Reviewerly/exHarmony huggingface dataset:
Coupling Mechanism of the Concealed Rock Body and Metallogenic Structure of the Sarakan Gold Deposit in Laos Investigated Using Magnetic and Electrical Methods

The Sarakan Gold Deposit is located in the vicinity of the Songsanu and Napafa villages, north of Sarakan County, Vientiane Province, Laos. It forms part of the Luang Prabang–Loei polymetallic metallogenic belt, and its metallogenic geological conditions are good and mineral resources are abundant. At present, most orebodies (mineralization) are deeply buried and greatly vary in terms of their characteristics; furthermore, the distribution and output of orebodies (mineralization) are not clear, which makes it difficult to find minerals in the area. Based on the comprehensive geological characteristics, geophysical anomalies, and interpretation results in the study area, it is believed that the Sarakan Gold Deposit is closely related to Indosinian felsic substance magmatic intrusions and is controlled by their structure. The type of deposits in the belt are brittle–ductile, shear structured, Quartz-pyrite, vein-type gold deposits. In this study, we carried out comprehensive geophysical methods, including the high-precision magnetic survey, induced polarization survey, and the transient electromagnetic measurement. Based on the characteristics of geophysical anomalies, geological inference, and interpretation, the integrated geophysical and geochemical prospecting criteria of the ore area have been determined: high magnetism, high polarization, and medium–high resistance are the signs pointing to concealed mineralization bodies in the study area, and this provided the best framework for us to discuss the deep geological bodies in the study area. The drilling verification results are consistent with the abnormalities delineated by the magnetic and electrical measurements. Our geophysical exploration results revealed the coupling relationship between the concealed intrusive body of the Sarakan Gold Deposit and the metallogenic structure, which provided a great opportunity for finding potentially similar deposits in the Luang Prabang–Loei polymetallic metallogenic belt.
An Open-Domain Event Extraction Method Incorporating Semantic and Dependent Syntactic Information

Open-domain event extraction is a fundamental task that aims to extract non-predefined types of events from news clusters. Some researchers have noticed that its performance can be enhanced by improving dependency relationships. Recently, graphical convolutional networks (GCNs) have been widely used to integrate dependency syntactic information into neural networks. However, they usually introduce noise and deteriorate the generalization. To tackle this issue, we propose using Bi-LSTM to obtain semantic representations of BERT intermediate layer features and infuse the dependent syntactic information. Compared to current methods, Bi-LSTM is more robust and has less dependency on word vectors and artificial features. Experiments on public datasets show that our approach is effective for open-domain event extraction tasks.
Research on Secure Storage Technology of Spatiotemporal Big Data Based on Blockchain

With the popularity of spatiotemporal big data applications, more and more sensitive data are generated by users, and the sharing and secure storage of spatiotemporal big data are faced with many challenges. In response to these challenges, the present paper puts forward a new technology called CSSoB (Classified Secure Storage Technology over Blockchain) that leverages blockchain technology to enable classified secure storage of spatiotemporal big data. This paper introduces a twofold approach to tackle challenges associated with spatiotemporal big data. First, the paper proposes a strategy to fragment and distribute space–time big data while enabling both encryption and nonencryption operations based on different data types. The sharing of sensitive data is enabled via smart contract technology. Second, CSSoB’s single-node storage performance was assessed under local and local area network (LAN) conditions, and results indicate that the read performance of CSSoB surpasses its write performance. In addition, read and write performance were observed to increase significantly as the file size increased. Finally, the transactions per second (TPS) of CSSoB and the Hadoop Distributed File System (HDFS) were compared under varying thread numbers. In particular, when the thread number was set to 100, CSSoB demonstrated a TPS improvement of 7.8% in comparison with HDFS. Given the remarkable performance of CSSoB, its adoption can not only enhance storage performance, but also improve storage security to a great extent. Moreover, the fragmentation processing technology employed in this study enables secure storage and rapid data querying while greatly improving spatiotemporal data processing capabilities.
Bi-LS-AttM: A Bidirectional LSTM and Attention Mechanism Model for Improving Image Captioning

The discipline of automatic image captioning represents an integration of two pivotal branches of artificial intelligence, namely computer vision (CV) and natural language processing (NLP). The principal functionality of this technology lies in transmuting the extracted visual features into semantic information of a higher order. The bidirectional long short-term memory (Bi-LSTM) has garnered wide acceptance in executing image captioning tasks. Of late, scholarly attention has been focused on modifying suitable models for innovative and precise subtitle captions, although tuning the parameters of the model does not invariably yield optimal outcomes. Given this, the current research proposes a model that effectively employs the bidirectional LSTM and attention mechanism (Bi-LS-AttM) for image captioning endeavors. This model exploits the contextual comprehension from both anterior and posterior aspects of the input data, synergistically with the attention mechanism, thereby augmenting the precision of visual language interpretation. The distinctiveness of this research is embodied in its incorporation of Bi-LSTM and the attention mechanism to engender sentences that are both structurally innovative and accurately reflective of the image content. To enhance temporal efficiency and accuracy, this study substitutes convolutional neural networks (CNNs) with fast region-based convolutional networks (Fast RCNNs). Additionally, it refines the process of generation and evaluation of common space, thus fostering improved efficiency. Our model was tested for its performance on Flickr30k and MSCOCO datasets (80 object categories). Comparative analyses of performance metrics reveal that our model, leveraging the Bi-LS-AttM, surpasses unidirectional and Bi-LSTM models. When applied to caption generation and image-sentence retrieval tasks, our model manifests time economies of approximately 36.5% and 26.3% vis-a-vis the Bi-LSTM model and the deep Bi-LSTM model, respectively.
Parallel CV-QRNG with Strict Entropy Evaluation

Continuous-variable quantum random number generators (CV-QRNGs) have promising application prospects thanks to their advantages such as high detection bandwidth, robustness of system, and integratability. In major CV-QRNGs, the generation of random numbers is based on homodyne detection and discretization of the quadrature fluctuations of the EM fields. Any defectiveness in physical realization may leak information correlated with the generated numbers and the maximal amount of randomness that can be extracted in presence of such side-information is evaluated by the so-called quantum conditional min-entropy. The parallel CV-QRNG overcomes the rate bottleneck of the previous serial type scheme. As a type of device-trusted QRNG, its security needs to be better guaranteed based on self-testing or monitoring that can be rigorously enforced. In this work, four sideband modes of vacuum state within 1.6 GHz detection bandwidth were extracted parallelly as the entropy source, and 16-bit analog-to-digital conversion in each channel was realized. Without making any ideal assumptions, the transfer function of the homodyne and quantization system was measured based on beat method to calibrate the evaluation of the min-entropy. Based on the rigorous entropy evaluation with a hash security parameter of εhash = 2−110, a real-time generation rate of 7.25 Gbps was finally achieved.
Re-Examining Bitcoin’s Price–Volume Relationship: A Time-Varying Spectral Analysis

This study employs continuous wavelet transforms to model the relationship between Bitcoin volume and prices across time and frequency space using daily data for the period between 17 September 2014 and 10 April 2023. The results show that Bitcoin price and volume have a long-term relationship at low frequency cycles mostly during the period after 2019. A statistically insignificant relationship between the price and volume of Bitcoin is observed prior to 2019 which coincides with a time of limited regulatory oversight of Bitcoin markets globally. Positive correlation is observed in the aftermath of this period, with stronger correlation recorded during and post the period of the Covid-19 pandemic. Furthermore, the findings reveal that fluc-tuations in the Bitcoin volume tends to affect the price at higher frequency synchronizations (short-term); whereas, at lower frequencies (long-term), a feedback loop is observed, whereby the price changes lead to alterations in the volume.
Identifying components existing in Agile software development for achieving “light but sufficient” documentation

Abstract The Agile nature allows changes within the development procedure which can sometimes be non-predictable and affect the cost and time of the software. Face-to-face communication appears to be a faster process in Agile Software Development (ASD) in practice, but issues such as understandability and communicativeness cause ambiguity between the customer and the developer. Among the existing works, achieving light but sufficient documentation in ASD remains a challenge. Most previous studies have simply highlighted the issues associated with less documentation, its limitations, and challenges, but no proper solution has yet been proposed. As an initial step in our theoretical study, we conducted a systematic literature review (SLR) to identify the related components and subcomponents that are applicable in supporting Agile documentation. As such, the fundamental concepts related to software documentation have been studied in order to align with documentation in ASD. The identification of these components can be useful in achieving the concept of “light but sufficient” documentation in Agile, which will aid in achieving customer satisfaction.
Windows memory forensics: Identification of (malicious) modifications in memory-mapped image files

Memory forensics plays a crucial role for the analysis of sophisticated malware, especially with memory-only variants, and has in the past extended its capabilities for detecting various attacker techniques. Several of these techniques affect the memory of victim processes, partly resulting in suspicious private memory regions, but others affect especially the memory-mapped image files (executables and DLLs). One infamous example are API hooks, which commonly are used to redirect the control flow by modifying a few bytes/instructions in the existing code of memory-mapped image files. Other examples are AMSI and ETW bypasses, which also modify just a few bytes, and Module Stomping which has a larger modification effect. While there are already tools for the detection of modified pages and these attacker techniques, one disadvantage they have in common is the inability to pinpoint the exact modified bytes. Instead, they either report modifications on a page level, which means to present 4096 instead of only 3 bytes that actually have been modified, or they use pattern matching in order to identify malicious traces. In this work, we will show that current detection approaches fail to reliably identify modified image-file pages, and even if not, miss some malicious modifications. We then present our novel approach to reliably detect modified pages and to reveal the exact bytes/instructions that have been modified, while filtering benign modifications. With this work we also release a Volatility 3 plugin named imgmalfind, which implements our approach and reports potentially malicious modifications, enriched with some analysis details.
Improving trace synthesis by utilizing computer vision for user action emulation

Forensic analyses are performed by skilled forensic practitioners who require reliable, state-of-the-art tooling and ongoing training. To provide both, education and academia rely on realistic training datasets. Those datasets are crucial to teaching investigators, validating forensic tools, advancing algorithms, and pursuing research. At the same time, the forensic community faces a shortcoming of realistic datasets, mainly due to ethical and legal reasons. To overcome this challenge, prior work introduced several frameworks aiming to create unproblematic replications of real evidence. Those frameworks generate synthetic datasets by populating disk images with traces of emulated user behavior. However, it is general consent that existing frameworks have some drawbacks concerning the quality of generated datasets, particularly due to the incorporation of unrealistic traces in GUI-based environments. Reviewing the implementation details of common frameworks, we found that current solutions miss realistic trace synthesis, reducing the quality and usefulness of synthesized datasets. By leveraging computer vision, this paper introduces a novel approach aiming to enhance the quality of synthetic datasets. We propose an architecture and provide an open-source implementation utilizing a hypervisor for creating Human Interface Device (HID) input, which is controlled by computer vision algorithms to imitate human-like user actions. In this way, we provide external GUI automation capabilities that enable more realistic trace synthesis than existing solutions and open up the applicability to a wide range of GUI-based operating systems. In contrast to previous research results, our approach is independent of software running in virtual machines, further optimizing the quality of generated datasets by omitting automation artifacts. Our experiments indicate that using external GUI automation for user action emulation results in a greater amount and a more widespread distribution of traces. Therefore our approach may refine the quality of datasets in this field.
Regularized linear discriminant analysis via a new difference-of-convex algorithm with extrapolation

Abstract In this paper, we transform the classical linear discriminant analysis (LDA) into a smooth difference-of-convex optimization problem. Then, a new difference-of-convex algorithm with extrapolation is introduced and the convergence of the algorithm is established. Finally, for a face recognition problem, the proposed algorithm achieves better classification performance compared with several current algorithms in the literature.
Classifying mental disorders through clinicians’ subjective approach based on three-way decisions

The most widely used technique for psychiatric diagnosis is a contemporary manual-based procedure based on prevailing culture-bound data for the classification of mental disorders. However, it has several inherent faults, including the misdiagnosis of complex patient phenomena and others. A potential mental patient from a minority culture could present with atypical symptoms that would be missed by the standard approach. Using the three-way decisions (3WD) as a framework, we propose a unified model that represents the subjective approach (CSA) of clinicians (psychiatrists and psychologists) consisting of three components: qualitative analysis, quantitative analysis, and evaluation-based analysis. The results of the qualitative and quantitative investigation are a classification list and a set of numerical weights based on malady severity levels according to the clinician's highest level of assumptions. Moreover, we construct a comparative classification of diseases into three categories with varying levels of importance; a three-way evaluation-based model is utilized in this study in order to better comprehend and communicate these results. This proposed method enables clinicians to consider identical data-driven individual behavioral symptoms of patients to be integrated with the current manual-based process as a complementary diagnostic instrument to improve the accuracy of mental disorder diagnosis.
Cognitive processing stages in mental rotation – How can cognitive modelling inform HsMM-EEG models?

The aspiration for insight into human cognitive processing has traditionally driven research in cognitive science. With methods such as the Hidden semi-Markov Model-Electroencephalography (HsMM-EEG) method, new approaches have been developed that help to understand the temporal structure of cognition by identifying temporally discrete processing stages. However, it remains challenging to assign concrete functional contributions by specific processing stages to the overall cognitive process. In this paper, we address this challenge by linking HsMM-EEG3 with cognitive modelling, with the aim of further validating the HsMM-EEG3 method and demonstrating the potential of cognitive models to facilitate functional interpretation of processing stages. For this purpose, we applied HsMM-EEG3 to data from a mental rotation task and developed an ACT-R cognitive model that is able to closely replicate human performance in this task. Applying HsMM-EEG3 to the mental rotation experiment data revealed a strong likelihood for 6 distinct stages of cognitive processing during trials, with an additional stage for non-rotated conditions. The cognitive model predicted intra-trial mental activity patterns that project well onto the processing stages, while explaining the additional stage as a marker of non-spatial shortcut use. Thereby, this combined methodology provided substantially more information than either method by itself and suggests conclusions for cognitive processing in general.
Physics-based optical flow estimation under varying illumination conditions

The physics-based optical flow (PBOF) model was derived from typical flow visualizations and it provided a more solid physical foundation for optical flow calculation. However, like many recent variational optical flow methods, it is not formulated to deal with the effect of illumination variation. In this paper, we propose a new efficient illumination-invariant optical flow estimation method, which improves the physics-based optical flow model. The proposed method introduces a multiplicative product of the velocity gradient and intensity from PBOF and an additive diffusion component to relax the brightness constancy assumption. The proposed method employs a linear transformation to reduce the influence of varying illumination on the data term. Then, a smoothness-sparsity regularization is used to constrain the minimization problem to have good edge-preserving for optical flow. Furthermore, the numerical implementation is given to solve the optimization problem. The accuracy of the developed method and the robustness against varying illumination are evaluated. The proposed method can provide motion estimation with acceptable accuracy for three optical flow datasets and cloud images of Jupiter’s great red spot, which have challenging illumination variations.
Local model dynamics for two qubits

The density matrix model described in an earlier paper “Model Dynamics for Quantum Computing” is applied to the case of two qubits. The earlier paper provided a density matrix dynamics with unitary time-dependent gates including qubit energy splitting, entropy constraints, external bath and noise effects. The Lindblad formulation as extended by Beretta was adopted in designing this model. Visualization was provided by examining the time-evolution of the polarization vector. In this paper, the same ideas are applied to two-qubit systems, where there are now 15 time-dependent observables; namely, six polarization vectors (P→A,P→B), and nine spin-correlations (T↔). These time-dependent observables provide ready visualization of two qubit dynamics. Special emphasis is placed on the CNOT gate, which is implemented following recent outstanding developments in using silicon-based dots. By invoking a different splitting for each qubit, plus a spin–spin interaction and a carefully designed Rabi oscillation, a CNOT gate is generated. Careful analysis of the time-dependence of these aspects provides insight into CNOT dynamics. The model can be used to ascertain the sensitivity and efficacy of such a CNOT gate when subject to external environmental effects such as noise disturbances, and noise compensation, using the single-system formulation of steepest-entropy-ascent (SEA) non-equilibrium quantum thermodynamics, suitable to model controlled-gate implementations as long as the two qubits remain localized and effectively behave as a single physical four-level system. The role of careful timing in constructing an efficient CNOT gate is illustrated. The formation of Bell states and the evolution of entanglement including noise, bath and entropy considerations are examined. Extensions to the two-qubit swap, and to the three-qubit Toffoli gate dynamics are outlined. It is also shown that a simple Lindblad form can be used to introduce weak and distinct qubit noise pulses. A simple scheme for noise compensation is designed to increase purity and decrease entropy, without invocation of quantum correction methodology. It is based on using the preexisting entanglement of the environment with the quantum system and carefully designed non-Hermitian Lindblad pulses.
Enhancing transferability of adversarial examples with pixel-level scale variation

The transferability of adversarial examples under the black-box attack setting has attracted extensive attention from the community. Input transformation is one of the most effective approaches to improve the transferability among all methods proposed recently. However, existing methods either only slightly improve transferability or are not robust to defense models. We delve into the generation process of adversarial examples and find that existing input transformation methods tend to craft adversarial examples by transforming the entire image, which we term image-level transformations. This naturally motivates us to perform pixel-level transformations, i.e., transforming only part pixels of the image. Experimental results show that pixel-level transformations can considerably enhance the transferability of the adversarial examples while still being robust to defense models. We believe that pixel-level transformations are more fine-grained than image-level transformations, and thus can achieve better performance. Based on this finding, we propose the pixel-level scale variation (PSV) method to further improve the transferability of adversarial examples. The proposed PSV randomly samples a set of scaled mask matrices and transforms the part pixels of the input image with the matrices to increase the pixel-level diversity. Empirical evaluations on the standard ImageNet dataset demonstrate the effectiveness and superior performance of the proposed PSV both on the normally trained (with the highest average attack success rate of 79.2%) and defense models (with the highest average attack success rate of 61.4%). Our method can further improve transferability (with the highest average attack success rate of 88.2%) by combining it with other input transformation methods.