Reviewerly/exHarmony huggingface dataset:
Electromagnetic and Power Side-Channel Analysis: Advanced Attacks and Low-Overhead Generic Countermeasures through White-Box Approach

Electromagnetic and power side-channel analysis (SCA) provides attackers a prominent tool to extract the secret key from the cryptographic engine. In this article, we present our cross-device deep learning (DL)-based side-channel attack (X-DeepSCA) which reduces the time to attack on embedded devices, thereby increasing the threat surface significantly. Consequently, with the knowledge of such advanced attacks, we performed a ground-up white-box analysis of the crypto IC to root-cause the source of the electromagnetic (EM) side-channel leakage. Equipped with the understanding that the higher-level metals significantly contribute to the EM leakage, we present STELLAR, which proposes to route the crypto core within the lower metals and then embed it within a current-domain signature attenuation (CDSA) hardware to ensure that the critical correlated signature gets suppressed before it reaches the top-level metal layers. CDSA-AES256 with local lower metal routing was fabricated in a TSMC 65 nm process and evaluated against different profiled and non-profiled attacks, showing protection beyond 1B encryptions, compared to ∼10K for the unprotected AES. Overall, the presented countermeasure achieved a 100× improvement over the state-of-the-art countermeasures available, with comparable power/area overheads and without any performance degradation. Moreover, it is a generic countermeasure and can be used to protect any crypto cores while preserving the legacy of the existing implementations.
Binding data mining and expert knowledge for one-day-ahead prediction of hourly global solar radiation

A new methodology to predict one-day-ahead hourly solar global radiation is proposed in this paper. This information is very useful to address many real problems; for instance, energy-market decision making is one of the contexts where that information is essential to ensure the correct integration of grid-connected photovoltaic solar systems. The developed methodology is based on the contribution of different experts to obtain improved data-driven models when included in the data mining process. The modelling phase, when models are induced and new patterns can be identified, is the one that most benefits from that expert knowledge. In this case, it is achieved by combining clustering, regression and classification methods that exploit meteorological data (directly measured or predicted by weather services). The developed models have been embedded in a prediction system that offers reliable forecasts on next-day hourly global solar radiation. As a result of the automatic learning process including the knowledge of different experts, 14 different types of day were identified based on the shape of hourly solar radiation throughout a day. The conventional definitions of types of days, that usually consider 4 options, are updated with this new proposal. The next-day prediction of hourly global radiation is obtained in two phases: in the first one, the next-day type is obtained from among the 14 possible types of day; in the second one, values of hourly global radiation are obtained using the centroid of the predicted type of day and extraterrestrial solar radiation. The relative root mean square error of the prediction model is less than 20%, meaning a significant reduction compared to previous models. Moreover, the proposed models can be recognized in the context of eXplainable Artificial Intelligence.
A Knowledge-Driven Multimedia Retrieval System Based on Semantics and Deep Features

In recent years the information user needs have been changed due to the heterogeneity of web contents which increasingly involve in multimedia contents. Although modern search engines provide visual queries, it is not easy to find systems that allow searching from a particular domain of interest and that perform such search by combining text and visual queries. Different approaches have been proposed during years and in the semantic research field many authors proposed techniques based on ontologies. On the other hand, in the context of image retrieval systems techniques based on deep learning have obtained excellent results. In this paper we presented novel approaches for image semantic retrieval and a possible combination for multimedia document analysis. Several results have been presented to show the performance of our approach compared with literature baselines.
Run-Time Assurance for Learning-Based Aircraft Taxiing

Aircraft systems that include learning-enabled components (LECs) and their software implementations are not amenable to verification and certification using current methods. We have produced a demonstration of a run-time assurance architecture based on a neural network aircraft taxiing application that shows how several advanced technologies could be used to ensure safe operation.
Educational Convergence with Digital Technology: Integrating a Global Society

The current society of change places the human being in a situation of new learning in emerging moments, therefore, it is necessary to place the teacher and the student in this new social paradigm. The objective of the present study was directed towards understanding the role of the educational technological society as an integrated component for the promotion of citizenship within virtual spaces. The methodology used in this study had a quantitative approach, this being a research with a descriptive level and a non-experimental and field design; where it was selected a sample of forty (40) faculty from three (3) universities in the city of Barranquilla for data collection, which was supported by a 120 question questionnaire related to the use of information technologies in education and the promotion of values within these environments. The results allowed to recognize that within the educational institutions that made part of the sample there is an optimal level in the use of information technologies, as well as in the promotion of the values in these institutions by teachers. The article concludes with a reflection of how the web spaces of today are the door for new generations to be more interconnected and informed, nourishing themselves in their growth process as individuals and citizens.
Adversarial robustness and attacks for multi-view deep models

Recent work has highlighted the vulnerability of many deep machine learning models to adversarial examples. It attracts increasing attention to adversarial attacks, which can be used to evaluate the security and robustness of models before they are deployed. However, to our best knowledge, there is no specific research on the adversarial robustness and attacks for multi-view deep models. Based on the fact that adversarial examples generalize well among different models, this paper takes the adversarial attack on the multi-view convolutional neural network as an example to investigate the adversarial robustness of multi-view deep models, and further proposes effective multi-view adversarial attacks. This paper proposes two strategies, two-stage attack (TSA) and end-to-end attack (ETEA), to attack against well-trained multi-view models. With the mild assumption that the single-view model on which the target multi-view model is based is known, we first propose the TSA strategy. The main idea of TSA is to attack the multi-view model with adversarial examples generated by attacking the associated single-view model, by which state-of-the-art single-view attack methods are directly extended to the multi-view scenario. Then we further propose the ETEA strategy where the multi-view model is provided publicly. The ETEA is applied to accomplish direct attacks on the target multi-view model, where we develop three effective multi-view attack methods. Extensive experimental results show that multi-view models are more robust than single-view models and demonstrate the effectiveness of the proposed multi-view adversarial attacks.
Tie Your Embeddings Down: Cross-Modal Latent Spaces for End-to-end Spoken Language Understanding

End-to-end (E2E) spoken language understanding (SLU) systems can infer the semantics of a spoken utterance directly from an audio signal. However, training an E2E system remains a challenge, largely due to the scarcity of paired audio-semantics data. In this paper, we consider an E2E system as a multi-modal model, with audio and text functioning as its two modalities, and use a cross-modal latent space (CMLS) architecture, where a shared latent space is learned between the ‘acoustic’ and ‘text’ embeddings. We propose using different multi-modal losses to explicitly align the acoustic embedding to the text embeddings (obtained via a semantically powerful pre-trained BERT model) in the latent space. We train the CMLS model on two publicly available E2E datasets and one internal dataset, across different cross-modal losses. Our proposed triplet loss function achieves the best performance. It achieves a relative improvement of 22.1% over an E2E model without a cross-modal space and a relative improvement of 2.8% over a previously published CMLS model using L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> loss on our internal dataset.
Desmoking Laparoscopy Surgery Images Using an Image-to-Image Translation Guided by an Embedded Dark Channel

In this paper, a method to remove the smoke effects in laparoscopic images is presented.The proposed method is based on an image-to-image conditional generative adversarial network endowed with a dark channel's embedded guide mask.The obtained experimental results were evaluated and quantitatively compared with desmoking state-of-art methods using the Peak Signal-to-Noise Ratio (PSNR) metrics and Structural Similarity (SSIM) index.Those results throw an improved performance compared with relevant works.Also, the processing time required by our method is 92 frames per second; a processing time that sets the foundation for a possible real-time implementation in a more modest embedded system.
On the Difference between the Information Bottleneck and the Deep Information Bottleneck

Combining the Information Bottleneck model with deep learning by replacing mutual information terms with deep neural nets has proved successful in areas ranging from generative modelling to interpreting deep neural networks. In this paper, we revisit the Deep Variational Information Bottleneck and the assumptions needed for its derivation. The two assumed properties of the data $X$, $Y$ and their latent representation $T$ take the form of two Markov chains $T-X-Y$ and $X-T-Y$. Requiring both to hold during the optimisation process can be limiting for the set of potential joint distributions $P(X,Y,T)$. We therefore show how to circumvent this limitation by optimising a lower bound for $I(T;Y)$ for which only the latter Markov chain has to be satisfied. The actual mutual information consists of the lower bound which is optimised in DVIB and cognate models in practice and of two terms measuring how much the former requirement $T-X-Y$ is violated. Finally, we propose to interpret the family of information bottleneck models as directed graphical models and show that in this framework the original and deep information bottlenecks are special cases of a fundamental IB model.
Quality-aware skill translation models for expert finding on StackOverflow

StackOverflow has become an emerging resource for talent recognition in recent years. While users exploit technical language on StackOverflow, recruiters try to find the relevant candidates for jobs using their own terminology. This procedure implies a gap which exists between recruiters and candidates terms. Due to this gap, the state-of-the-art expert finding models cannot effectively address the expert finding problem on StackOverflow. We propose two translation models to bridge this gap. The first approach is a statistical method and the second is based on word embedding approach. Utilizing several translations for a given query during the scoring step, the result of each intermediate query is blended together to obtain the final ranking. Here, we propose a new approach which takes the quality of documents into account in scoring step. We have made several observations to visualize the effectiveness of the translation approaches and also the quality-aware scoring approach. Our experiments indicate the following: First, while statistical and word embedding translation approaches provide different translations for each query, both can considerably improve the recall. Besides, the quality-aware scoring approach can improve the precision remarkably. Finally, our best proposed method can improve the MAP measure up to 46% on average, in comparison with the state-of-the-art expert finding approach.
UAV Path Planning Based on Dynamic Programming Algorithm On Photogrammetric DEMs

The aim of this paper is to propose a three-dimensional path planning algorithm based on dynamic programming to plan optimal trajectories for an Unmanned Aerial Vehicle (UAV) on a Digital Elevation Model (DEM) map. An RGB camera is employed to obtain point cloud data of the map using WebODEM. The point cloud data are processed in to a digital elevation map using GRASS-GIS. Then, dynamic programming approach is used to find an optimal path from a starting location to a target location. The effectiveness and practicality of this paper work are demonstrated by the simulation test results.
Computational approach for <scp>content‐based</scp> image retrieval of <scp>K‐similar</scp> images from brain <scp>MR</scp> image database

Abstract Content‐based medical image retrieval (CBMIR) is a mechanism to handle a huge quantity of image data generated in various medical imaging modalities. In recent years, due to the evolution of computer vision and digital imaging modalities, a large number of medical images are generated. Consequently, the task of retrieving medical images from a large image database becomes more tedious due to variation in the size and shape of the images. Hence, it is necessary to design an appropriate system for medical image retrieval. In this paper a methodology for CBMIR using features of an image such as colour, shape, and texture is proposed to represent and retrieve the images from a large database that are relevant to a given query image. This methodology is evaluated for the application of retrieving the brain MRI images of different planes (coronal, sagittal, and transverse) from a dataset of normal and demented subjects. The features are determined in terms of Grey level co‐occurrence based Haralik's features and histogram based cumulative distribution function (CDF). The image retrieval mechanism is designed using the K‐Nearest Neighbour algorithm by finding the minimum distance between query and database images. The performance parameters such as precision and recall are calculated. The average accuracy of 95.5% are obtained. The results provided ensures the capability to use it as assistive framework for radiologists in radiology image retrieval and classification.
Models for Hand Gesture Recognition using Deep Learning

According to the World Health organization, 5% of the world population, approximately 466 million people, is deaf and/or mute or has disabling hearing loss. There is often a wall of distinction between handicapped people and normal people. We communicate to share our thoughts but for a disabled person (mainly deaf and dumb), it becomes difficult to communicate. Inability to speak is considered to be a true form of disability. For such people, sign language or Braille is the only means of communication. Sign Language is a way of communication using hand gestures. However, it becomes difficult for them to communicate with others as most don't understand sign language. Hence, we aim at bridging this communication gap, between a deaf/mute person and others, by developing a system which acts as a mediator between both. We propose a hand gesture recognition system which works in 4 steps: 1. Generate a live stream of hand gestures using web-cam. 2. Form images from the video using video frames. 3. Preprocess these images. 4. Recognize sign language hand-gestures and convert into text/audio output. The system is implemented using the concepts of image processing and neural networks. We have tested the proposed models using Kaggle dataset, our dataset and a dataset formed after combining both. We propose to eliminate the ambiguity introduced in the results by inculcating variation in the background. Most of the models give similar accuracy of the test results for both plain and cluttered background.
The von Neumann Entropy for Mixed States

The Araki–Lieb inequality is commonly used to calculate the entropy of subsystems when they are initially in pure states, as this forces the entropy of the two subsystems to be equal after the complete system evolves. Then, it is easy to calculate the entropy of a large subsystem by finding the entropy of the small one. To the best of our knowledge, there does not exist a way of calculating the entropy when one of the subsystems is initially in a mixed state. For the case of a two-level atom interacting with a quantized field, we show that it is possible to use the Araki–Lieb inequality and find the von Neumann entropy for the large (infinite) system. We show this in the two-level atom-field interaction.
Incremental Learning Using a Grow-and-Prune Paradigm With Efficient Neural Networks

Deep neural networks (DNNs) have become a widely deployed model for numerous machine learning applications. However, their fixed architecture, substantial training cost, and significant model redundancy make it difficult to efficiently update them to accommodate previously unseen data. To solve these problems, we propose an incremental learning framework based on a grow-and-prune neural network synthesis paradigm. When new data arrive, the neural network first grows new connections based on the gradients to increase the network capacity to accommodate new data. Then, the framework iteratively prunes away connections based on the magnitude of weights to enhance network compactness, and hence recover efficiency. Finally, the model rests at a lightweight DNN that is both ready for inference and suitable for future grow- and-prune updates. The proposed framework improves accuracy, shrinks network size, and significantly reduces the additional training cost for incoming data compared to conventional approaches, such as training from scratch and network fine-tuning. For the LeNet-300-100 (LeNet-5) neural network architectures derived for the MNIST dataset, the framework reduces training cost by up to 64 (67), 63 (63), and 69 (73 percent) compared to training from scratch, network fine-tuning, and grow-and-prune from scratch, respectively. For the ResNet-18 architecture derived for the ImageNet dataset (DeepSpeech2 for the AN4 dataset), the corresponding training cost reductions against training from scratch, network fine-tunning, and grow-and-prune from scratch are 64 (67), 60 (62), and 72 (71 percent), respectively. Our derived models contain fewer network parameters but achieve higher accuracy relative to conventional baselines.
Fast-BCNN: Massive Neuron Skipping in Bayesian Convolutional Neural Networks

Bayesian Convolutional Neural Networks (BCNNs) have emerged as a robust form of Convolutional Neural Networks (CNNs) with the capability of uncertainty estimation. A BCNN model is implemented by adding a dropout layer after each convolutional layer in the original CNN. By executing the stochastic inferences many times, BCNNs are able to provide an output distribution that reflects the uncertainty of the final prediction. Repeated inferences in this process lead to much longer execution time, which makes it challenging to apply Bayesian technique to CNNs in real-world applications. In this study, we propose Fast-BCNN, an FPGA-based hardware accelerator design that intelligently skips the redundant computations for two types of neurons during repeated BCNN inferences. Firstly, within a sample inference, we aim to skip the dropped neurons that predetermined by dropout masks. Secondly, by leveraging the information from the first inference and dropout masks, we predict the zero neurons and skip all their corresponding computations during the following sample inferences. Particularly, an optimization algorithm is employed to guarantee the accuracy of zero neuron prediction while achieving the maximal computation reduction. To support our neuron skipping strategy at hardware level, we explore an efficient parallelism for CNN convolution to gracefully skip the corresponding computations for both types of neurons, we then propose a novel PE architecture that accommodates the parallel operation of convolution and prediction with negligible overhead. Experimental results demonstrate that our Fast-BCNN achieves 2.1~8.2× speedup and 44%~84% energy reduction over the baseline CNN accelerator.
Particle Swarm Optimization on Deep Reinforcement Learning for Detecting Social Spam Bots and Spam-Influential Users in Twitter Network

In online social networks (OSNs), detection of malicious social bots is an important research challenge to provide legitimacy of user profiles and trustworthy service ratings. Further, spam-influential users must be minimal to control the fake information-spread in OSNs. Learning from example patterns using supervised learning may not provide accurate results in cases where existing data items are biased and bot behavior dynamically changes over a period of time. Moreover, deep reinforcement learning provides improved learning by repeated interactions with the environment. However, a typical deep reinforcement leaning algorithm converges slower to find an optimal sequence of actions to reach out a goal state. In this article, we design a particle swarm optimization (PSO) based deep Q-learning algorithm for detecting social spam bots by integrating PSO with Q-value function. In addition, spam-influential users are identified using the proposed spam influence minimization model and it helps in restricting the flow of illegitimate tweets in Twitter network. Further, an influential community detection algorithm has been proposed to reduce the spreading of spam content through influential communities in Twitter network. Experimental results illustrate the efficacy of our proposed algorithms by considering two Twitter datasets and performance metrics such as precision, recall, and modularity.
Quantum annealing with capacitive-shunted flux qubits

Quantum annealing (QA) provides us with a way to solve combinatorial optimization problems. In the previous demonstration of the QA, a superconducting flux qubit (FQ) was used. However, the FQs in these demonstrations have a short coherence time such as tens of nano seconds. For the purpose to utilize quantum properties, it is necessary to use another qubit with a better coherence time. Here, we propose to use a capacitive-shunted flux qubit (CSFQ) for the implementation of the QA. The CSFQ has a few order of magnitude better coherence time than the FQ used in the QA. We theoretically show that, although it is difficult to perform the conventional QA with the CSFQ due to the form and strength of the interaction between the CSFQs, a spin-lock based QA can be implemented with the CSFQ even with the current technology. Our results pave the way for the realization of the practical QA that exploits quantum advantage with long-lived qubits.
Mixed Reality Interaction and Presentation Techniques for Medical Visualisations

Mixed, Augmented and Virtual reality technologies are burgeoning with new applications and use cases appearing rapidly. This chapter provides a brief overview of the fundamental display presentation methods; head-worn, hand-held and projector-based displays. We present a summary of visualisation methods that employ these technologies in the medical domain with a diverse range of examples presented including diagnostic and exploration, intervention and clinical, interaction and gestures, and education.