Reviewerly/exHarmony huggingface dataset:
Soil weathering behavior assessed by combined spectral ranges: Insights into aggregate analysis

Most research applying spectral sensors has focused on soil attributes quantification, with strong statistics and hundreds of data. Nevertheless, the explanation of the fundamental relationship between several spectral ranges and specific soil processes remains unclear. Soil sensing can be performed in many spectral ranges, however, the focus is usually on a specific one, which limits the knowledge of the phenomenon. Thus, this work investigated the synergistic performance of several spectral ranges (Gamma-ray; X-ray fluorescence, XRF; visible, Vis; near infrared, NIR; shortwave infrared, SWIR; and mid-infrared, MIR) on the weathering process of soils developed from magmatic material. Two soil profiles from Southeast Brazil (São Paulo state) were analyzed, assessing their chemical, physical and mineralogical data. The same profiles were analyzed by the indicated spectral ranges, which were related to mineralogy, weathering indices and mass balance (MB). The elements provided by Gamma-ray were related to the soil mineralogy and weathering degree of horizons. The XRF data enabled the calculation of MB and weathering indices, which were similar to those calculated by the traditional analysis. The spectra of the Vis-NIR-SWIR-MIR region presented alterations in their behavior related to the weathering degree of horizons. This synergic approach separated soil horizons by weathering degree more efficiently than a single spectral region. Single spectral measurements identified several elements and indices, which explained the weathering process. These results support further understanding of large datasets and their validation in statistical modeling.
Intelligent recognition of dance training movements based on machine learning and embedded system

Recognizing human movement is an important research topic in the field of human-computer interaction, and people expect it to be used in smart homes, virtual reality, and electronic games. Based on the interaction between humans and computers, more and more attention has been paid, especially in the field of smart home action recognition. Through observation, people can understand the intention of intelligent interaction is included in the main part. However, the current recognition algorithms still cannot meet the actual requirements of the accuracy, real-time and robustness of human motion recognition. Especially in order to recognize complex human movements in real time, it is imperative to solve several problems in motion capture and recognition. Establishing the feature parameter angle of the feature vector space of motion data, using the pre-recognition algorithm is based on multi-class support vector machines. The motion recognition algorithm takes advantage of the accurate and fast classification function of svm. Based on the structural differences of the motion data, most of the data can be correctly identified. The optimal motion recognition algorithm uses hmm to correct the svm error recognition result through the random constraint relationship between the error recognition data and the actual label. Based on data simulation and analysis, each variable determined by the grid search algorithm has the highest accuracy in the optimization of each variable of the support vector machine. Finally, a smart home simulation experiment interactive system was built, and a local database was created, including 1,300 processes. The real-time algorithm uses the data in the local database for training and testing. Experimental results show that the motion recognition algorithm in this paper improves the accuracy and robustness of complex motion recognition. While meeting the real-time recognition conditions, the correct answer rate of the final operation can reach 9.6%. The human motion trajectory recognition system uses the three-dimensional trajectory of gestures to recognize motion. The information in the three-dimensional space is more comprehensive, and the orbit recognition is more robust.
IGScript: An Interaction Grammar for Scientific Data Presentation

Most of the existing scientific visualizations toward interpretive grammar aim to enhance customizability in either the computation stage or the rendering stage or both, while few approaches focus on the data presentation stage. Besides, most of these approaches leverage the existing components from the general-purpose programming languages (GPLs) instead of developing a standalone compiler, which pose a great challenge about learning curves for the domain experts who have limited knowledge about programming. In this paper, we propose IGScript, a novel script-based interaction grammar tool, to help build scientific data presentation animations for communication. We design a dual-space interface and a compiler which converts natural language-like grammar statements or scripts into a data story animation to make an interactive customization on script-driven data presentations, and then develop a code generator (decompiler) to translate the interactive data exploration animations back into script codes to achieve statement parameters. IGScript makes the presentation animations editable, e.g., it allows to cut, copy, paste, append, or even delete some animation clips. We demonstrate the usability, customizability, and flexibility of IGScript by a user study, four case studies conducted by using four types of commonly-used scientific data, and performance evaluations.
Deep Reinforcement Learning for Robotic Hand Manipulation

Researchers have made a lot of progress in combining the advances in Deep Learning and the generalization and applicability of Reinforcement learning to the sequential decision-making process and introduce Deep Reinforcement Learning, which allows using Reinforcement Learning in high dimensional input space environments. Deep Reinforcement Learning achieved notable results in Atari-Games, continuous control tasks such as Robotics. In this project we benchmark the performance of three different deep reinforcement Learning techniques Deep Deterministic Policy Gradient ”DDPG” [1], Deep Deterministic Policy Gradient with Hindsight Experience Replay ”DDPG + HER” [2] and state-of-art policy gradient method Proximal Policy optimization ”PPO” [3], on multi-goal continuous control environments Fetch task and HandManipulate tasks, we benchmarked the three algorithms on six different environments using sparse and dense reward settings. Deep Deterministic Policy Gradient with Hindsight Experience Replay [2] achieves the best success-rate overall the environments when applied with sparse rewards, while both Proximal Policy Optimization [3] and Deep Deterministic Policy Gradient [1] were able to converge only on FetchReach environment.
A Framework to Optimize Implementations of Matrices

In this paper, we propose several reduction rules to optimize the given implementation of a binary matrix over \(\mathbb {F}_{2}\). Moreover, we design a top-layer framework which can make use of the existing search algorithms for solving SLP problems as well as our proposed reduction rules. Thus, efficient implementations of matrices with fewer Xor gates can be expected with the framework. Our framework outperforms algorithms such as Paar1, RPaar1, BP, BFI, RNBP, A1 and A2 when tested on random matrices with various densities and those matrices designed in recent literature. Notably, we find an implementation of AES MixColumns using only 91 Xors, which is currently the shortest implementation to the best of our knowledge.
Adaptive Video Encoding for Different Video Codecs

By 2022, we expect video traffic to reach 82% of the total internet traffic. Undoubtedly, the abundance of video-driven applications will likely lead internet video traffic percentage to a further increase in the near future, enabled by associate advances in video devices' capabilities. In response to this ever-growing demand, the Alliance for Open Media (AOM) and the Joint Video Experts Team (JVET) have demonstrated strong and renewed interest in developing new video codecs. In the fast-changing video codecs' landscape, there is thus, a genuine need to develop adaptive methods that can be universally applied to different codecs. In this study, we formulate video encoding as a multi-objective optimization process where video quality (as a function of VMAF and PSNR), bitrate demands, and encoding rate (in encoded frames per second) are jointly optimized, going beyond the standard video encoding approaches that focus on rate control targeting specific bandwidths. More specifically, we create a dense video encoding space (offline) and then employ regression to generate forward prediction models for each one of the afore-described optimization objectives, using only Pareto-optimal points. We demonstrate our adaptive video encoding approach that leverages the generated forward prediction models that qualify for real-time adaptation using different codecs (e.g., SVT-AV1 and x265) for a variety of video datasets and resolutions. To motivate our approach and establish the promise for future fast VVC encoders, we also perform a comparative performance evaluation using both subjective and objective metrics and report on bitrate savings among all possible pairs between VVC, SVT-AV1, x265, and VP9 codecs.
Capturing Banding in Images: Database Construction and Objective Assessment

With the fast technology advancement and the accelerated growth of high-quality image and video production and services, banding or false contour has become a frequently observed artifact in images, creating annoying negative impact on the visual quality-of-experience (QoE) of end users. Nevertheless, thorough investigations on the causes of banding, and effective and efficient methods to detect and reduce banding are largely lacking. This work targets at capturing and quantifying banding artifacts in images. We construct the first of its kind large-scale public database, consisting of 1,250 images with segmented banding regions and 169,501 image patches with class labels. We also develop a deep neural net-work based no-reference deep banding index (DBI), which not only produces an overall banding assessment of a given image, but also creates a banding map that indicates the variation of banding across the image space. Our experiments show that the proposed DBI method achieves accurate banding prediction with low computational cost. The database and the proposed algorithm are made publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Interpretation of Optimized Hyper Parameters in Associative Rule Learning using Eclat and Apriori

The classification of frequent item set is a vital problem in data mining. In recent area, where it deals with more outcome within limited time, there is a need of certain algorithm supported by faster computation. The aim of this research work is to generate a frequent item set so as to achieve a comparatively reduced time and effort. Frequent item set is a set of items that is often bought with another item, for example bread and butter. Here, two association rules mining algorithm, that is, Apriori and Eclat are used to predict the probability of subordinate items being bought if a prime item is obtained. Apriori algorithm works by picking the frequent individual items in a database and extending them to larger and larger sets whereas Eclat algorithm uses depth first search for discovering frequent item sets. In this paper, a dataset of transactions in a retail company is taken and corresponding support, confidence and lift is estimated. Here the relationship between these variables is explored. It is observed in Eclat algorithm that even after varying the minimum support the maximum and minimum support remains constant and using the Apriori algorithm that increasing the minimum lift value results in a gradual decrease in maximum support and small increase in minimum confidence values.
For God’s sake! Considering Religious Beliefs in HCI Research : A Case of Islamic HCI

HCI community believes in understanding socio-cultural norms and designing for users’ values - both of which can stem from users’ belief systems. Using stories from my research work in an Islamic context, I make a case for how religion can impact HCI research. In particular, I discuss a) the implications of socio-cultural norms and participants’ belief (e.g. hijab or ’veil’) on HCI research in these settings; b) how religion forms users’ individual and collective values and socio-cultural norms that impact users’ understanding, use, or perception of technologies; and c) how our presumptions about a belief system or our value tensions can impact reporting and viewing of such findings. Thus, HCI needs to look beyond engagement with populations to include the belief systems to understand the interpretations, negotiations, and enactments of these values, their implications on our research, and their results.
A Conference Goes Virtual: Lessons from Creating a Social Event in the Virtual Reality

Conferences and workshops are important activities in organizations for communication and collaboration, and in academia specifically to criticize and develop new research ideas. In order to enable social interactions during the pandemic situation, organizations use online solutions like video conference systems. A huge number of virtual conferences has been offered, and attracted more participants as the cost and participation effort were lower. However, socializing and informal exchange between conference participants during coffee breaks and conference dinners is nearly impossible in a virtual conference setting. Conferences are important to meet other researchers, to build a network and collaborate in the future, which is critical to society - to create frontier knowledge. Virtual reality could be an alternative to usual video conferences as they could allow social interaction between different participants and thus support to get in direct contact with future collaborators. Our paper explains how we created and executed a social event in virtual reality at an online conference in academia that included 22 participants in three Nordic countries. Based on 17 interviews with Swedish and Danish participants, we analyze perceived advantages and challenges that the event participants faced. Our results provide interesting insights and recommendations for organizers of virtual conferences to enable socializing in virtual reality.
Leveraging Acoustic and Linguistic Embeddings from Pretrained Speech and Language Models for Intent Classification

Intent classification is a task in spoken language understanding. An intent classification system is usually implemented as a pipeline process, with a speech recognition module followed by text processing that classifies the intents. There are also studies of end-to-end system that take acoustic features as input and classifies the intents directly. Such systems don’t take advantage of relevant linguistic information, and suffer from limited training data. In this work, we propose a novel intent classification framework that employs acoustic features extracted from a pretrained speech recognition system and linguistic features learned from a pretrained language model. We use knowledge distillation technique to map the acoustic embeddings towards linguistic embeddings. We perform fusion of both acoustic and linguistic embeddings through cross-attention approach to classify intents. With the pro-posed method, we achieve 90.86% and 99.07% accuracy on ATIS and Fluent speech corpus, respectively.
Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches

In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings. Specifically, we train a convolutional neural network to learn discriminative features by optimizing two popular versions of the Triplet Loss in order to study their clustering properties under the assumption of noisy labels. Additionally, we propose a new, simple Triplet Loss formulation, which shows desirable properties with respect to formal clustering objectives and outperforms the existing methods. We evaluate all three Triplet loss formulations for K-means and correlation clustering on the CIFAR-10 image classification dataset.
Privacy-Preserving Multivariant Regression Analysis over Blockchain-Based Encrypted IoMT Data

Most of the studies related to privacy-preserving linear regression training with the Internet of Medical Things (IoMT) data from various entities do not satisfy all the privacy issues of the data owner. This article proposes a secure design in order to protect privacy issues of IoMT data at the time of training a linear regression model. Blockchain is employed with a partially homomorphic cryptosystem known as Paillier to protect all participant’s data privacy. To eliminate the territory on a third-party, the proposed study unites secure building blocks in secure linear regression. Firstly, a guarded data-sharing platform is developed among various data providers, where encrypted IoMT data is registered on a shared ledger. Secondly, secure polynomial operation (SPO), and secure comparison (SC) are outlined using the homomorphic property of Paillier. Secure linear regression does not need any trusted third-party. It requires only three interplays in each iteration. Severe security inquiry proves that secure linear regression preserves sensitive data privacy for each data provider and analyst. The secure linear regression achieved 0.78, 0.066, and 0.196 adjusted \(R^{2}\) on BCWD, HDD, and DD datasets respectively. The performance of secure linear regression is nearly similar to the general linear regression.
A Comprehensive Review of Blockchain based Solutions in Food Supply Chain Management

In recent years, the focus on Food Quality has become paramount as it can act as an indicator of the living standards of the citizens and any lacunae in it can lead to large scale damage to the health and wellbeing of the people. Lack of information on food produce such as origin, quality, shelf time etc. for the consumers has resulted in distrust among them which can also affect the producers in terms of marketing and pricing. All this stems from the current food procurement and distribution systems that are centrally controlled and dependence on regulatory bodies for quality control. This has led to opacity about information about the food produce and also doubts on such available data if any among the stakeholders. The need of the hour is a system that can provide information in a transparent manner to its stakeholders especially its customers. This information made reliably available will build trust among the consumers and thereby improves brand value for sellers.Blockchain is a disruptive technology that has been exploited across several domains, the Food Procurement and Distribution system is no exception to it. Block chain allows for generating trust among the stakeholders because of its properties like immutability and transparency that any one part of the system can verify the authenticity of information stored in it. If all transactions happening in the Food Procurement and Distribution system from point of production to the point of consumption are recorded on the Block chain. Then it becomes easier for customers to verify whether the claims from the seller about the food quality are indeed true. This paper outlines a survey on the current work and research in the field of food supply chain management using Block chain technology and also specify the future research trends in the same area.
Jointly Trained Transformers Models for Spoken Language Translation

End-to-End and cascade (ASR-MT) spoken language translation (SLT) systems are reaching comparable performances, however, a large degradation is observed when translating the ASR hypothesis in comparison to using oracle input text. In this work, degradation in performance is reduced by creating an End-to-End differentiable pipeline between the ASR and MT systems. In this work, we train SLT systems with ASR objective as an auxiliary loss and both the networks are connected through the neural hidden representations. This training has an End-to-End differentiable path with respect to the final objective function and utilizes the ASR objective for better optimization. This architecture has improved the BLEU score from 41.21 to 44.69. Ensembling the proposed architecture with independently trained ASR and MT systems further improved the BLEU score from 44.69 to 46.9. All the experiments are reported on English-Portuguese speech translation task using the How2 corpus. The final BLEU score is on-par with the best speech translation system on How2 dataset without using any additional training data and language model and using fewer parameters.
Verifying authorship for forensic purposes: A computational protocol and its validation

Being able to identify the author of an anonymous or disputed document is an important task in forensic science. This can be treated as a form of pattern evidence based on writing style, but the subjective analysis of writing style may have all the well-known problems of other forms of subjective pattern evidence. In this paper, we demonstrate a computer program to address these issues. This program analyzes a pair of documents (a known document and a questioned document) to determine if they were written by the same author. More importantly, this paper also validates the accuracy of this program through a large-scale series of controlled experiments involving English language blogs. Across more than 32,000 different document pairs, the system achieved a measured accuracy of 77%. This paper concludes that this system not only addresses a key problem in forensic linguistics, but also provides the repeatability, reproducibility, and measured accuracy levels that are key to the advancement of forensic science.
Developing an Artificial Intelligence (A.I)‐based descriptor of facial appearance that fits with the assessments of makeup experts

Abstract Objective To develop an A.I‐based automatic descriptor that detects and grades, from selfie pictures, 23 facial signs, hairs included, as a help to making‐up procedures. Material and Methods The selfie images taken in very different conditions by 3326 women and men were used to create (90% of dataset) and validate (10% of dataset) a new algorithm architecture to appraise and grade 23 different facial signs such as lips, nose, eye color, eyebrows, eyelashes, and hair color as defined by makeup artists. Each selfie image was annotated by 12 experts and defined references to train Artificial Intelligence (A.I)‐based algorithm. Results As some the 23 signs present a continuous or discontinuous feature, these were analyzed by two different statistical approaches. The results provided by the automatic descriptor system were not only in good agreement with the expert's assessments but were even found of a better precision and reproducibility. This automatic descriptor system has proven a good and robust accuracy despite the very variable conditions in the acquisition of selfie pictures. Conclusion Such automatic descriptor system seems providing a valuable help in making‐up procedures and may extend to other activities such as Skincare or Haircare. As such it should allow large investigations to better evaluate the consumers’ needs of esthetical improvements.