Reviewerly/exHarmony huggingface dataset:
Adversarial Attack Against Deep Saliency Models Powered by Non-Redundant Priors

Saliency detection is an effective front-end process to many security-related tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> automatic drive and tracking. Adversarial attack serves as an efficient surrogate to evaluate the robustness of deep saliency models before they are deployed in real world. However, most of current adversarial attacks exploit the gradients spanning the entire image space to craft adversarial examples, ignoring the fact that natural images are high-dimensional and spatially over-redundant, thus causing expensive attack cost and poor perceptibility. To circumvent these issues, this paper builds an efficient bridge between the accessible <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">partially-white-box source</i> models and the unknown <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">black-box target</i> models. The proposed method includes two steps: 1) We design a new <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">partially-white-box</i> attack, which defines the cost function in the compact hidden space to punish a fraction of feature activations corresponding to the salient regions, instead of punishing every pixel spanning the entire dense output space. This <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">partially-white-box</i> attack reduces the redundancy of the adversarial perturbation. 2) We exploit the non-redundant perturbations from some <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">source</i> models as the prior cues, and use an iterative zeroth-order optimizer to compute the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">directional derivatives</i> along the non-redundant prior directions, in order to estimate the actual gradient of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">black-box target</i> model. The non-redundant priors boost the update of some “critical” pixels locating at non-zero coordinates of the prior cues, while keeping other redundant pixels locating at the zero coordinates unaffected. Our method achieves the best tradeoff between attack ability and perturbation redundancy. Finally, we conduct a comprehensive experiment to test the robustness of 18 state-of-the-art deep saliency models against 16 malicious attacks, under both of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">white-box</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">black-box</i> settings, which contributes a new robustness benchmark to the saliency community for the first time.
Multilayer Dynamic Encryption for Security OFDM-PON Using DNA-Reconstructed Chaotic Sequences Under Cryptanalysis

In this paper, a multilayer dynamic encryption scheme using deoxyribonucleic acid reconstructed chaotic sequences (DNA-RCS) under cryptanalysis is firstly proposed, which aims at enhancing the security of orthogonal frequency division multiplexing passive optical network (OFDM-PON).We adopt DNA coding to reconstruct chaotic sequences, the selected coding rules and the number of chaotic sequence blocks divided are then random, the randomness and security of encryption sequences are improved.The transmitted signal is encrypted in two layers.The first layer is hybrid chaotic permutation and diffusion.Each symbol can be encrypted by the combination of a single non-repetitive permutation and plaintextrelated diffusion.It makes encryption not only depend on the chaotic sequences but also relate to the order of permutation.The second layer is a dynamic Josephus permutation.By taking the unit as the permutation object, the scrambling efficiency is increased.Also, the counting period is randomly selected, which can enhance the security of the system.The number of tests needed to break a secure transmission for an attacker can reach up to 3.096 × 10 106 .An encryption signal with 22.06Gb/s is successfully demonstrated over a 25-km standard single-mode fiber (SSMF) and a back-to-back (BTB) system.It is proved that the proposed scheme does not degrade the system performance and can effectively resist various attacks by the performance analysis model based on cryptanalysis.
Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environments

Goal: To develop and validate a field-based data collection and assessment method for human activity recognition in the mountains with variations in terrain and fatigue using a single accelerometer and a deep learning model. Methods: The protocol generated an unsupervised labelled dataset of various long-term field-based activities including run, walk, stand, lay and obstacle climb. Activity was voluntary so transitions could not be determined a priori. Terrain variations included slope, crossing rivers, obstacles and surfaces including road, gravel, clay, mud, long grass and rough track. Fatigue levels were modulated between rested to physical exhaustion. The dataset was used to train a deep learning convolutional neural network (CNN) capable of being deployed on battery powered devices. The human activity recognition results were compared to a lab-based dataset with 1,098,204 samples and six features, uniform smooth surfaces, non-fatigued supervised participants and activity labelling defined by the protocol. Results: The trail run dataset had 3,829,759 samples with five features. The repetitive activities and single instance activities required hyper parameter tuning to reach an overall accuracy 0.978 with a minimum class precision for the one-off activity (climbing gate) of 0.802. Conclusion: The experimental results showed that the CNN deep learning model performed well with terrain and fatigue variations compared to the lab equivalents (accuracy 97.8% vs. 97.7% for trail vs. lab). Significance: To the authors knowledge this study demonstrated the first successful human activity recognition (HAR) in a mountain environment. A robust and repeatable protocol was developed to generate a validated trail running dataset when there were no observers present and activity types changed on a voluntary basis across variations in terrain surface and both cognitive and physical fatigue levels.
Aspect Based Multi-Labeling Using SVM Based Ensembler

Sentiment analysis is one of the most prominent sub-areas of research in Natural Language Processing (NLP), where it is important to consider implicit or explicit emotions conveyed by review material. Researchers also recognized that the generic feelings derived from the textual material are insufficient, so the sentiment analysis aspect based was coined to extract the emotions from textual data. Multi-labeling based on aspects data can resolve the issue of extracting emotion aspect based. In this work, a novel approach namely: Evolutionary Ensembler (EEn) is proposed to effectively boost the accuracy and diversity of multi-label learners. Unlike traditional multi-label training methods, EEn emphasizes the accuracy and diversity of multi-label-based models. We have used seven datasets (medical, hotel, movies, automobiles, proteins, birds, emotions, news). At first, we applied a pre-processing step to gain the refined and clean data. Second, we have applied the Vader tool with Bag of Words (BoW) for the feature extraction. Third, the word2vec method is applied to draw an association between words. Moreover, the SVM model (tuned with GA) is trained and tested on the refined data. The accuracy of the aspect-based multi-labeling using the SVM-GA on medical, hotel, movies, automobiles, proteins, birds, emotions, news are 93.13%, 94.32%, 94.0%, 95.10%, 90.20%, 93.22%, 90.0%, and 94.0%, respectively. Our proposed model with different dimensions of multi-label datasets shows that EEn is vastly superior to other popular techniques. Experimental outcomes validate the success of the implemented approach among existing benchmark techniques.
Direct U–Pb dating of carbonates from micron-scale femtosecond laser ablation inductively coupled plasma mass spectrometry images using robust regression

Abstract. Uranium–lead (U–Pb) dating of carbonates by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) spot analysis is an increasingly used method in the field of geosciences, as it brings very strong constraints over the geological history of basins, faults or reservoirs. Most ages currently published are based on the measurement of U and Pb ratios on spot ablations, using nanosecond lasers coupled to sector field or multi-collector ICP-MS. Here, we test a new strategy for the U–Pb dating of carbonates from 2D isotopic ratio maps, based on the use of a robust regression approach in the data reduction workflow. The isotopic maps, with a minimum area of 0.65 mm2 (∼ 1000 pixels of 13×25 µm resolution), are obtained using a 257 nm femtosecond laser ablation system at a high repetition rate (500 Hz) coupled to a high-resolution ICP-MS. The maps commonly show significant variations in isotope ratios at the pixel scale, allowing the plotting of pixel U–Pb ratios in concordia or isochron diagrams and the calculation of U–Pb ages. Due to the absence of individual ratio uncertainties, the ages are calculated using MM-robust linear regression rather than the more commonly used York-type regression. The goodness of fit to the data is assessed by the calculation of the residual standard error (RSE) of the regression and by the calculation of a mean square of weight deviates (MSWD) on discretised data. Several examples are provided that compare the ages calculated by robust regression with those obtained by other techniques (e.g. isotope dilution, LA-ICP-MS spot analyses and the pixel-pooling approach). For most samples, characterised by high U concentrations (&gt; 1 ppm), robust regression allows for the calculation of ages and uncertainties similar to those obtained with the other approaches. However, for samples with lower U concentrations (&lt; 0.5 ppm), the ages obtained are up to 10 % too young due to pixels with high U ∕ Pb acting as leverage points for the regression. We conclude that the U–Pb ages calculated by the regression method tested here, although statistically robust, should be critically analysed before validation, especially for samples with low U concentrations.
INCO-GAN: Variable-Length Music Generation Method Based on Inception Model-Based Conditional GAN

Deep learning has made significant progress in the field of automatic music generation. At present, the research on music generation via deep learning can be divided into two categories: predictive models and generative models. However, both categories have the same problems that need to be resolved. First, the length of the music must be determined artificially prior to generation. Second, although the convolutional neural network (CNN) is unexpectedly superior to the recurrent neural network (RNN), CNN still has several disadvantages. This paper proposes a conditional generative adversarial network approach using an inception model (INCO-GAN), which enables the generation of complete variable-length music automatically. By adding a time distribution layer that considers sequential data, CNN considers the time relationship in a manner similar to RNN. In addition, the inception model obtains richer features, which improves the quality of the generated music. In experiments conducted, the music generated by the proposed method and that by human composers were compared. High cosine similarity of up to 0.987 was achieved between the frequency vectors, indicating that the music generated by the proposed method is very similar to that created by a human composer.
A High-Performance Multimem SHA-256 Accelerator for Society 5.0

The development of a low-cost high-performance secure hash algorithm (SHA)-256 accelerator has recently received extensive interest because SHA-256 is important in widespread applications, such as cryptocurrencies, data security, data integrity, and digital signatures.Unfortunately, most current researches have focused on the performance of the SHA-256 accelerator but not on a system level, in which the data transfer between the external memory and accelerator occupies a large time fraction.In this paper, we solve the state-of-art problem with a novel SHA-256 architecture named the multimem SHA-256 accelerator that achieves high performance at the system on chip (SoC) level.Notably, our accelerator employs three novel techniques, the pipelined arithmetic logic unit (ALU), multimem processing element (PE), and shift buffer in shift buffer out (SBi-SBo), to reduce the critical path delay and significantly increase the processing rate.Experiments on a field-programmable gate array (FPGA) and an application-specific integrated circuit (ASIC) show that the proposed accelerator achieves significantly better processing rate and hardware efficiency than previous works.The accelerator accuracy is verified on a real hardware platform (FPGA ZCU102).The accelerator is synthesized and laid out with 180 nm complementary metal oxide semiconductor (CMOS) technology with a chip sized 8.5 mm × 8.5 mm, consumes 1.86 W, and provides a maximum processing rate of 40.96Gbps at 80 MHz and 1.8 V.With FPGA Xilinx 16 nm FinFET technology, the accelerator processing rate is as high as 284 Gbps.
Rise, Fall, and Recovery of Blockchains in the Maritime Technology Space

Blockchain technology, since its introduction, has been expected to be implemented in many areas. Cryptocurrency is one unique example that established a functioning application. On the other hand, blockchain technology is not immune to various challenges related to the nature of itself, privacy management, and antitrust laws, among others. This study lays out the nature of blockchain and applications in the maritime industry, while highlighting the bottlenecks. Potential resolutions and anticipated developments are proposed. To do this, we adopt a systematic approach and present an overview of blockchain in maritime literature. In addition, the fundamental problems with blockchain are investigated, beginning from their essentials to the pain points that are claimed to need improvement. For establishing a legitimate and practically meaningful blockchain platform, stakeholders need to achieve pluralism (consensus validation), privacy, and security of the system.
Ensemble Machine Learning Approaches for Detection of SQL Injection Attack

In the current era, SQL Injection Attack is a serious threat to the security of the ongoing cyber world particularly for many web applications that reside over the internet. Many webpages accept the sensitive information (e.g. username, passwords, bank details, etc.) from the users and store this information in the database that also resides over the internet. Despite the fact that this online database has much importance for remotely accessing the information by various business purposes but attackers can gain unrestricted access to these online databases or bypass authentication procedures with the help of SQL Injection Attack. This attack results in great damage and variation to database and has been ranked as the topmost security risk by OWASP TOP 10. Considering the trouble of distinguishing unknown attacks by the current principle coordinating technique, a strategy for SQL injection detection dependent on Machine Learning is proposed. Our motive is to detect this attack by splitting the queries into their corresponding tokens with the help of tokenization and then applying our algorithms over the tokenized dataset. We used four Ensemble Machine Learning algorithms: Gradient Boosting Machine (GBM), Adaptive Boosting (AdaBoost), Extended Gradient Boosting Machine (XGBM), and Light Gradient Boosting Machine (LGBM). The results yielded by our models are near to perfection with error rate being almost negligible. The best results are yielded by LGBM with an accuracy of 0.993371, and precision, recall, f1 as 0.993373, 0.993371, and 0.993370, respectively. The LGBM also yielded less error rate with False Positive Rate (FPR) and Root Mean Squared Error (RMSE) to be 0.120761 and 0.007, respectively. The worst results are yielded by AdaBoost with an accuracy of 0.991098, and precision, recall, f1 as 0.990733, 0.989175, and 0.989942, respectively. The AdaBoost also yielded high False Positive Rate (FPR) to be 0.009.
Adoption of Email Anti-Spoofing Schemes: A Large Scale Analysis

Sending forged emails by taking advantage of domain spoofing is a common technique used by attackers. The lack of appropriate email anti-spoofing schemes or their misconfiguration may lead to successful phishing attacks or spam dissemination. In this paper, we evaluate the extent of the SPF and DMARC deployment in two large-scale campaigns measuring their global adoption rate with a scan of 236 million domains and high-profile domains of 139 countries. We propose a new algorithm for identifying defensively registered domains and enumerating the domains with misconfigured SPF rules by emulating the SPF check_function. We define for the first time new threat models involving subdomain spoofing and present a methodology for preventing domain spoofing, a combination of good practices for managing SPF and DMARC records and analyzing DNS logs. Our measurement results show that a large part of the domains do not correctly configure the SPF and DMARC rules, which enables attackers to successfully deliver forged emails to user inboxes. Finally, we report on remediation and its effects by presenting the results of notifications sent to CSIRTs responsible for affected domains in two separate campaigns.
Improving novel extreme learning machine using PCA algorithms for multi-parametric modelling of municipal wastewater treatment plant

In order to develop a tool for modeling the efficiency of municipal wastewater treatment plants (MWWTP), a reliable prediction tool is essential.In this research, two scenarios (I and II) were investigated for modeling the performance of Nicosia MWWTP.The extreme learning machine (ELM), which is a newly developed black-box model, combined with principal component analysis was developed in scenario I and two principal components (PCs) variables were generated, while in scenario II, traditional multi-layer perceptron (MLP) neural network and multiple linear regression (MLR) models were established for comparison.The daily measured data obtained from new Nicosia MWWTP includes (pH inf , Conductivity inf , BOD inf , COD inf , Total-N inf , Total-P inf , NH 4 -N inf , SS inf and TSS inf ) as the input variables and (BOD eff , COD eff , Total-N eff , Total-P eff ) as the corresponding outputs.Taylor diagrams, box and whisker were also utilized to examine the similarities and comparisons between the observed and predicted values for both the ELM and PCs-ELM in scenario I.The obtained results based on the performance indices showed that the PCs-ELM model has higher performance accuracy than the novel ELM model.The results also showed increases of the PCs-ELM of about 12%, 2%, 20% and 6% for BOD eff , COD eff , TN eff (total nitrogen) and TP eff (total phosphorite) with regard to the ELM model.Also, the comparison results demonstrated that ELM and MLP revealed higher prediction accuracy than the MLR model, and the ELM model comparably outperformed the MLP model.The overall results indicated that both the PCs-ELM and two scenarios could be alternative reliable tools for modeling the performance of Nicosia MWWTP.
Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources

Warning: this paper contains content that may be offensive or upsetting. Commonsense knowledge bases (CSKB) are increasingly used for various natural language processing tasks. Since CSKBs are mostly human-generated and may reflect societal biases, it is important to ensure that such biases are not conflated with the notion of commonsense. Here we focus on two widely used CSKBs, ConceptNet and GenericsKB, and establish the presence of bias in the form of two types of representational harms, overgeneralization of polarized perceptions and representation disparity across different demographic groups in both CSKBs. Next, we find similar representational harms for downstream models that use ConceptNet. Finally, we propose a filtering-based approach for mitigating such harms, and observe that our filtered-based approach can reduce the issues in both resources and models but leads to a performance drop, leaving room for future work to build fairer and stronger commonsense models.
GAN-Based Multi-Style Photo Cartoonization

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Planning With Learned Dynamics: Probabilistic Guarantees on Safety and Reachability via Lipschitz Constants

We present a method for feedback motion planning of systems with unknown dynamics which provides probabilistic guarantees on safety, reachability, and goal stability. To find a domain in which a learned control-affine approximation of the true dynamics can be trusted, we estimate the Lipschitz constant of the difference between the true and learned dynamics, and ensure the estimate is valid with a given probability. Provided the system has at least as many controls as states, we also derive existence conditions for a one-step feedback law which can keep the real system within a small bound of a nominal trajectory planned with the learned dynamics. Our method imposes the feedback law existence as a constraint in a sampling-based planner, which returns a feedback policy around a nominal plan ensuring that, if the Lipschitz constant estimate is valid, the true system is safe during plan execution, reaches the goal, and is ultimately invariant in a small set about the goal. We demonstrate our approach by planning using learned models of a 6D quadrotor and a 7DOF Kuka arm. We show that a baseline which plans using the same learned dynamics <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without</i> considering the error bound or the existence of the feedback law can fail to stabilize around the plan and become unsafe.
A Deep Learning-based approach for forecasting off-gas production and consumption in the blast furnace

Abstract This article presents the application of a recent neural network topology known as the deep echo state network to the prediction and modeling of strongly nonlinear systems typical of the process industry. The article analyzes the results by introducing a comparison with one of the most common and efficient topologies, the long short-term memories, in order to highlight the strengths and weaknesses of a reservoir computing approach compared to one currently considered as a standard of recurrent neural network. As benchmark application, two specific processes common in the integrated steelworks are selected, with the purpose of forecasting the future energy exchanges and transformations. The procedures of training, validation and test are based on data analysis, outlier detection and reconciliation and variable selection starting from real field industrial data. The analysis of results shows the effectiveness of deep echo state networks and their strong forecasting capabilities with respect to standard recurrent methodologies both in terms of training procedures and accuracy.