Reviewerly/exHarmony huggingface dataset:
"First week is editorial, second week is algorithmic": platform gatekeepers and platformization of music curation

This article investigates the logics that underpin music curation, and particularly the work of music curators, working at digital music streaming platforms. Based on ethnographic research that combines participant observation and a set of interviews with key informants, the article questions the relationship between algorithmic and human curation and the specific workings of music curation as a form of platform gatekeeping. We argue that music streaming platforms in combining proprietary algorithms and human curators constitute the “new gatekeepers” in an industry previously dominated by human intermediaries such as radio programmers, journalists, and other experts. The paper suggests understanding this gatekeeping activity as a form of “algo-torial power”, that has the ability to set the ‘listening agendas’ of global music consumers.
Heliostat Dual-Axis Sun Tracking System: A Case Study in KSA

This paper demonstrates a case study for solar power tower (SPT) Heliostat, concentrated solar power topology in Dhahran, KSA.A MATLAB code is implemented for the solar position algorithm to calculate the incident angles in order to track the sunbeams during the whole year.A drive system composed of two linear actuators; DC motor-based drive is developed.The model has been validated experimentally using a rooftop Heliostat prototype.The experimental platform has been tested for several days in different seasons to confirm the prototype capabilities for sun tracking during the whole year.The experimental and simulation results are well agreed.
Facial expression recognition using local multidirectional score pattern descriptor and modified hidden Markov model

Facial expression recognition is an application used for biometric software that can be used to recognise special expressions in a digital image by comparing and analysing different patterns. Recognising faces becomes very difficult when there is a frequent change in facial expressions. In this paper, two tier modification of normal hidden Markov model (HMM) is used to identify continuous effective facial expressions. Two tier extension of HMM: bottom and upper tier. Bottom tier represents the atomic facial expressions which are made by mouth, eyes and nose separately and upper tier represents the joint of these atomic facial expressions such as neutral, sadness etc. This paper introduces the improved modified local decision based unsymmetrical trimmed median filter (IMLDBUTMF) for noise reduction, the local multidirectional score pattern (LMSP) for feature extraction and modified hidden Markov model for classification process. The experimental result shows that the proposed method gives enhanced performance of 85% of recognition rate on publicly available datasets JAFFE.
Evaluation of the predictive ability of ultrasound-based assessment of breast cancer using BI-RADS natural language reporting against commercial transcriptome-based tests

Purpose The objective of this study was to assess the classification capability of Breast Imaging Reporting and Data System (BI-RADS) ultrasound feature descriptors targeting established commercial transcriptomic gene signatures that guide management of breast cancer. Materials and methods This retrospective, single-institution analysis of 219 patients involved two cohorts using one of two FDA approved transcriptome-based tests that were performed as part of the clinical care of breast cancer patients at Harbor-UCLA Medical Center between April 2008 and January 2013. BI-RADS descriptive terminology was collected from the corresponding ultrasound reports for each patient in conjunction with transcriptomic test results. Recursive partitioning and regression trees were used to test and validate classification of the two cohorts. Results The area under the curve (AUC) of the receiver operator curves (ROC) for the regression classifier between the two FDA approved tests and ultrasound features were 0.77 and 0.65, respectively; they employed the ‘margins’, ‘retrotumoral’, and ‘internal echoes’ feature descriptors. Notably, the ‘retrotumoral’ and mass ‘margins’ features were used in both classification trees. The identification of sonographic correlates of gene tests provides added value to the ultrasound exam without incurring additional procedures or testing. Conclusions The predictive capability using structured language from diagnostic ultrasound reports (BI-RADS) was moderate for the two tests, and provides added value from ultrasound imaging without incurring any additional costs. Incorporation of additional measures, such as ultrasound contrast enhancement, with validation in larger, prospective studies may further substantiate these results and potentially demonstrate even greater predictive utility.
Identifying Malaria Infection in Red Blood Cells using Optimized Step-Increase Convolutional Neural Network Model

A vast number of image processing and neural network approaches are currently being utilized in the analysis of various medical conditions. Malaria is a disease which can be diagnosed by examining blood smears. But when it is examined manually by the microscopist, the accuracy of diagnosis can be error-prone because it depends upon the quality of the smear and the expertise of microscopist in examining the smears. Among the various machine learning techniques, convolutional neural networks (CNN) promise relatively higher accuracy. We propose an Optimized Step-Increase CNN (OSICNN) model to classify red blood cell images taken from thin blood smear samples into infected and non-infected with the malaria parasite. The proposed OSICNN model consists of four convolutional layers and is showing comparable results when compared with other state of the art models. The accuracy of identifying parasite in RBC has been found to be 98.3% with the proposed model.
bmlTUX: Design and control of experiments in virtual reality and beyond

Advances in virtual reality (VR) technology have made it a valuable new tool for vision and perception researchers. Coding VR experiments from scratch can be difficult and time-consuming so researchers rely on software such as Unity game engine to create and edit virtual scenes. However, Unity lacks built-in tools for controlling experiments. Existing third-party add-ins require complicated scripts to define experiments. This can be difficult and requires advanced coding knowledge, especially for multifactorial experimental designs. In this paper, we describe a new free and open-source tool called the BiomotionLab Toolkit for Unity Experiments (bmlTUX) that provides a simple interface for controlling experiments in Unity. In contrast to existing tools, bmlTUX provides a graphical interface to automatically handle combinatorics, counterbalancing, randomization, mixed designs, and blocking of trial order. The toolbox works “out-of-the-box” since simple experiments can be created with almost no coding. Furthermore, multiple design configurations can be swapped with a drag-and-drop interface allowing researchers to test new configurations iteratively while maintaining the ability to easily revert to previous configurations. Despite its simplicity, bmlTUX remains highly flexible and customizable, catering to coding novices and experts alike.
Human Fall Detection using Accelerometer and Gyroscope Sensors in Unconstrained Smartphone Positions

This study explored several methods for detecting body falls based on the data captured by the sensors (accelerometer and gyroscope) built in a smartphone carried by a person. The data for this study were collected by recording many sample units from each of the following human activity categories: stand-fall, walk-fall, stand-jump, stand-sit, stand, and walk. Several time-series data captured by the sensors were used as human motion features. One of the challenges of this study was the existence of human body motions whose features resembled those of body falls. In addition, unfixed smartphone positioning made human body falls harder to detect and can lead to high rate of misclassification (not detected as fall). This incident can caused serious bone fracture or even death if the person not handled as immediately as possible because of misclassification. To address this problem, we modified Resultant Acceleration and ∠Y formulas to address the problem of unconstrained smartphone positions. We proposed to combine five methods such as AGVeSR, Alim, ∠α, GyroReDi, and AGPeak to build a robust detector model to reduce the misclassification. The experiment results showed that the accuracy of the combination of both sensors (accelerometer and gyroscope) outperformed the accuracy of accelerometer only by more than 15%. The decision fusion that used voting involving five methods could boost the accuracy rate by up to 4.15%.
Data Cataloguing

After a brief reminder on general concepts used in data cataloguing activities, this chapter provides information concerning the architecture and design recommendations for the implementation of catalogue systems for the ENVRIplus community. The main objective of this catalogue is to offer a unified discovery service allowing cross-disciplinary search and access to data collections coming from Research Infrastructures (RIs). This catalogue focuses on metadata with a coarse level of granularity. It was decided to offer metadata representing different types of dataset series. Only metadata for so-called flagship products (as defined by each community) are covered by the scope of this catalogue. The data collections remain within each RI. For RIs, the aim is to improve the visibility of their results beyond their traditional user communities.
KETOD: Knowledge-Enriched Task-Oriented Dialogue

Existing studies in dialogue system research mostly treat task-oriented dialogue and chitchat as separate domains.Towards building a human-like assistant that can converse naturally and seamlessly with users, it is important to build a dialogue system that conducts both types of conversations effectively.In this work, we investigate how task-oriented dialogue and knowledge-grounded chit-chat can be effectively integrated into a single model.To this end, we create a new dataset, KETOD (Knowledge-Enriched Task-Oriented Dialogue), where we naturally enrich taskoriented dialogues with chit-chat based on relevant entity knowledge.We also propose two new models, SimpleToDPlus and Combiner, for the proposed task.Experimental results on both automatic and human evaluations show that the proposed methods can significantly improve the performance in knowledge-enriched response generation while maintaining a competitive task-oriented dialog performance.We believe our new dataset will be a valuable resource for future studies.
Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation

We introduce a novel setup for low-resource task-oriented semantic parsing which incorporates several constraints that may arise in real-world scenarios: (1) lack of similar datasets/models from a related domain, (2) inability to sample useful logical forms directly from a grammar, and (3) privacy requirements for unlabeled natural utterances.Our goal is to improve a low-resource semantic parser using utterances collected through user interactions.In this highly challenging but realistic setting, we investigate data augmentation approaches involving generating a set of structured canonical utterances corresponding to logical forms, before simulating corresponding natural language and filtering the resulting pairs.We find that such approaches are effective despite our restrictive setup: in a lowresource setting on the complex SMCalFlow calendaring dataset (Andreas et al., 2020), we observe 33% relative improvement over a nondata-augmented baseline in top-1 match.
Multi-Temporal Landsat-8 Images for Retrieval and Broad Scale Mapping of Soil Copper Concentration Using Empirical Models

Mapping soil heavy metal concentration using machine learning models based on readily available satellite remote sensing images is highly desirable. Accurate mapping relies on appropriate data, feature extraction, and model selection. To this end, a data processing pipeline for soil copper (Cu) concentration estimation has been designed. First, instead of using single Landsat scenes, the utilization of multiple Landsat scenes of the same location over time is considered. Second, to generate a preferred feature set as input to a regression model, a number of feature extraction methods are motivated and compared. Third, to find a preferred regression model, a variety of approaches are implemented and compared for accuracy. In this research, 11 Landsat-8 images from 2013 to 2017 of Gulin County, Sichuan China, and 138 soil samples with lab-measured Cu concentrations collected from the area in 2015 are used. A variety a metrics under cross-validation are used for comparison. The results indicate that multi-temporal images increase accuracy compared to single Landsat images. The preferred feature extraction varies based on the regression model used; however, the best results are obtained using support vector regression and the original data. The final soil Cu map generated using the recommended data processing pipeline shows a consistent spatial pattern with a ground-truth land cover classification map. These results indicate that machine learning has the ability to perform large-scale soil heavy metal mapping from widely available satellite remote sensing images.
Student Behavior Data Analysis Based on Association Rule Mining

Abstract With the advancement of intelligent campus data acquisition technology, student behavioral data are growing in size, variety, and real-time throughput, posing challenges to the storage capacity and computing power of traditional behavioral data analysis methods. The study focuses on the application of association rule mining in student behavioral data analysis. Data collection, storage, computation, and analysis all comprise integral parts of a four-layer data association mining architecture, and the three-step mining process from “data preprocessing” to “finding association rules” to “acquiring relevant knowledge” is described. The existing mining algorithm is updated to address the issues of overscanning of the original dataset and excess iterations. The findings from the case study reveal that the number of iterations in the modified mining algorithm is greatly lessened, effectively improving the mining efficiency of the massive student behavioral dataset.
The Role of Teacher–Student Relationships in Predicting Teachers’ Occupational Wellbeing, Emotional Exhaustion, and Enthusiasm

Wellbeing is regarded as a core dimension of an individual’s prosperity in the field of positive psychology. Underlying this multifaceted framework are emotive, mental, and societal forms of wellbeing, which can be based on constructive connections in the workplace. Career wellbeing among educators is linked to optimal mental functioning and their positive career experience is characterized in terms of the existence of constructive dimensions like enthusiasm at the workplace. Also, emotional exhaustion or fatigue is another central element in the research on educator burnout and it has an important effect on educators’ expert lives, alternatively, the excellence of teacher–student relationships is significant in the process of language learning. Due to the significant effect of the above-mentioned construct, this review tries to focus on the prominence of teacher–student relationships in this domain. The results from the review of the literature specified that high-value teacher–student relationships keep educators away from being emotionally exhausted since it can enhance the quantity of enthusiasm and lessen the amount of exhaustion. In a nutshell, this review of literature has suggestions for academics and experts in search of increasing teachers’ occupational wellbeing.
Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization

It is challenging to train a good intent classifier for a task-oriented dialogue system with only a few annotations.Recent studies have shown that fine-tuning pre-trained language models with a small amount of labeled utterances from public benchmarks in a supervised manner is extremely helpful.However, we find that supervised pre-training yields an anisotropic feature space, which may suppress the expressive power of the semantic representations.Inspired by recent research in isotropization, we propose to improve supervised pretraining by regularizing the feature space towards isotropy.We propose two regularizers based on contrastive learning and correlation matrix respectively, and demonstrate their effectiveness through extensive experiments.Our main finding is that it is promising to regularize supervised pre-training with isotropization to further improve the performance of few-shot intent detection.The source code can be found at https://github.com/fanolabs/isoIntentBert-main.
Runtime prediction of big data jobs: performance comparison of machine learning algorithms and analytical models

Abstract Due to the rapid growth of available data, various platforms offer parallel infrastructure that efficiently processes big data. One of the critical issues is how to use these platforms to optimise resources, and for this reason, performance prediction has been an important topic in the last few years. There are two main approaches to the problem of predicting performance. One is to fit data into an equation based on a analytical models. The other is to use machine learning (ML) in the form of regression algorithms. In this paper, we have investigated the difference in accuracy for these two approaches. While our experiments used an open-source platform called Apache Spark, the results obtained by this research are applicable to any parallel platform and are not constrained to this technology. We found that gradient boost, an ML regressor, is more accurate than any of the existing analytical models as long as the range of the prediction follows that of the training. We have investigated analytical and ML models based on interpolation and extrapolation methods with k-fold cross-validation techniques. Using the interpolation method, two analytical models, namely 2D-plate and fully-connected models, outperform older analytical models and kernel ridge regression algorithm but not the gradient boost regression algorithm. We found the average accuracy of 2D-plate and fully-connected models using interpolation are 0.962 and 0.961. However, when using the extrapolation method, the analytical models are much more accurate than the ML regressors, particularly two of the most recently proposed models (2D-plate and fully-connected). Both models are based on the communication patterns between the nodes. We found that using extrapolation, kernel ridge, gradient boost and two proposed analytical models average accuracy is 0.466, 0.677, 0.975, and 0.981, respectively. This study shows that practitioners can benefit from analytical models by being able to accurately predict the runtime outside of the range of the training data using only a few experimental operations.
VISUALLY ANNOTATED RESPONSIVE DIGITAL TWINS FOR REMOTE COLLABORATION IN MIXED REALITY ENVIRONMENTS

Abstract. Various forms of extended reality might empower remote collaboration in ways that the current de facto standards cannot facilitate. Especially when combined by a digital twin of the remote physical object, mixed reality (MR) opens up interesting new ways to support spatial communication. In this study, we explore the use of a digital twin to facilitate visuospatial communication in an expert-guided repair and maintenance operation scenario, supported by visual annotations. We developed two MR prototypes, one with a digital twin of the object of interest, and another where a first-person camera view was shown additionally. We tested these prototypes in a study with 19 participants (9 pairs) against a state-of-the art solution as a baseline and measured their usability, and obtained qualitative user feedback. Our findings suggest that digital twin supported mixed reality enriched with real time visual annotations can potentially improve remote collaboration tasks.
Fast, Searchable, Symmetric Encryption Scheme Supporting Ranked Search

Searchable encryption (SE) is one of the effective techniques for searching encrypted data without decrypting it. This technique can provide a secure indexing mechanism for encrypted data and utilize a secure trapdoor to search for the encrypted data directly, thus realizing a secure ciphertext retrieval function. Existing schemes usually build a secure index directly on the whole dataset and implement the retrieval of encrypted data by implementing a secure search algorithm on the index. However, this approach requires testing many non-relevant documents, which diminishes the query efficiency. In this paper, we adopt a clustering method to preclassify the dataset, which can filter out quite a portion of irrelevant documents, thus improving the query. Concretely, we first partition the dataset into multiple document clusters using the k-means clustering algorithm; then, we design index building and searching algorithms for these document clusters; finally, by using the asymmetric scalar-product-preserving encryption (ASPE) scheme to encrypt the indexes and queries, we propose a fast searchable symmetric encryption scheme that supports ranked search. Detailed security analysis demonstrates that the proposed scheme can guarantee the data and query security of the search process. In addition, theoretical and experimental analysis indicates that our scheme outperforms other similar schemes in terms of query efficiency.
Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach

Collaborative filtering (CF), as a standard method for recommendation with implicit feedback, tackles a semi-supervised learning problem where most interaction data are unobserved. Such a nature makes existing approaches highly rely on mining negatives for providing correct training signals. However, mining proper negatives is not a free lunch, encountering with a tricky trade-off between mining informative hard negatives and avoiding false ones. We devise a new approach named as Hardness-Aware Debiased Contrastive Collaborative Filtering (HDCCF) to resolve the dilemma. It could sufficiently explore hard negatives from two-fold aspects: 1) adaptively sharpening the gradients of harder instances through a set-wise objective, and 2) implicitly leveraging item/user frequency information with a new sampling strategy. To circumvent false negatives, we develop a principled approach to improve the reliability of negative instances and prove that the objective is an unbiased estimation of sampling from the true negative distribution. Extensive experiments demonstrate the superiority of the proposed model over existing CF models and hard negative mining methods.