Reviewerly/exHarmony huggingface dataset:
Coherent provenance analysis of terra rossa from the northern Adriatic based on heavy mineral assemblages reveals the emerged Adriatic shelf as the main recurring source of siliciclastic material for their formation

Coherent provenance studies of soils that take into account data processing and the limitations arising from the physiochemical properties of the source material are scarce. In this study, the provenance of four terra rossa soils on the Istrian peninsula and four terra rossa soils on the island of Susak is investigated based on the heavy mineral assemblages of the soils and their potential sources. The heavy mineral data are treated as compositional data, which means that the information is stored in the ratio between the components and not in absolute values. The provenance of the allochthonous soil components showed that they originated from the now submerged alluvial plain i.e., emerged Adriatic shelf, mainly fed by alpine material during low sea levels. In the case of Savudrija loess and red paleosol and partly Koreniki sections, the allochthonous material originated from the local Eocene flysch, also windblown from the emerged shelf. The degree of mineral weathering, which correlates with the Fed/Fet and the total portion of heavy minerals, shows that this allochthonous component is a parent material of the terra rossa soils, not an impurity. Thus, the main role of the carbonate rocks with which the terra rossa soils are associated is to maintain a specific and favorable conditions for their formation. The origin of the parent material is associated with sea level oscillations, i.e., in times of low sea level the material was windblown from the emerged shelf, onto the land, this recurrent process can probably be traced back to the Oligocene. During periods of high sea level, i.e., warmer periods, terra rossa formed. Thus, the formation of terra rossa soils in the northern Adriatic is a recurrent process that may have begun at least since the Miocene. Consequently, abundance of terra rossa on the eastern Adriatic coast is due to a large emerged shelf area that supplied the carbonate platform with siliciclastic material during low sea levels.
Unsupervised generalizable multi-source person re-identification: A Domain-specific adaptive framework

Domain generalization (DG) has attracted much attention in person re-identification (ReID) recently. It aims to make a model trained on multiple source domains generalize to an unseen target domain. Although achieving promising progress, existing methods usually need the source domains to be labeled, which could be a significant burden for practical ReID tasks. In this paper, we turn to investigate "unsupervised" domain generalization for ReID, by assuming that no label is available for any source domains. To address this challenging setting, we propose a simple and efficient domain-specific adaptive framework, and realize it with an adaptive normalization module designed upon the batch and instance normalization techniques. In doing so, we successfully yield reliable pseudo-labels to implement training and also enhance the domain generalization capability of the model as required. In addition, we show that our framework can even be applied to improve person ReID under the settings of supervised domain generalization and unsupervised domain adaptation, demonstrating competitive performance with respect to relevant methods. Extensive experimental study on benchmark datasets is conducted to validate the proposed framework. A significance of our work lies in that it shows the potential of unsupervised domain generalization for person ReID and sets a strong baseline for the further research on this topic. The code is available at https://github.com/Qi5Lei/DSAF.
Conditional quantum circuit Born machine based on a hybrid quantum–classical​ framework

As a branch of machine learning, generative models are widely used in supervised and unsupervised learning. To speedup certain machine learning tasks, quantum generative adversarial networks, quantum circuit Born machine (QCBM), and quantum Boltzmann machine have been proposed. These generative models can implement some specific generative tasks but have no control over the modes of the generated data. To make the generative model more intelligent and controllable, additional conditional information (such as category labels for MNIST digits) can be added to the model to guide the generation of data. A more in-depth study was carried out based on the QCBM, and a conditional quantum circuit Born machine (CQCBM) based on a hybrid quantum–classical (HQC) framework was proposed. The conditional information was encoded by adding extra qubits to guide the model training process. Experiments were conducted on both mixed Gaussian distribution and MNIST handwritten digit dataset. Numerical and experimental results show that the proposed CQCBM is able to generate the target distribution while satisfying the conditional constraints well. Compared to other conditional quantum generative models only applied to Bars and Stripes (BAS) or Chessboard datasets, the proposed model also performed well on more difficult image-generating tasks.
The potential impact of emerging technologies on democratic representation: Evidence from a field experiment

Advances in machine learning have led to the creation natural language models that can mimic human writing style and substance. Here we investigate the challenge that machine-generated content, such as that produced by the model GPT-3, presents to democratic representation by assessing the extent to which machine-generated content can pass as constituent sentiment. We conduct a field experiment in which we send both handwritten and machine-generated letters (a total of 32,398 emails) to 7132 state legislators. We compare legislative response rates for the human versus machine-generated constituency letters to gauge whether language models can approximate inauthentic constituency voices at scale. Legislators were only slightly less likely to respond to artificial intelligence (AI)-generated content than to human-written emails; the 2% difference in response rate was statistically significant but substantively small. Qualitative evidence sheds light on the potential perils that this technology presents for democratic representation, but also suggests potential techniques that legislators might employ to guard against misuses of language models.
Information Security Challenges During Digital Transformation

Since the proliferation of information technology (IT) into business processes, organisations have grown to rely on a large amount of data to improve their products and services and create added value. This development has made information the most valuable asset for any organisation, which, in turn, has made information security a primary concern for leaders. Despite the tremendous potential of digital transformation, prior empirical studies indicate that information security challenges must be overcome to realise the anticipated benefits. Analysing the data collected from 14 leaders through semi-structured interviews, this study identified six information security challenges facing organisations undertaking digital transformation—financial constraints, risk of security breaches, reduced productivity, reduced access and control over information, lack of expertise, and dynamic security management needs. Propositions, as well as the implication of the findings for research and practice, are discussed.
Towards Responsible Quantum Technology

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Security enhancement and analysis of images using a novel Sudoku-based encryption algorithm

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Video anomaly detection system using deep convolutional and recurrent models

Automatic identification of anomalies in video surveillance is an interesting research field. Even though interactive multimedia anomaly detection algorithms have been developed, it is still hard for video surveillance to find unusual things like illegal activities and crimes. In this study, a deep convolutional neural network (CNN) and a simple recurrent unit (SRU) are used to build an automated system that can find anomalies in videos. The ResNet architecture takes high-level feature representations from the video frames that come in, while the SRU collects temporal features. The SRU has expressive recurrence and allows for highly parallelized implementation, which makes the video anomaly detection system more accurate. In the study, three models to detect anomalies are suggested as ResNet18 + SRU, ResNet34 + SRU, and ResNet50 + SRU, respectively. The suggested models are examined using the UCF-Crime dataset. This study made a clear distinction between normal and unusual actions, showing that CNN + SRU were able to put each unusual action in the right category. Using the UCF-Crime dataset, ResNet18 + SRU achieved 88.92% accuracy, ResNet34 + SRU achieved 89.34% accuracy, and ResNet50 + SRU achieved 91.24% accuracy. Furthermore, the proposed models demonstrated significantly higher performance accuracy and outscored the comparable deep learning models.
Chaos-based optimal path planning of humanoid robot using hybridized regression-gravity search algorithm in static and dynamic terrains

The present work deals with the navigational analysis of humanoids in complex terrains by tuning the classical approach with reactive techniques. Here, the linear regression (LR)-based classical approach is merged with the gravitational search algorithm (GSA), called RGSA, and Chaos is added to achieve an optimum path. To improve upon the local traps, Chaos is introduced into the basic structure of the RGSA model to attain a global solution without effecting the higher convergence rate of the RGSA model. The Chaos further adds ergodicity to the dynamical system, gaining an optimal path length in the shortest time possible. The use of the proposed hybridization technique is found to be effective as compared to its counterparts. Various chaotic maps like the logistic map, Gauss map, piecewise linear chaotic map, and sinusoidal maps are used, and the results are compared. Also, the smoothness of the path is ensured during trajectory planning. The analysis is done in real-time lab conditions and simulation environments using single and multiple humanoids (Nao robots) with static and dynamic obstacles within the search space. The simulation for the path planning is done using the Webot software, while the choregraphe software is used for experimental path planning. The simulation and experimental work presented in the current paper shows good agreement and are within 6% acceptable limits. The proposed controller is then compared with a vision-based approach and another parallel sensor-based approach for optimum operational cost and smoother trajectory. The current work is a novel approach in which the humanoid’s interaction with the mobile robot is also analyzed. The proposed approach successfully avoided the dynamic obstacle in the form of the KHEPERA-II mobile robot. This concept can find potential applications in soccer scenarios employing mobile robots as opponent players to the Nao robots. Further, because of the lower operational cost, the proposed methodology can also be applied to other areas, such as multitasking, cooperative scheduling, power distribution systems, etc.
Avg-topk: A new pooling method for convolutional neural networks

The pooling layer is a layer used in Convolutional Neural Networks (CNN) that takes the output feature map of the previous convolutional layer and reduces the feature maps to smaller sizes. Furthermore, in CNN the pooling layer is one of the layers that determines the success of the model. The pooling layer, reduces the spatial dimension of a CNN, greatly reducing the learning time and computational cost of the model. The most common pooling methods are maximum and average pooling. Due to the fact that the pooling strategy reduces the amount of feature maps and model parameters, it is crucial to preserve the dominant information. In this study, a cost-effective new pooling method approach is proposed. The proposed pooling method is used by calculating the weighted average of the dominant features. The proposed pooling model has been developed to address the shortcomings of maximum pooling and average pooling. The proposed new Avg-TopK pooling model takes the pixels with the highest interaction as much as the specified K number and averages them. In this study, the performances of several pooling strategies for gray and RGB picture classification in 3 different datasets were compared and analyzed in depth. Extensive experiments have demonstrated that the Avg-TopK pooling method achieves significantly higher image classification accuracy than conventional pooling methods. It has been observed that using the AVG-TopK method in transfer learning models leads to much more successful results. Furthermore, studies in the literature have compared based on the performance metrics and it has been seen that the proposed method produces more successful outcomes. In research conducted on datasets using this method, the accuracy achieved for the CIFAR-10 dataset was 6.28% and 16.62% according to the maximum pooling and the average pooling, respectively. For the CIFAR-100 dataset, the accuracy rate increased by 7.76% compared to the maximum pooling and by 25% compared to the average pooling.
A security awareness and competency evaluation in the energy sector

The energy sector is highly vulnerable to cyber-attacks due to its inherently complex ecosystem of both physical and cyber infrastructure spreading across the globe. Cyber-security breaches in this domain could have a significant impact not only on the global economy but also on citizens’ lives. This paper aims at evaluating the security awareness and competency of European Electrical Power and Energy Systems (EPES) organisations’ workforce during the COVID-19 pandemic and the Ukrainian war. A targeted assessment campaign has been designed and conducted from 11th February 2022 until 18th March 2022. During that period, 132 participants, out of the 266 invited employees, participated in the campaign. The collected results were analysed from different perspectives unveiling significant findings regarding information security readiness and resilience of individuals and, consequently, organisations in the European energy sector. Key findings are discussed in detail concluding with various cyber-security recommendations addressing both the emerged vulnerabilities and the need for security culture evolution.
Two families of negacyclic BCH codes

Negacyclic BCH codes are a subclass of neagcyclic codes and are the best linear codes in many cases. However, there have been very few results on negacyclic BCH codes. Let q be an odd prime power and m be a positive integer. The objective of this paper is to study negacyclic BCH codes with length $$\frac{q^m-1}{2}$$ and $$\frac{q^m+1}{2}$$ over the finite field $${\textrm{GF}}(q)$$ and analyse their parameters. The negacyclic BCH codes presented in this paper have good parameters in general, and contain many optimal linear codes. For certain q and m, compared with cyclic codes with the same dimension and length, the negacyclic BCH codes presented in this paper have a larger minimum distance in some cases.
Named Entity Recognition of Chinese Crop Diseases and Pests Based on RoBERTa-wwm with Adversarial Training

This paper proposes a novel model for named entity recognition of Chinese crop diseases and pests. The model is intended to solve the problems of uneven entity distribution, incomplete recognition of complex terms, and unclear entity boundaries. First, a robustly optimized BERT pre-training approach-whole word masking (RoBERTa-wwm) model is used to extract diseases and pests’ text semantics, acquiring dynamic word vectors to solve the problem of incomplete word recognition. Adversarial training is then introduced to address unclear boundaries of diseases and pest entities and to improve the generalization ability of models in an effective manner. The context features are obtained by the bi-directional gated recurrent unit (BiGRU) neural network. Finally, the optimal tag sequence is obtained by conditional random fields (CRF) decoding. A focal loss function is introduced to optimize conditional random fields (CRF) and thus solve the problem of unbalanced label classification in the sequence. The experimental results show that the model’s precision, recall, and F1 values on the crop diseases and pests corpus reached 89.23%, 90.90%, and 90.04%, respectively, demonstrating effectiveness at improving the accuracy of named entity recognition for Chinese crop diseases and pests. The named entity recognition model proposed in this study can provide a high-quality technical basis for downstream tasks such as crop diseases and pests knowledge graphs and question-answering systems.
Integrated approach using petrophysical, gravity, and magnetic data to evaluate the geothermal resources at the Rahat Volcanic Field, Saudi Arabia

It is necessary to develop and explore geothermal resources to achieve sustainable development and clean renewable energy around the Globe. Geothermal energy is crucial to the future energy supply to meet the environmentally friendly energy demand of the World. The Rahat Volcanic Field (Kingdom of Saudi Arabia) is the oldest and lengthiest Cenozoic Volcanic Field in the World. It is a dominantly a mature mafic Volcanic Field that holds three major geological events; the historic eruption (1256 C.E.), the five fingers (∼4500–1500 BP), and the seismic swarm (1999 C.E.). These incidents were studied by utilizing geological information and geophysical data sets. Geophysical and geostatistical research includes gravity and magnetic survey data, including different log curves and major elements, obtained from water samples as well as of volcanic rocks obtained by X-ray fluorescence (XRF). To gain an understanding of the subsurface thermal structure, these datasets were analyzed. The primary goal of this study is to identify the prominent potential geothermal resources with the help of an available data set. Findings suggest that beneath the historic eruption site along with the fissure eruption, on the western side, there is a geothermal anomaly with a surface footprint of about 35 km 2 . Analyzing gravity and magnetic data as well as density and magnetic susceptibility variations in rock samples led to the mapping of this anomaly. It has been inferred through integrated study that statistical analysis of major elements will be helpful to validate the results of the outcome.
Exploring postselection-induced quantum phenomena with time-bidirectional state formalism

Here we present the time-bidirectional state formalism (TBSF) unifying in a general manner the standard quantum mechanical formalism with no postselection and the time-symmetrized two-state (density) vector formalism, which deals with postselected states. In the proposed approach, a quantum particle's state, called a time-bidirectional state, is equivalent to a joined state of two particles propagating in opposite time directions. For a general time-bidirectional state, we derive outcome probabilities of generalized measurements, as well as mean and weak values of Hermitian observables. We also show how the obtained expressions reduce to known ones in the special cases of no postselection and generalized two-state (density) vectors. Then we develop tomography protocols based on mutually unbiased bases and a symmetric informationally complete positive operator-valued measure, allowing experimental reconstruction of an unknown single qubit time-bidirectional state. Finally, we employ the developed techniques for tracking of a qubit's time-reversal journey in a quantum teleportation protocol realized with a cloud-accessible noisy superconducting quantum processor. The obtained results justify an existence of a postselection-induced qubit's proper time-arrow, which is different from the time-arrow of a classical observer, and demonstrate capabilities of the TBSF for exploring quantum phenomena brought forth by a postselection in the presence of noise.
Profiling the vendors of COVID‐19 related product on the Darknet: An observational study

In a time of unprecedented global change, the COVID-19 pandemic has led to a surge in demand of COVID-19 vaccines and related certifications. Mainly due to supply shortages, counterfeit vaccines, fake documentation, and alleged cures to illegal portfolios, have been offered on darkweb marketplaces (DWMs) with important public health consequences. We aimed to profile key DWMs and vendors by presenting some in-depth case studies.A non-systematic search for COVID-19 products was performed across 118 DWMs. Levels of activity, credibility, content, COVID-19 product listings, privacy protocols were among the features retrieved. Open web fora and other open web sources were also considered for further analysis of both functional and non functional DWMs. Collected data refers to the period between January 2020 and October 2021.A total of 42 relevant listings sold by 24 vendors across eight DWMs were identified. Four of these markets were active and well-established at the time of the study with good levels of credibility. COVID-19 products were listed alongside other marketplace content. Vendors had a trusted profile, communicated in English language and accepted payments in cryptocurrencies (Monero or Bitcoin). Their geographical location included the USA, Asia and Europe. While COVID-19 related goods were mostly available for regional supply, other listings were also shipped worldwide.Findings emerging from this study rise important questions about the health safety of certain DWMs activities and encourage the development of targeted interventions to overcome such new and rapidly expanding public health threats.CovSaf, National Research centre on Privacy, Harm Reduction and Adversarial Influence Online (REPHRAIN), Commonwealth Fund.
Geological Mapping via Convolutional Neural Network Based on Remote Sensing and Geochemical Survey Data in Vegetation Coverage Areas

Geological mapping in vegetation coverage areas is a challenging task. In this study, convolutional neural networks (CNNs) were employed for geological mapping in a vegetation coverage area based on remote sensing images and geochemical survey data. The Gram-Schmidt fusion technology was first applied to fuse Sentinel-2A and ASTER remote sensing images to enhance the spatial resolution and enrich spectral information of remote sensing data. The fused remote sensing images were then organically integrated with geochemical survey data according to the correlations between the geochemical element contents and spectral reflectance of the objects. A case study of mapping six lithologic units in Jilinbaolige, Inner Mongolia, China was implemented using a CNN model based on the fused data. The classification map obtained an overall accuracy of 83.0%, which exhibited a better performance in contrast to random forest (RF) model. The results showed that CNNs can take full advantage of the spatial features of fused data and solve the problems of the ‘salt and pepper phenomenon’ against the shallow machine learning algorithms, and the fusion of remote sensing and geochemical data can provide rich diagnostic information for geological mapping.