Reviewerly/exHarmony huggingface dataset:
Data Compression as a Comprehensive Framework for Graph Drawing and Representation Learning

Embedding a graph into feature space is a promising approach to understand its structure. Embedding into 2D or 3D space enables visualization; representation in higher-dimensional vector space (typically >100D) enables the application of data mining techniques. For the success of knowledge discovery it is essential that the distances between the embedded vertices truly reflect the structure of the graph. Our fundamental idea is to compress the adjacency matrix by predicting the existence of an edge from the Euclidean distance between the corresponding vertices in the embedding, and to use the achieved compression as a quality measure for the embedding. We call this quality measure Predictive Entropy (PE). PE uses a sigmoid function to define the probability which is monotonically decreasing with the Euclidean distance. We use this sigmoid probability to compress the adjacency matrix of the graph by an entropy coding. While PE could be used to assess the result of any graph drawing or representation learning method we particularly use it as objective function in our new method GEMPE (Graph Embedding by Minimizing the Predictive Entropy). We demonstrate in our experiments that GEMPE clearly outperforms comparison methods with respect to quality of the visual result, clustering and node-labeling accuracy on the discovered coordinates.
“I’m Disabled and Married to a Foreign Single Mother”. Public Service Chatbot’s Advice on Citizens’ Complex Lives

This paper describes a study of citizens' chats with a chatbot of a public agency. We have analyzed chat logs and identified citizens' lack of domain knowledge as a source of inadequate or failed chatbot responses. We identify three types of lack of domain knowledge: lack of the right vocabulary, uncertainty if a regulation fits the citizen's situation or the "shape sorting box" problem, or citizen's misunderstanding the regulations. The most serious failure is when a misunderstanding is not detected and corrected during the chat. The chatbot we studied is not able to make sense of badly formed questions from citizens. As implications for design we suggest making the chatbot limitations visible by not presenting it as a human-like avatar with a name. We also suggest to enable domain knowledge learning through its conversations.
New classes of entanglement-assisted quantum MDS codes

In this paper, two new classes of entanglement-assisted quantum MDS codes (EAQMDS codes for short) with length n being a factor of $$q^2\pm 1$$ are presented via cyclic codes over finite fields of odd characteristic. Among our constructions, there are several EAQMDS codes with new parameters which have never been reported. Moreover, some of them have much larger minimum distance than known results.
Motion Saliency Detection for Surveillance Systems Using Streaming Dynamic Mode Decomposition

Intelligent surveillance systems enable secured visibility features in the smart city era. One of the major models for pre-processing in intelligent surveillance systems is known as saliency detection, which provides facilities for multiple tasks such as object detection, object segmentation, video coding, image re-targeting, image-quality assessment, and image compression. Traditional models focus on improving detection accuracy at the cost of high complexity. However, these models are computationally expensive for real-world systems. To cope with this issue, we propose a fast-motion saliency method for surveillance systems under various background conditions. Our method is derived from streaming dynamic mode decomposition (s-DMD), which is a powerful tool in data science. First, DMD computes a set of modes in a streaming manner to derive spatial–temporal features, and a raw saliency map is generated from the sparse reconstruction process. Second, the final saliency map is refined using a difference-of-Gaussians filter in the frequency domain. The effectiveness of the proposed method is validated on a standard benchmark dataset. The experimental results show that the proposed method achieves competitive accuracy with lower complexity than state-of-the-art methods, which satisfies requirements in real-time applications.
Blind Poissonian Image Deblurring Regularized by a Denoiser Constraint and Deep Image Prior

The denoising and deblurring of Poisson images are opposite inverse problems. Single image deblurring methods are sensitive to image noise. A single noise filter can effectively remove noise in advance, but it also damages blurred information. To simultaneously solve the denoising and deblurring of Poissonian images better, we learn the implicit deep image prior from a single degraded image and use the denoiser as a regularization term to constrain the latent clear image. Combined with the explicit L 0 regularization prior of the image, the denoising and deblurring model of the Poisson image is established. Then, the split Bregman iteration strategy is used to optimize the point spread function estimation and latent clear image estimation. The experimental results demonstrate that the proposed method achieves good restoration results on a series of simulated and real blurred images with Poisson noise.
Recommendations Concerning the Selection of Artificial Intelligence Methods for Increasing of Cyber-Security

In the course of the implementation of a project funded by the Research Fund under the Ministry of Education and Science, on the base of multi-factor analysis of Cyber-security Threats and the adoption of military theory into Cyber-security procedures, the authors have formulated a new classification of the stages of the Cyber Defense and the classes of tasks that can be solved using Artificial Intelligence methods.
A Demonstration of an interactive Virtual Reality Prototype for Exploring the Cultural Heritage of Southern African Rock Art

In this paper, we describe an interactive implementation of a Virtual Reality prototype for creating rock art. The aim was to explore the possibilities of emerging technologies to safeguard the intangible cultural heritage of the San people in Namibia. The VR implementation combines an oral San tale narrative with the display of rock art paintings in a virtual cave. The user becomes a co-illustrator of the narrative being requested to paint or engrave the missing rock art in the cave. A user experience evaluation was conducted by eight San people from the Kalahari Desert. A logging system has been implemented to record detailed movements and interactions of the user throughout the system use. The interpretation of the logging data serves to understand user experiences and guide further system improvements.
Ensemble Clustering for Unsupervised Learning of Time Series Data using FPGAs

Ensemble Clustering is a form of Unsupervised Machine Learning where several clustering algorithms are combined in an ensemble to improve performance over a single clustering algorithm. Outputs from several clustering algorithms are combined together to determine the final output of the ensemble. A consensus function is used for combining outputs from the clustering algorithms to improve output from the ensemble. Selecting a consensus function without any apriori knowledge is difficult, and many types of consensus functions exist with various levels of computational complexity. In this paper, we consider the use of Ensemble Clustering as an unsupervised learning approach to time series data. Specifically, in this paper the challenge is to extract key relationships in time series data, gain insight and discover structure in data. The paper considers the advantages and disadvantages of ensembles in unsupervised machine learning followed by a discussion of clustering algorithms. A discussion of consensus functions for ensembles of clustering algorithms is provided and followed by a discussion of unsupervised learning of time series data. Furthermore, this paper considers implementation of Ensemble Clustering using Field Programmable Gate Arrays (FPGAs). The approach described in this paper applies to high performance, high throughput and scalable implementations for Big Data. Design data is provided for FPGA implementation of Ensemble Clustering. Finally, an example is provided based on the Xilinx UltraScale+ FPGAs to illustrate the concepts in this paper.
Effective crowd counting using multi-resolution context and image quality assessment-guided training

Crowd counting is the challenging task in the crowd scene analysis. To tackle the scale variant issue and to calculate the more accurate result in this target task, this paper designs an effective crowd counting method based on multi-resolution context and image quality assessment-guided training. Specially, a multi-resolution context module is designed to extract the multi-scale context adaptively to enhance the final counting performance through learning the imbalance between different scale paths. An image quality assessment-guided training approach is developed to facilitate the crowd counting network to generate high-quality density map and more accurate counting result. Extensive experiments on benchmarks demonstrate the effectiveness of the proposed method on crowd counting task, the generalization of the proposed method, and the generalization of the developed image quality assessment-guided training approach.
Quranic Topic Modelling Using Paragraph Vectors

The Quran is known for its linguistic and spiritual value. It comprises knowledge and topics that govern different aspects of people’s life. Acquiring and encoding this knowledge is not a trivial task due to the overlapping of meanings over its documents and passages. Analysing a text like the Quran requires learning approaches that go beyond word level to achieve sentence level representation. Thus, in this work, we follow a deep learning approach: paragraph vector to learn an informative representation of Quranic Verses. We use a recent breakthrough in embeddings that maps the passages of the Quran to vector representation that preserves more semantic and syntactic information. These vectors can be used as inputs for machine learning models, and leveraged for the topic analysis. Moreover, we evaluated the derived clusters of related verses against a tagged corpus, to add more significance to our conclusions. Using the paragraph vectors model, we managed to generate a document embedding space that model and explain word distribution in the Holy Quran. The dimensions in the space represent the semantic structure in the data and ultimately help to identify main topics and concepts in the text.
The Relation between Levels of Media Literacy and Attitudes and Beliefs Concerning Obesity in University Students

The prevalence of obesity in young people around the world. The written and visual media have an important role to play in increasing student awareness in the prevention of obesity. This study was planned with the aim of determining the effect on attitudes and beliefs concerning obesity of the levels of media literacy of university students. This cross-sectional descriptive study was conducted with 300 obese students. The Media Literacy Scale, and the Obesity Health Belief Model Scale were used in the collection of data for the study. The Student t test and the one-way ANOVA test were used in the analysis of the data. A weak, positive statistically significant correlation was found between media literacy and the subscales of perceived severity, perceived barriers, and perceived benefit (p < .05). A weak positive correlation was found between the subscales of possessing knowledge, the ability to analyze and form a response, and the ability to judge and see hidden messages, and the sub-scales of perceived severity, perceived barriers, and perceived benefit (p < .05). It was found in this study that the media literacy of obese students was at a medium level, and that media literacy affected their attitudes and beliefs regarding obesity. It is recommended that the visual media should broadcast short audiovisual educational films at regular intervals on the prevention of obesity.
Learning algebraic models of quantum entanglement

We review supervised learning and deep neural network design for learning membership on algebraic varieties. We demonstrate that these trained artificial neural networks can predict the entanglement type for quantum states. We give examples for detecting degenerate states, as well as border rank classification for up to 5 binary qubits and 3 qutrits (ternary qubits).
Security Measures for Safeguarding the Bioeconomy

Health SecurityVol. 18, No. 4 CommentarySecurity Measures for Safeguarding the BioeconomyMatthew Walsh and William StreileinMatthew WalshAddress correspondence to: Matthew Walsh, Associate Staff, Biological and Chemical Technologies Group, MIT Lincoln Laboratory, 244 Wood Street, Lexington, MA 02420 E-mail Address: Matthew.Walsh@ll.mit.eduMatthew Walsh is Associate Staff in the Biological and Chemical Technologies Group; and William Streilein, PhD, is Principal Staff in the Homeland Protection and Air Traffic Control Division; both at MIT Lincoln Laboratory, Lexington, MA.Search for more papers by this author and William StreileinMatthew Walsh is Associate Staff in the Biological and Chemical Technologies Group; and William Streilein, PhD, is Principal Staff in the Homeland Protection and Air Traffic Control Division; both at MIT Lincoln Laboratory, Lexington, MA.Search for more papers by this authorPublished Online:19 Aug 2020https://doi.org/10.1089/hs.2020.0029AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 18Issue 4Aug 2020 InformationCopyright 2020, Mary Ann Liebert, Inc., publishersTo cite this article:Matthew Walsh and William Streilein.Security Measures for Safeguarding the Bioeconomy.Health Security.Aug 2020.313-317.http://doi.org/10.1089/hs.2020.0029Published in Volume: 18 Issue 4: August 19, 2020KeywordsBioeconomyCybersecurityArtificial intelligenceBiotechnologyPersonnel securityPDF download
White learning methodology: A case study of cancer-related disease factors analysis in real-time PACS environment

Bayesian network is a probabilistic model of which the prediction accuracy may not be one of the highest in the machine learning family. Deep learning (DL) on the other hand possess of higher predictive power than many other models. How reliable the result is, how it is deduced, how interpretable the prediction by DL mean to users, remain obscure. DL functions like a black box. As a result, many medical practitioners are reductant to use deep learning as the only tool for critical machine learning application, such as aiding tool for cancer diagnosis. In this paper, a framework of white learning is being proposed which takes advantages of both black box learning and white box learning. Usually, black box learning will give a high standard of accuracy and white box learning will provide an explainable direct acyclic graph. According to our design, there are 3 stages of White Learning, loosely coupled WL, semi coupled WL and tightly coupled WL based on degree of fusion of the white box learning and black box learning. In our design, a case of loosely coupled WL is tested on breast cancer dataset. This approach uses deep learning and an incremental version of Naïve Bayes network. White learning is largely defied as a systemic fusion of machine learning models which result in an explainable Bayes network which could find out the hidden relations between features and class and deep learning which would give a higher accuracy of prediction than other algorithms. We designed a series of experiments for this loosely coupled WL model. The simulation results show that using WL compared to standard black-box deep learning, the levels of accuracy and kappa statistics could be enhanced up to 50%. The performance of WL seems more stable too in extreme conditions such as noise and high dimensional data. The relations by Bayesian network of WL are more concise and stronger in affinity too. The experiments results deliver positive signals that WL is possible to output both high classification accuracy and explainable relations graph between features and class.
Data Storage Security in the Cloud Environment Using the Honey Pot System

Cloud computing is a framework for delivering services through a shared network. It should follow certain procedures or mechanisms to protect and make the cloud data more reliable. In secure cloud computing, the implementation and service delivery model are dealt with. Cloud security could be viewed as a provider and customer responsibility depending on their design and level. There are cases where multiple cloud users can use the same information to create service abnormalities, which is called ‘multitenancy.' In a multi-tenant model, the resources are isolated between different users and it is done by segmentation to achieve the goal of data security between many users. In the user access area, user access to common resources is also limited. The software is aware of the actions of the client and acts accordingly if unauthorized access is detected. The authors propose a clear idea of the data storage security of cloud information through honeypot in cloud monitoring applications and various cloud security scheme key models used and also identifies the audit time and invalid response time.
Autonomous omnidirectional mobile robot navigation based on hierarchical fuzzy systems

Purpose This paper aims to propose a one-layer Mamdani hierarchical fuzzy system (HFS) to navigate autonomously an omnidirectional mobile robot to a target with a desired angle in unstructured environment. To avoid collision with unknown obstacles, Mamdani limpid hierarchical fuzzy systems (LHFS) are developed based on infrared sensors information and providing the appropriate linear speed controls. Design/methodology/approach The one-layer Mamdani HFS scheme consists of three fuzzy logic units corresponding to each degree of freedom of the holonomic mobile robot. This structure makes it possible to navigate with an optimized number of rules. Mamdani LHFS for obstacle avoidance consists of a number of fuzzy logic units of low dimension connected in a hierarchical structure. Hence, Mamdani LHFS has the advantage of optimizing the number of fuzzy rules compared to a standard fuzzy controller. Based on sensors information inputs of the Mamdani LHFS, appropriate linear speed controls are generated to avoid collision with static obstacles. Findings Simulation results are performed with MATLAB software in interaction with the environment test tool “Robotino Sim.” Experiments have been done on an omnidirectional mobile robot “Robotino.” Simulation results show that the proposed approaches lead to satisfied performances in navigation between static obstacles to reach the target with a desired angle and have the advantage that the total number of fuzzy rules is greatly reduced. Experimental results prove the efficiency and the validity of the proposed approaches for the navigation problem and obstacle avoidance collisions. Originality/value By comparing simulation results of the proposed Mamdani HFS to another navigational controller, it was found that it provides better results in terms of path length in the same environment. Moreover, it has the advantage that the number of fuzzy rules is greatly reduced compared to a standard Mamdani fuzzy controller. The use of Mamdani LHFS in obstacle avoidance greatly reduces the number of involved fuzzy rules and overcomes the complexity of high dimensionality of the infrared sensors data information.
Research on Relation Extraction Method of Chinese Electronic Medical Records Based on BERT

Relation extraction is a necessary step in obtaining information from electronic medical records. The deep learning methods for relation extraction are primarily based on word2vec and convolutional or recurrent neural network. However, word vectors generated by word2vec are static and cannot well reflect the different meanings of polysemy in different contexts and the feature extraction ability of RNN (Recurrent Neural Network) is not good enough. At the same time, the BERT (Bidirectional Encoder Representations from Transformers) pre-trained language model has achieved excellent results in many natural language processing tasks. In this paper, we propose a medical relation extraction model based on BERT. We combine the information of the whole sentence obtained from the pre-train language model with the corresponding information of two medical entities to complete relation extraction task. The experimental data were obtained from the Chinese electronic medical records provided by a hospital in Beijing. Experimental results on electronic medical records show that our model's accuracy, precision, recall, and F1-score reach 67.37%, 69.54%, 67.38%, 68.44%, which are higher than other three methods. Because named entity recognition task is the premise of relation extraction, we will combine the model with named entity recognition in the future work.
Improving relation extraction by multi-task learning

Relation extraction is a subtask of information extraction. Current relation extraction methods are mainly designed for relation extraction tasks, and they use limited knowledge. In this paper, we propose a relation extraction method based on multi-task learning. It uses multiple tasks to learn features that are hard to learn from the relation extraction task, and we add knowledge distillation to help the multi-task model perform better than its single-task counterparts. The experiments based on the pre-trained language model BERT show that our method performs better than most relation extraction methods on the SemEval2010-task8 dataset.
Exploiting minification for data hiding purposes

Nowadays various types of data hiding techniques are used to conceal data in different types of digital content, e.g. image, video, audio, text, or even network traffic. Such methods can be utilized for nefarious purposes, for instance, for confidential data exfiltration, enabling secret communication between the infected host and attacker's server or to download additional modules of malware. From this perspective, analyzing different schemes of data hiding allows to assess the preparedness of the current defensive systems. Minification is the process of the source code manipulation while preserving its functionality. In result, the size of the source code is reduced making the transmission more efficient. In this paper we investigate whether minification of JavaScript files can be exploited for data hiding purposes. The obtained results prove that this is feasible and thus countermeasures must be adjusted to take into account such threats.
Knowledge Model for Developing, Searching and Using Personalized Learning Content for Learners, Having Dyslexia Disability

Much research in the area of e-learning systems today is focused on personalization and adaptivity, but only a few of the proposed systems are intended for students, having learning disabilities. These learners are significant amount of all learners (children, having some dyslexia symptoms are between 10% and 20% of all children). Dyslexia symptoms can be specific for different learners and can require specific organization of learning. All the experts in learning say that suitable learning is a key for achieving excellent results for these learners.