Reviewerly/exHarmony huggingface dataset:
Four key challenges in the open‐data revolution

Abstract In Focus: Culina, A., Adriaensen, F., Bailey, L. D., et al. (2021) Connecting the data landscape of long‐term ecological studies: The SPI‐Birds data hub. Journal of Animal Ecology , https://doi.org/10.1111/1365‐2656.13388 . Long‐term, individual‐based datasets have been at the core of many key discoveries in ecology, and calls for the collection, curation and release of these kinds of ecological data are contributing to a flourishing open‐data revolution in ecology. Birds, in particular, have been the focus of international research for decades, resulting in a number of uniquely long‐term studies, but accessing these datasets has been historically challenging. Culina et al. (2021) introduce an online repository of individual‐level, long‐term bird records with ancillary data (e.g. genetics), which will enable key ecological questions to be answered on a global scale. As well as these opportunities, however, we argue that the ongoing open‐data revolution comes with four key challenges relating to the (1) harmonisation of, (2) biases in, (3) expertise in and (4) communication of, open ecological data. Here, we discuss these challenges and how key efforts such as those by Culina et al. are using FAIR (Findable, Accessible, Interoperable and Reproducible) principles to overcome them. The open‐data revolution will undoubtedly reshape our understanding of ecology, but with it the ecological community has a responsibility to ensure this revolution is ethical and effective.
Improved Variants of the Hutch++ Algorithm for Trace Estimation

This paper is concerned with two improved variants of the Hutch++ algorithm for estimating the trace of a square matrix, implicitly given through matrix-vector products. Hutch++ combines randomized low-rank approximation in a first phase with stochastic trace estimation in a second phase. In turn, Hutch++ only requires $O\left(\varepsilon^{-1}\right)$ matrix-vector products to approximate the trace within a relative error $\varepsilon$ with high probability, provided that the matrix is symmetric positive semidefinite. This compares favorably with the $O\left(\varepsilon^{-2}\right)$ matrix-vector products needed when using stochastic trace estimation alone. In Hutch++, the number of matrix-vector products is fixed a priori and distributed in a prescribed fashion among the two phases. In this work, we derive an adaptive variant of Hutch++, which outputs an estimate of the trace that is within some prescribed error tolerance with a controllable failure probability, while splitting the matrix-vector products in a near-optimal way among the two phases. For the special case of a symmetric positive semidefinite matrix, we present another variant of Hutch++, called NystrÃ¶m++, which utilizes the so-called NystrÃ¶m approximation and requires only one pass over the matrix, as compared to two passes with Hutch++. We extend the analysis of Hutch++ to NystrÃ¶m++. Numerical experiments demonstrate the effectiveness of our two new algorithms.
Deep Conditional Distribution Learning for Age Estimation

Age estimation is a challenging task not only because face appearance is affected by illumination, pose, and expression, but also because there exists age label ambiguity among different demographic groups. In this work, we first revisit different label distribution learning (LDL) based age estimation methods and propose a more general formulation, which can unify individual LDL-based age estimation methods, as well as the traditional regression, classification, and ranking based age estimation methods. Based on such a general formulation, we propose a novel deep conditional distribution learning (DCDL) method, which can flexibly leverage a varying number of auxiliary face attributes to achieve adaptive age-related feature learning and improve age estimation robustness against the challenges above. Experimental results on multiple age estimation datasets (MORPH II, AgeDB, FG-NET, MegaAge-Asian, CLAP2016, UTK-Face, and LFW+) show that the proposed approach outperforms the state-of-the-art age estimation methods by a large margin. In addition, the proposed approach can generalize well to other human attributes estimation tasks, like height, weight, and body mass index (BMI) estimation.
Multi-Modal Recommender Systems: Hands-On Exploration

Recommender systems typically learn from user-item preference data such as ratings and clicks. This information is sparse in nature, i.e., observed user-item preferences often represent less than 5% of possible interactions. One promising direction to alleviate data sparsity is to leverage auxiliary information that may encode additional clues on how users consume items. Examples of such data (referred to as modalities) are social networks, item’s descriptive text, product images. The objective of this tutorial is to offer a comprehensive review of recent advances to represent, transform and incorporate the different modalities into recommendation models. Moreover, through practical hands-on sessions, we consider cross model/modality comparisons to investigate the importance of different methods and modalities. The hands-on exercises are conducted with Cornac (https://cornac.preferred.ai ), a comparative framework for multimodal recommender systems. The materials are made available on https://preferred.ai/recsys21-tutorial/.
Garden optimization problems for benchmarking quantum annealers

We benchmark the 5000+ qubit system Advantage coupled with the Hybrid Solver Service 2 released by D-Wave Systems Inc. in September 2020 by using a new class of optimization problems called garden optimization problems known in companion planting. These problems are scalable to an arbitrarily large number of variables and intuitively find application in real-world scenarios. We derive their QUBO formulation and illustrate their relation to the quadratic assignment problem. We demonstrate that the Advantage system and the new hybrid solver can solve larger problems in less time than their predecessors. However, we also show that the solvers based on the 2000+ qubit system DW2000Q sometimes produce more favourable results if they can solve the problems.
Design, Application and Effectiveness of an Innovative Augmented Reality Teaching Proposal through 3P Model

Augmented reality (AR) has evolved hand in hand with advances in technology, and today is considered as an emerging technique in its own right. The aim of our study was to analyze students’ perceptions of how useful AR is in the school environment. A non-experimental quantitative design was used in the form of a questionnaire in which 106 primary sixth-grade students from six schools in the Region of Murcia (Spain) participated. During the study, a teaching proposal using AR related to the content of some curricular areas was put forward in the framework of the 3P learning model. The participants’ perceptions of this technique were analyzed according to each variable, both overall and by gender, via a questionnaire of our own making, which had previously been validated by AR experts, analyzing its psychometric qualities. The initial results indicate that this technique is, according to the students, useful for teaching the curriculum. The conclusion is that AR can increase students’ motivation and enthusiasm while enhancing teaching and learning at the same time.
Ripple Walk Training: A Subgraph-based Training Framework for Large and Deep Graph Neural Network

Graph neural networks (GNNs) have achieved outstanding performance in learning graph-structured data and various tasks. However, many current GNNs suffer from three common problems when facing large-size graphs or using a deeper structure: neighbors explosion, node dependence, and oversmoothing. Such problems attribute to the data structures of the graph itself or the designing of the multi-layers GNNs framework, and can lead to low training efficiency and high space complexity. To deal with these problems, in this paper, we propose a general subgraph-based training framework, namely Ripple Walk Training (RWT), for deep and large graph neural networks. RWT samples subgraphs from the full graph to constitute a mini-batch, and the full GNN is updated based on the mini-batch gradient. We analyze the high-quality sub graphs to train GNNs in a theoretical way. A novel sampling method Ripple Walk Sampler works for sampling these high-quality subgraphs to constitute the mini-batch, which considers both the randomness and connectivity of the graph-structured data. Extensive experiments on different sizes of graphs demonstrate the effectiveness and efficiency of RWT in training various GNNs (GCN & GAT). Our code is released in the https://github.com/anonymous2review/RippleWalk.
CDN and SDN Support and Player Interaction for HTTP Adaptive Video Streaming

Video streaming has become one of the most prevailing, bandwidth-hungry, and latency-sensitive Internet applications. HTTP Adaptive Streaming (HAS) has become the dominant video delivery mechanism over the Internet. Lack of coordination among the clients and lack of awareness of the network in pure client-based adaptive video bitrate approaches have caused problems, such as sub-optimal data throughput from Content Delivery Network (CDN) or origin servers, high CDN costs, and non-satisfactory users' experience. Recent studies have shown that network-assisted HAS techniques by utilizing modern networking paradigms, e.g., Software Defined Networking (SDN), Network Function Virtualization(NFV), and edge computing can significantly improve HAS system performance. In this doctoral study, we leverage the aforementioned modern networking paradigms and design network-assistance for/by HAS clients to improve HAS systems performance and CDN/network utilization. We present four fundamental research questions to target different challenges in devising a network-assisted HAS system.
DA-VSR: Domain Adaptable Volumetric Super-Resolution for Medical Images

Medical image super-resolution (SR) is an active research area that has many potential applications, including reducing scan time, bettering visual understanding, increasing robustness in downstream tasks, etc. However, applying deep-learning-based SR approaches for clinical applications often encounters issues of domain inconsistency, as the test data may be acquired by different machines or on different organs. In this work, we present a novel algorithm called domain adaptable volumetric super-resolution (DA-VSR) to better bridge the domain inconsistency gap. DA-VSR uses a unified feature extraction backbone and a series of network heads to improve image quality over different planes. Furthermore, DA-VSR leverages the in-plane and through-plane resolution differences on the test data to achieve a self-learned domain adaptation. As such, DA-VSR combines the advantages of a strong feature generator learned through supervised training and the ability to tune to the idiosyncrasies of the test volumes through unsupervised learning. Through experiments, we demonstrate that DA-VSR significantly improves super-resolution quality across numerous datasets of different domains, thereby taking a further step toward real clinical applications.
Towards an IMU-based Pen Online Handwriting Recognizer

Most online handwriting recognition systems require the use of specific writing surfaces to extract positional data. In this paper we present a online handwriting recognition system for word recognition which is based on inertial measurement units (IMUs) for digitizing text written on paper. This is obtained by means of a sensor-equipped pen that provides acceleration, angular velocity, and magnetic forces streamed via Bluetooth. Our model combines convolutional and bidirectional LSTM networks, and is trained with the Connectionist Temporal Classification loss that allows the interpretation of raw sensor data into words without the need of sequence segmentation. We use a dataset of words collected using multiple sensor-enhanced pens and evaluate our model on distinct test sets of seen and unseen words achieving a character error rate of 17.97% and 17.08%, respectively, without the use of a dictionary or language model.
Hybrid Sine Cosine and Fitness Dependent Optimizer for Global Optimization

The fitness-dependent optimizer (FDO), a newly proposed swarm intelligent algorithm, is focused on the reproductive mechanism of bee swarming and collective decision-making. To optimize the performance, FDO calculates velocity (pace) differently. FDO calculates weight using the fitness function values to update the search agent position during the exploration and exploitation phases. However, the FDO encounters slow convergence and unbalanced exploitation and exploration. Hence, this study proposes a novel hybrid of the sine cosine algorithm and fitness-dependent optimizer (SC-FDO) for updating the velocity (pace) using the sine cosine scheme. This proposed algorithm, SC-FDO, has been tested over 19 classical and 10 IEEE Congress of Evolutionary Computation (CEC-C06 2019) benchmark test functions. The findings revealed that SC-FDO achieved better performances in most cases than the original FDO and well-known optimization algorithms. The proposed SC-FDO improved the original FDO by achieving a better exploit-explore tradeoff with a faster convergence speed. Additionally, the SC-FDO was applied to the missing data estimation cases and refined the missingness as optimization problems. This is the first time, to our knowledge, that nature-inspired algorithms have been considered for handling time series datasets with low and high missingness problems (10%-90%). The impacts of missing data on the predictive ability of the proposed SC-FDO were evaluated using a large weather dataset from the year 1985 until 2020. The results revealed that the imputation sensitivity depends on the percentages of missingness and the imputation models. The findings demonstrated that the SC-FDO based multilayer perceptron (MLP) trainer outperformed the other three optimizer trainers with the highest average accuracy of 90% when treating the high-low missingness in the dataset.
Evaluation of Integrated Frameworks for Optimizing QoS in Serverless Computing

Serverless computing is an emerging cloud deployment model where developers can concentrate on developing application logic without worrying about the underlying architecture. It is similar to the platform as a service (PaaS) but at the functional level. Applications are usually deployed in the form of a set of functions independently and each function may be executed at separate servers thus also named as function as a service (FaaS). Serverless at the edge can handle thousands of concurrent functions invocations to process various kinds of events generated from resources like database, system logs, and other storage units, etc. A number of serverless frameworks like Openfaas, Openwhisk, Microsoft Azure, Amazon AWS allow dynamic scaling to handle the parallel request of stateless functions from the client-side. A separate container manager may be provisioned to handle distributed load for data processing. In this paper, we have evaluated the performance of serverless frameworks for parallel loads in terms of response time and throughput. In this paper, we have shown that the serverless framework is suitable for handling dynamic applications that can be executed on a number of stateless functions. An extensive comparison of the performance of serverless frameworks in handling concurrent invocations in terms of response time and throughput is also presented. It has been observed that Openwhisk is found to be the better serverless framework in terms of elasticity and scalability.
Graph Neural Networks for Heterogeneous Trust based Social Recommendation

In the current research, Graph Neural Networks (GNNs) play a decisive role in learning from network data structure. In a social recommender system, GNNs have a significant perspective to integrate the structure of a customer-customer social network and the customer-product bipartite network. Most of the existing trust-based social recommendation systems overlook heterogeneous trust relations among customers and heterogeneous interactions between customers and products. However, this is very challenging to capture these heterogeneous information. To address this challenge, we propose an approach to evaluate the authenticity of reviews written by customers on products. Varying authenticity introduces the heterogeneity in trust relations among customers and interactions between customers and products. This authenticity defines a customer's characteristic as a reviewer, whether the customer is reliable or biased. To the best of our knowledge, this is the first work which includes authenticity of reviews and customers to evaluate trust relationships and interactions. We develop a novel Graph Neural Network architecture for Trust-based Social Recommendation (GNNTSR) that systematically models two networks, i.e., customer-customer social network and customer-product bipartite network, and integrates heterogeneous trust and interaction. Extensive experiments are performed on real datasets, and empirical results show our model improves over the current best baseline by 2.16 - 5.74%.
Robust Physical-World Attacks on Face Recognition

Face recognition has been greatly facilitated by the development of deep neural networks (DNNs) and has been widely applied to many safety-critical applications. However, recent studies have shown that DNNs are very vulnerable to adversarial examples, raising severe concerns on the security of real-world face recognition. In this work, we study sticker-based physical attacks on face recognition for better understanding its adversarial robustness. To this end, we first analyze in-depth the complicated physical-world conditions confronted by attacking face recognition, including the different variations of stickers, faces, and environmental conditions. Then, we propose a novel robust physical attack framework, dubbed PadvFace, to model these challenging variations specifically. Furthermore, we reveal that the attack complexities vary under different physical-world conditions and propose an efficient Curriculum Adversarial Attack (CAA) algorithm that gradually adapts adversarial stickers to environmental variations from easy to complex. Finally, we construct a standardized testing protocol to facilitate the fair evaluation of physical attacks on face recognition, and extensive experiments on both physical dodging and impersonation attacks demonstrate the superior performance of the proposed method.
Statistical Analysis of Chemical Element Compositions in Food Science: Problems and Possibilities

In recent years, many analyses have been carried out to investigate the chemical components of food data. However, studies rarely consider the compositional pitfalls of such analyses. This is problematic as it may lead to arbitrary results when non-compositional statistical analysis is applied to compositional datasets. In this study, compositional data analysis (CoDa), which is widely used in other research fields, is compared with classical statistical analysis to demonstrate how the results vary depending on the approach and to show the best possible statistical analysis. For example, honey and saffron are highly susceptible to adulteration and imitation, so the determination of their chemical elements requires the best possible statistical analysis. Our study demonstrated how principle component analysis (PCA) and classification results are influenced by the pre-processing steps conducted on the raw data, and the replacement strategies for missing values and non-detects. Furthermore, it demonstrated the differences in results when compositional and non-compositional methods were applied. Our results suggested that the outcome of the log-ratio analysis provided better separation between the pure and adulterated data and allowed for easier interpretability of the results and a higher accuracy of classification. Similarly, it showed that classification with artificial neural networks (ANNs) works poorly if the CoDa pre-processing steps are left out. From these results, we advise the application of CoDa methods for analyses of the chemical elements of food and for the characterization and authentication of food products.
Deep Learning Based Engagement Recognition in Highly Imbalanced Data

Engagement recognition is a growing domain in paralinguistics evaluation due to its importance in many human-computer and human-robot applications. Current requirements for such systems are not only to interact, but also to engage the user into interaction as long as possible. To do so, the machine should differ among different levels of engagement to adjust its behavior properly. However, actual models are still far from it, partially due to data quality – usually, engagement recognition datasets are highly biased towards the high-engagement levels, because they are more often and naturally expressed by humans during interaction context. Thus, currently, the development of a reliable engagement recognition system able to detect all engagement levels is necessary. To facilitate it, we introduce a deep learning engagement recognition framework in the context of the DAiSEE corpus, which is a highly imbalanced dataset. We showed that the metric used formerly for evaluating the performance of the models on the DAiSEE dataset is inadequate due to its imbalance and conducted extensive experiments on DAiSEE, suggesting a new baseline performance based on the Unweighted Average Recall metric.
Negative Interactions for Improved Collaborative Filtering: Don’t go Deeper, go Higher

The recommendation-accuracy of collaborative filtering approaches is typically improved when taking into account higher-order interactions [5, 6, 9, 10, 11, 16, 18, 24, 25, 28, 31, 34, 36, 41, 42, 44]. While deep nonlinear models are theoretically able to learn higher-order interactions, their capabilities were, however, found to be quite limited in practice [5]. Moreover, the use of low-dimensional embeddings in deep networks may severely limit their expressiveness [8]. This motivated us in this paper to explore a simple extension of linear full-rank models that allow for higher-order interactions as additional explicit input-features. Interestingly, we observed that this model-class obtained by far the best ranking accuracies on the largest data set in our experiments, while it was still competitive with various state-of-the-art deep-learning models on the smaller data sets. Moreover, our approach can also be interpreted as a simple yet effective improvement of the (linear) HOSLIM [11] model: by simply removing the constraint that the learned higher-order interactions have to be non-negative, we observed that the accuracy-gains due to higher-order interactions more than doubled in our experiments. The reason for this large improvement was that large positive higher-order interactions (as used in HOSLIM [11]) are relatively infrequent compared to the number of large negative higher-order interactions in the three well-known data-sets used in our experiments. We further characterize the circumstances where the higher-order interactions provide the most significant improvements.