Reviewerly/exHarmony huggingface dataset:
Data Mining for Cyber Biosecurity Risk Management – a comprehensive review

Authors:
raw_author_name: Deepa D. Shankar
raw_affiliation_string: Abu Dhabi University
raw_author_name: Adresya Suresh Azhakath
raw_affiliation_string: Danmarks Tekniske Universitet
raw_author_name: Mahalakshmi T
raw_affiliation_string: Abu Dhabi University
raw_author_name: Sheeba K
raw_affiliation_string: Abu Dhabi University

Abstract: The world is currently facing the era of Cyberbiosecurity, also known as Biocybersecurity, or Digital Biosecurity, which poses a few unique security vulnerabilities. A significant percentage of the scientific, agricultural, and health communities are still unaware of the unique security complexities that have resulted from fusion of the supply chain, infrastructure, cyber, and life and medical sciences. Measurement, analysis, and mitigation of cyberattacks on biological systems are the goals of Cyberbiosecurity. Data mining is a promising avenue for further investigation as a means of mitigating cyber-attacks for research purposes. Data mining is the process of extracting useful patterns, information, and expertise from massive datasets. In the domain of Cyberbiosecurity, data mining techniques have received little attention. The purpose of this survey on data mining in cybersecurity is to determine the state of the art in cybersecurity issues, including different types of assaults and data mining methods that could be used to address these issues. This review's findings shed insight on the characteristics of security issues as well as gaps in Cyberbiosecurity. Furthermore, the review's conclusions show that most responses to Cyberbiosecurity attacks advocate for national or international government/private organizations to raise public awareness of these attacks and equip their clients/dealers with secure methods to transmit data online. These attempts to raise awareness can be divided into four distinct levels, each with its own set of techniques and objectives, resulting in a holistic strategy to addressing the difficulties of Cyberbiosecurity. A notable gap found in this review is the absence of appropriate countermeasures against cyberattacks employing data mining in the biological systems domain such as healthcare, agriculture, biomedical research, and other domains. The goal of this review is to increase knowledge among Cyberbiosecurity experts and to develop innovative solutions by utilizing cutting-edge data mining techniques in this sector.
Improved mixture differential attacks on 6-round AES-like ciphers towards time and data complexities

Authors:
raw_author_name: Xueping Yan
raw_affiliation_string: Information Engineering University, Zhengzhou, 450001, China
raw_author_name: Lin Tan
raw_affiliation_string: Information Engineering University, Zhengzhou, 450001, China
raw_author_name: Hong Xu
raw_affiliation_string: Information Engineering University, Zhengzhou, 450001, China
raw_author_name: Wenfeng Qi
raw_affiliation_string: Information Engineering University, Zhengzhou, 450001, China

Abstract: AES is the most widely used symmetric cipher. Many cryptographic primitives are designed following the structure of AES, called AES-like ciphers. Mixture differential cryptanalysis is a structural cryptanalysis technique for AES, which gave the best key recovery attack on 5-round AES in the chosen-plaintext setting and attacks with practical data and memory complexities on round-reduced AES. In this paper, we study the mixture differential attacks on 6 rounds of AES-like ciphers, and propose two improved attacks towards optimizing time and data complexities respectively. As an application to AES, we give the improved mixture differential attack on 6 rounds of AES-128 with time complexity 262.74, reducing by a factor of 210.26 compared with the previous mixture differential attack. For low data complexity, we show the improved 6-round mixture differential attack on AES-128 with data complexity 224.05, reducing by a factor of about 4. We also apply two attacks to 6 super-rounds of Saturnin, one of the second-round candidates in NIST lightweight cryptography standardization process, which is the first security evaluation of Saturnin against mixture differential cryptanalysis.
ِA new occurrence of rift-related damtjernite (ultramafic) lamprophyre, Gebel Anweiyib area, Arabian Nubian shield: Insights from bulk rock geochemistry and remote sensing data analysis

Authors:
raw_author_name: Gehad M. Saleh
raw_affiliation_string: Nuclear Materials Authority, P. O. Box 530, El-Maadi, Cairo, Egypt
raw_author_name: Samir Z. Kamh
raw_affiliation_string: Geology Department, Faculty of Science, Tanta University, Tanta, Egypt
raw_author_name: Fathy Abdalla
raw_affiliation_string: College of Science, King Saud University, P.O. Box 2455, Riyadh, Saudi Arabia
raw_author_name: El Saeed R. Lasheen
raw_affiliation_string: Geology Department, Faculty of Science, Al-Azhar University, Cairo, P.O. Box 11884, Egypt

Abstract: The occurrence of lamprophyre dikes is being reported first time in the Gebel Anweiyib area, south Eastern Desert, Egypt. The investigation of the ultramafic lamprophyre (damtjernite) dikes intruded into the crustal younger granites and metasediments based on their spectral characteristics, mineralogical and geochemical compositions. Three multispectral sensors of Landsat-8, ASTER and S2 have been used to detect and characterize the lithologic units and the lamprophyre dikes. The reflectance and absorption signatures of the granitic rocks and lamprophyre dikes were the basis for detecting them from the remotely sensed data using the application of advanced image processing algorithms including FCC, BR, PCA, MNF, and DS. Geochemical studies inferred that the damtjernite dikes are under-saturated with SiO2 (<38.89 wt%), alkaline with total alkalis (avg. 4 wt%), metaluminous with A/CNK (0.22–0.51), and ultramafic with MgO (13.88–19.23 wt%). They exhibit unique geochemical characteristics of mantle (with high MgO, Ni, Cr, and Mg#) coupled with crustal (high Sr, Ba, Zr, Ga and REEs) sources, similar to global ultramafic lamprophyres. High TiO2 and highly fractionated REEs with low HREEs (avg. 23 ppm) relative to LREEs (avg. 354 ppm) contents, yielding residual garnet in melts. Low partial melting of primitive garnet lherzolite can account the obtained REEs in the ultramafic lamprophyres. These features signify their derivation from a deeper enriched asthenospheric (plume-type) magma like OIB, which is attested by the high Nb/La (avg. 1.32), Nb/U (avg. 102), La/Yb (avg. 43), and low Y/Nb < 0.52, Rb/Sr < 1, Rb/Ba (<0.14), and Sr/Ba (<0.9), suggesting their derivation from a mantle-derived source rather than crustal rocks. High concentrations of TiO2 and Fe2O3 in the ultramafic lamprophyres, reflecting their formation from a fertile peridotite by mantle plume-derived melts with K-residual phlogopite rather than amphibole as obtained by low Ba/Rb and high Rb/Sr ratios. The Anweiyib lamprophyres are anorogenic and confined to being derived from rifting regions, and their magma is not contaminated with crustal sources during emplacement.
A two-stage model extraction attack on GANs with a small collected dataset

Authors:
raw_author_name: Hui Sun
raw_affiliation_string: School of Computer Science, China University of Geosciences (Wuhan), 430074, Wuhan, China
raw_author_name: Tianqing Zhu
raw_affiliation_string: School of Computer Science, University of Technology Sydney, 2008, NSW, Australia
raw_author_name: Wenhan Chang
raw_affiliation_string: School of Computer Science, China University of Geosciences (Wuhan), 430074, Wuhan, China
raw_author_name: Wanlei Zhou
raw_affiliation_string: Faculty of Data Science, City University of Macau, 999078, Macao, China

Abstract: Due to their capacity for image generation, GAN models may be considered as a solution for the use of private data, which enhances their commercial value. However, unlike discriminative models such as CNNs, extraction attacks on GANs have not received significant attention, with only a few relevant works available. This paper proposes a novel two-stage extraction attack on GANs that does not require access to the victim GAN model. In the first stage, data augmentation is performed using GAN inversion techniques and vector arithmetic, tailored to scenarios with small query budgets. The second stage comprises two types of training: the innocent transfer training on unprocessed collected samples and the additional training on the augmented set. We provide three options for additional training: training of the discriminator alone (D), training of the generator alone (G), and training of both (D+G). We then conduct a quantity and quality evaluation of our extraction attacks compared to the baseline attack. Experiment results demonstrate that our proposed approach can successfully extract a GAN model, where the distribution of the extracted GAN closely resembles the distribution of the target GAN, even with a hundred generated images of the target GAN. Specifically, we achieve stable performance with better extraction, a more disentangled latent space for the extracted GAN, and significant semantic attribute editing performance using the two-stage extraction with additional training of the discriminator (D). By demonstrating that even a hundred leaked images may result in severe extraction, this study raises serious concerns regarding GAN model leakage.
Demodulation of interferometry parameters with multiple-harmonic superposition based on the DTFT and semi-blind spectral analysis

Authors:
raw_author_name: Lin Chang
raw_affiliation_string: School of Engineering, Huzhou University, Huzhou 313000, Zhejiang, China
raw_author_name: Jiehua Gao
raw_affiliation_string: School of Engineering, Huzhou University, Huzhou 313000, Zhejiang, China
raw_author_name: Yongli Wang
raw_affiliation_string: School of Engineering, Huzhou University, Huzhou 313000, Zhejiang, China
raw_author_name: Yonghao Zhou
raw_affiliation_string: Department of Precision Mechanical Engineering, Shanghai University, Shanghai 200072, China

Abstract: The core issue of multi-surface wavelength-shifting interferometry at free cavity lengths is mainly centered on adaptively matching the tuning step. To avoid harmonic aliasing and undersampling, the discrete-time Fourier transform method with global spectrum densification is used to comprehensively analyze the spectral characteristics under different sampling cases and cavity lengths, then a composite multi-surface wavelength-shifting interferometry algorithm is proposed accordingly. The proposed method includes a preprocessing algorithm and three spectral criteria to identify and avoid undesirable sampling problems for in-situ measurements. The algorithm performance is quantitatively evaluated from several aspects of the simulation analysis. A flat plate with an average thickness of 20 mm was repeatedly measured using a Fizeau wavelength-shifting interferometer in the experiments, and the PVQ and RMS errors at different measurement distances were less than 5 mm and 1 mm, respectively.
An statistical model for the short-term albedo estimation applied to PV bifacial modules

Authors:
raw_author_name: Jose Rubén Gutierrez
raw_affiliation_string: Technological Institute for Microelectronics, University of the Basque Country, 48013 Bilbao, Spain

Abstract: Albedo estimation is an essential input parameter for bifacial PV modules. However, there was no clear agreement on which albedo values should be used and how they should be measured, either with satellite measurements or using onsite measurements which can be obtained from nearby meteorological stations. Since long-term measurements are not always available for new PV system locations, short-term albedo measurements are also used as input parameters in PV system performance estimation models. Short-term albedo presents high variability due to factors such as weather, seasonality or changes in the surrounding surface among others. In addition, apparently random albedo variations of 60% can be observed, even during consecutive days or within the same day. Therefore, this study presents a two-parameter exponential model that modelates the albedo data statistical distribution with an error of less than 5% in all cases and from which it is possible to determine the reliability of the obtained data. This model has been evaluated at a set of locations, with different surface types and climates, and assessed using onsite and satellite data. The impact of the methodology used to estimate the albedo data uncertainty and its reliability has also been studied as a function of several parameters such as the global horizontal irradiance and measurement time, allowing the process optimisation and enhancing its reliability.
Let model keep evolving: Incremental learning for encrypted traffic classification

Authors:
raw_author_name: Jiang Xie
raw_affiliation_string: Zhongguancun Laboratory, Beijing, China
raw_author_name: Qige Song
raw_affiliation_string: Zhongguancun Laboratory, Beijing, China
raw_author_name: Yafei Sang
raw_affiliation_string: Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
raw_author_name: Yongzheng Zhang
raw_affiliation_string: China Assets Cybersecurity Technology CO., Ltd., Beijing, China
raw_author_name: Shuhao Li
raw_affiliation_string: Zhongguancun Laboratory, Beijing, China
raw_author_name: Tianning Zang
raw_affiliation_string: Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

Abstract: Encrypted Traffic Classification (ETC) is valuable for many network management and security solutions as it provides insights into applications active on the network. However, the network environment constantly evolves, and new applications emerge in an endless stream daily, which gradually makes well-trained ETC models ineffective. The conventional approach to adapting new applications is to re-train the models on a re-formed dataset with both pre-existing and new application samples. The major limitation is that requiring redundant computing resources and sufficient storage spaces. In this work, we propose an Incremental Learning (IL) framework based on multi-view sequences fusion, MISS, to keep ETC models evolving with new applications. The key novelty of MISS is three-fold: extract cross-view information from multi-view sequences to capture sufficient knowledge; propose an exemplar selection algorithm from communication patterns to reduce redundant consumption; design a pair of branches from the learnability of parameters to mitigate accuracy loss during evolution. MISS outperforms the existing IL methods of ETC, and the state-of-the-art ETC models using the classic IL framework, on the real-world network traffic datasets, which achieves satisfactory improvements of 11.37%↑ and 1.58%↑. Furthermore, we comprehensively perform incremental experiments to evaluate the evolution ability of MISS, which is able to select representative exemplars of old applications, counteract the adverse effects of homogeneous applications, and keep evolving with unknown applications.
Third-party software library migration at the method-level using multi-objective evolutionary search

Authors:
raw_author_name: Niranjana Deshpande
raw_affiliation_string: Rochester Institute of Technology, Rochester, NY, USA
raw_author_name: Mohamed Wiem Mkaouer
raw_affiliation_string: Rochester Institute of Technology, Rochester, NY, USA
raw_author_name: Ali Ouni
raw_affiliation_string: ETS Montreal, University of Quebec, Montreal, QC, Canada
raw_author_name: Naveen Sharma
raw_affiliation_string: Rochester Institute of Technology, Rochester, NY, USA

Abstract: Software developers commonly use third-party software libraries to reduce implementation efforts and mitigate errors in their source code while building high-quality and reliable software. To support software evolution, newer libraries are released periodically with added features, improvements in performance as well as critical updates such as bug fixes. Hence, older, existing (source) libraries need to be replaced with their newer, updated (target) counterparts, in a process known as library migration. Typically, library migration is a time-consuming, manual, and error-prone process that requires developers to analyze the source and target library code and its documentation. Specifically, developers examine Application Programming Interface (API) implementations and documentation to replace each source API with a target API without modifying the underlying software functionality. While recent works have used various techniques to recommend suitable target library replacements, these approaches do not generalize well, e.g., when a source library method needs to be replaced by one or more target library APIs or methods. To address this limitation, we propose the use of multi-objective evolutionary algorithms to identify suitable method replacements during library migration. In particular, we formulate library migration at the method-level as a multi-objective combinatorial optimization problem and examine the performance of 7 multi-objective evolutionary algorithms: UNSGAIII, RNSGAII, AGEMOEA, SMSEMOA, NSGAII, IBEA and MOEAD. We use method signature and documentation similarity, and co-occurrence probability to accurately recommend one or more target library methods. We evaluate our approach by conducting an empirical study on 9 popular Java library migrations mined from 57,447 open-source projects on GitHub. Our results demonstrate that UNSGAIII, RNSGAII, AGEMOEA, SMSEMOA, NSGAII, IBEA and MOEAD achieve 90%, 89%, 94%, 90%, 91%, 94%, and 71% precision on average, and 83%, 23%, 58%, 63%, 58%, 60% and 17% average recall respectively. In the interest of reproducibility, we make all code and results publicly available at: http://bit.ly/MOO-api-migration.
LiDAR-based Online Control Barrier Function Synthesis for Safe Navigation in Unknown Environments

Authors:
raw_author_name: Made Widhi Surya Atman
raw_affiliation_string: Automation and Mechanical Engineering Unit, Tampere University, Tampere, Finland
raw_author_name: Azwirman Gusrialdi
raw_affiliation_string: Automation and Mechanical Engineering Unit, Tampere University, Tampere, Finland

Abstract: This paper presents a novel extension of the Control Barrier Function (CBF) as the low-level safety controller for autonomous mobile robots navigating in unknown environments. The main challenges of implementing CBF in real-world situations arise from the absence of a model or the lack of an exact one for the environment. Additionally, online learning is needed for the robot to maneuver in an unknown environment which leads to dealing with the sampled data set size, memory, and computational complexity. We address these challenges by designing an online non-parametric Lidar-based safety function using the Gaussian process (GP). It is both efficient in data size and eliminates the requirement to store previous data. Then, a CBF is synthesized using the proposed safety function to rectify the safe control input. The effectiveness of the Lidar-based CBF synthesis for navigation in unknown environments was validated by conducting experiments on unicycle-type robots.
Generating and Transferring Priors for Causal Bayesian Network Parameter Estimation in Robotic Tasks

Authors:
raw_author_name: Maximilian Diehl
raw_affiliation_string: Faculty of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
raw_author_name: Karinne Ramirez-Amaro
raw_affiliation_string: Faculty of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden

Abstract: Robots acting in human environments will often face new situations and can benefit from transferring prior experience. Priors could enable robots to handle new tasks zero-shot and help prevent failures, which can be particularly costly in real robot applications. Due to their interpretable nature, causal Bayesian Networks (CBN) are popular for modeling cause-effect relations between semantically meaningful environment features and their effects on action success. While the CBN structure is often intuitively transferable to a new context, its probability distribution might change, requiring data-intensive relearning. In this work, we propose three strategies that utilize semantic similarity and relatedness between the variables of two CBNs to generate and transfer informed CBN distribution priors. We evaluate the parameter prior accuracy in five different transfer scenarios, including sim-2-real, transferring parameters to more complex tasks with a larger number of parameters and even between two different tasks, which is particularly challenging. We show that the priors lead to better distribution estimates, particularly under a limited amount of new experiments, and improve the robot's ability to predict and prevent action failures by up to 50%.
Event-Triggered Image Moments Predictive Control for Tracking Evolving Features Using UAVs

Authors:
raw_author_name: Sotirios N. Aspragkathos
raw_affiliation_string: School of Mechanical Engineering, Control Systems Lab, National Technical University of Athens, Athens, Greece
raw_author_name: George C. Karras
raw_affiliation_string: Department of Informatics and Telecommunications, University of Thessaly, Lamia, Greece
raw_author_name: Kostas J. Kyriakopoulos
raw_affiliation_string: Engineering Division and Center for AI &amp; Robotics (CAIR), New York University, Abu Dhabi, UAE

Abstract: This paper presents a novel approach for tracking deformable contour targets using Unmanned Aerial Vehicles (UAVs). The proposed scheme combines image moments descriptor and Event-Triggered (ET) Nonlinear Model Predictive Control (NMPC) for efficient and accurate tracking. The deformable contour model allows adaptation to the evolving target's shape, while the proposed event-triggered scheme achieves improved computational efficiency and extended flight duration while generating new control sequences for the UAV. Real-world experimental validation as well as a comparative simulation performance analysis validate the scheme, showcasing its robustness in handling complex scenarios. This approach holds promise for various applications, such as surveillance and autonomous navigation.