Reviewerly/exHarmony huggingface dataset:
Monitoring perception reliability in autonomous driving: Distributional shift detection for estimating the impact of input data on prediction accuracy

Deep neural networks are at the heart of safety-critical applications such as autonomous driving. Distributional shift is a typical problem in predictive modeling, when the feature distribution of inputs and outputs varies between the training and test stages. When used on data different from the training distribution, neural networks provide little or no performance guarantees on such out-of-distribution (OOD) inputs. Monitoring distributional shift can help assess reliability of neural network predictions with the purpose of predicting potential safety-critical contexts. With our research, we evaluate state of the art OOD detection methods on autonomous driving camera data, while also demonstrating the influence of OOD data on the prediction reliability of neural networks. We evaluate three different OOD detection methods: As a baseline method we employ a variational autoencoder (VAE) trained on the similar data as the perception network (depth estimation) and use a reconstruction error based out of distribution measure. As a second approach, we choose to evaluate a method termed Likelihood Regret, which has been shown to be an efficient likelihood based OOD measure for VAEs. As a third approach, we evaluate another recently introduced method based on generative modelling termed SSD, which uses self-supervised representation learning followed by a distance based detection in the feature space, to calculate the outlier score. We compare all 3 methods and evaluate them concurrently with the error of an depth estimation network. Results show that while the reconstruction error based OOD metric is not able to differentiate between in and out of distribution data across all scenarios, the likelihood regret based OOD metric as well as the SSD outlier score perform fairly well in OOD detection. Their metrics are also highly correlated with perception error, rendering them promising candidates for an autonomous driving system reliability monitor.
Efficient Methods for Clickstream Pattern Mining on Incremental Databases

Sequence pattern mining is a core task of data mining in various fields.Clickstream pattern mining, a variant of sequence pattern mining, is very useful in e-commerce, and is used to analyze, evaluate and predict online customer behaviors, attracting the interest of many researchers.Clickstream pattern mining has become important due to its wide range of applications.However, most previous approaches are not suitable to apply when clickstreams are inserted into a database, because this task is too time consuming.The challenge of this problem is how to find a solution to minimize the runtimes and reduce the number of times the original databases are scanned to reduce the computational cost in the mining process on incremental databases.In this paper, we propose two effective methods for mining clickstream patterns from incremental databases, named inCMUB and Eff-inCMUB, based on the pre-large concept.inCMUB inserts new clickstreams from an inserted database into the existing tree and mines all frequent clickstream patterns, while Eff-inCMUB is a new approach, and builds a new tree from the inserted database to find pre-large 1-patterns and then it updates the pre-large clickstream patterns mined from the original database to extract frequent clickstream patterns.The experiments show that our proposed methods outperform the SMUB algorithm in terms of runtimes, memory usage and scalability on real-word clickstream databases.
Unsupervised deep learning based ego motion estimation with a downward facing camera

Abstract Knowing the robot's pose is a crucial prerequisite for mobile robot tasks such as collision avoidance or autonomous navigation. Using powerful predictive models to estimate transformations for visual odometry via downward facing cameras is an understudied area of research. This work proposes a novel approach based on deep learning for estimating ego motion with a downward looking camera. The network can be trained completely unsupervised and is not restricted to a specific motion model. We propose two neural network architectures based on the Early Fusion and Slow Fusion design principle: “EarlyBird” and “SlowBird”. Both networks share a Spatial Transformer layer for image warping and are trained with a modified structural similarity index (SSIM) loss function. Experiments carried out in simulation and for a real world differential drive robot show similar and partially better results of our proposed deep learning based approaches compared to a state-of-the-art method based on fast Fourier transformation.
Consistent-Contrastive Network With Temporality-Awareness for Robust-to-Anomaly Industrial Soft Sensor

The semisupervised soft sensor has gradually become a more practical solution due to the difficulty in collecting labels for the industrial soft sensors. Currently, the commonly used manifold regularization assumes similar inputs result in similar outputs, but such similarity assumption fails when temporality exists. Moreover, the performance of the soft sensor is vulnerable to the anomaly, so anomaly is generally discarded before modeling. The latent information in anomaly has seldom been explored for modeling yet. To overcome the limitation of manifold regularization and explore the usability of anomaly, a novel semisupervised soft sensor, named consistent-contrastive network (CC-Net), is proposed to build a temporality-aware and robust-to-anomaly soft sensor. Specifically, CC-Net consists of two branches, namely label branch and feature branch, to map the input into label and feature, respectively. In the label branch, labeled samples are used in a supervised manner. For unlabeled samples, two regularization terms, consistency and temporal consistency, are designed to constrain the pseudo-labels consistent against noise and along time, respectively, which can adapt to temporality. In the feature branch, the idea of contrastive learning is applied to separate normal samples and anomalies. Such a design properly utilizes the distribution information in anomaly as a regularizer. With the designed regularization terms and the utilization of anomalies, CC-Net implements a temporality-aware and robust-to-anomaly soft sensor, which is demonstrated by real denitrification and desulfurization cases.
VaPiD: A Rapid Vanishing Point Detector via Learned Optimizers

Being able to infer 3D structures from 2D images with geometric principles, vanishing points have been a well-recognized concept in 3D vision research. It has been widely used in autonomous driving, SLAM, and AR/VR for applications including road direction estimation, camera calibration, and camera pose estimation. Existing vanishing point detection methods often need to trade off between robustness, precision, and inference speed. In this paper, we introduce VaPiD, a novel neural network-based rapid Vanishing Point Detector that achieves unprecedented efficiency with learned vanishing point optimizers. The core of our method contains two components: a vanishing point proposal network that gives a set of vanishing point proposals as coarse estimations; and a neural vanishing point optimizer that iteratively optimizes the positions of the vanishing point proposals to achieve high-precision levels. Extensive experiments on both synthetic and real-world datasets show that our method provides competitive, if not better, performance as compared to the previous state-of-the-art vanishing point detection approaches, while being significantly faster.
Dual Distance Center Loss: The Improved Center Loss That Can Run Without the Combination of Softmax Loss, an Application for Vehicle Re-Identification and Person Re-Identification

Center loss is widely used as a supervision tool in deep learning method. However, the center loss also has some shortcomings, the most important of which is that it must be combined with softmax loss to run well. In this article, we sum up five shortcomings of center loss and solve all of them by proposing a dual distance center loss (DDCL). Compared with center loss, DDCL can run without the combination of softmax to supervise training the model. In addition, we verify the inconsistency between the proposed DDCL and softmax loss in the feature space. To be specifically, we add the Pearson distance on the basis of the Euclidean distance to the same center, which makes all features of the same class be confined to the intersection of a hypersphere and a hypercone in the feature space, strengthens the intraclass compactness of the center loss, and enhances the generalization ability of center loss. Moreover, by designing a Euclidean distance threshold between all center pairs, we not only strengthen the interclass separability of center loss, but also make the center loss (or DDCL) works well without the combination of softmax loss. We verify the effectiveness of DDCL in four datasets, two of which are widely used in the field of vehicle re-identification named VeRi-776 dataset and VehicleID dataset, and two other datasets are widely used in the field of person re-identification named Market1501 dataset and MSMT17 dataset. The experimental results of the proposed DDCL exceed that of the softmax loss in all the four datasets, indicating that our proposed method not only can run without the combination of softmax, but also has a high accuracy.
Struggling with digitized historical newspapers: Contextual barriers to information interaction in history research activities

Abstract On account of the complexities related to the use of digitized newspapers, researchers may encounter barriers when interacting with the collections' content. Overcoming barriers that could influence their information interaction should enhance the accessibility and utility of the newspapers. Hence, the study examined the barriers faced in history‐research tasks involving interaction with digitized historical newspapers, with focus on the barriers' contexts and the related task‐based activities. The analysis employed two datasets, from in‐depth interviews and demonstrations of newspaper‐use situations. Content analysis from these complementary data showed that barriers arose in multiple contexts, connected with the collection, task, tools, and socio‐organizational setting. Most barriers were associated with collection context and occurred in information searching and selection activities and in working with information items. Barriers related to the task or to socio‐organizational context arose most often in the planning and monitoring activities and in synthesizing and reporting. Such research‐based insight into the barriers faced can aid in illuminating what is required for providing good support to researchers working with digital newspaper content.
An empirical study of developers’ discussions about security challenges of different programming languages

Given programming languages can provide different types and levels of security support, it is critically important to consider security aspects while selecting programming languages for developing software systems. Inadequate consideration of security in the choice of a programming language may lead to potential ramifications for secure development. Whilst theoretical analysis of the supposed security properties of different programming languages has been conducted, there has been relatively little effort to empirically explore the actual security challenges experienced by developers. We have performed a large-scale study of the security challenges of 15 programming languages by quantitatively and qualitatively analysing the developers’ discussions from Stack Overflow and GitHub. By leveraging topic modelling, we have derived a taxonomy of 18 major security challenges for 6 topic categories. We have also conducted comparative analysis to understand how the identified challenges vary regarding the different programming languages and data sources. Our findings suggest that the challenges and their characteristics differ substantially for different programming languages and data sources, i.e., Stack Overflow and GitHub. The findings provide evidence-based insights and understanding of security challenges related to different programming languages to software professionals (i.e., practitioners or researchers). The reported taxonomy of security challenges can assist both practitioners and researchers in better understanding and traversing the secure development landscape. This study highlights the importance of the choice of technology, e.g., programming language, in secure software engineering. Hence, the findings are expected to motivate practitioners to consider the potential impact of the choice of programming languages on software security.
Omni: automated ensemble with unexpected models against adversarial evasion attack

Machine learning-based security detection models have become prevalent in modern malware and intrusion detection systems. However, previous studies show that such models are susceptible to adversarial evasion attacks. In this type of attack, inputs (i.e., adversarial examples) are specially crafted by intelligent malicious adversaries, with the aim of being misclassified by existing state-of-the-art models (e.g., deep neural networks). Once the attackers can fool a classifier to think that a malicious input is actually benign, they can render a machine learning-based malware or intrusion detection system ineffective. To help security practitioners and researchers build a more robust model against non-adaptive, white-box and non-targeted adversarial evasion attacks through the idea of ensemble model. We propose an approach called Omni, the main idea of which is to explore methods that create an ensemble of "unexpected models"; i.e., models whose control hyperparameters have a large distance to the hyperparameters of an adversary's target model, with which we then make an optimized weighted ensemble prediction. In studies with five types of adversarial evasion attacks (FGSM, BIM, JSMA, DeepFool and Carlini-Wagner) on five security datasets (NSL-KDD, CIC-IDS-2017, CSE-CIC-IDS2018, CICAndMal2017 and the Contagio PDF dataset), we show Omni is a promising approach as a defense strategy against adversarial attacks when compared with other baseline treatments. When employing ensemble defense against adversarial evasion attacks, we suggest to create ensemble with unexpected models that are distant from the attacker's expected model (i.e., target model) through methods such as hyperparameter optimization.
Joint neighborhood preserving and projected clustering for feature extraction

Neighborhood reconstruction is proved effective for dimensionality reduction because of the preservation of manifold structure. Conventional neighborhood preserving embedding (NPE) method first learns the affinity relationship or reconstruction relationship in the original space, and then learns the projection matrix to preserve the learned local information in low-dimensional space. However, the pre-learned manifold information may be inaccurate due to the noises and irrelevant features in real-world data. The performance of dimensionality reduction would be influenced as well. Besides, NPE and its variants only aim to preserve the local reconstruction relationship but ignore the fuzzy membership relationship between samples and cluster prototypes. To address these issues, we propose an adaptive neighborhood preserving discriminant projection model, where sparse reconstruction coefficients are updated in the process of dimensionality reduction to eliminate the influence of noises and irrelevant features. Meanwhile, we also learn the fuzzy membership relationships between data points and cluster prototypes to gather the samples belonging to the same class together in low-dimensional space. Neighborhood reconstruction learning and clustering are seamlessly connected in the learned subspace. To solve this model, an iterative algorithm is developed. The experimental results of recognition accuracy show the superiorities of the proposed methods over the state-of-the-arts.
Classification of malicious and benign websites by network features using supervised machine learning algorithms

Due to the increase in Internet usage through the past years, cyber-attacks have rapidly increased, leading to high personal information and financial loss. Cyberattacks can include phishing, spamming, and malware. Because websites, the most common element of the Internet, are widely used, hackers find their targets to attack. Therefore, the detection of malicious websites is critical for organizations and individuals to increase security. The earlier a malicious website is detected, the faster it is defended. In this paper, a dataset is analyzed and applied to multiple supervised machine learning models such as Random Forest, Artificial Neural Network, K-nearest neighbors, and Support Vector Machine. The dataset attributes are extracted based on the application layer and different network characteristics. The experimental studies with many benign and malicious websites obtained from real-life Internet resources show a high prediction performance. Due to the imbalanced dataset studied in this paper, the F1-score was measured instead of the accuracy. The support vector machine algorithm showed the highest performance over all the other algorithms studied, with a value of 92%.
ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior

Recent works show that convolutional neural network (CNN) architectures have a spectral bias towards lower frequencies, which has been leveraged for various image restoration tasks in the Deep Image Prior (DIP) framework. The benefit of the inductive bias the network imposes in the DIP framework depends on the architecture. Therefore, re-searchers have studied how to automate the search to de-termine the best-performing model. However, common neu-ral architecture search (NAS) techniques are resource and time-intensive. Moreover, best-performing models are de-termined for a whole dataset of images instead of for each image independently, which would be prohibitively expen-sive. In this work, we first show that optimal neural archi-tectures in the DIP framework are image-dependent. Lever-aging this insight, we then propose an image-specific NAS strategy for the DIP framework that requires substantially less training than typical NAS approaches, effectively en-abling image-specific NAS. We justify the proposed strat-egy's effectiveness by (1) demonstrating its performance on a NAS Dataset for DIP that includes 522 models from a particular search space (2) conducting extensive experi-ments on image denoising, inpainting, and super-resolution tasks. Our experiments show that image-specific metrics can reduce the search space to a small cohort of models, of which the best model outperforms current NAS approaches for image restoration. Codes and datasets are available at https://github.com/ozgurkara99/ISNAS-DIP.
Stretchable Textile Yarn Based on UHF RFID Helical Tag

In the context of wearable technology, several techniques have been used for the fabrication of radio frequency identification (RFID) tags such as 3D printing, inkjet printing, and even embroidery. In contrast to these methods where the tag is attached to the object by using sewing or simple sticking, the E-Thread® technology is a novel assembling method allowing for the integration of the RFID tag into a textile yarn and thus makes it embeddable into the object at the fabrication stage. The current E-Thread® yarn uses a RFID tag in which the antenna is a straight half-wave dipole that makes the solution vulnerable to mechanical strains (i.e., elongation). In this paper, we propose an alternative to the current RFID yarn solution with the use of an antenna having a helical geometry that answers to the mechanical issues and keeps quite similar electrical and radiative properties with respect to the present solution. The RFID helical tag was designed and simulated taking into consideration the constraints of the manufacturing process. The helical RFID tag was then fabricated using the E-Thread® technology and experimental characterization showed that the obtained structure exhibited good performance with 10.6 m of read range in the ultra high frequency (UHF) RFID band and 10% of tolerance in terms of elongation.
Spatial Filter Bank in the Spherical Harmonic Domain: Reconstruction and Application

Filter banks are an integral part of modern signal processing. They may also be applied to spatial filtering and the employed spatial filters can be designed with a specific shape for the analysis, e. g. suppressing side-lobes. After extracting spatially constrained signals from spherical harmonic (SH) input, i. e. filter bank analysis, many applications demand for a re-synthesis of the associated sector signals to the SH domain. This paper hence derives the complementary spatial filter bank reconstruction. The criterion for perfect reconstruction, and energy preserving reconstruction are given and implemented into the design. The filter bank is formulated such that for axisymmetric patterns both criteria can be met by only minor modification to the reconstruction stage. Its application is then demonstrated for both scenarios, perfect reconstruction and energy preservation of SH input signals.
Fusing hypergraph spectral features for shilling attack detection

Recommender systems can effectively improve user experience, but they are vulnerable to shilling attacks due to their open nature. Attackers inject fake user profiles to destroy the security and reliability of the recommender systems. Therefore, it is crucial to detect shilling attacks effectively. The primitive detection models are feasible but costly because of the dependence on plenty of hand-engineered explicit features based on statistical measures. Even though the upgraded models based on learning embeddings of the implicit features are more general, they fail to take some distinct features in distinguishing fake users into consideration. Moreover, these primitive and upgraded models are difficult to capture the high order relationships between users and items as the models usually learn the embedding from the first-order interactions. The representation and similarity information learned from the first-order interactions are not comprehensive enough, limiting the detection task. To this end, we propose a novel shilling attack detection model by fusing hypergraph spectral features (SpDetector). The proposed model combines the explicit and implicit features to balance the effectiveness and generality and deal with the high order relationships by hypergraphs-based embedding. From the implicit perspective, SpDetector constructs user hypergraphs and item hypergraphs for the high-order relationships hidden in the interaction and extracts spectral features from hypergraphs to capture high-order similarity for users and items, respectively. From the explicit perspective, it extracts two kinds of explicit features: item similarity offsets (ISO) based on item spectral features and rating prediction errors (RPE), for all users as their distinct capability of distinguishing fake users. Finally, the SpDetector learns to distinguish fake users by training a deep neural network with those features. Experiments conducted on MovieLens and Amazon datasets show that SpDetector outperforms state-of-the-art detection models.