Reviewerly/exHarmony huggingface dataset:
Utilizing Case-based Reasoning for Consumer Choice Prediction based on the Similarity of Compared Alternative Sets

This study suggests an alternative to the conventional collaborative filtering method for predicting consumer choice, using case-based reasoning.The algorithm of case-based reasoning determines the similarity between the alternative sets that each subject chooses.Case-based reasoning uses the inverse of the normalized Euclidian distance as a similarity measurement.This normalized distance is calculated by the ratio of difference between each attribute level relative to the maximum range between the lowest and highest level.The alternative case-based reasoning based on similarity predicts a target subject's choice by applying the utility values of the subjects most similar to the target subject to calculate the utility of the profiles that the target subject chooses.This approach assumes that subjects who deliberate in a similar alternative set may have similar preferences for each attribute level in decision making.The result shows the similarity between comparable alternatives the consumers consider buying is a significant factor to predict the consumer choice.Also the interaction effect has a positive influence on the predictive accuracy.This implies the consumers who looked into the same alternatives can probably pick up the same product at the end.The suggested alternative requires fewer predictors than conjoint analysis for predicting customer choices.
Development of an automated system model of information protection in the cross-border exchange

A model of an automated system for secure cross-border information exchange is considered. This automated system presents as a complex of the following modules which ensure the information security: data encryption, an electronic digital signature (EDS), access control to the stored information based on two-factor authentication, resolution of possible conflict situations. In this paper, models of two modules for an automated system are described: the electronic digital signature and access control to information based on two-factor authentication. The mathematical model of the formation and verification of the digital signature is described in detail. The phased software implementation of this model with the analysis of the results is described. The two-factor authentication algorithm based on the authenticator program and a mobile phone is considered. A secret string generator based on the method of exhaustive search was carried out. A generator of trigonometric functions is described, which is used to calculate a one-time password. The phased software implementation of this model is given. The analyzed results of the algorithm are presented.
A Critical Review on Adverse Effects of Concept Drift over Machine Learning Classification Models

Big Data (BD) is participating in the current computing revolution in a big way.Industries and organizations are utilizing their insights for Business Intelligence using Machine Learning Models (ML-Models).Deep Learning Models (DL-Models) have been proven to be a better selection than Shallow Learning Models (SL-Models).However, the dynamic characteristics of BD introduce many critical issues for DL-Models, Concept Drift (CD) is one of them.CD issue frequently appears in Online Supervised Learning environments in which data trends change over time.The problem may even worsen in the BD environment due to veracity and variability factors.Due to the CD issue, the accuracy of classification results degrades in ML-Models, which may make ML-Models not applicable.Therefore, ML-Models need to adapt quickly to changes to maintain the accuracy level of the results.In current solutions, a substantial improvement in accuracy and adaptability is needed to make ML-Models robust in a non-stationary environment.In the existing literature, the consolidated information on this issue is not available.Therefore, in this study, we have carried out a systematic critical literature review to discuss the Concept Drift taxonomy and identify the adverse effects and existing approaches to mitigate CD.
A modular approach to cataloguing marine science data

Abstract The ability to access and search metadata for marine science data is both a key requirement for answering fundamental principles of data management (making data Findable, Accessible, Interoperable and Reusable) and also in meeting domain-specific, community defined standards and legislative requirements placed on data publishers. This paper describes a modular data model to answer the functional requirements developed from these drivers and illustrates how this data model can be operationalised. The ability of this solution to meet the FAIR principles is then assessed.
An Approach of National and International Cybersecurity Laws and Standards to Mitigate Information Risks in Public Organizations of Ecuador

Today, cybersecurity is very important, and Ecuador could suffer serious security problems that would compromise the information of its citizens if it does not change its laws and standards in public institutions. The objective of this article was to analyze laws and standards in depth to be able to implement them in organizations public of Ecuador. It was used the deductive and exploratory research method to analyze the information of available media and scientific articles. It resulted in improvements in cybersecurity with the use of standards such as the International Organization for Standardization (ISO) and specific laws for cybersecurity. It was concluded that Ecuador need improve the cybersecurity in public organization, because had leak of information and implementation of laws and standards can help mitigate the risks the information.
ICE: An Interactive Configuration Explorer for High Dimensional Categorical Parameter Spaces

There are many applications where users seek to explore the impact of the settings of several categorical variables with respect to one dependent numerical variable. For example, a computer systems analyst might want to study how the type of file system or storage device affects system performance. A usual choice is the method of Parallel Sets designed to visualize multivariate categorical variables, However, we found that the magnitude of the parameter impacts on the numerical variable cannot be easily observed here. We also attempted a dimension reduction approach based on Multiple Correspondence Analysis but found that the SVD-generated 2D layout resulted in a loss of information. We hence propose a novel approach, the Interactive Configuration Explorer (ICE), which directly addresses the need of analysts to learn how the dependent numerical variable is affected by the parameter settings given multiple optimization objectives. No information is lost as ICE shows the complete distribution and statistics of the dependent variable in context with each categorical variable. Analysts can interactively filter the variables to optimize for certain goals such as achieving a system with maximum performance, low variance, etc. Our system was developed in tight collaboration with a group of systems performance researchers and its final effectiveness was evaluated with expert interviews, a comparative user study, and two case studies.
Vehicle detection method based on ADE-YOLOV3 algorithm

Aiming at the problem of repeated detection of YOLOV3 algorithm in vehicle detection, the ADE-YOLOV3 vehicle detection algorithm is proposed. The algorithm uses K-means clustering algorithm to determine the number of target candidate frames and aspect ratio according to the inherent width and height characteristics of the vehicle. Then, according to the results obtained by clustering, the anchor parameters are reset, which makes the ADE-YOLOV3 network have certain pertinence in vehicle detection. Finally, the migration learning method is used to improve the network structure, and the optimal weight model is obtained, which improves the training precision of the model. The experimental results show that compared with the original YOLOV3 method, the mAP is increased from 91.4% to 95%, the repeated detection rate is reduced from 5.6% to 2.1%, and the detection speed by 50fps. The detection accuracy is improved and the problem of repeated detection is effectively avoided.
HULK: An Energy Efficiency Benchmark Platform for Responsible Natural Language Processing

Computation-intensive pretrained models have been taking the lead of many natural language processing benchmarks such as GLUE. However, energy efficiency in the process of model training and inference becomes a critical bottleneck. We introduce HULK, a multi-task energy efficiency benchmarking platform for responsible natural language processing. With HULK, we compare pretrained models' energy efficiency from the perspectives of time and cost. Baseline benchmarking results are provided for further analysis. The fine-tuning efficiency of different pretrained models can differ a lot among different tasks and fewer parameter number does not necessarily imply better efficiency. We analyzed such phenomenon and demonstrate the method of comparing the multi-task efficiency of pretrained models. Our platform is available at https://sites.engineering.ucsb.edu/~xiyou/hulk/.
Semantic 3D Reconstruction with Learning MVS and 2D Segmentation of Aerial Images

Semantic modeling is a challenging task that has received widespread attention in recent years. With the help of mini Unmanned Aerial Vehicles (UAVs), multi-view high-resolution aerial images of large-scale scenes can be conveniently collected. In this paper, we propose a semantic Multi-View Stereo (MVS) method to reconstruct 3D semantic models from 2D images. Firstly, 2D semantic probability distribution is obtained by Convolutional Neural Network (CNN). Secondly, the calibrated cameras poses are determined by Structure from Motion (SfM), while the depth maps are estimated by learning MVS. Combining 2D segmentation and 3D geometry information, dense point clouds with semantic labels are generated by a probability-based semantic fusion method. In the final stage, the coarse 3D semantic point cloud is optimized by both local and global refinements. By making full use of the multi-view consistency, the proposed method efficiently produces a fine-level 3D semantic point cloud. The experimental result evaluated by re-projection maps achieves 88.4% Pixel Accuracy on the Urban Drone Dataset (UDD). In conclusion, our graph-based semantic fusion procedure and refinement based on local and global information can suppress and reduce the re-projection error.
MAST: A Memory-Augmented Self-supervised Tracker

Recent interest in self-supervised dense tracking has yielded rapid progress, but performance still remains far from supervised methods. We propose a dense tracking model trained on videos without any annotations that surpasses previous self-supervised methods on existing benchmarks by a significant margin (+15%), and achieves performance comparable to supervised methods. In this paper, we first reassess the traditional choices used for self-supervised training and reconstruction loss by conducting thorough experiments that finally elucidate the optimal choices. Second, we further improve on existing methods by augmenting our architecture with a crucial memory component. Third, we benchmark on large-scale semi-supervised video object segmentation(aka. dense tracking), and propose a new metric: generalizability. Our first two contributions yield a self-supervised network that for the first time is competitive with supervised methods on standard evaluation metrics of dense tracking. When measuring generalizability, we show self-supervised approaches are actually superior to the majority of supervised methods. We believe this new generalizability metric can better capture the real-world use-cases for dense tracking, and will spur new interest in this research direction.
Distributed deep learning platform for pedestrian detection on IT convergence environment

Abstract IT technology and traditional industries have been combined recently, resulting in IT convergence technology in various fields. Through convergence with the automobile, pedestrian detection technology, in particular, is used in the autonomous navigation control service of autonomous vehicles and also applied in various fields such as intelligent CCTV and robot recognition technology. For pedestrian detection, hierarchical classification and feature vector were used in early stage, and deep learning is under active progress. However, since deep learning for pedestrian detection is time-consuming for processing a large volume of image data, it requires a lot of computing resources, and hence building such a system is very expensive. Therefore, in this paper we shall present a distributed deep learning platform which can easily build a cluster, and execute deep learning process in the distributed cloud environment, while achieving performance improvement in various ways. Our platform provides a convenient interface for easily and efficiently executing the deep learning process in a distributed environment by providing a multilayered system architecture. Our system builds and utilizes computing power in easy and efficient way by leveraging container technique, so-called OS-level virtualization, rather than traditional hypervisor-based virtualization. In our system, we improve the whole performance by exploiting both of data and parameter parallelisms at once and reduce the synchronization overhead by exploiting asynchronous communication for parameter updates. Also, we propose an efficient resource allocation scheme for parameter servers and slaves which can improve the performance from the experiment.
A simplified ICA-based local similarity stereo matching

Abstract Since the existing stereo matching methods may fail in the regions of non-textures, boundaries and tiny details, a simplified independent component correlation algorithm (ICA)-based local similarity stereo matching algorithm is proposed. In order to improve the DispNetC, the proposed algorithm first offers the simplified independent component correlation algorithm (SICA) cost aggregation. Then, the algorithm introduces the matching cost volume pyramid, which simplifies the pre-processing process for the ICA. Also, the SICA loss function is defined. Next, the region-wise loss function combined with the pixel-wise loss function is defined as a local similarity loss function to improve the spatial structure of the disparity map. Finally, the SICA loss function is combined with the local similarity loss function, which is defined to estimate the disparity map and to compensate the edge information of the disparity map. Experimental results on KITTI dataset show that the average absolute error of the proposed algorithm is about 37% lower than that of the DispNetC, and its runtime consuming is about 0.6 s lower than that of GC-Net.
Gradient-Based Adversarial Training on Transformer Networks for Detecting Check-Worthy Factual Claims

We present a study on the efficacy of adversarial training on transformer neural network models, with respect to the task of detecting check-worthy claims. In this work, we introduce the first adversarially-regularized, transformer-based claim spotter model that achieves state-of-the-art results on multiple challenging benchmarks. We obtain a 4.70 point F1-score improvement over current state-of-the-art models on the ClaimBuster Dataset and CLEF2019 Dataset, respectively. In the process, we propose a method to apply adversarial training to transformer models, which has the potential to be generalized to many similar text classification tasks. Along with our results, we are releasing our codebase and manually labeled datasets. We also showcase our models' real world usage via a live public API.
Comparison of parameter estimation methods for normal inverse Gaussian distribution

This paper compares several methods for estimating parameters of normal inverse Gaussian distribution. Ordinary maximum likelihood estimation and the method of moment estimation often do not work properly due to restrictions on parameters. We examine the performance of adjusted estimation methods along with the ordinary maximum likelihood estimation and the method of moment estimation by simulation and real data application. We also see the effect of the initial value in estimation methods. The simulation results show that the ordinary maximum likelihood estimator is significantly affected by the initial value; in addition, the adjusted estimators have smaller root mean square error than ordinary estimators as well as less impact on the initial value. With real datasets, we obtain similar results to what we see in simulation studies. Based on the results of simulation and real data application, we suggest using adjusted maximum likelihood estimates with adjusted method of moment estimates as initial values to estimate the parameters of normal inverse Gaussian distribution.
A Scale-Adaptive Particle Filter Tracking Algorithm Based on Offline Trained Multi-Domain Deep Network

In this paper, a MDSPF method is proposed to learn a robust observation model for representing the targets by training a CNN with a number of video sequences. The CNN architecture is composed of three shared convolutional units, two shared fully connected (Fc) units and a multiple domain Fc unit, and it is offline trained by a multi-domain learning strategy. After training, the shared convolutional units are remained as an observation model for our tracking framework. The features from the shared convolutional units can well adapt to the challenges in tracking tasks. A scale-adaptive particle filter is also proposed in our framework to improve the robustness of particle filter method. Different from most existing particle filter tackers, it can efficiently shepherd each particle towards a more precise location and scale through similarity evaluation. Extensive experiments are conducted on Object Tracking Benchmark (OTB), UAV123 and LaSOT datasets to verify the efficiency of our proposed method.
Strong Authentication without Temper-Resistant Hardware and Application to Federated Identities

Shared credential is currently the most widespread form of end user authentication with its convenience, but it is also criticized for being vulnerable to credential database theft and phishing attacks.While several alternative mechanisms are proposed to offer strong authentication with cryptographic challenge-response protocols, they are cumbersome to use due to the need of tamper-resistant hardware modules at user end.In this paper, we propose the first strong authentication mechanism without the reliance on tamper-resistant hardware at user end.A user authenticates with a password-based credential via generating designated-verifiable authentication tokens.Our scheme is resistant to offline dictionary attacks in spite that the attacker can steal the password-protected credentials, and thus can be implemented on general-purpose devices.More specifically, we first introduce and formalize the notion of Password-Based Credential (PBC), which models the resistance of offline attacks and the unforageability of authentication tokens even if attackers can see authentication tokens and capture password-wrapped credentials of honest users.We then present a highly-efficient construction of PBC using a "randomize-thenprove" approach, and prove its security.The construction does not involve bilinear-pairings, and can be implemented with common cryptographic libraries for many platforms.We also present a technique to transform the PBC scheme to be publiclyverifiable, and present an application of PBC in federated identity systems to provide holder-of-key assertion mechanisms.Compared with current certificate-based approaches, it is more convenient and user-friendly, and can be used with the federation systems that employ privacy-preserving measures (e.g., Sign-in with Apple).We also implement the PBC scheme and evaluate its performance for different applications over various network environment.When PBC is used as a strong authentication mechanism for end users, it saves 26%-36% of time than the approach based on ECDSA with a tamper-resistant hardware module.As for its application in federation, it could even save more time when the user proves its possession of key to a Relying Party.
On Leveraging Pretrained GANs for Limited-Data Generation

Recent work has shown GANs can generate highly realistic images that areindistinguishable by human. Of particular interest here is the empiricalobservation that most generated images are not contained in training datasets,indicating potential generalization with GANs. That generalizability makes itappealing to exploit GANs to help applications with limited available data,e.g., augment training data to alleviate overfitting. To better facilitatetraining a GAN on limited data, we propose to leverage already-available GANmodels pretrained on large-scale datasets (like ImageNet) to introduceadditional common knowledge (which may not exist within the limited data)following the transfer learning idea. Specifically, exampled by natural imagegeneration tasks, we reveal the fact that low-level filters (those close toobservations) of both the generator and discriminator of pretrained GANs can betransferred to help the target limited-data generation. For better adaption ofthe transferred filters to the target domain, we introduce a new techniquenamed adaptive filter modulation (AdaFM), which provides boosted performanceover baseline methods. Unifying the transferred filters and the introducedtechniques, we present our method and conduct extensive experiments todemonstrate its training efficiency and better performance on limited-datageneration.
Cyber-Physical Security with RF Fingerprint Classification through Distance Measure Extensions of Generalized Relevance Learning Vector Quantization

Radio frequency (RF) fingerprinting extracts fingerprint features from RF signals to protect against masquerade attacks by enabling reliable authentication of communication devices at the “serial number” level. Facilitating the reliable authentication of communication devices are machine learning (ML) algorithms which find meaningful statistical differences between measured data. The Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier is one ML algorithm which has shown efficacy for RF fingerprinting device discrimination. GRLVQI extends the Learning Vector Quantization (LVQ) family of “winner take all” classifiers that develop prototype vectors (PVs) which represent data. In LVQ algorithms, distances are computed between exemplars and PVs, and PVs are iteratively moved to accurately represent the data. GRLVQI extends LVQ with a sigmoidal cost function, relevance learning, and PV update logic improvements. However, both LVQ and GRLVQI are limited due to a reliance on squared Euclidean distance measures and a seemingly complex algorithm structure if changes are made to the underlying distance measure. Herein, the authors (1) develop GRLVQI-D (distance), an extension of GRLVQI to consider alternative distance measures and (2) present the Cosine GRLVQI classifier using this framework. To evaluate this framework, the authors consider experimentally collected Z -wave RF signals and develop RF fingerprints to identify devices. Z -wave devices are low-cost, low-power communication technologies seen increasingly in critical infrastructure. Both classification and verification, claimed identity, and performance comparisons are made with the new Cosine GRLVQI algorithm. The results show more robust performance when using the Cosine GRLVQI algorithm when compared with four algorithms in the literature. Additionally, the methodology used to create Cosine GRLVQI is generalizable to alternative measures.