Reviewerly/exHarmony huggingface dataset:
An original deconvolution approach for oil production allocation based on geochemical fingerprinting

We tackle oil commingling scenarios and develop an original deconvolution approach for geochemical production allocation. This yields robust assessment of the proportions of oils forming a mixture originating from commingling oils associated with diverse reservoirs or, wells. Our study starts from considering that production allocation performed by means of geochemical fingerprinting is relevant in the context of modern and sustainable use of georesources, with the added benefit of favoring shared facilities and production equipment. A geochemical production allocation workflow is typically structured according to two steps: (i) determination of the chromatograms associated with the mixture (and eventually with each of the End Members, EMs, constituting the fluids in the mixture), and (ii) the use of a deconvolution algorithm to estimate the mass fraction of each EM. Concerning the latter step, we introduce an original approach and the ensuing deconvolution algorithm (hereafter termed PGM) that does not require additional laboratory efforts in comparison with traditional approaches. We also present extensions of widely used deconvolution algorithms, which we frame in a (stochastic) Monte Carlo context to improve their robustness and reliability. The new PGM approach is assessed jointly with a suite of typically used approaches and algorithms against new laboratory-based commingling scenarios. The latter are based on the design and introduction of a novel and low-cost experimental method. The results of the study (i) constitute a unique and rigorous comparison of the traditionally employed production allocation deconvolution algorithms, (ii) document the critical importance of the number of features of the chromatograms used during a quantitative deconvolution, and (iii) suggest that our new PGM approach is very robust and accurate compared to existing approaches.
Compact Representation of Interval Graphs of Bounded Degree and Chromatic Number

In this paper we initiate the study of designing parameterized compact data structures for interval graph <tex>$G$</tex>. First, we show that when the maximum degree of <tex>$G$</tex> is bounded by <tex>$\ell$</tex>, there exists an <tex>$(n\log_{2}\ell+O(n))$</tex>-bit data structure while supporting the standard navigational queries i.e., degree, adjacency, and neighborhood optimally. Our data structure takes less space than the information-theoretic lower bound when <tex>$\ell\ =\ O(n^{\epsilon})$</tex> where <tex>$0 &lt; \epsilon &lt; 1$</tex>. Furthermore, we show that the space requirement of our data structure is almost optimal by giving an explicit <tex>$(\frac{1}{6}n\log_{2}\ell-O(n))$</tex>-bit counting lower bound. Thus, we provide a compact representation of interval graphs with bounded degree for the first time in literature. Finally, we consider the interval graphs with bounded chromatic number <tex>$(\Delta)$</tex>, and design a <tex>$(\Delta- 1)n+o(\Delta n)$</tex> bit data structure with efficient query times for such graphs. This improves the worst-case information-theoretic lower bound when <tex>$\Delta=o(\log n)$</tex>.
Computing Lexicographic Parsings

We give memory-friendly algorithms computing the compression schemes plcpcomp or lexparse in linear or near-linear time, and give upper and lower bounds on the space requirements of our algorithm computing plcpcomp.
SortComp (Sort-and-Compress) - Towards a Universal Lossless Compression Scheme for Matrix and Tabular Data

A universal scheme is proposed for the lossless compression of two-dimensional tables and matrices. Instead of standard row- or column-based compression, we propose to sort each column first and record both the sorted table and the corresponding permutation table of the sorting permutations. These two tables are then separately compressed. In this new scheme, both intra- and inter-column correlations can be efficiently captured, giving rise to improved compression ratio in particular when both column-wise and row-wise dependencies cooccur. This scheme reduces the problem of the compression of an arbitrary two-dimensional table to that of a &#x2018;permutation table&#x2019; together with a &#x2018;sorted table&#x2019;, where the former is only dependent on the table dimension and the latter can be effectively compressed column-by-column using predictive methods. Based on this scheme, a new algorithm is proposed, SortComp (sort-and-compress). For correlated columns, we give an estimation of the asymptotic bit rate of the algorithm and compare it to column-oriented compression schemes. Numerical experiments on real-life csv datasets validate the advantages of SortComp compared to existing row- and column-oriented compression algorithms.
Design of Comprehensive Rating Algorithm for Classroom Teaching Effect under the Background of Sports Education Integration

Exploring the improvement of classroom teaching effect under the background of sports education integration in China has important practical significance and theoretical value. Integration of yard sports education and classroom course education in the school is a novel concept put forward by China’s sports and education circles to deepen the combination of sports and education. Although it is only a word different from the combination of sports and education to integrating sports and course education in the school, it is a groundbreaking theoretical and practical innovation for China in terms of changing the competitive sports development model and cultivating exceptional athletes. It is a new way to promote the sustainable development of sports and education according to the scientific outlook on development. Its fundamental significance is to change the closed state of sports and education departments, put sports and education in the background of economic and social development in a certain region, and fundamentally reform the content and mode of education. Therefore, this paper proposes a comprehensive rating model of classroom teaching effects based on deep learning (DL) and machine learning (ML) techniques. In this paper, the Jaffe expression dataset is used to train and test the utilized ML and DL models such as ResNet50, random forest (RF), logistic regression (LR), K-nearest neighbor (k-NN), and decision tree (DT). Further, with the help of artificial intelligence (AI) techniques, the algorithms can objectively evaluate the classroom teaching effect after the integration of physical education with classroom education and provide important guidance for the modernization and intellectualization of China’s educational methods in the future.
Insights into the Black Box Machine Learning Models Through Explainability and Interpretability

Artificial intelligence (AI) and machine learning (ML) technologies are considered to be the Holy Grail for the researchers across the world. The applications of AI and ML are proving disruptive across the global technological spectrum, and there is practically no area which has been left untouched by these technologies right from computer science to manufacturing, healthcare, insurance, credit ratings, cybersecurity, and many more. It would not be an exaggeration to say that it is the next big thing after the advent of the Internet and potentially holds a similar impact in touching the lives of human beings. Whilst most researchers using machine learning in research across diverse domains do not need to look beyond the model abstraction for their work, the need for understanding what is happening beneath the surface is sometimes necessary. This becomes especially important in the cases where the predictions are too good to be apparently true, and the researcher running the model is not sure about its validity as the logic for prediction is obscure. The process of feature engineering brings in more accuracy to predictions, but in the absence of intuitive background information regarding the features, the task gets more challenging. The scientific reasoning has been driven by logic through ages, and the scientist community remains sceptical of the results unless they can extract useful insights from the black box ML models. The paper applies five popular explainability algorithms being used by the research community to demystify the abstract nature of ML black box models and compare the relative clarity of the insights being provided individually by each from a practitioner’s perspective using the publicly available UCI wine quality dataset.
Mixed Huffman codes for on-line and off-line applications

In the paper Huffman codes that mix different r-nary code elements in one code, the mixed Huffman codes, are analyzed [1]. The Huffman code generalization usually leads to short-ening of average codeword length: a statistical test shows that for source alphabets longer than 8&#x2013;12 elements more than 99&#x0025; of the best compact codes are mixed Huffman ones. This is also true for practical mixed Huffman codes, which is demonstrated in experiments with data files containing up to milion elements for sources of size 12&#x2013;17 symbols. The codes are derived in the same way as other Huffman ones: iteratively by reducing in each step source size by r-1 elements, the only difference is that the <tex>$r$</tex> value may change from step to step (reasonable values of <tex>$r$</tex> are prime numbers). Search for optimal code is a trial and error process, nevertheless, usually they are several suboptimal mixed Huffman codes that are better than the binary ones, hence, exhaustive search for optimal solution is not necessary. It is worth to note that not described in this paper generated using simplified search rules dynamic mixed Huffman codes [2] are usually better than their binary counterparts, too. The mixed Huffman code is coded and decoded using modified Huffman tree, in which some nodes have <tex>$r$</tex> instead of 2 offsprings. The r-nary elements are grouped to form r-nary num-bers being close, but slightly smaller than some powers of number two, e.g. three 5-nary digits define numbers up to 124, which can be coded using seven bits. In fact, bit number can be lowered by variable-length coding, in the example above one 5-nary element is coded using 2.3333 bits, the value can be diminished to 2.3253 bits, while log 5 &#x003D; 2.3219. This technique implies that before sending r-nary elements should be collected from more than one codeword, leading to delays on the decoder side, which may be important in on-line applications. It is shown in the paper that delays can be kept short, while improved coding efficiency retained. For example, if each codeword contains at least one r-nary element, the delay introduced by this element is shorter than the size of buffer for its storing, e.g. in the example above for <tex>$r = 5$</tex> the delay is shorter than three codewords. As noted before there are usually many mixed Huffman codes better than the binary one, hence, the chance that such minimum delay and efficient code exists is high. Of course, in many applications coding delay is not a problem. Summarizing, coders and decoders for practical mixed Huffman codes are simple and fast, while theoretical considerations and experiments with true data show that indeed, their use usually leads to better data compression, if compared to that for the binary Huffman code.
Augmenting Blockchain With Competition Law for a Sustainable Economic Evolution

Since its introduction, blockchain technology has been revered, ridiculed, dismissed, embraced, and presently has become too large to ignore, witnessing exponential growth. The obvious indicator of this growth is that research revolving around blockchain technology has already raised competition in the form of directed acyclic graphs and hashgraph, all of which fall under the umbrella of distributed ledger technology (DLT). Segueing on the back of visibly positive effects of competition, we arrive at the essence of our paper. We show that the current competition regimes around the world are inefficient at promoting and maintaining competition around the world, dominated by the behemoth technology enterprises that have successfully monopolized and monetized data, which is indubitably, one of the most important assets in today’s digital age. Data gathered from users fuels the algorithms, machine learning, and artificial intelligence programs employed by these tech giants, which further entrenches their monopolistic hold over cyberspace. Blockchain and DLTs, just like any other technology, pose new threats to the competition law regimes, while also allowing the authorities to utilize the technology themselves to explore new horizons involving smart contracts, Decentralized Autonomous Organizations (DAOs), Web 3.0, and enforce competition more effectively. In our paper, we briefly illustrate the challenges presented before the competition authorities by the assimilation of blockchain in the existing establishments, and how the competition authorities can themselves collude with blockchain stakeholders to take a holistic approach and establish a symbiotic relationship, which ensures that both, survive, prosper, and enhance consumer welfare.
Graph-based construction of minimal models

Reasoning with minimal models is at the heart of many knowledge representation systems. Yet, it turns out that this task is formidable even when very simple theories are considered. It is, therefore, crucial to devise methods that attain good performances in most cases. To this end, a path to follow is to find ways to break the task at hand into several sub-tasks that can be solved separately and in parallel. And, in fact, we show that minimal models of positive propositional theories can be decomposed based on the structure of the dependency graph of the theories: this observation turns out to be useful for many applications involving computation with minimal models. In particular, we introduce a new algorithm for minimal model finding based on model decomposition. The algorithm temporal worst-case complexity is exponential in the size s of the largest connected component of the dependency graph, but the actual cost depends on the size of the largest component actually encountered at run time that can be far smaller than s, and on the class of theories to which components belong. For example, if all components reduce to either an Head Cycle Free or an Head Elementary-set Free theory, the algorithm is polynomial in the size of the theory.
Attribute-Decomposable Motion Compression Network for 3D MoCap Data

Motion Capture (MoCap) data is one type of fundamental asset for the digital entertainment. The progressively increasing 3D applications make MoCap data compression unprecedentedly important. In this paper, we propose an end-to-end attribute-decomposable motion compression network using the AutoEncoder architecture. Specifically, the algorithm consists of an LSTM-based encoder-decoder for compression and decompression. The encoder module decomposes human motion into multiple uncorrelated semantic attributes, including action content, arm space, and motion mirror. The decoder module is responsible for reconstructing vivid motion based on the decomposed high-level characteristics. Our method is computationally efficient with powerful compression ability, outperforming the state-of-the-art methods in terms of compression rate and compression error. Furthermore, our model can generate new motion data given a combination of different motion attributes while existing methods have no such capability.
Leukocyte Segmentation Method Based on Adaptive Retinex Correction and U-Net

To address the issues of uneven illumination and inconspicuous leukocyte properties in the gathered cell pictures, a leukocyte segmentation method based on adaptive retinex correction and U-net was proposed. The procedure begins by processing a peripheral blood image to clearly distinguish leukocytes from other components in the image. The adaptive retinex correction, which is based on multiscale retinex with colour replication (MSRCR), redefines the colour recovery function by introducing Michelson contrast. Then, the image is trained with the U-net convolutional neural network, and the leukocyte segmentation is completed. The innovation is in the manner of processing peripheral blood images, which improves the accuracy of leukocyte segmentation. This study conducts experimental evaluations on the Cellavision, BCCD, and LISC datasets. The experimental results show that the method in this study is better than the current best method, and the segmentation accuracy rate reaches 98.87%.
A location privacy protection method based on cipher‐text retrieval in cloud environment

Abstract In order to improve the efficiency of location query and the effect of privacy protection in cloud environment, a location privacy protection method based on cipher‐text retrieval was proposed. Firstly, the spatial area is divided into several square grids, and the big Morton code of each location is calculated, the spatial area in the grid is divided again, and the small Morton code of each location is calculated. Secondly, the location data is encrypted by the combination of public key encryption and scrambling encryption, and then outsourced to the cloud server. When querying the location data of the interest points, the query request is sent to the data owner. After obtaining the query trapdoor and the private key, the query trapdoor is sent to the location data server for query. On the server side, the interest points in the region are found according to the cipher‐text matching, and the cipher‐text top‐ k sorting is implemented. Finally, the cipher‐text query results are decrypted into plaintext by the private key. Experimental results show that the proposed method ensures the location data privacy, and improves query efficiency. At the same time, the balance between privacy protection security and query quality is achieved.
Creating Word Paintings Jointly Considering Semantics, Attention, and Aesthetics

In this article, we present a content-aware method for generating a word painting. Word painting is a composite artwork made from the assemblage of words extracted from a given text, which carries similar semantics and visual features to a given source image. However, word painting, usually created by skilled artists, involves tedious manual processes, especially when generating streamlines and laying out text. Hence, we provide an easy method to create word paintings for users. How to design textural layout that simultaneously conveys the input image and enables easy access to the semantic theme is the key challenge to generating a visually pleasing word painting. To address this issue, given an image and its content-related text, we first decompose the input image into several regions and approximate each region with a smooth vector field. At the same time, by analyzing the input text, we extract some weighted keywords as the graphic elements. Then, to measure the likelihood of positions in the input image that attract the observers’ attention, we generate a saliency map with our trained visual attention model. Finally, jointly considering visual attention and aesthetic rules, we propose an energy-based optimization framework to arrange extracted keywords into the decomposed regions and synthesize a word painting. Experimental results and user studies show that this method is able to generate a fashionable and appealing word painting.
Random Primes without Primality Testing

Numerous algorithms call for computation over the integers modulo a randomly-chosen large prime. In some cases, the quasi-cubic complexity of selecting a random prime can dominate the total running time. We propose a new variant of dynamic evaluation, applied to a randomly-chosen (composite) integer. The transformation we propose can apply to any algorithm in the algebraic RAM model, even allowing randomization. The resulting transformed algorithm avoids any primality tests and will, with constant positive probability, have the same result as the original computation modulo a randomly-chosen prime. As an application, we demonstrate how to compute the exact number of nonzero terms in an unknown integer polynomial in quasi-linear time. We also show how the same algorithmic transformation technique can be used for computing modulo random irreducible polynomials over a finite field.
Heterogeneous graph prompt for Community Question Answering

Summary Compared with general question answering, Community Question Answering (CQA), which has been widely used in various scenarios like E‐commerce and is well welcomed. In order to answer the user's question precisely, many CQA models resort to external knowledge sources such as Wikipedia. The main challenge of the task is knowledge extraction and utilization. Different from the traditional method of designing task‐specific knowledge modules, we propose a graph prompt‐based learning method that directly steers the pretrained language model to solve CQA tasks. Multiple information sources are organized as graph prompts to guide the generation of the model, naturally leveraging the knowledge learned in the pretrained step. Based on the pretrained bidirectional and autoregressive transformers, a large‐scale language model, a comparable performance is achieved with less than 10% of the full‐finetuning time by only optimizing the graph prompt parameters. Experiments on two standard CQA datasets show that compared with traditional sequential initialized prompts, graph prompt achieves 20.47% and 14.89% increments in terms of BLEU and ROUGE‐L scores on quick finetuning and outperforms in few‐shot learning.
DigiDrive: Making Driving School Management Effective

The Indian driving school system consists of schools ranging from small-scale setups to huge franchises with branches spread nationwide. Irrespective of the scale of the school, it needs to have a management system to handle its student and instructors data to ensure seamless communication. In today’s world, technology has transformed how industries handle their data management processes. However, from this study, it was observed that the Indian driving schools follow conventional and manual methods for managing their data which generate possibilities for errors. Furthermore, it also leads to several communication problems among the users. This study explores the data management methods, and the problems faced in driving schools through contextual inquiry, semi-structured interviews, and competitive analysis. Based on the problems identified, design intervention is proposed consisting of an in-vehicle infotainment system linked to a mobile and Web application to provide a seamless management experience.
Analysis of random sequential message passing algorithms for approximate inference

We analyze the dynamics of a random sequential message passing algorithm for approximate inference with large Gaussian latent variable models in a student-teacher scenario. To model nontrivial dependencies between the latent variables, we assume random covariance matrices drawn from rotation invariant ensembles. Moreover, we consider a model mismatching setting, where the teacher model and the one used by the student may be different. By means of dynamical functional approach, we obtain exact dynamical mean-field equations characterizing the dynamics of the inference algorithm. We also derive a range of model parameters for which the sequential algorithm does not converge. The boundary of this parameter range coincides with the de Almeida Thouless (AT) stability condition of the replica symmetric ansatz for the static probabilistic model.