Reviewerly/exHarmony huggingface dataset:
How Mandatory Second Factor Affects the Authentication User Experience

Recent years have seen growing organizational adoption of two-factor authentication as organizations seek to limit the damage caused by password breaches. However, research on the user experience of two-factor authentication in a real-world setting is relatively scant. To fill this gap, we conducted multiple waves of an online survey of users at a large public university during its multi-phase rollout of mandatory two-factor authentication for faculty, staff, and students. In addition, we examined multiple months of logs of all authentication events at the university. We found no significant changes in user experience and acceptance of two-factor authentication when it was mandatory for select systems that dealt with sensitive information. However, these factors degraded when users were forced to use two-factor authentication for logging into every single university resource. Our findings can serve as important guidance for the implementation of two-factor authentication in organizations in a way that can help achieve a balance between security and user experience.
Deep transfer neural network using hybrid representations of domain discrepancy

Transfer neural networks have been successfully applied in many domain adaptation tasks. The initiative of most of the current transfer networks, essentially, is optimizing a single distance metric between the source domain and target domain, while few studies integrate multiple metrics for training transfer networks. In this paper, we propose an architecture of transfer neural network equipped with hybrid representations of domain discrepancy, which could incorporate the advantages of different types of metrics as well as compensate their imperfections. In our architecture, the Maximum Mean Discrepancy (MMD) and H-divergence based domain adaptations are combined for simultaneous distribution alignment and domain confusion. Through extensive experiments, we find that the proposed method is able to achieve compelling transfer performance across the datasets with domain discrepancy from small scale to large scale. Especially, the proposed method can be promisingly used to predict the viewpoint of 3D-printed workpiece even trained without labels of real images. The visualization of learned features and adapted distributions by our transfer network highlights that the proposed approach could effectively learn the similar features between two domains and deal with a wide range of transfer tasks.
Variational Autoencoder Modular Bayesian Networks for Simulation of Heterogeneous Clinical Study Data

In the area of Big Data one of the major obstacles for the progress of biomedical research is the existence of data “silos”, because legal and ethical constraints often do not allow for sharing sensitive patient data from clinical studies across institutions. While federated machine learning now allows for building models from scattered data of the same format, there is still the need to investigate, mine and understand data of separate and very differently designed clinical studies that can only be accessed within each of the data hosting organizations. Simulation of sufficiently realistic virtual patients based on the data within each individual organization could be a way to fill this gap. In this work we propose a new machine learning approach (VAMBN) to learn a generative model of longitudinal clinical study data. VAMBN considers typical key aspects of such data, namely limited sample size coupled with comparable many variables of different numerical scales and statistical properties, and many missing values. We show that with VAMBN we can simulate virtual patients in a sufficiently realistic manner while making theoretical guarantees on data privacy. In addition, VAMBN allows for simulating counterfactual scenarios. Hence, VAMBN could facilitate data sharing as well as design of clinical trials.
Data Everyday: Data Literacy Practices in a Division I College Sports Context

Data analysis is central to sports training. Today, cutting-edge digital technologies are deployed to measure and improve athletes' performance. But too often researchers focus on the technology collecting performance data at the expense of understanding athletes' experiences with data. This is particularly the case in the understudied context of collegiate athletics, where competition is fierce, tools for data analysis abound, and the institution actively manages athletes' lives. By investigating how student-athletes analyze their performance data and are analyzed in turn, we can better understand the individual and institutional factors that make data literacy practices in athletics meaningful and productive-or not. Our pilot interview study of student-athletes at one Division I university reveals a set of opportunities for student-athletes to engage with and learn from data analytics practices. These opportunities come with a set of contextual tensions that should inform the design of new technologies for collegiate sports settings.
Neural network with multiple connection weights

Biological studies have shown that the interaction between neurons are based on neurotransmitters, which transmit signals between neurons, and that one neuron sends information to another neuron by releasing a number of different neurotransmitters, which play different roles. Motivated by this biological discovery, a novel neural networks model is proposed by extending the dimension of connection weights from one to multiple, i.e. there are multiple not only one connections between each two units. The number of dimensions of connection weight represents the number of categories of neurotransmitters and different components of the weight correspond to different neurotransmitters. In order to make these neurotransmitters collaborate and compete appropriately, the input and output for each unit in our proposed model have been heuristically defined. From the biological perspective, the proposed neural network is much closer to biological neural network. From the viewpoint of new model structure, the characteristic that the activation of each hidden unit is based on several filters, can improve the interpretability of features learned by the proposed neural network. Experimental results on MNIST, NORB and several other data sets have demonstrated that the performances of traditional neural networks can be improved by extending the dimension of connection weight between units, and the idea of multiple connection weights provides a new paradigm for the design of neural networks.
Efficient NewHope Cryptography Based Facial Security System on a GPU

With explosive era of machine learning development, human data, such as biometric images, videos, and particularly facial information, have become an essential training resource.The popularity of video surveillance systems and growing use of facial images have increased the risk of leaking personal information.On the other hand, traditional cryptography systems are still expensive, time consuming, and low security, leading to be threatened by the foreseeable attacks of quantum computers.This paper proposes a novel approach to fully protect facial images extracted from videos based on a post-quantum cryptosystem named NewHope cryptography.Applying the proposed technique to arrange input data for encryption and decryption processes significantly reduces encryption and decryption times.The proposed facial security system was successfully accelerated using data-parallel computing model on the recently launched Nvidia GTX 2080Ti Graphics Processing Unit (GPU).Average face frame extracted from video (190 × 190 pixel) required only 2.2 ms and 2.7 ms total encryption and decryption times with security parameters n = 1024 and n = 2048, respectively, which is approximately 9 times faster than previous approaches.Analysis results of security criteria proved that the proposed system offered comparable confidentiality to previous systems.
Ensemble Method for Sexual Predators Identification in Online Chats

Cyber grooming is a compelling problem worldwide nowadays and many reports strongly suggested that it becomes very urgent to tackle this problem to protect the children from sexual exploitation. In this study, we propose an effective method for sexual predator identification in online chats based on two-stage classification. The purpose of the first stage is to distinguish predatory conversations from the normal ones while the second stage aims to tell apart between the predator user and the victim within a single predatory conversation. Finally, some unique predators are derived from the second stage result. We investigate several machine learning classifiers including Naive Bayes, Support Vector Machine, Neural Network, Logistic Regression, Random Forest, K-Nearest Neighbors, and Decision Tree with Bag of Words features using several different term weighting methods for this task. We also proposed two ensemble techniques to improve the classification task. The experiment results on PAN12 dataset show that our best method using soft voting based ensemble for first stage and Naive Bayes based method for the second stage obtained an F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5</inf> -score of 0.9348, which would place as number one in the PAN12 competition ranking.
Single Image Haze Removal Using Deep Cellular Automata Learning

Deep learning is one of the most popular approaches to machine learning, which has been widely used for classification. In this paper, we propose a novel learning method based on a combination of an idea of the deep learning approach and the cellular automata model, called DeepCA for single image haze removal. DeepCA's learning is divided into two main parts. The first part is a cellular automata-based deep feature extraction: multi-layer cellular automata with the rules are used to extract the data feature matrices of the image, in which the matrices can be divided into several layers. Then, the score matrices were generated as the model in which was trained by the cellular automata rules. The second part is a decision stage: we used the score matrices to the mapping between the proper data. For demonstration, we take the single image haze removal task as an example to confirm the capability of the proposed method. In this regard, the dichromatic model is chosen as the major model to remove the haze of the image. The multi-layer cellular automata with the rules work as a mechanical extractor of the light source feature of the hazy image. The decision stage of DeepCA performs as the recognizer for properly predicting the global light source for dehazing. This aims to improve the light source and the transmission map that they are important compositions for haze-free image restoration. For performance evaluation, we perform quantitative and qualitative measures. For the qualitative performance of the haze removal, DeepCA did not even cause the halo artifact effect that occurred in other haze removal algorithms. The empirical results in quantitative measures show that DeepCA improved intensity, color saturation quality, and halo artifact when compared with the state-of-the-art methods.
Design and Development of Spherical Spy Robot for Surveillance Operation

Robots like humanoid, wheeled and legged robots are developed to perform operations such as pick and place, object detection, surveillance operation, search and rescue operations. The constraints of available robot are in terms of computational speed, cost, structure complexity and space. To overcome the difficulties of the current technology, the proposed spherical spy robot includes pendulum-based drive system with video streaming of live activities and obstacle detection. The 3D model/ structure of the spy robot is designed in CATIA and FEA analysis is performed. The motors, sensors and feedback drives will be interfaced with the structure and the system is driven using Arduino controller. The fabricated model consists of pendulum, drive systems and outer shell of the spherical spy robot. The android application is developed and is interfaced with the Bluetooth IP address to control the movement of the robot. Also, two cameras attached the spherical ball gives the 360-degree view.
University-Industry Interaction as an Element of the University’s «Third Mission»

In the context of the transformation of education, characterized by the rapid obsolescence of knowledge, the increasing role of technological and social innovations, the globalization of the economy and education, it is becoming increasingly important to ensure successful cooperation between universities and business. At the same time, the state gains an advantage due to the rapid transfer of new knowledge, research and innovation, enterprises and organizations increase opportunities for effective development. The aim of the study is to identify the most successful forms of interaction between educational organizations and enterprises, the mechanisms of influence of the University on the development of the region of its location, participation in socio-economic processes. The problem is considered through the prism of the concept of the “third mission” for universities, which involves active interaction of educational organizations with society, including employers. Besides, the authors use the provisions of the theory of stakeholders, which describes the parameters taking into account the interests of various actors in the process of University management. On the example of the Northern (Arctic) Federal University named after M.V. Lomonosov, the article analyzes the existing experience and successful practices of organizing such interaction, as well as the emerging problems. The information base of the study was a survey on the satisfaction of employers with the quality of University graduates’ training, conducted in SAFU in 2019. As findings, the article states the main systemic problems of interaction between universities and the sphere of production such as the lack of coordinated strategies for training in various specialties, discrepancies in understanding the sets of competencies and skills of graduates, the fragmented university-industry interaction in research and development areas, different motivations and values. The authors come to a conclusion that the University must undergo a path of transformation to ensure strategic interaction with business and industry, including the development of a common “entrepreneurial” culture in the team, as well as the expansion of interaction with public institutions and socio-economic actors in the framework of the “third mission”.
A Framework for Detecting Phishing Websites using GA based Feature Selection and ARTMAP based Website Classification

Nowadays, Phishing attack has gained more attention among all the other attacks existing in online social media. The fraudulent E-mail sent form the fake website that looks like the legitimate website is the initial carter for launching the phishing attacks. This is a kind of social engineering attack in which, the user is targeted for stealing the personal information, viz., user name, password, and banking credentials for committing the financial crimes. The existing phishing website detection methods suffer from two issues in terms of feature selection scheme that does not consider the right set of features for detection and the classifier which is trained with the poor hyper parameters. In this paper, the phishing websites are detected by extracting the various features from the collection of phishing and legitimate websites obtained from PhishTank and starting point directory service. This constructed feature vector is further processed by the proposed feature selection module GenFea to obtain the reduced set of features. This reduced feature vector is further processed by the proposed phishing detection module PhiDec to predict the type of a website. The performance of the proposed approach is compared with the existing machine learning classifiers and neural network classifiers. From the experimental results, it is observed that the proposed approach outperformed the other existing classifiers for detecting the phishing websites.
Stochastic Gradient Descent in Continuous Time: A Central Limit Theorem

Stochastic gradient descent in continuous time (SGDCT) provides a computationally efficient method for the statistical learning of continuous-time models, which are widely used in science, engineering, and finance. The SGDCT algorithm follows a (noisy) descent direction along a continuous stream of data. The parameter updates occur in continuous time and satisfy a stochastic differential equation. This paper analyzes the asymptotic convergence rate of the SGDCT algorithm by proving a central limit theorem for strongly convex objective functions and, under slightly stronger conditions, for nonconvex objective functions as well. An [Formula: see text] convergence rate is also proven for the algorithm in the strongly convex case. The mathematical analysis lies at the intersection of stochastic analysis and statistical learning.
B-MERODE: A Model-Driven Engineering and Artifact-Centric Approach to Generate Blockchain-Based Information Systems

Blockchain technology has the potential to facilitate the development and improvement of cross-organizational business processes. When it comes to developing systems relying on blockchain having this purpose, model-driven engineering is a promising approach that has been adopted by a number of solutions. This paper presents B-MERODE, a novel approach relying on model-driven engineering and artifact-centric business processes to generate blockchain-based information systems supporting cross-organizational collaborations. The feasibility of the approach is demonstrated by modeling the case of a rice supply chain. Compared to other solutions, B-MERODE provides more flexibility, more reusability and further leverages the automation potential offered by model-driven engineering.
A novel deep learning method for query task execution time prediction in graph database

The execution time prediction for query tasks in graph database has become difficult and challenging due to the complexity of query plan and system. It is difficult for Database Administrators (DBA) or Database Management System (DBMS) to catch the accurate execution time during and before the execution of a query task. Before executing a query task, predicting its execution time can help the DBA or DBMS to efficiently management in the fields of load management, task scheduling, permission control, progress monitoring, system scale customization, etc. Therefore, accurately and efficiently predicting the execution time for query tasks is a key technology in these fields. In this paper, motivated by the combination of artificial intelligence technologies and graph database theories, we first propose a novel deep learning method to predict the execution time for query tasks in graph database. First, each query plan tree of tasks is encoded into an operation sequence. Second, top-20 features are selected from 68 candidate system features using random forest (RF), and the selected top-20 features are reduced to five principal components using principal component analysis (PCA). Finally, an accurate and efficient model based on the long short-term memory (LSTM) is designed and implemented to predict the execution time. The model can predict the execution time in advance before executing a query task in graph database. The experimental results from six kinds of benchmarks with the public data set Yelp show that the average accuracy of the proposed model can reach 81.34% with a high prediction efficiency rate, which proves the feasibility of the deep learning method. In particular, the proposed model can achieve the state-of-the-art prediction performance for query task execution time.
Robust Coreset Construction for Distributed Machine Learning

Coreset, which is a summary of the original dataset in the form of a small weighted set in the same sample space, provides a promising approach to enable machine learning over distributed data. Although viewed as a proxy of the original dataset, each coreset is only designed to approximate the cost function of a specific machine learning problem, and thus different coresets are often required to solve different machine learning problems, increasing the communication overhead. We resolve this dilemma by developing robust coreset construction algorithms that can support a variety of machine learning problems. Motivated by empirical evidence that suitably-weighted k -clustering centers provide a robust coreset, we harden the observation by establishing theoretical conditions under which the coreset provides a guaranteed approximation for a broad range of machine learning problems, and developing both centralized and distributed algorithms to generate coresets satisfying the conditions. The robustness of the proposed algorithms is verified through extensive experiments on diverse datasets with respect to both supervised and unsupervised learning problems.
Optimal Multivehicle Motion Planning Using Bernstein Approximants

This article presents a computational framework to efficiently generate feasible and safe trajectories for multiple autonomous vehicle operations. We formulate the optimal motion planning problem as a continuous-time optimal control problem, and approximate its solutions in a discretized setting using Bernstein polynomials. The latter possess convenient properties that allow to efficiently compute and enforce constraints along the vehicles' trajectories, such as maximum speed and angular rates, minimum distance between trajectories and between the vehicles and known obstacles, etc. Thus, the proposed method is particularly suitable for generating trajectories in real-time for safe operations in complex environments and multiple vehicle missions. We show, using a rigorous mathematical framework, that the solution to the discretized optimal motion planning problem converges to that of the continuous-time one. The advantages of the proposed method are investigated through numerical examples.
Transferable Knowledge-Based Multi-Granularity Fusion Network for Weakly Supervised Temporal Action Detection

Despite remarkable progress, temporal action detection is still limited for real application due to the great amount of manual annotations. This issue motivates interest in addressing this task under weak supervision, namely, locating the action instances using only video-level class labels. Many current works on this task are mainly based on the Class Activation Sequence (CAS), which is generated by the video classification network to describe the probability of each snippet being in a specific action class of the video. However, the CAS generated by a simple classification network can only focus on local discriminative parts instead of locating the entire interval of target actions. In this paper, we present a novel framework to handle this issue. Specifically, we propose to utilize convolutional kernels with varied dilation rates to enlarge the receptive fields, which can transfer the discriminative information to the surrounding non-discriminative regions. Then, we design a cascaded module with the proposed Online Adversarial Erasing (OAE) mechanism to further mine more relevant regions of target actions by feeding the erased-feature maps of discovered regions back into the system. In addition, inspired by the transfer learning method, we adopt an additional module to transfer the knowledge from trimmed videos to untrimmed videos to promote the classification performance on untrimmed videos. Finally, we employ a boundary regression module embedded with Outer-Inner-Contrastive (OIC) loss to automatically predict the boundaries based on the enhanced CAS. Extensive experiments are conducted on two challenging datasets, THUMOS14 and ActivityNet-1.3, and the experimental results clearly demonstrate the superiority of our unified framework.