Reviewerly/exHarmony huggingface dataset:
Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-TaskLearning for Offensive Language Detection

Nowadays, offensive content in social media has become a serious problem, and automatically detecting offensive language is an essential task.In this paper, we build an offensive language detection system, which combines multi-task learning with BERT-based models.Using a pretrained language model such as BERT, we can effectively learn the representations for noisy text in social media.Besides, to boost the performance of offensive language detection, we leverage the supervision signals from other related tasks.In the OffensEval-2020 competition, our model achieves 91.51% F1 score in English Sub-task A, which is comparable to the first place (92.23%F1).An empirical analysis is provided to explain the effectiveness of our approaches.
piNET–An Automated Proliferation Index Calculator Framework for Ki67 Breast Cancer Images

In this work, a novel proliferation index (PI) calculator for Ki67 images called piNET is proposed. It is successfully tested on four datasets, from three scanners comprised of patches, tissue microarrays (TMAs) and whole slide images (WSI), representing a diverse multi-centre dataset for evaluating Ki67 quantification. Compared to state-of-the-art methods, piNET consistently performs the best over all datasets with an average PI difference of 5.603%, PI accuracy rate of 86% and correlation coefficient R = 0.927. The success of the system can be attributed to several innovations. Firstly, this tool is built based on deep learning, which can adapt to wide variability of medical images—and it was posed as a detection problem to mimic pathologists’ workflow which improves accuracy and efficiency. Secondly, the system is trained purely on tumor cells, which reduces false positives from non-tumor cells without needing the usual pre-requisite tumor segmentation step for Ki67 quantification. Thirdly, the concept of learning background regions through weak supervision is introduced, by providing the system with ideal and non-ideal (artifact) patches that further reduces false positives. Lastly, a novel hotspot analysis is proposed to allow automated methods to score patches from WSI that contain “significant” activity.
A Lightweight Deep Neural Model for SMS Spam Detection

The short messaging service (SMS) is one of the most popular and also most affordable telecommunication services. The popularity and affordability of SMS, however, have made it an ideal target for spamming. Spam is a major nuisance to mobile subscribers, but can also lead to security breaches or criminal activities. In this paper, we propose a novel lightweight deep neural model called Lightweight Gated Recurrent Unit (LGRU) for SMS spam detection. In addition, we incorporate enhancing semantics retrieved from external knowledge (WordNet) to augment the understanding of SMS text inputs for better classification. We compare the performance of our proposed model with that of more than 30 SMS spam classifiers that use various conventional machine learning and deep learning techniques. Experimental results show that our model outperforms these existing classifiers in terms of precision, recall and accuracy. In addition, our model requires fewer training parameters and incurs significantly less training time than state-of-the-art deep learning based classifiers.
Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of β-VAE

Learning Enabled Components (LECs) are widely being used in a variety of perceptions based autonomy tasks like image segmentation, object detection, end-to-end driving, etc.These components are trained with large image datasets with multimodal factors like weather conditions, time-of-day, trafficdensity, etc.The LECs learn from these factors during training, and while testing if there is variation in any of these factors, the components get confused resulting in low confidence predictions.Those images with factor values, not seen, during training are commonly referred to as Out-of-Distribution (OOD).For safe autonomy, it is important to identify the OOD images, so that a suitable mitigation strategy can be performed.Classical oneclass classifiers like SVM and SVDD are used to perform OOD detection.However, multiple labels attached to images in these datasets restrict the direct application of these techniques.We address this problem using the latent space of the β-Variational Autoencoder (β-VAE).We use the fact that compact latent space generated by an appropriately selected β-VAE will encode the information about these factors in a few latent variables, and that can be used for quick and computationally inexpensive detection.We evaluate our approach on the nuScenes dataset, and our results show the latent space of β-VAE is sensitive to encode changes in the values of the generative factor.
Enhancing the Security and Privacy of Self-Sovereign Identities on Hyperledger Indy Blockchain

Self-sovereign identities provide user autonomy and immutability to individual identities and full control to their identity owners. The immutability and control are possible by implementing identities in a decentralized manner on blockchains that are specially designed for identity operations such as Hyperledger Indy. As with any type of identity, self-sovereign identities too deal with Personally Identifiable Information (PII) of the identity holders and comes with the usual risks of privacy and security. This study examined certain scenarios of personal data disclosure via credential exchanges between such identities and risks of man-in-the-middle attacks in the blockchain based identity system Hyperledger Indy. On the basis of the findings, the paper proposes the following enhancements: 1) A novel attribute sensitivity score model for self-sovereign identity agents to ascertain the sensitivity of attributes shared in credential exchanges 2) A method of mitigating man-in-the-middle attacks between peer self-sovereign identities and 3) A novel quantitative model for determining a credential issuer's reputation based on the number of issued credentials in a window period, which is then utilized to calculate an overall confidence level score for the issuer.
Public-Coin Zero-Knowledge Arguments with (almost) Minimal Time and Space Overheads

Zero-knowledge protocols enable the truth of a mathematical statement to be certified by a verifier without revealing any other information. Such protocols are a cornerstone of modern cryptography and recently are becoming more and more practical. However, a major bottleneck in deployment is the efficiency of the prover and, in particular, the space-efficiency of the protocol. For every $${\mathsf {NP}}$$ relation that can be verified in time T and space S, we construct a public-coin zero-knowledge argument in which the prover runs in time $$T \cdot \mathrm {polylog}(T)$$ and space $$S \cdot \mathrm {polylog}(T)$$ . Our proofs have length $$\mathrm {polylog}(T)$$ and the verifier runs in time $$T \cdot \mathrm {polylog}(T)$$ (and space $$\mathrm {polylog}(T)$$ ). Our scheme is in the random oracle model and relies on the hardness of discrete log in prime-order groups. Our main technical contribution is a new space efficient polynomial commitment scheme for multi-linear polynomials. Recall that in such a scheme, a sender commits to a given multi-linear polynomial $$P:{{\mathbb {F}}}^n \rightarrow {{\mathbb {F}}}$$ so that later on it can prove to a receiver statements of the form " $$P(x)=y$$ ". In our scheme, which builds on commitments schemes of Bootle et al. (Eurocrypt 2016) and Bünz et al. (S&P 2018), we assume that the sender is given multi-pass streaming access to the evaluations of P on the Boolean hypercube and we show how to implement both the sender and receiver in roughly time $$2^n$$ and space n and with communication complexity roughly n.
Event Analysis for Vehicle Classification using Fast RCNN

Event analysis on object detection is critical as classification of vehicles and persons in events. This system proposed vehicles classification and counting for event system. The images from event video streaming camera are first featured extracted, segmentation, model building and presented to achieve improving optimization by Deep Neural Learning Fast R-CNN method with hyper-parameter optimization using the two datasets, modified Stanford car dataset and new Myanmar cars dataset. Experimental result demonstrated that Fast RCNN and hyper-parameter classifier with created Myanmar cars dataset attained the state-of-art precision result in vehicle classification and counting on real-life event Video streaming.
Fusion of region based extracted features for instance- and class-based CBIR applications

One of the major challenges in content-based image retrieval (CBIR) is understanding the actual semantic meaning of images. Generally, natural images possess a rich collection of different visual features, each of which have different contributions to the image semantics. These vary drastically in their local sub-regions. However, most of the state-of-the-art CBIR techniques give equal importance to all the visual features, which are not always effective for generating relevant retrieval outcomes. To overcome these issues, a CBIR scheme has been proposed which considers the image semantic properties of the different regions with their positional significance in the overall image. In this paper, a novel regions-of-attention (ROA) based feature extraction and fusion scheme for efficient image retrieval is suggested. Initially, a salient key-points and opponent-color feature based scheme is used to locate ROA of the image. Multi-directional texture, and spatial correlation-based color features are extracted from this ROA of the image to obtain most of the image’s semantic meanings. Spatial features from salient non-ROA local regions are also obtained to capture vital background information. A dissimilarity factor-based fusion scheme then is utilized to fuse the ROA and non-ROA features effectively, considering the importance and uniqueness of each feature to support the positional invariance notion in their respective sub-regions. Finally, the fused feature vector is used to perform instance- and class-based image retrieval in five different image datasets using different classifiers. The outcomes of the retrieval experiment have shown competitive performances as compared to the current state-of-the-art architectures.
Universal Composition with Global Subroutines: Capturing Global Setup Within Plain UC

The Global and Externalized UC frameworks [Canetti-Dodis-Pass-Walfish, TCC 07] extend the plain UC framework to additionally handle protocols that use a "global setup", namely a mechanism that is also used by entities outside the protocol. These frameworks have broad applicability: Examples include public-key infrastructures, common reference strings, shared synchronization mechanisms, global blockchains, or even abstractions such as the random oracle. However, the need to work in a specialized framework has been a source of confusion, incompatibility, and an impediment to broader use. We show how security in the presence of a global setup can be captured within the plain UC framework, thus significantly simplifying the treatment. This is done as follows: We also exemplify how our treatment can be used to streamline, within the plain UC model, proofs of security of systems that involve global set-up, thus providing greater simplicity and flexibility.
It Didn't Sound Good with My Cochlear Implants

How do deaf and hard of hearing (DHH) individuals use smart assistants (SAs)? Does the prominent use of voice interfaces in most SAs pose unique challenges for DHH users? In this work, we aim to answer these questions by conducting 4 in-depth interviews, as well as collecting survey data from 73 DHH individuals. Our findings show that individuals, even with profound deafness, can leverage SAs to accomplish complex daily tasks. However, we also identified a number of common challenges DHH individuals face when interacting with SAs (e.g., high pitch used in the default SA voice interfaces can be incompatible with hearing aids, difficulty using mobile SAs in public places with loud background noise). Based on these insights, we provide a set of suggestions for designing SAs that can better accommodate a wide range of hearing abilities. Specifically, SAs should provide more customization options to allow the user to tailor their SA to meet their hearing needs over time. For example, using a pitch-frequency test feature, much like audiograms conducted by audiologists, could allow users to calibrate their SA's voice to fit within their optimal range. We also see a need to provide more clear and actionable error messages conveyed beyond audio notifications, such as more meaningful light notifications. These recommendations and findings provide the first step forward toward a more inclusive SA by addressing accessibility needs unique to this group.
Histogram of Oriented Gradients Feature Extraction Without Normalization

In this paper, the effects of normalization in the histogram of oriented gradients (HOG) are studied and a HOG feature extraction pipeline without normalization is proposed. In the proposed pipeline, the functionality of normalization is merged into the gradient generation step by replacing the original linear difference based gradients with logarithmic gradients. Due to the discrete property of the pixel values, the logarithmic operation can be easily implemented using a lookup table (LUT) with a depth of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sup> , where N is the bit-width of the pixels. Theoretical analysis and experimental results show that the proposed normalization-free HOG feature based logarithmic gradient is close to the original version and can be used in the pedestrian detection algorithms without performance degradation. It is shown in the experiments that by skipping the time-consuming normalization step, the processing speed of HOG feature extraction can be significantly improved.
Enhanced versions of the shuffled shepherd optimization algorithm for the optimal design of skeletal structures

This paper consists of two main parts. The first part is devoted to developing a parameter-free variant of the shuffled shepherd optimization algorithm (SSOA), named as a parameter-free shuffled shepherd optimization algorithm (PF-SSOA). Next, a set-theoretical multi-phase framework is proposed for the population-based metaheuristic algorithms. The proposed framework's main idea is based on dividing the population of candidate solutions into some number of well-arranged sub-populations. The number of sub-populations gradually changes during the optimization process. The framework is applied to the PF-SSOA, leading to a set-theoretical multi-phase variant of PF-SSOA named STMP-SSOA. The STMP-SSOA stands for a set-theoretical multi-phase shuffled shepherd optimization algorithm. Some skeletal structures are optimized to verify the validity and efficiency of the proposed algorithms. The optimization problems include two truss size optimization problems with multiple frequency constraints and two planar frame structures with strength and displacement constraints. The results indicate that the developed algorithms perform better than the standard version of the SSOA, especially in terms of robustness and convergence characteristics.
A lowlight image enhancement method learning from both paired and unpaired data by adversarial training

In recent years, deep learning has been widely used in the field of lowlight image enhancement. In this paper, we propose a novel deep-learning-based lowlight image enhancement method, which could learn from both paired and unpaired data, and perform end-to-end contrast enhancement and noise reduction simultaneously. Concretely, we first employ a multi-level content loss on paired synthesized data for training enhancer to recover detail better. Then, we propose a GAN-based domain adaptation mechanism, which applies an adversarial training strategy and a novel gamma-correction-based self-supervised content loss on abundant unpaired real data, for training the enhancer to perform better on real lowlight images. Furthermore, a Patch-GAN-based noise reduction mechanism is proposed to adversarially train the enhancer to better reduce noise in real lowlight images. Finally, we improve the enhancer by introduce attention mechanism and global feature to original U-net, make it more suitable for lowlight image enhancement task. We conduct experiments on several common datasets and the results show that our method outperforms other state-of-the-arts under a variety of image quality assessment metrics. And when applied as pre-processing module, our method can improve the classification accuracy on lowlight dataset by 1.7%, outperforming other methods too.
OpinionMine: A Bayesian-based framework for opinion mining using Twitter Data

This article studies opinion mining from social media with probabilistic logic reasoning. As it is known, Twitter is one of the most active social networks, with millions of tweets sent daily, where multiple users express their opinion about traveling, economic issues, political decisions etc. As such, it offers a valuable source of information for opinion mining. In this paper we present OpinionMine, a Bayesian-based framework for opinion mining, exploiting Twitter Data. Initially, our framework imports Tweets massively by using Twitter’s API. Next, the imported Tweets are further processed automatically for constructing a set of untrained rules and random variables. Then, a Bayesian Network is derived by using the set of untrained rules, the random variables and an evidence set. After that, the trained model can be used for the evaluation of new Tweets. Finally, the constructed model can be retrained incrementally, thus becoming more robust. As application domain for the development of our methodology we have selected tourism because it is one of the most popular topics in social media. Our framework can predict users’ intention to visit a place. Among the advantages of our framework is that it follows an incremental learning strategy. That is, the derived model can be retrained incrementally with new training sets thus becoming more robust. Further, our framework can be easily adapted to opinion mining from social media on other topics, whereas the rules of the derived model are constructed in an efficient way and automatically.
Training Noise-Resilient Recurrent Photonic Networks for Financial Time Series Analysis

Photonic-based neuromorphic hardware holds the credentials for providing fast and energy efficient implementations of computationally complex Deep Learning (DL) models. At the same time, the unique nature of neuromorphic photonics also imposes a number of limitations that hinders its application, including the need to re-train DL models in order to be compliant with the underlying hardware architecture, as well as the existence of various noise sources, which are prevalent in virtually all neuromorphic photonic architectures and negatively affect the accuracy of the deployed models. In this paper we propose a novel noise-aware approach for training neural networks realized on photonic hardware, which can alleviate some of these limitations. To this end we first provide an extensive characterization of the various noise sources that affect sigmoid-based recurrent photonic architectures, as well as provide an extensive study on the effect of various signal-to-noise-ratios (SNRs) levels on the performance of such DL models. The effectiveness of the proposed method is demonstrated on a challenging forecasting problem that involves high frequency financial time series using a state-of-the-art recurrent photonic architecture, which natu-rally fits the requirements of such latency-critical applications. Apart from providing more accurate models, the proposed method opens several interesting future research directions on co-designing neuromorphic photonics, including developing DL models that can work on lower SNRs, leading to more energy efficient solutions.
Accelerating Binarized Neural Networks via Bit-Tensor-Cores in Turing GPUs

Despite foreseeing tremendous speedups over conventional deep neural networks, the performance advantage of binarized neural networks (BNNs) has merely been showcased on general-purpose processors such as CPUs and GPUs. In fact, due to being unable to leverage bit-level-parallelism with a word-based architecture, GPUs have been criticized for extremely low utilization (1 percent) when executing BNNs. Consequently, the latest tensorcores in NVIDIA Turing GPUs start to experimentally support bit computation. In this article, we look into this brand new bit computation capability and characterize its unique features. We show that the stride of memory access can significantly affect performance delivery and a data-format co-design is highly desired to support the tensorcores for achieving superior performance than existing software solutions without tensorcores. We realize the tensorcore-accelerated BNN design, particularly the major functions for fully-connect and convolution layers - bit matrix multiplication and bit convolution. Evaluations on two NVIDIA Turing GPUs show that, with ResNet-18, our BTC-BNN design can process ImageNet at a rate of 5.6K images per second, 77 percent faster than state-of-the-art. Our BNN approach is released on https://github.com/pnnl/TCBNN.
Consistency-aware and Inconsistency-aware Graph-based Multi-view Clustering

Multi-view data analysis has gained increasing popularity because multi-view data are frequently encountered in machine learning applications. A simple but promising approach for clustering of multi-view data is multi-view clustering (MVC), which has been developed extensively to classify given subjects into some clustered groups by learning latent common features that are shared across multi-view data. Among existing approaches, graph-based multi-view clustering (GMVC) achieves state-of-the-art performance by leveraging a shared graph matrix called the unified matrix. However, existing methods including GMVC do not explicitly address inconsistent parts of input graph matrices. Consequently, they are adversely affected by unacceptable clustering performance. To this end, this paper proposes a new GMVC method that incorporates consistent and inconsistent parts lying across multiple views. This proposal is designated as CI-GMVC. Numerical evaluations of real-world datasets demonstrate the effectiveness of the proposed CI-GMVC.