Reviewerly/exHarmony huggingface dataset:
Gaussian mixture model with an extended ultrametric covariance structure

Gaussian Mixture Models (GMMs) are one of the most widespread methodologies for model-based clustering. They assume a multivariate Gaussian distribution for each component of the mixture, centered at the mean vector and with volume, shape and orientation derived by the covariance matrix. To reduce the large number of parameters produced by the covariance matrices, parsimonious parameterizations of the latter were proposed in literature, e.g., the eigen-decomposition and the parsimonious GMMs based on mixtures of probabilistic principal component analyzers and mixtures of factor analyzers. We introduce a new parameterization of a covariance matrix by defining an extended ultrametric covariance matrix and we implement it into a GMM. This structure can be used to describe multidimensional phenomena which are characterized by nested latent concepts having different levels of abstraction, from the most specific to the most general. The proposal is able to pinpoint a hierarchical structure on variables for each component of the GMM, thus identifying a different characterization of a multidimensional phenomenon for each component (cluster, subpopulation) of the mixture. At the same time, it defines a new parsimonious GMM since the ultrametric covariance structure reconstructs the relationships among variables with a limited number of parameters. The proposal is applied on synthetic and real data. On the former it shows good performance in terms of classification when compared to the other existing parameterizations, and on the latter it also provides insight into the hierarchical relationships among the variables for each cluster.
Regional and local controls of hydrothermal fluid flow and gold mineralization in the Sheba and Fairview mines, Barberton Greenstone Belt, South Africa

High-grade gold-quartz-sulphide lodes of the Sheba-Fairview Complex (SFC) of gold mines in the Barberton Greenstone Belt of South Africa describe a wide range of geometries, orientations, controls, and a number of different and often contradicting models have been postulated to account for their formation. This paper aims to present a holistic model that describes the focussing of a regional-scale fluid flow into narrower, mineralized structures during the late-stage refolding and tightening of the first-order folds that host the mineralization. The auriferous reefs of the SFC are hosted by well-bedded, low-grade metamorphic, clastic sedimentary rocks of the Paleoarchaean Fig Tree and Moodies groups that are preserved in two regional-scale, strongly curved and refolded synclinal structures along the northern margin of the belt. The majority (85 %) of presently and historically mined orebodies occur in the broad hinge zone of the refolded synclines. This suggests the regional-scale fluid redistribution from fold limbs into the first-order fold hinge following regional hydraulic gradients during refolding. Ubiquitous bedding-parallel quartz (-carbonate-sulphide) veins and associated alteration underline the significance of bedding anisotropies and bedding-parallel flexural slip for regional-scale fluid flow during refolding. The focussing of these fluids and economic-grade gold mineralization occurs preferentially along contacts between lithologies with pronounced rheological contrasts. Lithological contacts combine the effects of strain localization in weaker units and the formation of fracture permeabilities in competent units across these contacts. Examples include kilometer-scale hinge-parallel ore shoots that are controlled by undulations (jogs) along low-displacement slip horizons between retrogressed ultramafic rocks and competent metasediments. In contrast, low-angle thrusts or steep reverse faults form accommodation structures related to the late-stage tightening of the first-order folds. These structures transect bedding at high angles and are, thus, ideally orientated to collect fluid from the regional system of bedding-parallel fluid flow. Mineralization in the SFC may be explained by the spatial and temporal coincidence of (1) late-stage NW-SE shortening and refolding, of (2) a well-bedded, anisotropic wall-rock sequence, associated with (3) regional-scale fluid redistribution along bedding planes during flexural slip, leading to (4) fluid focussing into either jog geometries or bedding discordant structures. The combination of these factors is unique to the SFC and accounts for the location of the main Barberton gold mines along the northwestern margin of the greenstone belt.
Portkey: hypervisor-assisted container migration in nested cloud environments

Derivative cloud service providers use nesting to provision virtual computational entities (VCE) within VCEs, e.g., containers runtimes within virtual machines. As part of resource management and ensuring application performance, migration of nested containers is an important and useful mechanism. Checkpoint Restore In Userspace (CRIU) is the dominant method for migration, used by Docker and other container technologies. While CRIU works well for container migration from host to host, it suffers from significant increase in resource requirements in nested setups. The overheads are primarily due to the high network virtualization overhead in nested environments. While techniques such as SR-IOV can mitigate the overheads, they require additional hardware features and tight coupling of network endpoints. Based on our insights of network virtualization being the main bottleneck, we present Portkey - a software-based solution for efficient nested container migration that significantly reduces CPU utilization at both the source and destination hosts. Our solution relies on interposing a layer that directly coordinates network IO from within a virtual machine with the hypervisor. A new set of hypercalls provide this interfacing along with a control loop that minimizes the hypercall path usage. Extensive evaluation of our solution shows that Portkey reduces CPU usage by up to 75% and 82% at the source and destination hosts, respectively.
Toward everyday negotiation and resistance under data-driven surveillance

research-article Share on Toward everyday negotiation and resistance under data-driven surveillance Author: Alex Jiahong Lu University of Michigan University of MichiganView Profile Authors Info & Claims InteractionsVolume 29Issue 2March - April 2022 pp 34–38https://doi.org/10.1145/3516427Published:25 February 2022Publication History 1citation610DownloadsMetricsTotal Citations1Total Downloads610Last 12 Months610Last 6 weeks51 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Augmented Reality and Speech Control from Automobile Showcasing

Augmented Reality (AR) is an expanding field in marketing research and practice due to its capacity to link virtual information with the real world. Significant advances in handheld or smartphone technology have aroused people's attention towards augmented reality and resulted in a rapid development of AR applications and games. There are numerous applications of augmented reality applications such as education, medical training and designing and modeling. Voice command and speech technology has also been an important milestone of the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> Century allowing human interaction with devices and applications using speech recognition. It provides the users with interface solutions that do not require physically touching or pressing the buttons to perform a particular action. This paper provides an insight on the consumers' perception of and engagement with the brand that use augmented reality and voice commands for marketing and retail. It provides a comprehensive review of the augmented reality applications, along with speech recognition can be used in shopping and retail by providing the consumer with a straightforward way to shop for products from the comfort of their own home.
The Human Factor of Cybercrime

This volume highlights the central role of the human factor in cybercrime and the need to develop a more interdisciplinary research agenda to understand better the constant evolution of online harms and craft more effective responses. The term “human factor” is understood very broadly and encompasses individual, institutional, and societal dimensions. It covers individual human behaviors and the social structures that enable collective action by groups and communities of various sizes, as well as the different types of institutional assemblages that shape societal responses. This volume is organized around three general themes whose complementary perspectives allow us to map the complex interplay between offenders, machines, and victims, moving beyond static typologies to offer a more dynamic analysis of the cybercrime ecology and its underlying behaviors. The contributions use quantitative and qualitative methodologies and bring together researchers from the United States, the United Kingdom, the Netherlands, Denmark, Australia, and Canada.
Automatic Inference of BGP Location Communities

The Border Gateway Protocol (BGP) orchestrates Internet communications between and inside Autonomous Systems. BGP's flexibility allows operators to express complex policies and deploy advanced traffic engineering systems. A key mechanism to provide this flexibility is tagging route announcements with BGP communities, which have arbitrary, operator-defined semantics, to pass information or requests from router to router. Typical uses of BGP communities include attaching metadata to route announcements, such as where a route was learned or whether it was received from a customer, and controlling route propagation, for example to steer traffic to preferred paths or blackhole DDoS traffic. However, there is no standard for specifying the semantics nor a centralized repository that catalogs the meaning of BGP communities. The lack of standards and central repositories complicates the use of communities by the operator and research communities. In this paper, we present a set of techniques to infer the semantics of BGP communities from public BGP data. Our techniques infer communities related to the entities or locations traversed by a route by correlating communities with AS paths. We also propose a set of heuristics to filter incorrect inferences introduced by misbehaving networks, sharing of BGP communities among sibling autonomous systems, and inconsistent BGP dumps. We apply our techniques to billions of routing records from public BGP collectors and make available a public database with more than 15 thousand location communities. Our comparison with manually-built databases shows our techniques provide high precision (up to 93%), better coverage (up to 81% recall), and dynamic updates, complementing operators' and researchers' abilities to reason about BGP community semantics.
Super-Resolution Reconstruction of 3T-Like Images From 0.35T MRI Using a Hybrid Attention Residual Network

Magnetic resonance (MR) images from low-field scanners present poorer signal-to-noise ratios (SNRs) than those from high-field scanners at the same spatial resolution. To obtain a clinically acceptable SNR, radiologists operating the low-field scanners use a much smaller acquisition matrix than high-field scanners. Thus, the current state of the image quality indicates the need for further research to improve the image quality of low-field systems. Strategies based on super-resolution (SR) techniques can be alternatives for image reconstruction. However, predetermined degradation methods embedded in these techniques, such as bicubic downsampling, seem to impose a performance drop when the actual degradation is different from the pre-defined assumption. In this study, we collected a unique dataset by scanning 70 participants to address this problem. The anatomical locations of the scanned image slices were the same for 0.35T and 3T data. Low-resolution (LR) images (0.35T) and high-resolution (HR) images (3T) were the image pairs used for data training. Herein, we introduce a novel CNN-based network with hybrid attention mechanisms (HybridAttentionResNet, HARN) to adaptively capture diverse information and reconstruct super-resolution 0.35T MR images (3T-like MR images). Specifically, the proposed dense block combines variant dense blocks and attention blocks to extract abundant features from LR images. The experimental results demonstrate that our proposed residual network efficiently recovers significant textures while rendering a high peak signal-to-noise ratio (PSNR) and an appealing structural similarity index (SSIM). Moreover, an extensive subjective-mean-opinion-score (SMOS) proves to be promising in the clinical application using HARN.
r-fat linearized polynomials over finite fields

r -fat polynomials are a natural generalization of scattered polynomials. They define linear sets of the projective line PG ( 1 , q n ) of rank n with r points of weight larger than one. Using techniques on algebraic curves and function fields, we obtain numerical bounds for r and the non-existence of exceptional r -fat polynomials with r > 0 . We completely determine the possible values of r when considering linearized polynomials over F q 4 and we also provide one family of 1-fat polynomials in PG ( 1 , q 5 ) . Furthermore, we investigate LP-polynomials (i.e. polynomials of type f ( x ) = x + δ x q 2 s ∈ F q n [ x ] , gcd ⁡ ( n , s ) = 1 ), determining the spectrum of values r for which such polynomials are r -fat.
Back to futures

Common approaches to concurrent programming begin with languages whose semantics are naturally sequential and add new constructs that provide limited access to concurrency, as exemplified by futures. This approach has been quite successful, but often does not provide a satisfactory theoretical backing for the concurrency constructs, and it can be difficult to give a good semantics that allows a programmer to use more than one of these constructs at a time. We take a different approach, starting with a concurrent language based on a Curry-Howard interpretation of adjoint logic, to which we add three atomic primitives that allow us to encode sequential composition and various forms of synchronization. The resulting language is highly expressive, allowing us to encode futures, fork/join parallelism, and monadic concurrency in the same framework. Notably, since our language is based on adjoint logic, we are able to give a formal account of linear futures, which have been used in complexity analysis by Blelloch and Reid-Miller. The uniformity of this approach means that we can similarly work with many of the other concurrency primitives in a linear fashion, and that we can mix several of these forms of concurrency in the same program to serve different purposes.
BioFors: A Large Biomedical Image Forensics Dataset

Research in media forensics has gained traction to combat the spread of misinformation. However, most of this research has been directed towards content generated on social media. Biomedical image forensics is a related problem, where manipulation or misuse of images reported in biomedical research documents is of serious concern. The problem has failed to gain momentum beyond an academic discussion due to an absence of benchmark datasets and standardized tasks. In this paper we present BioFors <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> – the first dataset for benchmarking common biomedical image manipulations. BioFors comprises 47,805 images extracted from 1,031 open-source research papers. Images in BioFors are divided into four categories – Microscopy, Blot/Gel, FACS and Macroscopy. We also propose three tasks for forensic analysis – external duplication detection, internal duplication detection and cut/sharp-transition detection. We benchmark BioFors on all tasks with suitable state-of-the-art algorithms. Our results and analysis show that existing algorithms developed on common computer vision datasets are not robust when applied to biomedical images, validating that more research is required to address the unique challenges of biomedical image forensics.
Free2Shard

In this paper, we study a canonical distributed resource allocation problem arising in blockchains. While distributed resource allocation is a well-studied problem in networking, the blockchain setting additionally requires the solution to be resilient to adversarial behavior from a fraction of nodes. Scaling blockchain performance is a basic research topic; a plethora of solutions (under the umbrella of sharding ) have been proposed in recent years. Although the various sharding solutions share a common thread (they cryptographically stitch together multiple parallel chains), architectural differences lead to differing resource allocation problems. In this paper we make three main contributions: (a) we categorize the different sharding proposals under a common architectural framework, allowing for the emergence of a new, uniformly improved, uni-consensus sharding architecture. (b) We formulate and exactly solve a core resource allocation problem in the uni-consensus sharding architecture -- our solution, Free2shard, is adversary-resistant and achieves optimal throughput. The key technical contribution is a mathematical connection to the classical work of Blackwell approachability in dynamic game theory. (c) We implement the sharding architecture atop a full-stack blockchain in 3000 lines of code in Rust -- we achieve a throughput of more than 250,000 transactions per second with 6 shards, a vast improvement over state-of-the-art.
CWM-CGAN Method for Renewable Energy Scenario Generation Based on Weather Label Multi-Factor Definition

With the increasing installed capacity of renewable energy in the energy system, the uncertainty of renewable energy has an increasingly prominent impact on power system planning and operation. Renewable energy such as wind and solar energy is greatly affected by the external weather. How to use a reasonable method to describe the relationship between weather and renewable energy output, so as to measure the uncertainty of renewable energy more accurately, is an important problem. To solve this problem, this paper proposes a renewable energy scenario generation method based on a conditional generation countermeasure network and combination weighting method (CWM-CGAN). In this method, the combination of AHP and the entropy weight method is used to analyze the meteorological factors, the weather classification is defined as the condition label in the conditional generation countermeasure network, and the energy scenario is generated by the conditional generation confrontation network. In this paper, the proposed method is tested with actual PV data, and the results show that the proposed model can describe the uncertainty of PV more accurately.
Single View Physical Distance Estimation using Human Pose

We propose a fully automated system that simultaneously estimates the camera intrinsics, the ground plane, and physical distances between people from a single RGB image or video captured by a camera viewing a 3-D scene from a fixed vantage point. To automate camera calibration and distance estimation, we leverage priors about human pose and develop a novel direct formulation for pose-based auto-calibration and distance estimation, which shows state-of-the-art performance on publicly available datasets. The proposed approach enables existing camera systems to measure physical distances without needing a dedicated calibration process or range sensors, and is applicable to a broad range of use cases such as social distancing and workplace safety. Furthermore, to enable evaluation and drive research in this area, we contribute to the publicly available MEVA dataset with additional distance annotations, resulting in "MEVADA" – an evaluation benchmark for the pose-based auto-calibration and distance estimation problem.
Encapsulated Search Index: Public-Key, Sub-linear, Distributed, and Delegatable

We build the first sub-linear (in fact, potentially constant-time) public-key searchable encryption system: We call such a primitive Encapsulated Search Index (ESI). Our ESI scheme can be made (t, n)-distributed among n servers in the best possible way: non-interactive, verifiable, and resilient to any coalition of up to $$(t-1)$$ malicious servers. We also introduce the notion of delegatable ESI and show how to extend our construction to this setting. Our solution — including public indexing, sub-linear search, delegation, and distributed token generation — is deployed as a commercial application by a real-world company.
Sustainability of Cooperation in the International Development of African Higher Education*

This study compares an ideal model of a sustainable research institution and the dominating model used by Sida. It uses a complementary capacity building (CCB) model aimed to promote an improved research culture for strengthening the sustainability of research partnerships in economics and management disciplines. This study uses bibliometric data to show post-graduation publication results from graduated Ph.D. students from the Sida supported research partnership in Rwanda and data from public records to show results from the implementation of the CCB model in the same country. The results show an increased publication activity among a significant proportion of the faculty members targeted by the CCB model and its contribution to improved research culture. Based on the evidence, the paper provides some recommendations to induce improvements in outcomes, including activities promoting a more active research culture in research partnerships with sub-Saharan African institutions.
An expert recommendation model to electric projects based on KG2E and collaborative filtering

Science and technology projects are an essential starting point for the digital transformation of electric companies. Irrelevant projects’ reviewers will cause unrealistic research results. The companies will also waste research funding. We use knowledge graphs and collaborative filtering to recommend experts for projects in the electric power field to solve this problem. First, we constructed an electric project knowledge graph through the project abstract and CNKI database. Then, we use semantic and collaborative similarity to find the experts most relevant to the project. Finally, we discussed the outperformance and conditions of the proposed model and compared the recommendation results with state-of-the-art methods. Research indicates: (1) The knowledge graph can effectively solve the cold-start problem of collaborative filtering. (2) The KG2E-CF model can improve the relevance between the results of the recommendation. (3) The proposed model should be combined with the theme words extraction algorithm to increase the relevance of the recommendation results. Therefore, the expert recommendation in the electric power field can adopt the model proposed in this paper.
Linguistically annotated dataset for four official South African languages with a conjunctive orthography: IsiNdebele, isiXhosa, isiZulu, and Siswati

This data article presents a linguistically annotated data set for four official South African languages with a conjunctive orthography, namely isiNdebele, isiXhosa, isiZulu and Siswati. The data set is parallel for all four languages and can be used for language-specific as well as cross-language development and evaluation of Natural Language Processing (NLP) core technologies. In addition, it can be used for corpus linguistic studies. The article describes how the data was collected, what type of texts it contains and it provides some details on the three different types of linguistic annotation added (morphology, part-of-speech and lemmas), including an example.