Reviewerly/exHarmony huggingface dataset:
The gold potential of the northern Black Sea region

The article presents the results of the analysis and evaluation of gold occurrences of the Northern Black Sea and Crimea. The increased gold content of this region is associated primarily with magmatism and volcanism of the paleo&lt;island arc and the Ukrainian crystalline massif. Analysis of the field studies and laboratory results made it possible to determine the main factors of the origin of a gold mineralization. The magmatism of the Crimean Mountains formed both primary ore and alluvial occurrences, which extend in series from the Karadag in the east to Heraclea and the Lomonosov massif. The main source of fine gold placers are the weathering crusts of the Ukrainian shield rocks and secondary products of alluvium repeatedly redeposited in recent times, especially in post&lt;glacial periods, when there was a powerful outflow of unconsolidated sediments. The authors note the decisive role of ancient river systems both in the accumulation of gold&lt;bearing sands and in the formation of a potential source of outflow for the alluvial strata of numerous small rivers. Alluvial sands and alluvial-proluvial cover pebbles of the Beregansky and Sulsky climatoliths are particularly interesting for their gold content. They form clad covers on watersheds and fill the paleo&lt;valleys of large rivers. The authors also studied the role of the Paleo&lt;Dnieper and other main rivers of the Northern Azov Sea region during the melting of glaciers, when huge masses of sandy&lt;silty material, including fine and ultrafine gold, were transported over long distances. Thus, the identification of factors affecting the location, structure and composition of gold deposits is important for determining the prediction criteria of conditions potentially favorable for the formation of gold placers within the Northern Black Sea and Crimea. Data on the gold content of the Black Sea region currently is not sufficient. It is necessary to conduct further research on gold occurrences to determine the scientific and practical interest of this phenomenon. So far, we can state only the presence of gold occurrences, which provides the basis for further research and prospecting.
Reinforcement based Communication Topology Construction for Decentralized Learning with Non-IID Data

Federated Learning (FL) allows Internet-of-Things (IoT) devices to train a global model collaboratively and circumvent the security issue. However, the current FL framework has three main drawbacks, the huge network overhead, single point of failure, and accuracy degradation in non-independent-and-identically-distributed (non-IID) data distribution. We propose a novel Deep Reinforcement Learning (DRL) based Decentralized Learning (DL) framework, DeepSelect, to 1) reduce the network overhead of conventional FL, 2) construct a good communication topology adaptively to mitigate the effect of non-IID data, and 3) accelerate the DL training by balancing the effects of hitting time (HT) and data bias. Moreover, DeepSelect with a subtly-designed DRL agent is reusable with different levels of non-IID data distributions. To the best of our knowledge, this paper is the first one to indicate that proper neighbor selection for exchanging parameters (not raw data) can counterbalance the data bias's effect and improve the DL convergence with non-IID data. The experiment results show that DeepSelect can reduce 18%-51% training rounds than the other heuristics on FashionMNIST and CIFAR-10 with non-IID data distributions.
Skills, Practices, and Emotions for Teaching in Times of Pandemic

This chapter describes how Argentine teachers at the pre-school, primary, and secondary levels adapted to emergency distance education during the Preventive and Compulsory Social Isolation (ASPO) that was established in Argentina due to the COVID-19 pandemic in 2020. From an exploitative-descriptive strategy, a non-probabilistic survey was carried out through an online questionnaire during June 2020, whose sample consisted of 411 cases. Among the results, it is highlighted that the teachers applied their knowledge and skills prior to the pandemic, but they also had to learn to use new resources to adapt to the educational conditions of the pandemic, which led to greater effort and difficulties for older teachers. In accordance with this, the predominance of joy and pride in the younger teachers indicates that they were more satisfied than the older ones. It is concluded that despite the fact that all teachers expressed having adapted, the older ones encountered greater difficulties and, consequently, had to resort to a greater diversity of adaptation strategies.
Shadow Detection and Tracking in Video

Advance video surveillance systems deal with shadows for segmentation of moving objects. Till date most of the techniques present does the penumbra shadow detection successfully but these techniques are not capable of detecting umbra shadows. Umbra shadows are detected as moving object in some cases and hence it reduces the performance of system. In this paper proposes a method to detect both umbra and penumbra shadows successfully. Firstly a statistical non parametric approach is presented based on computational colour model in which brightness and colour differences are calculated, which removes moving cast shadow from moving object of interest. The region corresponding to shadow is detected based on colour statistics. To improve the performance of shadow detection further, tracking based algorithm is used and data association checks for temporal consistency between object and shadow. There is huge amount of data is consumed in video surveillance. Storing capacity required for this is very high hence videos are compressed. The proposed algorithm uses median filter for background subtraction which reduces edge detection problem. Experimental results show that proposed system gives more shadow detection rate and almost 100 % shadow discrimination rate as compared to current methods.
Multi-Input Operational Databases

This chapter focuses on case studies where multiple operational databases are used as inputs to the data warehouse. It introduces two concepts, namely, vertical stacking and horizontal stacking, each discussed with a case study. A vertical stacking is where multiple fact tables are vertically stacked into one fact table, whereas a horizontal stacking is where multiple tables are stacked horizontally into one fact table.
Image Super-Resolution Reconstruction Based on Multi-scale Residual Learning

Current image super-resolution (SR) reconstruction methods based on deep learning focus more on using the learning capabilities of deeper networks to obtain better reconstruction results, but the increase in depth will increase the complexity of the network structure and the difficulty of training. To address this problem, considering the advantages of residual learning that can overcome the problem of low learning efficiency, and the ability of multiscale networks that can extract global and local features at different scales, this paper proposes a multi-scale residual block (MSRB) for feature learning. Based on the constructed MSRB, a cascaded multi-scale residual network (CMSRN) is developed for image SR reconstruction. In the network, to reconstruct richer image texture details, multiple multi-scale residual blocks are cascaded to construct the residual feature learning part. Experimental results on four datasets show that the proposed network can obtain better reconstruction results, and is superior to state-of-the-art SR reconstruction methods in terms of subjective observation and objective quantitative evaluation.
Translation of Long English Sentences Based on Clause Complex Theory

The translation of long sentences in English is hard to handle for translators in the English-Chinese translation. The first and very key factor contributing to this problem is the high complexity of sentence structures and constitute relationships in long English sentences. Additionally, since English and Chinese are two unrelated languages with huge differences, translators’ understanding and handling of these differences, to a large extent, affects their performance in translation practice. Taking above-mentioned factors into consideration, numerous methods were proposed by scholars previously in the practice of translating long English sentences. However, there is no decent standard and sufficient sample analyses provided for translators to judge from. More importantly, analyzing sentences in traditional linguistic ways is not always practical in translation practice. Therefore, Clause Complex Theory is proposed to provide framework for clause-based analysis of clause complex structures in both English and Chinese. Following the Clause Complex Theory, this paper introduces an English-Chinese clause alignment annotation system, including English clause tagging and Chinese translation generation and combination. Furthermore, selected research samples together with concrete analysis are anticipated to be presented. To conclude, this study has described an effective Component Sharing based method for the proper treatment of long sentences in English-Chinese translation.
Human Capital Empowerment: The Importance of Trust, Data Safeguards, and Protection of Vulnerable Groups

No AccessOct 2021Human Capital Empowerment: The Importance of Trust, Data Safeguards, and Protection of Vulnerable GroupsAuthors/Editors: Sajitha Bashir, Carl J. Dahlman, Naoto Kanehira, Klaus TilmesSajitha BashirSearch for more papers by this author, Carl J. DahlmanSearch for more papers by this author, Naoto KanehiraSearch for more papers by this author, Klaus TilmesSearch for more papers by this authorhttps://doi.org/10.1596/978-1-4648-1719-9_ch5AboutView ChaptersPDF (0.5 MB) ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinked In Abstract: Discusses three issues related to the governance of converging technologies central to the inclusion and empowerment agenda for human capital: (1) trust in technology use; (2) data governance; and (3) governance of converging and dual-use technologies. Trust underlies the effective deployment of all technologies, but the role of trust has become amplified in the context of converging technologies. Power asymmetries, the opacity of technology value chains, and the spread of digital misinformation and targeting of vulnerable groups dominate global discourse. The role of data governance proves especially important because the possibility of using and combining data from different sources lies at the heart of converging technologies. Specifically, the linking of sensitive health, education, and social protection data with digital identification can work to advance surveillance and profiling of vulnerable populations and individuals. Protections for the collection, storage, and use of data, especially of children, women, and marginalized communities, remain an important component of agency and empowerment. Previous chapterNext chapter FiguresreferencesRecommendeddetails View Published: October 2021ISBN: 978-1-4648-1719-9e-ISBN: 978-1-4648-1720-5 Copyright & Permissions Related RegionsSouth AsiaRelated CountriesIndiaPakistanRelated TopicsInformation and Communication TechnologiesSocial Development KeywordsCASE STUDYHUMAN CAPITALTECHNOLOGY DEVELOPMENTINNOVATIONEMPOWERMENTTRUSTINFORMATION ASYMMETRYMISINFORMATIONVULNERABLE GROUPSINFORMATION SECURITYPRIVACYDATA STORAGE PDF DownloadLoading ...
Cross-Domain Knowledge Transfer Using High Dynamic Range Imaging in Synthetic Datasets

Herein, we propose a framework for the generation of photorealistic synthetic datasets using High Dynamic Range Imaging (HDRI) that include all kinds of information computer vision algorithms need. Furthermore, by utilizing the proposed framework, we demonstrate cross-domain knowledge transfer in a semantic segmentation scenario. We found that deep neural networks trained with our synthetic images or with a mix of real and synthetic perform equal to or in cases better than those trained solely on real images. To our knowledge, this is the first work that uses HDRI to successfully transfer knowledge from the synthetic domain to the real world.
Fuzzy Numerical Morphological approach for Super Resolution of MR Brain Images

Magnetic resonance imaging (MRI) is an incredible medicinal technology provides relative information regarding the parts of the human body. For the diagnosis, high resolution MR images are essential to extract the detailed information about the diseases. But high resolution (HR) images will be constructed from number of low resolution (LR) images. It is time consuming process to construct a HR image from the number of LR images. However, with the measured single MR images it’s a challenging issue in extracting the detailed information associated to disease for the posterior analysis or treatment. In general, to improve the contrast of MR image histogram equalization has to be performed. In the proposal, for the resolution enhancement of the MR brain images a soft computing approach i.e. fuzzy mathematical approach is implemented. Irrespective of the region, it is possible to manipulate the intensities over the entire region with the help of fuzzy logic so that the resolution will be improved. To analyze the proposal qualitatively and quantitatively different performance image metrics are evaluated like PSNR, entropy etc. And from the results it can be observed that better results are obtained with the proposal when compared with the conventional methods.
Adversarial Learning for Hiding Wireless Signals

Spectrum access in the next generation congested cognitive wireless networks will be vulnerable to malicious intents of strong adversaries. One of the ways to obscure an adversary is to hide a signal to avoid the threats of being intercepted or jammed. In this paper, we present such a wireless steganography technique, where we hide the secret message as a form of noise. We use adversarial learning model to transform the domain of secret message, from bits to complex signal that is statistically identical to hardware noise of a transmitter. A three-node neural network, an encoder, a decoder, and a critic (steganalyzer), is optimized jointly to encode and decode a secret message while adhering to statistically identical properties of the encoded covert signal and hardware generated noise. Once the domain transfer is complete, this covert signal can be carried by any cover signal, independent of its waveform or modulation order. We provide an information-theoretic analysis to evaluate the embedding capability of the learning model. Our results show that the covert signal achieves a throughput of 12Mbps at 12dB SNR and is resilient to different levels of hardware noise.
Improved U2Net-based liver segmentation

The analysis of CT images of the abdomen has become increasingly important as lesions occurring in the liver are not easily detectable. The development of medical imaging technology has led to an explosive growth in the number of pathological sections of CT images. In the field of medical imaging, the use of computer-aided techniques for liver segmentation of abdominal CT images is particularly important. To further improve the accuracy of liver segmentation in abdominal CT images, this paper proposes the U2-OANet liver segmentation network on the basis of U2Net. This paper combines the new octave convolution module to design the OCRSU module, which produces better results for segmentation of liver boundaries while reducing the video memory usage. At the same time, this paper adds a dual attention module including the bottom attention module and the mixed attention module, to ensure the liver edge details while alleviating the noise problem and the false-positive problem, and further optimizes the liver segmentation network. Compared with the U2Net network, the U2-OANet network proposed in this paper has effectively improved the liver segmentation accuracy on CHAOS and 3DIRCADB datasets.
Automated Assessment Generation in Intelligent Tutoring Systems

Intelligent Tutoring Systems have a huge potential to improve the efficiency of learning and teaching, especially for personalized adaptive self-learning. The investigation presented in this paper focuses on the implementation of a sub-module, the Automated Question Generation for Assessment unit, using a combined rule based and machine learning based methods. The proposed module is based on a proposed background domain ontology, and it works in two phases. A generator automaton is used to construct the answer templates in the first phase. In the next phase, an encoder-decoder neural network translator unit was used to generate the question sentence templates for the selected answer templates. According to the tests with the implemented prototype assessment generator application on the SQL domain, the proposed framework is suitable to generate assessment tasks in a very efficient way.
Delegates

A delegate is a type used to reference a method. This allows methods to be assigned to variables and passed as arguments. The delegate’s declaration specifies the method signature to which objects of the delegate type can refer. Delegates are by convention named with each word initially capitalized, followed by Delegate at the end of the name.
Ground Monitoring Method of Overhead Line Traction Tension Setting out Process Based on Information Integration

To better ensure the operation quality and safety of overhead lines and solve the problem of the poor management effect of overhead line traction tension setting out, a ground monitoring method for the overhead line traction tension setting out process based on information integration is proposed in this paper. On the basis of optimizing the process of overhead line traction tension setting out, combined with the principle of information integration, the abnormal information in the process of setting out is excavated to realize the real-time monitoring of overhead line traction tension setting out process and ensure the monitoring quality. The experimental results show that this method has high effectiveness in practical applications, can better ensure the effect of overhead line traction tension setting out management, and improve the quality of overhead line management.
Research on Gesture Recognition Based on Improved Template Matching Algorithm

Aiming at the problem of low recognition rate when recognizing the left and right hands with traditional gesture features, a feature <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$H$</tex> (Hand-to-hand angle) based on the angle between the hands is designed to describe the positional relationship of the left and right hands. At the same time, a new gesture recognition algorithm is proposed to solve the interference problem of undefined gestures in the recognition process. Based on the template matching method, similarity judgment is introduced to filter out interfering gestures. Finally, real-time classification and recognition of static gestures are realized. Experiments show that this gesture recognition algorithm can effectively filter out interfering gestures, and the recognition rate of static gestures is up to 96%.
A Survey on the Identification of Causal Relation in Texts

Causality is the basis for humans to make rational decisions and is widely mentioned in different fields. In the natural language processing (NLP) community, the problem of causality is complex and challenging. This paper serves as an effort to briefly discuss the causal relation identification in texts, from the existing causal resources, research methodology, and the robustness problems. First, we introduce relevant causal datasets and resources. Second, the existing typical approaches that have been used in causal relation identification are categorized into unsupervised and supervised methods. In addition, the robustness of causality identification models is discussed succinctly. Finally, we try to list the research challenges at present and raise the future research directions in this field.
Spatial resolution of complex-valued reservoir computing in aspect classification using InSAR data

Convolutional neural networks (CNNs) have been utilized for SAR image analyses such as aspect classification and land cover grouping. However, reservoir computing-based approach was also proposed for aspect classification because CNNs have serious problems of resolution degradation and large computational cost. In this paper, we conduct an experiment of aspect classification by using complex-valued reservoir computing (CVRC) and complex-valued CNNs (CVCNNs). We also discuss the tradeoff between the spatial resolution of the CVRC results and noise robustness. In addition, we compare these approaches in terms of segmented image resolution and computational cost. Experimental results show that CVRC realizes one-hundredth learning time as well as one-fifth classification time, compared with CVCNNs, with the same resolution as that of original interferogram.
Capturing Invalid Input Manipulations for Memory Corruption Diagnosis

Memory corruption diagnosis, especially at the binary level where all high-level program abstractions are missing, is a tedious and time-consuming task. Given a crash, memory corruption diagnosis is expected to not only locate the root cause of the vulnerability, but also deliver rich semantics to understand the vulnerability. However, existing techniques can barely satisfy the above requirements. In this article, we present <inline-formula><tex-math notation="LaTeX">${{\sf MemRay}}$</tex-math></inline-formula> , a dynamic memory corruption diagnosis technique. The insight behind our approach is that most memory corruption is caused by malformed inputs, which further leads the vulnerable program to manipulate inputs by referencing invalid data structures. We design the “data structure reference sequence” to characterize how a program references various data structures to manipulate program inputs. Then, we identify memory corruptions by detecting violations in the input manipulations via data structures. We demonstrate the effectiveness of <inline-formula><tex-math notation="LaTeX">${{\sf MemRay}}$</tex-math></inline-formula> on a wide range of memory-corruption vulnerabilities. The result shows that <inline-formula><tex-math notation="LaTeX">${{\sf MemRay}}$</tex-math></inline-formula> precisely locates the root cause of vulnerabilities. Moreover, the “data structure reference” enables <inline-formula><tex-math notation="LaTeX">${{\sf MemRay}}$</tex-math></inline-formula> to deliver rich semantics and context information to assist vulnerability diagnosis on binary code.
Learning Assisted Identification of Scenarios Where Network Optimization Algorithms Under-Perform

We present a generative adversarial method that uses deep learning to identify network load traffic conditions in which network optimization algorithms under-perform other known algorithms: the Deep Convolutional Failure Generator (DCFG). The spatial distribution of network load presents challenges for network operators for tasks such as load balancing, in which a network optimizer attempts to maintain high quality communication while at the same time abiding capacity constraints. Testing a network optimizer for all possible load distributions is challenging if not impossible. We propose a novel method that searches for load situations where a target network optimization method underperforms baseline, which are key test cases that can be used for future refinement and performance optimization. By modeling a realistic network simulator&#x0027;s quality assessments with a deep network and, in parallel, optimizing a load generation network, our method efficiently searches the high dimensional space of load patterns and reliably finds cases in which a target network optimization method under-performs a baseline by a significant margin.