Reviewerly/exHarmony huggingface dataset:
Development of web browser prototype with embedded classification capability for mitigating Cross-Site Scripting attacks

Mitigation of Cross-Site Scripting (XSS) with machine learning techniques is the recent interest of researchers. A large amount of research work is reported in this domain. A lack of real-time tools working on the basis of these approaches is a gap in this domain. In this work, a web browser that works on machine learning classification to mitigate XSS attacks is developed. This browser classifies webpages into malicious and non-malicious pages using features identified by observation of malicious web pages and features collected from the different authors works. Classification experiments are conducted to evaluate the effectiveness of these features, and it is found that this approach performs better than other proposed methods in terms of classification accuracy, precision, recall, and F1-score. A web browser is implemented with the open-source browser WebKit. Experiments are conducted to assess the overhead created by the added functionality of classification in the web browser. The browser is found effective in classifying web pages and in real-time browsing scenarios with very less generated overhead. This makes web browser better than other proposed solutions to mitigate (XSS) attacks with minimal overhead. This developed web browser will be beneficial not only for researchers working in this domain but also for the users who can be the victims of XSS attacks.
Sundanese Twitter Dataset for Emotion Classification

Sundanese is the second-largest tribe in Indonesia which possesses many dialects. This condition has gained attention for many researchers to analyze emotion especially on social media. However, with barely available Sundanese dataset, this condition makes understanding sundanese emotion is a challenging task. In this research, we proposed a dataset for emotion classification of Sundanese text. The preprocessing includes case folding, stopwords removal, stemming, tokenizing, and text representation. Prior to classification, for the feature generation, we utilize term frequency-inverse document frequency (TFIDF). We evaluated our dataset using k-Fold Cross Validation. Our experiments with the proposed method exhibit an effective result for machine learning classification. Furthermore, as far as we know, this is the first Sundanese emotion dataset available for public.
Deriving Effective Human Activity Recognition Systems through Objective Task Complexity Assessment

Research in sensor based human activity recognition (HAR) has been a core concern of the mobile and ubiquitous computing community. Sophisticated systems have been developed with the main view on applications of HAR methods in research settings. This work addresses a related yet practically different problem that mainly focuses on users of HAR technology. We acknowledge that practitioners from outside the core HAR research community are motivated to employ HAR methods for practical deployments. Even though standard processing approaches exist, arguably, often times substantial modifications are necessary to derive effective analysis systems. It is not always clear a-priori how challenging a HAR task actually is and what dimensions of an analysis pipeline are crucial for successful automated assessments. In practice this can lead to disappointing results or disproportionate efforts that have to be invested into the optimization of data analysis pipelines, that were supposed to work "out of the box". We present a framework for the objective complexity assessment of HAR tasks that directly supports practitioners' decision making of whether and how to employ HAR for their deployments. We map a HAR task onto a vectorial representation that allows us to analyse the inherent challenges of the task and to draw conclusions through similarity analysis with regards to existing tasks. We validate our complexity assessment framework on 23 HAR datasets and derive a data-driven categorization of human activity recognition. We demonstrate how our objective analysis can be used to inform the deployment of HAR systems in practical scenarios.
Minimum-Norm Adversarial Examples on KNN and KNN based Models

We study the robustness against adversarial examples of kNN classifiers and classifiers that combine kNN with neural networks. The main difficulty lies in the fact that finding an optimal attack on kNN is intractable for typical datasets. In this work, we propose a gradient-based attack on kNN and kNN-based defenses, inspired by the previous work by Sitawarin & Wagner [1]. We demonstrate that our attack outperforms their method on all of the models we tested with only a minimal increase in the computation time. The attack also beats the state-of-the-art attack [2] on kNN when using less than 1% of its running time. We hope that this attack can be used as a new baseline for evaluating the robustness of kNN and its variants.
A new nonlocal means based framework for mixed noise removal

Many image-denoising approaches seek to remove either additive white Gaussian noise (AWGN) or impulse noise (IN), because both types are easier to process when considered separately. However, images can be corrupted by a mixture of AWGN and IN during image acquisition and transmission. The major difficulty of mixed noise removal arises through the complex distribution of noise, which cannot be fitted by a simple parametric model. In this paper, a new nonlocal means based framework (NMF) is proposed. A median-type filter is used to detect the locations of outlier pixels; these pixels are then replaced by their nonlocal means, which makes the mixed noise distribution approximately Gaussian. To prove the effectiveness of our NMF, a low rank approximation combined with NMF (LRNM) model is presented for mixed noise removal. In the LRNM, we group similar nonlocal patches in a matrix and apply a low rank approximation to reconstruct the clean image. Gradient regularization is added to better preserve the image texture details. A convolutional neural network (CNN) combined with the NMF (NMF-CNN) is also presented, to prove the generality of the NMF. Experimental results show that LRNM and NMF-CNN achieve a strong mixed noise removal performance and also produce visually pleasing denoising results.
Contesting narratives of repression: Experimental evidence from Sisi’s Egypt

Authoritarian regimes frequently attempt to justify repression by accusing their opponents of violent behavior. Are such claims successful at persuading the public to accept state-sponsored violence, and can these claims be contested effectively by human rights organizations seeking to publicize evidence contradicting the regime’s narrative? To evaluate these questions, we conducted a survey experiment in Egypt using Facebook advertisements to recruit respondents safely. The experiment evaluates the persuasiveness of competing information provided by a human rights organization and the Egyptian security forces in shaping attitudes toward an incident of state-sponsored violence in which security forces killed several leaders of the opposition Muslim Brotherhood. We find evidence for the ability of Egyptian security forces to increase support for this repression when they control the narrative about why violence was used. However, we also find that the effects of this propaganda disappear when paired with information from Human Rights Watch that counters the security forces’ justifications. These findings provide experimental evidence that propaganda can help authoritarian regimes to increase public support for repression, but they also indicate that human rights organizations can play some role in mitigating this support when they succeed at disseminating countervalent information in these contexts.
Combating Deepfakes: Multi-LSTM and Blockchain as Proof of Authenticity for Digital Media

Malicious use of deep learning algorithms has allowed the proliferation of high realism fake digital content such as text, images, and videos, to exist on the internet as readily available and accessible consumable content. False information provided through algorithmically modified footage, images, audios, and videos (known as deepfakes), coupled with the virality of social networks, may cause major social unrest. The emergence of misinformation from fabricated digital content suggests the necessity for anti-disinformation methods such as deepfake detection algorithms or immutable metadata in order to verify the validity of digital content. Permissioned blockchain, notably Hyperledger Fabric 2.0, coupled with LSTMs for audio/video/descriptive captioning is a step towards providing a feasible tool for combating deepfake media. Original content would require the original artist attestation of untampered data. The smart contract combines a varied multiple LSTM networks into a process that allows for the tracing and tracking of a digital content's historical provenance. The result is a theoretical framework that enables proof of authenticity (PoA) for digital media using a decentralized blockchain using multiple LSTMs as a deep encoder for creating unique discriminative features; which is then compressed and hashed into a transaction. Our work assumes we trust the video at the point of reception. Our contribution is a decentralized blockchain framework of deep discriminative digital media to combat deepfakes.
Biometric Identification and Presentation-Attack Detection using Micro- and Macro-Movements of the Eyes

We study involuntary micro-movements of both eyes, in addition to saccadic macro-movements, as biometric characteristic. We develop a deep convolutional neural network that processes binocular oculomotoric signals and identifies the viewer. In order to be able to detect presentation attacks, we develop a model in which the movements are a response to a controlled stimulus. The model detects replay attacks by processing both the controlled but randomized stimulus and the ocular response to this stimulus. We acquire eye movement data from 150 participants, with 4 sessions per participant. We observe that the model detects replay attacks reliably; compared to prior work, the model attains substantially lower error rates.
LT-GAN: Self-Supervised GAN with Latent Transformation Detection

Generative Adversarial Networks (GANs) coupled with self-supervised tasks have shown promising results in unconditional and semi-supervised image generation. We propose a self-supervised approach (LT-GAN) to improve the generation quality and diversity of images by estimating the GAN-induced transformation (i.e. transformation induced in the generated images by perturbing the latent space of generator). Specifically, given two pairs of images where each pair comprises of a generated image and its transformed version, the self-supervision task aims to identify whether the latent transformation applied in the given pair is same as that of the other pair. Hence, this auxiliary loss encourages the generator to produce images that are distinguishable by the auxiliary network, which in turn promotes the synthesis of semantically consistent images with respect to latent transformations. We show the efficacy of this pretext task by improving the image generation quality in terms of FID on state-of-the-art models in conditional and unconditional settings on CIFAR-10, CelebA-HQ and ImageNet datasets. Moreover, we empirically show that LT-GAN helps in improving controlled image editing for CelebA-HQ, and ImageNet over baseline models. We experimentally demonstrate that our proposed LT self-supervision task can be effectively combined with other state-of-the-art training techniques for added benefits. Consequently, we show that our approach achieves the new state-of-the-art FID score of 9.8 on conditional CIFAR-10 image generation.
Incremental Contract-based Verification of Software Updates for Safety-Critical Cyber-Physical Systems

Software updates are indispensable for the continuous development of Cyber Physical Systems (CPS): They allow for low-cost bug-fixing, fast adaptation to new or changing environments, or adding new functionality throughout the CPS’s life-cycle. Due to the urgent need for some safety-critical updates, their verification and validation may need to happen as fast as possible without loss of quality. For this reason, incremental checks targeting specifically the introduced changes and their impact on the system are essential as they speed up the validation process. In this paper, we introduce a concept for such an incremental verification for different types of updates by using contract-based design and verifying the integration of the introduced changes by checking their compliance with the contractually agreed assumptions and guarantees. We demonstrate our approach by applying two update types to an Adaptive Cruise Control (ACC) system and verifying the impact of the changes within the environment of the changed module(s).
Noisy Concurrent Training for Efficient Learning under Label Noise

Deep neural networks (DNNs) fail to learn effectively under label noise and have been shown to memorize random labels which affect their generalization performance. We consider learning in isolation, using one-hot encoded labels as the sole source of supervision, and a lack of regularization to discourage memorization as the major shortcomings of the standard training procedure. Thus, we propose Noisy Concurrent Training (NCT) which leverages collaborative learning to use the consensus between two models as an additional source of supervision. Furthermore, inspired by trial-to-trial variability in the brain, we propose a counter-intuitive regularization technique, target variability, which entails randomly changing the labels of a percentage of training samples in each batch as a deterrent to memorization and over-generalization in DNNs. Target variability is applied independently to each model to keep them diverged and avoid the confirmation bias. As DNNs tend to prioritize learning simple patterns first before memorizing the noisy labels, we employ a dynamic learning scheme whereby as the training progresses, the two models increasingly rely more on their consensus. NCT also progressively increases the target variability to avoid memorization in later stages. We demonstrate the effectiveness of our approach on both synthetic and real-world noisy benchmark datasets.
Direct Anonymous Attestation With Optimal TPM Signing Efficiency

Direct Anonymous Attestation (DAA) is an anonymous signature scheme, which allows the Trusted Platform Module (TPM), a small chip embedded in a host computer, to attest to the state of the host system, while preserving the privacy of the user. DAA provides two signature modes: fully anonymous signatures and pseudonymous signatures. One main goal of designing DAA schemes is to reduce the TPM signing workload as much as possible, as the TPM has only limited resources. In an optimal DAA scheme, the signing workload on the TPM will be no more than that required for a normal signature like ECSchnorr. To date, no scheme has achieved the optimal signing efficiency for both signature modes. In this paper, we propose the first DAA scheme which achieves the optimal TPM signing efficiency for both signature modes. In this scheme, the TPM takes only a single exponentiation to generate a signature, and this single exponentiation can be pre-computed. Our scheme can be implemented using the existing TPM 2.0 commands, and thus is compatible with the TPM 2.0 specification. We benchmarked the TPM 2.0 commands needed for three DAA use cases on an Infineon TPM 2.0 chip, and also implemented the host signing and verification algorithm for our DAA scheme on a laptop with 1.80GHz Intel Core i7-8550U CPU. Our experimental results show that our DAA scheme obtains a total signing time of about 144 ms for either signature mode, while with pre-computation we can obtain a signing time of about 65 ms. Based on our benchmark results for the pseudonymous signature mode, our scheme is roughly <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> (resp., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5\times $ </tex-math></inline-formula> ) faster than the existing DAA schemes supported by TPM 2.0 in terms of total (resp., online) signing efficiency.
View-invariant action recognition via Unsupervised AttentioN Transfer (UANT)

With wide applications in surveillance and human-robot interaction, view-invariant human action recognition is critical, however, challenging, due to the action occlusion and information loss caused by view change. Current methods mainly seek for a common feature space for different views. However, such solutions become invalid when there exist few common features, e.g. large view change. To tackle the problem, we propose an Unsupervised AttentioN Transfer (UANT) approach for view-invariant action recognition. Other than transferring feature knowledge, UANT transfers attention from one selected reference view to arbitrary views, which correctly emphasizes crucial body joints and their relations for view-invariant representation. In addition, the attention calculation method taking into account both recognition contribution and reliability of skeleton joints generates effective attention. Experiments showed its effectiveness for correctly locating crucial body joints in action sequences. We exhaustively evaluate our approach on the UESTC and the NTU dataset, performing unsupervised view-invariant evaluations, i.e. X-view and Arbitrary-view recognition. Experiment results demonstrate its superiority in view-invariant representation and recognition.
Deep learning with GPUs

Deep learning has been extensively researched in various areas and scales up very fast in the last decade. It has deeply permeated into our daily life, such as image classification, video synthesis, autonomous driving, voice recognition, and personalized recommendation systems. The main challenge for most deep learning models, including convolutional neural networks, recurrent neural networks, and recommendation models, is their large amount of computation. Fortunately, most computations in deep learning applications are parallelizable, therefore they can be effectively handled by throughput processors, such as Graphics Processing Units (GPUs). GPUs support high throughput, parallel processing performance, and high memory bandwidth and becomes the most popularly adopted device for deep learning. As a matter of fact, many deep learning workloads from mobile devices to data centers are performed by GPUs. In particular, modern GPU systems provide specialized hardware modules and software stacks for deep learning workloads. In this chapter, we present detailed analysis on the evolution of GPU architectures and the recent hardware and software supports for more efficient acceleration of deep learning in GPUs. Furthermore, we introduce leading-edge researches, challenges, and opportunities of running deep learning workloads on GPUs.
Tool recommender system in Galaxy using deep learning

Galaxy is a web-based and open-source scientific data-processing platform. Researchers compose pipelines in Galaxy to analyse scientific data. These pipelines, also known as workflows, can be complex and difficult to create from thousands of tools, especially for researchers new to Galaxy. To help researchers with creating workflows, a system is developed to recommend tools that can facilitate further data analysis.A model is developed to recommend tools using a deep learning approach by analysing workflows composed by researchers on the European Galaxy server. The higher-order dependencies in workflows, represented as directed acyclic graphs, are learned by training a gated recurrent units neural network, a variant of a recurrent neural network. In the neural network training, the weights of tools used are derived from their usage frequencies over time and the sequences of tools are uniformly sampled from training data. Hyperparameters of the neural network are optimized using Bayesian optimization. Mean accuracy of 98% in recommending tools is achieved for the top-1 metric.The model is accessed by a Galaxy API to provide researchers with recommended tools in an interactive manner using multiple user interface integrations on the European Galaxy server. High-quality and highly used tools are shown at the top of the recommendations. The scripts and data to create the recommendation system are available under MIT license at https://github.com/anuprulez/galaxy_tool_recommendation.
Mental models for assessing coastal social-ecological systems following disasters

Understanding abrupt change in social-ecological systems (SES), such as following major disasters, is key for restoration, adaptation, and sustainability. We describe an approach and case study using fuzzy cognitive mapping (FCM) and mental models to collectively represent local perceptions of Hurricane Irma in Key West, Florida. Using in-person FCM interviews, we sought to answer three overarching questions: 1) how did residents perceive SES relationships post-disaster, 2) how did these perceptions vary across three stakeholder groups consisting of those working in ocean recreation, resorts and inns, and non-tourism sectors, and 3) how did the aggregation of diverse opinions and perceptions affect the understanding of SES trade-offs regarding the coastal region's resilience? Across all respondents, housing was the most commonly mentioned concept occurring in 39% of all models. Coral reefs were included in 71% of ocean recreation models, whereas tourism was included in 50% of resorts and inns models. While mangroves, seawalls, and beaches were rarely to never mentioned until prompted by the interviewer, scenario analyses revealed that shorelines play an important role in residents' perceptions of how Hurricane Irma collectively impacted people and the environment. Sensitivity analyses revealed that the community, composed of a diverse representation of stakeholders, viewed core SES components similarly and trade-offs were acknowledged. Yet, different stakeholder groups perceived the SES in which they live in different ways, with each group perceiving greater damage to specific system components based on direct experience and knowledge. Our study demonstrates an approach for representing the collective perceptions of communities in post-disaster research, rebuilding, and restoration efforts.
Transition characteristics for a small tail-sitter unmanned aerial vehicle

Research on the transition phase of tail-sitter Unmanned Aerial Vehicles (UAVs) is crucial for trajectory planning and performance analysis. This study focuses on the analysis of the transition characteristics and path of a small dual-rotor tail-sitter UAV, including static and dynamic computations. The system input time delay and actuator dynamics are specifically considered during the dynamic analysis, and these actual physical properties ensure that the computation results are reliable and reasonable. The UAV steady-state limit is obtained through static analysis, which is also adopted to verify the correctness of the dynamic results. In regard to the dynamic analysis, several typical transition approaches are computed based on different initial states and optimization objective functions, and the different computations are applicable under specific task conditions. The off-line dynamic results of the transition path and actuator output sequence could also be adopted as reference values for the transition process during real flight. A comparison of the static and dynamic results illustrates the necessity of combining these two methods for UAV transition characteristic analysis. Furthermore, the UAV conceptual parameters related to the transition path are also studied, and the obtained quantitative characteristics provide feedback for the UAV conceptual design.