Reviewerly/exHarmony huggingface dataset:
Encoding Power Traces as Images for Efficient Side-Channel Analysis

Side-Channel Attacks (SCAs) are a powerful method to attack implementations of cryptographic algorithms. State-of-the-art techniques such as template attacks and stochastic models usually require a lot of manual preprocessing and feature extraction by the attacker. Deep Learning (DL) methods have been introduced to simplify SCAs and simultaneously lowering the amount of required side-channel traces for a successful attack. However, the general success of DL is largely driven by their capability to classify images, a field in which they easily outperform humans. In this paper, we present a novel technique to interpret 1D traces as 2D images. We show and compare several techniques to transform power traces into images, and apply these on different implementations of the Advanced Encryption Standard (AES). By allowing the neural network to interpret the trace as an image, we are able to significantly reduce the number of required attack traces for a correct key guess. We also demonstrate that the attack efficiency can be improved by using multiple 2D images in the depth channel as an input. Furthermore, by applying image-based data augmentation, we show how the number of profiling traces is reduced by a factor of 50 while simultaneously enhancing the attack performance. This is a crucial improvement, as the amount of traces that can be recorded by an attacker is often very limited in real-life applications.
Artificial intelligence and robotic technologies in tourism and hospitality industry

Artificial intelligence applications and robotic technologies, which are rapidly spreading and widely used throughout the world, are discussed by different disciplines in the literature. The field of tourism draws attention as one of the disciplines in which studies on these issues have been carried out in recent years. In this context, robots come to the fore in the application areas of the tourism sector. However, it is known that there are many artificial intelligence applications that are becoming widespread or likely to become widespread day by day in the tourism sector. From this point of view, in this conceptual study, firstly artificial intelligence applications and robotic technologies were evaluated, the development of these technologies was revealed, then the current technologies used in the tourism and hospitality industry were examined, and as a result, the future of these technologies in the tourism and hospitality industry was discussed. In this context, it can be said that this study, in which the current situation is revealed and sector-experienced writers make inferences for the future, is an important study that can contribute to the literature and industry practitioners.
Atlas-ISTN: Joint segmentation, registration and atlas construction with image-and-spatial transformer networks

Deep learning models for semantic segmentation are able to learn powerful representations for pixel-wise predictions, but are sensitive to noise at test time and may lead to implausible topologies. Image registration models on the other hand are able to warp known topologies to target images as a means of segmentation, but typically require large amounts of training data, and have not widely been benchmarked against pixel-wise segmentation models. We propose the Atlas Image-and-Spatial Transformer Network (Atlas-ISTN), a framework that jointly learns segmentation and registration on 2D and 3D image data, and constructs a population-derived atlas in the process. Atlas-ISTN learns to segment multiple structures of interest and to register the constructed atlas labelmap to an intermediate pixel-wise segmentation. Additionally, Atlas-ISTN allows for test time refinement of the model's parameters to optimize the alignment of the atlas labelmap to an intermediate pixel-wise segmentation. This process both mitigates for noise in the target image that can result in spurious pixel-wise predictions, as well as improves upon the one-pass prediction of the model. Benefits of the Atlas-ISTN framework are demonstrated qualitatively and quantitatively on 2D synthetic data and 3D cardiac computed tomography and brain magnetic resonance image data, out-performing both segmentation and registration baseline models. Atlas-ISTN also provides inter-subject correspondence of the structures of interest.
Development of an Intraoperative Pipeline for Holographic Mixed Reality Visualization During Spinal Fusion Surgery

Objective. Holographic mixed reality (HMR) allows for the superimposition of computer-generated virtual objects onto the operator's view of the world. Innovative solutions can be developed to enable the use of this technology during surgery. The authors developed and iteratively optimized a pipeline to construct, visualize, and register intraoperative holographic models of patient landmarks during spinal fusion surgery. Methods. The study was carried out in two phases. In phase 1, the custom intraoperative pipeline to generate patient-specific holographic models was developed over 7 patients. In phase 2, registration accuracy was optimized iteratively for 6 patients in a real-time operative setting. Results. In phase 1, an intraoperative pipeline was successfully employed to generate and deploy patient-specific holographic models. In phase 2, the registration error with the native hand-gesture registration was 20.2 ± 10.8 mm (n = 7 test points). Custom controller-based registration significantly reduced the mean registration error to 4.18 ± 2.83 mm (n = 24 test points, P < .01). Accuracy improved over time (B = -.69, P < .0001) with the final patient achieving a registration error of 2.30 ± .58 mm. Across both phases, the average model generation time was 18.0 ± 6.1 minutes (n = 6) for isolated spinal hardware and 33.8 ± 8.6 minutes (n = 6) for spinal anatomy. Conclusions. A custom pipeline is described for the generation of intraoperative 3D holographic models during spine surgery. Registration accuracy dramatically improved with iterative optimization of the pipeline and technique. While significant improvements and advancements need to be made to enable clinical utility, HMR demonstrates significant potential as the next frontier of intraoperative visualization.
GAN-Based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer’s Disease Diagnosis

Unsupervised learning can discover various diseases, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer’s Disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with disease stages. Therefore, we propose a two-step method using Generative Adversarial Network-based multiple adjacent brain MRI slice reconstruction to detect AD at various stages: (Reconstruction) Wasserstein loss with Gradient Penalty $$+$$ $$\ell _1$$ loss—trained on 3 healthy slices to reconstruct the next 3 ones—reconstructs unseen healthy/AD cases; (Diagnosis) Average/Maximum loss (e.g., $$\ell _2$$ loss) per scan discriminates them, comparing the reconstructed/ground truth images. The results show that we can reliably detect AD at a very early stage with Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) 0.780 while also detecting AD at a late stage much more accurately with ROC-AUC 0.917; since our method is fully unsupervised, it should also discover and alert any anomalies including rare disease.
Quantum correlations and non-classical properties for two superconducting qubits interacting with a quantized field in the context of deformed Heisenberg algebra

In the present work, we examine qualitatively the entanglement and parameter estimation in a two superconducting qubits (SC-qubits) coupled to a nonlinear field in the existence of time-varying coupling. We show the effects of the nonlinearity of the field and time-varying coupling on the evolution of such quantities of current interests by considering the von Neumann entropy, entanglement of formation and quantum Fisher information (QFI). Moreover, we explain the relationship between the information quantifiers during the dynamics. The obtained results can suggest new prospects to explore and understand the nonlinearity phenomena on the behaviour of the information quantifiers in SC-qubits.
Research on environmental landscape design based on virtual reality technology and deep learning

Virtual Reality (VR) provides immersive visualization and intuitive interaction. The VR is used to enable any biomedical profession to develop a deep learning (DL) model for image classification. The Deep Neural Network (DNN) models can be used as a powerful tool for data analysis, but they are also challenging to understand and develop. To make deep learning more convenient and fast operation, it have established a landscape of DNN development environment based on virtual reality. In this environment, users can move concrete objects only to build neural networks with their own hands. It automatically transforms these configurations into a trainable model and reports on the real-time test dataset. In addition to realizing the insights users are developing into DNN models, it has also visually enriched the virtual environmental landscape objects with the parts of the model. In this way bridge the gap between professionals in different disciplines, providing a new perspective on the model analysis and data interaction. This system further demonstrates that learning and visualization technologies developed in Shenzhen can benefit from integrating virtual reality.
A Multi-User Ciphertext Policy Attribute-Based Encryption Scheme with Keyword Search for Medical Cloud System

Population aging is currently a tough problem of many countries. How to utilize modern technologies (including both information and medical technologies) to improve the service quality of health information is an important issue. Personal Health Record (PHR) could be regarded as a kind of health information records of individuals. A ciphertext policy attribute-based encryption (CP-ABE) is a cryptographic primitive for fine-grained access control of outsourced data in clouds. In order to enable patients to effectively store his medical records and PHR data in medical clouds, we propose an improved multi-user CP-ABE scheme with the functionality of keyword search which enables data users to seek for specific ciphertext in the cloud server by using a specific keyword. Additionally, we adopt an independent proxy server in the proposed system architecture to isolate the communication between clients and the cloud server, so as to prevent cloud servers from suffering direct attacks and also reduce the computational loading of cloud servers. Compared with the previous approach, the proposed encryption algorithm takes less running time and the ciphertext length is also relatively short. Moreover, the procedures of re-encryption and pre-decryption only require one exponentiation computation, respectively.
Real-time Relevant Recommendation Suggestion

Users of recommendation systems usually focus on one topic at a time. When finishing reading an item, users may want to access more relevant items related to the last read one as extended reading. However, conventional recommendation systems are hard to provide the continuous extended reading function of these relevant items, since the main recommendation results should be diversified. In this paper, we propose a new task named recommendation suggestion, which aims to (1) predict whether users want extended reading, and (2) provide appropriate relevant items as suggestions. These recommended relevant items are arranged in a relevant box and instantly inserted below the clicked item in the main feed. The challenge of recommendation suggestion on relevant items is that it should further consider semantic relevance and information gain besides CTR-related factors. Moreover, the real-time relevant box insertion may also harm the overall performance when users do not want extended reading. To address these issues, we propose a novel Real-time relevant recommendation suggestion (R3S) framework, which consists of an Item recommender and a Box trigger. We extract features from multiple aspects including feature interaction, semantic similarity and information gain as different experts, and propose a new Multi-critic multi-gate mixture-of-experts (M3oE) strategy to jointly consider different experts with multi-head critics. In experiments, we conduct both offline and online evaluations on a real-world recommendation system with detailed ablation tests. The significant improvements in item/box related metrics verify the effectiveness of R3S. Moreover, we have deployed R3S on WeChat Top Stories, which affects millions of users. The source codes are in https://github.com/modriczhang/R3S.
Toward Lightweight In-situ Self-reporting

In-situ self-reporting is an important measurement method used for capturing daily experience data right-in-the-moment in dynamic contexts. Research has been conducted to reduce the demand placed on users for manually reporting data in context. In this regard, smartwatches offer inherent benefits for making self-reporting more convenient and facilitate data gathering. However, self-reporting on the small touchscreen under various contextual conditions can be burdensome and challenging. In this study, to gain insights into designing smartwatch-based self-report interfaces, we conducted an exploratory user study with eight design probes and twenty-four participants under three simulated scenarios: walking, gaming, and social chatting. Findings showed that users' subjective perception of interface features (e.g., input methods and option layouts) varied with changes in context. Participants leveraged different features (e.g., hierarchical layout and discrete input) to micro-schedule self-report tasks (i.e., create one or multiple opportune moments) or to conduct eyes-free interaction with the assistance of smartwatch attributes (e.g., the physical frame of a smartwatch). We discuss implications for smartwatch-based self-report interface designs by considering context and designing interface features to support users' coping strategies.
Permissioned blockchain based public procurement system

Hundreds of public procurement projects are undertaken every day all over the country. The tenders for these projects are given to the winning contractor in an auction-like setting which have massive security issues. After a contractor wins a tender, the specifics of the progress of the work done are rarely properly monitored. The details of the finances spent on the project can be easily manipulated. To enable integrity, non-repudiation and immutability to the data requires the desirable technology to support the above requirements. Hence, the proposed system uses blockchain technology to provide transparency and trust to all parties involved in the network. The entire system consists of two modules such as the Tender Bidding system and Tender Monitoring system using a multi-organization blockchain network in the Hyperledger Fabric. The whole bidding process is improved by creating a decentralized descending auction system that will carry it out fairly and transparently. The Tender Monitoring system employs a custom endorsement policy to attain 100% consensus for attesting every transaction made regarding the progress of the project so that vital steps are ratified and recorded with evidence supporting their integrity. The main aspects of the system, its many components, deployment and drawbacks are viewed.
A Predictive Analysis Model of Customer Purchase Behavior using Modified Random Forest Algorithm in Cloud Environment

Prediction analysis of customer purchase behavior is an interesting and challenging task in modern-day life. Our objective is to introduce the concept of machine learning using a random forest algorithm in depth. In this paper, a model has been proposed for predicting which cloud services have been purchased on a number of factors. A random forest model is built using different parameters such as advertisement click sequence, previously purchased cloud services, etc. and training our model. For the execution of this proposed model, an advertisement log dataset has been taken and the necessary modifications have been done on it. As an outcome, for a customer, this proposed model is providing high accuracy in prediction. Five factors have been considered which affect purchasing decision-making of customers in cloud services, such as previous purchasing habits, a sequence of advertisements viewed, customer location, etc..
BranchSpec: Information Leakage Attacks Exploiting Speculative Branch Instruction Executions

Recent studies on attacks exploiting processor hardware vulnerabilities have raised significant concern for information security. Particularly, transient execution attacks such as Spectre augment microarchitectural side channels with speculative executions that lead to exfiltration of secretive data not intended to be accessed. Many prior works have demonstrated the manipulation of branch predictors for triggering speculative executions, and thereafter leaking sensitive information through processor microarchitectural components. In this paper, we present a new class of microarchitectural attack, called BranchSpec, that performs information leakage by exploiting state changes of branch predictors in speculative path. Our key observation is that, branch instruction executions in speculative path alter the states of branch pattern history, which are not restored even after the speculatively executed branches are eventually squashed. Unfortunately, this enables adversaries to harness branch predictors as the transmitting medium in transient execution attacks. More importantly, as compared to existing speculative attacks (e.g., Spectre), BranchSpec can take advantage of much simpler code patterns in victim's code base, making the impact of such exploitation potentially even more severe. To demonstrate this security vulnerability, we have implemented two variants of BranchSpec attacks: a side channel where a malicious spy process infers cross-boundary secrets via victim's speculatively executed nested branches, and a covert channel that communicates secrets through intentionally perturbing the branch pattern history structure via speculative branch executions. Our evaluation on Intel Skylake- and Coffee Lake-based processors reveals that these information leakage attacks are highly accurate and successful. To the best of our knowledge, this is the first work to reveal the information leakage threat due to speculative state update in branch predictor. Our studies further broaden the attack surface of processor microarchitecture, and highlight the needs for branch prediction mechanisms that are secure in transient executions.
Spectral Unmixing for Mapping a Hydrothermal Field in a Volcanic Environment Applied on ASTER, Landsat-8/OLI, and Sentinel-2 MSI Satellite Multispectral Data: The Nisyros (Greece) Case Study

The aim of this study was to propose a methodology that provides a detailed description of the argillic zone of a hydrothermal field, based on satellite multispectral data. More specifically, we developed a method based on spectral unmixing where hydroxyl-bearing alteration is represented by a single endmember (representing clays) and the three (nearly) non-altered primary volcanic lithologies, namely, two types of lava flows (basic and acidic compositions) and the loose materials (alluvial/beach deposits, scree, pyroclastic deposits, etc.), are represented by three endmembers. We also used one endmember representing elemental sulfur that is present in fumarolic vents hosted by active hydrothermal craters. The methodology was applied in the south part of Lakki plain inside the Nisyros volcano caldera (Greece), using Sentinel-2, Landsat-8/OLI, and ASTER satellite multispectral datasets. Specifically, it was applied separately to each one of the three datasets. The spectral unmixing results, combined with the relative geological map, provide quantitative estimations of the primary volcanic and loose material areas affected by alteration. In addition, pixels with high abundance values of hydroxyl-bearing alteration corresponded to mapped areas with strong hydrothermal alteration. The developed methodology is superior to conventional approaches (e.g., alteration spectral index) in terms of its ability to describe the overall pattern of the hydrothermal field. The most accurate results were taken when applied to ASTER or Sentinel-2 MSI data.
Image and Text fusion for UPMC Food-101 using BERT and CNNs

The modern digital world is becoming more and more multimodal. Looking on the internet, images are often associated with the text, so classification problems with these two modalities are very common. In this paper, we examine multimodal classification using textual information and visual representations of the same concept. We investigate two main basic methods to perform multimodal fusion and adapt them with stacking techniques to better handle this type of problem. Here, we use UPMC Food-101, which is a difficult and noisy multimodal dataset that well represents this category of multimodal problems. Our results show that the proposed early fusion technique combined with a stacking-based approach exceeds the state of the art on the dataset used.
Gender recognition in the wild: a robustness evaluation over corrupted images

Abstract In the era of deep learning, the methods for gender recognition from face images achieve remarkable performance over most of the standard datasets. However, the common experimental analyses do not take into account that the face images given as input to the neural networks are often affected by strong corruptions not always represented in standard datasets. In this paper, we propose an experimental framework for gender recognition “in the wild”. We produce a corrupted version of the popular LFW+ and GENDER-FERET datasets, that we call LFW+C and GENDER-FERET-C, and evaluate the accuracy of nine different network architectures in presence of specific, suitably designed, corruptions; in addition, we perform an experiment on the MIVIA-Gender dataset, recorded in real environments, to analyze the effects of mixed image corruptions happening in the wild. The experimental analysis demonstrates that the robustness of the considered methods can be further improved, since all of them are affected by a performance drop on images collected in the wild or manually corrupted. Starting from the experimental results, we are able to provide useful insights for choosing the best currently available architecture in specific real conditions. The proposed experimental framework, whose code is publicly available, is general enough to be applicable also on different datasets; thus, it can act as a forerunner for future investigations.