Reviewerly/exHarmony huggingface dataset:
Predicting Customer Value with Social Relationships via Motif-based Graph Attention Networks

Customer value is essential for successful customer relationship management. Although growing evidence suggests that customers’ purchase decisions can be influenced by social relationships, social influence is largely overlooked in previous research. In this work, we fill this gap with a novel framework — Motif-based Multi-view Graph Attention Networks with Gated Fusion (MAG), which jointly considers customer demographics, past behaviors, and social network structures. Specifically, (1) to make the best use of higher-order information in complex social networks, we design a motif-based multi-view graph attention module, which explicitly captures different higher-order structures, along with the attention mechanism auto-assigning high weights to informative ones. (2) To model the complex effects of customer attributes and social influence, we propose a gated fusion module with two gates: one depicts the susceptibility to social influence and the other depicts the dependency of the two factors. Extensive experiments on two large-scale datasets show superior performance of our model over the state-of-the-art baselines. Further, we discover that the increase of motifs does not guarantee better performances and identify how motifs play different roles. These findings shed light on how to understand socio-economic relationships among customers and find high-value customers.
Semi-Open Information Extraction

Open Information Extraction (OIE), the task aimed at discovering all textual facts organized in the form of (subject, predicate, object) found within a sentence, has gained much attention recently. However, in some knowledge-driven applications such as question answering, we often have a target entity and hope to obtain its structured factual knowledge for better understanding, instead of extracting all possible facts aimlessly from the corpus. In this paper, we define a new task, namely Semi-Open Information Extraction (SOIE), to address this need. The goal of SOIE is to discover domain-independent facts towards a particular entity from general and diverse web text. To facilitate research on this new task, we propose a large-scale human-annotated benchmark called SOIED, consisting of 61,984 facts for 8,013 subject entities annotated on 24,000 Chinese sentences collected from the web search engine.
EXTRA: Explanation Ranking Datasets for Explainable Recommendation

Recently, research on explainable recommender systems has drawn much attention from both academia and industry, resulting in a variety of explainable models. As a consequence, their evaluation approaches vary from model to model, which makes it quite difficult to compare the explainability of different models. To achieve a standard way of evaluating recommendation explanations, we provide three benchmark datasets for EXplanaTion RAnking (denoted as EXTRA), on which explainability can be measured by ranking-oriented metrics. Constructing such datasets, however, poses great challenges. First, user-item-explanation triplet interactions are rare in existing recommender systems, so how to find alternatives becomes a challenge. Our solution is to identify nearly identical sentences from user reviews. This idea then leads to the second challenge, i.e., how to efficiently categorize the sentences in a dataset into different groups, since it has quadratic runtime complexity to estimate the similarity between any two sentences. To mitigate this issue, we provide a more efficient method based on Locality Sensitive Hashing (LSH) that can detect near-duplicates in sub-linear time for a given query. Moreover, we make our code publicly available to allow researchers in the community to create their own datasets.
Named Entity Recognition and Relation Extraction with Graph Neural Networks in Semi Structured Documents

The use of administrative documents to communicate and leave record of business information requires of methods able to automatically extract and understand the content from such documents in a robust and efficient way. In addition, the semi-structured nature of these reports is specially suited for the use of graph-based representations which are flexible enough to adapt to the deformations from the different document templates. Moreover, Graph Neural Networks provide the proper methodology to learn relations among the data elements in these documents. In this work we study the use of Graph Neural Network architectures to tackle the problem of entity recognition and relation extraction in semi-structured documents. Our approach achieves state of the art results in the three tasks involved in the process. Additionally, the experimentation with two datasets of different nature demonstrates the good generalization ability of our approach.
Deep convolutional neural networks for surface coal mines determination from sentinel-2 images

Coal is a principal source of energy and the combustion of coal supplies around one-third of the global electricity generation. Coal mines are also an important source of CH4 emissions, the second most important greenhouse gas. Monitoring CH4 emissions caused by coal mining using earth observation will require the exact location of coal mines. This paper aims to determine surface coal mines from satellite images through deep learning techniques by treating them as a land use/land cover classification task. This is achieved using Convolutional Neural Networks (CNN) that has proven to be capable of complex land use/land cover classification tasks. With a list of known coal mine locations from various countries, a training dataset of "Coal Mine" and "No Coal Mine" image patches is prepared using Sentinel-2 satellite images with 13 spectral bands. Various pre-trained CNN network architectures (VGG, ResNet, DenseNet) are trained and validated with our prepared coal mine dataset of 3500 "Coal Mine" and 3000 "No Coal Mine" image patches. After several experiments with the VGG network combined with transfer learning is found to be an optimal model for this task. Classification accuracy of 98% has been achieved for the validation dataset of the pre-trained VGG architecture. The model produces more than 95% overall accuracy when tested on unseen satellite images from different countries outside the training dataset and evaluated against visual classification.
DR-BFT: A consensus algorithm for blockchain-based multi-layer data integrity framework in dynamic edge computing system

Edge computing, as a part a distributed computing architecture, has become an increasingly popular paradigm. It expands the capacity of cloud by facilitating data from the end devices to be stored and processed at the edge of the network closer to the data instead of delivering it to the cloud. Data integrity is a big concern in edge computing. As a promising solution to data integrity, blockchain is viable to protect the data in edge computing systems. However, most existing consensus algorithms cannot meet the requirements of edge computing in a dynamic network, where the nodes may join or leave the blockchain network dynamically. In this paper, we introduce a two-layer blockchain-based framework to provide data integrity in edge computing, and propose a novel Dynamic Random Byzantine Fault Tolerance (DR-BFT) consensus algorithm. DR-BFT consists of three sub-algorithms, String Consensus, Data Correctness Validation, and Binary Consensus. String consensus tries to reach consensus on the data of end devices or edge servers, and the sub-algorithm is based on an agreement and borrows some ideas from the Phase King Protocol. If the string consensus fails “early termination”, each node will agree on a value from a random primary node and go through data correctness validation sub-algorithm. The system then reaches consensus on the data with binary consensus sub-algorithm which is a variant of Ben-Or and Michael’s Random Consensus. We also propose an improved quorum method to cope with contention and dynamic node leaving/joining. We analyze DR-BFT with regard to consensus correctness, security, and system overhead. DR-BFT satisfies agreement, validity and termination. We conduct experiments through simulations. Experimental results show that the proposed consensus DR-BFT can effectively improve the performance in dynamic edge computing, including communication overhead and consensus latency.
A multi-objective evolutionary algorithm based on length reduction for large-scale instance selection

Instance selection, as an important data pre-processing task, is widely used in supervised classification. Recently, a series of instance selection algorithms with different techniques have been suggested. Among them, evolutionary algorithms (EAs) have shown competitive performance. However, when the size of instance set is large, these EA-based algorithms may face great challenges on search efficiency and computational cost. To this end, in this paper, a multi-objective evolutionary algorithm based on length reduction, termed as LRIS, is proposed for large-scale instance selection, where a length reduction strategy is suggested to recursively shorten the length of each individual in the population, and improve the computational efficiency of LRIS greatly. Specifically, in the proposed length reduction strategy of LRIS, each gene in the individuals has a probability of being deleted, whose probability is obtained according to the importance of the corresponding instance in the instance set and the importance of the corresponding gene in the population. Then, two evolutionary operators (e.g. crossover and mutation) based on the length reduction strategy are developed to generate offspring population from the reduced population. In addition, an individual repairing operator is also designed to repair the length of over-reduced individuals. Experimental results on 12 large-scale data sets have demonstrated the efficiency and the effectiveness of the proposed LRIS in comparison with the state-of-the-art EA-based instance selection algorithms.
From Collaboration to Solitude and Back: Remote Pair Programming During COVID-19

Abstract Along with the increasing popularity of agile software development, software work has become much more social than ever. Contemporary software teams rely on a variety of collaborative practices, such as pair programming, the topic of our study. Many agilists advocated the importance of collocation, face-to-face interaction, and physical artefacts incorporated in the shared workspace, which the COVID-19 pandemic made unavailable; most software companies around the world were forced to send their engineers to work from home. As software projects and teams overnight turned into distributed collaborations, we question what happened to the pair programming practice in the work-from-home mode. This paper reports on a longitudinal study of remote pair programming in two companies. We conducted 38 interviews with 30 engineers from Norway, Sweden, and the USA, and used the results of a survey in one of the case companies. Our study is unique as we collected the data longitudinally in April/May 2020, Sep/Oct 2020, and Jan/Feb 2021. We found that pair programming has decreased and some interviewees report not pairing at all for almost a full year. The experiences of those who paired vary from actively co-editing the code by using special tools to more passively co-reading and discussing the code and solutions by sharing the screen. Finally, we found that the interest in and the use of PP over time, since the first months of the forced work from home to early 2021, has admittedly increased, also as a social practice.
Effects of Avatar player-similarity and player-construction on gaming performance

There are a variety of ways in which the similarity of an avatar to its user has been achieved. In addition to the characteristics of an avatar, the source of the design can also impact the avatars utility. The current study focuses on how an avatar's similarity to the individual user through customization (similar vs. dissimilar) and who is the designer (player-vs other-construction) contribute to presence and performance. Results supported the separable importance of both avatar similarity and playerconstruction of avatars for game performance, as well as virtual presence, perception of performance, and perception of control. Performance was influenced by these design factors even in the absence of social engagement. Overall, the results of this study highlight the different effects that design, construction source and visual appearance have on avatar-player relationships and related performance.
Generating Senses and RoLes: An End-to-End Model for Dependency- and Span-based Semantic Role Labeling

Despite the recent great success of the sequence-to-sequence paradigm in Natural Language Processing, the majority of current studies in Semantic Role Labeling (SRL) still frame the problem as a sequence labeling task. In this paper we go against the flow and propose GSRL (Generating Senses and RoLes), the first sequence-to-sequence model for end-to-end SRL. Our approach benefits from recently-proposed decoder-side pretraining techniques to generate both sense and role labels for all the predicates in an input sentence at once, in an end-to-end fashion. Evaluated on standard gold benchmarks, GSRL achieves state-of-the-art results in both dependency- and span-based English SRL, proving empirically that our simple generation-based model can learn to produce complex predicate-argument structures. Finally, we propose a framework for evaluating the robustness of an SRL model in a variety of synthetic low-resource scenarios which can aid human annotators in the creation of better, more diverse, and more challenging gold datasets. We release GSRL at github.com/SapienzaNLP/gsrl.
Negative sampling strategies for contrastive self-supervised learning of graph representations

Contrastive learning has become a successful approach for learning powerful text and image representations in a self-supervised manner. Contrastive frameworks learn to distinguish between representations coming from augmentations of the same data point (positive pairs) and those of other (negative) examples. Recent studies aim at extending methods from contrastive learning to graph data. In this work, we propose a general framework for learning node representations in a self supervised manner called Graph Constrastive Learning (GraphCL). It learns node embeddings by maximizing the similarity between the nodes representations of two randomly perturbed versions of the same graph. We use graph neural networks to produce two representations of the same node and leverage a contrastive learning loss to maximize agreement between them. We investigate different standard and new negative sampling strategies as well as a comparison without negative sampling approach. We demonstrate that our approach significantly outperforms the state-of-the-art in unsupervised learning on a number of node classification benchmarks in both transductive and inductive learning setups.
Graph Attention Networks for Anti-Spoofing

The cues needed to detect spoofing attacks against automatic speaker verification are often located in specific spectral sub-bands or temporal segments. Previous works show the potential to learn these using either spectral or temporal self-attention mechanisms but not the relationships between neighbouring sub-bands or segments. This paper reports our use of graph attention networks (GATs) to model these relationships and to improve spoofing detection performance. GATs leverage a self-attention mechanism over graph structured data to model the data manifold and the relationships between nodes. Our graph is constructed from representations produced by a ResNet. Nodes in the graph represent information either in specific sub-bands or temporal segments. Experiments performed on the ASVspoof 2019 logical access database show that our GAT-based model with temporal attention outperforms all of our baseline single systems. Furthermore, GAT-based systems are complementary to a set of existing systems. The fusion of GAT-based models with more conventional countermeasures delivers a 47% relative improvement in performance compared to the best performing single GAT system.
Finding Solutions of the Navier‐Stokes Equations through Quantum Computing—Recent Progress, a Generalization, and Next Steps Forward

Abstract Efficient simulation of a quantum system's dynamics is expected to be an important application area for quantum computers as existing classical computers cannot do this. However, quantum systems are not unique in being hard to simulate. For example, classical nonlinear continuum systems and fields are governed by nonlinear partial differential equations whose solution is also hard for classical computers. Solving such equations is essential for many economically important industries/applications such as the aerospace industry, weather‐forecasting, fiber‐optics communication, and plasma magneto‐hydrodynamics. This raises the question: can a quantum computer speed‐up solving these equations? In this Review, a new quantum algorithm is described for solving nonlinear partial differential equations for which the answer is yes. First, a new quantum algorithm is discussed for solving the Navier‐Stokes nonlinear partial differential equations which govern the flow of a viscous fluid. Its construction, verification, and computational cost are described, and it is shown that a significant quantum speed‐up is possible. Its generalization to a quantum algorithm for solving nonlinear partial differential equations is described. The Review closes with a discussion of next steps forward. These new quantum algorithms open up a large new application area for quantum computing with substantial economic impact, including the trillion‐dollar aerospace industry.
Similarity measures for Collaborative Filtering-based Recommender Systems: Review and experimental comparison

Collaborative Filtering (CF) filters the flow of data that can be recommended, by a Recommender System (RS), to a target user according to his taste and his preferences. The target user’s profile is built based on his similarity with other users. For this reason, CF technique is very sensitive to the similarity measure used to quantify the dependency strength between two users (or two items). In this paper we provide an in-depth review on similarity measures used for CF-based RS. For each measure, we outline its fundamental background and we test its performance through an experimental study. Experiments are carried out on three standard datasets (MovieLens100k, MovieLens1M and Jester) and reveal many important conclusions. In fact, results show that ITR and IPWR are the most suitable similarity measures for a user-based RS while AMI is the best choice for an item-based RS. Evaluation metrics show that under the user-based approach, ITR obtains an MAE equal to 0.786 and 0.731 on MovieLens100k and MovieLens1M, respectively. Whereas, IPWR reach an MAE equal to 3.256 on Jester. Also, AMI gets under the item-based approach an MAE equal to 0.745, 0.724 and 3.281 on MovieLens100k, MovieLens1M and Jester, respectively.
The value of human data annotation for machine learning based anomaly detection in environmental systems

Anomaly detection is the process of identifying unexpected data samples in datasets. Automated anomaly detection is either performed using supervised machine learning models, which require a labelled dataset for their calibration, or unsupervised models, which do not require labels. While academic research has produced a vast array of tools and machine learning models for automated anomaly detection, the research community focused on environmental systems still lacks a comparative analysis that is simultaneously comprehensive, objective, and systematic. This knowledge gap is addressed for the first time in this study, where 15 different supervised and unsupervised anomaly detection models are evaluated on 5 different environmental datasets from engineered and natural aquatic systems. To this end, anomaly detection performance, labelling efforts, as well as the impact of model and algorithm tuning are taken into account. As a result, our analysis reveals the relative strengths and weaknesses of the different approaches in an objective manner without bias for any particular paradigm in machine learning. Most importantly, our results show that expert-based data annotation is extremely valuable for anomaly detection based on machine learning.
Modeling Users’ Empowerment in E-Health Systems

In conventional e-health initiatives, customers (patients) are mostly perceived as recipients of medical care, where they play little roles in the process of health decision making. Empowerment in e-health enables individuals to access their health data and consult online with medical staff. The proposed concept is intended to improve the existing e-health theory in relation to patient empowerment. The study suggests a model that expands the role of customers (patient) in three dimensions: as an individual health actor, social health agent, and medical partner. This study is theoretically significant because it explores a comprehensive approach of patient empowerment in e-health systems to achieve best practice customer service, establish long-term customer relationships to improve customer satisfaction, and achieve better health literacy of individuals.
Efficient mining of cross-level high-utility itemsets in taxonomy quantitative databases

In contrast to frequent itemset mining (FIM) algorithms that focus on identifying itemsets with high occurrence frequency, high-utility itemset mining algorithms can reveal the most profitable sets of items in transaction databases. Several algorithms were proposed to perform the task efficiently. Nevertheless, most of them ignore the item categorizations. This useful information is provided in many real-world transaction databases. Previous works, such as CLH-Miner and ML-HUI Miner were proposed to solve this limitation to discover cross-level and multi-level HUIs. However, the CLH-Miner has a long runtime and high memory usage. To address these drawbacks, this study extends tight upper bounds to propose effective pruning strategies. A novel algorithm named FEACP (Fast and Efficient Algorithm for Cross-level high-utility Pattern mining) is introduced, which adopts the proposed strategies to efficiently identify cross-level HUIs in taxonomy-based databases. It can be seen from a thorough performance evaluation that FEACP can identify useful itemsets of different abstraction levels in transaction databases with high efficiency, that is up to 8 times faster than the state-of-the-art algorithm on the tested sparse databases and up to 177 times on the tested dense databases. FEACP reduces memory usage by up to half over the CLH-Miner algorithm.
Experimental critical quantum metrology with the Heisenberg scaling

Abstract Critical quantum metrology, which exploits quantum critical systems as probes to estimate a physical parameter, has gained increasing attention recently. However, the critical quantum metrology with a continuous quantum phase transition (QPT) is experimentally challenging since a continuous QPT only occurs at the thermodynamic limit. Here, we propose an adiabatic scheme on a perturbed Ising spin model with a first-order QPT. By introducing a small transverse magnetic field, we can not only encode an unknown parameter in the ground state but also tune the energy gap to control the evolution time of the adiabatic passage. Moreover, we experimentally implement the critical quantum metrology scheme using nuclear magnetic resonance techniques and show that at the critical point the precision achieves the Heisenberg scaling as 1/ T . As a theoretical proposal and experimental implementation of the adiabatic scheme of critical quantum metrology and its advantages of easy implementation, inherent robustness against decays and tunable energy gap, our adiabatic scheme is promising for exploring potential applications of critical quantum metrology on various physical systems.