Reviewerly/exHarmony huggingface dataset:
Paragenetic Association of Platinum and Gold Minerals in Placers of the Anabar River in the Northeast of the Siberian Platform

Areal placers of diamond and precious metals (platinum and gold) of unknown origin are widespread in the Anabar River basin on the northeastern part of the Siberian Platform. This article discusses the typomorphic features of palladium gold (porpezite) and ferroan platinum, which, in addition to fragmented individual grains, sometimes form close growths, which indicates their obvious genetic relationship. This can be used to delimit the primary sources of commercial components of complex placers by their genetic types. The composition of minerals was determined on a Camebax-Micro (Cameca, France) microprobe analyzer, and their microstructural relationships were studied using the scanning microscope JSM-6480LV JEOL. Determination of the structure and parameters of elementary lattices of minerals was carried out on the D8 Discover diffractometer. According to microprobe analysis, the Pd content in porpezite ranges from 0.73% to 12.83%, Ag does not exceed 2.75% and Cu reaches 3–4%. Considering the composition, such a gold–platinum mineral association from placers of the Anabar river is close to precious metals from placers of the Gulinsky massif, as well as Au–PGE ore occurrences related to ultramafic–mafic intrusions of other regions of the world. Complex gold–platinum-metal mineralizations are usually closely related to parent rocks and are often observed in sulfide and chromite ores of layered ultramafic–mafic intrusions with complex metasomatic and hydrothermal transformations. It is shown that in such cases gold and platinum form a magmatogenic paragenesis of minerals that coexist until the separation of hydrothermal solutions from magma, which, as a rule, transports Au and Ag with a small fraction of PGE from the fluid-ore-magmatic system in accordance with the model of the formation of gold–porphyry deposits. Within the Anabar diamond-bearing region, according to modern geophysical data, a significant number of local gravimagnetic anomalies associated with the presence of intrusive massifs of basic and alkaline-ultrabasic rocks in the cover and within the basement have been identified. This allows us to assume that the buried parent rocks of the Anabar Au–Pt placers may be located in both the Precambrian and Phanerozoic strata.
ST-PBFT: An Optimized PBFT Consensus Algorithm for Intellectual Property Transaction Scenarios

For the current Intellectual Property (IP) transaction scenario, consensus nodes need to simultaneously consensus transactions of the same transaction type, resulting in low consensus efficiency, accuracy, and reliability, which seriously hinders the development of intellectual property. Based on the consortium chain, this paper proposes a secure and efficient blockchain-distributed consensus algorithm, ST-PBFT (Shard Transaction Practical Byzantine Fault Tolerance), applied to the IP transaction scenario. The main contributions of ST-PBFT include the following: first, a grouping method based on the principle of consistent hashing is proposed to group consensus nodes, and nodes group consensus, which reduces the complexity of communication. Second, the transaction consensus group can process IP transactions in parallel, which improves the throughput of the algorithm. Third, a node reputation evaluation model is proposed, which can prevent byzantine nodes from being repeatedly elected as primary nodes. The experimental results show that ST-PBFT can significantly improve the consensus efficiency and reliability and reduce consensus latency.
An efficient audio watermarking scheme with scrambled medical images for secure medical internet of things systems

In recent times, the security of communication channels between healthcare entities in Medical Internet of Things (MIoT) systems becomes a significant issue to facilitate and guarantee the exchange of medical image and expertise securely. This paper presents an efficient audio watermarking scheme employing professionally Wavelet-based Image Fusion, Arnold transforms, and Singular Value Decomposition (SVD) for the secure transmission of medical images and reports in the MIoT applications. The essential consequence of the proposed scheme is to first syndicate two medical watermarks into a fused watermark to upsurge the payload of the inserted medical images. The fused watermark is then scrambled utilizing Arnold transform. Lastly, the Arnold fused watermark is inserted in the audio signal using the SVD algorithm following converting it into a 2D format. The choice of the Arnold transform for watermark is ascribed to settling robustness that skirmishes respective types of severe attacks. Several assessment metrics such as SNR, LLR, SNRseg, SD, and Cr are used to evaluate the audio watermarked signal and extracted watermarks The results reveal that the proposed audio watermarking scheme increases the capacity with more embedded medical images and security of implanted medical images transmission deprived of affecting the quality of audio signals, especially for IoT-based Telemedicine systems.
Runtime Management of Service Level Agreements through Proactive Resource Provisioning for a Cloud Environment

By leveraging the Internet, cloud computing allows users to have on-demand access to large pools of configurable computing resources. PaaS (Platform as a Service), IaaS (Infrastructure as a Service), and SaaS (Software as a Service) are three basic categories for the services provided by cloud the computing environments. Quality of service (QoS) metrics like reliability, availability, performance, and cost determine which resources and services are available in a cloud computing scenario. Provider and the user-specified performance characteristics, such as, rejection rate, throughput, response time, financial cost, and energy consumption, form the basis for QoS. To fulfil the needs of its customers, cloud computing must ensure that its services are given with the appropriate quality of service QoS. A “A legally enforceable agreement known as a “Service Level Agreement” (SLA) between a service provider and a customer that outlines service objectives, quality of service requirements, and any associated financial penalties for falling short. We, therefore, presented “A Proactive Resource Supply based Run-time Monitoring of SLA in Cloud Computing”, which allows for the proactive management of SLAs during run-time via the provisioning of cloud services and resources. Within the framework of the proposed work, SLAs are negotiated between cloud users and providers at run-time utilizing SLA Manager. Resources are proactively allocated via the Resource Manager to cut down on SLA violations and misdetection costs. As metrics of performance, we looked at the frequency with which SLAs were broken and the money lost due to false positives. We compared the proposed PRP-RM-SLA model’s simulated performance to the popular existing SLA-based allocation strategy SCOOTER. According to simulation data, the suggested PRP-RM-SLA model is 25% more effective than the current work SCOOTER at reducing SLA breaches and the cost of misdetection.
Impacts, sustainability, and resilience on the Egyptian tourism and hospitality industry after the Russian airplane crash in 2015

The Russian airplane crash in 2015 has severely affected the tourism industry in Egypt. The present article is focused on the impacts of economic, political, and social impacts on Egyptian tourism after this crisis. The present study aimed at dealing with the crisis to mitigate the consequences and allow the companies to make the best use of it to restore their business. The present article has also discussed the crisis impacts on the financial situation of company A which is one of the top five hospitality companies in Egypt. Crisis management and investments in human resources were the most effective strategies to overcome the harsh consequences of the crisis on tourism sector. The present study data is based on the Egyptian ministry of tourism figures, internet, articles, and international channels reports aimed to explain how the political, social, and economic events had a negative impact on the hotel industry in Egypt.
A speaker-aware multiparty dialogue discourse parser with heterogeneous graph neural network

Discourse parsing for multiparty dialogue aims to detect the discourse structure and relations in a dialogue to obtain a discourse dependency graph. Existing models for this task have proven the effect of speaker information. However, no model explicitly learns representations of the speaker for parsing the discourse structure of dialogues. In this paper, to further exploit the effect of speaker information, we propose a novel model HG-MDP, which uses a heterogeneous graph neural network to encode dialogue graphs and we use an iterative update for the aggregation of speaker nodes and utterance nodes. Finally, we adopt updated utterance nodes to predict discourse dependency links and relations using the biaffine module. To validate our HG-MDP model, we perform experiments on the two existing benchmarks STAC and Molweni corpus. The results prove the effectiveness of the speaker modeling module on two datasets and we achieve the state-of-the-art on the Molweni dataset.
A Multi-Blockchain Scheme for Distributed Spectrum Sharing in CBRS System

To overcome the spectrum scarcity issues, the citizens broadband radio service (CBRS) presents a centralized spectrum management solution. The efficiency of spectrum utilization could be further improved by introducing spectrum trading. Blockchain-based spectrum trading has been considered as a decentralized, flexible, and secure approach. However, current studies rarely investigate the interference to incumbent users caused by spectrum trading between CBRS devices (CBSDs), and the scalability issues in blockchain-based spectrum trading are rarely discussed. To address the problems above, this paper develops the blockchain-based spectrum trading mechanisms for CBRS. Particularly, we propose a queuing mechanism for intra-coexistence group (CxG) trading, in which spectrum trading is leveraged to reduce the aggregated interference to incumbents. A new parameter termed “network feature” is proposed to prioritize spectrum transactions in different queues, which helps to achieve the trade-off between the interference to incumbents and resource requirements in spectrum transactions. Furthermore, we propose a multi-blockchain architecture and a corresponding cross-chain mechanism to improve the speed of inter-CxG spectrum trading. Simulation results show that the aggregated interference to incumbents can be reduced by adopting the proposed method. Meanwhile, when adopting the proposed cross-chain spectrum trading mechanism, the network throughput can be improved by up to 24% compared with the traditional CBRS spectrum management framework.
A statistical model approach based on the Gaussian Mixture Model for the diagnosis and classification of bone fractures

Medical imaging has significantly influenced the field of image processing in recent years. Several advancements have been made in medical imaging that is now being used, and all of them are geared at making disease diagnosis more accurate. Researchers recently explored how to diagnose, define, and classify bone fractures, and numerous approaches have been proposed. However, a uniform categorization has yet to be developed for all detected fractures. This article focuses on the methods for identifying realignments among the bone structures utilizing model-based approaches, and this methodology is highlighted here. The performance metrics are used to assess the findings, and the constructed model has shown substantial results in binary classification and multi-classification of bone fractures. The model that has been suggested is called the Finite Beta Gaussian Mixture Model (FBGMM), and its performance may be evaluated with the use of a confusion matrix. This project intends to construct an image processing system that can identify bone fractures promptly and reliably by including data from X-rays. FGBMM achieves good accuracy in both binary and multiclassification of fractures.
Concurrent translation on collaborative platforms

Abstract The advent of AI-supported, cloud-based collaborative translation platforms have enabled a new form of online collaborative translation – ‘concurrent translation’ (CT). CT refers to commercial translation performed on such platforms by multiple agents (translators, editors, subject-matter experts etc.) simultaneously, via concurrent access. Although the practice has recently gained more ground, research on CT is scarce. The present article reports on selected key findings of a study that investigates translators’ experiences with CT via a survey of 804 professional translators working in CT mode across different commercial platforms. Despite the affordances such as peer learning, positive competition, speed, flexibility of the volume of work and working time, and reduced responsibility and reduced stress, CT workflow comes with its substantial challenges such as time pressure, negative competition, reduced self-revision and research, all of which result in quality compromised for speed.
Seeing the faces is so important—Experiences from online team meetings on commercial virtual reality platforms

During the Covid-19 pandemic, online meetings became common for daily teamwork in the home office. To understand the opportunities and challenges of meeting in virtual reality (VR) compared to video conferences, we conducted the weekly team meetings of our human-computer interaction research lab on five off-the-shelf online meeting platforms over four months. After each of the 12 meetings, we asked the participants (N = 32) to share their experiences, resulting in 200 completed online questionnaires. We evaluated the ratings of the overall meeting experience and conducted an exploratory factor analysis of the quantitative data to compare VR meetings and video calls in terms of meeting involvement and co-presence. In addition, a thematic analysis of the qualitative data revealed genuine insights covering five themes: spatial aspects, meeting atmosphere, expression of emotions, meeting productivity, and user needs. We reflect on our findings gained under authentic working conditions, derive lessons learned for running successful team meetings in VR supporting different kinds of meeting formats, and discuss the team's long-term platform choice.
Characters as graphs: Interpretable handwritten Chinese character recognition via Pyramid Graph Transformer

It is meaningful but challenging to teach machines to recognize handwritten Chinese characters. However, conventional approaches typically view handwritten Chinese characters as either static images or temporal trajectories, which may ignore the inherent geometric semantics of characters. Instead, here we first propose to represent handwritten characters as skeleton graphs, explicitly considering the natural characteristics of characters (i.e., characters as graphs). Furthermore, we propose a novel Pyramid Graph Transformer (PyGT) to specifically process the graph-structured characters, which fully integrates the advantages of Transformers and graph convolutional networks. Specifically, our PyGT can learn better graph features through (i) capturing the global information from all nodes with graph attention mechanism and (ii) modelling the explicit local adjacency structures of nodes with graph convolutions. Furthermore, the PyGT learns the multi-resolution features by constructing a progressive shrinking pyramid. Compared with existing approaches, it is more interpretable to recognize characters as geometric graphs. Moreover, the proposed method is generic for both online and offline handwritten Chinese character recognition (HCCR), and it also can be feasibly extended to handwritten text recognition. Extensive experiments empirically demonstrate the superiority of PyGT over the prevalent approaches including 2D-CNN, RNN/1D-CNN, and Vision Transformer (ViT) for HCCR. The code is available at https://github.com/ganji15/PyGT-HCCR.
Backward Reachability Analysis for Neural Feedback Loops

The increasing prevalence of neural networks (NNs) in safety-critical applications calls for methods to certify their behavior and guarantee safety. This paper presents a backward reachability approach for safety verification of neural feedback loops (NFLs), i.e., closed-loop systems with NN control policies. While recent works have focused on forward reachability as a strategy for safety certification of NFLs, backward reachability offers advantages over the forward strategy, particularly in obstacle avoidance scenarios. Prior works have developed techniques for backward reachability analysis for systems without NNs, but the presence of NNs in the feedback loop presents a unique set of problems due to the nonlinearities in their activation functions and because NN models are generally not invertible. To overcome these challenges, we use existing forward NN analysis tools to find affine bounds on the control inputs and solve a series of linear programs (LPs) to efficiently find an approximation of the backprojection (BP) set, i.e., the set of states for which the NN control policy will drive the system to a given target set. We present an algorithm to iteratively find BP set estimates over a given time horizon and demonstrate the ability to reduce conservativeness in the BP set estimates by up to 88% with low additional computational cost. We use numerical results from a double integrator model to verify the efficacy of these algorithms and demonstrate the ability to certify safety for a linearized ground robot model in a collision avoidance scenario where forward reachability fails.
Escaping High-order Saddles in Policy Optimization for Linear Quadratic Gaussian (LQG) Control

First-order policy optimization has been widely used in reinforcement learning. It guarantees to find the optimal policy for the state-feedback linear quadratic regulator (LQR). However, the performance of policy optimization remains unclear for the linear quadratic Gaussian (LQG) control where the LQG cost has spurious suboptimal stationary points. In this paper, we introduce a novel perturbed policy gradient (PGD) method to escape a large class of bad stationary points (including high-order saddles). In particular, based on the specific structure of LQG, we introduce a novel reparameterization procedure that converts the iterate from a high-order saddle to a strict saddle, from which standard random perturbations in PGD can escape efficiently. We further characterize a class of high-order saddles that can be escaped by our algorithm.
Annotating for Artificial Intelligence Applications in Digital Pathology: A Practical Guide for Pathologists and Researchers

Training machine learning models for artificial intelligence (AI) applications in pathology often requires extensive annotation by human experts, but there is little guidance on the subject. In this work, we aimed to describe our experience and provide a simple, useful, and practical guide addressing annotation strategies for AI development in computational pathology. Annotation methodology will vary significantly depending on the specific study's objectives, but common difficulties will be present across different settings. We summarize key aspects and issue guiding principles regarding team interaction, ground-truth quality assessment, different annotation types, and available software and hardware options and address common difficulties while annotating. This guide was specifically designed for pathology annotation, intending to help pathologists, other researchers, and AI developers with this process.
FedDQ: Communication-Efficient Federated Learning with Descending Quantization

Federated learning (FL) is an emerging learning paradigm without violating users' privacy. However, large model size and frequent model aggregation cause serious communication bottleneck for FL. To reduce the communication volume, techniques such as model compression and quantization have been proposed. Besides the fixed-bit quantization, existing adaptive quantization schemes use ascending-trend quantization, where the quantization level increases with the training stages. In this paper, we first investigate the impact of quantization on model convergence, and show that the optimal quantization level is directly related to the range of the model updates. Given the model is supposed to converge with the progress of the training, the range of the model updates will gradually shrink, indicating that the quantization level should decrease with the training stages. Based on the theoretical analysis, a descending quantization scheme named FedDQ is proposed. Experimental results show that the proposed descending quantization scheme can save up to 65.2% of the communicated bit volume and up to 68% of the communication rounds, when compared with existing schemes.
Hefty Algebras: Modular Elaboration of Higher-Order Algebraic Effects

Algebraic effects and handlers is an increasingly popular approach to programming with effects. An attraction of the approach is its modularity: effectful programs are written against an interface of declared operations, which allows the implementation of these operations to be defined and refined without changing or recompiling programs written against the interface. However, higher-order operations (i.e., operations that take computations as arguments) break this modularity. While it is possible to encode higher-order operations by elaborating them into more primitive algebraic effects and handlers, such elaborations are typically not modular. In particular, operations defined by elaboration are typically not a part of any effect interface, so we cannot define and refine their implementation without changing or recompiling programs. To resolve this problem, a recent line of research focuses on developing new and improved effect handlers. In this paper we present a (surprisingly) simple alternative solution to the modularity problem with higher-order operations: we modularize the previously non-modular elaborations commonly used to encode higher-order operations. Our solution is as expressive as the state of the art in effects and handlers.
Textual Pre-Trained Models for Gender Identification Across Community Question-Answering Members

Promoting engagement and participation is vital for online social networks such as community Question-Answering (cQA) sites. One way of increasing the contribution of their members is by connecting their content with the right target audience. To achieve this goal, demographic analysis is pivotal in deciphering the interest of each community fellow. Indeed, demographic factors such as gender are fundamental in reducing the gender disparity across distinct topics. This work assesses the classification rate of assorted state-of-the-art transformer-based models (e.g., BERT and FNET) on the task of gender identification across cQA fellows. For this purpose, it benefited from a massive text-oriented corpus encompassing 548,375 member profiles including their respective full-questions, answers and self-descriptions. This assisted in conducting large-scale experiments considering distinct combinations of encoders and sources. Contrary to our initial intuition, in average terms, self-descriptions were detrimental due to their sparseness. In effect, the best transformer models achieved an AUC of 0.92 by taking full-questions and answers into account (i.e., DeBERTa and MobileBERT). Our qualitative results reveal that fine-tuning on user-generated content is affected by pre-training on clean corpora, and that this adverse effect can be mitigated by correcting the case of words.