Reviewerly/exHarmony huggingface dataset:
Understanding the Privacy Dimension of Wearables through Machine Learning-enabled Inferences

To keep up with the ever-growing user expectations, manufacturers and developers keep adding new features, including input/output (I/O) interfaces, to augment the use cases of wearable technologies, such as fitness trackers, augmented reality head-mounted devices (AR HMDs), and smart watches, without considering their security and privacy implications. In this talk, we will discuss some of our recent results on understanding the privacy dimension of wearable technologies through inference attacks facilitated by advances in machine learning. Our attacks target unconventional and new I/O mechanisms that allow an adversary to breach application-specific features. First, we will present an exploration of the attack surface introduced by fitness trackers [1, 4]. We propose an inference attack that breaches location privacy through the elevation profiles collected by fitness trackers. Our attack highlights that adversaries can infer the location from elevation profiles collected via fitness trackers. Second, we will review the attack surface introduced by smartwatches [5]. For that, we develop an inference attack that exploits the smartwatch microphone to capture the acoustic emanations of physical keyboards and successfully infers what the user has been typing. Third, we will present an exploration of AR HMDs security [2, 3, 6]. We design an inference attack that exploits the geometric projection of hand movements in the air. The attack framework predicts the typed text on an in-air tapping keyboard, which is only visible to the user. We will conclude with lessons learned, defense directions, and open research directions.
Toward a New World in Scholarly Communication: The 9th PUBMET2022 Conference on Scholarly Communication in the Context of Open Science

Open access has emerged from the need to make scholarly communication freely available to the scientific community and not hidden behind a paywall [...]
A Quantitative Analysis of Non-Profiled Side-Channel Attacks Based on Attention Mechanism

In recent years, the deep learning method has emerged as a mainstream approach to non-profiled side-channel attacks. However, most existing methods of deep learning-based non-profiled side-channel attack rely on traditional metrics such as loss and accuracy, which often suffer from unclear results in practical scenarios. Furthermore, most previous studies have not fully considered the properties of power traces as long time-series data. In this paper, a novel non-profiled side-channel attack architecture is proposed, which incorporates the attention mechanism and derives a corresponding attention metric. By attaching the attention mechanism after the network layers, the attention mechanism provides a quantitative prediction of correct key. Moreover, this architecture can effectively extract and analyze the features from long power traces. The success rate on different datasets is at least 86%, which demonstrates the superior reliability of this architecture compared to other works when facing various countermeasures and noise. Notably, even in scenarios where traditional loss and accuracy metrics fail to provide reliable results, the proposed attention metric remains capable of accurately distinguishing the correct key.
Demonstrating Arena-Web: A Web-based Development and Benchmarking Platform for Autonomous Navigation Approaches

In recent years, mobile robot navigation approaches have become increasingly important due to various application areas ranging from healthcare to warehouse logistics.In particular, Deep Reinforcement Learning approaches have gained popularity for robot navigation but are not easily accessible to non-experts and complex to develop.In recent years, efforts have been made to make these sophisticated approaches accessible to a wider audience.In this paper, we present Arena-Web, a web-based development and evaluation suite for developing, training, and testing DRL-based navigation planners for various robotic platforms and scenarios.The interface is designed to be intuitive and engaging to appeal to non-experts and make the technology accessible to a wider audience.With Arena-Web and its interface, training and developing Deep Reinforcement Learning agents is simplified and made easy without a single line of code.The web-app is free to use and openly available at https://app.arena-rosnav.com.
Applying Few-Shot Learning for In-the-Wild Camera-Trap Species Classification

Few-shot learning (FSL) describes the challenge of learning a new task using a minimum amount of labeled data, and we have observed significant progress made in this area. In this paper, we explore the effectiveness of the FSL theory by considering a real-world problem where labels are hard to obtain. To assist a large study on chimpanzee hunting activities, we aim to classify various animal species that appear in our in-the-wild camera traps located in Senegal. Using the philosophy of FSL, we aim to train an FSL network to learn to separate animal species using large public datasets and implement the network on our data with its novel species/classes and unseen environments, needing only to label a few images per new species. Here, we first discuss constraints and challenges caused by having in-the-wild uncurated data, which are often not addressed in benchmark FSL datasets. Considering these new challenges, we create two experiments and corresponding evaluation metrics to determine a network’s usefulness in a real-world implementation scenario. We then compare results from various FSL networks, and describe how factors may affect a network’s potential real-world usefulness. We consider network design factors such as distance metrics or extra pre-training, and examine their roles in a real-world implementation setting. We also consider additional factors such as support set selection and ease of implementation, which are usually ignored when a benchmark dataset has been established.
Domain Knowledge Graph Question Answering Based on Semantic Analysis and Data Augmentation

Information retrieval-based question answering (IRQA) and knowledge-based question answering (KBQA) are the main forms of question answering (QA) systems. The answer generated by the IRQA system is extracted from the relevant text but has a certain degree of randomness, while the KBQA system retrieves the answer from structured data, and its accuracy is relatively high. In the field of policy and regulations such as household registration, the QA system requires precise and rigorous answers. Therefore, we design a QA system based on the household registration knowledge graph, aiming to provide rigorous and accurate answers for relevant household registration inquiries. The QA system uses a semantic analysis-based approach to simplify one question into a simple problem consisting of a single event entity and a single intention relationship, and quickly generates accurate answers by searching in the household registration knowledge graph. Due to the scarcity and imbalance of QA corpus data in the field of household registration, we use GPT3.5 to augment the collected questions dataset and explore the impact of data augmentation on the QA system. The experiment results show that the accuracy rate of the QA system using the augmented dataset reaches 93%, which is 6% higher than before.
Imaging Characteristics of Disturbance Flow Field Surrounding a Hypersonic Target

The disturbance flow field arises naturally with hypersonic target flying in near space. In situations where traditional infrared and radar systems lose effectiveness, space-based optical detection of this surrounding flow can serve as an alternative method for detecting high-speed targets. This paper presents a remote sensing imaging analysis of the disturbance flow field surrounding a hypersnoic target at different flight altitudes and Mach numbers. Utilizing Fourier Optics and Background-Oriented Schlieren, in conjunction with the fourth-order Runge-Kutta ray tracing algorithm, the imaging blurring and imaging deviation of three typical backgrounds under the influence of the disturbance flow field are obtained. Additionally, the study analyzes the influence of flight conditions and parameters of the imaging system on the imaging characteristics, and provides optical design recommendations. The results indicate that the presence of disturbance flow fields leads to varying degrees of visually apparent blurring effects and indiscernible deviation effects on the background images. Furthermore, the profiles of the disturbance flow field are extracted, in agreement with current experimental research. This study verifies the feasibility of space-based optical detection of hypersonic targets through disturbance flow field remote sensing imaging and contributes to the advancement of imaging research in this field.
Trace Element Analysis of Pyrite and Arsenopyrite Using the LA-ICPMS Technique in Pulai, Central Belt of Peninsular Malaysia

The Pulai gold deposit is one of the most promising gold prospects in the Central Belt of Peninsular Malaysia. It is found within the Permian-Triassic Gua Musang sequence of metasediments and metavolcanics and in a structurally controlled NE-SW major fault. Various ore minerals, including pyrite, arsenopyrite, chalcopyrite, sphalerite, pyrrhotite, and galena are typically associated with this deposit. Four types of pyrite (Pyrite 1, Pyrite 2, Pyrite 3, and Pyrite 4) and two types of arsenopyrite (Arsenopyrite 1 and Arsenopyrite 2) were characterised based on their morphological and textural differences. In this region, gold occurs as refractory gold in the nanoparticle form and in the state of Au+ within the structure of sulphides of variable concentrations. Through the detailed laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS) trace element mapping analysis of pyrite and arsenopyrite, the main Au-bearing sulphides were found within vein-hosted Pyrite 4 and Arsenopyrite 2 during late phase mineralisation, while Pyrite 3 had the lowest Au concentration. Two phases of Au enrichment were recorded in Pyrite 4, mainly in the core (2 to 11.7 ppm; average 1.4 ppm) and margin of the grain (0.3 to 8.8 ppm; average 1.2 ppm), whereas the highest Au content was detected in the core of Arsenopyrite 2 (0.3 to 137.1 ppm; mean 31.9 ppm). The enrichment of Au is associated with As, forming a zoning elemental pattern distribution. Other trace elements, including Co, Ni, Sb, Pb, Bi, Cu, and Zn, show systematic variation in their composition between the various types of pyrite and arsenopyrite. For early-phase sulphides, the Au enrichment localised at the margin of Pyrite 2 and Arsenopyrite 1, together with Co–Ni, Pb–Bi–Sb, and Ag in the same oriented pattern, suggesting the remobilisation and redistribution of Au in sulfides. Meanwhile, the late crystallisation phase of vein-hosted sulphides formed a rich Au–As ore zoning pattern in the core of Arsenopyrite 2 and Pyrite 4. The second phase of Au enrichment continued at the margin of Pyrite 4 through the remobilisation and precipitation of Au together with Ni, Co, Sb, Pb, Bi, Ag, and Cu. Subsequent deformation then reactivated the late fluid system with the enrichment of Sr, Ba, Rb, Ag, and Zn along the fractures and outermost rim of Pyrite 4 and Rb–Sr–Ba–Pb–Bi along the rim of Arsenopyrite 2. The Pulai gold prospect is interpreted as an orogenic-style gold mineralisation where arsenic can be used as an indicator for proximity to ore mineralisation in exploration.
Universal Adversarial Training Using Auxiliary Conditional Generative Model-Based Adversarial Attack Generation

While Machine Learning has become the holy grail of modern-day computing, it has many security flaws that have yet to be addressed and resolved. Adversarial attacks are one of these security flaws, in which an attacker appends noise to data samples that machine learning models take as input with the aim of fooling the model. Various adversarial training methods have been proposed that augment adversarial examples in the training dataset for defense against such attacks. However, a general limitation exists where a robust model can only protect itself against adversarial attacks that are known or similar to those it was trained on. To address this limitation, this paper proposes a Universal Adversarial Training algorithm using adversarial examples generated by an Auxiliary Classifier Generative Adversarial Network (AC-GAN) in parallel with other data augmentation techniques, such as the mixup method. This method builds on a previously proposed technique, Adversarial Training, in which adversarial examples produced by gradient-based methods are augmented and added to the training data. Our method improves the AC-GAN architecture for adversarial example generation to make it more suitable for adversarial training by updating different loss terms and testing its performance against various attacks compared to other robust adversarial models. In this way, it becomes apparent that generative models are better suited for boosting adversarial robustness through adversarial training. When tested using various attack types, our proposed model had an average accuracy of 97.48% on the MNIST dataset and 94.02% on the CelebA dataset, proving that generative models have a higher chance of boosting adversarial security through adversarial training.
Control of a qubit under Markovian and non-Markovian noise

An essential action in quantum information processing is the manipulation (control) of a single qubit, ideally a closed two-level system. However, in realistic applications, quantum processes are often under the influence of the external environment, e.g., presenting some degree of dissipation and decoherence. In this paper we address the emerging difficulties in the (tracking) quantum control of a two-level system under the influence of both Markovian and non-Markovian noise. We employ a same framework to treat both situations, a Lindblad-type equation, but considering that for the former (latter) case, the decay rate $\mathrm{\ensuremath{\Gamma}}$ is time independent (dependent). We discuss the conditions leading to a breakdown of the quantum control and eventual ways to overcome the problem, like employing a fast control scheme or controlling the off-diagonal terms of the system density matrix. Surprisingly, for Markovian noise such breakdown time decreases with $\mathrm{\ensuremath{\Gamma}}$ not as an exponential but as a power law. This indicates that the quantum control should be possible for a coupling between the system and the environment stronger than previously expected. Moreover, we find that for non-Markovian noise, the breakdown time is longer when there is backflow, i.e., $\mathrm{\ensuremath{\Gamma}}(t)$ can be negative. The present theoretical results point to certain favorable scenarios to operate qubits even in a noisy medium.
Human Action Representation Learning Using an Attention-Driven Residual 3DCNN Network

The recognition of human activities using vision-based techniques has become a crucial research field in video analytics. Over the last decade, there have been numerous advancements in deep learning algorithms aimed at accurately detecting complex human actions in video streams. While these algorithms have demonstrated impressive performance in activity recognition, they often exhibit a bias towards either model performance or computational efficiency. This biased trade-off between robustness and efficiency poses challenges when addressing complex human activity recognition problems. To address this issue, this paper presents a computationally efficient yet robust approach, exploiting saliency-aware spatial and temporal features for human action recognition in videos. To achieve effective representation of human actions, we propose an efficient approach called the dual-attentional Residual 3D Convolutional Neural Network (DA-R3DCNN). Our proposed method utilizes a unified channel-spatial attention mechanism, allowing it to efficiently extract significant human-centric features from video frames. By combining dual channel-spatial attention layers with residual 3D convolution layers, the network becomes more discerning in capturing spatial receptive fields containing objects within the feature maps. To assess the effectiveness and robustness of our proposed method, we have conducted extensive experiments on four well-established benchmark datasets for human action recognition. The quantitative results obtained validate the efficiency of our method, showcasing significant improvements in accuracy of up to 11% as compared to state-of-the-art human action recognition methods. Additionally, our evaluation of inference time reveals that the proposed method achieves up to a 74× improvement in frames per second (FPS) compared to existing approaches, thus showing the suitability and effectiveness of the proposed DA-R3DCNN for real-time human activity recognition.
Managing Complex Knowledge in Sustainable Planning: A Semantic-Based Model for Multiagent Water-Related Concepts

The concepts of green infrastructures, nature-based solutions and ecosystem services are today considered an integral part of the broader theme of the urban bioregion, with an intrinsic character of complexity. It is certainly difficult to structure bioregional processes in a balanced and sustainable way, able to keep local energy production and consumption cycles closed. It is a complex issue of knowledge bases, and problems are increased by the participatory dimension of environmental planning. In fact, when rational planning models have failed in the face of prominent individual needs and environmental complexity, a path has emerged towards the inclusion of multiple citizens’ and stakeholders’ knowledge. The cognitive structure of the plans has thus changed from systems of exclusively expert, formal knowledge to systems of diffused, multi-agent knowledge. This has involved richness but also significant problems in understanding and managing knowledge bases. In this complexity, there are some common peculiarities when it comes to socio-environmental systems. A common feature of the reference domains of ecosystem services, nature-based solutions and green infrastructures is the water resource. A management model of hydrological data, which are structurally relevant and cross-sectoral in environmental planning actions, could represent a flagship initiative. The used approach could be conveyed to more complex and extensive areas of the environmental domain in a perspective of sustainable planning. The present paper is part of a research work oriented toward handling complex environmental subjects, such as green infrastructures, nature-based solutions or ecosystem services, with a knowledge modelling approach. This approach is based on semantic extensions, elaborated form the concept of semantic web, to allow shared interpretations of knowledge coming from different languages and scientific domains. It is also based on using applied ontologies, elaborated from the concept of ontology-based classification, to support a structured organization of knowledge contents. The main research objective is therefore to investigate about a knowledge management system with semantic extensions, populated with hydrological knowledge contents, as well as to propose a preliminary functional architecture. A simple ontology of data is extracted, aiming at clarifying and improving inter-domain communication, so as to enhance a common semantic understanding in a complex environmental system.
Sudden death of entanglement with a Hamiltonian ensemble assisted by auxiliary qubits

In this paper, we theoretically propose a method to simulate the longitudinal relaxation of a single qubit by coupling it to an auxiliary qubit. In order to mimic the finite-temperature relaxation, we utilize the Hamiltonian-ensemble approach [Kropf, Gneiting, and Buchleitner, Phys. Rev. X 6, 031023 (2016)] and in each realization the auxiliary qubit possesses a random level spacing. The longitudinal relaxation arises as a consequence of the ensemble average and the interaction between the working qubit and the auxiliary qubit. Furthermore, we apply this approach to investigate the influence of the longitudinal relaxation and the transverse relaxation on the entanglement dynamics of two qubits. It is discovered that the sudden death of the entanglement will occur as long as the longitudinal relaxation is present. The transverse relaxation assists the longitudinal relaxation and thus accelerates the finite-time disentanglement.
MILP Modeling of S-box: Divide and Merge Approach

Mixed Integer Linear Programming (MILP) solvers are used to ascertain the resistance of block ciphers against the differential attack. This requires the translation of active S-box minimization and probability optimization problems into the MILP model. The possible and impossible transitions in the difference distribution table (DDT) are used to generate the linear inequalities for the S-box. There are a large number of linear inequalities in this set that need to be minimized to construct an efficient MILP model. At ToSC 2020, Boura and Coggia presented an approach to minimize the set of linear inequalities, but the complexity to get an optimal solution is high due to the trade-off between time complexity and optimal solution. In this paper, we aim to optimize this trade-off and propose an algorithm based on the divide and merge approach. We experimented with various configurations of the proposed algorithm for 4-bit and 5-bit S-boxes and compared the results. The results show that the divide and merge approach outperforms the existing approach with large advantage factors.
Existence of Global Minimizer for Elastic Variational Obstacle Avoidance Problems on Riemannian Manifolds

This work is devoted to studying existence of global minimizers for optimal control problems with obstacle avoidance. We show the existence of global extrema in the general setting of Riemannian manifolds. This is a problem that consists of minimizing a suitable energy functional among a set of admissible configurations. The given energy functional depends on state variables (trajectory), control variables (velocity, covariant acceleration), and on an artificial potential function used for avoiding obstacles.
A User Authentication Schema Under the Integration of Mobile Edge Computing and Blockchain Technology

The safe and efficient authentication of users is the basis for the realization of the Internet of Things. A reliable and fast user authentication schema that combines mobile edge computing and blockchain technology is proposed. This schema adopts the polling hosting method to optimize the practical Byzantine Fault Tolerance (PBFT) algorithm, it reduces the impact of tokens in the algorithm, and ensures trusted authentication. The two-way authentication protocol between the cluster head and the base station, and the one-way authentication protocol between the base station and the sensor node are designed to effectively simplify the authentication process of sensor nodes, which further guarantees the security and speed of user authentication and effectively meets the security, reliability, and convenience requirements of the Internet of Things. Simulation experiments show that the schema can achieve efficient information verification, and the identity authentication time and protocol authentication times are only 23.58 ms and 25.06 ms, respectively, which has obvious performance advantages over the Paillier algorithm, the hybrid Paillier-blowfish algorithm, and the ElGamal algorithm.