Reviewerly/exHarmony huggingface dataset:
GazeMeter: Exploring the Usage of Gaze Behaviour to Enhance Password Assessments

We investigate the use of gaze behaviour as a means to assess password strength as perceived by users. We contribute to the effort of making users choose passwords that are robust against guessing-attacks. Our particular idea is to consider also the users' understanding of password strength in security mechanisms. We demonstrate how eye tracking can enable this: by analysing people's gaze behaviour during password creation, its strength can be determined. To demonstrate the feasibility of this approach, we present a proof of concept study (N = 15) in which we asked participants to create weak and strong passwords. Our findings reveal that it is possible to estimate password strength from gaze behaviour with an accuracy of 86% using Machine Learning. Thus, we enable research on novel interfaces that consider users' understanding with the ultimate goal of making users choose stronger passwords.
RBP: a website fingerprinting obfuscation method against intelligent fingerprinting attacks

Abstract Edge computing has developed rapidly in recent years due to its advantages of low bandwidth overhead and low delay, but it also brings challenges in data security and privacy. Website fingerprinting (WF) is a passive traffic analysis attack that threatens website privacy which poses a great threat to user’s privacy and web security. It collects network packets generated while a user accesses website, and then uses a series of techniques to discover patterns of network packets to infer the type of website user accesses. Many anonymous networks such as Tor can meet the need of hide identity from users in network activities, but they are also threatened by WF attacks. In this paper, we propose a website fingerprinting obfuscation method against intelligent fingerprinting attacks, called Random Bidirectional Padding (RBP). It is a novel website fingerprinting defense technology based on time sampling and random bidirectional packets padding, which can covert the real packets distribution to destroy the Inter-Arrival Time (IAT) features in the traffic sequence and increase the difference between the datasets with random bidirectional virtual packets padding. We evaluate the defense against state-of-the-art website fingerprinting attacks in real scenarios, and show its effectiveness.
Controllable and Identity-Aware Facial Attribute Transformation

Modifying facial attributes without the paired dataset proves to be a challenging task. Previously, approaches either required supervision from a ground-truth transformed image or required training a separate model for mapping every pair of attributes. These limit the scalability of the models to accommodate a larger set of attributes since the number of models that we need to train grows exponentially large. Another major drawback of the previous approaches is the unintentional gain of the identity of the person as they transform the facial attributes. We propose a method that allows for controllable and identity-aware transformations across multiple facial attributes using only a single model. Our approach is to train a generative adversarial network (GAN) with a multitask conditional discriminator that recognizes the identity of the face, distinguishes real images from fake, as well as identifies facial attributes present in an image. This guides the generator into producing an output that is realistic while preserving the person's identity and facial attributes. Through this framework, our model also learns meaningful image representations in a lower dimensional latent space and semantically associate separate parts of the encoded vector with both the person's identity and facial attributes. This opens up the possibility of generating new faces and other transformations such as making the face thinner or chubbier. Furthermore, our model only encodes the image once and allows for multiple transformations using the encoded vector. This allows for faster transformations since it does not need to reprocess the entire image for every transformation. We show the effectiveness of our proposed method through both qualitative and quantitative evaluations, such as ablative studies, visual inspection, and face verification. Competitive results are achieved compared to the main competition (CycleGAN), however, at great space and extensibility gain by using a single model.
On Self-Supervised Multimodal Representation Learning: An Application To Alzheimer’s Disease

Introspection of deep supervised predictive models trained on functional and structural brain imaging may uncover novel markers of Alzheimer's disease (AD). However, supervised training is prone to learning from spurious features (shortcut learning), impairing its value in the discovery process. Deep unsupervised and, recently, contrastive self-supervised approaches, not biased to classification, are better candidates for the task. Their multimodal options specifically offer additional regularization via modality interactions. This paper introduces a way to exhaustively consider multimodal architectures for a contrastive self-supervised fusion of fMRI and MRI of AD patients and controls. We show that this multimodal fusion results in representations that improve the downstream classification results for both modalities. We investigate the fused self-supervised features projected into the brain space and introduce a numerically stable way to do so.
Hybrid optimization assisted deep convolutional neural network for hardening prediction in steel

Hardness is a property that prevents forced scraping or surface penetration of material surfaces against deformation. Indeed, some methods in the tradition of forecasting the mechanical properties of the steel used to recommend a new hardening forecast using a profound learning model. More particularly, an Optimized Deep Convolutional Neural Network (DCNN) framework is used that makes the prediction more accurate and precise. The input given to the model is the chemical composition of steel along with the distance from the quenched end, which directly predicts the hardening of steel as the model already knows of it. Moreover, to make the prediction more accurate, this paper aims to make a fine-tuning of Convolutional layers in DCNN. This paper suggests a new hybrid algorithm for optimal tuned, which is then hybridized Sea Lion Optimization (SLNO), Dragonfly Algorithm (DA), and Sea Lion insisted on Dragon Fly Modification (SL-DU). This is an optimal tuning. Finally, the performance of the proposed work is compared and validated over other state-of-the-art models for error measures. Finally, the performance of the adopted system was evaluated compared with other traditional systems and the results were achieved. According to the analysis, the MAE of the pattern used for distance 1.5 was 77.16%, 9.84%, 12.71%, and 23.36% better than regression, MVR, ANN, and CNN.
Image-Based Projection Labeling for Mobile Eye Tracking

The annotation of gaze data concerning investigated areas of interest (AOIs) poses a time-consuming step in the analysis procedure of eye tracking experiments. For data from mobile eye tracking glasses, the annotation effort is further increased because each recording has to be investigated individually. Automated approaches based on supervised machine learning require pre-trained categories which are hard to obtain without human interpretation, i.e., labeling ground truth data. We present an interactive visualization approach that supports efficient annotation of gaze data based on image content participants with eye tracking glasses focused on. Recordings can be segmented individually to reduce the annotation effort. Thumbnails represent segments visually and are projected on a 2D plane for a fast comparison of AOIs. Annotated scanpaths can then be interpreted directly with the timeline visualization. We showcase our approach with three different scenarios.
Generalize Ultrasound Image Segmentation Via Instant And Plug &amp; Play Style Transfer

Deep segmentation models that generalize to images with unknown appearance are important for real-world medical image analysis. Retraining models leads to high latency and complex pipelines, which are impractical in clinical settings. The situation becomes more severe for ultrasound image analysis because of their large appearance shifts. In this paper, we propose a novel method for robust segmentation under unknown appearance shifts. Our contribution is three-fold. First, we advance a one-stage plug-and-play solution by embedding hierarchical style transfer units into a segmentation architecture. Our solution can remove appearance shifts and perform segmentation simultaneously. Second, we adopt Dynamic Instance Normalization to conduct precise and dynamic style transfer in a learnable manner, rather than previously fixed style normalization. Third, our solution is fast and lightweight for routine clinical adoption. Given 400×400 image input, our solution only needs an additional 0.2 ms and 1.92M FLOPs to handle appearance shifts compared to the baseline pipeline. Extensive experiments are conducted on a large dataset from three vendors demonstrate our proposed method enhances the robustness of deep segmentation models.
An Efficient Secure System for Fetching Data From the Outsourced Encrypted Databases

Recently, database users have begun to use cloud database services to outsource their databases. The reason for this is the high computation speed and the huge storage capacity that cloud owners provide at low prices. However, despite the attractiveness of the cloud computing environment to database users, privacy issues remain a cause for concern for database owners since data access is out of their control. Encryption is the only way of assuaging users' fears surrounding data privacy, but executing Structured Query Language (SQL) queries over encrypted data is a challenging task, especially if the data are encrypted by a randomized encryption algorithm. Many researchers have addressed the privacy issues by encrypting the data using deterministic, onion layer, or homomorphic encryption. Nevertheless, even with these systems, the encrypted data can still be subjected to attack. In this research, we first propose an indexing scheme to encode the original table's tuples into bit vectors (BVs) prior to the encryption. The resulting index is then used to narrow the range of retrieved encrypted records from the cloud to a small set of records that are candidates for the user's query. Based on the indexing scheme, we then design a system to execute SQL queries over the encrypted data. The data are encrypted by a single randomized encryption algorithm, namely the Advanced Encryption Standard-Cipher-Block Chaining (AES-CBC). In the proposed scheme, we store the index values (BVs) at user's side, and we extend the system to support most of relational algebra operators, such as select, join, etc. Implementation and evaluation of the proposed system reveals that it is practical and efficient at reducing both the computation and space overhead when compared with state-of-the-art systems like CryptDB.
Performance Analysis of Cloud Computing Encryption Algorithms

Cloud computing has become an essential need in our daily life, especially in our present days after the pandemic of our era COVID-19 and the need to conduct all business and meetings from our home and remotely. The emergence of the Internet and the concepts of parallel distributed/networked computing have changed the face of the world. Also, the current situation indicates that the future of information technology will be closely related to the use of the cloud. The need has become more urgent to access resources, applications, massive storage capabilities and data processing from anywhere, flexibly and securely. To send data securely from one place to another, the data has to send in an encrypted form. In this paper, we provide a sufficient theoretical background of cloud computing and analyze the performance of the encryption algorithms (ECC and RSA) of cloud computing. Our simulation result shows that ECC performs better than RSA in total time.
Volleyball Data Analysis System and Method Based on Machine Learning

After the reform and the opening up, the economy of my country has grown rapidly and people’s lives have become better and better. As a result, there is a lot of time to pay attention to their health, which has promoted the rapid development of my country’s sports industry. Since the 2008 Beijing Olympics, the successful hosting of the Beijing Olympics has been further strengthened. With the rise of the development of sports in our country, the use of machine learning in a large amount of information can process this data and analyze it well. Based on this, this article is aimed at making volleyball players and coaches better understand the technical structure of hiking and the technique of hiking. The paper understands the characteristics of muscle activity over time and uses machine learning methods to analyze a large number of volleyball sports data. In this experiment, 12 volleyball players from a college of physical education were selected. According to the actual situation of the students’ physical fitness and skills, it is more reasonable to divide them into two arms with preswing technology (A type) group and two-arms without preswing technology (B type) group. Mainly study the volleyball spiking action, select the representative front-row 4th position strong attack and the back-row 6th position for comparison and analysis, and analyze the process from the take-off stage to the aerial shot stage in the four stages of the smash through the kinematics, dynamics, and surface electromyography parameters. Experiments have shown that for type A, the left gluteus maximus integral EMG sum value is significantly different between the front and rear rows ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> ). The discharge volume of the left gluteus maximus during the front-row spiking process is greater than that of the back-row spiking. This difference is mainly reflected in the kicking stage and the air attack stage. It shows that volleyball data analysis has a very broad prospect of exploration and application, which can create huge social and economic benefits. How to analyze kinematics is also a very demanding research project and is also part of the future analysis of sports data. Academic value and broad practical significance are important.
Machine Learning in Cardiac Imaging: Exploring the Art of Cluster Analysis

Love for impressionist art is now almost universal, but it was not so forthcoming when the art form made its debut. Indeed, the commentator Louis Leroy, who is credited with coining the term “impressionist,” originally intended it to be an insult directed at Claude Monet's Impression, Sunrise (Impression, soleil levant). Leroy wrote of this piece, “A preliminary drawing for a wallpaper pattern is more finished than this seascape.” To those used to the traditional methods of the time, this new technique represented no more than unfinished brushstrokes and a disorganized mess. But to the more open-minded observer, standing back and looking at the whole picture allowed this disorganized mess to reveal a more complete—and indeed truthful—image. Through the hodgepodge of color, the impressionists had in fact brilliantly reconstructed the basic process of how our eyes perceive various color components and synthesize them to form a whole image. A new art form was born, and the rest is history. At one time derided, impressionism came to be regarded as one of the most important developments in art history.
Magneto-optical design of anomalous Nernst thermopile

Abstract The introduction of spin caloritronics into thermoelectric conversion has paved a new path for versatile energy harvesting and heat sensing technologies. In particular, thermoelectric generation based on the anomalous Nernst effect (ANE) is an appealing approach as it shows considerable potential to realize efficient, large-area, and flexible use of heat energy. To make ANE applications viable, not only the improvement of thermoelectric performance but also the simplification of device structures is essential. Here, we demonstrate the construction of an anomalous Nernst thermopile with a substantially enhanced thermoelectric output and simple structure comprising a single ferromagnetic material. These improvements are achieved by combining the ANE with the magneto-optical recording technique called all-optical helicity-dependent switching of magnetization. Our thermopile consists only of Co/Pt multilayer wires arranged in a zigzag configuration, which simplifies microfabrication processes. When the out-of-plane magnetization of the neighboring wires is reversed alternately by local illumination with circularly polarized light, the ANE-induced voltage in the thermopile shows an order of magnitude enhancement, confirming the concept of a magneto-optically designed anomalous Nernst thermopile. The sign of the enhanced ANE-induced voltage can be controlled reversibly by changing the light polarization. The engineering concept demonstrated here promotes effective utilization of the characteristics of the ANE and will contribute to realizing its thermoelectric applications.
Statistical methods and models based on quality of experience distributions

Abstract Due to biased assumptions on the underlying ordinal rating scale in subjective Quality of Experience (QoE) studies, Mean Opinion Score (MOS)-based evaluations provide results, which are hard to interpret and can be misleading. This paper proposes to consider the full QoE distribution for evaluating, reporting, and modeling QoE results instead of relying on MOS-based metrics derived from results based on ordinal rating scales. The QoE distribution can be represented in a concise way by using the parameters of a multinomial distribution without losing any information about the underlying QoE ratings, and even keeps backward compatibility with previous, biased MOS-based results. Considering QoE results as a realization of a multinomial distribution allows to rely on a well-established theoretical background, which enables meaningful evaluations also for ordinal rating scales. Moreover, QoE models based on QoE distributions keep detailed information from the results of a QoE study of a technical system, and thus, give an unprecedented richness of insights into the end users’ experience with the technical system. In this work, existing and novel statistical methods for QoE distributions are summarized and exemplary evaluations are outlined. Furthermore, using the novel concept of quality steps, simulative and analytical QoE models based on QoE distributions are presented and showcased. The goal is to demonstrate the fundamental advantages of considering QoE distributions over MOS-based evaluations if the underlying rating data is ordinal in nature.
The Power of Linked Eye Movement Data Visualizations

In this paper we showcase several eye movement data visualizations and how they can be interactively linked to design a flexible visualization tool for eye movement data. The aim of this project is to create a user-friendly and easy accessible tool to interpret visual attention patterns and to facilitate data analysis for eye movement data. Hence, to increase accessibility and usability we provide a web-based solution. Users can upload their own eye movement data set and inspect it from several perspectives simultaneously. Insights can be shared and collaboratively be discussed with others. The currently available visualization techniques are a 2D density plot, a scanpath representation, a bee swarm, and a scarf plot, all supporting several standard interaction techniques. Moreover, due to the linking feature, users can select data in one visualization, and the same data points will be highlighted in all active visualizations for solving comparison tasks. The tool also provides functions that make it possible to upload both, private or public data sets, and can generate URLs to share the data and settings of customized visualizations. A user study showed that the tool is understandable and that providing linked customizable views is beneficial for analyzing eye movement data.
Database workload characterization with query plan encoders

Smart databases are adopting artificial intelligence (AI) technologies to achieve instance optimality , and in the future, databases will come with prepackaged AI models within their core components. The reason is that every database runs on different workloads, demands specific resources, and settings to achieve optimal performance. It prompts the necessity to understand workloads running in the system along with their features comprehensively, which we dub as workload characterization. To address this workload characterization problem, we propose our query plan encoders that learn essential features and their correlations from query plans. Our pretrained encoders captures the structural and the computational performance of queries independently. We show that our pretrained encoders are adaptable to workloads that expedites the transfer learning process. We performed independent assessments of structural encoder and performance encoders with multiple downstream tasks. For the overall evaluation of our query plan encoders, we architect two downstream tasks (i) query latency prediction and (ii) query classification. These tasks show the importance of feature-based workload characterization. We also performed extensive experiments on individual encoders to verify the effectiveness of representation learning, and domain adaptability.
Deep Semi-Supervised Bias Field Correction Of Mr Images

A bias field is an artifact inherent to MRI scanners which is manifested by a smooth intensity variation across the scans. We present an innovative generative approach to address the inverse problem of bias field estimation and removal in a semi-supervised manner. The key contribution is the construction of a compound framework of four interacting, adversarial neural networks. Specifically, we simultaneously train a pair of neural networks, one for the reconstruction of the plain bias field and the other for the reconstruction of a bias-free MRI scan, such that the output of each together with the input biased scans define the loss of the other network. A third network, trained as a bias-field discriminator provides an additional loss to the bias field generator while an MRI segmentation network provides an additional loss to the bias-free MRI generator. We trained and validated our framework using real MRI scans with simulated bias fields and tested it on publicly available brain data-sets as well as private data yielding results competitive with state-of-the-art methods. Code is available upon request.