Reviewerly/exHarmony huggingface dataset:
Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using Zero-Shot Learning

While billions of non-English speaking users rely on search engines every day, the problem of ad-hoc information retrieval is rarely studied for non-English languages. This is primarily due to a lack of data set that are suitable to train ranking algorithms. In this paper, we tackle the lack of data by leveraging pre-trained multilingual language models to transfer a retrieval system trained on English collections to non-English queries and documents. Our model is evaluated in a zero-shot setting, meaning that we use them to predict relevance scores for query-document pairs in languages never seen during training. Our results show that the proposed approach can significantly outperform unsupervised retrieval techniques for Arabic, Chinese Mandarin, and Spanish. We also show that augmenting the English training collection with some examples from the target language can sometimes improve performance.
WaveTTS: Tacotron-based TTS with Joint Time-Frequency Domain Loss

Tacotron-based text-to-speech (TTS) systems directly synthesize speech from text input. Such frameworks typically consist of a feature prediction network that maps character sequences to frequency-domain acoustic features, followed by a waveform reconstruction algorithm or a neural vocoder that generates the time-domain waveform from acoustic features. As the loss function is usually calculated only for frequency-domain acoustic features, that doesn't directly control the quality of the generated time-domain waveform. To address this problem, we propose a new training scheme for Tacotron-based TTS, referred to as WaveTTS, that has 2 loss functions: 1) time-domain loss, denoted as the waveform loss, that measures the distortion between the natural and generated waveform; and 2) frequency-domain loss, that measures the Mel-scale acoustic feature loss between the natural and generated acoustic features. WaveTTS ensures both the quality of the acoustic features and the resulting speech waveform. To our best knowledge, this is the first implementation of Tacotron with joint time-frequency domain loss. Experimental results show that the proposed framework outperforms the baselines and achieves high-quality synthesized speech.
Collective Support and Independent Learning with a Voice-Based Literacy Technology in Rural Communities

Access to literacy is critical to children's futures, but formal education may be insufficient for fostering early literacy, especially in low-resource contexts. Educational technologies used at home may be able to help, but it is unclear whether or how children (and families) will use such technologies at home in rural communities, particularly in low-literate families. In this paper, we investigate these questions with a voice-based literacy technology deployed with families in 8 rural communities in Côte d'Ivoire for 4 months. We use interviews and observations with 37 families to investigate motivations, methods, and barriers for rural families' engagement with a literacy technology accessible via feature phones. We contribute insights into how families view digital literacy as a learning goal, leverage networks of supporters, and over time, transition from explicit to implicit support for children's learning.
RNN-based demand awareness in smart library using CRFID

To provide more intelligence service in the smart library, we need to better perceive the reader's preferences. In addition to perceiving online records based on readers' search history and borrowing records, advanced information technologies give us more chance to perceive the behavior of readers in the actual reading process and further discover the need for reading. In this paper, we use CRFID and RNN deep learning network to recognize book motions in the reading process, so as to judge readers' need degree for the book, which can provide a basis for library book purchases and readers personalized service. In order to improve the recognition accuracy, we use the RSS as well as acceleration magnitude gathered from CRFID as the input data for RNN, and design a new encoding scheme. We trained and tested the deep learning network using real-world data, recorded during actual reading in our lab environment which mimics a typical reading room, from the experimental results, we conclude that our approach is feasible to recognize different reading phase to perceiving the needs of the readers.
The Paradigm Discovery Problem

This work treats the paradigm discovery problem (PDP)-the task of learning an inflectional morphological system from unannotated sentences.We formalize the PDP and develop evaluation metrics for judging systems.Using currently available resources, we construct datasets for the task.We also devise a heuristic benchmark for the PDP and report empirical results on five diverse languages.Our benchmark system first makes use of word embeddings and string similarity to cluster forms by cell and by paradigm.Then, we bootstrap a neural transducer on top of the clustered data to predict words to realize the empty paradigm slots.An error analysis of our system suggests clustering by cell across different inflection classes is the most pressing challenge for future work.
Machine Learning Explainability in Breast Cancer Survival.

Machine Learning (ML) can improve the diagnosis, treatment decisions, and understanding of cancer. However, the low explainability of how "black box" ML methods produce their output hinders their clinical adoption. In this paper, we used data from the Netherlands Cancer Registry to generate a ML-based model to predict 10-year overall survival of breast cancer patients. Then, we used Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to interpret the model's predictions. We found that, overall, LIME and SHAP tend to be consistent when explaining the contribution of different features. Nevertheless, the feature ranges where they have a mismatch can also be of interest, since they can help us identifying "turning points" where features go from favoring survived to favoring deceased (or vice versa). Explainability techniques can pave the way for better acceptance of ML techniques. However, their evaluation and translation to real-life scenarios need to be researched further.
Deep Learning for Matching in Search and Recommendation

Matching is the key problem in both search and recommendation, that is to measure the relevance of a document to a query or the interest of a user on an item. Previously, machine methods have been exploited to address the problem, which learns a matching function from labeled data, also referred to as learning to match''. In recent years, deep has been successfully applied to matching and significant progresses have been made. Deep semantic matching models for search and neural collaborative filtering models for recommendation are becoming the state-of-the-art technologies. The key to the success of the deep approach is its strong ability in of representations and generalization of matching patterns from raw data (e.g., queries, documents, users, and items, particularly in their raw forms). In this tutorial, we aim to give a comprehensive survey on recent progress in deep for matching in search and recommendation. Our tutorial is unique in that we try to give a unified view on search and recommendation. In this way, we expect researchers from the two fields can get deep understanding and accurate insight on the spaces, stimulate more ideas and discussions, and promote developments of technologies. The tutorial mainly consists of three parts. Firstly, we introduce the general problem of matching, which is fundamental in both search and recommendation. Secondly, we explain how traditional machine techniques are utilized to address the matching problem in search and recommendation. Lastly, we elaborate how deep can be effectively used to solve the matching problems in both tasks.
QuPiD Attack: Machine Learning-Based Privacy Quantification Mechanism for PIR Protocols in Health-Related Web Search

With the advancement in ICT, web search engines have become a preferred source to find health-related information published over the Internet. Google alone receives more than one billion health-related queries on a daily basis. However, in order to provide the results most relevant to the user, WSEs maintain the users’ profiles. These profiles may contain private and sensitive information such as the user’s health condition, disease status, and others. Health-related queries contain privacy-sensitive information that may infringe user’s privacy, as the identity of a user is exposed and may be misused by the WSE and third parties. This raises serious concerns since the identity of a user is exposed and may be misused by third parties. One well-known solution to preserve privacy involves issuing the queries via peer-to-peer private information retrieval protocol, such as useless user profile (UUP), thereby hiding the user’s identity from the WSE. This paper investigates the level of protection offered by UUP. For this purpose, we present QuPiD (query profile distance) attack: a machine learning-based attack that evaluates the effectiveness of UUP in privacy protection. QuPiD attack determines the distance between the user’s profile (web search history) and upcoming query using our proposed novel feature vector. The experiments were conducted using ten classification algorithms belonging to the tree-based, rule-based, lazy learner, metaheuristic, and Bayesian families for the sake of comparison. Furthermore, two subsets of an America Online dataset (noisy and clean datasets) were used for experimentation. The results show that the proposed QuPiD attack associates more than 70% queries to the correct user with a precision of over 72% for the clean dataset, while for the noisy dataset, the proposed QuPiD attack associates more than 40% queries to the correct user with 70% precision.
Data-centricity

Rethinking the content of introductory computing around a data-centric approach to better engage and support a diversity of students.
Non-Deterministic Outlier Detection Method Based on the Variable Precision Rough Set Model

This study presents a method for the detection of outliers based on the Variable Precision Rough Set Model (VPRSM). The basis of this model is the generalisation of the standard concept of a set inclusion relation on which the Rough Set Basic Model (RSBM) is based. The primary contribution of this study is the improvement in detection quality, which is achieved due to the generalisation allowed by the classification system that allows a certain degree of uncertainty. From this method, a computationally efficient algorithm is proposed. The experiments performed with a real scenario and a comparison of the results with the RSBM-based method demonstrate the effectiveness of the method as well as the algorithm’s efficiency in diverse contexts, which also involve large amounts of data.
On Adaptive Security of Delayed-Input Sigma Protocols and Fiat-Shamir NIZKs

We study adaptive security of delayed-input Sigma protocols and non-interactive zero-knowledge (NIZK) proof systems in the common reference string (CRS) model. Our contributions are threefold: An attractive feature of our first two compilers is that they allow obtaining efficient delayed-input Sigma protocols with adaptive security, and efficient Fiat-Shamir NIZKs with adaptive soundness (and non-adaptive zero knowledge) in the CRS model. Prior to our work, the latter was only possible using generic NP reductions.
Improving Deep Item-Based Collaborative Filtering with Bayesian Personalized Ranking for MOOC Course Recommendation

With the advancement of big data and education technology, MOOCs (Massive Online Open Courses) has become a popular education model in online education community. A large number of online courses with diverse disciplinary background are offered freely to global learners. When a learner finishes a series of courses, it is very important to effectively and efficiently recommend the most relevant courses to study next. Traditional item-based recommendation methods are all pointwise approaches where models bias towards estimating the precise rating or relevance score of each item. It would be better to model this problem from a pairwise learning perspective which is more close to the ranking nature of course recommendation. In this paper, we combine item-based collaborative filtering and Bayesian Personalized Ranking for course recommendation problem. We theoretically derive the optimization schema based on Bayesian Personalized Ranking and develop a novel neural network model, called Bayesian Personalized Ranking Network (BPRN), which learns pairwise course preference for each user given her historically enrolled courses. With extensive experiments on a large-scale MOOCs enrollment dataset from XuetangX, we empirically demonstrate that our BPRN framework performs better than state-of-the-art item-based course recommendation methods.
Chinese Text Classification Based on Hybrid Model of CNN and LSTM

Text classification is one of the basic tasks of natural language processing. In recent years, deep learning has been widely used in text classification tasks. The representative one is the convolutional neural network. The convolutional neural network(CNN) is limited by the size of the local window and can only extract local features of the text. For long texts like news, CNN cannot learn the longterm dependence of the long text. Another model of deep learning recurrent neural networks based on long short-term memory (LSTM) can learn the long-term dependence of text. Therefore, in the work of this paper, combining the advantages of CNN and LSTM, a LSTM_CNN Hybrid model is constructed for Chinese news text classification tasks. We first use LSTM to learn the longterm dependence of text, then we design a shallow convolution structure to further extract the semantic features of the text, and finally use the max-pooling operation to filter to obtain important features for classification. The model we proposed has achieved good results on the News dataset.
Assessing the nexus between knowledge management and firm performance: A data article

This data article highlights the contingent role of company size in the relationship between knowledge management practices and firm performance. It also investigates the possible direct impact of these practices on performance. Data was collected from managers in large and medium-sized industrial companies in Jordan, using a self-administered questionnaire. 170 responses were obtained, 91 from medium-sized companies and 79 from large companies. Confirmatory factor analysis was used to ensure the validity and reliability of the measurement model. Multiple group structural equation modeling was used to check for significant differences in the path coefficients of the research model. The fitness indicators of the multi-group model showed that there was no significant difference in the interpretation of the measurement model; also, the path model was equivalent for both large and medium-sized companies. Testing the hypotheses showed that the application of knowledge has the greatest explanatory power for performance, whether in medium or large companies; however, for knowledge capturing the explanatory power on performance was only for medium-sized companies, and there was no effect on large companies. Moreover, the acquisition and sharing of knowledge had no statistically significant effect on performance in either group.
Showing Data About People: A Design Space of Anthropographics

When showing data about people, visualization designers and data journalists often use design strategies that presumably help the audience relate to those people. The term anthropographics has been recently coined to refer to this practice and the resulting visualizations. Anthropographics is a rich and growing area, but the work so far has remained scattered. Despite preliminary empirical work and a few web essays written by practitioners, there is a lack of clear language for thinking about and communicating about anthropographics. We address this gap by introducing a conceptual framework and a design space for anthropographics. Our design space consists of seven elementary design dimensions that can be reasonably hypothesized to have some effect on prosocial feelings or behavior. It extends a previous design space and is informed by an analysis of 105 visualizations collected from newspapers, websites, and research articles. We use our conceptual framework and design space to discuss trade-offs, common design strategies, as well as future opportunities for design and research in the area of anthropographics.
Privacy-Preserving Spatio-Temporal Patient Data Publishing

As more data become available to the public, the value of information seems to be diminishing with concern over what constitute privacy of individual. Despite benefit to data publishing, preserving privacy of individuals remains a major concern because linking of data from heterogeneous source become easier due to the vast availability of artificial intelligence tools. In this paper, we focus on preserving privacy of spatio-temporal data publishing. Specifically, we present a framework consisting of (i) a 5-level temporal hierarchy to protect the temporal attributes and (ii) temporal representative point (TRP) differential privacy to protect the spatial attributes. Evaluation results on big datasets show that our framework keeps a good balance of utility and privacy. To a further extent, our solution is expected be extendable for privacy-preserving data publishing for the spatio-temporal data of coronavirus disease 2019 (COVID-19) patients.
Linear Attack on Round-Reduced DES Using Deep Learning

Linear attack is a powerful known-plaintext cryptanalysis method on block ciphers, which has been successfully applied in DES, KATAN, SPECK and other ciphers. In this paper, we use deep learning networks to achieve linear attack on DES with plain-cipher pairs. Comparing with traditional linear attack algorithm, our work requires less knowledge about complex cryptanalysis as neural network can work well by data-driven. Thus, this paper has three main contributions. First, a new linear attack architecture based on deep residual network was proposed to train discriminative neural networks with auto-generated plain-cipher pair data. The results indicate that trained neural networks can effectively learn algorithmic representations of the XOR distributions of given linear expression on DES. Second, several novel neural network-based algorithms were designed to efficiently enforce key recovery on round-reduced DES using trained networks with moderate full and partial bits of linear expression as inputs. Third, as far as we know, it is the first time that neural networks are used to achieve known-plaintext attack on complex block ciphers.
A flexible numerical calculation method of angular spectrum based on matrix product

Fast Fourier transform (FFT) is the most commonly used mathematical method in numerical calculation, and the FFT-based angular spectrum method (ASM) is also used widely in diffraction calculation. However, the frequency and spatial sampling rules in FFT limit the effective propagation distance and the observation window range of ASM. A novel method for calculating the angular spectrum based on the matrix product is proposed in this Letter. This method realizes the fast calculation of discrete Fourier transform (DFT) based on the matrix product, in which the sampling matrix is orthogonally decomposed into two vectors. Instead of FFT, angular spectrum diffraction calculation is carried out based on the matrix product, which is named the matrix product ASM. The method in this Letter uses a simple mathematical transformation to achieve maximum compression of the sampling interval in the frequency domain, which significantly increases the effective propagation distance of the angular spectrum. Additionally, the size of the observation window can be enlarged to obtain a wider calculation range by changing the spatial sampling of the output plane.
Span-Based Joint Entity and Relation Extraction with Transformer Pre-Training.

We introduce SpERT, an attention model for span-based joint entity and relation extraction. Our key contribution is a light-weight reasoning on BERT embeddings, which features entity recognition and filtering, as well as relation classification with a localized, marker-free context representation. The model is trained using strong within-sentence negative samples, which are efficiently extracted in a single BERT pass. These aspects facilitate a search over all spans in the sentence. In ablation studies, we demonstrate the benefits of pre-training, strong negative sampling and localized context. Our model outperforms prior work by up to 2.6% F1 score on several datasets for joint entity and relation extraction.
Assessment of speckle pattern quality in digital image correlation from the perspective of mean bias error

Digital image correlation (DIC) technology is severely affected by the speckle pattern quality. Most of the existing assessment methods always use only one parameter to evaluate both mean bias error and standard deviation error, such as the mean intensity gradient (MIG). However, the principles of these two error models are quite different. The mean bias error is closely related to the first-order and second-order gradients of speckle pattern intensity. A parameter named Ef, based on the mean bias error, is proposed to evaluate the speckle pattern quality in this work. Numerical translations are applied to eight different speckle patterns to justify its correctness. The results indicate that the existing MIG is efficient to assess speckle pattern quality by evaluating the standard deviation error, while the parameter Ef is efficient to assess the speckle pattern quality from the perspective of mean bias error.
New PcN and APcN functions over finite fields

Functions with low c-differential uniformity were proposed in 2020 and attracted lots of attention, especially the PcN and APcN functions, due to their applications in cryptography. The objective of this paper is to study PcN and APcN functions. As a consequence, we propose two classes of PcN functions and three classes of APcN functions by using the cyclotomic technique and the switching method. In addition, four classes of PcN or APcN functions are presented by virtue of the (generalized) AGW criterion.
Application of Corrected Methods for High-Resolution XRF Core Scanning Elements in Lake Sediments

Contemporary studies emphasize theoretical and analytical aspects of monitoring water quality within lacustrine settings. The X-ray fluorescence (XRF) core scanner provides the most rapid, non-destructive high-resolution elemental measurements for unprocessed sediments. However, the analytical precision of measured elemental composition may be offset due to water content and inhomogeneities in the physical properties of the sediment. A range of calibration approaches developed specifically for converting XRF scanning intensities to element fractions has been made available. Here, two lake sediment-cores retrieved from southwest China were used to evaluate the performance of various calibration methods. In particular, the influence of sediment properties on XRF scanning intensities was assessed by redundancy analyses (RDA) and the generalized additive model (GAM). The results demonstrate that for fine-grained sediments, the impact of grain size results in only minor deviations in the XRF scanning intensities. Water content of the lake sediment was shown to be the most important factor influencing the XRF scanning intensities, especially for light elements (e.g., Al to Fe). Significant decreases in XRF scanning intensities may occur when sediment water content is greater than 47%. We recommend testing the element fractions obtained via conventional techniques throughout the core and applying the multivariate log-ratio calibration for high-resolution XRF scanning elements within lake sediments.