Reviewerly/exHarmony huggingface dataset:
HSDet: A Representative Sampling Based Object Detector in Cervical Cancer Cell Images

Automated detection of cervical cancer cells has the potential to reduce error and increase productivity in cervical cancer screening. However, the existing object detection methods to detect the cervical cancer cells make inadequate use of negative samples and fail to balance the number of hard samples and easy samples. In this work, we propose a image-level sampling method, called pair sampling, which can extract the representative negative samples and generate the sample pair image. Then, based on the sample pair image, we propose the hybrid sampling, a proposal-level sampling method, to balance the number of hard samples and easy samples. Combining the proposed sampling methods, we design a representative sampling based cervical cell detector, which can detect the cervical cancer cells effectively. In order to comprehensively evaluate our method, we use a dataset which is consisted of 16000 annotated cervical cell images with size of $$1024\times 1024$$ . The experimental result shows that our method achieves 57.1% mean Average Precision (mAP), and it is higher than cascade R-CNN and Faster R-CNN by 4.7% and 5.8%, respectively.
An Empirical Analysis and Application of the Expectation-Maximization and Matrix Completion Algorithms for Varying Degrees of Missing Data

Incomplete data sets have been a problem in most studies, however, few studies have come to realise that imputation is a solution to this problem. Incomplete data can have a significant effect on the conclusion drawn and decision made. To solve the problem of incomplete data, one should use techniques to recover those missing values, depending on how much the data is missing, how big is the data, how the data has gone missing, etc. In this report, we aimed to compare the performance of the EM algorithm and matrix completion when imputing the missing values for varying degrees of missing data.Kullback-Leibler (KL) divergence was used as an evaluation metric to observe the performance of Expectation-Maximization (EM) algorithm and matrix completion when estimating missing values relative to the ground-truth distribution. The findings of this research shows that the EM algorithm outperformed matrix completion in both the theoretical (the simulated scenarios of learning from varying degrees of missing data) and the application (the application of theoretical model on real-world data on credit card fraud) models. Few similarities of the algorithms were observed when recovering missing values such as the increasing trend of error as missing values increases and the impact of increasing number of variables in a data set.Matrix completion only performed better when missing values were beyond approximately 75%. Therefore, from our findings, we conclude that when less than 50% of the data is missing, EM algorithm produces accurate predictions. The EM algorithm performed better compared to the matrix completion since it first learned the data itself and used maximum likelihood procedures to estimate the parameters of the model while the matrix completion analysed the existing pattern from rows and columns and imputes them using the pattern learned in the data.
DeepDoT: Deep Framework for Detection of Tables in Document Images

An efficient table detection process offers a solution for enterprises dealing with automated analysis of digital documents. Table detection is a challenging task due to low inter-class and high intra-class dissimilarities in document images. Further, the foreground-background class imbalance problem limits the performance of table detectors (especially single stage table detectors). The existing table detectors rely on a bottom-up scheme that efficiently captures the semantic features but fails in accounting for the resolution enriched features, thus, affecting the overall detection performance. We propose an end to end trainable framework (DeepDoT), which effectively detect the tables (of different sizes) over arbitrary scales in document images. The DeepDoT utilizes a top-down as well as a bottom-up approach, and additionally, it uses focal loss for handling the pervasive class imbalance problem for accurate predictions. We consider multiple benchmark datasets: ICDAR-2013, UNLV, ICDAR-2017 POD, and MARMOT for a thorough evaluation. The proposed approach yields comparatively better performance in terms of F1-score as compared to state-of-the-art table detection approaches.
Distill2Vec: Dynamic Graph Representation Learning with Knowledge Distillation

Dynamic graph representation learning strategies are based on different neural architectures to capture the graph evolution over time. However, the underlying neural architectures require a large amount of parameters to train and suffer from high online inference latency, that is several model parameters have to be updated when new data arrive online. In this study we propose Distill2Vec, a knowledge distillation strategy to train a compact model with a low number of trainable parameters, so as to reduce the latency of online inference and maintain the model accuracy high. We design a distillation loss function based on Kullback-Leibler divergence to transfer the acquired knowledge from a teacher model trained on offline data, to a small-size student model for online data. Our experiments with publicly available datasets show the superiority of our proposed model over several state-of-the-art approaches with relative gains up to 5% in the link prediction task. In addition, we demonstrate the effectiveness of our knowledge distillation strategy, in terms of number of required parameters, where Distill2Vec achieves a compression ratio up to 7:100 when compared with baseline approaches. For reproduction purposes, our implementation is publicly available at https://stefanosantaris.github.io/Distill2Vec.
Ad Click-Through Rate Prediction: A Survey

Ad click-through rate prediction (CTR), as an essential task of charging advertisers in the field of E-commerce, provides users with appropriate advertisements according to user interests to increase users’ click-through rate based on user clicks. The performance of CTR models plays a crucial role in advertising. Recently, there are many approaches to improving the performance of CTR. In this paper, we present a survey to analyze state-of-art models of CTR via types of models comprehensively. Finally, we summarize some practical challenges and then open perspective problems of CTR.
You Get What You Chat: Using Conversations to Personalize Search-Based Recommendations

Prior work on personalized recommendations has focused on exploiting explicit signals from user-specific queries, clicks, likes and ratings. This paper investigates tapping into a different source of implicit signals of interests and tastes: online chats between users. The paper develops an expressive model and effective methods for personalizing search-based entity recommendations. User models derived from chats augment different methods for re-ranking entity answers for medium-grained queries. The paper presents specific techniques to enhance the user models by capturing domain-specific vocabularies and by entity-based expansion. Experiments are based on a collection of online chats from a controlled user study covering three domains: books, travel, food. We evaluate different configurations and compare chat-based user models against concise user profiles from questionnaires. Overall, these two variants perform on par in terms of NCDG@20, but each has advantages on certain domains.
Digital safeguard of laminated historical manuscripts: the treatise “Poem in Rajaz on medicine” as a case study

In this paper, we analyze and discuss the characteristics of a system for the effective digital preservation and fruition of historical manuscripts degraded by the process of lamination. As a case study, we will make reference to the “Poem in Rajaz on medicine”, written by Abubacer in the XII century, and conserved in the Al Quaraouiyine Library located in Fez, Morocco. The conceived system should have at least four main functionalities: image acquisition (i.e. digitization), image enhancement, text encoding, and linguistic analysis. Based on the evaluation of the manuscript damages, the acquisition set up should be designed in such a way to be able to avoid reflections as much as possible. Suitable digital image processing techniques should also be devised to correct the residual degradations and enhance the text for an easier legibility. Finally, semi-automatic transcription, scholarly encoding and linguistic analysis, to be performed on the virtually restored pages, should adapt existing tools to the specificity of the primary source writing system and language. The feasibility study for the realization of such a system is of general utility, in that it can provide guidelines for the digitization, the enhancement and the text encoding of the many laminated manuscripts conserved in other historical archives. On the other hand, from the cultural heritage point of view, the experimentation on the “Poem in Rajaz on medicine” could foster the systematic philological and ontological study of a unique piece of our documental heritage: the longest poem of medieval Islamic medical literature.
Aspect-Based Sentiment Analysis on Convolution Neural Network and Multi-Hierarchical Attention

Aspect-based sentiment analysis as fine-grained sentiment analysis problem, mainly aims to clarify the sentiment of different aspect. Previous fine-grained sentiment analysis methods usually used Recurrent Neural Networks as feature extractors, and achieved good results in sentiment analysis. However, they were limited to the sequential processing of sequence by RNN. Its training and operation efficiency are relatively low and time consuming. Aiming at this problem, combining the multi-head attention mechanism in Transformer, fusion model of convolution neural network and hierarchical attention coding network is proposed, and convolution neural network is used as feature encoder to improve the efficiency of model training. At the same time, multi-head attention is used to calculate the degree of relevance between aspect words and context, thus capturing emotional elements corresponding to entity words. Experiments on the SemEval2014 dataset have improved the training and inference efficiency of aspect-based sentiment analysis.
Wingsuit Flying Search Enhanced by Spherical Evolution

Wingsuit flying search (WFS) is a novel metaheuristic search algorithm that mimics the process of a flier to land on the earth's surface within their range, i.e., a global minimum of the search space. The main advantage of this algorithm is parameter-free except for the population size and the maximal number of iterations. Spherical evolution (SE) uses a spherical search style which possesses a strong exploring ability especially for multimodal optimization problems. This paper proposes a novel mechanism to incorporate SE into WFS for considerably exploiting the search space while maintaining the strong exploration capability. The resultant hybrid algorithm WFSSE is tested on 30 classical benchmark functions. Experimental results in comparison with other state-of-the art algorithms verify the superior performance of WFSSE in terms of robustness and effectiveness.
Performance Analysis of Stego-Image Security in Machine Learning

A numerical way to deal with steganalysis is introduced in this paper with straight steganography being the primary core interest. A numerically formal meaning of steganalysis is given trailed by definitions for uninvolved and dynamic steganalysis. The steganalysis issue is figured as visually impaired framework ID and conditions for recognizability (effective steganalysis) are determined. A methodology to deliberately misuse any accessible spatial and worldly variety data for proficient steganalysis is additionally examined. Test results are given for steganalysis of Gaussian circulated, spread range picture steganography and watermarking. The proposed procedure is seen to deliver great outcomes for an assortment of execution measures.
Improving Trace Link Recovery Using Semantic Relation Graphs and Spreading Activation

[Context & Motivation] Trace Link Recovery tries to identify and link related existing requirements with each other to support further engineering tasks. Existing approaches are mainly based on algebraic Information Retrieval or machine-learning. [Question/Problem] Machine-learning approaches usually demand reasonably large and labeled datasets to train. Algebraic Information Retrieval approaches like distance between tf-idf scores also work on smaller datasets without training but are limited in considering the context of semantic statements. [Principal Ideas/Results] In this work, we revise our existing Trace Link Recovery approach that is based on an explicit representation of the content of requirements as a semantic relation graph and uses Spreading Activation to answer trace queries over this graph. The approach generates sorted candidate lists and is fully automated including an NLP pipeline to transform unrestricted natural language requirements into a graph and does not require any external knowledge bases or other resources. [Contribution] To improve the performance, we take a detailed look at five common datasets and adapt the graph structure and semantic search algorithm. Depending on the selected configuration, the predictive power strongly varies. With the best tested configuration, the approach achieves a mean average precision of 50%, a Lag of 30% and a recall of 90%.
Graph Autoencoder Combined with Attribute Information in Graph

Network embedding is an important method to learn the low-dimensional representation of nodes. It can be used to reduce the dimension of the network for downstream tasks. Many existing embeddings learn the representation only based on the reconstruction of the topological structure, yet nodes with attributes can provide important information in many real networks. Thus, learning attributed graph embedding should not only be based on the reconstruction of topological structure but also node features. In this paper, we propose a reasonable and interpretable graph autoencoder based on structure and attributes. This model can also be extended in both supervised and unsupervised tasks. Experiments including link prediction, clustering, and graph visualization have verified the effectiveness of our proposed GAE model.
Digital Evidence Case Management Tool for Collaborative Digital Forensics Investigation

Digital forensics investigation process begins with the acquisition, investigation until the presentation of investigation findings. Investigators are required to manage bits and pieces of digital evidence in the cloud and to correlate with evidence found in physical machines and network. The process could be made easy with a proper case management tool that is hosted in the web. The challenge of maintaining chain of custody, determining access to evidence, assignment of forensics investigator could be overcome when digital evidence is fully integrated in a single platform. Our proposed case management tool streamlines information gathering and integrates information on different platforms, shares information, tracks cases, and uploads data directly into a database. In addition, the case management tool facilitates the collaboration of investigators through sharing of forensics findings. These features allow case owner or administrator to track and monitor investigation progress in a forensically sound manner.
Forum 4.0: An Open-Source User Comment Analysis Framework

With the increasing number of user comments in diverse domains, including comments on online journalism and e-commerce websites, the manual content analysis of these comments becomes time-consuming and challenging.However, research showed that user comments contain useful information for different domain experts, which is thus worth finding and utilizing.This paper introduces Forum 4.0, an opensource framework to semi-automatically analyze, aggregate, and visualize user comments based on labels defined by domain experts.We demonstrate the applicability of Forum 4.0 with comments analytics scenarios within the domains of online journalism and app stores.We outline the underlying container architecture, including the web-based user interface, the machine learning component, and the task manager for time-consuming tasks.We finally conduct machine learning experiments with simulated annotations and different sampling strategies on existing datasets from both domains to evaluate Forum 4.0's performance.Forum 4.0 achieves promising classification results (ROC-AUC ≥ 0.9 with 100 annotated samples), utilizing transformer-based embeddings with a lightweight logistic regression model.We explain how Forum 4.0's architecture is applicable for millions of user comments in real-time, yet at feasible training and classification costs.
Review and Evaluation of Systems Supporting Data Journalism

In recent years, a new form of journalism has developed a strong dynamic: journalism based on data. The success of the move is so great that technologies and applications that support it have begun to emerge. Although technologies are in a period of mature productivity, their evaluation is an area lagging in development. It is a fact that lack of evaluation based on a reliable methodology is evident in the literature. Finding a suitable methodology for this purpose is particularly important in order to (a) evaluate systems supporting data journalism resulting in a specific ranking of potential, and (b) make it easier for the user to choose the application that best suits his / her requirements and cognitive level. A comparative evaluation of 9 applications related to the visual imaging component was attempted with usability and functionality criteria. The aim of this study is to provide a quick evaluation method, open to proposed improvements and further refinement, in order to establish a framework for qualitative assessment of data journalism applications.
Semantic Web and IoT

In this chapter, we provide an overview of the current trends in using semantic technologies in the IoT domain, presenting practical applications and use cases in different domains, such as in the healthcare domain (home care and occupational health), disaster management, public events, precision agriculture, intelligent transportation, building and infrastructure management. More specifically, we elaborate on semantic web-enabled middleware, frameworks and architectures (e.g. semantic descriptors for M2M) proposed to overcome the limitations of device and data heterogeneity. We present recent advances in structuring, modelling (e.g. RDFa, JSON-LD) and semantically enriching data and information derived from sensor environments, focusing on the advanced conceptual modelling capabilities offered by semantic web ontology languages (e.g. RDF/OWL2). Querying and validation solutions on top of RDF graphs and Linked Data (e.g. SPARQL, SPIN and SHACL) are also presented. Furthermore, insights are provided on reasoning, aggregation, fusion and interpretation solutions that aim to intelligently process and ingest sensor information, infusing also human awareness for advanced situational awareness.
A Study on Classification Accuracy of Different Features in Identification of Parasite Eggs from Microscopic Images of Fecal Samples

Selecting and extracting a robust feature set is very much important in automatic identification or classification of parasitic eggs in microscopic images. In this paper, we have explored different types of features and used in identification of the segmented microscopic images of three different types of parasitic eggs namely Ascaris Lumbricoides, Necator Americanus and Trichuris Trichiura. We have extracted four types of image moments as four different feature sets from the segmented gray-scale images of parasite eggs. We have also extracted several features from gray level pixel values and some texture features along with a few shape-based features to automatically identify the above mentioned types of parasite eggs. The performances of the different feature sets are examined using three different classifiers viz SVM, ANN and kNN. The highest classification accuracy of 96.5% is obtained by SVM using texture and shaped based features.
Metallogenic Prediction of Metal Minerals Based on Remote Sensing Technology

Abstract Human beings have many indispensable material resources in the development of the earth, and mineral resources are an important part of them. In China, mineral resources are also one of the important products since ancient times. In today’s era, China has entered an important period of building a well-off society in an all-round way. With the deepening of urbanization, social production and people’s growing living needs, China’s demand for mineral resources is also rising. However, due to endless mining and development, mineral resources in our country have become increasingly scarce, the reserve reserves are seriously insufficient, and the supply of mineral resources in the development and use is in short supply. In order to solve this difficult problem, this paper studies the metallogenic prediction of metal minerals by remote sensing technology, analyzes the relevant data, and provides a scientific basis for further resource exploration.
National Defense Cloud Strategy

Recently, the U.S. Department of Defense is carrying forward the JEDI project that moves and integrates about 3.4 millions of users, 4 millions of devices to cloud basis on a scale of S 10 billion from 2019 in order to improve data processing and analysis speed with cloud-based military intelligence classification and AI technology. South Korea is also pushing ahead actively policies to promote relevant industries and construct ecosystem such as investing in the smart electronic government service (G-cloud) to efficiently manage IT information resources by establishing the cloud act in 2015. Despite the effort of government, however, it has trouble in initial diffusion for reasons of extant anxiety about cloud such as security issues, burden of initial construction and maintenance expenses, and in addition to this, it is not easy to invigorate due to various regulations and systems of individual laws that hinder the introduction of cloud. Also in national defense field, it continues to make efforts such as preparing guidelines to preferentially examine cloud when introducing, moving and transferring military information systems according to the government policy, performing studies to apply cloud, and carrying out pilot projects, but it seemed the practical application process is not easy due to a lot of limitations such as task characteristics and each military information system operation diversity. Therefore, this study suggests policy directions and strategies to dispel the anxiety of cloud, form a consensus, and spread national defense cloud (D-cloud (tentative name)) with a focus on the defense integrated data center (DIDC).