Reviewerly/exHarmony huggingface dataset:
Chapter 24: Muruntau, Uzbekistan: The World’s Largest Epigenetic Gold Deposit

Abstract Muruntau in the Central Kyzylkum desert of the South Tien Shan, western Uzbekistan, with past production of ~3,000 metric tons (t) Au since 1967, present annual production of ~60 t Au, and large remaining resources, is the world’s largest epigenetic Au deposit. The host rocks are the mainly Cambrian-Ordovician siliciclastic flysch of the Besapan sequence. The rocks were deformed into a broadly east-west fold-and-thrust belt prior to ca. 300 Ma during ocean closure along the South Tien Shan suture. A subsequent tectonic transition was characterized by left-lateral motion on regional splays from the suture and by a massive thermal event documented by widespread 300 to 275 Ma magmatism. The Besapan rocks were subjected to middle to upper greenschist-facies regional metamorphism, an overprinting more local thermal metamorphism to produce a large hornfels aureole, and then Au-related hydrothermal activity all during early parts of the thermal event. The giant Muruntau Au deposit formed in the low-strain hornfels rocks at ca. 288 Ma at the intersection of one of the east-west splays, the Sangruntau-Tamdytau shear zone, with a NE-trending regional fault zone, the Muruntau-Daugyztau fault, which likely formed as a cross fault during the onset of left-lateral translation on the regional splays. Interaction between the two faults opened a large dilational zone along a plunging anticlinorium fold nose that served as a major site for hydrothermal fluid focusing. The Au ores are dominantly present as a series of moderately to steeply dipping quartz ± K-feldspar stockwork systems surrounding uncommon central veins and with widespread lower Au-grade metasomatites (i.e., disseminated ores). Pervasive alteration is biotite-K-feldspar, although locally albitization is dominant. Sulfides are mainly arsenopyrite, pyrite, and lesser pyrrhotite, and scheelite may be present both in preore ductile veins and in the more brittle auriferous stockwork systems. The low-salinity, aqueous-carbonic ore-forming fluids probably deposited the bulk of the ore at 400° ± 50°C and 6-to 10-km paleodepth. The genesis of the deposit remains controversial with metamorphic, thermal aureole gold (TAG), and models related to mantle upwelling all having been suggested in recent years. More importantly, the question as to why there was such a focusing of so much Au and fluid into this one location, forming an ore system an order of magnitude larger than other giant Au deposits in metamorphic terranes, remains unresolved.
Using handheld user interface and direct manipulation for architectural modeling in immersive virtual reality: An exploratory study

Abstract This study comparatively investigates the affordances and constraints of two modeling methods for three‐dimensional modeling in immersive virtual reality (IVR): handheld user interface (HUI) and direct manipulation modeling (DM). The experimental study was conducted with 20 graduate students and two domain experts. Data were collected with screen capture videos of the modeling screen and video recordings of the users' gestures during the modeling session. To triangulate the findings for better reliability, an open‐ended questionnaire was administered just after the modeling session to collect written data about the modeling experience and the investigated modeling methods. Collected data were analyzed by qualitative coding methods. Analyses focused on affordances and constraints of modeling techniques, user preferences about virtual environments, effects of instant scale feature during modeling, locomotion techniques in IVR, ergonomics of the hardware related to modeling tasks, and usability of modeling interfaces in IVR. The results showed that DM modeling could be supported by (i) numeric input via a novel type of keyboard, (ii) a convenient HUI's menu interface with menu hierarchy wherever needed, and (iii) better locomotion techniques. This study generated suggestions for ensuring that efficient modeling applications for design studios, computer aided design courses, and professional practices in IVR can be implemented by investigating affordances and constraints.
Hierarchical Graph Pathomic Network for Progression Free Survival Prediction

High resolution histology images contain information related to disease prognosis. However, survival prediction based on current clinical grading systems, which rely heavily on a pathologist’s histological assessment, has significant limitations due to the heterogeneity and complexity of tissue phenotypes. To address these challenges, we propose a deep learning framework that leverages hierarchical graph-based representations to enable more precise prediction of progression-free survival in prostate cancer patients. Unlike conventional approaches that analyze patch-based or cell-based pathomic features alone without considering their spatial connectivity, we explore multi-scale topological structures of whole slide images in an integrative context. Extensive experiments have demonstrated the effectiveness of our model for better progression prediction.
A Deep Learning Approach to Detect Depression from Bengali Text

Most of the dimensional sentiment analysis methods are established on deep learning algorithms in natural language processing which can categorize the sentiment of the Bengali text or paragraph by creating a definite pole. Our purpose is to identify the depression-related 'sad' post using the above method from the Bengali dataset. To implement this work, we have collected Bengali text from different platforms such as social media, Bengali blogs, and quotes of Noble persons. And to classify the sentiment as happy or sad from those texts by using our model. The data preprocessing of Bengali text is one of the toughest parts of this model. For tokenizing the data to train the model, we have used Keras tokenizer. During this experiment, we have applied a recurrent neural network with a long short-term memory algorithm and achieved 98% accuracy and also able to detect the sentiment from the given dataset.
Anonymous Sealed-Bid Auction on Ethereum

In a competitive market, online auction systems enable optimal trading of digital products and services. Bidders can participate in existing blockchain-based auctions while protecting the confidentiality of their bids in a decentralized, transparent, secure, and auditable manner. However, in a competitive market, parties would prefer not to disclose their interests to competitors, and to remain anonymous during auctions. In this paper, we firstly analyze the specific requirements for blockchain-based anonymous fair auctions. We present a formal model tailored to study auction systems that facilitate anonymity, as well as a generic protocol for achieving bid confidentiality and bidder anonymity using existing cryptographic primitives such as designated verifier ring signature. We demonstrate that it is secure using the security model we presented. Towards the end, we demonstrate through extensive simulation results on Ethereum blockchain that the proposed protocol is practical and has minimal associated overhead. Furthermore, we discuss the complexity and vulnerabilities that a blockchain environment might introduce during implementation.
Implementing and Measuring KEMTLS

KEMTLS is a novel alternative to the Transport Layer Security (TLS) handshake that integrates post-quantum algorithms. It uses key encapsulation mechanisms (KEMs) for both confidentiality and authentication, achieving post-quantum security while obviating the need for expensive post-quantum signatures. The original KEMTLS paper presents a security analysis, Rust implementation, and benchmarks over emulated networks. In this work, we provide full Go implementations of KEMTLS and other post-quantum handshake alternatives, describe their integration into a distributed system, and provide performance evaluations over real network conditions. We compare the standard (non-quantum-resistant) TLS 1.3 handshake with three alternatives: one that uses post-quantum signatures in combination with post-quantum KEMs (PQTLS), one that uses KEMTLS, and one that is a reduced round trip version of KEMTLS (KEMTLS-PDK). In addition to the performance evaluations, we discuss how the design of these protocols impacts TLS from an implementation and configuration perspective.
Fast Flux Entangling Gate for Fluxonium Circuits

We analyze a high-fidelity two-qubit gate using fast flux pulses on superconducting fluxonium qubits. The gate is realized by temporarily detuning magnetic flux through fluxonium loop away from the half flux quantum sweet spot. We simulate dynamics of two capacitively coupled fluxoniums during the flux pulses and optimize the pulse parameters to obtain a highly accurate $\sqrt{i\mathrm{swap}}$-like entangling gate. We also evaluate the effect of the flux noise and qubit relaxation on the gate fidelity. Our results demonstrate that the gate error remains below $10^{-4}$ for currently achievable magnitude of the flux noise and qubit relaxation time.
Multi-Agent Reinforcement Learning based Distributed Renewable Energy Matching for Datacenters

The rapid growth of cloud computing in cloud datacenters in recent decades greatly increases the brown energy consumption in datacenters, and hence significant increase of carbon emission that negatively impacts on the environment as well as the monetary cost. More and more cloud service providers are adopting renewable energy as the energy supply to offset the consumption of brown energy. Meanwhile, an increasing number of renewable energy generators have been built to meet the needs. However, the instability of the renewable energy cannot guarantee the support to the datacenter and the energy competition of different datacenters may lead to datacenter energy outage. In this paper, we focus on the problem of how to match different renewable energy generators to the datacenters from different cloud providers to minimize the carbon emission, monetary cost, and service level objective (SLO) violation due to renewable energy shortage. The challenges here are that the datacenters may compete in energy requesting, the renewable energy generation is not stable and the decision should be made quickly. There have been no previous efforts devoting to this problem. To solve the problem, we first test several machine learning techniques on long-term prediction accuracy on renewable energy generation and energy demand using real traces and identify SARIMA for the prediction. We then propose a multi-agent reinforcement learning based method (MARL) for each datacenter to determine how much renewable energy to request from each generator based on the predicted results. We also propose a deadline guaranteed job postponement method (DGJP) to postpone executing unurgent jobs upon insufficient renewable energy supply. The trace-driven experiments show that MARL outperforms other methods by increasing up to 35% SLO satisfaction ratio, and reducing up to 19% (0.33 billion dollars in 90 days) total monetary cost and 33% total carbon emission, and DGJP further improves the performance.
Robust Fuzzy K-Means Clustering With Shrunk Patterns Learning

Fuzzy K-Means (FKM) clustering regards each cluster as a fuzzy set and assigns each sample to multiple clusters with a certain degree of membership. However, conventional FKM methods perform clustering on original data directly where the intrinsic structure of data may be corrupted by the noise. According, the performance of these methods would be challenged. In this paper, we present a novel fuzzy K-Means clustering model to conduct clustering tasks on the flexible manifold. Technically, we perform fuzzy clustering based on the shrunk patterns which have desired manifold structure. The shrunk patterns can be viewed as an approximation to the original data; and a penalty term is employed to measure the mismatch between them. Moreover, we integrate the learning of shrunk patterns and the learning of membership degree between shrunk patterns and clusters into a unified framework. Furthermore, we extend the proposed model for projected FKM clustering to find a suitable subspace to fit the non-linear manifold structure of data, reduce the interference of the noise and redundant features and gather homogeneous samples together simultaneously. Two alternating iterative algorithms are derived to solve these two models, respectively. Extensive experimental results demonstrate the feasibility and effectiveness of our proposed clustering algorithms.
TAFA: A Task-Agnostic Fingerprinting Algorithm for Neural Networks

Well-trained deep neural networks (DNN) are an indispensable part of the intellectual property of the model owner. However, the confidentiality of models are threatened by model piracy, which steals a DNN and obfuscates the pirated model with post-processing techniques. To counter model piracy, recent works propose several model fingerprinting methods, which are commonly based on a special set of adversarial examples of the owner’s classifier as the fingerprints, and verify whether a suspect model is pirated based on whether the predictions on the fingerprints from the suspect model and from the owner’s model match with one another. However, existing fingerprinting schemes are limited to models for classification and usually require access to the training data. In this paper, we propose the first Task-Agnostic Fingerprinting Algorithm (TAFA) for the broad family of neural networks with rectified linear units. Compared with existing adversarial example-based fingerprinting algorithms, TAFA enables model fingerprinting for DNNs on a variety of downstream tasks including but not limited to classification, regression and generative modeling, with no assumption on training data access. Extensive experimental results on three typical scenarios strongly validate the effectiveness and the robustness of TAFA.
Investigating consumers’ motives for consumer brand-cyberbullying on social media

In this article we offer the first survey-based study on the motivations that spur consumers to bully others about the brands they support on social media, a phenomenon we term “Consumer Brand-Cyberbullying” (CBC). Analyzing data from 1,203 participants of online brand communities, we find that consumers who seek to be popular and attractive are more likely to engage in CBC, while those who seek to affiliate with close others and help the community are less likely to do so. Consumers who identify with and are loyal to a particular brand are more likely to engage in CBC. Taken together, our study moves us toward a systematic analysis of the relationship between brands and cyberbullying on social media.
Optimizing Massively Parallel Winograd Convolution on ARM Processor

Convolution Neural Network (CNN) has gained a great success in deep learning applications and been accelerated by dedicated convolutional algorithms. Winograd-based algorithm can greatly reduce the number of arithmetic operations required in convolution. However, our experiments show that existing implementations in deep learning libraries cannot achieve expected parallel performance on ARM manycore CPUs with last-level cache (LLC). Compared to multicore processor, ARM manycore CPUs have more cores, more NUMA nodes and the parallel performance is more easily restricted by memory bandwidth, cache contention, NUMA configuration and etc. In this paper, we propose an optimized implementation for single-precision Winograd-based algorithm on ARM manycore CPUs. Our algorithm adjusts the data layout according to the input shape and is optimized for the characteristics of ARM processor, thus reducing the matrix transformation overhead and achieving high arithmetic intensity. We redesign the parallel algorithm for Winograd-based convolution to achieve a more efficient implementation for manycore CPUs. The experimental results with 32 cores show that for modern ConvNets, our implementation achieves speedups ranging from 3 × to 5 × over the state-of-the-art Winograd-based convolution on ARM processor. Even conducted on a set of convolutional benchmarks executing on a 128-core system with 4 NUMA nodes, the results show that our implementation can also achieve better performance than existing implementations on ARM processor.
Decoding techniques applied to the compilation of CNOT circuits for NISQ architectures

Current proposals for quantum compilers require the synthesis and optimization of linear reversible circuits and among them CNOT circuits. Since these circuits represent a significant part of the cost of running an entire quantum circuit, we aim at reducing their size. In this paper we present a new algorithm for the synthesis of CNOT circuits based on the solution of the syndrome decoding problem. Our method addresses the case of ideal hardware with an all-to-all qubit connectivity and the case of near-term quantum devices with restricted connectivity. For both cases, we present benchmarks showing that our algorithm outperforms existing algorithms.
Mask or Non-Mask? Robust Face Mask Detector via Triplet-Consistency Representation Learning

In the absence of vaccines or medicines to stop COVID-19, one of the effective methods to slow the spread of the coronavirus and reduce the overloading of healthcare is to wear a face mask. Nevertheless, to mandate the use of face masks or coverings in public areas, additional human resources are required, which is tedious and attention-intensive. To automate the monitoring process, one of the promising solutions is to leverage existing object detection models to detect the faces with or without masks. As such, security officers do not have to stare at the monitoring devices or crowds, and only have to deal with the alerts triggered by the detection of faces without masks. Existing object detection models usually focus on designing the CNN-based network architectures for extracting discriminative features. However, the size of training datasets of face mask detection is small, while the difference between faces with and without masks is subtle. Therefore, in this article, we propose a face mask detection framework that uses the context attention module to enable the effective attention of the feed-forward convolution neural network by adapting their attention maps’ feature refinement. Moreover, we further propose an anchor-free detector with Triplet-Consistency Representation Learning by integrating the consistency loss and the triplet loss to deal with the small-scale training data and the similarity between masks and occlusions. Extensive experimental results show that our method outperforms the other state-of-the-art methods. The source code is released as a public download to improve public health at https://github.com/wei-1006/MaskFaceDetection .
THE USEFULNESS OF ONLINE LEARNING DURING THE COVID19 PANDEMIC AS PERCEIVED BY ENGINEERING EDUCATION STUDENTS: A MULTIDIMENSIONAL MODEL

With the unprecedented crisis created by the Coronavirus pandemic, online teaching and learning became a global solution - embraced by the whole world - that tried to cover, at least in part, in part the problems encountered by the educational systems. An exclusively online format of teaching and learning proved to be a challenge for both the didactic and the learning process. Although the extant research during the pandemic is very abundant, there are few approaches addressing the different perceptions reported by university students enrolled in various programs. This research aims to explore the usefulness of online learning as perceived by civil engineering students, from a multidimensional perspective. A multidimensional model has been developed that manifests on four dimensions: social usefulness of online lectures, the usefulness of online learning platform, learning effectiveness, and, usefulness for school performance. The results show a relatively low perception of the online learning usefulness in an exclusively online educational context, which disables active learning through hands-on experience in laboratory and real-life building sites (considering the regular study process). The model has been cross-validated on a sample of students enrolled in various educational programs then the differences between samples have been analyzed and discussed. Keywords: Covid-19 pandemic, engineering students, learning platform, online education, perceived usefulness
BV-Person: A Large-scale Dataset for Bird-view Person Re-identification

Person Re-IDentification (ReID) aims at re-identifying persons from non-overlapping cameras. Existing person ReID studies focus on horizontal-view ReID tasks, in which the person images are captured by the cameras from a (nearly) horizontal view. In this work we introduce a new ReID task, bird-view person ReID, which aims at searching for a person in a gallery of horizontal-view images with the query images taken from a bird's-eye view, i.e., an elevated view of an object from above. The task is important because there are a large number of video surveillance cameras capturing persons from such an elevated view at public places. However, it is a challenging task in that the images from the bird view (i) provide limited person appearance information and (ii) have a large discrepancy compared to the persons in the horizontal view. We aim to facilitate the development of person ReID from this line by introducing a large-scale real-world dataset for this task. The proposed dataset, named BV-Person, contains 114k images of 18k identities in which nearly 20k images of 7.4k identities are taken from the bird's-eye view. We further introduce a novel model for this new ReID task. Large-scale experiments are performed to evaluate our model and 11 current state-of-the-art ReID models on BV-Person to establish performance benchmarks from multiple perspectives. The empirical results show that our model consistently and substantially outperforms the state-of-the-art models on all five datasets derived from BV-Person. Our model also achieves state-of-the-art performance on two general ReID datasets. The BV-Person dataset is available at: https://git.io/BVPerson
Optimal Verifiable Data Streaming Protocol with Data Auditing

As smart devices connected to networks like Internet of Things and 5G become popular, the volume of data generated over time (i.e., stream data) by them is growing rapidly. As a consequence, for these resources-limited client-side devices, it becomes very challenging to store the continuously generated stream data locally. Although the cloud storage provides a perfect solution to this problem, the data owner still needs to ensure the integrity of the outsourced stream data, since various applications built upon stream data are sensitive of both its context and order. To this end, the notion of verifiable data streaming (VDS) was proposed to effectively append and update stream data outsourced to an untrusted cloud server, and has received significant attention. However, previous VDS constructions adopt Merkle hash tree to capture the integrity of outsourced data, and thus inevitably have logarithmic costs. In this paper, we further optimize the construction of VDS in terms of communication and computation costs. Specifically, we use the digital signature scheme to ensure the integrity of outsourced stream data, and employ a recently proposed RSA accumulator (v.s. Merkle hash tree) to invalidate the corresponding signature after each data update operation. Benefited from this approach, the resulted VDS construction achieves optimal, i.e., having constant costs. Furthermore, by specifying the underly signature scheme with the BLS short signature and carefully combining it with the RSA accumulator, we finally obtain an optimal verifiable data streaming protocol with data auditing. We prove the security of the proposed VDS construction in the random oracle model.