Reviewerly/exHarmony huggingface dataset:
Diversifying Search Results using Self-Attention Network

Search results returned by search engines need to be diversified in order to satisfy different information needs of different users. Several supervised learning models have been proposed for diversifying search results in recent years. Most of the existing supervised methods greedily compare each candidate document with the selected document sequence and select the next local optimal document. However, the information utility of each candidate document is not independent with each other, and research has shown that the selection of a candidate document will affect the utilities of other candidate documents. As a result, the local optimal document rankings will not lead to the global optimal rankings. In this paper, we propose a new supervised diversification framework to address this issue. Based on a self-attention encoder-decoder structure, the model can take the whole candidate document sequence as input, and simultaneously leverage both the novelty and the subtopic coverage of the candidate documents. We call this framework Diversity Encoder with Self-Attention (DESA). Comparing with existing supervised methods, this framework can model the interactions between all candidate documents and return their diversification scores based on the whole candidate document sequence. Experimental results show that our proposed framework outperforms existing methods. These results confirm the effectiveness of modeling all the candidate documents for the overall novelty and subtopic coverage globally, instead of comparing every single candidate document with the selected sequence document selection.
COVID-19 and library social media use

Purpose The study aims to explore Greek libraries’ social media presence and library operation and social media use during the coronavirus disease 2019 (COVID-19) lockdown. Design/methodology/approach Primary data were collected via an online questionnaire that was distributed to the Greek libraries. The final sample comprised 189 libraries of all types with the exception of school libraries. Findings Results indicated that Facebook is the most widely used platform, while social media are mainly used for sharing announcements about library operations and for the promotion of events. During the COVID-19 lockdown, libraries responded quickly to the new circumstances by taking many of their services online. However, they did not fully use social media for service provision but rather used social media as a static communication channel. Only a few of the libraries grasped the opportunity to highlight their role in the promotion of public health by providing timely and reliable information. Practical implications Library leaders who are looking to harness the power of social media for service promotion and outreach should build a strategy that takes platform popularity, current social media trends, patron preferences and the specific promotional objectives of their library into consideration. Originality/value To the best of the authors’ knowledge, the current research is the first which attempts to explore the social media presence of Greek libraries of all types and changes made to library operations and social media use in response to the COVID-19 lockdown.
Universal Graph Transformer Self-Attention Networks

We introduce a transformer-based GNN model, named UGformer, to learn graph representations. In particular, we present two UGformer variants, wherein the first variant (publicized in September 2019) is to leverage the transformer on a set of sampled neighbors for each input node, while the second (publicized in May 2021) is to leverage the transformer on all input nodes. Experimental results demonstrate that the first UGformer variant achieves state-of-the-art accuracies on benchmark datasets for graph classification in both inductive setting and unsupervised transductive setting; and the second UGformer variant obtains state-of-the-art accuracies for inductive text classification. The code is available at: https://github.com/daiquocnguyen/Graph-Transformer.
Eliciting Attribute-Level User Needs From Online Reviews With Deep Language Models and Information Extraction

Abstract Eliciting user needs for individual components and features of a product or a service on a large scale is a key requirement for innovative design. Synthesizing data as an initial discovery phase of a design process is usually accomplished with a small number of participants, employing qualitative research methods such as observations, focus groups, and interviews. This leaves an entire swath of pertinent user behavior, preferences, and opinions not captured. Sentiment analysis is a key enabler for large-scale need finding from online user reviews generated on a regular basis. A major limitation of current sentiment analysis approaches used in design sciences, however, is the need for laborious labeling and annotation of large review datasets for training, which in turn hinders their scalability and transferability across different domains. This article proposes an efficient and scalable methodology for automated and large-scale elicitation of attribute-level user needs. The methodology builds on the state-of-the-art pretrained deep language model, BERT (Bidirectional Encoder Representations from Transformers), with new convolutional net and named entity recognition (NER) layers for extracting attribute, description, and sentiment words from online user review corpora. The machine translation algorithm BLEU (BiLingual Evaluation Understudy) is utilized to extract need expressions in the form of predefined part-of-speech combinations (e.g., adjective–noun, verb–noun). Numerical experiments are conducted on a large dataset scraped from a major e-commerce retail store for apparel and footwear to demonstrate the performance, feasibility, and potentials of the developed methodology.
Object Recognition at Night Scene Based on DCGAN and Faster R-CNN

In the past few years, with the rapid development of computer execution capabilities, target recognition strategies based on convolutional neural networks have become mainstream algorithms in the field of object detection. However, due to the blurred background and dim light, the object detection task in the night environment still faces greater visual challenges. This article is strongly inspired by DCGAN (Deep Convolution Generative Adversarial Networks). We use night images as input, generate virtual target scenes similar to the daytime environment through game training of generators and discriminators; and to obtain high-precision detection results, we combine the currently very advanced Faster R-CNN (Region-based Convolution Neural Networks) target detection system, through deep convolution feature fusion and multi-scale ROI (Region Of Interest) pooling. A series of experimental results show that our method achieves an mAP of 82.6% in the detection of its own night scene dataset, which is significantly higher than the original Faster R-CNN alone of 80.4%. Therefore, our method can meet the actual needs of target detection tasks in night scene. We sincerely hope that our approach will contribute to future research.
DeepHistReg: Unsupervised Deep Learning Registration Framework for Differently Stained Histology Samples

The use of several stains during histology sample preparation can be useful for fusing complementary information about different tissue structures. It reveals distinct tissue properties that combined may be useful for grading, classification, or 3-D reconstruction. Nevertheless, since the slide preparation is different for each stain and the procedure uses consecutive slices, the tissue undergoes complex and possibly large deformations. Therefore, a nonrigid registration is required before further processing. The nonrigid registration of differently stained histology images is a challenging task because: (i) the registration must be fully automatic, (ii) the histology images are extremely high-resolution, (iii) the registration should be as fast as possible, (iv) there are significant differences in the tissue appearance, and (v) there are not many unique features due to a repetitive texture. In this article, we propose a deep learning-based solution to the histology registration. We describe a registration framework dedicated to high-resolution histology images that can perform the registration in real-time. The framework consists of an automatic background segmentation, iterative initial rotation search and learning-based affine/nonrigid registration. We evaluate our approach using an open dataset provided for the Automatic Non-rigid Histological Image Registration (ANHIR) challenge organized jointly with the IEEE ISBI 2019 conference. We compare our solution to the challenge participants using a server-side evaluation tool provided by the challenge organizers. Following the challenge evaluation criteria, we use the target registration error (TRE) as the evaluation metric. Our algorithm provides registration accuracy close to the best scoring teams (median rTRE 0.19% of the image diagonal) while being significantly faster (the average registration time is about 2 seconds). The proposed framework provides results, in terms of the TRE, comparable to the best-performing state-of-the-art methods. However, it is significantly faster, thus potentially more useful in clinical practice where a large number of histology images are being processed. The proposed method is of particular interest to researchers requiring an accurate, real-time, nonrigid registration of high-resolution histology images for whom the processing time of traditional, iterative methods in unacceptable. We provide free access to the software implementation of the method, including training and inference code, as well as pretrained models. Since the ANHIR dataset is open, this makes the results fully and easily reproducible.
Towards Locality-Aware Meta-Learning of Tail Node Embeddings on Networks

Network embedding is an active research area due to the prevalence of network-structured data. While the state of the art often learns high-quality embedding vectors for high-degree nodes with abundant structural connectivity, the quality of the embedding vectors for low-degree or tail nodes is often suboptimal due to their limited structural connectivity. While many real-world networks are long-tailed, to date little effort has been devoted to tail node embedding. In this paper, we formulate the goal of learning tail node embeddings as a few-shot regression problem, given the few links on each tail node. In particular, since each node resides in its own local context, we personalize the regression model for each tail node. To reduce overfitting in the personalization, we propose a locality-aware meta-learning framework, called meta-tail2vec, which learns to learn the regression model for the tail nodes at different localities. Finally, we conduct extensive experiments and demonstrate the promising results of meta-tail2vec. (Supplemental materials including code and data are available at https://github.com/smufang/meta-tail2vec.)
Robotic Path Planning Using Recurrent Neural Networks

Every autonomous vehicle or any other application which requires reaching a destination, path planning is an important task. Once a destination is determined there can be various ways to reach there, but optimum use of resources to reach there is important. Hence path planning is an important part of navigation. There have been many developments over the years to realize the best path using different techniques. Recent developments on Neural Networks made it an important research topic for path planning. Many classifiers, learning algorithms have been tested and experimented for path planning. Deep Neural Networks have recently developed and seems to given better results compared to other machine learning techniques and there is larger space for research as well. CNN (Convolutional Neural Network) under the deep neural networks are observed and regarded to be usually applicable for path planning. But in this study Recurrent Neural Network technique (RNN) is being proposed. This is because RNN uses past inputs for present output which makes it applicable for temporal data. Hence in dynamic obstacle avoidance environment this method can help to predict path quicker than CNN. LSTM (Long Short Term Memory) is the algorithm that is mainly focused in this paper. LSTM is an extended version of RNN and this makes it suitable for congested environment. In this paper LSTM is applied for path planning in environment with obstacles and compared with the results of highly popular A* algorithm.
The Era of Intelligent Recommendation: Editorial on Intelligent Recommendation with Advanced AI and Learning

The articles in this special section address intelligent recommender systems using advanced artificial intelligence (AI) learning applications.
On the Forensic Validity of Approximated Audit Logs

Auditing is an increasingly essential tool for the defense of computing systems, but the unwieldy nature of log data imposes significant burdens on administrators and analysts. To address this issue, a variety of techniques have been proposed for approximating the contents of raw audit logs, facilitating efficient storage and analysis. However, the security value of these approximated logs is difficult to measure—relative to the original log, it is unclear if these techniques retain the forensic evidence needed to effectively investigate threats. Unfortunately, prior work has only investigated this issue anecdotally, demonstrating sufficient evidence is retained for specific attack scenarios.
A statistical approach to signal denoising based on data-driven multiscale representation

We develop a data-driven approach for signal denoising that utilizes variational mode decomposition (VMD) algorithm and Cramer Von Misses (CVM) statistic. In comparison with the classical empirical mode decomposition (EMD), VMD enjoys superior mathematical and theoretical framework that makes it robust to noise and mode mixing. These desirable properties of VMD materialize in segregation of a major part of noise into a few final modes while majority of the signal content is distributed among the earlier ones. To exploit this representation for denoising purpose, we propose to estimate the distribution of noise from the predominantly noisy modes and then use it to detect and reject noise from the remaining modes. The proposed approach first selects the predominantly noisy modes using the CVM measure of statistical distance. Next, CVM statistic is used locally on the remaining modes to test how closely the modes fit the estimated noise distribution; the modes that yield closer fit to the noise distribution are rejected (set to zero). Extensive experiments demonstrate the superiority of the proposed method as compared to the state of the art in signal denoising and underscore its utility in practical applications where noise distribution is not known a priori.
A Comparative Analysis for Machine Learning based Software Defect Prediction Systems

In the Software Engineering concept, the prediction of the software defects plays a vital role in increasing the quality of the software systems, which is one of the most critical and expensive phases of the software development lifecycle. While the use of software systems is increasing in our daily lives, their dependencies and complexities are also increasing, and this results in a suitable environment for defects. Due to the existence of software defects, the software produces incorrect results and behaviors. What is more critical than defects, is finding them before they occur. Therefore detection (and also prediction) of the software defects enables the managers of the software to make an efficient allocation of the resources for the maintenance and testing phases. In the literature, there are different proposals for the prediction of software defects. In this paper, we made a comparative analysis about the machine learning-based software defect prediction systems by comparing 10 learning algorithms like Decision Tree, Naive Bayes, K-Nearest Neighbor, Support Vector Machine, Random Forest, Extra Trees, Adaboost, Gradient Boosting, Bagging, and Multi-Layer Perceptron, on the public datasets CM1, KC1, KC2, JM1, and PC1 from the PROMISE warehouse. The experimental results showed that proposed models result in proper accuracy levels for software defect prediction to increase the quality of the software.
VPSL: Verifiable Privacy-Preserving Data Search for Cloud-Assisted Internet of Things

Cloud-assisted Internet of Things (IoT) is increasingly prevalent used in various fields, such as the healthcare system. While in such a scenario, sensitive data (e.g., personal electronic medical records) can be easily revealed, which incurs potential security challenges. Thus, Symmetric Searchable Encryption (SSE) has been extensively studied due to its capability of supporting efficient search on encrypted data. However, most SSE schemes require the data owner to share the complete key with query users and take malicious cloud servers out of consideration. Seeking to address these limitations, in this article we propose a Verifiable Privacy-preserving data Search scheme with Limited key-disclosure (VPSL) for cloud-assisted Internet of Things. VPSL first designs a trapdoor generation protocol for obtaining a trapdoor with disclosing limited key information and without revealing plaintext query points to others. Then, VPSL provides an efficient result verification and search processing by employing the Merkle hash tree structure and k-means clustering technique, respectively. VPSL is secure against the level-2 attack. Finally, an enhanced VPSL (called VPSL+) resisting the level-3 attack is constructed by introducing the random splitting technique. Empirical experiments demonstrate the accuracy and efficiency of VPSL or VPSL+ using real-world datasets.
A Checkpoint Enabled Scalable Blockchain Architecture for Industrial Internet of Things

Industry 4.0 represents the fourth industrial revolution that will leverage the industrial Internet of Things (IIoT) to introduce adaptive and autonomous systems that can self-heal and self-learn. IIoT aims to promote businesses and industries by realizing intelligent industrialization. However, the constantly surging data volumes that are generated by IIoT environments present security issues like data integrity and system scalability. Blockchain is a promising candidate to address these problems, which offers distributed system design principles. Though blockchain-based IIoT frameworks may have the potential to support the demands and services of next-generation industrial systems, their integration is still constrained by significant challenges in scalability and security. Therefore, blockchain in its original structure with traditional proof-of-work consensus is not suitable for IIoT. To address this, we propose a blockchain architecture that uses a dynamic proof-of-work consensus with a block checkpoint mechanism. The dynamic consensus functions with different mining difficulty levels allow the architecture to efficiently scale with increase in communication traffic of IIoT devices, whereas the checkpoint defines an alternative mechanism to generate the next block hash in the blockchain. To study the scalability and feasibility of the architecture, thorough performance and security analyses are presented, which attest that it can scale and offer enhanced security fidelity with a minimal increase in block mining time.
Learning to Factorize and Relight a City

We propose a learning-based framework for disentangling outdoor scenes into temporally-varying illumination and permanent scene factors. Inspired by the classic intrinsic image decomposition, our learning signal builds upon two insights: 1) combining the disentangled factors should reconstruct the original image, and 2) the permanent factors should stay constant across multiple temporal samples of the same scene. To facilitate training, we assemble a city-scale dataset of outdoor timelapse imagery from Google Street View, where the same locations are captured repeatedly through time. This data represents an unprecedented scale of spatio-temporal outdoor imagery. We show that our learned disentangled factors can be used to manipulate novel images in realistic ways, such as changing lighting effects and scene geometry. Please visit http://factorize-a-city.github.io/ for animated results.
Improving Sentiment Analysis of Arabic Tweets by One-way ANOVA

Social media is an indispensable necessity for modern life. As a result, it is full of people’s opinions, emotions, ideas, and attitudes, whether positive or negative. This abundance of views creates many opportunities for applying sentiment analysis to the education sector, which reflects how countries and cultures develop. In this research, a real-world Twitter dataset was collected, containing approximately 8144 tweets related to Qassim University, Saudi Arabia. The main aim of this experimental study was to explore the possibility of using a one-way analysis of variance (ANOVA) as a feature selection method to considerably reduce the number of features when classifying opinions conveyed through Arabic tweets. The primary motivation for this research was that no previous studies had examined one-way ANOVA comprehensively to tackle the curse of dimensionality and to enhance classification performance in sentiment analysis for Arabic tweets. Therefore, various experiments were conducted to investigate the effects of one-way ANOVA and to select important features concerning the performance of different supervised machine learning classifiers. Support Vector Machine and Naïve Bayes achieved the best results with one-way ANOVA as compared to the baseline experimental results in the collected dataset. Furthermore, the differences between all results have been statistically analyzed in this study. As further evidence, one-way ANOVA with Support Vector Machine represented an excellent combination across different Arabic benchmark datasets, with its results outperforming other studies.
Benchmarking parallelism in FaaS platforms

Serverless computing has seen a myriad of work exploring its potential. Some systems tackle Function-as-a-Service (FaaS) properties on automatic elasticity and scale to run highly-parallel computing jobs. However, they focus on specific platforms and convey that their ideas can be extrapolated to any FaaS runtime. An important question arises: do all FaaS platforms fit parallel computations? In this paper, we argue that not all of them provide the necessary means to host highly-parallel applications. To validate our hypothesis, we create a comparative framework and categorize the architectures of four cloud FaaS offerings, emphasizing parallel performance. We attest and extend this description with an empirical experiment that consists in plotting in deep detail the evolution of a parallel computing job on each service. The analysis of our results evinces that FaaS is not inherently good for parallel computations and architectural differences across platforms are decisive to categorize their performance. A key insight is the importance of virtualization technologies and the scheduling approach of FaaS platforms. Parallelism improves with lighter virtualization and proactive scheduling due to finer resource allocation and faster elasticity. This causes some platforms like AWS and IBM to perform well for highly-parallel computations, while others such as Azure present difficulties to achieve the required parallelism degree. Consequently, the information in this paper becomes of special interest to help users choose the most adequate infrastructure for their parallel applications.