Reviewerly/exHarmony huggingface dataset:
Information Needs, Queries, and Query Performance Prediction

The query performance prediction (QPP) task is to estimate the effectiveness of a search performed in response to a query with no relevance judgments. Existing QPP methods do not account for the effectiveness of a query in representing the underlying information need. We demonstrate the far-reaching implications of this reality using standard TREC-based evaluation of QPP methods: their relative prediction quality patterns vary with respect to the effectiveness of queries used to represent the information needs. Motivated by our findings, we revise the basic probabilistic formulation of the QPP task by accounting for the information need and its connection to the query. We further explore this connection by proposing a novel QPP approach that utilizes information about a set of queries representing the same information need. Predictors instantiated from our approach using a wide variety of existing QPP methods post prediction quality that substantially transcends that of applying these methods, as is standard, using a single query representing the information need. Additional in-depth empirical analysis of different aspects of our approach further attests to the crucial role of query effectiveness in QPP.
Object removal from complex videos using a few annotations

We present a system for the removal of objects from videos. As input, the system only needs a user to draw a few strokes on the first frame, roughly delimiting the objects to be removed. To the best of our knowledge, this is the first system allowing the semi-automatic removal of objects from videos with complex backgrounds. The key steps of our system are the following: after initialization, segmentation masks are first refined and then automatically propagated through the video. Missing regions are then synthesized using video inpainting techniques. Our system can deal with multiple, possibly crossing objects, with complex motions, and with dynamic textures. This results in a computational tool that can alleviate tedious manual operations for editing high-quality videos.
A Visual Measurement of Water Content of Crude Oil Based on Image Grayscale Accumulated Value Difference

In the process of oil exploitation, the water level of an oil well can be predicted and the position of reservoir can be estimated by measuring the water content of crude oil, with reference for the automatic production of high efficiency in the oil field. In this paper, a visual measuring method for water content of crude oil is proposed. The oil and water in crude oil samples were separated by centrifugation, distillation or electric dehydration, and a water-oil layered mixture was formed according to the unequal densities. Then the volume ratio of water and oil was analyzed by digital image processing, and the water content and oil content was able to be calculated. A new method for measuring water content of crude oil based on IGAVD (image grayscale accumulated value difference) is proposed, which overcomes the uncertainty caused by environmental illumination and improves the measurement accuracy. In order to verify the effectiveness of the algorithm, a miniaturization and low-cost system prototype was developed. The experimental results show that the average power consumption is about 165 mW and the measuring error is less than 1.0%. At the same time, the real-time and remote transmission about measurement results can be realized.
Generate adversarial examples by spatially perturbing on the meaningful area

Recently, research on adversarial attack and adversarial defense received more and more attention. Because although deep neural networks (DNNs) outperform humans in many tasks, studies have shown that they are easily fooled by human invisible disturbances. Identifying vulnerabilities in existing networks and further improving the robustness of the network is critical to the application of DNNs in real life. In this paper, a spatial transformed attack method based on the attention mechanism is proposed. The attention mechanism based on gradient-weighted class activation mapping (Grad-CAM) is used to find an meaningful attack area and the spatial transformation is performed on the this area to achieve adversarial attack. The experimental results show that the obtained method can reduce the magnitude of the disturbances needed to achieve the attack while ensuring the effectiveness. The adversarial examples generated by the obtained method shows smaller difference from the original sample under several different metrics.
Sustainable industrial technology for recovery of cellulose from banknote production waste and reprocessing into cellulose nanocrystals

Banknote printing is the industry that has a deep influence on financial operations on market, reflecting the economic performance of governments. Therefore, it is surrounded by a great secrecy even when dealing with production waste and end-of-life products, which are utilized very carefully by controlled combustion or incineration after crushing using special techniques designed to ensure that the waste does not come into possession by third parties. This waste contains significant amounts of cotton, percentage of which sometimes can reach up to 100% of the weight of banknote paper. It is clear that disposal of such waste by simply incinerating it is contrary to the principles of sustainability and preservation of resources. In order to achieve these principles, this research aims to develop a new strategy called “Banknote for Banknote (BFB)” to recover and refine cotton from Banknote waste and later reuse it in the production of banknote paper or reprocess it into cellulose nanocrystals suitable for use in many advanced applications through a sustainable technology. The experiments were performed only on the Banknote Production Waste (BPW) (which is estimated as ˜3% of the total volume of Banknote production) because the end-of-life banknotes are usually contaminated with many substances during the circulation. Some of them can become hazardous to the extent of being contaminated with drugs (e.g. cocaine, etc.); that requires many additional analyses and special treatment. In the developed technology BPW was treated by using five sequential processes that were selected based on the banknote design, structure, need to reach minimum gas emissions and high recovery rate. The technology began from crushing treatment at the Central Bank for safety reasons, then leaching process was used to dissolve the heavy metals from the ink layer (Al, Fe, Ni, etc.) and separate other pigment metal elements (Si, Ti, etc.) in the form of suspended particles, while the dissolution and bleaching stages were employed to dissolve the remaining organic components and purify the cotton substrate. Finally, acid hydrolysis was used to prepare Cellulose nanocrystals with average size 75 nm from the purified cotton. Although the experiments were conducted on Egyptian Banknote, the advanced technology is applicable to all types of cotton based-banknotes with high profitability.
Self-Adaptation in Mobile Apps: a Systematic Literature Study

With their increase, smartphones have become more integral components of our lives but due to their mobile nature it is not possible to develop a mobile application the same way another software system would be built.In order to always provide the full service, a mobile application needs to be able to detect and deal with changes of context it may be presented with.A suitable method to achieve this goal is self-adaptation.However, as of today it is difficult to have a clear view of existing research on self-adaptation in the context of mobile applications.In this paper, we apply the systematic literature review methodology on selected peer-reviewed papers focusing on selfadaptability in the context of mobile applications.Out of 607 potentially relevant studies, we select 44 primary studies via carefully-defined exclusion and inclusion criteria.We use known modelling dimensions for self-adaptive software systems as our classification framework, which we apply to all selected primary studies.From the synthesized data we obtained, we produce an overview of the state of the art.The results of this study give a solid foundation to plan for future research and practice on engineering self-adaptive mobile applications.
Medical Image Analysis Using Artificial Intelligence

Purpose Automated analytical systems have begun to emerge as a database system that enables the scanning of medical images to be performed on computers and the construction of big data. Deep-learning artificial intelligence (AI) architectures have been developed and applied to medical images, making high-precision diagnosis possible. Materials and Methods For diagnosis, the medical images need to be labeled and standardized. After pre-processing the data and entering them into the deep-learning architecture, the final diagnosis results can be obtained quickly and accurately. To solve the problem of overfitting because of an insufficient amount of labeled data, data augmentation is performed through rotation, using left and right flips to artificially increase the amount of data. Because various deep-learning architectures have been developed and publicized over the past few years, the results of the diagnosis can be obtained by entering a medical image. Results Classification and regression are performed by a supervised machine-learning method and clustering and generation are performed by an unsupervised machine-learning method. When the convolutional neural network (CNN) method is applied to the deep-learning layer, feature extraction can be used to classify diseases very efficiently and thus to diagnose various diseases. Conclusions AI, using a deep-learning architecture, has expertise in medical image analysis of the nerves, retina, lungs, digital pathology, breast, heart, abdomen, and musculo-skeletal system.
Student data privacy in MOOCs: a sentiment analysis

Student data privacy as a research focus and practice is relatively under-researched in the context of massive open online courses (MOOCs). Central to researching student data privacy in the context of online service providers are the various user agreements that participants must accept or opt into before they can use a MOOC platform. Such documents form the basis for any recourse individuals might have if they have concerns about data privacy. Of specific interest in this research is the emotive use of language used in these documents. Research in other genres suggest that emotive language can be intentionally used to facilitate buy-in or to soften the implications of the agreement between user and provider. This article shares our findings of a sentiment analysis of the terms and conditions and privacy statements of three major MOOC providers.
Classification of Breast Cancer using Fast Fuzzy Clustering based on Kernel

Abstract Breast cancer is the second leading cause of death in women in the world. The classification is the initial process of executing patient treatment, which is important as it increases life expectancy as well as quality. In this paper, a new method is proposed based on kernel, which is modified from KC-Means: it combines K-Means, Fuzzy C-Means algorithm, and kernel function. The C-Means algorithm is applied on the centers of a fixed number of groups founded by K-Means, and the kernel function is expected to improve the accuracy of classification with its ability to separate data which cannot be separated linearly. We applied the proposed method on a dataset of 201 breast cancer and 85 non-breast cancer samples from the UC Irvine Machine Learning Repository. Results concluded that fast fuzzy clustering has an accuracy of 85.26%, but fast fuzzy clustering based on kernel is 89.74%, with a better running time on average than 90.95% with the same method.
The future's hybrid nature

column Share on The future's hybrid nature Author: Verena Fuchsberger University of Salzburg University of SalzburgView Profile Authors Info & Claims InteractionsVolume 26Issue 4July-August 2019 pp 26–31https://doi.org/10.1145/3328481Published:26 June 2019Publication History 12citation932DownloadsMetricsTotal Citations12Total Downloads932Last 12 Months240Last 6 weeks33 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Image Recognition by Predicted User Click Feature With Multidomain Multitask Transfer Deep Network

The click feature of an image, defined as a user click count vector based on click data, has been demonstrated to be effective for reducing the semantic gap for image recognition. Unfortunately, most of the traditional image recognition datasets do not contain click data. To address this problem, researchers have begun to develop a click prediction model using assistant datasets containing click information and have adapted this predictor to a common click-free dataset for different tasks. This method can be customized to our problem, but it has two main limitations: 1) the predicted click feature often performs badly in the recognition task since the prediction model is constructed independently of the subsequent recognition problem and 2) transferring the predictor from one dataset to another is challenging due to the large cross-domain diversity. In this paper, we devise a multitask and multidomain deep network with varied modals (MTMDD-VM) to formulate image recognition and click prediction tasks in a unified framework. Datasets with and without click information are integrated in the training. Furthermore, a nonlinear word embedding with a position-sensitive loss function is designed to discover the visual click correlation. We evaluate the proposed method on three public dog breed image datasets, and we utilize the Clickture-Dog dataset as the auxiliary dataset that provides click data. The experimental results show that: 1) the nonlinear word embedding and position-sensitive loss function largely enhance the predicted click feature in the recognition task, realizing a 32% improvement in accuracy; 2) the multitask learning framework improves accuracies in both image recognition and click prediction; and 3) the unified training using the combined dataset with and without click data further improves the performance. Compared with the state-of-the-art methods, the proposed approach not only performs much better in accuracy but also achieves good scalability and one-shot learning ability.
Content-based image retrieval based on supervised learning and statistical-based moments

Content-based image retrieval (CBIR) system generally retrieves images based on the matching of the query image from all the images of the database. This exhaustive matching and searching slow down the image retrieval process. In this paper, a fast and effective CBIR system is proposed which uses supervised learning-based image management and retrieval techniques. It utilizes machine learning approaches as a prior step for speeding up image retrieval in the large database. For the implementation of this, first, we extract statistical moments and the orthogonal-combination of local binary patterns (OC-LBP)-based computationally light weighted color and texture features. Further, using some ground truth annotation of images, we have trained the multi-class support vector machine (SVM) classifier. This classifier works as a manager and categorizes the remaining images into different libraries. However, at the query time, the same features are extracted and fed to the SVM classifier. SVM detects the class of query and searching is narrowed down to the corresponding library. This supervised model with weighted Euclidean Distance (ED) filters out maximum irrelevant images and speeds up the searching time. This work is evaluated and compared with the conventional model of the CBIR system on two benchmark databases, and it is found that the proposed work is significantly encouraging in terms of retrieval accuracy and response time for the same set of used features.
Terrorism and Social Media: Global Evidence

The study assesses the relationship between terrorism and social media from a cross section of 148 countries with data for the year 2012. The empirical evidence is based on Ordinary Least Squares, Negative Binomial and Quantile regressions. The main finding is that there is a positive relationship between social media in terms of Facebook penetration and terrorism. The positive relationship is driven by below-median quantiles of terrorism. In other words, countries in which existing levels of terrorism are low are more significantly associated with a positive Facebook-terrorism nexus. The established positive relationship is confirmed from other externalities of terrorism: terrorism fatalities, terrorism incidents, terrorism injuries and terrorism-related property damages. The terrorism externalities are constituents of the composite dependent variable.
Growing Media Skills and Know-How in Situ: Technology-Enhanced Practices and Collaborative Support in Mobile News-Reporting

Over the past decade, mobile news production has had a growing prevalence and has been established as a new type by modern journalism industry. Journalists understand content capturing and sharing as parts of their role in newsrooms. Mobile journalism (mojo) is an evolving form of reporting in which where people use only a smartphone to create and file stories, and it has been gaining ground during the last decade. This paper aims to examine the difficulties, issues, and challenges in real-world mojo scenarios, analyzing the efficacy of prototype machine-assisted reporting services (MoJo-MATE). A usability evaluation is conducted in quantitative and qualitative terms, paying attention to the media literacy support provided through implemented tools and the proposed collaborations. Students of the School of Journalism and Mass Communications, along with postgraduate-level researchers and professional journalists, form the sample for this investigation, which has a two-folded target: To guide the rapid prototyping process for system development and to validate specific hypotheses by answering the corresponding research questions. The results indicate the impact of mobile/on-demand support and training on journalistic practices and the attitudes of future journalists towards specialized technology in the era of constantly evolving digital journalism.
How Practitioners Perceive Coding Proficiency

Coding proficiency is essential to software practitioners. Unfortunately, our understanding on coding proficiency often translates to vague stereotypes, e.g., "able to write good code". The lack of specificity hinders employers from measuring a software engineer's coding proficiency, and software engineers from improving their coding proficiency skills. This raises an important question: what skills matter to improve one's coding proficiency. To answer this question, we perform an empirical study by surveying 340 software practitioners from 33 countries across 5 continents. We first identify 38 coding proficiency skills grouped into nine categories by interviewing 15 developers from three companies. We then ask our survey respondents to rate the level of importance for these skills, and provide rationales of their ratings. Our study highlights a total of 21 important skills that receive an average rating of 4.0 and above (important and very important), along with rationales given by proponents and dissenters. We discuss implications of our findings to researchers, educators, and practitioners.
Multiple Convolutional Neural Networks Fusion Using Improved Fuzzy Integral for Facial Emotion Recognition

Facial expressions are indispensable in human cognitive behaviors since it can instantly reveal human emotions. Therefore, in this study, Multiple Convolutional Neural Networks using Improved Fuzzy Integral (MCNNs-IFI) were proposed for recognizing facial emotions. Since effective facial expression features are difficult to design; deep learning CNN is used in the study. Each CNN has its own advantages and disadvantages, thus combining multiple CNNs can yield superior results. Moreover, multiple CNNs combined with improved fuzzy integral, in which its fuzzy density value is optimized through particle swarm optimization (PSO), overcomes the majority decision drawback in the traditional voting method. Two Multi-PIE and CK+ databases and three main CNN structures, namely AlexNet, GoogLeNet, and LeNet, were used in the experiments. To verify the results, a cross-validation method was used, and experimental results indicated that the proposed MCNNs-IFI exhibited 12.84% higher accuracy than that of the three CNNs.
Robust 3D image reconstruction of pancreatic cancer tumors from histopathological images with different stains and its quantitative performance evaluation

Histopathological imaging is widely used for the analysis and diagnosis of multiple diseases. Several methods have been proposed for the 3D reconstruction of pathological images, captured from thin sections of a given specimen, which get nonlinearly deformed due to the preparation process. The majority of the available methods for registering such images use the degree of matching of adjacent images as the criteria for registration, which can result in unnatural deformations of the anatomical structures. Moreover, most methods assume that the same staining is used for all images, when in fact multiple staining is usually applied in order to enhance different structures in the images.This paper proposes a non-rigid 3D reconstruction method based on the assumption that internal structures on the original tissue must be smooth and continuous. Landmarks are detected along anatomical structures using template matching based on normalized cross-correlation (NCC), forming jagged shape trajectories that traverse several slices. The registration process smooths out these trajectories and deforms the images accordingly. Artifacts are automatically handled by using the confidence of the NCC in order to reject unreliable landmarks.The proposed method was applied to a large series of histological sections from the pancreas of a KPC mouse. Some portions were dyed primarily with HE stain, while others were dyed alternately with HE, CK19, MT and Ki67 stains. A new evaluation method is proposed to quantitatively evaluate the smoothness and isotropy of the obtained reconstructions, both for single and multiple staining.The experimental results show that the proposed method produces smooth and nearly isotropic 3D reconstructions of pathological images with either single or multiple stains. From these reconstructions, microanatomical structures enhanced by different stains can be simultaneously observed.
Unsupervised learning of API aliasing specifications

Real world applications make heavy use of powerful libraries and frameworks, posing a significant challenge for static analysis as the library implementation may be very complex or unavailable. Thus, obtaining specifications that summarize the behaviors of the library is important as it enables static analyzers to precisely track the effects of APIs on the client program, without requiring the actual API implementation.