Reviewerly/exHarmony huggingface dataset:
A Novel Preprocessing Technique for Toxic Comment Classification

The threat of online abuse and harassment is increasing day by day in the cyber community. To tackle this problem, many platforms have devised policies. But these policies require prior identification of the content that is inappropriate and offensive. Furthermore, the data contains various aspects of negativity, for example, a particular piece of comment can express, disgust, disbelief, and threat at the same time. It points that even the negativity/toxicity exhibited in a comment can have various facets. Hence, the challenge is to identify what exactly is exhibited in comments so that respective policies can be formulated and applied to penalize the offender. This study makes use of two approaches to identify these underlying toxicities in the comments. The first approach is to train separate classifiers against each facet of the toxicity in comments. The second approach deals with the problem as a multi-label classification problem. Different machine learning approaches including logistic regression, Naïve Bayes, and decision tree classification are employed to carry out this study. The dataset is taken from Kaggle and 10-fold cross-validation is used to report the robustness of the model. The study uses a novel preprocessing scheme that transforms the multi-label classification problem into the multi-class classification problem. The preprocessing strategy has shown a significant improvement in the accuracies when employed for simple classification models encouraging its use for other sophisticated models as well. Experimental results show that in both the binary classification and the multi-classification, logistic regression turns out to be a better performer. This indicates the potential use of the preprocessing for the neural classification models.
Punishing Artificial Intelligence: Legal Fiction or Science Fiction

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An Optimal Computing Budget Allocation Tree Policy for Monte Carlo Tree Search

We analyze a tree search problem with an underlying Markov decision process, in which the goal is to identify the best action at the root that achieves the highest cumulative reward. We present a new tree policy that optimally allocates a limited computing budget to maximize a lower bound on the probability of correctly selecting the best action at each node. Compared to widely used upper confidence bound (UCB) tree policies, the new tree policy presents a more balanced approach to manage the exploration and exploitation tradeoff when the sampling budget is limited. Furthermore, UCB assumes that the support of reward distribution is known, whereas our algorithm relaxes this assumption. Numerical experiments demonstrate the efficiency of our algorithm in selecting the best action at the root.
SDCRKL-GP: Scalable deep convolutional random kernel learning in gaussian process for image recognition

Deep convolutional neural networks have shown great potential in image recognition tasks. However, the fact that the mechanism of deep learning is difficult to explain hinders its development. It involves a large amount of parameter learning, which results in high computational complexity. Moreover, deep convolutional neural networks are often limited by overfitting in regimes in which the number of training samples is limited. Conversely, kernel learning methods have a clear mathematical theory, fewer parameters, and can contend with small sample sizes; however, they are not able to handle high-dimensional data, e.g., images. It is important to achieve a performance and complexity trade-off in complicated tasks. In this paper, we propose a novel scalable deep convolutional random kernel learning in Gaussian process architecture called SDCRKL-GP, which is characterized by excellent performance and low complexity. First, we successfully incorporated the deep convolutional architecture into kernel learning by implementing the random Fourier feature transform for Gaussian processes, which can effectively capture hierarchical and local image-level features. This approach enabled the kernel method to effectively handle image processing problems. Second, we optimized the parameters of deep convolutional filters and Gaussian kernels by stochastic variational inference. Then, we derived the lower variational bound of the marginal likelihood. Finally, we explored the model architecture design space selection method to determine the appropriate network architecture for different datasets. The design space consists of the number of layers, the channels per layer, and so on. Different design space selections improved the scalability of the SDCRKL-GP architecture. We evaluated SDCRKL-GP on the MNIST, FMNIST, CIFAR10, and CALTECH4 benchmark datasets. Taking MNIST as an example, the error rate of classification is 0.60%, and the number of parameters, number of computations and memory access cost of the architecture are 19.088k, 0.984M, and 1.057M, respectively. The experimental results verified that the proposed SDCRKL-GP method outperforms several state-of-the-art algorithms in both accuracy and speed in image recognition tasks. The code is available at https://github.com/w-tingting/deep-rff-pytorch.
Performance-Adaptive Sampling Strategy Towards Fast and Accurate Graph Neural Networks

The main challenge of adapting Graph convolutional networks (GCNs) to large-scale graphs is the scalability issue due to the uncontrollable neighborhood expansion in the aggregation stage. Several sampling algorithms have been proposed to limit the neighborhood expansion. However, these algorithms focus on minimizing the variance in sampling to approximate the original aggregation. This leads to two critical problems: 1) low accuracy because the sampling policy is agnostic to the performance of the target task, and 2) vulnerability to noise or adversarial attacks on the graph.
Demographic Fairness in Biometric Systems: What Do the Experts Say?

Biometric technologies <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> have become an integral component of many personal, commercial, and governmental identity management systems worldwide. Biometrics rely on highly distinctive characteristics of human beings, which make it possible for individuals to be reliably recognized using fully automated algorithms. Prominent examples of biometric characteristics used for recognition purposes are face, fingerprint, iris, and voice. Application scenarios of biometrics beyond personal devices (see <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> ) include, but are not limited to, border control (see <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> , <xref ref-type="bibr" rid="ref4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</xref> , and <xref ref-type="bibr" rid="ref5" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[5]</xref> ), forensic investigations, law enforcement (see <xref ref-type="bibr" rid="ref6" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[6]</xref> and <xref ref-type="bibr" rid="ref7" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[7]</xref> ), and national ID systems (see <xref ref-type="bibr" rid="ref8" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[8]</xref> ).
Exploiting the Sensitivity of L2 Adversarial Examples to Erase-and-Restore

By adding carefully crafted perturbations to input images, adversarial examples (AEs) can be generated to mislead neural-network-based image classifiers. L2 adversarial perturbations by Carlini and Wagner (CW) are among the most effective but difficult-to-detect attacks. While many countermeasures against AEs have been proposed, detection of adaptive CW-L2 AEs is still an open question. We find that, by randomly erasing some pixels in an L2 AE and then restoring it with an inpainting technique, the AE, before and after the steps, tends to have different classification results, while a benign sample does not show this symptom. We thus propose a novel AE detection technique, Erase-and-Restore (E&R), that exploits the intriguing sensitivity of L2 attacks. Experiments conducted on two popular image datasets, CIFAR-10 and ImageNet, show that the proposed technique is able to detect over 98% of L2 AEs and has a very low false positive rate on benign images. The detection technique exhibits high transferability: a detection system trained using CW-L2 AEs can accurately detect AEs generated using another L2 attack method. More importantly, our approach demonstrates strong resilience to adaptive L2 attacks, filling a critical gap in AE detection. Finally, we interpret the detection technique through both visualization and quantification.
One-shot dual-projection topography enhanced by phase-shifting logical moiré

In conventional dual-projection moiré topography, the additive-type moiré pattern has an amplitude modulated form, and therefore poor visibility, unable to be improved through low-pass filtering. To overcome this difficulty, this paper presents an enhanced dual-projection moiré topography by means of logical moiré. In its implementation, a couple of projectors project two fringe patterns of different colors onto the object simultaneously and then a color camera captures the deformed fringe pattern. Instead of directly processing the additive-type moiré fringes, we separate the deformed fringes of two chromatic channels, binarize them, and then calculate their exclusive OR (XOR) moiré pattern. By differentiating one of the two deformed fringe patterns, a second XOR moiré pattern having exactly a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>π<!-- π --></mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> -rad phase shift can also be generated, so that the phases are simply calculated by using an arctangent function after low-pass filtering to the XOR moiré patterns. By doing so, we can measure the three-dimensional shape of an object in an efficient way requiring a single-shot implementation. The validity of this method has been demonstrated through simulation and experimental results.
Supervised Sentiment Analysis on Amazon Product Reviews: A survey

Sentiment Analysis (SA), which is also known as Opinion Mining, is a hot-fastest growing research area, making it challenging to follow all the activities in such areas. It intends to study people's thoughts, feelings, and attitudes about topics, events, issues, entities, individuals, and their attributes in social media (e.g., social networking sites, forums, blogs, etc.) expressed by either text reviews or comments. Amazon is an example of the world's largest online retailer that allows its customers to rate its products and freely write reviews. Analyzing these reviews into positive or negative; will assist customers' decision making, which varies from purchasing a product like a camera, mobile phone, etc., to writing a review about movies and making investments - all of these decisions will have a significant impact on the daily life. Sentiment analysis draws the attention of both scientific and market research in Natural Language Processing and Machine Learning fields. In general, the machine learning approach consists of supervised and unsupervised algorithms. In this research study, a detailed typical workflow process often adopted by the researchers is presented. Moreover, traditional supervised machine learning classification techniques have been investigated on various categories of Amazon product reviews to find the best method that provides a reliable result of sentiment analysis and assists future research in this newly emerging area.
Differentially private average consensus with general directed graphs

Differential privacy, a strict privacy notion, prevents the presence or absence of a single data from being identified. This paper proposes a novel average consensus algorithm to achieve differentially private average consensus on multi-agent systems (MASs) with general directed graphs. For MASs under the proposed algorithm, necessary and sufficient conditions are established on achieving average consensus and preserving differential privacy of the states of the agents at any time instant. Finally, numerical examples are provided to illustrate these results.
On Decision Support for Quantum Application Developers: Categorization, Comparison, and Analysis of Existing Technologies

Quantum computers have been significantly advanced in recent years. Offered as cloud services, quantum computers have become accessible to a broad range of users. Along with the physical advances, the landscape of technologies supporting quantum application development has also grown rapidly in recent years. However, there is a variety of tools, services, and techniques available for the development of quantum applications, and which ones are best suited for a particular use case depends, among other things, on the quantum algorithm and quantum hardware. Thus, their selection is a manual and cumbersome process. To tackle this challenge, we introduce a categorization and a taxonomy of available tools, services, and techniques for quantum application development to enable their analysis and comparison. Based on that we further present a comparison framework to support quantum application developers in their decision for certain technologies.
TriTSA: Triple Tree-Seed Algorithm for dimensional continuous optimization and constrained engineering problems

Tree-Seed Algorithm (TSA) is a meta-heuristic optimization algorithm with good performance in solving continuous optimization problems. However, there is an imbalance between its exploration and exploitation when solving complex problems, mainly lack exploration. To overcome this problem, this paper proposes two tree migration mechanisms: triple-learning-based migration mechanism and sine-random-distribution migration mechanism. The target tree position is migrated by learning from the first three trees in the current iteration. The sine function is added to the tree migration formula to enhance the randomness of tree distribution. In order to verify these migration mechanisms, Triple Tree-Seed Algorithm (TriTSA) has been proposed and compared with TSA on 30 well-known test functions from IEEE CEC 2014. In addition, STSA, SCA, PSO, ABC, Jaya, and TLBO are adopted in some comparative experiments on different dimensions. The experiments show that the tree migration mechanism can improve the optimization capability of the original algorithm effectively. On all 30 benchmark test functions, TriTSA outperforms TSA on 10, 30, 50, and 100 dimensions by 70%, 90%, 90%, and 97% respectively. Finally, the proposed TriTSA is compared with TSA, ABC, PSO, and SCA on solving two classical engineering design problems. It is proved that the proposed algorithm is more applicable in solving practical problems.
KompaRe

Reasoning is a fundamental capability for harnessing valuable insight, knowledge and patterns from knowledge graphs. Existing work has primarily been focusing on point-wise reasoning, including search, link prediction, entity prediction, subgraph matching and so on. This paper introduces comparative reasoning over knowledge graphs, which aims to infer the commonality and inconsistency with respect to multiple pieces of clues. We envision that the comparative reasoning will complement and expand the existing point-wise reasoning over knowledge graphs. In detail, we develop KompaRe, the first of its kind prototype system that provides comparative reasoning capability over large knowledge graphs. We present both the system architecture and its core algorithms, including knowledge segment extraction, pairwise reasoning and collective reasoning. Empirical evaluations demonstrate the efficacy of the proposed KompaRe.
CovChain: Blockchain-Enabled Identity Preservation and Anti-Infodemics for COVID-19

The intangible nature of the COVID-19 virus and the changing strains have created panic and fear among people, leading to an increase in discrimination against COVID-19 positive patients and their families in society. Moreover, the spread of infodemics creates daunting challenges for peaceful livelihood and fighting the pandemic. In this article, we propose CovChain, a blockchain-based Internet of Things solution for identity preservation and anti-infodemics. Toward this, we propose storing patient information on a private blockchain and exploiting its immutability feature, which helps combat infodemics. We further propose encrypting the data using a distributed attribute-based encryption (d-ABE) scheme to facilitate restricted access to information based on the clearance level. We also propose reducing the load on the miners by using geographically-aware fog nodes. As data in a blockchain is immutable, we delete the blocks corresponding to recovering patients and store the information in a forked CovChain. The cardinality of the two chains helps in maintaining a global census by the cloud servers. Through system implementations, we present the feasibility of CovChain on resource-constrained devices within tolerable delays of 1 s, upload and download rates of 35 kb/s, and CPU (single-core) and memory utilization under 70 percent.
A deep sequence multi-distribution adversarial model for bearing abnormal condition detection

• Proposed model that use normal data training are more suitable for practical applications. • Apply adversarial learning and LSTM structure to anomaly detection. • Multi-distribution features are utilized to construct an anomaly index. • DSMDA can prove sufficient robustness for anomalies detection of low SNR signals. Time series anomaly detection is one of the key challenges in the field of condition monitoring. Many anomaly detection methods are inefficient and easy to lose effective information due to manual features extracting. Deep learning-based methods can solve the problem effectively, but the detection accuracy is still not satisfactory. In addition, most of the methods cannot take the time-ordered specialty into account which is significant for time-series-based anomaly detection. To address these issues, a novel method named deep sequence multi-distribution adversarial model (DSMDA) is proposed to improve the accuracy of anomaly detection in bearing condition monitoring. The proposed model utilizes the data reconstruction capability of the Variational Autoencoder (VAE) under the framework of generative adversarial network (GAN) to make full use of information. The feedforward neural network layer of VAE is replaced by the long-term and short-term memory (LSTM) layer, which uses the forgetting mechanism of LSTM to effectively avoid the false alarms caused by the excessive influence of the old sequences. Additionally, the fault-attention abnormal state index can be constructed by the real-time spatial distribution and latent spatial distribution features learned by the double discriminators. To verify the effectiveness of the proposed approach, experiments on two public datasets are carried out with only healthy data in training stage that is more suitable for practical industrial applications. The results show that the proposed method is superior to GANomaly and other advanced methods. Furthermore, the 2-D visualization results can indicate the level of fault while the last feature space of the two discriminators is combined and embedded into the visualization, and the fault-attention abnormal state indictor constructed on these features can indicate abnormalities well.
Efficient Data-specific Model Search for Collaborative Filtering

Collaborative filtering (CF), as a fundamental approach for recommender systems, is usually built on the latent factor model with learnable parameters to predict users' preferences towards items. However, designing a proper CF model for a given data is not easy, since the properties of datasets are highly diverse. In this paper, motivated by the recent advances in automated machine learning (AutoML), we propose to design a data-specific CF model by AutoML techniques. The key here is a new framework that unifies state-of-the-art (SOTA) CF methods and splits them into disjoint stages of input encoding, embedding function, interaction function, and prediction function. We further develop an easy-to-use, robust, and efficient search strategy, which utilizes random search and a performance predictor for efficient searching within the above framework. In this way, we can combinatorially generalize data-specific CF models, which have not been visited in the literature, from SOTA ones. Extensive experiments on five real-world datasets demonstrate that our method can consistently outperform SOTA ones for various CF tasks. Further experiments verify the rationality of the proposed framework and the efficiency of the search strategy. The searched CF models can also provide insights for exploring more effective methods in the future.
Automatic Helmet Violation Detection of Motorcyclists from Surveillance Videos using Deep Learning Approaches of Computer Vision

Automatic detection of helmet for motorcyclists from real-time surveillance videos is a rising application in computer science. Object detection and classification using deep learning was recently well-known over the years. Researchers used these techniques for solving several surveillance-related problems. Several deep learning models adopt for automatic detection of helmet for motorcyclists but they cannot achieve state-of-the-art results due to different difficulties such as low resolution, whether conditions, occlusion and illumination etc. In this paper, we proposed a methodology for surveillance videos that automatically detect the helmet wear by motorcyclist or not. For this purpose, we used the Faster R-CNN model. First, we apply Region Proposal Network (RPN) starting with the input image that has been delivered into the backbone. Then RPN weights are settled and proposals from RPN are utilized to train the Faster RCNN model. For training, we used the self-generated dataset of three different locations in Lahore, Pakistan. The experimental results detect 97.26% accuracy on real-time surveillance videos for the detection of helmet for motorcyclists.
Hazy Re-ID: An Interference Suppression Model for Domain Adaptation Person Re-Identification Under Inclement Weather Condition

In a conventional domain adaptation person Re-identification (Re-ID) task, both the training and test images in target domain are collected under the sunny weather. However, in reality, the pedestrians to be retrieved may be obtained under severe weather conditions such as hazy, dusty and snowing, etc. This paper proposes a novel Interference Suppression Model (ISM) to deal with the interference caused by the hazy weather in domain adaptation person Re-ID. A teacher-student model is used in the ISM to distill the interference information at the feature level by reducing the discrepancy between the clear and the hazy intrinsic similarity matrix. Furthermore, in the distribution level, the extra discriminator is introduced to assist the student model make the interference feature distribution more clear. The experimental results show that the proposed method achieves the superior performance on two synthetic datasets than the state-of-the-art methods. The related code will be released online https://github.com/pangjian123/ISM-ReID.