Reviewerly/exHarmony huggingface dataset:
A Sensor-Based Hand Gesture Recognition System for Japanese Sign Language

In this paper, we propose a sensor-based data acquisition glove for Japanese Sign Language (JSL) hand gesture recognition. Five flex sensors, an Inertial Measurement Unit (IMU), and three Force Sensing Resistors (FSRs) are used to detect the bending degree of fingers and hand movement information. The detected data are transmitted to the computer by an Arduino Micro. The average accuracy of the hand gesture recognition for a single subject, using the Support Vector Machine (SVM) based and the Dynamic Time Wrapping (DTW) based algorithm are 96.9% and 94.5%, respectively. Our proposed system also achieves an average recognition accuracy of about 82.5% for the cross-recognition among three subjects. The experimental results indicate that our proposed system has great potential for JSL hand gesture recognition.
Time-Weighted Collaborative Filtering Algorithm Based on Improved Mini Batch K-Means Clustering

The traditional collaborative filtering recommendation algorithm has the defects of sparse score matrix, weak scalability and user interest deviation, which lead to the low efficiency of algorithm and low accuracy of score prediction. Aiming at the above problems, this paper proposed a time-weighted collaborative filtering algorithm based on improved Mini Batch K-Means clustering. Firstly, the algorithm selected the Pearson correlation coefficient to improve the Mini Batch K-Means clustering, and used the improved Mini Batch K-Means algorithm to cluster the sparse scoring matrix, calculated the user interest score to complete the filling of the sparse matrix. Then, considering the influence of user interest drift with time, the algorithm introduced the Newton cooling time-weighted to improve user similarity. And then calculated user similarity based on the filled score matrix, which helped to get the last predicted score of unrated items The experimental results show that, compared with the traditional collaborative filtering algorithms, the mean absolute error of Proposed improved algorithm is d, and the Precision, Recall and F1 value of MBKT-CF also get a large improvement, which has a higher rating prediction accuracy.
ECLIPSE: Envisioning CLoud Induced Perturbations in Solar Energy

Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency. A promising approach to forecasting the temporal variability of solar irradiance resulting from the cloud cover dynamics is based on the analysis of sequences of ground-taken sky images or satellite observations. Despite encouraging results, a recurrent limitation of existing deep learning approaches lies in the ubiquitous tendency of reacting to past observations rather than actively anticipating future events. This leads to a frequent temporal lag and limited ability to predict sudden events. To address this challenge, we introduce ECLIPSE, a spatio-temporal neural network architecture that models cloud motion from sky images to not only predict future irradiance levels and associated uncertainties, but also segmented images, which provide richer information on the local irradiance map. We show that ECLIPSE anticipates critical events and reduces temporal delay while generating visually realistic futures. The model characteristics and properties are investigated with an ablation study and a comparative study on the benefits and different ways to integrate auxiliary data into the modelling. The model predictions are also interpreted through an analysis of the principal spatio-temporal components learned during network training.
Neural Embedding Singular Value Decomposition for Collaborative Filtering

Singular value decomposition (SVD) is one of the most effective algorithms in recommender systems (RSs). Due to the iterative nature of SVD algorithms, one big challenge is initialization that has a major impact on the convergence and performance of RSs. Unfortunately, existing SVD algorithms in the literature typically initialize the user and item features in a random manner; thus, data information is not fully utilized. This work addresses the challenge of developing an efficient initialization method for SVD algorithms. We propose a general neural embedding initialization framework, where a low-complexity probabilistic autoencoder neural network initializes the features of user and item. This framework supports explicit and implicit feedback data sets. The design details of our proposed framework are elaborated and discussed. Experimental results show that RSs based on our proposed initialization framework outperform the state-of-the-art methods in rating prediction. Moreover, regarding item ranking, our proposed framework shows an improvement of at least 2.20% ~5.74% than existing SVD algorithms and other matrix factorization methods in the literature.
Ordinal losses for classification of cervical cancer risk

Cervical cancer is the fourth leading cause of cancer-related deaths in women, especially in low to middle-income countries. Despite the outburst of recent scientific advances, there is no totally effective treatment, especially when diagnosed in an advanced stage. Screening tests, such as cytology or colposcopy, have been responsible for a substantial decrease in cervical cancer deaths. Cervical cancer automatic screening via Pap smear is a highly valuable cell imaging-based detection tool, where cells must be classified as being within one of a multitude of ordinal classes, ranging from abnormal to normal. Current approaches to ordinal inference for neural networks are found to not sufficiently take advantage of the ordinal problem or to be too uncompromising. A non-parametric ordinal loss for neuronal networks is proposed that promotes the output probabilities to follow a unimodal distribution. This is done by imposing a set of different constraints over all pairs of consecutive labels which allows for a more flexible decision boundary relative to approaches from the literature. Our proposed loss is contrasted against other methods from the literature by using a plethora of deep architectures. A first conclusion is the benefit of using non-parametric ordinal losses against parametric losses in cervical cancer risk prediction. Additionally, the proposed loss is found to be the top-performer in several cases. The best performing model scores an accuracy of 75.6% for seven classes and 81.3% for four classes.
A Gibbs Sampling Algorithm with Monotonicity Constraints for Diagnostic Classification Models

Diagnostic classification models (DCMs) are restricted latent class models with a set of cross-class equality constraints and additional monotonicity constraints on their item parameters, both of which are needed to ensure the meaning of classes and model parameters. In this paper, we develop an efficient, Gibbs sampling-based Bayesian Markov chain Monte Carlo estimation method for general DCMs with monotonicity constraints. A simulation study was conducted to evaluate parameter recovery of the algorithm which showed accurate estimation of model parameters. Moreover, the proposed algorithm was compared to a previously developed Gibbs sampling algorithm which imposed constraints on only the main effect item parameters of the log-linear cognitive diagnosis model. The newly proposed algorithm showed less bias and faster convergence. An analysis of the 2000 Programme for International Student Assessment reading assessment data using this algorithm was also conducted.
Que2Search: Fast and Accurate Query and Document Understanding for Search at Facebook

In this paper, we present Que2Search, a deployed query and product understanding system for search. Que2Search leverages multi-task and multi-modal learning approaches to train query and product representations. We achieve over 5% absolute offline relevance improvement and over 4% online engagement gain over state-of-the-art Facebook product understanding system by combining the latest multilingual natural language understanding architectures like XLM and XLM-R with multi-modal fusion techniques. In this paper, we describe how we deploy XLM-based search query understanding model that runs <1.5ms @P99 on CPU at Facebook scale, which has been a significant challenge in the industry. We also describe what model optimizations worked (and what did not) based on numerous offline and online A/B experiments. We deploy Que2Search to Facebook Marketplace Search and share our deployment experience to production and tuning tricks to achieve higher efficiency in online A/B experiments. Que2Search has demonstrated gains in production applications and operates at Facebook scale.
Bots or inattentive humans? Identifying sources of low-quality data in online platforms

Online data collection has become indispensable to the social sciences, polling, marketing, and corporate research. However, in recent years, online data collection has been inundated with low quality data. Low quality data threatens the validity of online research and, at times, invalidates entire studies. It is often assumed that random, inconsistent, and fraudulent data in online surveys comes from ‘bots.’ But little is known about whether bad data is caused by bots or ill-intentioned or inattentive humans. We examined this issue on Mechanical Turk (MTurk), a popular online data collection platform. In the summer of 2018, researchers noticed a sharp increase in the number of data quality problems on MTurk, problems that were commonly attributed to bots. Despite this assumption, few studies have directly examined whether problematic data on MTurk are from bots or inattentive humans, even though identifying the source of bad data has important implications for creating the right solutions. Using CloudResearch’s data quality tools to identify problematic participants in 2018 and 2020, we provide evidence that much of the data quality problems on MTurk can be tied to fraudulent users from outside of the U.S. who pose as American workers. Hence, our evidence strongly suggests that the source of low quality data is real humans, not bots. We additionally present evidence that these fraudulent users are behind data quality problems on other platforms.
Learning Based Proximity Matrix Factorization for Node Embedding

Node embedding learns a low-dimensional representation for each node in the graph. Recent progress on node embedding shows that proximity matrix factorization methods gain superb performance and scale to large graphs with millions of nodes. Existing approaches first define a proximity matrix and then learn the embeddings that fit the proximity by matrix factorization. Most existing matrix factorization methods adopt the same proximity for different tasks, while it is observed that different tasks and datasets may require different proximity, limiting their representation power.
Precise control of squeezing angle to generate 11 dB entangled state

The strength of the quantum correlations of a continuous-variable entangled state is determined by several relative phases in the preparation, transmission, and detection processes of entangled states. In this paper, we report the first experimental and theoretical demonstrations of the precision of relative phases associated with the strength of quadrature correlations. Based on the interrelations of the relative phases, three precisely phase-locking methodologies are established: ultralow RAM control loops for the lengths and relative phases stabilization of the DOPAs, difference DC locking for the relative phase between the two squeezed beams, and DC-AC joint locking for the relative phases in BHDs. The phase-locking loops ensure the total phase noise to be 9.7±0.32/11.1±0.36 mrad. Finally, all the relative phase deviations are controlled to be in the range of −35 to 35 mrad, which enhances the correlations of the amplitude and phase quadratures to −11.1 and −11.3 dB. The entanglement also exhibits a broadband squeezing bandwidth up to 100 MHz. This paves a valuable resource for experimental realization and applications in quantum information and precision measurement.
SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC)

In this paper, we introduce the first SemEval task on Multilingual and Cross-Lingual Wordin-Context disambiguation (MCL-WiC).This task allows the largely under-investigated inherent ability of systems to discriminate between word senses within and across languages to be evaluated, dropping the requirement of a fixed sense inventory.Framed as a binary classification, our task is divided into two parts.In the multilingual sub-task, participating systems are required to determine whether two target words, each occurring in a different context within the same language, express the same meaning or not.Instead, in the crosslingual part, systems are asked to perform the task in a cross-lingual scenario, in which the two target words and their corresponding contexts are provided in two different languages.We illustrate our task, as well as the construction of our manually-created dataset including five languages, namely Arabic, Chinese, English, French and Russian, and the results of the participating systems.Datasets and results are available at:
Potential technologies and applications based on deep learning in the 6G networks

The fifth generation (5G) networks provide high speed, low latency, and reliable communication services, and have been making great differences in our daily life. However, it might be difficult to meet the growing demands of Internet of Things (IoT) devices based on the existing technologies in the future, so the concept of sixth generation (6G) networks were raised to improve the existing 5G networks and promote the development of intelligent applications furthermore. Therefore, we introduce the characteristics, challenges, and potential technologies of 6G networks, and find that deep learning is potential to overcome these challenges with the help of big data and cloud computing. In addition, Deep learning approaches and potential applications in 6G networks are also discussed. We summarize the important viewpoints and studies, and provide some new ideas for developing 6G networks.
The Zero Resource Speech Challenge 2021: Spoken Language Modelling

We present the Zero Resource Speech Challenge 2021, which asks participants to learn a language model directly from audio, without any text or labels. The challenge is based on the Libri-light dataset, which provides up to 60k hours of audio from English audio books without any associated text. We provide a pipeline baseline system consisting on an encoder based on contrastive predictive coding (CPC), a quantizer ($k$-means) and a standard language model (BERT or LSTM). The metrics evaluate the learned representations at the acoustic (ABX discrimination), lexical (spot-the-word), syntactic (acceptability judgment) and semantic levels (similarity judgment). We present an overview of the eight submitted systems from four groups and discuss the main results.
Review of white box methods for explanations of convolutional neural networks in image classification tasks

In recent years, deep learning has become prevalent to solve applications from multiple domains. Convolutional neural networks (CNNs) particularly have demonstrated state-of-the-art performance for the task of image classification. However, the decisions made by these networks are not transparent and cannot be directly interpreted by a human. Several approaches have been proposed to explain the reasoning behind a prediction made by a network. We propose a topology of grouping these methods based on their assumptions and implementations. We focus primarily on white box methods that leverage the information of the internal architecture of a network to explain its decision. Given the task of image classification and a trained CNN, our work aims to provide a comprehensive and detailed overview of a set of methods that can be used to create explanation maps for a particular image, which assign an importance score to each pixel of the image based on its contribution to the decision of the network. We also propose a further classification of the white box methods based on their implementations to enable better comparisons and help researchers find methods best suited for different scenarios.
Disparate Vulnerability to Membership Inference Attacks

A membership inference attack (MIA) against a machine-learning model enables an attacker to determine whether a given data record was part of the model's training data or not. In this paper, we provide an in-depth study of the phenomenon of disparate vulnerability against MIAs: unequal success rate of MIAs against different population subgroups. We first establish necessary and sufficient conditions for MIAs to be prevented, both on average and for population subgroups, using a notion of distributional generalization. Second, we derive connections of disparate vulnerability to algorithmic fairness and to differential privacy. We show that fairness can only prevent disparate vulnerability against limited classes of adversaries. Differential privacy bounds disparate vulnerability but can significantly reduce the accuracy of the model. We show that estimating disparate vulnerability to MIAs by naively applying existing attacks can lead to overestimation. We then establish which attacks are suitable for estimating disparate vulnerability, and provide a statistical framework for doing so reliably. We conduct experiments on synthetic and real-world data finding statistically significant evidence of disparate vulnerability in realistic settings. The code is available at this https URL
Cloud Security Service for Identifying Unauthorized User Behaviour

Recently, an innovative trend like cloud computing has progressed quickly in Information Technology. For a background of distributed networks, the extensive sprawl of internet resources on the Web and the increasing number of service providers helped cloud computing technologies grow into a substantial scaled Information Technology service model. The cloud computing environment extracts the execution details of services and systems from end-users and developers. Additionally, through the system’s virtualization accomplished using resource pooling, cloud computing resources become more accessible. The attempt to design and develop a solution that assures reliable and protected authentication and authorization service in such cloud environments is described in this paper. With the help of multi-agents, we attempt to represent Open-Identity (ID) design to find a solution that would offer trustworthy and secured authentication and authorization services to software services based on the cloud. This research aims to determine how authentication and authorization services were provided in an agreeable and preventive manner. Based on attack-oriented threat model security, the evaluation works. By considering security for both authentication and authorization systems, possible security threats are analyzed by the proposed security systems.
More Efficient Post-quantum KEMTLS with Pre-distributed Public Keys

While server-only authentication with certificates is the most widely used mode of operation for the Transport Layer Security (TLS) protocol on the world wide web, there are many applications where TLS is used in a different way or with different constraints. For example, embedded Internet-of-Things clients may have a server certificate pre-programmed and be highly constrained in terms of communication bandwidth or computation power. As post-quantum algorithms have a wider range of performance trade-offs, designs other than traditional “signed-key-exchange” may be worthwhile. The KEMTLS protocol, presented at ACM CCS 2020, uses key encapsulation mechanisms (KEMs) rather than signatures for authentication in the TLS 1.3 handshake, a benefit since most post-quantum KEMs are more efficient than PQ signatures. However, KEMTLS has some drawbacks, especially in the client authentication scenario which requires a full additional roundtrip.We explore how the situation changes with pre-distributed public keys, which may be viable in many scenarios, for example pre-installed public keys in apps, on embedded devices, cached public keys, or keys distributed out of band. Our variant of KEMTLS with pre-distributed keys, called \(\mathsf{KEMTLS\text {-}PDK}\), is more efficient in terms of both bandwidth and computation compared to post-quantum signed-KEM TLS (even cached public keys), and has a smaller trusted code base. When client authentication is used, \(\mathsf{KEMTLS\text {-}PDK}\) is more bandwidth efficient than KEMTLS yet can complete client authentication in one fewer round trips, and has stronger authentication properties. Interestingly, using pre-distributed keys in \(\mathsf{KEMTLS\text {-}PDK}\) changes the landscape on suitability of PQ algorithms: schemes where public keys are larger than ciphertexts/signatures (such as Classic McEliece and Rainbow) can be viable, and the differences between some lattice-based schemes is reduced. We also discuss how using pre-distributed public keys provides privacy benefits compared to pre-shared symmetric keys in TLS.
GraphSVX: Shapley Value Explanations for Graph Neural Networks

Graph Neural Networks (GNNs) achieve significant performance for various learning tasks on geometric data due to the incorporation of graph structure into the learning of node representations, which renders their comprehension challenging. In this paper, we first propose a unified framework satisfied by most existing GNN explainers. Then, we introduce GraphSVX, a post hoc local model-agnostic explanation method specifically designed for GNNs. GraphSVX is a decomposition technique that captures the “fair” contribution of each feature and node towards the explained prediction by constructing a surrogate model on a perturbed dataset. It extends to graphs and ultimately provides as explanation the Shapley Values from game theory. Experiments on real-world and synthetic datasets demonstrate that GraphSVX achieves state-of-the-art performance compared to baseline models while presenting core theoretical and human-centric properties.
A review of cluster analysis techniques and their uses in library and information science research:<i>k-means</i>and<i>k-medoids</i>clustering

Purpose This literature review explores the definitions and characteristics of cluster analysis, a machine-learning technique that is frequently implemented to identify groupings in big datasets and its applicability to library and information science (LIS) research. This overview is intended for researchers who are interested in expanding their data analysis repertory to include cluster analysis, rather than for existing experts in this area. Design/methodology/approach A review of LIS articles included in the Library and Information Source (EBSCO) database that employ cluster analysis is performed. An overview of cluster analysis in general (how it works from a statistical standpoint, and how it can be performed by researchers), the most popular cluster analysis techniques and the uses of cluster analysis in LIS is presented. Findings The number of LIS studies that employ a cluster analytic approach has grown from about 5 per year in the early 2000s to an average of 35 studies per year in the mid- and late-2010s. The journal Scientometrics has the most articles published within LIS that use cluster analysis (102 studies). Scientometrics is the most common subject area to employ a cluster analytic approach (152 studies). The findings of this review indicate that cluster analysis could make LIS research more accessible by providing an innovative and insightful process of knowledge discovery. Originality/value This review is the first to present cluster analysis as an accessible data analysis approach, specifically from an LIS perspective.