Reviewerly/exHarmony huggingface dataset:
TOP-Rank: A TopicalPostionRank for Extraction and Classification of Keyphrases in Text

Keyphrase extraction is the task of extracting the most important phrases from a document. Automatic keyphrase extraction attempts to itemize a document content as metainformation and facilitate efficient information retrieval. In this paper we propose TOP-Rank, an approach for keyphrase extraction and keyphrase classification. For keyphrase extraction, we build an approach based on the position of keyphrases in the document and expand it with topical ranking of keyphrases. In particular, keyphrase extraction technique analyzes the documents and extracts keyphrases from the document by giving a higher rank to topical phrases. After keyphrase extraction, we classify keyphrases as process, material and task. Our evaluation on diverse datasets shows that TOP-Rank achieves F1-score of 0.73 for keyphrase classification improving upon state-of-the-art methods by a huge margin.
Risk Factors of Cervical Cancer using Classification in Data Mining

Abstract According to World Health Organization, cervical cancer is the fourth most frequent cancer that have high mortality rate which affected women all around the world especially in low and middle-income countries. As the computer science and information technology field growth, researches on analysing medical datasets such as diabetes, cervical cancer and liver disease and etc also growth. This paper is set to studies classification techniques in data mining on risk factor of cervical cancer datasets. The clssification techniques such as Naive Bayes (NB), C4.5 Decision Tree (C4.5), k-Nearest Neighbors (kNN), Sequential Minimal Optimization (SMO), Random Forest Decision Tree (RF), Multilayer Perceptron (MLP) Neural Network and Simple Logistic Regression (SLR) have been used to classify the dataset whether healthy or cancer result for cervical cancer diagnostic. The dataset is needed to be undergoing intense data pre-processing phase due to imbalance and have a lot of missing value. The performance of classification were evaluated using 10-folds cross validation where accuracy, precision and recall as evaluation metric were measured using confusion matrix to determine the performance power for all classification techniques.
Histologic tissue components provide major cues for machine learning-based prostate cancer detection and grading on prostatectomy specimens

Abstract Automatically detecting and grading cancerous regions on radical prostatectomy (RP) sections facilitates graphical and quantitative pathology reporting, potentially benefitting post-surgery prognosis, recurrence prediction, and treatment planning after RP. Promising results for detecting and grading prostate cancer on digital histopathology images have been reported using machine learning techniques. However, the importance and applicability of those methods have not been fully investigated. We computed three-class tissue component maps (TCMs) from the images, where each pixel was labeled as nuclei, lumina, or other. We applied seven different machine learning approaches: three non-deep learning classifiers with features extracted from TCMs, and four deep learning, using transfer learning with the 1) TCMs, 2) nuclei maps, 3) lumina maps, and 4) raw images for cancer detection and grading on whole-mount RP tissue sections. We performed leave-one-patient-out cross-validation against expert annotations using 286 whole-slide images from 68 patients. For both cancer detection and grading, transfer learning using TCMs performed best. Transfer learning using nuclei maps yielded slightly inferior overall performance, but the best performance for classifying higher-grade cancer. This suggests that 3-class TCMs provide the major cues for cancer detection and grading primarily using nucleus features, which are the most important information for identifying higher-grade cancer.
Experience Fleming’s rule in Electromagnetism Using Augmented Reality: Analyzing Impact on Students Learning

Education trends are changing from traditional teaching to Electronic-learning and Mobile-learning. Through the development of information and communication technology, many e-learning systems were developed but, they lack in terms of user interaction. This affects student’s learning, motivation, performance, thinking level, technical skills, and expertise. The accessibility of practical labs in science and engineering education with expensive equipment and apparatus is restricted for a limited amount of time for large number of students. According to the previous research, it was found that there are fewer studies in the field of electromagnetism by using Augmented Reality (AR) application. Moreover, students are facing difficulties to understand Fleming’s rule and for this, no practical experiment is available in the physics lab. The solution to overcome these problems is through the use of Augmented Reality (AR) and Virtual Reality (VR) labs. In this research the Concept of Fleming’s rule in Electromagnetism is explained by using AR technique and the visualization of magnetic field lines, electric current and force exerted on current-carrying conductor has been done. The efficiency of the proposed system was calculated and team-based activity among secondary and applied engineering students was analyzed. The knowledge gain by the students was measured using the pre-test and post-test method. The outcomes of this research revealed that AR technology was more efficient in enhancing students’ knowledge in the field of Electromagnetism. The investigation also suggests that AR application helps the students to gain higher flow understanding levels than those attained by a web-based application.
Causality-based Explanation of Classification Outcomes

We propose a simple definition of an explanation for the outcome of a classifier based on concepts from causality. We compare it with previously proposed notions of explanation, and study their complexity. We conduct an experimental evaluation with two real datasets from the financial domain.
The Impact of Artificial Intelligence and Information Technologies on the Efficiency of Knowledge Management at Modern Organizations: A Systematic Review

In the twenty-first century, with the evolution of Information Technology and Artificial Intelligence, Knowledge Management seems to be more advanced and sophisticated. Knowledge management has been studied and discussed for a long time by several researchers from academia and business sector due to its vitality for the success of organizations. Additionally, many leading companies around the world have adopted several knowledge management practices to ensure that they remain ahead of their rival companies in today's competitive business world. Hence, companies continue to look for ways to improve these knowledge management practices. Several studies were conducted to have more profound understanding of the recent research trend of knowledge management processes and its best practices at organizations. However, this subject requires further investigation from other perspectives. Most of the previous studies neglect exploring the impact of applying artificial intelligence (AI) and information technology (IT) concepts and techniques on the efficiency of knowledge management at modern organizations in particular. This research systematically reviews and highlights the current knowledge management practices that have relied on IT and AI mechanisms and their impacts on modern organizations along with their challenges and limitations. The current research aims to provide a profound analysis of 15 (out of 844) research papers published in highly standard journals and conference proceedings throughout the past 10 years, from 2009 to 2019. This review study can be considered a pivotal reference for scholars as it fills in some gaps in knowledge management especially in IT and AI related studies.
Duolingo English Test, Revised Version July 2019

The Duolingo English Test (DET) is a computer adaptive test of English proficiency that is increasingly used for English-medium university admissions purposes.During the COVID-19 pandemic of 2020, test centers were shut down in many countries, and major tests including the TOEFL iBT and IELTS could not be administered.The DET is an "at home" test, and thus many universities began accepting DET scores as ameasure of applicants' English proficiency.Because a revised version of the DET was launched in July, 2019, and because of the large increase in universities accepting DET scores, a critical review of the DET is warranted.The current review lauds the accessibility of the test (e.g., it is an inexpensive "at home" test that can be taken anywhere, in less than an hour, with scores returned in 48 hours).However, the test has multiple shortcomings: the test tasks have little in common with the types of language tasks university students engage in; the test does not assess test takers' academic language ability, discourse level competence, or interactional competence; it is susceptible to cheating and test preparation; and it has a potential for negative washback on learners and learning systems.In addition, there is a lack of independent research validating the use of DET scores for admissions.Given these shortcomings, the use of DET scores cannot be recommended for university admissions purposes.
UAV Positioning Mechanisms in Landing Stations: Classification and Engineering Design Review

Landing platforms' automation is aimed at servicing vertical take-off and landing UAVs between flights and maintaining their airworthiness. Over the last few years, different designs for the landing platforms have been proposed. This shows a strong development and establishment of automatic landing platforms with UAV positioning devices on the landing site. Positioning and safe fixation of the UAV are some of the main features of the landing platform, especially if it is mounted on a movable vehicle. This article focuses exclusively on the landing platform and its elements that provide the positioning of the UAV by affecting it during and after the landing. Both active devices and mechanisms and passive elements used for positioning are considered. This article, based on the review of recent patents and publications, gives the classification of positioning approaches used in landing stations with the analysis of the required landing precision, as well as the pros and cons of each type of approach.
Achieving Efficient and Privacy-Preserving Exact Set Similarity Search over Encrypted Data

Set similarity search, aiming to search the similar sets to a query set, has wide application in today's recommendation services. Meanwhile, the rapid advance in cloud technique has promoted the boom of data outsourcing. However, since the cloud is not fully trustable and the data may be sensitive, data should be encrypted before outsourced to the cloud. Undoubtedly, data encryption will hinder some basic functionalities, e.g., set similarity search. For achieving set similarity search over encrypted data, many solutions were proposed, yet they either only satisfy weak security requirements, or only achieve approximate similarity, or have low efficiency or under the model of two cloud servers. Therefore, in this article, we propose a new efficient and privacy-preserving exact set similarity search scheme under a single cloud server. Specifically, we first design a symmetric-key predicate encryption (SPE-Sim) scheme, which can support similarity search over binary vectors. Then, we represent the set records to be binary vectors and employ the B+ tree to build an index for them. After that, based on SPE-Sim and the B+ tree-based index, we propose our scheme and it can achieve efficient set similarity search while preserving the privacy of set records and query contents. Finally, security analysis and performance evaluation indicate that our scheme is privacy-preserving and efficient.
Tag-informed collaborative topic modeling for cross domain recommendations

Collaborative topic modeling is powerful to alleviate data sparsity in recommender systems owing to the incorporation of collaborative filtering and topic models. However sufficient textual data is not always available. On the other hand, tags serving as supplementary description of items can reflect users’ interests in item attributes. But previous works only mine the effect of tags on ratings in one domain and ignore that in related domains items can be related in attributes. Tags encode similar properties of items and can be transferred across domains to mutually benefit recommendations for both domains. In this study we propose a TagCDCTR (Tag-informed Cross Domain Collaborative Topic Regression) model, which exploits shared tags as bridges to link related domains through an extended collaborative topic modeling framework. The model exploits the inter-domain relations by encoding cross domain item–item similarity based on common tags and jointly learning a shared set of topics from all domains together. Collectively factorizing the rating matrices of multiple domains into common user latent factors and domain-specific item latent factors, so that the learned item latent factors are linked through the inter-domain relations, helping to capture the items more comprehensively. The rich information reused in multiple domains alleviates data sparsity and the semantic advantage of topics and tags provides a better interpretability of recommendations. The experiments conducted on three datasets demonstrate that TagCDCTR outperforms state-of-the-art collaborative-topic-based models and cross-domain-based models.
Hate speech detection is not as easy as you may think: A closer look at model validation (extended version)

Hate speech is an important problem that is seriously affecting the dynamics and usefulness of online social communities. Large scale social platforms are currently investing important resources into automatically detecting and classifying hateful content, without much success. On the other hand, the results reported by state-of-the-art systems indicate that supervised approaches achieve almost perfect performance but only within specific datasets, most of them in English language. In this work, we analyze this apparent contradiction between existing literature and actual applications. We study closely the experimental methodology used in prior work and their generalizability to other datasets. Our findings evidence methodological issues, as well as an important dataset bias. As a consequence, performance claims of the current state-of-the-art have become significantly overestimated. The problems that we have found are mostly related to data overfitting and sampling issues. We discuss the implications for current research and re-conduct experiments to give a more accurate picture of the current state-of-the art methods. Moreover, we design some baseline approaches to perform cross-lingual experiments, using English and Spanish datasets.
Clustering Documents using the Document to Vector Model for Dimensionality Reduction

The TF-IDF model is the most common way of representing documents in the vector space. However, its results are highly dimensional, posing problems to the classic clustering algorithms due to the curse of dimensionality. Recent word embeddings based techniques can reduce the documents representations dimensionality while also preserving the semantic relationships between words. In this paper, we analyze the accuracy of four different classical clustering algorithms (K-Means, Spherical K-Means, LDA, and DBSCAN) in combination with the Document to Vector model.
Freehand-Steering Locomotion Techniques for Immersive Virtual Environments: A Comparative Evaluation

Virtual reality has achieved significant popularity in recent years, and allowing users to move freely within an immersive virtual world has become an important factor critical to realize.The user's interactions are generally designed to increase the perceived realism, but the locomotion techniques and how these affect the user's task performance still represent an open issue, much discussed in the literature.In this article, we evaluate the efficiency and effectiveness of, and user preferences relating to, freehand locomotion techniques designed for an immersive virtual environment performed through hand gestures tracked by a sensor placed in the egocentric position and experienced through a head-mounted display.Three freehand locomotion techniques have been implemented and compared with each other, and with a baseline technique based on a controller, through qualitative and quantitative measures.An extensive user study conducted with 60 subjects shows that the proposed methods have a performance comparable to the use of the controller, further revealing the users' preference for decoupling the locomotion in sub-tasks, even if this means renouncing precision and adapting the interaction to the possibilities of the tracker sensor.
Intuitive Development to Examine Collaborative IoT Supply Chain System Underlying Privacy and Security Levels and Perspective Powering through Proactive Blockchain

Undoubtedly, the supply chain management (SCM) system is an important part of many organizations worldwide; over time, the technologies used to manage a supply chain ecosystem have, therefore, a great impact on businesses' effectiveness. Among others, numerous developments have been made that targeted to have robust supply chain systems to efficiently manage the growing demands of various supplies, considering the underlying requirements and main challenges such as scalability, specifically privacy and security, of various business networks. Internet of things (IoT) comes with a solution to manage a complex, scalable supply chain system, but to provide and attain enough security during information exchange, along with keeping the privacy of its users, is the great inherent challenge of IoT. To fulfill these limitations, this study designs and models a scaled IoT-based supply chain (IoT-SC) system, comprising several operations and participants, and deploys mechanisms to leverage the security, mainly confidentially, integrity, authentication (CIA), and a digital signature scheme to leverage potentially secured non-repudiation security service for the worst-case scenario, and to leverage privacy to keep users sensitive personal and location information protected against adversarial entities to the IoT-SC system. Indeed, a scaled IoT-SC system certainly opens new challenges to manage privacy and security while communicating. Therefore, in the IoT-SC system, each transaction writes from edge computing nodes to the IoT-SC controller is thoroughly examined to ensure the proposed solutions in bi-directional communication, and their robustness against adversarial behaviors. Future research works, employing blockchain and its integrations, are detailed as paces to accelerate the privacy and security of the IoT-SC system, for example, migrating IoT-centric computing to an immutable, decentralized platform.
Learning Models over Relational Data Using Sparse Tensors and Functional Dependencies

Integrated solutions for analytics over relational databases are of great practical importance as they avoid the costly repeated loop data scientists have to deal with on a daily basis: select features from data residing in relational databases using feature extraction queries involving joins, projections, and aggregations; export the training dataset defined by such queries; convert this dataset into the format of an external learning tool; and train the desired model using this tool. These integrated solutions are also a fertile ground of theoretically fundamental and challenging problems at the intersection of relational and statistical data models. This article introduces a unified framework for training and evaluating a class of statistical learning models over relational databases. This class includes ridge linear regression, polynomial regression, factorization machines, and principal component analysis. We show that, by synergizing key tools from database theory such as schema information, query structure, functional dependencies, recent advances in query evaluation algorithms, and from linear algebra such as tensor and matrix operations, one can formulate relational analytics problems and design efficient (query and data) structure-aware algorithms to solve them. This theoretical development informed the design and implementation of the AC/DC system for structure-aware learning. We benchmark the performance of AC/DC against R, MADlib, libFM, and TensorFlow. For typical retail forecasting and advertisement planning applications, AC/DC can learn polynomial regression models and factorization machines with at least the same accuracy as its competitors and up to three orders of magnitude faster than its competitors whenever they do not run out of memory, exceed 24-hour timeout, or encounter internal design limitations.
A fast self-attention cascaded network for object detection in large scene remote sensing images

Aiming at the real-time detection of multiple objects and micro-objects in large-scene remote sensing images, a cascaded convolutional neural network real-time object-detection framework for remote sensing images is proposed, which integrates visual perception and convolutional memory network reasoning. The detection framework is composed of two fully convolutional networks, namely, the strengthened object self-attention pre-screening fully convolutional network (SOSA-FCN) and the object accurate detection fully convolutional network (AD-FCN). SOSA-FCN introduces a self-attention module to extract attention feature maps and constructs a deep feature pyramid to optimize the attention feature maps by combining convolutional long-term and short-term memory networks. It guides the acquisition of potential sub-regions of the object in the scene, reduces the computational complexity, and enhances the network’s ability to extract multi-scale object features. It adapts to the complex background and small object characteristics of a large-scene remote sensing image. In AD-FCN, the object mask and object orientation estimation layer are designed to achieve fine positioning of candidate frames. The performance of the proposed algorithm is compared with that of other advanced methods on NWPU_VHR-10, DOTA, UCAS-AOD, and other open datasets. The experimental results show that the proposed algorithm significantly improves the efficiency of object detection while ensuring detection accuracy and has high adaptability. It has extensive engineering application prospects.
Computational LEGO technic design

research-article Free AccessComputational LEGO technic design Share on Authors: Hao Xu Search about this author , Ka-Hei Hui Search about this author , Chi-Wing Fu Search about this author , Hao Zhang Search about this author Authors Info & Affiliations ACM Transactions on GraphicsVolume 38Issue 6November 2019 Article No.: 196https://doi.org/10.1145/3355089.3356504Published:08 November 2019 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 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 SiteeReaderPDF