Reviewerly/exHarmony huggingface dataset:
Montmorillonite Estimation in Clay–Quartz–Calcite Samples from Laboratory SWIR Imaging Spectroscopy: A Comparative Study of Spectral Preprocessings and Unmixing Methods

Clay minerals play an important role in shrinking–swelling of soils and off–road vehicle mobility mainly due to the presence of smectites including montmorillonites. Since soils are composed of different minerals intimately mixed, an accurate estimation of its abundance is challenging. Imaging spectroscopy in the short wave infrared spectral region (SWIR) combined with unmixing methods is a good candidate to estimate clay mineral abundance. However, the performance of unmixing methods is mineral-dependent and may be enhanced by using appropriate spectral preprocessings. The objective of this paper is to carry out a comparative study in order to determine the best couple spectral preprocessing/unmixing method to quantify montmorillonite in intimate mixtures with clays, such as montmorillonite, kaolinite and illite, and no-clay minerals, such as calcite and quartz. To this end, a spectral database is built with laboratory hyperspectral imagery from 51 dry pure mineral samples and intimate mineral mixtures of controlled abundances. Six spectral preprocessings, standard normal variate (SNV), continuum removal (CR), continuous wavelet transform (CWT), Hapke model, first derivative (1st SGD) and pseudo–absorbance (Log(1/R)), are applied and compared with reflectance spectra. Two linear unmixing methods, fully constrained least square method (FCLS) and multiple endmember spectral mixture analysis (MESMA), and two non-linear unmixing methods, generalized bilinear method (GBM) and multi-linear model (MLM), are compared. Global results showed that the benefit of spectral preprocessings occurs when spectral absorption features of minerals overlap for SNV, CR, CWT and 1st SGD, whereas the use of reflectance spectra performs the best when no overlap is present. With one mineral having no spectral feature (quartz), montmorillonite abundance estimation is difficult and gives RMSE higher than 50%. For the other mixtures, performances of linear and non-linear unmixing methods are similar. Consequently, the recommended couple spectral preprocessing/unmixing method based on the trade-off between its simplicity and performance is 1st SGD/FCLS for clay binary and ternary mixtures (RMSE of 9.2% for montmorillonite–illite mixtures, 13.9% for montmorillonite–kaolinite mixtures and 10.8% for montmorillonite–illite–kaolinite mixtures) and reflectance/FCLS for binary mixtures with calcite (RMSE of 8.8% for montmorillonite–calcite mixtures). These performances open the way to improve the classification of expansive soils.
Automated gleason grading on prostate biopsy slides by statistical representations of homology profile

Background and Objective:Gleason grading system is currently the clinical gold standard for determining prostate cancer aggressiveness. Prostate cancer is typically classified into one of 5 different categories with 1 representing the most indolent disease and 5 reflecting the most aggressive disease. Grades 3 and 4 are the most common and difficult patterns to be discriminated in clinical practice. Even though the degree of gland differentiation is the strongest determinant of Gleason grade, manual grading is subjective and is hampered by substantial inter-reader disagreement, especially with regard to intermediate grade groups. Methods:To capture the topological characteristics and the degree of connectivity between nuclei around the gland, the concept of Homology Profile (HP) for prostate cancer grading is presented in this paper. HP is an algebraic tool, whereby, certain algebraic invariants are computed based on the structure of a topological space. We utilized the Statistical Representation of Homology Profile (SRHP) features to quantify the extent of glandular differentiation. The quantitative characteristics which represent the image patch are fed into a supervised classifier model for discrimination of grade patterns 3 and 4. Results:On the basis of the novel homology profile, we evaluated 43 digitized images of prostate biopsy slides annotated for regions corresponding to Grades 3 and 4. The quantitative patch-level evaluation results showed that our approach achieved an Area Under Curve (AUC) of 0.96 and an accuracy of 0.89 in terms of discriminating Grade 3 and 4 patches. Our approach was found to be superior to comparative methods including handcrafted cellular features, Stacked Sparse Autoencoder (SSAE) algorithm and end-to-end supervised learning method (DLGg). Also, slide-level quantitative and qualitative evaluation results reflect the ability of our approach in discriminating Gleason Grade 3 from 4 patterns on H&E tissue images. Conclusions:We presented a novel Statistical Representation of Homology Profile (SRHP) approach for automated Gleason grading on prostate biopsy slides. The most discriminating topological descriptions of cancerous regions for grade 3 and 4 in prostate cancer were identified. Moreover, these characteristics of homology profile are interpretable, visually meaningful and highly consistent with the rubric employed by pathologists for the task of Gleason grading.
Statistical estimation of hourly diffuse radiation intensity of Budapest City

Abstract The vulnerability of fossil fuel prices to worldwide events such as the recent coronavirus (COVID‐19) pandemics increases the interest in renewable energy resources that offer more stable energy generation costs. Solar energy is one of the most abundant renewable energy resources that have gained significant interest in the last decades with various challenges related to the forecastingof the energy production from these systems. Solar radiation intensity varies due to the daily and seasonal changes in the sun's position in addition to the variation in the sky clearness from one location to another which is considered as an important factor that affects the deployment of solar energy systems. This study aims to develop statistical models—mainly regression models and parametric model based on ASHRAE model—to estimate the hourly diffuse radiation in Budapest as a case study using the measured hourly global and diffuse radiation between 2011 and 2018. The prediction models relate the clearness index (which is obtained from the extraterrestrial and global radiation) and the global radiation through a generalized equation. The parametric model was developed by finding the optimal site‐specific constants of ASHRAE model for Budapest using the measured data that minimize the root mean square error. In addition, this study presents a comparison between the results from the developed models and the models reported in the literature. The results indicate that all the developed regression models had close correlation coefficients ( R 2 ) where the linear, power, and exponential models had the largest R 2 (.69). Finally, the linear model was evaluated on a dataset outside the test data range where the linear model was capable of predicting the diffuse radiation with much better R 2 (.93).
What HCI Can Learn from ASMR: Becoming Enchanted with the Mundane

In this paper we explore how the qualities of Autonomous Sensory Meridian Response (ASMR) media - its pairing of sonic and visual design, ability to subvert fast-paced technology for slow experiences, production of somatic responses, and attention to the everyday-might reveal new design possibilities for interactions with wearable technology. We recount our year-long design inquiry into the subject which began with an interview with a "live" ASMR creator and design probes, a series of first-person design exercises, and resulted in the creation of two interactive garments for attending, noticing, and becoming enchanted with our our everyday surroundings. We conclude by suggesting that these ASMR inspired designs cultivate personal, intimate, embodied, and felt practices of attention within our everyday, mundane, environments.
Runtime Enforcement for Control System Security

With the explosion of Industry 4.0, industrial facilities and critical infrastructures are transforming into “smart” systems that dynamically adapt to external events. The result is an ecosystem of heterogeneous physical and cyber components, such as programmable logic controllers, which are more and more exposed to cyber-physical attacks, i.e., security breaches in cyberspace that adversely affect the physical processes at the core of industrial control systems. We apply runtime enforcement techniques, based on an ad-hoc sub-class of Ligatti et al.'s edit automata, to enforce specification compliance in networks of potentially compromised controllers, formalised in Hennessy and Regan's Timed Process Language. We define a synthesis algorithm that, given an alphabet P of observable actions and an enforceable regular expression e capturing a timed property for controllers, returns a monitor that enforces the property e during the execution of any (potentially corrupted) controller with alphabet P and complying with the property e. Our monitors correct and suppress incorrect actions coming from corrupted controllers and emit actions in full autonomy when the controller under scrutiny is not able to do so in a correct manner. Besides classical properties, such as transparency and soundness, the proposed enforcement ensures non-obvious properties, such as polynomial complexity of the synthesis, deadlock- and diverge-freedom of monitored controllers, together with scalability when dealing with networks of controllers.
Using source code density to improve the accuracy of automatic commit classification into maintenance activities

Source code is changed for a reason, e.g., to adapt, correct, or adapt it. This reason can provide valuable insight into the development process but is rarely explicitly documented when the change is committed to a source code repository. Automatic commit classification uses features extracted from commits to estimate this reason. We introduce source code density, a measure of the net size of a commit, and show how it improves the accuracy of automatic commit classification compared to previous size-based classifications. We also investigate how preceding generations of commits affect the class of a commit, and whether taking the code density of previous commits into account can improve the accuracy further. We achieve up to 89% accuracy and a Kappa of 0.82 for the cross-project commit classification where the model is trained on one project and applied to other projects. Models trained on single projects yield accuracies of up to 93% with a Kappa approaching 0.90. The accuracy of the automatic commit classification has a direct impact on software (process) quality analyses that exploit the classification, so our improvements to the accuracy will also improve the confidence in such analyses.
Index-Free Approach with Theoretical Guarantee for Efficient Random Walk with Restart Query

Due to the prevalence of graph data, graph analysis is very important nowadays. One popular analysis on graph data is Random Walk with Restart (RWR) since it provides a good metric for measuring the proximity of two nodes in a graph. Although RWR is important, it is challenging to design an algorithm for RWR. To the best of our knowledge, there are no existing RWR algorithms which, at the same time, (1) are index-free, (2) return answers with a theoretical guarantee and (3) are efficient. Motivated by this, in this paper, we propose an index-free algorithm called Residue-Accumulated approach (ResAcc) which returns answers with a theoretical guarantee efficiently. Our experimental evaluations on large-scale real graphs show that ResAcc is up to 4 times faster than the best-known previous algorithm, guaranteeing the same accuracy. Under typical settings, the best-known algorithm ran around 1000 seconds on a large dataset containing 41.7 million nodes, which is too time-consuming, while ResAcc finished in 275 seconds with the same accuracy. Moreover, ResAcc is up to 6 orders of magnitude more accurate than the best-known algorithm in practice with the same execution time, which is considered as a substantial improvement.
MEBOW: Monocular Estimation of Body Orientation in the Wild

Body orientation estimation provides crucial visual cues in many applications, including robotics and autonomous driving. It is particularly desirable when 3-D pose estimation is difficult to infer due to poor image resolution, occlusion or indistinguishable body parts. We present COCO-MEBOW (Monocular Estimation of Body Orientation in the Wild), a new large-scale dataset for orientation estimation from a single in-the-wild image. The body-orientation labels for around 130K human bodies within 55K images from the COCO dataset have been collected using an efficient and high-precision annotation pipeline. We also validated the benefits of the dataset. First, we show that our dataset can substantially improve the performance and the robustness of a human body orientation estimation model, the development of which was previously limited by the scale and diversity of the available training data. Additionally, we present a novel triple-source solution for 3-D human pose estimation, where 3-D pose labels, 2-D pose labels, and our body-orientation labels are all used in joint training. Our model significantly outperforms state-of-the-art dual-source solutions for monocular 3-D human pose estimation, where training only uses 3-D pose labels and 2-D pose labels. This substantiates an important advantage of MEBOW for 3-D human pose estimation, which is particularly appealing because the per-instance labeling cost for body orientations is far less than that for 3-D poses. The work demonstrates high potential of MEBOW in addressing real-world challenges involving understanding human behaviors. Further information of this work is available at https://chenyanwu.github.io/MEBOW/.
Studying the Effects of Cognitive Biases in Evaluation of Conversational Agents

Humans quite frequently interact with conversational agents. The rapid advancement in generative language modeling through neural networks has helped advance the creation of intelligent conversational agents. Researchers typically evaluate the output of their models through crowdsourced judgments, but there are no established best practices for conducting such studies. Moreover, it is unclear if cognitive biases in decision-making are affecting crowdsourced workers' judgments when they undertake these tasks. To investigate, we conducted a between-subjects study with 77 crowdsourced workers to understand the role of cognitive biases, specifically anchoring bias, when humans are asked to evaluate the output of conversational agents. Our results provide insight into how best to evaluate conversational agents. We find increased consistency in ratings across two experimental conditions may be a result of anchoring bias. We also determine that external factors such as time and prior experience in similar tasks have effects on inter-rater consistency.
Answering (Unions of) Conjunctive Queries using Random Access and Random-Order Enumeration

As data analytics becomes more crucial to digital systems, so grows the importance of characterizing the database queries that admit a more efficient evaluation. We consider the tractability yardstick of answer enumeration with a polylogarithmic delay after a linear-time preprocessing phase. Such an evaluation is obtained by constructing, in the preprocessing phase, a data structure that supports polylogarithmic-delay enumeration. In this paper, we seek a structure that supports the more demanding task of a "random permutation": polylogarithmic-delay enumeration in truly random order. Enumeration of this kind is required if downstream applications assume that the intermediate results are representative of the whole result set in a statistically valuable manner. An even more demanding task is that of a "random access": polylogarithmic-time retrieval of an answer whose position is given. We establish that the free-connex acyclic CQs are tractable in all three senses: enumeration, random-order enumeration, and random access; and in the absence of self-joins, it follows from past results that every other CQ is intractable by each of the three (under some fine-grained complexity assumptions). However, the three yardsticks are separated in the case of a union of CQs (UCQ): while a union of free-connex acyclic CQs has a tractable enumeration, it may (provably) admit no random access. For such UCQs we devise a random-order enumeration whose delay is logarithmic in expectation. We also identify a subclass of UCQs for which we can provide random access with polylogarithmic access time. Finally, we present an implementation and an empirical study that show a considerable practical superiority of our random-order enumeration approach over state-of-the-art alternatives.
TransN: Heterogeneous Network Representation Learning by Translating Node Embeddings

Learning network embeddings has attracted growing attention in recent years. However, most of the existing methods focus on homogeneous networks, which cannot capture the important type information in heterogeneous networks. To address this problem, in this paper, we propose TransN, a novel multi-view network embedding framework for heterogeneous networks. Compared with the existing methods, TransN is an unsupervised framework which does not require node labels or user-specified meta-paths as inputs. In addition, TransN is capable of handling more general types of heterogeneous networks than the previous works. Specifically, in our framework TransN, we propose a novel algorithm to capture the proximity information inside each single view. Moreover, to transfer the learned information across views, we propose an algorithm to translate the node embeddings between different views based on the dual-learning mechanism, which can both capture the complex relations between node embeddings in different views, and preserve the proximity information inside each view during the translation. We conduct extensive experiments on real-world heterogeneous networks, whose results demonstrate that the node embeddings generated by TransN outperform those of competitors in various network mining tasks.
Expedient Information Retrieval System for Web Pages Using the Natural Language Modeling

Retrieving of information from the huge set of data flowing due to the day to day development in the technologies has become more popular as it assists in searching for the valuable information in a structured, unstructured or a semi structured data set like text, database, multimedia, documents, and internet etc. The retrieval of information is performed employing any one of the models starting from the simple Boolean model for retrieving information, or using other frame works such as probabilistic, vector space and the natural language modelling. The paper is emphasis on using a natural language model based information retrieval to recover the meaning insights from the enormous amount of data. The method proposed in the paper uses the latent semantic analysis to retrieve significant information’s from the question raised by the user or the bulk documents. The carried out method utilizes the fundamentals of semantic factor occurring in the data set to identify the useful insights. The experiment analysis of the proposed method is carried out with few state of art dataset such as TIME, LISA, CACM and the NPL etc. and the results obtained demonstrate the superiority of the method proposed in terms of precision, recall and F-score.
CrossFusion net: Deep 3D object detection based on RGB images and point clouds in autonomous driving

In recent years, accurate 3D detection plays an important role in a lot of applications. Autonomous driving, for instance, is one of typical representatives. This paper aims to design an accurate 3D detector that takes both Li-DAR point clouds and RGB images as inputs according to the fact that both LiDAR and camera have their own merits. A deep novel end-to-end two-stream learnable architecture, CrossFusion Net, is designed to exploit features from both LiDAR point clouds as well as RGB images through a hierarchical fusion structure. Specifically, CrossFusion Net utilizes bird's eye view (BEV) of point clouds through projection. Besides, these two feature maps of different streams are fused through the newly introduced CrossFusion(CF) layer. The proposed CF layer transforms feature maps of one stream to another based on the spatial relationship between the BEV and RGB images. Additionally, we apply attention mechanism on the transformed feature map and the original one to automatically decide the importance of the two feature maps from the two sensors. Experiments on the challenging KITTI car 3D detection benchmark and BEV detection benchmark show that the presented approach outperforms the other state-of-the-art methods in average precision(AP), specifically, as well as outperforms UberATG-ContFuse [3] of 8% AP in moderate 3D car detection. Furthermore, the proposed network learns an effective representation in perception of circumstances via RGB feature maps and BEV feature maps.
Human Object Interaction Detection via Multi-level Conditioned Network

As one of the essential problems in scene understanding, human object interaction detection (HOID) aims to recognize fine-grained object-specific human actions, which demands the capabilities of both visual perception and reasoning. Existing methods based on convolutional neural network (CNN) utilize diverse visual features for HOID, which are insufficient for complex human object interaction understanding. To enhance the reasoning capablity of CNN, we propose a novel multi-level conditioned network that fuses extra spatial-semantic knowledge with visual features. Specifically, we construct a multi-branch CNN as backbone for multi-level visual representation. We then encode extra knowledge including human body structure and object context as condition to dynamically influence the feature extraction of CNN by affine transformation and attention mechanism. Finally, we fuse the modulated multimodal features to distinguish the interactions. The proposed method is evaluated on two most frequently-used benchmarks, HICO-DET and V-COCO. The experiment results show that our method is superior to the state-of-the-arts.
DRUBER

In the latest years, there has been a growing interest in autonomous drone delivery. This is due to the increasing demand for efficient delivery services, and to the concurrent inability of existing ground based systems to provide guaranteed availability, and delivery time. However, the cost for implementing a centralized drone-based delivery service can only be afforded by large commercial organizations.