Reviewerly/exHarmony huggingface dataset:
Silicon Photonic Integrated Reservoir Computing Processor with Ultra-high Tunability for High-speed IM/DD Equalization

Intensity modulation and direct detection (IM/DD) technology still dominates the optical fiber communication region for the sake of cost and energy efficiency. Reservoir Computing (RC), a special machine learning algorithm suitable for sequence models, has recently been applied to reduce the inter-symbol interference (ISI) caused by dispersion and Kerr nonlinearity in IM/DD systems. In this paper, we designed and numerically simulated a Photonic Integrated Reservoir Computing Processor (PIRCP) with two recurrent nodes using a standard silicon-on-insulator platform. The PIRCP exhibits ultra-high tunability of phase, intensity, delay time and detuning frequency of the optical carrier, which greatly facilitates parameter sweeping for the obtaining of the optimal processing performance. To validate the efficiency of our design, we implemented the PIRCP along with a Feed Forward Equalizer (FFE) in the receiver-end, and finally achieved sub HD-FEC performance for 112 Gbps/λ transmission over 60 km standard single-mode fiber (SSMF) with an ROP of -15 dBm, showing an improvement of 5 dBm compared with non-RC scheme.
Digital Dispossession and Gendered Discrimination in Use of Smartphone by Women in Marginal Settings in India

Women are becoming avid user of mobile phones and are increasingly turning to technology to up-skill themselves which offers scope to reduce gender inequalities even in marginal settings. Technology has ushered in a development offering which holds tangible promise to reduce social barriers by providing training that boosts proficiency. Digital media offers scope and opportunities of reshaping systemic gendered imbalances of social structure. Women, in marginalized settings, hemmed in by deeply rooted patriarchal structures may find emancipation by use of the digital technologies in their everyday routine which may accelerate restructuring of their social and cultural realities. The role of digital media in struggle for women’s inclusion in the civil society and gender equality has been considered as emancipatory. This is particularly so because technology provides an avenue for women’s engagement where their physical mobility is restricted and voices deliberately muted. However, patterns of social attitudes may underpin internet cultures if viewed through a gendered prism. There is a persistent gender gap in mobile phone ownership and usage in the low income setting areas and women tend to experience certain barriers more acutely than men. This deprivation in terms of being unconnected widens the gap of marginalization. The present study is an attempt to explore entry of digital technologies in a given culture of slum area in urban settings and subsequent negotiations for the concomitant changes in the discriminatory roles and responses. Two research objectives that have been explored are- factors that engender marginalisation of women and their gendered aspects constraining their digital literacy and competency; the second objective is to describe disruptive impact of digital devices on traditional gender roles and responsibilities that determine the relationships and status in the family. A sizeable percentage of the slum dwellings have electricity, television and smartphones even though lacking toilets. The paper uses qualitative method and tools of observation, focus group discussions and interviews to study mobile and smartphone usage of women living in slums to capture their everyday mobile experience and internet adoption in the context of existing gender inequalities. The study finds that power dynamics within the household play roles that mark digital in/exclusion and (dis) empowerment. It is obvious that digital divide exists even among marginalised as do categorical differences because women are conditioned to accept and continue male dominance in subtle ways. Augmenting the efforts to provide digital literacy and competency to marginalised women may turn out to be real game changer in bringing about social change.
QUALITY OF SERVICE MANAGEMENT FOR EFFECTIVE DISTANCE LEARNING

The present time is characterized by the active use of information and communication technologies in education, in particular distance educational technologies. The widespread use of distance learning is due to the ability to study at a convenient time, in a convenient place and pace, providing students with equal opportunities to receive education regardless of their place of residence, health status and material situation. Moreover, and due to the current epidemic in the world, distance learning has become a necessity, accompanied by an increased load on the Internet, therefore, and based on the importance of distance learning in our time, it is necessary to develop effective data transfer algorithms to ensure the optimal operation of the distance learning process. This paper discusses the various mechanism and network protocols used in distance learning, and technical requirements almost for each of them, suggests a new mechanism and algorithm for supporting the transmission of multimedia packets which are very essential and important in the process of distance learning, explains the concept of quality-of-service QoS in computer networks and its importance to provide a good data transmission through the network, Quality of service management is based on the management of such data transmission parameters as network bandwidth, information transmission delay, percentage of packet loss in the network and jitter, every type of network application imposes its own QoS requirements to ensure successful and effective data transfer, and our main task will be to optimize quality of service parameters in order to provide the best data transmission for distance learning.
THE IMPACT OF E-LEARNING ON THE FUTURE JOB MARKET – PREDICTING A NEW EDUCATIONAL TYPE OF LEARNING STYLE FOR THE NEXT GENERATIONS

This paper aims to present a theoretical analysis of a new educational type of learning styles for next generations. We are in a new era of technological breakthrough regarding Artificial Intelligence and Robotics that will change the jobs as we know them and will create others. In the next decades, machines and robots are expected to perform more current tasks than humans and there will be a need of new skilled workers that could program, design and handle the Artificial Intelligence. What happens with the ones that will be left out ? It will be a critical issue for the business to take a proactive role in supporting their existing employees through reconverting to new areas of competencies and the educational institutes to create an environment that facilitates workforce transformation. The change in the educational paradigm should have two key components: soft skills and technical skills. Soft skills are the key abilities for the ones that will reconvert from jobs that will disappear and technical skills are key abilities for the jobs that aren't created yet. The best scenario is the combination of the two skill sets and the challenge to combine them in a new educational platform for students and reconverting education for adults and elders. The studies show us that we are not proactive on the issue of preparing the workforce that will fill the job market in the future. This article will review a series of predictions regarding jobs that will be created based on the digitization of old industries, new technological areas and a modern way of education.
Computing Classifier-Based Embeddings with the Help of Text2ddc

We introduce a method for computing classifier-based semantic spaces on top of text2ddc . To this end, we optimize text2ddc, a neural network-based classifier for the Dewey Decimal Classification (DDC). By using a wide range of linguistic features, including sense embeddings, we achieve an F-score of 87,4%. To show that our approach is language independent, we evaluate text2ddc by classifying texts in six different languages. Based thereon, we develop a topic model that generates probability distributions over topics for linguistic input at the word (sense), sentence and text level. In contrast to related approaches, these probabilities are estimated with text2ddc, so that each dimension of the resulting embeddings corresponds to a separate DDC class. We finally evaluate this Classifier-based Semantic space (CaSe) in the context of text classification and show that it improves the classification results.
Streaming State Validation Technique for Textual Big Data Using Apache Flink

Data processing comes on the top of list while handling large amount of data. Batch processing and stream processing are two main frameworks for handling big data. Stateful stream processing has been grasping attention due to its applications into an extensive range of scenarios. Stream processors are emerging as a framework that analytical focused but also focus on handling the core of insistent application logic. Thus, the growing systems needs support for application state with robust consistency guarantee, partial failures, and adaptive cluster reconfigurations. Apache Flink, an open-source stream processing engine served such needs as main design principle by providing state management feature. However, there is absence of strong consistency and accuracy guarantee for returned values of state. The proposed research provides the approach to access state via state processor API recently introduce by Apache Flink and validation process for calculated state.
Active Learning to Select Unlabeled Examples with Effective Features for Document Classification

In this paper, for document classification task and text mining based on machine learning, I propose a new pool-based active learning method to select unlabeled data that have effective features not found in the training data. Given a small set of training data and a large set of unlabeled data, the active learner selects the most uncertain data that has effective features not found in the training data from the unlabeled data and asks to label it. After capturing these uncertain data from the unlabeled data repeatedly, I apply the existing pool-based active learning to select training data from the unlabeled data efficiently. Therefore, by adding data with effective features from unlabeled data to training data, I consider that it is effective to improve the performance of the pool-based active learning. To evaluate the efficiency of the proposed method, I conduct some experiments and show that my active learning method achieves consistently higher accuracy than the existing algorithm.
Things we just don't talk about: ethics in mineral collection

Minerals occupy a unique position as specimens yielding scientific information, objects with aesthetic and monetary value, and substances necessary for modern society. Aside from minerals in industrial use, a broad range of people are interested in minerals such as geologists, gemologists and jewelers, and people involved in minerals as collectors&amp;#8217; items including miners, dealers, private and institutional collectors, curators -- even art collectors and anthropologists. Traditionally, we have taken the extractive nature of these minerals for granted with little discussion of where and how they are obtained.Adherence to legalities, disclosure of information, and personal and institutions responsibility are among ethical considerations. Legal considerations include how a mineral specimen is obtained in its initial extraction, its movement through opaque supply chains, export and import requirements, and ownership transfer. Disclosure issues range from curators and institutions maintaining and relaying accurate information about a mineral&amp;#8217;s authenticity, sourcing, and history. What ethical responsibilities do individuals and institutions possess to ensure acquisition policies that address these issues? And, finally, as mineral extraction becomes a more pressing issue in the world&amp;#8217;s move from a fossil-fuel economy, where does ethics lie in educating the public in the role minerals play in the environment and society?Some of these questions raise issues which, in their complexity, have no apparent or easy solution. This paper presents the results of a literature survey on ethics of mineral specimens and raises questions for geoscientists.
Geochemical and geochronologic investigations into carbonate veins from historical drill cores in undercover Mt Isa, NW Queensland

This study investigates carbonate veins in five drill cores archived at Geological Survey of Queensland from NW Queensland, ~100 km south of Mt Isa, with geochemical and geochronologic approaches in order to characterise origins of fluids and their mineralisation potentials. Carbonate veins are mostly hosted in Proterozoic age (approximately 1800-1650 Ma) supra-crustal rocks, which are inferred from geophysics to be covered by 350 to 2,000 m of younger sedimentary rocks of the Eromanga and Georgina basins. Data regarding the nature of fluid activities is important for comparison between the undercover southern Mt Isa and outcropped Mt Isa mineral district, which is a world-class mineral province.Multiple-phase carbonate veins (mostly calcite and dolomite) are identified, including late formed brittle veins, pyrite/chalcopyrite mineralisation-bearing carbonate veins, and calcite hosted in crackle breccias. Samples are prepared into one-inch polished mounts and studied with SEM-EDS, and in situ laser ablation ICP-MS for geochemical and U-Pb geochronological studies. Relatively pure calcite phases are also prepared with microdrill for stable isotope C and O analysis. Trace element datasets suggest enrichments in rare earth elements and ytterbium (REE+Y) with distinct negative Eu anomalies. Stable isotopes range ~-5 to &amp;#8211; 15 (&amp;#948;13CVPDB&amp;#8240;) and ~10 to 25 (&amp;#948;18OVSMOW&amp;#8240;). Trace element data, Yb/La and Yb/Ca ratios, and stable isotope signatures imply that these carbonate veins have hydrothermal origins. 206Pb/238U geochronology data has indicated multi-phase calcite formation ranging from the late Neoproterozoic to Cretaceous. Our results provide a novel dataset to demonstrate the use of carbonate veins in revealing fluid activities in a mineral district and help the future exploration of critical mineral deposits in undercover southern Mt Isa when interpreted against regional structural data and well-documented mineralisation events in the northern Mt Isa district.&amp;#160;
Access control and reporting at the Data Center for Deep Geothermal Energy

The CDGP [https://cdgp.u-strasbg.fr], Data Center for Deep Geothermal Energy, is a Research repository for deep geothermal data in Alsace (France) originating from academic observatories or industrial providers. It collects seismological (catalogues, waveforms, focal mechanisms), seismic, hydraulic, geological, and all data related to anthropogenic hazard from the different phases of a geothermal project, mainly from the exploration and development stages at Soultz-sous-For&amp;#234;ts, Rittershoffen and Vendenheim EGS geothermal sites. Data are verified, validated, and curated; they are then described with metadata following the ISO 191115/19139 standards and have owner-defined distribution rules associated with them. A persistent Digital Object Identifier (DOI) is associated with the collections as well as a &amp;#8220;how to cite&amp;#8221; statement, for better citation. Data are converted into standard formats and archived in data warehouses. The CDGP is also a node of the EPOS TCS-AH platform [https://tcs.ah-epos.eu/] and provides Episodes metadata and data.Metadata are open and harvested, they are also pushed on the TCS- AH platform. Data follow FAIR&amp;#8217;s statement &amp;#8220;as open as possible, as closed as necessary&amp;#8221;. CDGP has set an authentication, authorization, and accounting infrastructure (AAAI) to compare distribution rules and user affiliation. Access to data on the TCS-AH platform is conditional on academic membership, and data are provided on-demand. Access controls are made as near the user as possible (subsidiarity principle). Monitoring and reporting are part of the AAAI, and usage is described in terms of use and privacy. Reports are sent to data providers every semester. CDGP operates a regular bibliographic follow-up, e.g. with Google Scholar or by questioning users that downloaded data. On his side, the TCS-AH can only provide general statistics on user engagement, origin, etc.
Hyperspectral mineral mapping for underground mining

Future mining will increasingly require rapid and informed decisions to optimise ore extraction and valuation. In this context, the use of hyperspectral imaging has been proven to be effective for geological mapping in surface mining operations. The potential of hyperspectral methods in underground mining environments, however, remains underexplored due to challenges associated with illumination and surface water. Our contribution addresses this gap by evaluating different lighting setups and the effect of moisture on the spectral quality of hyperspectral data in a laboratory setup. We also compared three commercially available, visible-near infrared to shortwave infrared sensors to assess their suitability for underground hyperspectral scanning. As a demonstration, we acquired hyperspectral data from three adjacent outcrops in the visitor&amp;#8217;s mine of Zinnwald, Germany, where rocks of a Late Variscan Sn-W-Li greisen-type deposit are exposed in representative underground mining conditions. A photogrammetric 3D digital outcrop model was used to correct for illumination effects in the data. We then estimated mineral abundance and lithium content across the mine face employing an adapted workflow that combines quantitative XRD measurements with hyperspectral unmixing techniques. Laser-induced breakdown spectroscopy was used to validate the results. While there are still challenges to overcome, this study proves that hyperspectral imaging techniques can be applied underground to yield rapid and accurate geological information. This application will pave the way for the safe, digital and automated underground mine of the future.
Detection of Deep Low-Frequency Tremors from Continuous Paper Records at a Station in Southwest Japan About 50 Years Ago Based on Convolutional Neural Network

The establishment of the High Sensitivity Seismograph Network (Hi-net) in Japan has led to the discovery of deep low-frequency tremors. Since such tremors are considered to be associated with large earthquakes adjacent to tremors on the same subducting plate interface, it is important in seismology to investigate these tremors before establishing modern seismograph networks that record seismic data digitally. We propose a deep-learning method to detect evidence of tremors from seismogram images recorded on paper more than 50 years ago. In this study, we trained a convolutional neural network (CNN) based on the Residual Network (ResNet) with seismogram images converted from real seismic data recorded by Hi-net. The CNN trained by fine-tuning achieved an accuracy of 98.64% for determining whether an input image contains tremors. The Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps for visualizing model predictions indicated that the CNN successfully detects tremors without being affected by&amp;#160;teleseisms. The trained CNN was applied to the past seismograms recorded from 1966 to 1977 at the Kumano observatory, in southwest Japan, operated by Earthquake Research Institute, The University of Tokyo. The CNN showed potential for detecting tremors from past seismogram images for broader applications, such as publishing a new tremor catalog, although further training using data including more variables such as the thickness of the pen would be required to develop a universally applicable model.
Hyperspectral sensing as a tool for smart environmental monitoring in mine waste management

The use of hyperspectral imaging (HSI) at different scales has become a big ally for a large range of applications in remote sensing, especially within the mining life cycle. In this contribution, we explore the use of HSI sensing, particularly to monitor mine waste at different constellations that might face environmental damaging phenomena: Acid Mine Drainage (AMD). Failure to accurately monitor and remediate such a complex process, leads to long-term impacts on ecosystems and human health, in addition to significant financial consequences and reputational damage to operators. We propose a workflow to integrate hyperspectral visible to near-infrared (VNIR) data, together with mineralogical and geochemical data to precisely map the extent of acid mine drainage using machine learning algorithms. Collected data from the field and further laboratory analyses on a few specimens provide the ground-truth and training data to support the proposed mapping algorithms. Results consist of semi-quantified concentration of dissolved metals, physicochemical properties in water bodies, and associated AMD minerals sub-products (e.g., goethite, jarosite, schwertmannite) within mine waste materials, as well as mineralogical characterization for predictive modeling at laboratory scale. &amp;#160;Regardless of the scale of acquisition, spectral imaging represents a cost-effective tool to enhance the quality of classical environmental analyses both in active and post-mining scenarios, which can increase the overall accuracy of the monitoring, allowing frequent and multi-temporal observations to reveal risk scenarios, take fast corrective actions and keep a continuous control.
Earthquake swarm September/October 2022 on Disko Island, West Greenland

During 25 days in September and August 2022, the island of Disko in central west Greenland experienced a swarm of around 200 small earthquakes, ranging from ML 1.0 to ML 3.1. The majority of the earthquakes were concentrated on a small peninsula measuring 15 x 25 km. We have not received any felt reports, with &amp;#160;the closest inhabited area is approximately 20 km away. The island of Disko is part of the Palaeogene North Atlantic province. Swarms has been observed on and around Disko Island earlier, but not in the same place as the September/October 2022 swarm. In 2010, 27 earthquakes were detected in an area&amp;#160; around 20 km to the east [1], and in 2016 a swarm of over 250 earthquakes occurred following three mb 4.5+ earthquakes. These earthquakes occurred offshore and south of Disko Island, 60 km to the SSE [2]. The coverage with seismic stations is Greenland is sparse, with distances between stations in hundreds of km, but since 2019 three additional stations have been in operation in central west Greenland due to monitoring for landslide events, enabling better locations. The closest station to the swarm is GDH, at 60 km distance. The largest earthquakes in the swarm could be observed at a distance of more than 1600 km.Larsen, T.B., et al., Earthquake swarms in Greenland. Geological Survey of Denmark and Greenland (GEUS) Bulletin, 2014. 31: p. 75-78. Dahl-Jensen, T., P.H. Voss, and T.B. Larsen, [S01-4-01] Recent earthquakes at Disko Island, Greenland, with focal mechanisms in IAG-IASPEI Joint Scientific Assembly. 2017: Kobe, Japan.