Reviewerly/exHarmony huggingface dataset:
Performance comparison of neural and non-neural approaches to session-based recommendation

The benefits of neural approaches are undisputed in many application areas. However, today's research practice in applied machine learning---where researchers often use a variety of baselines, datasets, and evaluation procedures---can make it difficult to understand how much progress is actually achieved through novel technical approaches. In this work, we focus on the fast-developing area of session-based recommendation and aim to contribute to a better understanding of what represents the state-of-the-art.
Hyperspectral Images Super-Resolution via Learning High-Order Coupled Tensor Ring Representation

Hyperspectral image (HSI) super-resolution is a hot topic in remote sensing and computer vision. Recently, tensor analysis has been proven to be an efficient technology for HSI image processing. However, the existing tensor-based methods of HSI super-resolution are not able to capture the high-order correlations in HSI. In this article, we propose to learn a high-order coupled tensor ring (TR) representation for HSI super-resolution. The proposed method first tensorizes the HSI to be estimated into a high-order tensor in which multiscale spatial structures and the original spectral structure are represented. Then, a coupled TR representation model is proposed to fuse the low-resolution HSI (LR-HSI) and high-resolution multispectral image (HR-MSI). In the proposed model, some latent core tensors in TR of the LR-HSI and the HR-MSI are shared, and we use the relationship between the spectral core tensors to reconstruct the HSI. In addition, the graph-Laplacian regularization is introduced to the spectral core tensors to preserve the spectral information. To enhance the robustness of the proposed model, Frobenius norm regularizations are introduced to the other core tensors. Experimental results on both synthetic and real data sets show that the proposed method achieves the state-of-the-art super-resolution performance.
Extracting Training Data from Large Language Models

It has become common to publish large (billion parameter) language models that have been trained on private datasets. This paper demonstrates that in such settings, an adversary can perform a training data extraction attack to recover individual training examples by querying the language model. We demonstrate our attack on GPT-2, a language model trained on scrapes of the public Internet, and are able to extract hundreds of verbatim text sequences from the model's training data. These extracted examples include (public) personally identifiable information (names, phone numbers, and email addresses), IRC conversations, code, and 128-bit UUIDs. Our attack is possible even though each of the above sequences are included in just one document in the training data. We comprehensively evaluate our extraction attack to understand the factors that contribute to its success. Worryingly, we find that larger models are more vulnerable than smaller models. We conclude by drawing lessons and discussing possible safeguards for training large language models.
Towards Improving Neural Named Entity Recognition with Gazetteers

Most of the recently proposed neural models for named entity recognition have been purely data-driven, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features.This could increase the chance of overfitting since the models cannot access any supervision signal beyond the small amount of annotated data, limiting their power to generalize beyond the annotated entities.In this work, we show that properly utilizing external gazetteers could benefit segmental neural NER models.We add a simple module on the recently proposed hybrid semi-Markov CRF architecture and observe some promising results.
RawNet: Advanced End-to-End Deep Neural Network Using Raw Waveforms for Text-Independent Speaker Verification

Recently, direct modeling of raw waveforms using deep neural networks has been widely studied for a number of tasks in audio domains. In speaker verification, however, utilization of raw waveforms is in its preliminary phase, requiring further investigation. In this study, we explore end-to-end deep neural networks that input raw waveforms to improve various aspects: front-end speaker embedding extraction including model architecture, pre-training scheme, additional objective functions, and back-end classification. Adjustment of model architecture using a pre-training scheme can extract speaker embeddings, giving a significant improvement in performance. Additional objective functions simplify the process of extracting speaker embeddings by merging conventional two-phase processes: extracting utterance-level features such as i-vectors or x-vectors and the feature enhancement phase, e.g., linear discriminant analysis. Effective back-end classification models that suit the proposed speaker embedding are also explored. We propose an end-to-end system that comprises two deep neural networks, one front-end for utterance-level speaker embedding extraction and the other for back-end classification. Experiments conducted on the VoxCeleb1 dataset demonstrate that the proposed model achieves state-of-the-art performance among systems without data augmentation. The proposed system is also comparable to the state-of-the-art x-vector system that adopts data augmentation.
Sentiment Analysis in Tamil Texts: A Study on Machine Learning Techniques and Feature Representation

Sentiment Analysis (SA) is an application of Natural Language Processing (NLP) to extract the sentiments expressed in the text. In this paper, we experimented five approaches to perform SA, namely, Lexicon based approach, Supervised Machine learning based approach, Hybrid approach, K-means with Bag of Word (BoW) approach and K-modes with BoW approach. We have experimented these approaches using five corpora with different feature representation techniques to predict the best approach to perform SA in Tamil texts. In this research we used Basic features such as word count and punctuation count in addition to traditional features such as Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) included to check their influence in the prediction. We have compared these approaches, features and the corpora. From the evaluation the highest accuracy of 79% is obtained for UJ_Corpus_Opinions_Nouns corpus with fastText for supervised Machine learning based approach.
Education in precarious times: a comparative study across six countries to identify design priorities for mobile learning in a pandemic

Purpose This paper is based on the emergency changes we have had to make in the European DEIMP Project (2017-2020), “Designing and Evaluating Innovative Mobile Pedagogies” (DEIMP). DEIMP is undertaken by a transnational consortium comprising partner institutions and schools from the UK (coordinating), Australia, Belgium, Cyprus, Ireland and The Netherlands. As well as the enforced changes to the project, there have been major adjustments in how education is being provided in each of our countries, across all sectors: primary, secondary and tertiary. The purpose of this paper is to provide pragmatic guidelines that will help us respond effectively in the uncertain present, and plan systematically for an unpredictable, post-pandemic future. Design/methodology/approach The authors outline 21 design principles underpinning innovative mobile learning, which will be of pragmatic use to all using mobile learning in the COVID-19 pandemic. These principles have emerged in the context of the three-year European DEIMP Project (2017-2020). The authors also examine major educational changes that have recently been imposed upon teachers and educational researchers, and key aspects of the current emergency response in education internationally, and resultant implications for educational technology and mobile learning. Findings A living record highlighting what is currently happening in the educational systems of the DEIMP project’s respective partner countries. The paper outlines design concerns and issues, which will need to be addressed as the authors endeavour to bridge both the digital divide and digital use divide in remote education. Furthermore, the paper illustrates 21 pragmatic design principles underpinning innovative mobile pedagogies. Originality/value A comparative study of the effects of the pandemic across six countries, including The UK, Australia, Belgium, Cyprus, Ireland and The Netherlands. The authors outline 21 design principles for mobile learning, which is hoped will help us respond effectively in the uncertain present, and plan systematically for an unpredictable, post-pandemic future.
Vulnerabilities in Federated Learning

With more regulations tackling the protection of users’ privacy-sensitive data in recent years, access to such data has become increasingly restricted. A new decentralized training paradigm, known as Federated Learning (FL), enables multiple clients located at different geographical locations to learn a machine learning model collaboratively without sharing their data. While FL has recently emerged as a promising solution to preserve users’ privacy, this new paradigm’s potential security implications may hinder its widespread adoption. The existing FL protocols exhibit new unique vulnerabilities that adversaries can exploit to compromise the trained model. FL is often preferred in learning environments where security and privacy are the key concerns. Therefore, it is crucial to raise awareness of the consequences resulting from the new threats to FL systems. To date, the security of traditional machine learning systems has been widely examined. However, many open challenges and complex questions are still surrounding FL security. In this paper, we bridge the gap in FL literature by providing a comprehensive survey of the unique security vulnerabilities exposed by the FL ecosystem. We highlight the vulnerabilities sources, key attacks on FL, defenses, as well as their unique challenges, and discuss promising future research directions towards more robust FL.
Commonsense Knowledge Base Completion with Structural and Semantic Context

Automatic KB completion for commonsense knowledge graphs (e.g., ATOMIC and ConceptNet) poses unique challenges compared to the much studied conventional knowledge bases (e.g., Freebase). Commonsense knowledge graphs use free-form text to represent nodes, resulting in orders of magnitude more nodes compared to conventional KBs ( ∼18x more nodes in ATOMIC compared to Freebase (FB15K-237)). Importantly, this implies significantly sparser graph structures — a major challenge for existing KB completion methods that assume densely connected graphs over a relatively smaller set of nodes.In this paper, we present novel KB completion models that can address these challenges by exploiting the structural and semantic context of nodes. Specifically, we investigate two key ideas: (1) learning from local graph structure, using graph convolutional networks and automatic graph densification and (2) transfer learning from pre-trained language models to knowledge graphs for enhanced contextual representation of knowledge. We describe our method to incorporate information from both these sources in a joint model and provide the first empirical results for KB completion on ATOMIC and evaluation with ranking metrics on ConceptNet. Our results demonstrate the effectiveness of language model representations in boosting link prediction performance and the advantages of learning from local graph structure (+1.5 points in MRR for ConceptNet) when training on subgraphs for computational efficiency. Further analysis on model predictions shines light on the types of commonsense knowledge that language models capture well.
Graph-based Path Planning for Autonomous Robotic Exploration in Subterranean Environments

This paper presents a novel strategy for autonomous graph-based exploration path planning in subterranean environments. Attuned to the fact that subterranean settings, such as underground mines, are often large-scale networks of narrow tunnel-like and multi-branched topologies, the proposed planner is structured around a bifurcated local-and global-planner architecture. The local planner employs a rapidly-exploring random graph to reliably and efficiently identify collision-free paths that optimize an exploration gain within a local subspace. Accounting for the robot endurance limitations and the possibility that the local planner reaches a dead-end (e.g. a mine heading), the global planner is engaged when a return-to-home path must be derived or when the robot should be re-positioned towards an edge of the exploration space. The proposed planner is field evaluated in a collection of deployments inside both active and abandoned underground mines in the U.S. and in Switzerland.
rgee: An R package for interacting with Google Earth Engine

Google Earth Engine (Gorelick et al., 2017) is a cloud computing platform designed for planetary-scale environmental data analysis.Its multi-petabyte data catalog and computation services are accessed via an Internet-accessible API.The API is exposed through JavaScript and Python client libraries.Google provides a browser-based IDE for the JavaScript API, and while convenient and useful for rapid data exploration and script development, it does not allow third-party package integration, relying solely on Google Maps and Google Charts for data visualization, and proprietary systems for metadata viewing and asset management.In contrast, the Python and Node.js distributions offer much flexibility for developers to integrate with third-party libraries.However, without the structure of a dedicated IDE, casual users can be left directionless and daunted.A significant gap exists between these two offerings (Google-supported JavaScript IDE and base client libraries) where convenience and flexibility meet.We propose to fill this gap with an R package that wraps the Earth Engine Python API to provide R users with a familiar interface, rapid development features, and flexibility to analyze data using open-source, third-party packages.
Evaluating differentially private machine learning in practice

Differential privacy is a strong notion for privacy that can be used to prove formal guarantees, in terms of a privacy budget, e, about how much information is leaked by a mechanism. When used in privacy-preserving machine learning, the goal is typically to limit what can be inferred from the model about individual training records. However, the calibration of the privacy budget is not well understood. Implementations of privacy-preserving machine learning often select large values of e in order to get acceptable utility of the model, with little understanding of the impact of such choices on meaningful privacy. Moreover, in scenarios where iterative learning procedures are used, relaxed definitions of differential privacy are often used which appear to reduce the needed privacy budget but present poorly understood trade-offs between privacy and utility. In this paper, we quantify the impact of these choices on privacy in experiments with logistic regression and neural network models. Our main finding is that there is no way to obtain privacy for free--relaxed definitions of differential privacy that reduce the amount of noise needed to improve utility also increase the measured privacy leakage. Current mechanisms for differentially private machine learning rarely offer acceptable utility-privacy trade-offs for complex learning tasks: settings that provide limited accuracy loss provide little effective privacy, and settings that provide strong privacy result in useless models.
Deep Learning for Modulation Recognition: A Survey With a Demonstration

In this paper, we review a variety of deep learning algorithms and models for modulation recognition and classification of wireless communication signals. Specifically, deep learning (DL) has shown overwhelming advantages in computer vision, robotics, and voice recognition. Recently, DL has been proposed to apply to wireless communications for signal detection and classification in order to better learn the active users for electromagnetic spectrum sharing purposes. Therefore, we aim to provide a survey on the most recent techniques which use DL for recognizing and classifying a wireless signal. We focus on the most widely used DL models, emphasize the advantages and limitations, and discuss the challenges as well as future directions. In addition, we also apply a DL algorithm, convolutional neural network (CNN), to demonstrate the feasibility of using CNN to recognize and classify the over-the-air wireless signals using Mathworks DL toolbox with PlutoSDR and Universal Software Radio Peripheral (USRP), respectively.
A blockchain-empowered AAA scheme in the large-scale HetNet

A Large-Scale Heterogeneous Network (LS-HetNet) integrates different networks into one uniform network system to provide seamless one-world network coverage. In LS-HetNet, various devices use different technologies to access heterogeneous networks and generate a large amount of data. For dealing with a large number of access requirements, these data are usually stored in the HetNet Domain Management Server (HDMS) of the current domain, and HDMS uses a centralized Authentication/Authorization/Auditing (AAA) scheme to protect the data. However, this centralized method easily causes the data to be modified or disclosed. To address this issue, we propose a blockchain-empowered AAA scheme for accessing data of LS-HetNet. Firstly, the account address of the blockchain is used as the identity authentication, and the access control permission of data is redesigned and stored on the blockchain, then processes of AAA are redefined. Finally, the experimental model on Ethereum private chain is built, and the results show that the scheme is not only secure but also decentral, without tampering and trustworthiness.
Motion Guided 3D Pose Estimation from Videos

We propose a new loss function, called motion loss, for supervising models for monocular 3D Human pose estimation from videos. It works by comparing the motion pattern of the prediction against ground truth key point trajectories. In computing motion loss, we introduce pairwise motion encoding, a simple yet effective representation for keypoint motion. We design a new graph convolutional network architecture, U-shaped GCN (UGCN). It captures both short-term and long-term motion information to fully leverage the supervision from the motion loss (Codes and models at http://wangjingbo.top/papers/ECCV2020-Video-Pose/MotionLossPage.html ). We experiment training UGCN with the motion loss on two large scale benchmarks: Human3.6M and MPI-INF-3DHP. Our models surpass other state-of-the-art models by a large margin. It also demonstrates strong capacity in producing smooth 3D sequences and recovering keypoint motion.
Dual-Level Representation Enhancement on Characteristic and Context for Image-Text Retrieval

Image-text retrieval is a fundamental and vital task in multi-media retrieval and has received growing attention since it connects heterogeneous data. Previous methods that perform well on image-text retrieval mainly focus on the interaction between image regions and text words. But these approaches lack joint exploration of characteristics and contexts of regions and words, which will cause semantic confusion of similar objects and loss of contextual understanding. To address these issues, a dual-level representation enhancement network (DREN) is proposed to strength the characteristic and contextual representations by innovative block-level and instance-level representation enhancement modules, respectively. The block-level module focuses on mining the potential relations between multiple blocks within each instance representation, while the instance-level module concentrates on learning the contextual relations between different instances. To facilitate the accurate matching of image-text pairs, we propose the graph correlation inference and weighted adaptive filtering to conduct the local and global matching between image-text pairs. Extensive experiments on two challenging datasets (i.e., Flickr30K and MSCOCO) verify the superiority of our method for image-text retrieval.
Structural Neural Encoders for

Marco Damonte, Shay B. Cohen. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Deep learning assisted mitotic counting for breast cancer

As part of routine histological grading, for every invasive breast cancer the mitotic count is assessed by counting mitoses in the (visually selected) region with the highest proliferative activity. Because this procedure is prone to subjectivity, the present study compares visual mitotic counting with deep learning based automated mitotic counting and fully automated hotspot selection. Two cohorts were used in this study. Cohort A comprised 90 prospectively included tumors which were selected based on the mitotic frequency scores given during routine glass slide diagnostics. This pathologist additionally assessed the mitotic count in these tumors in whole slide images (WSI) within a preselected hotspot. A second observer performed the same procedures on this cohort. The preselected hotspot was generated by a convolutional neural network (CNN) trained to detect all mitotic figures in digitized hematoxylin and eosin (H&E) sections. The second cohort comprised a multicenter, retrospective TNBC cohort (n = 298), of which the mitotic count was assessed by three independent observers on glass slides. The same CNN was applied on this cohort and the absolute number of mitotic figures in the hotspot was compared to the averaged mitotic count of the observers. Baseline interobserver agreement for glass slide assessment in cohort A was good (kappa 0.689; 95% CI 0.580-0.799). Using the CNN generated hotspot in WSI, the agreement score increased to 0.814 (95% CI 0.719-0.909). Automated counting by the CNN in comparison with observers counting in the predefined hotspot region yielded an average kappa of 0.724. We conclude that manual mitotic counting is not affected by assessment modality (glass slides, WSI) and that counting mitotic figures in WSI is feasible. Using a predefined hotspot area considerably improves reproducibility. Also, fully automated assessment of mitotic score appears to be feasible without introducing additional bias or variability.
8×8 reconfigurable quantum photonic processor based on silicon nitride waveguides

The development of large-scale optical quantum information processing circuits ground on the stability and reconfigurability enabled by integrated photonics.We demonstrate a reconfigurable 8×8 integrated linear optical network based on silicon nitride waveguides for quantum information processing.Our processor implements a novel optical architecture enabling any arbitrary linear transformation and constitutes the largest programmable circuit reported so far on this platform.We validate a variety of photonic quantum information processing primitives, in the form of Hong-Ou-Mandel interference, bosonic coalescence/anticoalescence and high-dimensional single-photon quantum gates.We achieve fidelities that clearly demonstrate the promising future for large-scale photonic quantum information processing using low-loss silicon nitride.