Reviewerly/exHarmony huggingface dataset:
Significance of ferruginous pisoliths and interface sampling for gold exploration in the covered terrains

The deeply weathered Yamarna Terrane is the easternmost, least explored Terrane of the Yilgarn Craton in Western Australia. Investigation of the landscape evolution, mineralogy and geochemistry of the transported cover showed that the near surface ferruginous regolith can provide a significant tool for exploration under cover. Three types of ferruginous materials were identified and examined in three gold prospects: Smokebush, Toppin Hill and Santana. The ferruginous materials include: 1) lateritic residuum and its detrital ferruginous clasts of underlying or nearby bedrock; 2) authigenic pisoliths and nodules of Permian glacial sediments (PPS); and 3) authigenic pisoliths of aeolian sand (PAS) overlying the other two. Our findings show that As is the main pathfinder element for Au mineralization. Arsenic and Au are elevated and form near surface anomalies in lateritic residuum, detrital ferruginous clasts, PAS, <75 μm and <2 μm soil size fractions at Smokebush. At Toppin Hill, Au migrated directly from the mineralization through the Permian cover and became enriched at the surface in the PPS above mineralization. Laser ablation ICP-MS mapping shows Au in the PPS as nano-grains, microcrystalline aggregates and veinlet fillings. This is supported by partial extraction analyses, where Au was extracted mainly by K-iodide and K-cyanide, indicating that it is dispersed in particulate and soluble forms. The absence of an As anomaly in transported cover over the Toppin Hill prospect compared to Smokebush is related to the absence of arsenopyrite in the Toppin Hill Au mineralization. Arsenic, which was extracted mainly by 0.1 M tetra‑sodium pyrophosphates, is associated with organic compounds and has a different fractionation pathway from Au near surface. This is also shown by an As anomaly in the Eucalyptus foliage over the Smokebush Au mineralization. At the Santana prospect, there are no Au and/or As anomalies in the PPS over the mineralization. The PPS at Santana are reworked from barren Permian cover and could be deposited with ferruginous clasts from different sources, as shown by textural and mineralogical variations between the grains and matrix. Furthermore, there is no evidence of hydromorphic dispersion of Au and As from the underlying mineralization. Thus, the PPS at Santana cannot be used to vector towards the mineralization. However, sampling at the unconformity (interface sampling) is more effective in locating the mineralization at Santana. This study shows that ferruginous pisoliths and clasts in the cover are potential sampling media for Au exploration in the Yamarna Terrane. The exploration methods used in this study can be also applied in similar covered terrains not only in Australia but also in areas with complex weathering histories in the tropics and sub-tropics.
GAN-Based Image Deblurring Using DCT Discriminator

In this paper, we propose high quality image deblurring by using discrete cosine transform (DCT) with less computational complexity. Recently, Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) based algorithms have been proposed for image deblurring. Moreover, multi-scale architecture of CNN restores blurred image cleary and suppresses more ringing artifacts or block noise, but it takes much time to process. To solve these problems, we propose a method that preserves texture and suppresses ringing artifacts in the restored image without multi-scale architecture using DCT based loss named “DeblurDCTGAN.”. It compares frequency domain of the images made from deblurred image and ground truth image by using DCT. Hereby, DeblurDCTGAN can reduce block noise or ringing artifacts while maintaining deblurring performance. Our experimental results show that DeblurDCTGAN gets the highest performances on both PSNR and SSIM comparing with other conventional methods in GoPro, DVD, NFS and HIDE test Dataset. Also, the running time per pair of DeblurDCTGAN is faster than others.
Discovering nonlinear resonances through physics-informed machine learning

For an ensemble of nonlinear systems that model, for instance, molecules or photonic systems, we propose a method that finds efficiently the configuration that has prescribed transfer properties. Specifically, we use physics-informed machine-learning (PIML) techniques to find the parameters for the efficient transfer of an electron (or photon) to a targeted state in a non-linear dimer. We create a machine learning model containing two variables, $\chi_D$, and $\chi_A$, representing the non-linear terms in the donor and acceptor target system states. We then introduce a data-free physics-informed loss function as $1.0 - P_j$, where $P_j$ is the probability, the electron being in the targeted state, $j$. By minimizing the loss function, we maximize the occupation probability to the targeted state. The method recovers known results in the Targeted Energy Transfer (TET) model, and it is then applied to a more complex system with an additional intermediate state. In this trimer configuration, the PIML approach discovers desired resonant paths from the donor to acceptor units. The proposed PIML method is general and may be used in the chemical design of molecular complexes or engineering design of quantum or photonic systems.
KUMITRON: Artificial Intelligence System to Monitor Karate Fights that Synchronize Aerial Images with Physiological and Inertial Signals

New technologies make it possible to develop tools that allow more efficient and personalized interaction in unsuspected areas such as martial arts. From the point of view of the modelling of human movement in relation to the learning of complex motor skills, martial arts are of interest because they are articulated around a system of movements that are predefined -or at least, bounded- and governed by the Laws of Physics. Their execution must be learned after continuous practice over time. Artificial Intelligence algorithms can be used to obtain motion patterns that can be used to compare a learners' practice against the execution of an expert, as well as to analyse its temporal evolution during learning. In this paper we introduce KUMITRON, which collects motion data from wearable sensors and integrates computer vision and machine learning algorithms to help karate practitioners improve their skills in combat. The current version focuses on using the computer vision algorithms to identify the anticipation of the opponent's movements. This information is computed in real time and can be communicated to the learner together with a recommendation of the type of strategy to use in the combat.
On the effect of incompleteness to check requirement-to-method traces

Requirement-to-method traces reveal the code location(s) where a requirement is implemented. This is helpful to software engineers when they have to perform tasks such as software maintenance or bug fixing. Indeed, being aware of the method(s) that implement a requirement saves engineers' time, as it pinpoints the exact code region that needs to be edited to perform a bug fix or a maintenance task. Engineers produce traces manually as well as automatically. Nevertheless, traces are incomplete. This limits the amount of information that could be used by an automated technique to check further traces. Therefore, since traces are incomplete, we would like to study the effect of incompleteness on the automated assessment of requirement-to-method traces. In this paper, we apply machine learning on either incomplete or complete tracing information and we evaluate the effect of incompleteness on checking trace information. We demonstrate that the use of complete traces might yield a higher precision but yields a lower recall. Also, the use of incomplete traces yields a higher recall but a lower precision.
Design and Implementation of Digital Platform of Academic Test in Colleges and Universities

Abstract With the rapid development of Internet technology, academic testing in colleges and universities also ushered in changes. The disadvantages of a test paper are becoming more and more obvious. This paper expounds in detail the design and implementation process of the online academic test digital platform designed and developed by using Java development language and MySQL database and eclipse development tools. The test results show that the digital platform of academic test in colleges and universities is flexible in practical application, and it is convenient to group papers, test and mark papers, and data collection and analysis, which provides work efficiency.
Shopping Recommendation System Design Based On Deep Learning

With the development of internet shopping, the amount of user data generated is increasing day by day. In this paper, a shopping recommendation system based on deep learning is constructed. The user data crawling module and shopping recommendation module are mainly designed. Firstly, obtain important user review information and product information from Jingdong Mall by python crawler and build a user data crawling module. Then a shopping recommendation system was constructed based on deep learning, combined with recommendation algorithm. The system extracts the characteristics of users and commodities through neural network algorithms, proposing a coupled recommendation algorithm (referred to as U-S recommendation algorithm) based on user characteristics and product similarity. The algorithm calculates the best match rate between users and commodities. The results show that the proposed algorithm can improve the effectiveness of the recommendation system, compared with the algorithm based on similarity of products.
Security Threats and Challenges in Public Cloud Storage

Cloud computing is a modern way for many businesses to offer computing tools and services that have transformed their business at a low cost. But the main concern is protection and privacy in cloud computing using a security program, encrypted file systems, and data loss software the problem and threats decreased. But the safety risks still exist in current days. Many cloud computing organizations and consumers also do not trust cloud providers to store their confidential data in public cloud storage because of the scarcity of accuracy in the protection of information sharing. This paper focuses primarily on security risks and problems that different cloud platforms face (Education, Enterprise, and Healthcare).
Graph Coloring Using Coupled Oscillator-Based Dynamical Systems

Graph coloring is a NP-hard problem, and computing the solution on a digital computer entails an exponential increase in the computing resources (time, memory) with increasing problem size. This has motivated the search for alternate and more efficient non-Boolean approaches. Here, we experimentally demonstrate the solution to this problem using the phase dynamics of coupled oscillators. Using a 30-oscillator IC platform with reconfigurable all-to-all coupling and minimal post-processing, our approach achieves 98% accuracy in detecting (near-) optimal solutions within 1 color of the optimal solution in comparison to the 77% accuracy achieved with the heuristic Johnson algorithm. Additionally, we propose a new local search-based post-processing scheme to improve the quality of the coloring solution. Finally, using circuit simulations, we demonstrate the scalability and speed up (~ 100×) achievable with the above approach in larger graphs.
Attack Detection and Countermeasures for Autonomous Navigation

Advances in artificial intelligence, machine learning, and robotics have profoundly impacted the field of autonomous navigation and driving. However, sensor spoofing attacks can compromise critical components and the control mechanisms of mobile robots. Therefore, understanding vulnerabilities in autonomous driving and developing countermeasures remains imperative for the safety of unmanned vehicles. Hence, we demonstrate cross-validation techniques for detecting spoofing attacks on the sensor data in autonomous driving in this work. First, we discuss how visual and inertial odometry (VIO) algorithms can provide a root-of-trust during navigation. Then, we develop examples for sensor data spoofing attacks using the open-source driving dataset. Next, we design an attack detection technique using VIO algorithms that cross-validates the navigation parameters using the IMU and the visual data. Following, we consider hardware-dependent attack survival mechanisms that support an autonomous system during an attack. Finally, we also provide an example of spoofing survival technique using on-board hardware oscillators. Our work demonstrates the applicability of classical mobile robotics algorithms and hardware security primitives in defending autonomous vehicles from targeted cyber attacks.
Exploring selfie practices and their geographies in the digital society

Drawing on a practical perspective and a posthuman approach, this article maps the quotidian digital geographies of selfie practices. The empirical analysis of this research is based on a qualitative study that took place in a Chinese city – Guangzhou. The empirical sections of this research investigate the digital geographies of daily selfie practices by focusing on the process of preparing for, taking, editing, and sharing selfies which are facilitated by both non‐human and human agencies. The findings of this article indicate that selfie practices are simultaneous a technology of self and a reflexive performance by which people construct diverse selves in extraordinary scenes and an obsessive way of seeing, consuming, and communicating shaped by wider digital cultures and social norms. More broadly, this article suggests that local contexts still matter in shaping the visual cultures and practices in contemporary digital society.
Redefining insider threats: a distinction between insider hazards and insider threats

This article suggests a new definition of insiders and insider threats. It refrains from applying a harm-oriented perspective that concentrates on the insider’s intention to cause harm because it defines the insider threat either too narrow or too broad. Instead, a privilege-oriented perspective is applied that focuses on the insider’s intention to misuse his privileged access to or knowledge about the organizational assets. Because existing privilege-oriented definitions refrain from making an explicit and clear-cut division between intentional and unintentional misuse of privilege, a new conceptualization is suggested that distinguishes insider hazards from insider threats. If the insider unintentionally misuses his insider privilege, it concerns an insider hazard. If the insider intentionally misuses his insider privilege, it is regarded as an insider threat.
A method of terminology alignment in Historical Books based on the correspondence between Pivot language and position

Abstract In view of the most difficult problem of terminology translation in the translation of historical books, this paper extracts term translation pairs from bilingual parallel corpus as a reference to improve the quality and efficiency of the translation of historical books. However, the small size of the corpus of historical books leads to poor word alignment, which brings a large number of errors to term alignment. At the same time, the special grammar of ancient Chinese will also affect term alignment. In order to solve the above two problems, this paper proposes a method of term alignment of historical books based on pivot language and position correspondence. This method introduces modern Chinese as the pivot language, makes use of the position correspondence between modern Chinese and English terms in sentences, and uses the sliding window method to generate candidate term pairs, which avoids the influence of word alignment errors on term alignment. In addition, if we first align to modern Chinese, the ancient Chinese terminology will complete the omitted characters in modern Chinese and replace the false characters, so as to avoid the influence of the special usage in ancient Chinese on the term alignment. The experimental results in Historical Records show that the accuracy of this method is slightly better than that of the method based on word alignment, which verifies the effectiveness of this method without word alignment.
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations.

While the need for interpretable machine learning has been established, many common approaches are slow, lack fidelity, or hard to evaluate. Amortized explanation methods reduce the cost of providing interpretations by learning a global selector model that returns feature importances for a single instance of data. The selector model is trained to optimize the fidelity of the interpretations, as evaluated by a predictor model for the target. Popular methods learn the selector and predictor model in concert, which we show allows predictions to be encoded within interpretations. We introduce EVAL-X as a method to quantitatively evaluate interpretations and REAL-X as an amortized explanation method, which learn a predictor model that approximates the true data generating distribution given any subset of the input. We show EVAL-X can detect when predictions are encoded in interpretations and show the advantages of REAL-X through quantitative and radiologist evaluation.
Seed picking crossover optimisation algorithm for semantic segmentation from images

Semantic image segmentation treats the issues involved in the object recognition and image segmentation as a combined task. The chief notion of semantic segmentation is to partition the image into visually uniform regions and to discriminate the class of the partitioned regions. Pixel classification is done over the segmented regions by assigning semantic labels. In general, inference frameworks are fed with the combination of low-level features and high-level contextual cues to segment an image. Since these combinations are rarely object consistent, result with minimum classification accuracy because of choosing non-influencing features and cues to track specific objects. To overcome this problem, a nature-inspired meta-heuristic optimization algorithm called Seed Picking Crossover Optimization (SPCO) is proposed to optimize i.e. train the CRF (Conditional Random Field) for choosing relevant feature to segment the object with high accuracy. To meritoriously recognize the objects, a semi-segmentation process is initially performed using Simple Linear Iterative Clustering (SLIC) algorithm. For pixel transformation and pixel association, Dirichlet process mixture model and CRF are employed. Optimized CRFs are used where the parametric optimization is done using the proposed SPCO algorithm. The proposed work results with 84% on classification accuracy and the performance evaluations are done using MSRC-21 dataset.
Efficient crowd counting model using feature pyramid network and ResNeXt

Crowd counting is one of the most challenging issues in the computer vision community for safety and security through surveillance systems. It has extensive range of applications, such as disaster management, surveillance event detection, intelligence gathering and analysis, public safety control, traffic monitoring, design of public spaces, anomaly detection and military. Early approaches still encounter many issues like non-uniform density distribution, partial occlusion and discrepancies in scale and perspective. To address the above problems, feature pyramid networks are introduced in deep convolution networks for counting the individuals in the crowd. The designed network has extracted the features at all resolutions and is constructed rapidly from only one input image. This method achieves outperformance results compared to the well-known networks on three standard crowd counting datasets.
VD-tree

Efficient similarity search is a core issue for retrieval operations on large amounts of complex data, often relying on Metric Access Methods (MAMs) to speed up the Range and k-NN queries. Among the most used MAMs are those based on covering radius, which create balanced structures, and enable efficient data retrieval and dynamic maintenance. MAMs typically suffer from node overlapping, which increases retrieval costs. Some strategies aim to reduce node over-lapping by employing global pivots to improve the filtering process during queries, but result at significant costs to maintain the pivots, whereas not completely removing the overlaps, which impacts queries over large databases. Other strategies use hyper-plane-based MAMs, which can get rid of overlaps but with large costs to create and update the index. We propose VD-Tree, a MAM which combines a covering radius strategy with a Voronoi-like organization. VD-Tree retains index flexibility for updates whereas reducing the node overlap using dynamic swap of elements among nodes. The method relies on only the solid organization fostered by Voronoi, and does not require storing further information to the tree. Experimental analysis using five real-world image datasets and four feature extractors shows that VD-Tree reduced node overlaps up to 43% and the average time needed to answer similarity queries by up to 28%, when compared to its closest competitor.
Towards annotation-free evaluation of cross-lingual image captioning

Cross-lingual image captioning, with its ability to caption an unlabeled image in a target language other than English, is an emerging topic in the multimedia field. In order to save the precious human resource from re-writing reference sentences per target language, in this paper we make a brave attempt towards annotation-free evaluation of cross-lingual image captioning. Depending on whether we assume the availability of English references, two scenarios are investigated. For the first scenario with the references available, we propose two metrics, i.e., WMDRel and CLinRel. WMDRel measures the semantic relevance between a model-generated caption and machine translation of an English reference using their Word Mover's Distance. By projecting both captions into a deep visual feature space, CLinRel is a visual-oriented cross-lingual relevance measure. As for the second scenario, which has zero reference and is thus more challenging, we propose CMedRel to compute a cross-media relevance between the generated caption and the image content, in the same visual feature space as used by CLinRel. We have conducted a number of experiments to evaluate the effectiveness of the three proposed metrics. The combination of WMDRel, CLinRel and CMedRel has a Spearman's rank correlation of 0.952 with the sum of BLEU-4, METEOR, ROUGE-L and CIDEr, four standard metrics computed using references in the target language. CMedRel alone has a Spearman's rank correlation of 0.786 with the standard metrics. The promising results show high potential of the new metrics for evaluation with no need of references in the target language.
Developing a Personal Voting Machine for the Estonian Internet Voting System

As the world's population is increasingly mobile, remote voting becomes more and more a necessity. Vote casting by paper mail is slow, expensive and error-prone. Voting over Internet, on the other hand, is a subject to a wide range of cyber attacks. One of the weakest points in current Internet voting (i-voting) schemes is the end user environment that is hard to control against both vote integrity and privacy attacks. The essence of the problem is that conventional end user devices (PC-s, mobile phones, etc.) are in some sense too powerful, being able to run malware without the voter having efficient means to detect it.