Reviewerly/exHarmony huggingface dataset:
User-as-Graph: User Modeling with Heterogeneous Graph Pooling for News Recommendation

Accurate user modeling is critical for news recommendation. Existing news recommendation methods usually model users' interest from their behaviors via sequential or attentive models. However, they cannot model the rich relatedness between user behaviors, which can provide useful contexts of these behaviors for user interest modeling. In this paper, we propose a novel user modeling approach for news recommendation, which models each user as a personalized heterogeneous graph built from user behaviors to better capture the fine-grained behavior relatedness. In addition, in order to learn user interest embedding from the personalized heterogeneous graph, we propose a novel heterogeneous graph pooling method, which can summarize both node features and graph topology, and be aware of the varied characteristics of different types of nodes. Experiments on large-scale benchmark dataset show the proposed methods can effectively improve the performance of user modeling for news recommendation.
A novel cross-project software defect prediction algorithm based on transfer learning

Software Defect Prediction (SDP) technology is an effective tool for improving software system quality that has attracted much attention in recent years. However, the prediction of cross-project data remains a challenge for the traditional SDP method due to the different distributions of the training and testing datasets. Another major difficulty is the class imbalance issue that must be addressed in Cross-Project Defect Prediction (CPDP). In this work, we propose a transfer-leaning algorithm (TSboostDF) that considers both knowledge transfer and class imbalance for CPDP. The experimental results demonstrate that the performance achieved by TSboostDF is better than those of existing CPDP methods.
Evaluation of extreme weather impacts on utility-scale photovoltaic plant performance in the United States

The global energy system is undergoing significant changes, including a shift in energy generating technologies to more renewable energy sources. However, the dependence of renewable energy sources on local environmental conditions could also increase disruptions in service through exposures to compound, extreme weather events. By fusing three diverse datasets (operations and maintenance tickets, weather data, and production data), this analysis presents a novel methodology to identify and evaluate performance impacts arising from extreme weather events across diverse geographical regions. Text analysis of maintenance tickets identified snow, hurricanes, and storms as the leading extreme weather events affecting photovoltaic plants in the United States. Statistical techniques and machine learning were then implemented to identify the magnitude and variability of these extreme weather impacts on site performance. Impacts varied between event and non-event days, with snow events causing the greatest reductions in performance (54.5%), followed by hurricanes (12.6%) and storms (1.1%). Machine learning analysis identified key features in determining if a day is categorized as low performing, such as low irradiance, geographic location, weather features, and site size. This analysis improves our understanding of compound, extreme weather event impacts on photovoltaic systems. These insights can inform planning activities, especially as renewable energy continues to expand into new geographic and climatic regions around the world.
Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification

Fine-grained classification involves dealing with datasets with larger number of classes with subtle differences between them.Guiding the model to focus on differentiating dimensions between these commonly confusable classes is key to improving performance on fine-grained tasks.In this work, we analyse the contrastive fine-tuning of pre-trained language models on two fine-grained text classification tasks, emotion classification and sentiment analysis.We adaptively embed class relationships into a contrastive objective function to help differently weigh the positives and negatives, and in particular, weighting closely confusable negatives more than less similar negative examples.We find that Label-aware Contrastive Loss outperforms previous contrastive methods, in the presence of larger number and/or more confusable classes, and helps models to produce output distributions that are more differentiated.
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks

Increasing concerns and regulations about data privacy and sparsity necessitate the study of privacy-preserving, decentralized learning methods for natural language processing (NLP) tasks.Federated learning (FL) provides promising approaches for a large number of clients (e.g., personal devices or organizations) to collaboratively learn a shared global model to benefit all clients while allowing users to keep their data locally.Despite interest in studying FL methods for NLP tasks, a systematic comparison and analysis is lacking in the literature.Herein, we present the FedNLP 1 , a benchmarking framework for evaluating federated learning methods on four common formulations of NLP tasks: text classification, sequence tagging, question answering, and seq2seq generation.We propose a universal interface between Transformerbased language models (e.g., BERT, BART) and FL methods under various non-IID partitioning strategies.Our extensive experiments with FedNLP provide empirical comparisons between FL methods and help us better understand the inherent challenges of this direction.The comprehensive analysis points to intriguing and exciting future research aimed at developing FL methods for NLP tasks.
Quantum minimal surfaces from quantum error correction

We show that complementary state-specific reconstruction of logical (bulk) operators is equivalent to the existence of a quantum minimal surface prescription for physical (boundary) entropies. This significantly generalizes both sides of an equivalence previously shown by Harlow; in particular, we do not require the entanglement wedge to be the same for all states in the code space. In developing this theorem, we construct an emergent bulk geometry for general quantum codes, defining "areas" associated to arbitrary logical subsystems, and argue that this definition is "functionally unique." We also formalize a definition of bulk reconstruction that we call "state-specific product unitary" reconstruction. This definition captures the quantum error correction (QEC) properties present in holographic codes and has potential independent interest as a very broad generalization of QEC; it includes most traditional versions of QEC as special cases. Our results extend to approximate codes, and even to the "non-isometric codes" that seem to describe the interior of a black hole at late times.
A Modified HSIFT Descriptor for Medical Image Classification of Anatomy Objects

Modeling low level features to high level semantics in medical imaging is an important aspect in filtering anatomy objects. Bag of Visual Words (BOVW) representations have been proven effective to model these low level features to mid level representations. Convolutional neural nets are learning systems that can automatically extract high-quality representations from raw images. However, their deployment in the medical field is still a bit challenging due to the lack of training data. In this paper, learned features that are obtained by training convolutional neural networks are compared with our proposed hand-crafted HSIFT features. The HSIFT feature is a symmetric fusion of a Harris corner detector and the Scale Invariance Transform process (SIFT) with BOVW representation. The SIFT process is enhanced as well as the classification technique by adopting bagging with a surrogate split method. Quantitative evaluation shows that our proposed hand-crafted HSIFT feature outperforms the learned features from convolutional neural networks in discriminating anatomy image classes.
Analyzing the performance of variational quantum factoring on a superconducting quantum processor

Abstract In the near-term, hybrid quantum-classical algorithms hold great potential for outperforming classical approaches. Understanding how these two computing paradigms work in tandem is critical for identifying areas where such hybrid algorithms could provide a quantum advantage. In this work, we study a QAOA-based quantum optimization approach by implementing the Variational Quantum Factoring (VQF) algorithm. We execute experimental demonstrations using a superconducting quantum processor, and investigate the trade off between quantum resources (number of qubits and circuit depth) and the probability that a given biprime is successfully factored. In our experiments, the integers 1099551473989, 3127, and 6557 are factored with 3, 4, and 5 qubits, respectively, using a QAOA ansatz with up to 8 layers and we are able to identify the optimal number of circuit layers for a given instance to maximize success probability. Furthermore, we demonstrate the impact of different noise sources on the performance of QAOA, and reveal the coherent error caused by the residual Z Z -coupling between qubits as a dominant source of error in a near-term superconducting quantum processor.
Optimization of STP Innovation Management Mechanisms Driven by Advanced Evolutionary IoT Arithmetic

Since industrialization, manufacturing has been an important pillar of a country's economic development. Under the dual pressure of the new trend of global manufacturing development and the loss of competitive advantage of manufacturing industry, it is especially important to accelerate the enhancement of national high technology innovation capacity and the optimization of high technology policy innovation management mechanism driven by advanced evolutionary Internet of Things (IoT) arithmetic. The main of this paper thus introduces the effective method of optimization of high technology policy innovation management mechanism driven by advanced evolutionary IoT arithmetic. To study the optimization of high technology policy innovation management mechanism, a conceptual analysis of currently popular information technologies, such as big data technologies, artificial intelligence technologies, and Internet of Things technologies, and an overview of the application of these technologies in microgrids are given. In the paper, all factors are studied using the STP innovation management mechanism-based approach, and finally, all factors are classified into two categories of cause and effect factors by this approach, and the importance of all factors is ranked. Secondly, a wind power prediction algorithm based on data mining technology and an improved algorithm and a PV power prediction algorithm based on a deep neural network were established with the technical support of high-tech information technology such as big data and artificial intelligence. Finally, the majorization of high technology policy innovation management mechanism driven by advanced evolutionary IoT arithmetic is proposed.
Introduction to the Special Issue on First-Person Methods in HCI

In this introduction to the special issue on First-Person Methods in (Human-Computer Interaction) HCI, we present a brief overview of first-person methods, their origin, and their use in Human-Computer Interaction. We also detail the difference between first-person methods, second-person, and third-person methods, as a way to guide the reader when engaging the special issue articles. We articulate our motivation for putting together this special issue: we wanted a collection of works that would allow HCI researchers to develop further, define, and outline practices, techniques and implications of first-person methods. We trace links between the articles in this special issue and conclude with questions and directions for future work in this methodological space: working with boundaries, risk, and accountability.
Quantum Filter Diagonalization with Compressed Double-Factorized Hamiltonians

We demonstrate a method that merges the quantum filter diagonalization (QFD) approach for hybrid quantum-classical solution of the time-independent electronic Schrödinger equation with a low-rank double factorization (DF) approach for the representation of the electronic Hamiltonian. In particular, we explore the use of a novel sparse "compressed" double factorization (C-DF) truncation of the Hamiltonian within the time-propagation elements of QFD, while retaining a similarly compressed but numerically converged double-factorized representation of the Hamiltonian for the operator expectation values needed in the QFD quantum matrix elements. The new C-DF method is found to provide substantial additional compression at any given accuracy metric over the traditional "explicit" double factorization approach. Together with significant circuit reduction optimizations and number-preserving postselection and echo-sequencing error mitigation strategies, the method is found to provide accurate predictions for low-lying eigenspectra in a number of representative molecular systems, while requiring reasonably short circuit depths and modest measurement costs. The method is demonstrated by experiments on noise-free simulators, simulations including models of decoherence and shot-noise, and real quantum hardware.12 MoreReceived 7 May 2021Revised 8 September 2021Accepted 3 November 2021DOI:https://doi.org/10.1103/PRXQuantum.2.040352Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsQuantum computationTechniquesQuantum chemistry methodsQuantum Information
A tutorial on optimal control and reinforcement learning methods for quantum technologies

Quantum Optimal Control is an established field of research which is necessary for the development of Quantum Technologies. In recent years, Machine Learning techniques have been proved usefull to tackle a variety of quantum problems. In particular, Reinforcement Learning has been employed to address typical problems of control of quantum systems. In this tutorial we introduce the methods of Quantum Optimal Control and Reinforcement Learning by applying them to the problem of three-level population transfer. The jupyter notebooks to reproduce some of our results are open-sourced and available on github.
A Binary Multi-Objective Chimp Optimizer With Dual Archive for Feature Selection in the Healthcare Domain

Medical datasets frequently include vast feature sets with numerous features that are related to one another. As a result, the curse of dimensionality affects learning from a medical dataset to discover significant characteristics, making it necessary to minimize the feature set. Feature selection (FS) is a major step in classification and also in reducing the dimension. This study attempts a novel Binary Multi-objective Chimp Optimization Algorithm (BMOChOA) with dual archive and k-nearest neighbors (KNN) classifier for mining relevant aspects from medical data. In this research, 12 versions of BMOChOA are implemented based on the group information and types of chaotic functions used. The best Pareto front obtained from suggested BMOChOA variations is compared with three benchmark multi-objective FS methods by taking 14 popular medical datasets of variable dimensions. By analyzing the experimental outputs using four multi-objective performance evaluators, it is found that the proposed FS method is superior in finding the best trade-off between the two objective functions: the number of features and classification performance.
Sign Language Gloss Translation using Deep Learning Models

Converting sign language to a form of natural language is one of the recent areas of the machine learning domain.Many research efforts have focused on categorizing sign language into gesture or facial recognition.However, these efforts ignore the linguistic structure and the context of natural sentences.Traditional translation methods have low translation quality, poor scalability of their underlying models, and are timeconsuming.The contribution of this paper is twofold.First, it proposes a deep learning approach for bidirectional translation using GRU and LSTM.In each of the proposed models, Bahdanau and Luong's attention mechanisms are used.Second, the paper experiments proposed models on two sign languages corpora: namely, ASLG-PC12 and Phoenix-2014T.The experiment conducted on 16 models reveals that the proposed model outperforms the other previous work on the same corpus.The results on the ASLG-12 corpus, when translating from text to gloss, reveal that the GRU model with Bahdanau attention gives the best result with ROUGE (Recall-Oriented Understudy for Gisting Evaluation) score 94.37% and BLEU (Bilingual Evaluation Understudy)-4 score 83.98%.When translating from gloss to text, the results also show that the GRU model with Bahdanau attention achieves the best result with ROUGE score 87.31% and BLEU-4 66.59%.On Phoenix-2014T corpus, the results of text to gloss translation show that the GRU model with Bahdanau attention gives the best result in ROUGE with a score of 42.96%, while the GRU model with Luong attention gives the best result in BLEU-4 with 10.53%.When translating from gloss to text, the results report that the GRU model with Luong attention achieves the best result in ROUGE with a score of 45.69% and BLEU-4 with a score of 19.56%.
Data Integration Challenges for Machine Learning in Precision Medicine

A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.
Deep Learning Approaches for Predicting Glaucoma Progression Using Electronic Health Records and Natural Language Processing

Advances in artificial intelligence have produced a few predictive models in glaucoma, including a logistic regression model predicting glaucoma progression to surgery. However, uncertainty exists regarding how to integrate the wealth of information in free-text clinical notes. The purpose of this study was to predict glaucoma progression requiring surgery using deep learning (DL) approaches on data from electronic health records (EHRs), including features from structured clinical data and from natural language processing of clinical free-text notes.Development of DL predictive model in an observational cohort.Adult patients with glaucoma at a single center treated from 2008 through 2020.Ophthalmology clinical notes of patients with glaucoma were identified from EHRs. Available structured data included patient demographic information, diagnosis codes, prior surgeries, and clinical information including intraocular pressure, visual acuity, and central corneal thickness. In addition, words from patients' first 120 days of notes were mapped to ophthalmology domain-specific neural word embeddings trained on PubMed ophthalmology abstracts. Word embeddings and structured clinical data were used as inputs to DL models to predict subsequent glaucoma surgery.Evaluation metrics included area under the receiver operating characteristic curve (AUC) and F1 score, the harmonic mean of positive predictive value, and sensitivity on a held-out test set.Seven hundred forty-eight of 4512 patients with glaucoma underwent surgery. The model that incorporated both structured clinical features as well as input features from clinical notes achieved an AUC of 73% and F1 of 40%, compared with only structured clinical features, (AUC, 66%; F1, 34%) and only clinical free-text features (AUC, 70%; F1, 42%). All models outperformed predictions from a glaucoma specialist's review of clinical notes (F1, 29.5%).We can successfully predict which patients with glaucoma will need surgery using DL models on EHRs unstructured text. Models incorporating free-text data outperformed those using only structured inputs. Future predictive models using EHRs should make use of information from within clinical free-text notes to improve predictive performance. Additional research is needed to investigate optimal methods of incorporating imaging data into future predictive models as well.
Indicator Minerals, Pathfinder Elements, and Portable Analytical Instruments in Mineral Exploration Studies

Until recently, the classic approach to mineral exploration studies was to bring the field samples/drill cores collected during field studies to the laboratory, followed by laborious analysis procedures to generate the analytical data. This is very expensive, time-consuming, and difficult for exploring vast areas. However, rapid technological advances in field-portable analytical instruments, such as portable visible and near-infrared spectrophotometers, gamma-ray spectrometer, pXRF, pXRD, pLIBS, and µRaman spectrometer, have changed this scenario completely and increased their on-site applications in mineral exploration studies. LED fluorimeter is a potential portable tool in the hydrogeochemical prospecting studies of uranium. These instruments are currently providing direct, rapid, on-site, real-time, non-destructive, cost-effective identification, and determination of target elements, indicator minerals and pathfinder elements in rock, ore, soil, sediment, and water samples. These portable analytical instruments are currently helping to obtain accurate chemical and mineralogical information directly in the field with minimal or no sample preparation and providing decision-making support during fieldwork, as well as during drilling operations in several successful mineral exploration programs. In this article, the developments in these portable devices, and their contributions in the platinum group elements (PGE), rare earth elements (REE), gold, base metals, and lithium exploration studies both on land and on the ocean bed, have been summarized with examples.