Reviewerly/exHarmony huggingface dataset:
Curriculum learning-based strategy for low-density archaeological mound detection from historical maps in India and Pakistan

This paper presents two algorithms for the large-scale automatic detection and instance segmentation of potential archaeological mounds on historical maps. Historical maps present a unique source of information for the reconstruction of ancient landscapes. The last 100 years have seen unprecedented landscape modifications with the introduction and large-scale implementation of mechanised agriculture, channel-based irrigation schemes, and urban expansion to name but a few. Historical maps offer a window onto disappearing landscapes where many historical and archaeological elements that no longer exist today are depicted. The algorithms focus on the detection and shape extraction of mound features with high probability of being archaeological settlements, mounds being one of the most commonly documented archaeological features to be found in the Survey of India historical map series, although not necessarily recognised as such at the time of surveying. Mound features with high archaeological potential are most commonly depicted through hachures or contour-equivalent form-lines, therefore, an algorithm has been designed to detect each of those features. Our proposed approach addresses two of the most common issues in archaeological automated survey, the low-density of archaeological features to be detected, and the small amount of training data available. It has been applied to all types of maps available of the historic 1″ to 1-mile series, thus increasing the complexity of the detection. Moreover, the inclusion of synthetic data, along with a Curriculum Learning strategy, has allowed the algorithm to better understand what the mound features look like. Likewise, a series of filters based on topographic setting, form, and size have been applied to improve the accuracy of the models. The resulting algorithms have a recall value of 52.61% and a precision of 82.31% for the hachure mounds, and a recall value of 70.80% and a precision of 70.29% for the form-line mounds, which allowed the detection of nearly 6000 mound features over an area of 470,500 km2, the largest such approach to have ever been applied. If we restrict our focus to the maps most similar to those used in the algorithm training, we reach recall values greater than 60% and precision values greater than 90%. This approach has shown the potential to implement an adaptive algorithm that allows, after a small amount of retraining with data detected from a new map, a better general mound feature detection in the same map.
Characterizing and Understanding Software Security Vulnerabilities in Machine Learning Libraries

The application of machine learning (ML) libraries has tremendously increased in many domains, including autonomous driving systems, medical, and critical industries. Vulnerabilities of such libraries could result in irreparable consequences. However, the characteristics of software security vulnerabilities have not been well studied. In this paper, to bridge this gap, we take the first step toward characterizing and understanding the security vulnerabilities of seven well-known ML libraries, including TensorFlow, PyTorch, Scikit-learn, Mlpack, Pandas, Numpy, and Scipy. To do so, we collected 683 security vulnerabilities to explore four major factors: 1) vulnerability types, 2) root causes, 3) symptoms, and 4) fixing patterns of security vulnerabilities in the studied ML libraries. The findings of this study can help developers and researchers understand the characteristics of security vulnerabilities across the studied ML libraries.
Extremely large Lamb shift in a deep-strongly coupled circuit QED system with a multimode resonator

We report experimental and theoretical results on the extremely large Lamb shift in a multimode circuit quantum electrodynamics (QED) system in the deep-strong coupling (DSC) regime, where the qubit-resonator coupling strength is comparable to or larger than the qubit and resonator frequencies. The system comprises a superconducting flux qubit (FQ) and a quarter-wavelength coplanar waveguide resonator ([Formula: see text] CPWR) that are coupled inductively through a shared edge that contains a Josephson junction to achieve the DSC regime. Spectroscopy is performed around the frequency of the fundamental mode of the CPWR, and the spectrum is fitted by the single-mode quantum Rabi Hamiltonian to obtain the system parameters. Since the qubit is also coupled to a large number of higher modes in the resonator, the single-mode fitting does not provide the bare qubit energy but a value that incorporates the renormalization from all the other modes. We derive theoretical formulas for the Lamb shift in the multimode resonator system. As shown in previous studies, there is a cut-off frequency [Formula: see text] for the coupling between the FQ and the modes in the CPWR, where the coupling grows as [Formula: see text] for [Formula: see text] and decreases as [Formula: see text] for [Formula: see text]. Here [Formula: see text] is the frequency of the nth mode. The cut-off effect occurs because the qubit acts as an obstacle for the current in the resonator, which suppresses the current of the modes above [Formula: see text] at the location of the qubit and results in a reduced coupling strength. Using our observed spectrum and theoretical formulas, we estimate that the Lamb shift from the fundamental mode is 82.3% and the total Lamb shift from all the modes is 96.5%. This result illustrates that the coupling to the large number of modes in a CPWR yields an extremely large Lamb shift but does not suppress the qubit energy to zero, which would happen in the absence of a high-frequency cut-off.
Quantum interferometric power and Bures distance entanglement versus normalized steered coherence under random telegraph noise

This study examines the impact of random telegraph noise on non-separability, non-classicality, and steered coherence in a bipartite system initially prepared in a Gisin state and embedded in both Markovian and non-Markovian environments. To quantify non-separability, we employ the Bures distance entanglement measure ([Formula: see text]); for non-classicality detection, we utilize the quantum interferometric power ([Formula: see text]); and to measure steered coherence, we employ the normalized steered coherence ([Formula: see text]). We analyze the dynamics of these three metrics under the effects of the random telegraph noise through various theoretical and numerical techniques. Our findings demonstrate that the amount of quantum correlations in the system is closely tied to the parameters defining the random telegraph noise and the initial system state. Our results also reveal that all three measures exhibit oscillatory behavior in the non-Markovian regime and monotonic changes with time in the Markovian regime. These results provide a deeper understanding of the robustness and stability of non-separability and coherence under noisy conditions and may have implications for the design of noise-resistant quantum systems.
Minimum resolution requirements of digital pathology images for accurate classification

Digitisation of pathology has been proposed as an essential mitigation strategy for the severe staffing crisis facing most pathology departments. Despite its benefits, several barriers have prevented widespread adoption of digital workflows, including cost and pathologist reluctance due to subjective image quality concerns. In this work, we quantitatively determine the minimum image quality requirements for binary classification of histopathology images of breast tissue in terms of spatial and sampling resolution. We train an ensemble of deep learning classifier models on publicly available datasets to obtain a baseline accuracy and computationally degrade these images according to our derived theoretical model to identify the minimum resolution necessary for acceptable diagnostic accuracy. Our results show that images can be degraded significantly below the resolution of most commercial whole-slide imaging systems while maintaining reasonable accuracy, demonstrating that macroscopic features are sufficient for binary classification of stained breast tissue. A rapid low-cost imaging system capable of identifying healthy tissue not requiring human assessment could serve as a triage system for reducing caseloads and alleviating the significant strain on the current workforce.
Large kernel convolution YOLO for ship detection in surveillance video

&lt;abstract&gt; &lt;p&gt;At present, ship detectors have many problems, such as too many hyperparameter, poor recognition accuracy and imprecise regression boundary. In this article, we designed a large kernel convolutional YOLO (Lk-YOLO) detection model based on Anchor free for one-stage ship detection. First, we discuss the introduction of large size convolution kernel in the residual module of the backbone network, so that the backbone network has a stronger feature extraction capability. Second, in order to solve the problem of conflict regression and classification fusion under the coupling of detection heads, we split the detection head into two branches, so that the detection head has better representation ability for different branches of the task and improves the accuracy of the model in regression tasks. Finally, in order to solve the problem of complex and computationally intensive anchor hyperparameter design of ship data sets, we use anchor free algorithm to predict ships. Moreover, the model adopts an improved sampling matching strategy for both positive and negative samples to expand the number of positive samples in GT (Ground Truth) while achieving high-quality sample data and reducing the imbalance between positive and negative samples caused by anchor. We used NVIDIA 1080Ti GPU as the experimental environment, and the results showed that the mAP@50 Reaching 97.7%, mAP@.5:.95 achieved 78.4%, achieving the best accuracy among all models. Therefore, the proposed method does not need to design the parameters of the anchor, and achieves better detection efficiency and robustness without hyperparameter input.&lt;/p&gt; &lt;/abstract&gt;
How far does the central bank influence its economies? The example of Algeria

This study focused on Algeria and it looked at how education quality affected the knowledge economy. The theoretical anchor was the endogenous or new growth hypothesis. Secondary sources were used to get panel-structured data that spanned 48 Algerian provinces in 22 years (1999–2020). The equations were estimated using the fixed-random effect model and Hausman test, Also the Kao (Engle Ganger-based) cointegration test proved that the series did indeed have a long-run connection. The findings indicated that the knowledge economy was boosted by a rise in students’ Baccalaureate and Intermediate test success rates. This is due to the fact that a rise in exam success rates indicates that individuals at various educational levels are now receiving education of a higher caliber, and the economy depends on this caliber of education. The rise in students and professors is a sign that there are more knowledgeable people and specialized educators accessible to fuel the knowledge economy. As more educated citizens pass their exams and graduate from college, they are hired from the labor market into industries as significant production factors whose skills, values, and knowledge acquired through specialized training are expected to promote machine handling and coordination of other human and nonhuman production factors to fuel the knowledge economy. Teachers should be provided with ongoing professional development opportunities to help them provide instruction at all educational levels, according to a recommendation. This would improve their ability to educate and inspire students and help them do better on their intermediate and baccalaureate exams.
Bounded Verification for Finite-Field-Blasting

Abstract Zero Knowledge Proofs (ZKPs) are cryptographic protocols by which a prover convinces a verifier of the truth of a statement without revealing any other information. Typically, statements are expressed in a high-level language and then compiled to a low-level representation on which the ZKP operates. Thus, a bug in a ZKP compiler can compromise the statement that the ZK proof is supposed to establish. This paper takes a step towards ZKP compiler correctness by partially verifying a field-blasting compiler pass, a pass that translates Boolean and bit-vector logic into equivalent operations in a finite field. First, we define correctness for field-blasters and ZKP compilers more generally. Next, we describe the specific field-blaster using a set of encoding rules and define verification conditions for individual rules. Finally, we connect the rules and the correctness definition by showing that if our verification conditions hold, the field-blaster is correct. We have implemented our approach in the CirC ZKP compiler and have proved bounded versions of the corresponding verification conditions. We show that our partially verified field-blaster does not hurt the performance of the compiler or its output; we also report on four bugs uncovered during verification.
Construction of a New 2D Hyperchaotic Map with Application in Efficient Pseudo-Random Number Generator Design and Color Image Encryption

This paper proposes a new two-dimensional discrete hyperchaotic system and utilizes it to design a pseudo-random number generator (PRNG) and an efficient color image encryption algorithm. This hyperchaotic system has very complex dynamic properties and can generate highly random chaotic sequences. The complex hyperchaotic characteristics of the system are confirmed via bifurcation diagram, chaotic attractor, Lyapunov exponents, correlation analysis, approximate entropy and permutation entropy. Compared with some traditional discrete chaotic systems, the new chaotic system has a larger range of chaotic parameters and more complex hyperchaotic characteristics, making it more suitable for application in information encryption. The proposed PRNG can generate highly random bit sequences that can fully pass all NIST testing items. The proposed color image encryption algorithm achieves cross-channel permutation and diffusion of pixels in parallel. These strategies not only greatly improve the encryption speed of color images, but also enhance the security level of cipher images. The simulation experiments and security analysis results show that the algorithm has strong robustness against differential attacks, statistical attacks and interference attacks, and has good application potential in real-time secure communication applications of color images.
Towards a sustainable future: developing a cybersecurity framework for manufacturing

Purpose This study aims to propose a cybersecurity framework that prioritizes sustainability in the manufacturing sector by identifying necessary resources and capabilities for effective cybersecurity management. The proposed framework aims to enhance resource protection and safeguard data confidentiality, integrity and accessibility, provide proactive steps for predicting cyber threats and highlight the importance of educating employees at all levels of the organization. Design/methodology/approach A thorough review of existing literature and analysis was conducted to develop the proposed cybersecurity framework. Several frameworks, including the NIST cybersecurity framework, were reviewed to identify the necessary skills and resources required to combat cyber threats and keep businesses sustainable. Findings The proposed framework includes proactive steps, such as predicting cyber threats, and emphasizes the importance of educating employees and raising awareness at all levels of the organization. Resilience is also emphasized, which refers to an organization's ability to recover and continue operations following a cyberattack. Implementing this framework may require a significant budget and time investment, and small organizations may face limitations in applying all aspects of the framework. Originality/value This study proposes a cybersecurity framework that prioritizes sustainability in the manufacturing sector, which provides added protection for organizations. The framework's key functions can be adopted partially or fully, making it suitable for organizations of varying sizes. Future research can focus on addressing the framework's limitations and shortcomings to further reduce cyber risks for sustainable manufacturing, establishing the scale of an industry based on its economy and extending the framework to non-manufacturing businesses.
Real-time detection and location of reserved anchor hole in coal mine roadway support steel belt

Based on the current coal mine roadway using supporting steel belt steel belt auxiliary anchor support, the location of the supporting steel belt anchor holes is mainly done manually; if the location is not accurate, there will be large safety hazards and other problems. An intelligent real-time detection and location method of anchor holes in coal mine roadway support steel belt based on deep learning model and depth camera is proposed. First, to reduce the influence of water mist and dust on the camera and improve the image quality of the camera, the image is pre-processed using contrast limited adaptive histogram equalization. Second, the YOLOv5s model is improved by adding SPD-Conv and coordinate attention mechanisms to improve the detection capability of the model. Third, a real-time depth map restoration method that fully preserves object edge features is proposed to avoid errors caused by areas with depth values of 0 in the depth map when locating anchor holes in combination with a depth camera. Finally, the improved YOLOv5s model proposed in this paper combined with the repaired depth map was used to achieve the detection and location of anchor holes in a laboratory simulated tunnel with a location error of less than 5 mm and an average FPS of 28.9. In summary, the real-time target detection and location method based on a deep learning model and a depth camera is feasible in an unstructured environment in coal mines.
Typing High-Speed Cryptography against Spectre v1

The current gold standard of cryptographic software is to write efficient libraries with systematic protections against timing attacks. In order to meet this goal, cryptographic engineers increasingly use high-assurance cryptography tools. These tools guide programmers and provide rigorous guarantees that can be verified independently by library users. However, high-assurance tools reason about overly simple execution models that elide transient execution leakage. Thus, implementations validated by high-assurance cryptography tools remain potentially vulnerable to transient execution attacks such as Spectre or Meltdown. Moreover, proposed countermeasures are not used in practice due to performance overhead.We propose, analyze, implement and evaluate an approach for writing efficient cryptographic implementations that are protected against Spectre v1 attacks. Our approach ensures speculative constant-time, an information flow property which guarantees that programs are protected against Spectre v1. Speculative constant-time is enforced by means of a (value-dependent) information flow type system. The type system tracks security levels depending on whether execution is misspeculating. We implement our approach in the Jasmin framework for high-assurance cryptography, and use it for protecting all implementations of an experimental cryptographic library that includes highly optimized implementations of symmetric primitives, of elliptic-curve cryptography, and of Kyber, a lattice-based KEM recently selected by NIST for standardization. The performance impact of our protections is very low; for example, less than 1% for Kyber and essentially zero for X25519.
News Aggregation using Web Scraping News Portals

In today's fast-paced world, staying informed through reliable and high-quality news is important. However, with an overwhelming number of online publishers and platforms, accessing information from multiple sources can be time-consuming and challenging. To simplify this process, news aggregators have emerged as a solution. These platforms allow users to customize their news consumption by selecting preferred websites and receiving curated articles from those sources in one centralized location. This saves valuable time and effort but also streamlines information gathering in our daily routines. To develop an influential online news aggregator, web scraping for data structuring is crucial. Web scraping involves analyzing a website's HTML structure to extract the desired data. By understanding the underlying structure of web pages, developers can fetch relevant information such as article titles, summaries, authors, and publication dates. People often struggle to find valuable news sources amid the vast sea of information available online. News aggregators address this issue by providing personalized news feeds based on individual interests. Users serve as valuable resources for knowledge tailored to specific preferences. While news aggregators are widely recognized for their popularity and utility, there is still room for improvement in their role in software development. Enhancements could include refining algorithms for more accurate content curation. In addition, Users could include implementing better user interfaces for enhanced user experiences and exploring innovative features like sentiment analysis and topic clustering. By continually iterating and improving upon existing news aggregator platforms, developers can ensure users have access to timely, relevant, and trustworthy news conveniently and efficiently.
Improving Diagnostics with Deep Forest Applied to Electronic Health Records

An electronic health record (EHR) is a vital high-dimensional part of medical concepts. Discovering implicit correlations in the information of this data set and the research and informative aspects can improve the treatment and management process. The challenge of concern is the data sources’ limitations in finding a stable model to relate medical concepts and use these existing connections. This paper presents Patient Forest, a novel end-to-end approach for learning patient representations from tree-structured data for readmission and mortality prediction tasks. By leveraging statistical features, the proposed model is able to provide an accurate and reliable classifier for predicting readmission and mortality. Experiments on MIMIC-III and eICU datasets demonstrate Patient Forest outperforms existing machine learning models, especially when the training data are limited. Additionally, a qualitative evaluation of Patient Forest is conducted by visualising the learnt representations in 2D space using the t-SNE, which further confirms the effectiveness of the proposed model in learning EHR representations.
Calibration method for a multi-focus microscopic 3D imaging system

This Letter presents a novel, to the best of our knowledge, method to calibrate multi-focus microscopic structured-light three-dimensional (3D) imaging systems with an electrically adjustable camera focal length. We first leverage the conventional method to calibrate the system with a reference focal length f 0 . Then we calibrate the system with other discrete focal lengths f i by determining virtual features on a reconstructed white plane using f 0 . Finally, we fit the polynomial function model using the discrete calibration results for f i . Experimental results demonstrate that our proposed method can calibrate the system consistently and accurately.