Reviewerly/exHarmony huggingface dataset:
Attention history-based attention for abstractive text summarization

Recently, encoder-decoder model using attention has shown meaningful results in the abstractive summarization tasks. In the attention mechanism, the attention distribution is generated based only on the current decoder state. However, since there are patterns in the process of writing summaries, patterns will exist even in the process of paying attention. In this work, we propose the attention history-based attention model that considers such patterns of the attention history. We build an additional recurrent network, the attention reader network to model the attention patterns. Also, we employ an accumulation vector that keeps the total amount of effective attention to each part of the input text, which is guided by an additional network named the accumulation network. Both the attention reader network and the accumulation vector are used as the additional inputs to the attention mechanism. The evaluation results on the CNN/Daily Mail dataset show that our method better captures the attention pattern and achieves higher ROUGE scores than strong baselines.
Enhancing Sustainable Customer Dining Experience Through QR Code and Geo-Fencing

The hotel industry is the one of the trendsetters since its inception and has been following the latest technological advancement to keep them growing with the globalizing world. The technology has made a subsequent shift in the industry and has resulted profitably. Today when growth of mobile user is tremendous and mobiles available in market are also very user friendly, this indicates that people are comfortable with mobile technology. In this paper we are discussing a concept of mobile application, which will work with QR code technology and in a coordinated way with Geo-Fencing technology. Which will enhance the guest experience in a farm to table by providing him the detailed farm to table fork information for each and every ingredient (for example; Harvested, Stored, Transported, Prepared and served) on his order. The paper discusses a model based on QR code and Geo-Fencing technique which will help to enhance user experience. The advantage of Geo-Fencing and QR technology in the supply chain of the restaurant starting from the receiving area to the service of the food in high end establishments. The information will be also vital for the guest and regulatory authorities who are conscious about the meals and the safe practices involved therein.
Signed heterogeneous network embedding in social media

In today's social networks users can express positive or negative attitudes towards others, which forms sentiment links in addition to social links. This link heterogeneity builds a new network topology called signed heterogeneous networks. Meanwhile, network embeddings have emerged as convenient methods for conducting machine learning over networked data. Consequently, extending embedding methods for signed heterogeneous networks allows interesting analytical scenarios such as predicting the sign of sentiment links. Previous works mainly focus on heterogeneous network embedding discarding the sign information in the network. In this paper, we propose SiHet, a fast and scalable embedding method suited for Signed Heterogeneous networks. Our method extends classical network embedding algorithms through a new loss function and a sampling strategy which allows to integrate sign and social network information. Empirical evaluations on two real-world datasets demonstrate effectiveness and efficiency of SiHet compared to the state-of-the-art methods.
Hash Based Integrity Verification for Vehicular Cloud Environment

Vehicular Cloud Computing (VCC) facilitates the customers to share resources ranging from storage to computing power to renting it to other users over the internet. VCC technology is combination of VANET and cloud computing. However, this integration of vehicular network and cloud computing introduces new challenges for the research community. For security reasons, users would like to verify the integrity of their data once storing it in the VCC. In this paper, we will introduce a scheme for the integrity checking with the support of the Road Side Unit (RSU) for dynamic vehicular cloud. The hash functions and signatures are employed for integrity checking of messages which are uploaded from the vehicle to the cloud. We have analysed effectiveness of proposed scheme against typical attacks like tampering attack, node impersonation and application attack etc. Extensive simulations are conducted to verify the performance of the proposed scheme and the results are compared with existing research based on ID-based cryptographic scheme. The proposed scheme performs better in terms of computation cost and communication cost.
Domain sentiment dictionary construction and optimization based on multi-source information fusion

Sentiment analysis of text data, such as reviews, can help users and merchants make more favorable decisions. It is difficult to use the popular supervised learning method to complete the sentiment classification task because marking data manually is time-consuming and laborious. Unsupervised senti ment classification methods are mostly based on sentiment lexicons. The existing sentiment lexicons are simply not capable of domain sentiment classification, it still requires to construct a domain sentiment lexicon. There are still many problems with the advanced domain sentiment lexicon construction methods, e.g., rely heavily on labeled data, poor accuracy. We propose a labeled data extension idea to reduce the dependence of supervised learning methods on labeled data. In order to solve the problems of domain sentiment lexicon construction, we proposed a novel framework based on multi-source information fusion (MSIF) for learning. We extracted four kinds of emotional information, which are lexicon emotional information, emotional word co-occurrence information, emotional word polarity information and polarity relationship information of emotional word pair. When extracting the co-occurrence information, a novel method based on the data extension idea is proposed to enhance its accuracy and coverage. In order to accelerate the solution of the fusion model, an optimization method based on the ADMM algorithm is applied. Experimental results on five Amazon product review datasets show that the sentiment dictionary constructed by the proposed method can significantly improve the performance of review sentiment classification compared with the current popular baseline and the state-of-the-art methods.
A decision-theoretic approach to acquire environmental information for improved subsea search performance

Abstract This study addresses subsea search applications where an autonomous underwater vehicle (AUV) is tasked with finding the target density in a given search region within finite time. We assume that AUV is equipped with a side-scan sonar sensor that detects the targets at the sampled location. We consider that sensor performance is dependent on local environmental conditions (e.g., clutter density, sediment type) that vary throughout the search region, and we presume that environmental conditions are unknown or partially known. Due to uncertain and varying environmental conditions, resulting search performance is also uncertain and it varies by location. This paper specifically considers the cases where environmental information can be acquired either by a separate vehicle or by the same vehicle that performs the search task. Our main contribution is to formally derive a decision-theoretic cost function to compute the locations where the environmental information should be acquired so that the performance of the search task can be improved. For the cases where computing the optimal locations to sample the environment is computationally expensive, we offer an approximation approach that yields provable near-optimal paths. We show that our decision-theoretic cost function outperforms the information-maximization approach, which is often employed in similar applications.
The similarity-consensus regularized multi-view learning for dimension reduction

During the last decades, learning a low-dimensional space with discriminative information for dimension reduction (DR) has gained a surge of interest. However, it is not accessible for these DR methods to achieve satisfactory performance when dealing with the features from multiple views. For multi-view learning problems, one instance can be represented by multiple heterogeneous features, which are highly relevant but sometimes look different from each other. In addition, correlations between features from multiple views always vary greatly, which challenges the capability of multi-view learning methods. Consequently, constructing a multi-view learning framework with generalization and scalability, which could take advantage of multi-view information as much as possible, is extremely necessary but challenging. To implement the above target, this paper proposes a novel multi-view learning framework based on similarity consensus, which makes full use of correlations among multi-view features while considering the scalability and robustness of the framework. It aims to straightforwardly extend those existing DR methods into multi-view learning domain by preserving the similarity consensus between different views to capture the low-dimensional embedding. Two schemes based on pairwise-consensus and centroid-consensus are separately proposed to force multiple views to learn from each other, then an iterative alternating strategy is developed to obtain the optimal solution. The proposed method is evaluated on 5 benchmark datasets and comprehensive experiments show that our proposed multi-view framework can yield comparable and promising performance with some famous methods.
Authoring logically sequenced visual data stories with Gravity

Visual data stories have been shown to significantly aid the comprehension and short-term memorability of statistical facts and value messages. Hence, they are a promising medium for communicating complex information to a target audience, fulfilling the communicative goal of information visualisation. However, creating visual data stories often requires a plethora of tools to execute the visual data story creation process – creating visualisations, developing a logical connection between visualisations, and presenting the story based on the meaningfully sequenced visualisations. Current information visualisation tools either focus primarily on exploration or lack sequencing models for visualisation presentation, even though transitioning and sequencing have been shown to affect user understanding and interpretation of visualisations. We present Gravity, a system that consolidates the different phases of the visual data story creation process, and recommends and visualises logically sequenced sets of interactive visualisations to support the presentation of coherent narratives and visual data stories. We also report on an evaluation study with representative participants. Our results show that participants learned to use the system within a short time and successfully created visual data stories with minimal guidance.
Studying Developer Reading Behavior on Stack Overflow during API Summarization Tasks

Stack Overflow is commonly used by software developers to help solve problems they face while working on software tasks such as fixing bugs or building new features. Recent research has explored how the content of Stack Overflow posts affects attraction and how the reputation of users attracts more visitors. However, there is very little evidence on the effect that visual attractors and content quantity have on directing gaze toward parts of a post, and which parts hold the attention of a user longer. Moreover, little is known about how these attractors help developers (students and professionals) answer comprehension questions. This paper presents an eye tracking study on thirty developers constrained to reading only Stack Overflow posts while summarizing four open source methods or classes. Results indicate that on average paragraphs and code snippets were fixated upon most often and longest. When ranking pages by number of appearance of code blocks and paragraphs, we found that while the presence of more code blocks did not affect number of fixations, the presence of increasing numbers of plain text paragraphs significantly drove down the fixations on comments. SO posts that were looked at only by students had longer fixation times on code elements within the first ten fixations. We found that 16 developer summaries contained 5 or more meaningful terms from SO posts they viewed. We discuss how our observations of reading behavior could benefit how users structure their posts.
Automatic tracing and extraction of text‐line and word segments directly in JPEG compressed document images

JPEG is one of the popular and efficient compression algorithms supported in the consumer electronics world. Excessive usage of mobile phones and e-governance applications have all resulted in a huge collection of JPEG compressed document images. The major challenge with these images is that its processing becomes expensive as it requires repeated decompression and recompression operations. Recently, it has been proved that developing algorithms to operate directly on the compressed data is one of the solutions in overcoming the above issue. This research study investigates a novel algorithm for segmentation of text-lines and words directly from JPEG compressed handwritten document images. Segmenting a handwritten document is challenging due to the presence of uneven spacing, variable font sizes, overlapping and touching components, and it becomes much more challenging if it is to be done directly in the compressed image. The proposed technique virtually fixes a vertical stripe at the beginning of the document to detect starting points of text-lines. Then a moving window-based space penetration algorithm is used for tracing the exact line boundary between two text-lines, resolving the issues of space and font variations, touching and overlapping components. Subsequently, a word boundary tracing algorithm is used to segment words.
Fourier and Wavelet Transforms

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Comparing BERT and XLNet from the Perspective of Computational Characteristics

Exploiting attention mechanism, Transformer provides superior performance compared to traditional CNN and RNN models on various NLP (Natural Language Processing) tasks. BERT and XLNet are two popular models utilizing Transformer. In this paper, we compare the computational characteristics of the inference of BERT and XLNet using MPRC (Microsoft Research Paraphrase Corpus), one of the popular language understanding benchmarks. Through evaluation, we observe that the both models exhibit similar computational characteristics except the target-position-aware representation and relative position encoding features of XLNet, leading to a better benchmark score at the cost of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathit{1.2}\times$</tex> arithmetic operations and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathit{1.5}\times$</tex> execution time on a modern CPU.
A Sparse Sampling-Based Framework for Semantic Fast-Forward of First-Person Videos

Technological advances in sensors have paved the way for digital cameras to become increasingly ubiquitous, which, in turn, led to the popularity of the self-recording culture. As a result, the amount of visual data on the Internet is moving in the opposite direction of the available time and patience of the users. Thus, most of the uploaded videos are doomed to be forgotten and unwatched stashed away in some computer folder or website. In this paper, we address the problem of creating smooth fast-forward videos without losing the relevant content. We present a new adaptive frame selection formulated as a weighted minimum reconstruction problem. Using a smoothing frame transition and filling visual gaps between segments, our approach accelerates first-person videos emphasizing the relevant segments and avoids visual discontinuities. Experiments conducted on controlled videos and also on an unconstrained dataset of First-Person Videos (FPVs) show that, when creating fast-forward videos, our method is able to retain as much relevant information and smoothness as the state-of-the-art techniques, but in less processing time.
Sequence-to-sequence models for workload interference prediction on batch processing datacenters

Co-scheduling of jobs in data-centers is a challenging scenario, where jobs can compete for resources yielding to severe slowdowns or failed executions. Efficient job placement on environments where resources are shared requires awareness on how jobs interfere during execution, to go far beyond ineffective resource overbooking techniques. Current techniques, most of them already involving machine learning and job modeling, are based on workload behavior summarization across time, instead of focusing on effective job requirements at each instant of the execution. In this work we propose a methodology for modeling co-scheduling of jobs on data-centers, based on their behavior towards resources and execution time, using sequence-to-sequence models based on recurrent neural networks. The goal is to forecast co-executed jobs footprint on resources along their execution time, from the profile shown by the individual jobs, to enhance resource managers and schedulers placement decisions. The methods here presented are validated using High Performance Computing benchmarks based on different frameworks (like Hadoop and Spark) and applications (CPU bound, IO bound, machine learning, SQL queries...). Experiments show that the model can correctly identify the resource usage trends from previously seen and even unseen co-scheduled jobs.
The Improved Siamese Network in Face Recognition

This paper is an application of Siamese Network in face recognition; the learning process is minimizing the logistic regression loss function that drives the metric to be small for the same persons, and significant for different persons. In practice, we hope that the distinction between the same persons and different persons is as significant as possible. Nevertheless, the standard sigmoid function does not characterize this feature very well. This paper improves the standard sigmoid function for this defect. The hypothesis verified by experiments and has achieved a useful application in engineering. This paper introduces the complete experimental process, analyzes the experimental results, and provides a theoretical explanation for the results.
Semantic similarity to improve question understanding in a virtual patient

In medicine, a communicating virtual patient or doctor allows students to train in medical diagnosis and develop skills to conduct a medical consultation. In this paper, we describe a conversational virtual standardized patient system to allow medical students to simulate a diagnosis strategy of an abdominal surgical emergency. We exploited the semantic properties captured by distributed word representations to search for similar questions in the virtual patient dialogue system. We created two dialogue systems that were evaluated on datasets collected during tests with students. The first system based on hand-crafted rules obtains 92.29% as F1-score on the studied clinical case while the second system that combines rules and semantic similarity achieves 94.88%. It represents an error reduction of 9, 70% as compared to the rules-only-based system.
Challenges of Cloud Computing in Warehousing Operations with Respect to Chennai Port Trust

Cloud computing plays an important role in data storage and computing power. The term cloud computing is used to be a data centres to users through internet. Cloud based applications are being connected with the user and to maintain all information is stored and is quickly accessible online in 24/7 format, apart from this various kinds of tools like desktops, laptops, smart phones and tablets. Cloud computing carries warehouse management system software it just come into existence. This study was conducted with warehouse operators in Chennai port trust. Survey method with structured questionnaire was developed and deployed based on the literature study and the data collected were analyzed quantitatively. Convenience sampling techniques were adopted. A warehouse management system may be an info driven pc application, to enhance the potency of the warehouse and to keep up correct inventory by recording warehouse transactions. This article presents the challenges of cloud in warehouse and its various operations and also to discuss to improve operational efficiency of warehouses.
Automatically Extracting Subroutine Summary Descriptions from Unstructured Comments

Summary descriptions of subroutines are short (usually one-sentence) natural language explanations of a subroutine's behavior and purpose in a program. These summaries are ubiquitous in documentation, and many tools such as JavaDocs and Doxygen generate documentation built around them. And yet, extracting summaries from unstructured source code repositories remains a difficult research problem - it is very difficult to generate clean structured documentation unless the summaries are annotated by programmers. This becomes a problem in large repositories of legacy code, since it is cost prohibitive to retroactively annotate summaries in dozens or hundreds of old programs. Likewise, it is a problem for creators of automatic documentation generation algorithms, since these algorithms usually must learn from large annotated datasets, which do not exist for many programming languages. In this paper, we present a semi-automated approach via crowdsourcing and a fully-automated approach for annotating summaries from unstructured code comments. We present experiments validating the approaches, and provide recommendations and cost estimates for automatically annotating large repositories.
Trending Topic Classification for Single-Label Using Multinomial Naive Bayes (MNB) and Multi-Label Using K-Nearest Neighbors (KNN)

Trending Topic is one of the features found of Twitter in a short text. However, the short text used as a trending topic on Twitter sometimes confuses its users, so they need to be classified into several labels, but one tweet can have more than one label called multilable. The lables are politics, sports, entertainment, tourism, business, and other news. Another problem is the multi-labeling of classifications. Single-label will classify a trending topic into one label, while multi-label classifies into more than one label. This paper aimed to classify Twitter's trending topic using Multinomial Naive Bayes (MNB) for single-label data and K-Nearest Neighbor (KNN) for multi-label data. The steps were to collect trending topic data along with their tweets, labeling and text preprocessing, weighting TF-IDF, single-label classification using MNB and multi-label classification using KNN with the Binary Relevance approach, finally evaluation and analysis of results. By using K=3, the results show that KNN have 88.05% accuracy for multi-label data, whilst, MNB has a good result for single-label data 82.53% accuracy.