Reviewerly/exHarmony huggingface dataset:
An XAI method for convolutional neural networks in self-driving cars

eXplainable Artificial Intelligence (XAI) is a new trend of machine learning. Machine learning models are used to predict or decide something, and they derive output based on a large volume of data set. Here, the problem is that it is hard to know why such prediction was derived, especially when using deep learning models. It makes the models unreliable in the case of reliability-critical applications. So, it is required to explain how they derived such output. It is a reliability-critical application for self-driving cars because the mistakes made by the computers inside them can lead to critical accidents. So, it is necessary to adopt XAI models in this field. In this paper, we propose an XAI method based on computing and explaining the difference of the output values of the neurons in the last hidden layer of convolutional neural networks. First, we input the original image and some modified images of it. Then we derive output values for each image and compare these values. Then, we introduce the Sensitivity Analysis technique to explain which parts of the original image are needed to distinguish the category. In detail, we divide the image into several parts and fill these parts with shades. First, we compute the influence value on the vector indicating the last hidden layer of the model for each of these parts. Then we draw shades whose darkness is in proportion to the influence values. The experimental results show that our approach for XAI in self-driving cars finds the parts needed to distinguish the category of these images accurately.
Synchronization phenomena in dual-transistor spiking oscillators realized experimentally towards physical reservoirs

Transistor-based chaotic oscillators are known to realize highly diverse dynamics despite having elementary circuit topologies. This work investigates, numerically and experimentally using a ring network, a recently-introduced dual-transistor circuit that generates neural-like spike trains. A multitude of non-trivial effects are observed as a function of the supply voltage and coupling strength, including pattern formation under incomplete synchronization and sensitivity to additional long-distance links. Globally-applied noise exerts a synchronizing effect that interacts with the other control parameters. When the network is partitioned in halves at different levels of granularity, their interplay gives rise to adversarial route-to-synchronization phenomena. These results highlight the generative ability of this circuit and motivate its consideration towards the future realization of physical reservoirs.
Proxemic behavior in navigation tasks using reinforcement learning

Abstract Human interaction starts with a person approaching another one, respecting their personal space to prevent uncomfortable feelings. Spatial behavior, called proxemics, allows defining an acceptable distance so that the interaction process begins appropriately. In recent decades, human-agent interaction has been an area of interest for researchers, where it is proposed that artificial agents naturally interact with people. Thus, new alternatives are needed to allow optimal communication, avoiding humans feeling uncomfortable. Several works consider proxemic behavior with cognitive agents, where human-robot interaction techniques and machine learning are implemented. However, it is assumed that the personal space is fixed and known in advance, and the agent is only expected to make an optimal trajectory toward the person. In this work, we focus on studying the behavior of a reinforcement learning agent in a proxemic-based environment. Experiments were carried out implementing a grid-world problem and a continuous simulated robotic approaching environment. These environments assume that there is an issuer agent that provides non-conformity information. Our results suggest that the agent can identify regions where the issuer feels uncomfortable and find the best path to approach the issuer. The results obtained highlight the usefulness of reinforcement learning in order to identify proxemic regions.
Catalyst of hate? Ethnic insulting on YouTube in the aftermath of terror attacks in France, Germany and the United Kingdom 2014–2017

In the last 20 years, several major terror attacks conducted in the name of political Islam hit Western Europe. We examine the impact of such terror attacks on hostile behaviour on social media from a cross-national perspective. To this end, we draw upon time-stamped, behavioural data from YouTube and focus on the frequency and popularity ('likes') of ethnically insulting comments among a corpus of approximately one hundred thousand comments. We study aggregate change and use individual-level panel data to investigate within-user change in ethnic insulting in periods leading up to and following major terror events in Germany, France and the UK. Results indicate that terror attacks boost interest in immigration-related topics in general, and lead to a disproportional increase in hate speech in particular. Moreover, we find that attack effects spill over to other countries in several, but not all, instances. Deeper analyses suggest, however, that this pattern is mainly driven by changes in the composition of users and not by changing behaviour of individual users. That is, a surge in ethnic insulting comes from hateful users newly entering online discussions, rather than previous users becoming more hateful following an attack.
Exploration of the Virtual Reality Teleportation Methods Using Hand-Tracking, Eye-Tracking, and EEG

Although recent developments in VR tracking technology have enabled VR to become more portable and convenient, VR must overcome limited traversable space to be used for more general purposes. Since future VR HMDs aim to embed bio-sensors, such as eye-tracking and electroencephalography (EEG), the present study investigates how bio-sensors could be integrated into VR locomotion and how usability could be improved. In this paper, teleportation methods were developed by combining eye-tracking, EEG, and hand-tracking for location targeting and teleport triggering processes. In a static teleportation task, we compared the efficiency, accuracy, and usability of each method as well as the interactions between different combinations of methods. The experimental results verified that these locomotion methods were overall suitable for hands-free VR contents, and revealed relative superiority of eye-tracking for location targeting and hand-tracking for teleport triggering. Importantly, we identified specific combinations of locomotion methods appropriate for context by showing the interactions between experimental factors, and components that require improvement for future application. The results of this study contribute to development and understanding of novel VR locomotion methods utilizing bio-signal data, for more efficient and natural VR locomotion experience.
A novel steganography method for binary and color halftone images

Digital steganography is the science of establishing hidden communication on electronics; the aim is to transmit a secret message to a particular recipient using unsuspicious carriers such as digital images, documents, and audio files with the help of specific hiding methods. This article proposes a novel steganography method that can hide plaintext payloads on digital halftone images. The proposed method distributes the secret message over multiple output copies and scatters parts of the message randomly within each output copy for increased security. A payload extraction algorithm, where plain carrier is not required, is implemented and presented as well. Results gained from conducted objective and subjective tests prove that the proposed steganography method is secure and can hide large payloads.
Hierarchical autoregressive neural networks for statistical systems

It was recently proposed that neural networks could be used to approximate many-dimensional probability distributions that appear e.g. in lattice field theories or statistical mechanics. Subsequently they can be used as variational approximators to asses extensive properties of statistical systems, like free energy, and also as neural samplers used in Monte Carlo simulations. The practical application of this approach is unfortunately limited by its unfavorable scaling both of the numerical cost required for training, and the memory requirements with the system size. This is due to the fact that the original proposition involved a neural network of width which scaled with the total number of degrees of freedom, e.g. $L^2$ in case of a two dimensional $L\times L$ lattice. In this work we propose a hierarchical association of physical degrees of freedom, for instance spins, to neurons which replaces it with the scaling with the linear extent $L$ of the system. We demonstrate our approach on the two-dimensional Ising model by simulating lattices of various sizes up to $128 \times 128$ spins, with time benchmarks reaching lattices of size $512 \times 512$. We observe that our proposal improves the quality of neural network training, i.e. the approximated probability distribution is closer to the target that could be previously achieved. As a consequence, the variational free energy reaches a value closer to its theoretical expectation and, if applied in a Markov Chain Monte Carlo algorithm, the resulting autocorrelation time is smaller. Finally, the replacement of a single neural network by a hierarchy of smaller networks considerably reduces the memory requirements.
Bi-directional long short term memory-gated recurrent unit model for Amharic next word prediction

The next word prediction is useful for the users and helps them to write more accurately and quickly. Next word prediction is vital for the Amharic Language since different characters can be written by pressing the same consonants along with different vowels, combinations of vowels, and special keys. As a result, we present a Bi-directional Long Short Term-Gated Recurrent Unit (BLST-GRU) network model for the prediction of the next word for the Amharic Language. We evaluate the proposed network model with 63,300 Amharic sentence and produces 78.6% accuracy. In addition, we have compared the proposed model with state-of-the-art models such as LSTM, GRU, and BLSTM. The experimental result shows, that the proposed network model produces a promising result.
zVote: A Blockchain-based Privacy-preserving Platform for Remote E-voting

Despite decades of development progress, the current e-voting systems still suffer several challenges especially in terms of security and privacy. Some of the key reasons behind this issue include lack of transparency in the process and inadequate privacy guarantee for the voters. To address these issues, we propose and construct zVote, a blockchain-based e-voting platform that aims to create a transparent and secure system for remote e-voting. With the use of blockchain, the voting result can be verifiable by the public, thereby eliminating the trust in the election authorities. Furthermore, we leverage homomorphic encryptions and zero-knowledge proofs to protect the privacy of users, especially in terms of anonymity and membership privacy, while retaining the correctness and verifiability of the votes. We provide formal definitions and proofs of the construction’s security. Our proof-of-concept implementation demonstrates the feasibility and practicality of zVote.
Sentiment analysis on twitter data based on spider monkey optimization and deep learning for future prediction of the brands

Summary In this manuscript, a deep neural network is proposed by integrating improved adaptive‐network‐based fuzzy inference system (IANFIS) for branding online products to overcome these issues. Here, the sentiment analysis (SA) and prediction on future branding of products that are extracted from the twitter data is carried out. After the review process classifying the products as positive, negative, and neutral assessments completely concentrated in three folds, prediction of a future brand that is carried out by IANFIS for weighting the products finally classify them. This scheme helps the respective retailers/retail brands with their digital marketing team to understand their brand perception as opposed to others. The performance of the proposed method is compared with the existing methods, such as sentiment analysis on twitter data based on particle swarm optimization and genetic algorithm, sentiment analysis on twitter data based on particle swarm optimization and convolutional neural network, sentiment analysis on twitter data based on whale optimization algorithm and support vector machine, and sentiment analysis on twitter data based on convolutional neural network and long short term memory. The simulation results show that the proposed method outperforms the state of art methods.
Label‐only membership inference attacks on machine unlearning without dependence of posteriors

Machine unlearning is the process through which a deployed machine learning model is enforced to forget about some of its training data items. It normally generates two machine learning models, the original model and the unlearned model, indicating training results before and after data items are deleted. However, recent studies find that machine unlearning is vulnerable to membership inference attacks—as the directivity of training and nontraining data (i.e., data items in the training set have high posterior probabilities), the attackers can utilize this property to infer whether an item has been used for original model training. Nevertheless, such attacks are incapable in label-only settings, in which the attackers are infeasible to get the posteriors. In this paper, we propose a new label-only membership inference attack scheme targeted at machine unlearning to eliminate the dependence on posteriors. Our heuristic is that injected turbulence on candidate samples will present different behaviors for training and nontraining data. Thus, in our scheme, the attacker iteratively query on the original/unlearned models and inject turbulence to change their predicting labels; it determines whether an item is having-been-delated by observing the disturbance amplitude. Extensive experiments (i.e., on MNIST, CIFAR10, CIFAR100, and STL10 data sets) show that our method achieves high inference accuracy (measured by AUC) in label-only settings, for example, AUC = 0.96 for MNIST data set. Besides, we analyze the existing countermeasures in mitigating inference attacks and find that our scheme can bypass most of them.
Prediction and evaluation of combination pharmacotherapy using natural language processing, machine learning and patient electronic health records

Combination pharmacotherapy targets key disease pathways in a synergistic or additive manner and has high potential in treating complex diseases. Computational methods have been developed to identifying combination pharmacotherapy by analyzing large amounts of biomedical data. Existing computational approaches are often underpowered due to their reliance on our limited understanding of disease mechanisms. On the other hand, observable phenotypic inter-relationships among thousands of diseases often reflect their underlying shared genetic and molecular underpinnings, therefore can offer unique opportunities to design computational models to discover novel combinational therapies by automatically transferring knowledge among phenotypically related diseases. We developed a novel phenome-driven drug discovery system, named TuSDC, which leverages knowledge of existing drug combinations, disease comorbidities, and disease treatments of thousands of disease and drug entities extracted from over 31.5 million biomedical research articles using natural language processing techniques. TuSDC predicts combination pharmacotherapy by extracting representations of diseases and drugs using tensor factorization approaches. In external validation, TuSDC achieved an average precision of 0.77 for top ranked candidates, outperforming a state of art mechanism-based method for discovering drug combinations in treating hypertension. We evaluated top ranked anti-hypertension drug combinations using electronic health records of 84.7 million unique patients and showed that a novel drug combination hydrochlorothiazide-digoxin was associated with significantly lower hazards of subsequent hypertension as compared to the monotherapy hydrochlorothiazide alone (HR: 0.769, 95% CI [0.732, 0.807]) and digoxin alone (0.857, 95% CI [0.785, 0.936]). Data-driven informatics analyses reveal that the renin-angiotensin system is involved in the synergistical interactions of hydrochlorothiazide and digoxin on regulating hypertension. The prediction model's code with PyTorch version 1.5 is available at http://nlp.case.edu/public/data/TuSDC/.
Conceptualizing Saudi women's participation in the knowledge economy: the role of education

This paper is part of a project that aims to conceptualize the knowledge economy (KE) in the Saudi context, focusing on gender in relation to education, employment, human development index, innovation, and ICT. It uses a quantitative methodology. However, the used data is secondary data collected from different government and non-government sources. Different statistical analysis methods were conducted including descriptive statistics, graphs, correlation, and trend analysis. The paper found that despite the importance placed on KE and women empowerment (Saudi Vision, 2030), gender discrepancies were observed in relation to employment, innovation and ICT. Those components, according to our data, are positively correlated with the current Saudi ranking under KEI. The paper shows that although the rate of female graduates from higher education is slightly higher than male, this difference was not translated into participation in the labor market, particularly in jobs related to KE. It was also found that though Saudi Arabia's ranking under innovation and ICT was internationally low, there was a lack of public information regarding the gender dimension. This suggests that gender was not understood as a crucial factor in improving the country's ranking under those components. The paper concludes by highlighting the national innovation system's shortfalls as reflected by education, patents, and innovation. It suggests further investigations into utilizing women under jobs related to KE and calls for including the gender dimension as a variable in any future planning or studies related to the knowledge economy.
Multispectral discrimination of spectrally similar hydrothermal minerals in mafic crust: A 5000 km2 ASTER alteration map of the Oman–UAE ophiolite

Multispectral remote sensing of hydrothermal alteration in volcanogenic massive sulfide (VMS) ore systems in mafic crust is relatively uncommon, in part due to the short-wave infrared spectral similarity of several key alteration minerals: epidote, chlorite, actinolite, and serpentine. In this study, we developed regional mosaic generation and classification workflows for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery to discriminate these minerals over the entire crust of the Semail ophiolite (Oman–UAE). Spectral discrimination was achieved through adaptation of the ASTER (pre-)processing workflow to the specific mapping targets, available datasets, and location of this study. Necessary steps included the pre-selection of ASTER scenes without residual atmospheric water features, mosaic normalization based solely on overlapping target outcrops, correcting cross-mosaic ramp errors, and alteration map classification based on image-derived reference data. The resulting alteration map, validated through comparison with field mapping and sampling, is the most areally extensive continuous survey of hydrothermal alteration yet presented for oceanic crust, providing a renewed framework for research and mineral exploration of Earth's largest ophiolite. Our map confirms that the vast majority of the upper oceanic crust is regionally altered to a spilite type secondary mineral assemblage. Localized areas of epidosite alteration, marking focused hydrothermal flow paths, are confined to the upper oceanic crust, whereas areas of previously unrecognized but intense actinolite alteration are common in both the lower and upper oceanic crust. Our methodological developments expand the standard considerations necessary for regional geological mapping using infrared image mosaics. They further demonstrate the underappreciated capability of multispectral data for mapping spectrally similar rock types. Although the specifics of the method are necessarily optimized for the Oman–UAE ophiolite, re-optimization based on local reference data should allow similar results to be achieved in other well-exposed mafic-hosted VMS districts.
The virtual machines scheduling strategy based on M/M/<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e806" altimg="si1.svg"><mml:mi>c</mml:mi></mml:math> queueing model with vacation

To enhance the energy savings of cloud systems and ensure the quality of service, energy saving measures was proposed for virtual machines (VMs) in cloud data centers. Considering the disadvantages of synchronous vacation and asynchronous vacation and the energy consumption caused by frequent VM state switching. In this study, a policy combining synchronous vacation and asynchronous vacation is proposed. The scheduling policy is modeled as a two-dimensional Markov stochastic model with multiple servers. Then, the analytical expressions of the equations are obtained using the quasi-birth-and-death process and the matrix-geometric solution, such as the average delay of tasks, the average number of busy state VMs and the average number of idle state VMs. Eventually, numerical experiments demonstrate that the strategy can decrease the energy consumption of the system effectively.
Revisiting the debate: Are code metrics useful for measuring maintenance effort?

Evaluating and predicting software maintenance effort using source code metrics is one of the holy grails of software engineering. Unfortunately, previous research has provided contradictory evidence in this regard. The debate is still open: as a community we are not certain about the relationship between code metrics and maintenance impact. In this study we investigate whether source code metrics can indeed establish maintenance effort at the previously unexplored method level granularity. We consider $\sim $ 730K Java methods originating from 47 popular open source projects. After considering seven popular method level code metrics and using change proneness as a maintenance effort indicator, we demonstrate why past studies contradict one another while examining the same data. We also show that evaluation context is king. Therefore, future research should step away from trying to devise generic maintenance models and should develop models that account for the maintenance indicator being used and the size of the methods being analyzed. Ultimately, we show that future source code metrics can be applied reliably and that these metrics can provide insight into maintenance effort when they are applied in a judiciously context-sensitive manner.