Reviewerly/exHarmony huggingface dataset:
Near Field Air-Gap Covert Channel Attack

Air-gapped systems are isolated from the Internet due to the sensitive information they handle.This paper presents a new covert channel attack that enables the leaking of sensitive information from highly isolated, air-gapped systems to nearby mobile phones. Malware running on an air-gapped computer can generate radio waves by executing crafted code on the target system. The malicious code exploits the dynamic power consumption of modern computers and manipulates the momentary loads on CPU cores. With this technique, malware can control the computer's internal utilization and generate low-frequency electromagnetic radiation in the 0-60 kHz band. Sensitive information (e.g., files, encryption keys, biometric data, and keylogging) can be modulated over the emanated signals and received by a nearby mobile phone at a max speed of 1000 bit/sec. We show that a standard smartphone with a simple antenna carried by a malicious insider or visitor can be used as a covert receiver. Finally, we present a set of countermeasures to this air-gap attack.
A Trusted, Verifiable and Differential Cyber Threat Intelligence Sharing Framework using Blockchain

Cyber Threat Intelligence (CTI) is the knowledge of cyber and physical threats that help mitigate potential cyber attacks. The rapid evolution of the current threat landscape has seen many organisations share CTI to strengthen their security posture for mutual benefit. However, in many cases, CTI data contains attributes (e.g., software versions) that have the potential to leak sensitive information or cause reputational damage to the sharing organisation. While current approaches allow restricting CTI sharing to trusted organisations, they lack solutions where the shared data can be verified and disseminated ‘differentially’ (i.e., selective information sharing) with policies and metrics flexibly defined by an organisation. In this paper, we propose a blockchain-based CTI sharing framework that allows organisations to share sensitive CTI data in a trusted, verifiable and differential manner. We discuss the limitations associated with existing approaches and highlight the advantages of the proposed CTI sharing framework. We further present a detailed proof of concept using the Ethereum blockchain network. Our experimental results show that the proposed framework can facilitate the exchange of CTI without creating significant additional overheads.
Development of Blockchain-based e-Voting System: Requirements, Design and Security Perspective

Elections make a fundamental contribution to democratic governance and are very important to select the appropriate person to determine the fate of a nation while a large number of citizens do not trust the ballot-paper-based voting system. As a result, e-Voting is being adopted by various countries throughout the world. With advantages, there are a number of constraints of e-voting systems, a single vulnerability can lead to large-scale manipulations of voting results. Leveraging and intersecting both cutting-edge technologies including biometric and blockchain technology can address the limitations of current e-Voting frameworks. In this research, we analyze the requirements for systems modeling of voting technology and possible opportunities to adopt biometrics and blockchain technology. Based on the requirements analysis, we propose a biometric-enabled and hyperledger fabric-based voting framework to automate identity verification that will ensure transparency and security of electronic voting. We utilize the software architecture analysis method and active reviews for intermediate designs for evaluating the proposed framework. Demonstration indicates the efficiency and quality attributes of architectural design that shall lead us to implement the system in real-world scenarios in future studies. Initially, we develop a Webbased prototype to demonstrate the framework. In future studies, we aim to implement on large scale and evaluate the prototype against the requirements and security standard.
Privacy-Preserving Data Analysis without Trusted Third Party

With the spread of IoT devices, various data about our lives are being collected, such as heart rate, physical activity, number of steps, pulse, oxygen intake, calorie consumption, etc. If these data can be analyzed, it will be possible to learn the signs of disease. However, it is dangerous for a person’s activity status to be managed on an external server with the view of privacy. To solve this problem, local differential privacy (LDP), which is a technique for randomly adding local noise to data, has been proposed. While ensuring privacy by LDP is certainly important, it degrades the usefulness of the analysis of data with added noise. In this paper, we propose a new mechanism to protect data privacy in both phases of training and testing based on LDP. We also make sure feasibility of our mechanism in two cases of breast cancer screening data and ionosphere data set.
AR Museum: A Virtual Museum using Marker less Augmented Reality System for Mobile Devices

As the most effective hand-held devices are readily available in the world, while not the latest topic, Augmented Reality (AR) has become one of the fastest growing research topics. These robust devices can calculate large amounts of data in the blink of an eye. Enabling the integration of manufactured, interactive, and graphic computing in the real world to enhance the in-depth user experience. In this paper, we developed a Mobile Augmented Reality Experiences [1] through an application that allows users to explore and interact with virtual museum locally using marker-less AR. till now this work is not been done in this field. Using this AR technology, it enables museums to exhibit a whole collection of artifacts in limitedspace. Typically, in conventional artifacts exhibitions, the artifacts are protected in galleries which require wide spaces, and this causes only part of the artifacts to be exhibited. Thus, by digitizing the artifacts, there is no need to put the virtual artifacts in the gallery since it is a virtual model. Consequently, available space can be used to display the other artifacts to the public. The app was developed using the Google AR Core SDK and Unity 3D and will serve as a useful tool for producing a museum anytime and anywhere, giving the user virtual stimulation of museum.
BareSkinNet: De‐makeup and De‐lighting via 3D Face Reconstruction

Abstract We propose BareSkinNet, a novel method that simultaneously removes makeup and lighting influences from the face image. Our method leverages a 3D morphable model and does not require a reference clean face image or a specified light condition. By combining the process of 3D face reconstruction, we can easily obtain 3D geometry and coarse 3D textures. Using this information, we can infer normalized 3D face texture maps (diffuse, normal, roughness, and specular) by an image‐translation network. Consequently, reconstructed 3D face textures without undesirable information will significantly benefit subsequent processes, such as re‐lighting or re‐makeup. In experiments, we show that BareSkinNet outperforms state‐of‐the‐art makeup removal methods. In addition, our method is remarkably helpful in removing makeup to generate consistent high‐fidelity texture maps, which makes it extendable to many realistic face generation applications. It can also automatically build graphic assets of face makeup images before and after with corresponding 3D data. This will assist artists in accelerating their work, such as 3D makeup avatar creation.
Eye‐Tracking‐Based Prediction of User Experience in VR Locomotion Using Machine Learning

Abstract VR locomotion is one of the most important design features of VR applications and is widely studied. When evaluating locomotion techniques, user experience is usually the first consideration, as it provides direct insights into the usability of the locomotion technique and users' thoughts about it. In the literature, user experience is typically measured with post‐hoc questionnaires or surveys, while users' behavioral (i.e., eye‐tracking) data during locomotion, which can reveal deeper subconscious thoughts of users, has rarely been considered and thus remains to be explored. To this end, we investigate the feasibility of classifying users experiencing VR locomotion into L‐UE and H‐UE (i.e., low‐ and high‐user‐experience groups) based on eye‐tracking data alone. To collect data, a user study was conducted in which participants navigated a virtual environment using five locomotion techniques and their eye‐tracking data was recorded. A standard questionnaire assessing the usability and participants' perception of the locomotion technique was used to establish the ground truth of the user experience. We trained our machine learning models on the eye‐tracking features extracted from the time‐series data using a sliding window approach. The best random forest model achieved an average accuracy of over 0.7 in 50 runs. Moreover, the SHapley Additive exPlanations (SHAP) approach uncovered the underlying relationships between eye‐tracking features and user experience, and these findings were further supported by the statistical results. Our research provides a viable tool for assessing user experience with VR locomotion, which can further drive the improvement of locomotion techniques. Moreover, our research benefits not only VR locomotion, but also VR systems whose design needs to be improved to provide a good user experience.
What Remains from Covid-19? Agile Software Development in Hybrid Work Organization: A Single Case Study

Context: The Covid-19 pandemic led to a comprehensive transformation of how organizations operate and people work. Many companies, especially in the software development area, sent their employees to work from home in order to contain the spread of the virus. Nowadays, after two years of the pandemic, the companies introduce new regulations concerning the work organization for their employees. We notice a high variety of work organization types in practice, like work from home, work from anywhere, onsite work or mixed approaches. While some organizations integrated approaches like work from anywhere, others specify rules for part-time organizations onsite and work from home. This situation leads to new challenges for agile software development teams, as the agile practices in use are originally designed for onsite work and focusing on social aspects like collaboration. Also, the adaptions of specific agile practices due to the remote work during Covid-19 by the teams may do not cover the new circumstances of hybrid (distributed) work settings, while some team members work from home, others onsite in the office. Method: We conducted a quantitative survey in two agile software development teams in one company operating in a work from anywhere setting. Results: The results of this study show that the team members prefer to work remotely in future. However, some team members also want to work one or two days per week onsite in the office. This heterogeneous preferences of the workplace will lead to hybrid work organization and thus, to new challenges and advantages for agile software development team members. Conclusion: Based on our results, we discuss the needs of agile software development teams working in hybrid settings concerning equipment in the office, new software tools supporting the agile approach in use and the adaption of agile practices. Furthermore, we provide practical implications related to the need for sustainable flexibility on employee side concerning selecting the workplace.
DNN Surgery: Accelerating DNN Inference on the Edge through Layer Partitioning

Recent advances in deep neural networks have substantially improved the accuracy and speed of various intelligent applications. Nevertheless, one obstacle is that DNN inference imposes a heavy computation burden on end devices, but offloading inference tasks to the cloud causes a large volume of data transmission. Motivated by the fact that the data size of some intermediate DNN layers is significantly smaller than that of raw input data, we designed the DNN surgery, which allows partitioned DNN to be processed at both the edge and cloud while limiting the data transmission. The challenge is twofold: (1) Network dynamics substantially influence the performance of DNN partition, and (2) State-of-the-art DNNs are characterized by a directed acyclic graph rather than a chain, so that partition is incredibly complicated. To solve the issues, We design a Dynamic Adaptive DNN Surgery(DADS) scheme, which optimally partitions the DNN under different network conditions. We also study the partition problem under the cost-constrained system, where the resource of the cloud for inference is limited. Then, a real-world prototype based on the selif-driving car video dataset is implemented, showing that compared with current approaches, DNN surgery can improve latency up to 6.45 times and improve throughput up to 8.31 times. We further evaluate DNN surgery through two case studies where we use DNN surgery to support an indoor intrusion detection application and a campus traffic monitor application, and DNN surgery shows consistently high throughput and low latency.
RelaHash: Deep Hashing With Relative Position

Deep hashing has been widely used as a solution to encoding binary hash code for approximating nearest neighbor problem. It has been showing superior performance in terms of its ability to index high-level features by learning compact binary code. Many recent state-of-the-art deep hashing methods often use multiple loss terms at once, thus introducing optimization difficulty and may result in sub-optimal hash codes. OrthoHash [1] was proposed to replace those losses with just a single loss function. However, the quantization error minimization problem in OrthoHash is still not addressed effectively. In this paper, we take one step further - propose a single-loss model that can effectively minimize the quantization error without explicit loss terms. Specifically, we introduce a new way to measure the similarity between the relaxed codes with centroids, called relative similarity. The relative similarity is the similarity between the relative position representation of continuous codes and the normalized centroids. The resulting model outperforms many state-of-the-art deep hashing models on popular benchmark datasets.
Fast and Efficient Image Generation Using Variational Autoencoders and K-Nearest Neighbor OveRsampling Approach

Researchers gravitate towards Generative Adversarial Networks (GAN) to create artificial images. However, GANs suffer from convergence issues, mode collapse, and overall complexity in balancing the Nash Equilibrium. Images generated are often distorted, rendering them useless. We propose a combination of Variational Autoencoders (VAEs) and a statistical oversampling method called K-Nearest Neighbor OveRsampling (KNNOR) to create artificial images. This combination of VAE and KNNOR results in more life-like images with reduced distortion. We fine-tune several pre-trained networks on a separate set of real and fake face images to test images generated by our method against images generated by conventional Deep Convolutional GANs (DCGANs). We also compare the combination of VAEs and Synthetic Minority Oversampling Technique (SMOTE) to establish the efficacy of KNNOR against naive oversampling methods. Not only are our methods better able to convince the classifiers that the images generated are authentic, but the models are also half in size of DCGANs. The code is available at GitHub for public use.
Efficient Person Reidentification for IoT-Assisted Cyber–Physical Systems

The main objective of this study is to propose a cyber–physical system (CPS)-based person reidentification (P-ReID) framework for smart surveillance. The Internet of Things (IoT)-based interconnected vision sensors in smart cities are considered essential elements of a CPS, and contribute significantly to urban security. However, the reidentification of targeted persons using emerging edge AI techniques still faces certain challenges. To improve efficiency at the edge and overcome the traditional sensing of video cameras, we employed an AI-based P-ReID framework for CPS that is functional in IoT environments. In addition, we present dual attention dilated network (DADNet), which integrates an energy-efficient convolutional neural network (CNN) with a self-attention module to substantially improve the person matching probability. Furthermore, we applied dual feature fusion to intelligently integrate discriminative and robust features using early and late fusion strategies that allow DADNet to significantly consider the foreground and marginally utilize the background information. Furthermore, we impose diversity orthogonality regularization over several CNN layers, which boosts the performance of DADNet, resulting in an appropriate usage over IoT networks. A comprehensive set of ablation studies, comparison with other state-of-the-art approaches, and a time complexity analysis confirm the strength of our DADNet for reidentification tasks in AI-enabled IoT settings that are well suited for a CPS.
Using data‐driven phenotyping to investigate the impact of sex on <scp>3D</scp> human facial surface morphology

The effects of sex on human facial morphology have been widely documented. Because sexual dimorphism is relevant to a variety of scientific and applied disciplines, it is imperative to have a complete and accurate account of how and where male and female faces differ. We apply a comprehensive facial phenotyping strategy to a large set of existing 3D facial surface images. We investigate facial sexual dimorphism in terms of size, shape, and shape variance. We also assess the ability to correctly assign sex based on shape, both for the whole face and for subregions. We applied a predefined data-driven segmentation to partition the 3D facial surfaces of 2446 adults into 63 hierarchically linked regions, ranging from global (whole face) to highly localized subparts. Each facial region was then analyzed with spatially dense geometric morphometrics. To describe the major modes of shape variation, principal components analysis was applied to the Procrustes aligned 3D points comprising each of the 63 facial regions. Both nonparametric and permutation-based statistics were then used to quantify the facial size and shape differences and visualizations were generated. Males were significantly larger than females for all 63 facial regions. Statistically significant sex differences in shape were also seen in all regions and the effects tended to be more pronounced for the upper lip and forehead, with more subtle changes emerging as the facial regions became more granular. Males also showed greater levels of shape variance, with the largest effect observed for the central forehead. Classification accuracy was highest for the full face (97%), while most facial regions showed an accuracy of 75% or greater. In summary, sex differences in both size and shape were present across every part of the face. By breaking the face into subparts, some shape differences emerged that were not apparent when analyzing the face as a whole. The increase in facial shape variance suggests possible evolutionary origins and may offer insights for understanding congenital facial malformations. Our classification results indicate that a high degree of accuracy is possible with only parts of the face, which may have implications for biometrics applications.
Fast and Accurate Deep Leakage from Gradients Based on Wasserstein Distance

Shared gradients are widely used to protect the private information of training data in distributed machine learning systems. However, Deep Leakage from Gradients (DLG) research has found that private training data can be recovered from shared gradients. The DLG method still has some issues such as the “Exploding Gradient,” low attack success rate, and low fidelity of recovered data. In this study, a Wasserstein DLG method, named WDLG, is proposed; the theoretical analysis shows that under the premise that the output layer of the model has a “bias” term, predicting the “label” of the data by whether the “bias” is “negative” or not is independent of the approximation of the shared gradient, and thus, the label of the data can be recovered with 100% accuracy. In the proposed method, the Wasserstein distance is used to calculate the error loss between the shared gradient and the virtual gradient, which improves model training stability, solves the “Exploding Gradient” phenomenon, and improves the fidelity of the recovered data. Moreover, a large learning rate strategy is designed to improve model training convergence speed in-depth. Finally, the WDLG method is validated on datasets from MNIST, Fashion MNIST, SVHN, CIFAR-100, and LFW. Experiments results show that the proposed WDLG method provides more stable updates for virtual data, a higher attack success rate, faster model convergence, higher image fidelity during recovery, and support for designing large learning rate strategies.
An integrated adaptive Kalman filter for improving the reliability of navigation systems

Abstract Integrated GPS/INS using Kalman filter is the best technique for improving navigation accuracy. Assuming that the covariance matrices are known and constant, a conventional Kalman filter (CKF) is usually used, however, when they are unknown and time-varying, several adaptive estimation approaches have to be developed to estimate the statistical information of the measurement ( R ), process ( Q ), and state ( P ) covariance matrices. In many situations, blunders/faults in the measurement model and/or sudden changes in the dynamic model may occur during the navigation period. Therefore, the CKF, as well as the adaptive Kalman filter (AKF) will exhibit abnormal behavior and may lead the filter to be suboptimal or even diverge. In this study, the Sage-Husa adaptive Kalman filter (SHAKF) and innovation-based adaptive Kalman filter (IAKF) approaches are employed for adapting the measurement covariance matrix( R ). In the case of abrupt changes in the dynamic model, the state covariance matrix ( P ) is adapted using the strong tracking filter (STF). The performance of these adaptive approaches is evaluated before and after simulating a fault of different sizes in the measurement and dynamic models. The results show that with a large window width, the SHAKF outperforms the CKF and IAKF. However, when the system encounters any fault either in the measurement or dynamic model, the SHAKF loses its optimality and diverges. The sensitivity of the SHAKF to the fault is because the R matrix accumulates with the propagation of the recursive noise estimator. On the other hand, the IAKF and STF provide better performance than both the CKF and SHAKF because the gain matrix is adaptively adjusted to mitigate the influence of the fault, and therefore, they behave normally when a fault of any size occurs in the measurement and/or dynamic model.