Reviewerly/exHarmony huggingface dataset:
Biometric multi‐factor authentication: On the usability of the <scp>FingerPIN</scp> scheme

Abstract Fingerprint‐based authentication has been successfully adopted in a wide range of applications, including law enforcement and immigration, due to its numerous advantages over traditional password‐based authentication. Despite the usability and accuracy of this technology, some significant concerns still exist, which can potentially hinder its further adoption. For instance, a fingerprint is permanently associated with an individual and, once stolen, cannot be replaced, thus compromising biometric‐based authentication. To mitigate this concern, we previously designed a multi‐factor authentication approach that integrates Type‐1 and Type‐3 authentication factors into a fingerprint‐based personal identification number (PIN). To authenticate, a subject is required to present a sequence of fingerprints corresponding to the digits of the PIN, based on a predefined secret mapping between digits and fingers. We conducted a preliminary vulnerability analysis and demonstrated that this approach is robust to the compromise of one or more of the subject's fingerprints. The objective of the work presented in this paper is to identify any usability issues for this FingerPIN scheme, collect qualitative and quantitative data through a user study, and determine the participants' satisfaction with the authentication mechanism. We carried out systematic usability tests, designed suitable performance metrics for assessing authentication usability on an initial cohort of 100 individuals, and performed a comparative analysis of the FingerPIN scheme against traditional sequential multi‐factor authentication schemes.
Cryptosystems Based on Tropical Congruent Transformation of Symmetric Matrices

Recently, public-key cryptography based on tropical semi-rings have been proposed. However, the majority of them are damaged. The main reason is that they use a public matrix to construct commutative matrix semi-rings. New public-key cryptosystems are proposed in this paper. They are based on tropical congruent transformation of symmetric matrix by circular matrix. The NP-hard problem of solving a tropical system of nonlinear equations underlies the cryptosystem’s security. Since a known matrix cannot express the used commutative subsemi-rings of circular matrices and there is no tropical matrix addition operation and power of matrix, the cryptosystems can withstand known attacks, including the KU attack, RM attack, and IK attack. The length of the public key and private key of the new cryptosystems is half that of those described in the literature.
Mapping hydrothermal alteration of the Au-Cu deposits in the Zhenghe magmatic-hydrothermal mineralization system, SE China, using Short Wavelength Infrared (SWIR) reflectance spectroscopy

Magmatic–hydrothermal mineralization developed in Zhenghe area, where a potential of blind porphyry Cu polymetallic deposits is expected. To explore alteration characteristics and their relationship with mineralization, alteration mineral mapping with the help of SWIR reflectance spectroscopy was carried out in the Dongji Au deposit, the Maluntou Au deposit, the Shizigang Cu polymetallic deposit, and the Dongkeng volcanic basin. The results suggest that illite ± chlorite assemblage predominates in the Dongji deposit, whereas phengitic illite ± chlorite ± epidote occurred in the Maluntou deposit. Chlorite ± epidote ± actinolite developed pervasively in the Shizigang deposit. The alteration feature and ore geology indicate the Dongji and Maluntou deposits formed in a low-intermediate sulfidation epithermal environment with moderate-low temperatures. Maluntou is more likely approached to a heat source. Four alteration zones including potassic–silicic zone, propylitic zone, phyllic zone, and surficial argillic zone are divided, and a heat center is roughly inferred in the Shizigang district, suggesting a proximal porphyry-hydrothermal alteration style. A model demonstrating mineralization and alteration is established, along with mineral spectral vectors to mineralization. Illite with Pos2200 (wavelength of sericite Al-OH absorption peak position) of about 2203 nm is correlative with Au mineralization in the Dongji deposit and phengitic illite with Pos2200 ranging from 2215 nm to 2219 nm is the product of metallogenesis in the Maluntou deposit. Illite with Pos2200 of approximately 2205 nm–2206 nm in the Nanyuan Formation and illite with Pos2200 of about 2207 nm in the Huangkeng Formation are formed in the non-mineralization stage or weathering. Muscovite with the Pos2200 of roughly 2206 nm has the potential to be an orebody vector in the Shizigang district. Pos2250 (wavelength of chlorite Fe-OH absorption peak position) variation in the Shizigang district indicates the location of drill hole ZK1-1 is approximal to the hydrothermal center. Shizigang Cu, Maluntou Au, Dongji Au, and other deposits in Zhenghe are probably part of an integrated metallogenic system with epithermal-porphyry features. High-intensity magmatic-hydrothermal activity indicates the potential for deep prospecting in the region.
Dual global-aware propagation for few-shot learning

Few-shot learning remains a challenging problem because it needs to classify unseen categories with only a few samples as limited supervision. The tasks and samples can be extremely different in various few-shot problems, which makes it even more difficult. Due to the local connectivity in CNN, it can not capture the global description of the samples and the features are not discriminative enough from a global viewpoint. Meanwhile, a sample usually reveals similar features in different tasks, which does not consider the global information of the task and weakens the discrimination of features. To address the above issue, we proposed a Dual Global-Aware method for label Propagation (DGAP) to encode two kinds of global description to enhance the discriminative power of the learned features. On the sample level, the global-aware sample module (GSM) is employed to get the contextual description and enhance the feature representation capability of each sample. On the task level, the global-aware task module (GTM) is used to embed the features in the current task to a more appropriate and discriminative position in the feature space which is task-oriented. In the end, a feature fusion module is adopted to combine the features obtained from both global sample and global task respects. Based on the label propagation method, the proposed DGAP improves the performance approximately 2–5% over the baseline on different benchmarks (mini-Imagenet and tiered-Imagenet) across different structures (Conv4 structure and ResNet12 structure), which reaches the state-of-the-art.
POFMakeup: A style transfer method for Peking Opera makeup

When using standard neural network transfer methods for portrait style transfer, semantically correct transfers often cannot be guaranteed; the texture details in the style examples tend to be ignored. This paper proposes a style transfer method for Peking Opera faces called POFMakeup. This method can transfer the style of a portrait with a Peking Opera face to another portrait. This method uses two guides, namely, a position guide and an appearance guide. The position guide ensures semantic consistency in the transfer process, while the appearance guide ensures the appearance of the target subject can be preserved during transfer. The experimental results show that this method not only solves the problem of traditional portrait style transfer but also can quickly and perfectly realize the style transfer of the Peking Opera face. The proposed method showed a 17% improvement in structural similarity (SSIM) over conventional methods.
Toward Sustainable Fashion Product Development: The use of 3D Virtual Prototyping Technologies in the Synchronous Remote Learning Classroom

This study examined the use of 3D garment prototyping technology as a remote learning facilitator to create new instructional designs for a product development course. The new instructional designs in a flipped learning approach for the remote, synchronous computer-aided fashion product development course was created, implemented, and evaluated within the Addie framework at one of the largest fashion colleges in the U.S. The students’ submitted final semester projects demonstrated that the new instructional designs were effective for the students’ synchronous remote learning achievement at a high level. The students had an enormous experiential learning experience from the instructor-student collaborative 3D avatar fashion show promoted on the college website and social media. The outcomes of this research can be used as a toolkit to create a new instructional design using a 3D prototyping technology as a learning facilitation tool in a synchronous remote classroom. More research must be conducted to focus on challenges in helping all students adapt to novel online learning environments. The current study must be further expanded to determine the use of other 3D prototyping technologies in different remote courses across disciplines.
Achieving small-batch accuracy with large-batch scalability via Hessian-aware learning rate adjustment

We consider synchronous data-parallel neural network training with a fixed large batch size. While the large batch size provides a high degree of parallelism, it degrades the generalization performance due to the low gradient noise scale. We propose a general learning rate adjustment framework and three critical heuristics that tackle the poor generalization issue. The key idea is to adjust the learning rate based on geometric information of loss landscape and encourage the model to converge into a flat minimum that is known to better generalize to the unknown data. Our empirical study demonstrates that the Hessian-aware learning rate schedule remarkably improves the generalization performance in large-batch training. For CIFAR-10 classification with ResNet20, our method achieves 92.31% accuracy using 16,384 batch size, which is close to 92.83% achieved using 128 batch size, at a negligible extra computational cost.
Impact of overloading of photovoltaic arrays on the evaluation of photovoltaic power generation forecasts

Photovoltaic power generation (PV output) forecast is based on solar irradiance forecasts; therefore, an increase in overloading of PV arrays may affect errors in the PV output forecast. To study the impact of overloading of PV arrays on evaluation of PV output forecasts, we compared the estimated and forecasted values of PV output under various overloading conditions and investigated the characteristics of forecast errors resulting from overloading. We used data of actual estimated PV output and forecasted PV output for 2016 to calculate the estimated and forecasted values of overloaded P PV output. Our findings indicate that compared with over-forecasted errors, errors due to under-forecasting undergo higher reduction in both time and magnitude with increasing inverter loading ratio. However, contrary to the initial assumption, the effect of PV module overloading was smaller than expected, which could be attributed to the smoothing effect of PV systems in a large area. PV module and array prices have been decreasing in the long term, and the number of overloaded PV systems is expected to increase worldwide. This study provides useful information for the stable operation of power systems including overloaded PV systems and for improving the economics of PV systems.
Kernel methods with asymmetric and robust loss function

The least squares support vector machine (LSSVM) has achieved great success in various fields, but it still has certain limitations. Firstly, it treats all points equally and does not take into account the impact of different sample locations. Secondly, it is vulnerable to outliers and noise. Moreover, when class-imbalanced data are encountered, the decision boundary is biased toward the minority class. To address above problems, this paper introduces a bounded linear-exponential (BLINEX) loss function into LSSVM (LSKB), where the asymmetry and boundedness of the BLINEX loss function allow LSKB to assign distinct weights to each sample and be insensitive to noise and outliers. Further, this paper extends LSKB to a cost sensitive (CSLSKB) form to make it adapt to the class-imbalanced data. A fast optimization algorithm based on NESVM is developed to solve non-convex LSKB and CSLSKB (NEBLL). Numerous experiments demonstrate their effectiveness in dealing with class-balanced as well as class-imbalanced problems.
Controls on the mineralogical and geochemical dispersion in soil and water around a tailing storage facility in the epithermal gold–silver mine in Central Kalimantan, Indonesia

The mineralogical and geochemical parameters on soil, waste, and tailings are major components for water quality assessments because they dictate the behavior of the chemical composition and water–rock interactions. Arsenic and trace metal contamination occasionally occurs because of the oxidation of arsenic-rich minerals and hydrothermal alteration or because of anthropogenic influences, such as industrial mining activity. This study aimed to understand the characteristics of tailings, soils, surface water, and groundwater to evaluate the possible geochemical dispersion of metals employed in tailing storage facilities. A study site at the former epithermal Au–Ag mine in Central Kalimantan, Indonesia, was selected because it was suitable for investigating the geochemical behavior of trace metals in water and solid samples and their possible contribution to metal contamination in tropical regions. A detailed study of the tailings comparing mineralogical and geochemical analysis indicates that the presence of unaltered, intermediate, and oxidized zone within the tailings can be related to the sulfides alteration indices (SAI). These zones include an unoxidized zone in the submerged areas where sulfides, including pyrite with less sphalerite and galena, remain fresh. In the intermediate zone, pyrite starts to change to iron oxyhydroxide. The SAI parameters from the intermediate zone, extending 50 cm below the tailing surface, indicate that only a few grains of pyrite in this zone are weakly altered and are capped by a thin (<10 μm) iron oxyhydroxide. The oxidized zone is indicated by traces of remaining pyrite and iron oxyhydroxide as the primary mineral. Heavy metals and rare earth elements from tailings storage facilities were scavenged from the soil solutions by groundwater. The stable isotopes of oxygen, hydrogen, and dissolved rare earth elements (REEs) were used as natural tracers in systems affected by acid mine drainage. The results showed that stable isotope and rare earth elements data do not support contamination between the surface and groundwater. This study found that the oxidation of ferrous iron to ferric ion and precipitation of Fe-oxyhydroxides in soil and water has a close spatial relationship with rare earth elements, which are concentrated only in intermediate and oxide zones.
A distributed real-time recommender system for big data streams

Recommender Systems (RS) play a crucial role in our lives. As users become continuously connected to the internet, they are less tolerant of obsolete recommendations made by an RS. Online RS has to address three requirements: continuous training and recommendation, handling concept drifts, and the ability to scale. Streaming RS proposed in the literature address the first two requirements only. That is because they run the training process on a single machine. To tackle the third challenge, we propose a Splitting and Replication mechanism for distributed streaming RS. Our mechanism is inspired by the shared-nothing architecture that underpins contemporary big data processing systems. We have applied our mechanism to two well-known approaches for online RS, namely, matrix factorization and item-based collaborative filtering. We conducted experiments comparing the performance with the baseline (single machine). Evaluating different data sets, experiments show online recall improvement by 40% with more than 50% less memory consumption.
A new approach based on convolutional neural network and feature selection for recognizing vehicle types

The number of vehicles used in traffic life has reached enormous dimensions today. The increase in the number of vehicles day by day causes some traffic problems along with it; such as traffic congestion, accidents, pollution, and safety. To overcome all these problems, convolutional neural networks (CNN) methods are one of the trend methods used in recent years due to their success. In this study, a new approach is proposed to use this power of CNN in low-power devices. First of all, MobileNetv1, MobileNetv2, and NASNetMobile models were optimized to increase accuracy performance. Then, an approach is proposed in which these optimized mobile CNN approaches are used only as feature extractors, and methods such as combining, selecting, and classifying the obtained features are used together. As a result of the classification made with this approach, the classification accuracy has increased by approximately 5%.
Explanation-Based Negotiation Protocol for Nutrition Virtual Coaching

People's awareness about the importance of healthy lifestyles is rising. This opens new possibilities for personalized intelligent health and coaching applications. In particular, there is a need for more than simple recommendations and mechanistic interactions. Recent studies have identified nutrition virtual coaching systems (NVC) as a technological solution, possibly bridging technologies such as recommender, informative, persuasive, and argumentation systems. Enabling NVC to explain recommendations and discuss (argument) dietary solutions and alternative items or behaviors is crucial to improve the transparency of these applications and enhance user acceptability and retain their engagement. This study primarily focuses on virtual agents personalizing the generation of food recipes recommendation according to users' allergies, eating habits, lifestyles, nutritional values, etc. Although the agent would nudge the user to consume healthier food, users may tend to object in favor of tastier food. To resolve this divergence, we propose a user-agent negotiation interacting over the revision of the recommendation (via feedback and explanations) or convincing (via explainable arguments) the user of its benefits and importance. Finally, the paper presents our initial findings on the acceptability and usability of such a system obtained via tests with real users. Our preliminary experimental results show that the majority of the participants appreciate the ability to express their feedback as well as receive explanations of the recommendations, while there is still room for improvement in the persuasiveness of the explanations.
Assessing Cyber Risks of an INS Using the MITRE ATT&amp;CK Framework

Shipping performed by contemporary vessels is the backbone of global trade. Modern vessels are equipped with many computerized systems to enhance safety and operational efficiency. One such system developed is the integrated navigation system (INS), which combines information and functions for the bridge team onboard. An INS comprises many marine components involving cyber threats and vulnerabilities. This study aims to assess the cyber risks of such components. To this end, a methodology considering the MITRE ATT&CK framework, which provides adversarial tactics, techniques, and mitigation measures, was applied by modifying for cyber risks at sea. We assessed cyber risks of 25 components on the bridge by implementing the extended methodology in this study. As a result of the assessment, we found 1850 risks. We classified our results as 1805 low, 32 medium, 9 high, and 4 critical levels for 22 components. Three components did not include any cyber risks. Scientists, ship operators, and product developers could use the findings to protect navigation systems onboard from potential cyber threats and vulnerabilities.
Towards energy efficient autonomous exploration of Mars lava tube with a Martian coaxial quadrotor

Mapping and exploration of a Martian terrain with an aerial vehicle has become an emerging research direction, since the successful flight demonstration of the Mars helicopter Ingenuity. Although the autonomy and navigation capability of the state of the art Mars helicopter has proven to be efficient in an open environment, the next area of interest for exploration on Mars are caves or ancient lava tube like environments, especially towards the never-ending search of life on other planets. This article presents an autonomous exploration mission based on a modified frontier approach along with a risk aware planning and integrated collision avoidance scheme with a special focus on energy aspects of a custom designed Mars Coaxial Quadrotor (MCQ) in a Martian simulated lava tube. One of the biggest novelties of the article stems from addressing the exploration capability, while rapidly exploring in local areas and intelligently global re-positioning of the MCQ when reaching dead ends in order to to efficiently use the battery based consumed energy, while increasing the volume of the exploration. The proposed three layer cost based global re-position point selection assists in rapidly redirecting the MCQ to previously partially seen areas that could lead to more unexplored part of the lava tube. The Martian fully simulated mission presented in this article takes into consideration the fidelity of physics of Mars condition in terms of thin atmosphere, low surface pressure and low gravity of the planet, while proves the efficiency of the proposed scheme in exploring an area that is particularly challenging due to the subterranean-like environment. The proposed exploration-planning framework is also validated in simulation by comparing it against the graph based exploration planner.
Adaptive boosted random forest-support vector machine based classification scheme for speaker identification

At each integrated work stage, many technologies and algebraic formulations are required to identify the speakers from speech signals. Due to advancements in machine learning and artificial intelligence, speaker recognition technologies are gaining popularity and are being used in a variety of applications. This paper presents a multi-feature adaptive boosted hybrid random forest support vector machine (AB-RFSVM) model that can process real-time, uninterrupted speech with 98 percent accuracy. To build the speaker recognition model, the extracted temporal and spectral speaker-specific information was applied as a feature vector to a suggested hybrid machine learning classifier. The goal of this work is to enhance the RF-SVM classifier's categorization performance by adaptively boosting the decision of RFSVM. SVM moves around the decision trees positioned in the experimentally discovered subareas around the hyperplane dividing the multi-classes of correctly and incorrectly identified speakers is subject to boosting of classification decisions collected using the RFSVM classifier. The predetermined default configuration value was utilized while building the RFSVM classifier. Finally, the efficacy of the AB-RFSVM method is evaluated against that of other baseline methods using a variety of typical and real-time speaker databases. The experimental results revealed that AB-RFSVM yielded a 99 percent specificity and a loss function of less than 2%. This paper's findings show that the hybrid AB-RFSVM classification method is more effective for identifying and verifying speaker data.