Reviewerly/exHarmony huggingface dataset:
Interaction-Aware Factorization Machines for Recommender Systems

Factorization Machine (FM) is a widely used supervised learning approach by effectively modeling of feature interactions. Despite the successful application of FM and its many deep learning variants, treating every feature interaction fairly may degrade the performance. For example, the interactions of a useless feature may introduce noises; the importance of a feature may also differ when interacting with different features. In this work, we propose a novel model named Interaction-aware Factorization Machine (IFM) by introducing Interaction-Aware Mechanism (IAM), which comprises the feature aspect and the field aspect, to learn flexible interactions on two levels. The feature aspect learns feature interaction importance via an attention network while the field aspect learns the feature interaction effect as a parametric similarity of the feature interaction vector and the corresponding field interaction prototype. IFM introduces more structured control and learns feature interaction importance in a stratified manner, which allows for more leverage in tweaking the interactions on both feature-wise and field-wise levels. Besides, we give a more generalized architecture and propose Interaction-aware Neural Network (INN) and DeepIFM to capture higher-order interactions. To further improve both the performance and efficiency of IFM, a sampling scheme is developed to select interactions based on the field aspect importance. The experimental results from two well-known datasets show the superiority of the proposed models over the state-of-the-art methods.
Cross-Domain Facial Expression Recognition Based on Transductive Deep Transfer Learning

In this paper, we proposed a novel end-to-end transductive deep transfer learning network (TDTLN) to deal with the challenging cross-domain expression recognition problem, in which both the source and target databases are utilized to jointly learn optimal nonlinear discriminative features so as to improve the label prediction performance of the target data samples. As part of the network parameters, the labels of the target samples are also optimized when optimizing the parameters of TDTLN, such that the cross-entropy loss of source domain data and the regression loss of target domain data can be simultaneously calculated. Finally, to evaluate the recognition performance of the proposed TDTLN method, we conduct extensive cross-database experiments on four commonly used multi-view facial expression databases, namely the BU-3DEF, Multi-PIE, SFEW, and RAF database. The experimental results show that the proposed TDTLN method outperforms state-of-the-art methods.
Adversarial Transfer for Named Entity Boundary Detection with Pointer Networks

In this paper, we focus on named entity boundary detection, which aims to detect the start and end boundaries of an entity mention in text, without predicting its type. A more accurate and robust detection approach is desired to alleviate error propagation in downstream applications, such as entity linking and fine-grained typing systems. Here, we first develop a novel entity boundary labeling approach with pointer networks, where the output dictionary size depends on the input, which is variable. Furthermore, we propose AT-Bdry, which incorporates adversarial transfer learning into an end-to-end sequence labeling model to encourage domain-invariant representations. More importantly, AT-Bdry can reduce domain difference in data distributions between the source and target domains, via an unsupervised transfer learning approach (i.e., no annotated target-domain data is necessary). We conduct Formal Text to Formal Text, Formal Text to Informal Text and ablation evaluations on five benchmark datasets. Experimental results show that AT-Bdry achieves state-of-the-art transferring performance against recent baselines.
CulturalERICA: A conversational agent improving the exploration of European cultural heritage

In the past decade, public institutions and private entities have launched large campaigns of digitization of cultural artefacts leading to the creation of massive digital collections like Europeana, Europe's digital cultural library, museum, and archive. It offers public access to millions of digital objects from thousands of contributing heritage collections all across the European Union, using a multilingual interface, built upon the principles of the Semantic Web. However, such large digital libraries suffer due to low accessibility for the general public and difficult search and navigation through their items. Intelligent conversational agents have the potential of facilitating user-friendly access to the vast amount of information in the Semantic Web by natural language interaction and by providing structured answers to the user's queries. More recently, new technologies additionally fostered the development of intelligent conversational agents for a wide range of applications. Following this approach, we propose a solution to improve the accessibility and search accuracy of digital cultural heritage resources from Europeana through a system which integrates artificial intelligence, natural language technologies, web services, and APIs.
Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings

Chemical patents are an important resource for chemical information.However, few chemical Named Entity Recognition (NER) systems have been evaluated on patent documents, due in part to their structural and linguistic complexity.In this paper, we explore the NER performance of a BiLSTM-CRF model utilising pre-trained word embeddings, characterlevel word representations and contextualized ELMo word representations for chemical patents.We compare word embeddings pre-trained on biomedical and chemical patent corpora.The effect of tokenizers optimized for the chemical domain on NER performance in chemical patents is also explored.The results on two patent corpora show that contextualized word representations generated from ELMo substantially improve chemical NER performance w.r.t. the current state-of-the-art.We also show that domain-specific resources such as word embeddings trained on chemical patents and chemical-specific tokenizers have a positive impact on NER performance.
How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing

Under special circumstances, summaries should conform to a particular style with patterns, such as court judgments and abstracts in academic papers.To this end, the prototype document-summary pairs can be utilized to generate better summaries.There are two main challenges in this task: (1) the model needs to incorporate learned patterns from the prototype, but (2) should avoid copying contents other than the patternized wordssuch as irrelevant facts-into the generated summaries.To tackle these challenges, we design a model named Prototype Editing based Summary Generator (PESG).PESG first learns summary patterns and prototype facts by analyzing the correlation between a prototype document and its summary.Prototype facts are then utilized to help extract facts from the input document.Next, an editing generator generates new summary based on the summary pattern or extracted facts.Finally, to address the second challenge, a fact checker is used to estimate mutual information between the input document and generated summary, providing an additional signal for the generator.Extensive experiments conducted on a large-scale real-world text summarization dataset 1 show that PESG achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations.
Debiasing Embeddings for Reduced Gender Bias in Text Classification

(Bolukbasi et al., 2016) demonstrated that pretrained word embeddings can inherit gender bias from the data they were trained on. We investigate how this bias affects downstream classification tasks, using the case study of occupation classification (De-Arteaga et al., 2019). We show that traditional techniques for debiasing embeddings can actually worsen the bias of the downstream classifier by providing a less noisy channel for communicating gender information. With a relatively minor adjustment, however, we show how these same techniques can be used to simultaneously reduce bias and maintain high classification accuracy.
A consortium blockchain paradigm on hyperledger-based peer-to-peer lending system

This paper focuses on how to use consortium blockchain to improve the regulation of peer-to-peer (P2P) lending market. The partial decentralized consortium blockchain with limited pre-set nodes can well improve transparency and security, which is suitable for financial regulation. Considering irregularities of the P2P lending market, the Hyper-ledger-based Peer-to-Peer Lending System (HyperP2PLS) is proposed. First elaborate the application scenario and business logic of the system, where a national P2P Lending Trading Center will be established to integrate all transactions and information of P2P lending market. Then construct the system architecture consisting of the blockchain network, HTTP server, and applications. The algorithm of implementation process and the web application for users have been well illustrated. The performance analysis shows that HyperP2PLS can guarantee the reliability, safety, transparency and efficiency.
Building certified concurrent OS kernels

Operating system (OS) kernels form the backbone of system software. They can have a significant impact on the resilience and security of today's computers. Recent efforts have demonstrated the feasibility of formally verifying simple general-purpose kernels, but they have ignored the important issues of concurrency, which include not just user and I/O concurrency on a single core, but also multi-core parallelism with fine-grained locking. In this work, we present CertiKOS, a novel compositional framework for building verified concurrent OS kernels. Concurrency allows interleaved execution of programs belonging to different abstraction layers and running on different CPUs/threads. Each such layer can have a different set of observable events. In CertiKOS, these layers and their observable events can be formally specified, and each module can then be verified at the abstraction level it belongs to. To link all the verified pieces together, CertiKOS enforces a so-called contextual refinement property for every such piece, which states that the implementation will behave like its specification under any concurrent context with any valid interleaving. Using CertiKOS, we have successfully developed a practical concurrent OS kernel, called mC2, and built the formal proofs of its correctness in Coq. The mC2 kernel is written in 6500 lines of C and x86 assembly and runs on stock x86 multicore machines. To our knowledge, this is the first correctness proof of a general-purpose concurrent OS kernel with fine-grained locking.
Low-Overhead Remote User Authentication Protocol for IoT Based on a Fuzzy Extractor and Feature Extraction

The Internet of things (IoT) is a system of smart technologies and services that mutually communicate data between users and devices or between devices via the Internet.Since data are shared between a remote user and various sensing devices over a network, it is essential to design a secure, lightweight and efficient remote user authentication protocol for the IoT environment.In the context of security and network privacy, mutual authentication is necessary for securely accessing the services of the IoT environment.However, the IoT faces substantial new challenges realizing mutual authentication due to IoT devices constraints.In this paper, we present a lightweight, robust and secure authentication protocol that satisfies constraints on IoT devices.The proposed protocol is based on level 3 feature extraction, fuzzy extraction of the user's biometrics, one-way hash functions and XOR operations and includes (1) three-factor authentication (user password, biometrics and smart devices), (2) mutual authentication, (3) a session key, and (4) key freshness.Furthermore, we have used the Burrows-Abadi-Needham logic to prove the authentication of our proposed protocol.In addition, our proposed protocol does not require additional hardware or a resource-constrained cryptosystem, and for that reason; hence, it has the lowest computational cost on the IoT nodes (0.003_ms), the lowest total computational cost (0.071_ms), and the lowest communication cost (2784 bits) compared with other relevant works.Moreover, we have conducted an informal security analysis to prove its ability to withstand well-known malicious attacks, such as replay attacks, impersonation attacks, password change attacks, man-in-the-middle (MITM) attacks, and denial of service (DOS) attacks.
A Survey on Personalized PageRank Computation Algorithms

Personalized PageRank (PPR) is an important variation of PageRank, which is a widely applied popularity measure for Web search. Unlike the original PageRank, PPR is a node proximity measure that represents the degree of closeness among multiple nodes within a graph. It is also widely applied to diverse domains, such as information retrieval, recommendations, and knowledge discovery, due to its theoretical simplicity and flexibility. However, computing PPR in large graphs using naïve algorithms such as iterative matrix multiplication and matrix inversion is not fast enough for many of these applications. Therefore, devising efficient PPR algorithms has been one of the most important subjects in large-scale graph processing. In this paper, we review the algorithms for efficient PPR computations, organizing them into five categories based on their core ideas. Along with detailed explanations including recent advances and their applications, we provide a multifaceted comparison of the algorithms.
Channel Equalization and Detection With ELM-Based Regressors for OFDM Systems

Extreme learning machine (ELM) is commonly adopted and best known for its extremely fast learning capability and notable performance. In this paper, a multiple split-complex ELM (Multi-SCELM) regressor based equalization and detection method is proposed for OFDM systems. This method combines ELM regressors for equalization and minimum-distance based symbol slicers for symbol detection. Furthermore, the proposed Multi-SCELM is extended to fully complex ELM (CELM) for channel equalization and detection. Simulations demonstrate that compared to existing ELM based methods, the proposed one owns the advantages of lower computational complexity, higher detection accuracy, stronger activation function adaptability, shorter training length and better subchannel number adaptability especially in strong frequency selective channels. Compared to the benchmark MMSE method, the proposed method has minor performance degradation but significant reduction in computational complexity.
Evaluation of augmented reality technology for the design of an evacuation training game

Abstract Building evacuation training systems and training employees in an organization have a vital role in emergency cases in which people need to know what to do exactly. In every building, procedures, rules, and actions are attractively shown on the walls, but most of the people living in that building are not aware of these procedures and do not have any experience what to do in these dangerous situations. In order to be able to apply these procedures properly in an emergency situation, community members should be trained with the state-of-the-art equipment and technologies, but to do so, up-front investment and development of such a system are necessary. In this study, augmented reality (AR) technology was applied to realize a game-based evacuation training system that implements gamification practices. The architectural plans of a university were used to model the floors and the relevant environment. Employees are trained to learn how to reach the nearest exit location in the event of a fire or earthquake, and also, the system provides the shortest path for the evacuation. In addition to these features, our training game has educational animations about the fire, chemical attack, and earthquake events. A mobile application was implemented to train employees working in the building and inform them to know how to escape in an emergency situation. The technology acceptance model and the related questionnaire form were applied, and the response of 36 participants was analyzed. It was demonstrated that AR and relevant tools provide a flexible environment to develop evacuation systems in a university, our mobile application enabled participants to be trained in a realistic environment, and trainees were highly satisfied with the system. Educational animations were also another benefit for the trainees.
Reborn Filters: Pruning Convolutional Neural Networks with Limited Data

Channel pruning is effective in compressing the pretrained CNNs for their deployment on low-end edge devices. Most existing methods independently prune some of the original channels and need the complete original dataset to fix the performance drop after pruning. However, due to commercial protection or data privacy, users may only have access to a tiny portion of training examples, which could be insufficient for the performance recovery. In this paper, for pruning with limited data, we propose to use all original filters to directly develop new compact filters, named reborn filters, so that all useful structure priors in the original filters can be well preserved into the pruned networks, alleviating the performance drop accordingly. During training, reborn filters can be easily implemented via 1×1 convolutional layers and then be fused in the inference stage for acceleration. Based on reborn filters, the proposed channel pruning algorithm shows its effectiveness and superiority on extensive experiments.
Deep Smart Scheduling: A Deep Learning Approach for Automated Big Data Scheduling Over the Cloud

With the widespread adoption of Internet of Thing (IoT) and the exponential growth in the volumes of generated data, cloud providers tend to receive massive waves of demands on their storage and computing resources. To help providers deal with such demands without sacrificing performance, the concept of cloud automation had recently arisen to improve the performance and reduce the manual efforts related to the management of cloud computing workloads. In this context, we propose in this paper, Deep learning Smart Scheduling (DSS), an automated big data task scheduling approach in cloud computing environments. DSS combines Deep Reinforcement Learning (DRL) and Long Short-Term Memory (LSTM) to automatically predict the Virtual Machines (VMs) to which each incoming big data task should be scheduled to so as to improve the performance of big data analytics and reduce their resource execution cost. Experiments conducted using real-world datasets from Google Cloud Platform show that our solution minimizes the CPU usage cost by 28.8% compared to the Shortest Job First (SJF), and by 14% compared to both the Round Robin (RR) and improved Particle Swarm Optimization (PSO) approaches. Moreover, our solution decreases the RAM memory usage cost by 31.25% compared to the SJF, by 25% compared to the RR, and by 18.78% compared to the improved PSO.
Geomorphological modelling and mapping of the Peru-Chile Trench by GMT

Abstract The author presents a geospatial analysis of the Peru-Chile Trench located in the South Pacific Ocean by the Generic Mapping Tool (GMT) scripting toolset used to process and model data sets. The study goal is to perform geomorphological modelling by the comparison of two segments of the trench located in northern (Peruvian) and southern (Chilean) parts. The aim of the study is to perform automatic digitizing profiles using GMT and several scripting modules. Orthogonal cross-section profiles transecting the trench in a perpendicular direction were automatically digitized, and the profiles visualized and compared. The profiles show variations in the geomorphology of the trench in the northern and southern segments. To visualize geological and geophysical settings, a set of the thematic maps was visualized by GMT modules: free-air gravity anomaly, geoid, geology and bathymetry. The results of the descriptive statistical analysis of the bathymetry in both segments show that the most frequent depths for the Peruvian segment of the Peru-Chile Trench range from -4,000 to -4,200 (827 recorded samples) versus the range of -4,500 to -4,700 m for the Peruvian segment (1,410 samples). The Peruvian segment of the trench is deeper and its geomorphology steeper with abrupt slopes compared to the Chilean segment. A comparison of the data distribution for both segments gives the following results. The Peruvian segment has the majority of data (23%) reaching 1,410 (-4,500 m to -4,700 m). This peak shows a steep pattern in data distribution, while other data in the neighbouring diapason are significantly lower: 559 (-4,700 m to -5,000 m) and 807 (-4,200 m to -4,400 m). The Chilean segment has more unified data distribution for depths of -6,000 m to -7,000 m. This paper presents GMT workflow for the cartographic automatic modelling and mapping deep-sea trench geomorphology.
CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification

Remote sensing image scene classification has a high application value in the agricultural, military, as well as other fields. A large amount of remote sensing data is obtained every day. After learning the new batch data, scene classification algorithms based on deep learning face the problem of catastrophic forgetting, that is, they cannot maintain the performance of the old batch data. Therefore, it has become more and more important to ensure that the scene classification model has the ability of continual learning, that is, to learn new batch data without forgetting the performance of the old batch data. However, the existing remote sensing image scene classification datasets all use static benchmarks and lack the standard to divide the datasets into a number of sequential learning training batches, which largely limits the development of continual learning in remote sensing image scene classification. First, this study gives the criteria for training batches that have been partitioned into three continual learning scenarios, and proposes a large-scale remote sensing image scene classification database called the Continual Learning Benchmark for Remote Sensing (CLRS). The goal of CLRS is to help develop state-of-the-art continual learning algorithms in the field of remote sensing image scene classification. In addition, in this paper, a new method of constructing a large-scale remote sensing image classification database based on the target detection pretrained model is proposed, which can effectively reduce manual annotations. Finally, several mainstream continual learning methods are tested and analyzed under three continual learning scenarios, and the results can be used as a baseline for future work.