Reviewerly/exHarmony huggingface dataset:
Monitoring sedimentary areas from mine waste products with Sentinel‐2 satellite images: A case study in the <scp>SE</scp> of Spain

Abstract Torrential rainfall regimes, among others, are the origin of accelerated soil erosion. The Spanish southeastern Mediterranean region is characterised by an arid climate regime affected by extreme erosion episodes with an important loss of sediments. Soil erosion effects are even more noticeable in areas where soil has been anthropically degraded, as in the mining district of Sierra Minera de Cartagena . The present research focuses on monitoring and mapping the changes in the sediment distribution of iron oxides and hydroxyl (OH − ) bearing minerals caused by a cold drop known in Spanish as 'DANA' in September 2019 on the Rambla del Beal . This short rambla is fed by sediments from its drainage basin and by mining residues, irrigating a wide agricultural area. When discharges overflow the rambla channel, residues spread over its floodplain and reach the ecological protected coastal lagoon Mar Menor . The objective of the study was mapping the mineral distribution of the mining materials eroded from the source areas and sedimented in Rambla del Beal during a DANA. The study was carried out using a pre‐ and a post‐DANA image from the Sentinel‐2 satellite. After masking vegetation, urban areas and water bodies, different band ratios (B4/B3, B11/B12, B8A/B6) and a Spectral Angle Mapper (SAM) classification were applied. Sediment deposits were identified in wider areas after the DANA. Iron oxides increased their extension by 11.08% in the central area (B3/B4 with R 2 of 0.84) and hydroxyl‐bearing minerals increased by 8.95% in the Rambla del Beal 's headwaters (B11/B12 with R 2 of 0.71). The SAM classification (with a 0.1 rad threshold and an overall accuracy of 87.33%) allowed the differentiation and classification of two ferric iron oxides (haematite and goethite) and one iron hydrous sulphate mineral (jarosite). Additionally, band ratio images were spatially overlaid with the soil land uses map layer of the cadastre in order to plot the land uses most affected by the transported sediments during the DANA. These results highlighted agricultural land as the areas (land uses) most affected by iron oxides deposition, as oxidation processes occur more rapidly in these areas. However, grassland and scrubland were the areas with the highest content of hydroxyl‐bearing minerals, as water is accumulated in these places, which favours hydrolysis reactions. Highlights Torrential events can relocate large volumes of contaminated sediments from former mining areas. Sentinel‐2 allows monitoring mine waste iron‐sulphate changes caused by weathering alteration. Spatial mineralogical pattern in sediments is controlled by geomorphology and flood dynamics. The proposed digital image analysis allows mapping short‐term evolution of mine waste sediments.
STUDENTS’ PERCEPTIONS IN LEARNING LISTENING

One of the English skills that the students have to be comprehended is listening. Listening plays an important role in learning English. This article describes on the answers about investigating into the students’ perceptions regarding listening comprehension in English at Senior High School in Muhammadiyah Luwuk. It used descriptive qualitative and interview in collecting the data. The major problems emphasized by the students were dealing with listening skill is more difficult than other skill the teacher gave them the monotonous activities and the material too long (conversation and monologue) in learning listening. It made them feel hard to comprehend the meaning of the material. In addition, most of the students thought that the difficult in learning listening related with their feeling anxiety, unable to focus when answer and listen at the same time, poor quality of tapes recording, and unfamiliar words. Furthermore, students thought that learning listening was very difficult because of their memory problem in remembering the listening material, noisy surrounding, high speed of delivery text, and unclear pronunciation from the native speakers and the obstacles that many encountered in learning listening were lack of vocabulary.
AdaNS: Adaptive negative sampling for unsupervised graph representation learning

Recently, unsupervised graph representation learning has attracted considerable attention through effectively encoding graph-structured data without semantic annotations. To accelerate its training, noise contrastive estimation (NCE) samples uniformly negative examples to fit an unnormalized graph model. However, this uniform sampling strategy may easily lead to slow convergence, even the vanishing gradient problem. In this paper, we theoretically show that sampling those hard negatives close to the current anchor can relieve the above difficulties. With this finding, we then propose an Adaptive Negative Sampling strategy, namely AdaNS, which efficiently samples the hard negatives from the mixing distribution regarding the dimensional elements of the current node representation. Experiments show that our AdaNS sampling strategy applied on top of representative unsupervised models, e.g., DeepWalk, GraphSAGE, can outperform the existing negative sampling strategies in the tasks of node classification and visualization. This also further demonstrates that sampling those hard negatives can bring performance improvements for learning the node representations.
Security defense against long-term and stealthy cyberattacks

Modern cyberattacks such as advanced persistent threats have become sophisticated. Hackers can stay undetected for an extended time and defenders do not have sufficient countermeasures to prevent advanced cyberattacks. Reflecting on this phenomenon, we propose a game-theoretic model to analyze strategic decisions made by a hacker and a defender in equilibrium. In our game model, the hacker launches stealthy cyberattacks for a long time and the defender decides when to disable a suspicious user based on noisy observations of the user’s activities. Damages caused by the hacker can be enormous if the defender does not immediately ban a suspicious user under certain circumstances, which can explain the emerging sophisticated cyberattacks with detrimental consequences. Our model also predicts that the hacker may opt to be behavioral to avoid worst cases. This is because behavioral cyberattacks are less threatening and the defender decides not to immediately block a suspicious user to reduce cost of false detection.
Data Augmentation in 3D Object Detection for self-driving vehicles: the role of original and augmented training samples

Abstract Safe self-driving vehicles require precise 3D Object Identification. LiDAR sensors are key in accomplishing such a task, as LiDARs produce high-definition point clouds. Such point clouds are then processed by 3D Object Detection models to finally detect objects. Most Object Detection models require massive amounts of data to be trained. Gathering and processing this data is an expensive and time-consuming task, which is why the information taken from each sample must be fully harnessed. Such can be done through Data Augmentation. Data Augmentation contributes significantly for improving performance, being at least as relevant as the advances in the Object Detection models themselves. A few studies have been reported regarding the effectiveness of Data Augmentation. However, the role played by original and augmented samples has been neglected. This work reports the first-ever detailed quantification of the impact that the inclusion of original and augmented samples in a dataset has in 3D Object Detection in the context of autonomous driving. The obtained results show that although a good augmentation strategy is crucial to the model’s performance, it is only as good as the quality of the original samples allows it to be.
Continuous-variable measurement-device-independent quantum key distribution in free-space channels

The field of space communications is the realm of communication technologies where diffraction and atmospheric effects, both of which contribute to loss and noise, become overriding. The pertinent questions here are how and at which rate information (secret keys) can be securely transferred (shared) among users under such supposedly severe circumstances. In the present work we study continuous-variable (CV) quantum key distribution (QKD) in a measurement-device-independent (MDI) configuration over free-space optical (FSO) links. We assess the turbulence regime and provide a composable finite-size key rate analysis of the protocol for FSO links. We study both short-range, horizontal communication links as well as slant paths to, e.g., high-altitude platform station (HAPS) systems.
Breast cancer classification from histopathological images using transformers

Accurate detection and classification of breast cancer is a critical task in medical imaging due to the complexity of breast tissues. Transformers have recently impacted the computer vision area, also in the eld of medical image analysis. However, breast cancer classification from histopathological images using transformers faces a big challenge: Transformers are not suitable for small sets of data and medical datasets are more difficult to obtain. Therefore, breast cancer classification from histopathological images using Transformers is of great significance. In this study, we propose a breast cancer classification method using transformers without large sets of data. The network automatically extracts features through a supervised phase from images with specified size, and presents the result as a probability matrix as either a positive sample (malignant) or a negative sample (benign). The proposed model can achieve the accuracy about 89% when training from scratch on public dataset BreaKHis. The simple and compact model is made accessible to those equipped with basic computing resources and trained them in less than half hour. Consequently, the proposed method is better than the traditional ones, as it automatically learns the best possible features and experimental results show that the model outperformed the previously proposed transformers.
PEENet for phase unwrapping in fringe projection profilometry

In this paper, we propose a lightweight deep convolution neural network, named PEENet, for high resolution image phase unwrapping in fringe projection profilometry on the device with limited performance. In our method, the dilated convolution strategy is applied to the networks, which increases the receptive field of the network while reducing the amount of network parameters. In the PEENet, we use Atrous Spatial Pyramid Pooling (ASPP) structure which can reduce the network parameters (total 0.48 million) while can extract the deep features of image. We also use Edge-Enhanced Block (EEB) structure, which can enhance the edge features of the image. We conducted ablation experiments to explore the effect of different network structures on network performance and then we compare our method with the other lightweight deep convolution neural network with the same training and testing datasets. We also build a new dataset that contain more different situations which can enhance the generalization ability of the network. The results show that our method achieves higher accuracy with fewer parameters and the new dataset works well.
Cryptocurrency Methodologies and Techniques

Cryptocurrency is an eight-year-old technology that uses an encrypted peer-to-peer network to facilitate digital barter. Bitcoin, the first and most popular cryptocurrency, is paving the way as a disruptive technology to long-standing and unchanging financial payment systems. While cryptocurrencies are unlikely to replace traditional fiat currency, they have the potential to alter how Internet-connected global markets interact with one another, removing the restrictions that exist around traditional national currencies and exchange rates. Technology advances at a breakneck pace, and a technology’s success is almost entirely determined by the market it tries to improve. Cryptocurrencies have the potential to change digital trade marketplaces by enabling a fee-free trading mechanism. A SWOT analysis of bitcoin is offered, which highlights some of the recent events and movements that may have an impact on whether bitcoin contributes to a paradigm change in economics.
High-density Integrated Photonic Tensor Processing Unit with a Matrix Multiply Compile

With the increasing demanding for data processing and application-specific hardware, photonics can out-perform electrical IC in terms of speed and energy saving. Here, we present our Photonic Tensor Core architecture, showing high components density, with error rate lower than 4% on image edge detection.
A High-Speed Photonic Tensor Accelerator

We propose a coherent multi-dimensional (wavelength, spatial mode, polarization, etc.) photonic tensor accelerator capable of matrix-vector, matrix-matrix, and batch matrix multiplications in a single clock cycle. A proof-of-concept $2\times2$ matrix-matrix multiplication at 25GBd with 4.67 bit precision was experimentally demonstrated.
The Sound of Silence: Efficiency of First Digit Features in Synthetic Audio Detection

The recent integration of generative neural strategies and audio processing techniques have fostered the widespread of synthetic speech synthesis or transformation algorithms. This capability proves to be harmful in many legal and informative processes (news, biometric authentication, audio evidence in courts, etc.). Thus, the development of efficient detection algorithms is both crucial and challenging due to the heterogeneity of forgery techniques.This work investigates the discriminative role of silenced parts in synthetic speech detection and shows how first digit statistics extracted from MFCC coefficients can efficiently enable a robust detection. The proposed procedure is computationally-lightweight and effective on many different algorithms since it does not rely on large neural detection architecture and obtains an accuracy above 90% in most of the classes of the ASVSpoof dataset.
Quantum Photonic Chip for Binary Classification of Financial Data

A binary classifier of financial data is demonstrated for different kinds of Figure classify problems. The scheme is based on quantum photonic chip, and we directly trained the parameter of Mach Zehnder Interferometer (MZI) instead of interpreting the combination of MZI as quantum gates.
Automatic Self-calibration of Programmable Photonic Processors

In this work, we present and demonstrate a fully-automated routine to characterize the calibration curves of every phase actuator in highly-coupled programmable photonic processors. We apply and experimentally validate it to a 72-cell silicon hexagonal mesh arrangement.
Producing Successful Design Website of Video Based Learning in Hybrid Learning of Higher Education

Video-based learning is one of the digital platforms that can assist teachers in teaching with face-to-face collaboration and online learning during the hybrid learning period. But the fact is that in recent years many teachers are not proficient in making interesting video-based learning and the lack of availability of supporting media equipment to make it easier for teachers to understand making video-based learning or students who have difficulty accessing video-based learning due to infrastructure constraints. This research refers to the topic of learning technology. Therefore, in this research paper, we try to create a web design solution to overcome the problems that occur by using the theory of Nielsen Attributes of Usability and User-Centered Design in testing user needs and also testing usability in producing website solutions. This study collects data from 112 respondents in April 2022. The respondents are those who work as teachers or students in universities. respondents watched the interactive user interface video of the website (Prototype) before using it. The use of questionnaires to determine user needs and usability testing of the resulting website solutions. By using Nielsen Attributes of Usability to test learning ability, efficiency, memory, error, and satisfaction. and User-Centered Design for us to define the context of use, user requirements, and existing design solutions: Home page, Forum page, Create video page, video tutorial page, Chat page, and Video list page. This study aims to determine user expectations regarding video-based learning and user interfaces based on user recommendations. Based on the results of the data analysis the respondents who filled out were teachers 53.6% and students 46.4%, as many as 55.4% of respondents understood how to use and functions of the website, and 58% of respondents were interested in using the website. therefore the website design can be accepted and used properly by the user and the user is interested in using the website.
Classification of Road Damage Using Supervised Learning to Assist Visual Assessment of Road Damage

Roads are an important aspect of improving the welfare and productivity of the people that affect the country's economic growth. Therefore, monitoring of road conditions must be carried out. The Department of Highways and Spatial Planning of West Java Province in collaboration with the Bandung Institute of Technology has developed the Road Pavement Condition Survey application which makes it easier to monitor road conditions. However, some features of this application are still carried out semi-automatically, one of which is the road damage classification feature. This causes long working time. Classification of road damage itself needs a visual assessment by humans. Therefore, a solution is needed in the form of a machine learning-based road damage classification system that can help users visually assess road damage. The system uses the supervised learning method to build a classification model. The system still requires a visual assessment by the user to validate the system classification results. The system that has been built is considered to be able to help classify road damage by assisting the user's visual assessment, although the classification results by the system have not been able to provide a correct classification of road damage.
A Systematic Literature Review of RegTech: Technologies, Characteristics, and Architectures

Regulatory Technology, or RegTech, uses information technology and digital innovation to help the financial services industry realize regulatory compliance. RegTech helps them to streamline compliance processes. Although dominant in the financial sector, ideally, RegTech can be implemented in various sectors other than finance. The critical elements of RegTech are regulated entities, supervisors and/or regulators, regulatory data, and data generated by regulated entities' activities. The technology at RegTech will bring all these elements together to achieve regulatory compliance. This study describes RegTech regarding technology utilization, characteristics, and architecture in the financial industry. The results of this study can be adopted as a basis for answering the challenges of implementing RegTech in various sectors of activity. Therefore, we used the systematic literature review approach to review several prior studies. Based on the search results, thirty-seven articles were chosen successfully for further review. RegTech focussed on four characteristics: agility, speed, integration, and analytics. Various technologies are used in RegTech, such as big data analytics, artificial intelligence, machine learning, cloud computing, blockchain, etc. Generally, RegTech architecture components can be grouped into enterprise architecture (EA) layers: business, data, application, and technology.
Classification of Tumor Metastasis Data by Using Quantum kernel-based Algorithms

Tumor metastasis is a dynamic process, and its fatality rate is relatively high. Different genes are regulated during the metastasis process. Next-generation sequencing technology is a new approach that enables rapid and high-throughput whole-transcriptome measurements. Support vector algorithms make use of kernel functions to classify data effectively. A few studies suggest that quantum support vector machine algorithms can perform well in classification problems. If biomarkers can be identified to predict tumor metastasis accurately, it will be an important step toward precision medicine. In this study, we use both the SVM and QSVM classifiers with the addition of a certain number of features, we can achieve very good distinctions between patients with or without metastasis. This is a positive result for precision medicine studies. Also, we evaluate the performance of quantum and classical algorithms in classifying tumor metastasis data. Our preliminary study indicates that the classical kernel-based classifier performs better than the quantum version.
Blockchain-based Smart Contract in Service-Oriented Business Processes

Service systems' business processes show the existence of mutual interaction between users. Running a particular business subdomain requires the role of each user. This role is in line with the concept contained in blockchain-based smart contracts. However, determining the subdomains that require smart contract coding is challenging in developing business processes that will run on the blockchain. We aim to determine the service-oriented business process (in the case of marketplace service) and the subdomain of the business process that requires smart contract coding. This article is essential because it shows how developing IT services without a smart contract differs from developing IT services with a smart contract. The main differences between developing services that need smart contracts and those that do not are that certain process can be run automatically and the record of activity in blockchain cannot be changed by anyone (tamper-proof). We outlined methods for designing marketplace business processes and defined subdomains to be encoded into smart contract code. After reviewing the three marketplace platforms, we determined eight subdomains of the marketplace business process. Two subdomains require smart contract coding from eight, namely the payment and complaint subdomains.
From Hindsight to Foresight: Enhancing Design Artifacts for Business Logic Flaw Discovery

Web applications have encroached on our lives, handling important tasks and sensitive information. There are many tools that check application code for implementation-level vulnerabilities but they are often blind to flaws caused by violation of design-level assumptions. Fixing such flaws after code deployment is costly. In this work, we seek to retroactively identify business logic flaws or design-level flaws by generating security tests during the design phase using available software artifacts. Specifically, we take in use case scenarios and automatically generate misuse case scenarios based on user-defined design constraints. By running those misuse case scenarios using already existing testing code written for functional use cases, we can discover potential design flaws during the coding phase. We apply our approach to two widely used open-source applications which have high-quality feature files. Running our generated misuse case scenarios discovers, and hence, potentially prevents seven flaws. Among them, several were only fixed in hindsight after someone stumbled upon a bug, with the remaining being new issues.