a Service Function Chain, based on the user requests, targeted security requirements, and measured reliability levels. The paper concludes by further explaining the functionalities of the proposed architecture through a use case.
A Hybrid CNN-LSTM Architecture for Path Planning of Mobile Robots in Unknow Environments

Path planning algorithms generally require several steps including mapping, localization, sensor data processing, etc. Deep learning-based approach has been proposed to achieve end-to-end path planning, alleviating human design tasks and saving the cost of building maps. In this paper, a CNN-LSTM model which combines convolutional neural network (CNN) with Long Short-Term Memory (LSTM) is constructed to accomplish the path planning task of mobile robots. The CNN structure extracts environment features from the sensor data and calculates the proper command velocity while the LSTM module aims to learn the relationship between continuous actions and smooth the velocity of the robot. The input data of the model is acquired by a lidar, which abstracts the environment information and increases the generalization capability of different environments. In the Robot Operating System (ROS)-based simulator, the proposed model is tested in both the training map and a more complex new map. It performs well in the path planning task even in the untrained map. Compared to CNN model, the CNN-LSTM model has a great advantage in continuous planning and velocity smoothing. Furthermore, both the linear and angular velocity calculated by this model is within limited range and real time performance of this algorithm is satisfactory.