Reviewerly/exHarmony huggingface dataset:
Over-optimistic evaluation and reporting of novel cluster algorithms: an illustrative study

Abstract When researchers publish new cluster algorithms, they usually demonstrate the strengths of their novel approaches by comparing the algorithms’ performance with existing competitors. However, such studies are likely to be optimistically biased towards the new algorithms, as the authors have a vested interest in presenting their method as favorably as possible in order to increase their chances of getting published. Therefore, the superior performance of newly introduced cluster algorithms is over-optimistic and might not be confirmed in independent benchmark studies performed by neutral and unbiased authors. This problem is known among many researchers, but so far, the different mechanisms leading to over-optimism in cluster algorithm evaluation have never been systematically studied and discussed. Researchers are thus often not aware of the full extent of the problem. We present an illustrative study to illuminate the mechanisms by which authors—consciously or unconsciously—paint their cluster algorithm’s performance in an over-optimistic light. Using the recently published cluster algorithm Rock as an example, we demonstrate how optimization of the used datasets or data characteristics, of the algorithm’s parameters and of the choice of the competing cluster algorithms leads to Rock’s performance appearing better than it actually is. Our study is thus a cautionary tale that illustrates how easy it can be for researchers to claim apparent “superiority” of a new cluster algorithm. This illuminates the vital importance of strategies for avoiding the problems of over-optimism (such as, e.g., neutral benchmark studies), which we also discuss in the article.
Iron Ore Tailing Composition Estimation Using Fused Visible–Near Infrared and Thermal Infrared Spectra by Outer Product Analysis

Iron ore tailings are mainly composed of SiO2 and iron, whose content determines the potential reuse strategy of the tailings. Compared with the traditional wet chemistry approach, spectroscopy has proven its superior effectiveness in characterizing and predicting minerals, such as iron oxides, clay, and SiO2. This study aims to estimate the content of SiO2 and TFe in iron ore tailings based on visible–near infrared (VIS–NIR, 350–2500 nm) and thermal infrared (TIR, 8–14 μm) spectroscopy. The outer product analysis (OPA) method is used to combine VIS–NIR and TIR spectral domains, from which an outer product matrix of fusion data can be generated. The study area is the iron ore tailing dam from Waitoushan, which is one of the super-large iron deposits in the Anshan–Benxi iron cluster of northeastern China. The spectral analysis results demonstrated the following: (1) The reflectance feature at 1163–2499 nm in the VIS–NIR range correlates with TFe and the emissivity feature at 8–9.4 and 10.7–12 μm in the TIR range correlates with SiO2. (2) Compared with the original absorbance spectra, the correlation coefficients of fusion spectra improve from 0.66 to 0.87 for TFe and from 0.64 to 0.84 for SiO2. (3) The partial least squares regression, random forest (RF), and extreme learning machine exploiting particle swarm optimization modeling methods are established for SiO2 and TFe estimation. The prediction accuracy results indicate that the prediction model with OPA-fused spectra performs significantly better than with individual VIS–NIR and TIR spectra. The RF model with input-fused spectra provides the highest accuracy with the coefficients of determination of 0.95 and 0.91, root mean square errors of 0.97% and 0.96%, and ratios of performance to interquartile distance of 6.49 and 2.31 for SiO2 and TFe content estimation, respectively. These outcomes provide a theoretical basis and technical support for tailing composition estimation using spectroscopy.
SASC: A simple approach to synthetic cohorts for generating longitudinal observational patient cohorts from COVID-19 clinical data

One of the impacts of the coronavirus disease 2019 (COVID-19) pandemic has been a push for researchers to better exploit synthetic data and accelerate the design, analysis, and modeling of clinical trials. The unprecedented clinical efforts caused by COVID-19's emergence will certainly boost future robust and innovative approaches of statistical sciences applied to clinical fields. Here, we report the development of SASC, a simple but efficient approach to generate COVID-19-related synthetic clinical data through a web application. SASC takes basic summary statistics for each group of patients and attempts to generate single variables according to internal correlations. To assess the "reliability" of the results, statistical comparisons with Synthea, a known synthetic patient generator tool, and, more importantly, with clinical data of real COVID-19 patients are provided. The source code and web application are available on GitHub, Zenodo, and Mendeley Data.
Analysis of Sports Video Intelligent Classification Technology Based on Neural Network Algorithm and Transfer Learning

With the rapid development of information technology, digital content shows an explosive growth trend. Sports video classification is of great significance for digital content archiving in the server. Therefore, the accurate classification of sports video categories is realized by using deep neural network algorithm (DNN), convolutional neural network (CNN), and transfer learning. Block brightness comparison coding (BICC) and block color histogram are proposed, which reflect the brightness relationship between different regions in video and the color information in the region. The maximum mean difference (MMD) algorithm is adopted to achieve the purpose of transfer learning. On the basis of obtaining the features of sports video images, the sports video image classification method based on deep learning coding model is adopted to realize sports video classification. The results show that, for different types of sports videos, the overall classification effect of this method is obviously better than other current sports video classification methods, which greatly improves the classification effect of sports videos.
OPEC

Bare-metal embedded systems usually lack security isolation. Attackers can subvert the whole system with a single vulnerability. Previous research intends to enforce both privilege isolation (to run application code at the unprivileged level) and resource isolation for global variables and peripherals. However, it suffers from partition-time and execution-time over-privilege issues, due to the limited hardware resources (MPU regions) and the improper way to partition a program.
Quantifying data volatility for IoT forensics with examples from Contiki OS

Forensic investigations are often conducted under limited resource availability such as time, equipment, and people. As data acquisition is resource-demanding already, a higher emphasis needs to be put on prioritizing the investigative steps to optimize the probability of collecting the relevant evidence. Data volatility measures how quickly data disappears from a system and is an essential part of assessing the likelihood of collecting the most valuable evidence. An investigator can use a model for the volatility to estimate the probability of the existence of evidence. This work motivates and details a model for data volatility and exemplifies it for the Coffee File System used in Contiki OS, an operating system for IoT devices. We conducted experiments to test how well the model corresponds to the collected simulated data and cross-validate the model with observations from file system operations. The results revealed that an approximated model based on the known workings of the file system underestimated the volatility. While there are many sources describing volatility qualitatively, there is little research on quantitative volatility, and this paper is a stepping stone to understanding a quantitative approach to evidence volatility.
Learning ordinal constraint binary codes for fast similarity search

Similarity search with hashing has become one of the fundamental research topics in computer vision and multimedia. The current researches on semantic-preserving hashing mainly focus on exploring the semantic similarities between pointwise or pairwise samples in the visual space to generate discriminative hash codes. However, such learning schemes fail to explore the intrinsic latent features embedded in the high-dimensional feature space and they are difficult to capture the underlying topological structure of data, yielding low-quality hash codes for image retrieval. In this paper, we propose an ordinal-preserving latent graph hashing (OLGH) method, which derives the objective hash codes from the latent space and preserves the high-order locally topological structure of data into the learned hash codes. Specifically, we conceive a triplet constrained topology-preserving loss to uncover the ordinal-inferred local features in binary representation learning. By virtue of this, the learning system can implicitly capture the high-order similarities among samples during the feature learning process. Moreover, the well-designed latent subspace learning is built to acquire the noise-free latent features based on the sparse constrained supervised learning. As such, the latent under-explored characteristics of data are fully employed in subspace construction. Furthermore, the latent ordinal graph hashing is formulated by jointly exploiting latent space construction and ordinal graph learning. An efficient optimization algorithm is developed to solve the resulting problem to achieve the optimal solution. Extensive experiments conducted on diverse datasets show the effectiveness and superiority of the proposed method when compared to some advanced learning to hash algorithms for fast image retrieval. The source codes of this paper are available at https://github.com/DarrenZZhang/OLGH .
B-GPS: Blockchain-Based Global Positioning System for Improved Data Integrity and Reliability

When surveying national reference points using a global positioning system (GPS), appropriate work regulations pertaining to the surveying time must be observed. However, such data can be modified easily, so identifying non-compliance with work regulations and forgeries is challenging. If such incidents occur in cadastral surveys, it may result in financial damages to stakeholders, such as citizens and the state. Therefore, it is necessary to improve the reliability by ensuring the integrity of the GPS positioning data and allowing anyone to track them. In this study, a prototype system was developed to record GPS data and the corrections generated during survey processes using the Ethereum blockchain network. Blockchain is a distributed ledger system that prevents the manipulation of uploaded data without the need for a centralized institution by allowing anyone to check the data. Unlike in the past, the proposed system improves the data integrity and reliability for the entire survey process through blockchain, thereby ensuring transparency of the checks using smart contract addresses.
Recognition of a widespread Paleoproterozoic hydrothermal system in the southern McArthur Basin, northern Australia, by in-situ analysis of fine-grained pyrite and spatially-associated solid bitumen in the Lamont Pass palaeohigh

The Paleoproterozoic hydrothermal system in the southern McArthur Basin, northern Australia, played a key role in the formation of the world-class clastic-dominant (CD)-type McArthur River Zn-Pb deposit, as well as several small-sized deposits nearby in this basin. However, the spatial scale of this hydrothermal system and its role in controlling mineralisation are uncertain. Such understanding is crucial for improving mineral exploration efficiency. In this study, we characterised the geochemistry of the fine-grained (<10 μm) pyrite and the co-existing solid bitumen in the non-mineralised Paleoproterozoic Barney Creek Formation (BCF) intersected by the drill-hole Lamont Pass 3, with a novel combination of Raman spectroscopy and nanoscale secondary ion mass spectrometry (Nano-SIMS), to underpin any potential hydrothermal activities distal (∼50 km) to the McArthur River deposit. In the studied samples, fine-grained pyrites display sub-rounded, elongated and ultrafine-grained morphologies and occur as grain clusters spatially closely-associated with solid bitumen. Elemental maps obtained from Nano-SIMS reveal that some pyrites are enriched in trace elements, such as Pb, Tl, Ni, Co, and Cu, occurring as individual grains or overgrowth over the trace element-depleted pyrites. Fine-grained pyrites in the studied samples show similar trace element enrichment patterns to those often observed in the McArthur River deposit. The trace element-rich and -poor pyrites are interpreted to form as a result of hydrothermal and diagenetic processes, respectively. Formation temperatures of solid bitumen were estimated as ∼ 170 ℃ based on Raman spectral and reflectance data, approximating temperatures of hydrothermal fluids responsible for the McArthur River deposit. It is interpreted that solid bitumen may be either formed syngenetically with diagenetic pyrites and thermally reworked by hydrothermal fluids or formed contemporaneously with hydrothermal pyrite. The in-situ analysis of fine-grained pyrite and spatially-associated solid bitumen indicates that hydrothermal activity in the study area is consistent with the fluids responsible for the McArthur River deposit with respect to the trace element composition and fluid temperature. Hydrothermal fluid flow in the study area was likely a far-field effect of the McArthur River mineralisation extending along the Emu Fault. The widespread hydrothermal activity in the Barney Creek Formation strongly suggests that other factors, such as the fluid migration conduits (e.g. large-scale faults), are important controls for mineralization in this region.
Tracking Multiple Zebrafish Larvae Using YOLOv5 and DeepSORT

Accurate tracking of zebrafish larva movements is essential to examine their dynamic behaviors in biomedical and pharmaceutical applications. However, the characteristic “burst” and sleep-like stationary movements of zebrafish will cause inconsistency in the detection and tracking, which hinders the observation of their identities and trajectories. To address these problems, this paper develops an accurate and reliable tracking system for multiple zebrafish larvae based on the current state-of-the-art detection technology YOLOv5 and multi-target tracking technology DeepSORT. The detection-based tracking system divided into two parts: detection and tracking. In the detection stage, the zebrafish larvae’s head position detected by using the trained YOLOv5 model. In the DeepSORT tracking phase, a Cascade matching algorithm utilized to match the identification of zebrafish larvae, which utilizes information on movement and appearance of larvae. On the one hand, the Mahalanobis distance used to evaluate the predicted value by the Kalman filter and the detected value by the YOLOv5 model. On the other hand, the appearance features model of the fish head trained and utilizes Cosine distance to evaluate between the stored values of the appearance features and the current frame appearance information. The proposed tracking system has been evaluated by using CLEAR MOT Metrics on five groups of zebrafish larvae videos under various complex imaging conditions. The results showed that the system had good performance in reducing the ID switch problem in complex environments, and the tracking accuracy could be up to 88.8%.
Comment: Understanding Simpson’s Paradox

chapter Share on Comment: Understanding Simpson's Paradox Author: Judea Pearl Search about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 399–412https://doi.org/10.1145/3501714.3501738Online:04 March 2022Publication History 0citation6DownloadsMetricsTotal Citations0Total Downloads6Last 12 Months6Last 6 weeks2 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
Distributed fusion cross‐chain model and architecture

Abstract Cross‐chain is the key technology to achieve interconnectivity and interoperability across various blockchain networks. It surpasses traditional single‐chain architecture in terms of the performance, capacity, privacy, economic and technical scalability. Cross‐chain can break barriers between institutions, industries, and regions. This paper integrates existing blockchain interoperability technologies and proposes a distributed fusion cross‐chain model in financial industry. In order to support large scale computing and high availability requirements in financial industry, the model is built on a decentralized architecture and highly scalable. It supports large‐scale isomorphic or heterogeneous blockchain cross‐chain data and state interaction. And it achieves reliable financial value circulation in various underlying blockchain networks.
Performance and Power Prediction for Concurrent Execution on GPUs

The unprecedented growth of edge computing and 5G has led to an increased offloading of mobile applications to cloud servers or edge cloudlets. 1 The most prominent workloads comprise computer vision applications. Conventional wisdom suggests that computer vision workloads perform significantly well on SIMD/SIMT architectures such as GPUs owing to the dominance of linear algebra kernels in their composition. In this work, we debunk this popular belief by performing a lot of experiments with the concurrent execution of these workloads, which is the most popular pattern in which these workloads are executed on cloud servers. We show that the performance of these applications on GPUs does not scale well with an increase in the number of concurrent applications primarily because of contention at the shared resources and lack of efficient virtualization techniques for GPUs. Hence, there is a need to accurately predict the performance and power of such ensemble workloads on a GPU. Sadly, most of the prior work in the area of performance/power prediction is for only a single application. To the best of our knowledge, we propose the first machine learning-based predictor to predict the performance and power of an ensemble of applications on a GPU. In this article, we show that by using the execution statistics of stand-alone workloads and the fairness of execution when these workloads are executed with three representative microbenchmarks, we can get a reasonably accurate prediction. This is the first such work in the direction of performance and power prediction for concurrent applications that does not rely on the features extracted from concurrent executions or GPU profiling data. Our predictors achieve an accuracy of 91% and 96% in estimating the performance and power of executing two applications concurrently, respectively. We also demonstrate a method to extend our models to four or five concurrently running applications on modern GPUs.
A Multi-Entity Knowledge Joint Extraction Method of Communication Equipment Faults for Industrial IoT

The Industrial Internet of Things (IIoT) deploys massive communication devices for information collection and process control. Once it reaches failure, it will seriously affect the operation of the industrial system. This paper proposes a new method for multi-entity knowledge joint extraction (MEKJE) of IIoT communication equipment faults. This method constructs a multi-task tightly coupled model of fault entity and relationship extraction. We use it to implement word embedding and bidirectional semantic capture to generate computable text vectors. At the same time, a multi-entity segmentation method is proposed, which uses noise filtering to distinguish the multi-fault relationship of single corpus. We constructed a dataset of communication failures in power IIoT and conducted experiments. The experimental results show that the method performs best in tests with the Faulty Text dataset and the CLUENER dataset. In particular, the model achieves an F1 value of 78.6% in the evaluation of relationship extraction for multiple entities, and a significant improvement of 5–8% in its accuracy and recall. It enables effective mapping and accurate extraction of fault knowledge data.
Health Record Maintenance using Cloud Computing and Multi Authority Attribute Based Encryption

The health records of various patients are monitored, stored and accessed using cloud providers. There may a chance of accessing and data exposure to third party servers which are unauthorized. In order to provide a secured access to data, Encryption is considered to be a most promising method. Though many issues are concerned in encryption such as key management, efficient access and user revocation the most prominent changeling is ensuring fine grained. This proposed method states various framework and architectures which would provide a control over the data access and also helps in storing the patient&#x0027;s record in semi trusted servers. In order to achieve fine grained and secured data access over health records, Multi-Authority Attribute based encryption (MA-ABE) technique helps in encrypting each and every person health record. The suggested encryption standards have a centric view on multiple data that divides the users into multiple domains which in reduces the complicity of key management for both users and owners. High level of privacy is provided by exploiting multi authority encryption method. This also governs dynamic change in accessing policies and also supports efficient user revocation under emergency situation. The experimental results help in providing the scalability, efficiency of proposed method.
Patterns in CAOSS: Distributed representations predict variation in relational interpretations for familiar and novel compound words

While distributional semantic models that represent word meanings as high-dimensional vectors induced from large text corpora have been shown to successfully predict human behavior across a wide range of tasks, they have also received criticism from different directions. These include concerns over their interpretability (how can numbers specifying abstract, latent dimensions represent meaning?) and their ability to capture variation in meaning (how can a single vector representation capture multiple different interpretations for the same expression?). Here, we demonstrate that semantic vectors can indeed rise up to these challenges, by training a mapping system (a simple linear regression) that predicts inter-individual variation in relational interpretations for compounds such as wood brush (for example brush FOR wood, or brush MADE OF wood) from (compositional) semantic vectors representing the meanings of these compounds. These predictions consistently beat different random baselines, both for familiar compounds (moon light, Experiment 1) as well as novel compounds (wood brush, Experiment 2), demonstrating that distributional semantic vectors encode variations in qualitative interpretations that can be decoded using techniques as simple as linear regression.