Reviewerly/exHarmony huggingface dataset:
Electronic Medical Records Data Analysis Technologies and Services for the Cardiovascular Diseases Risk Prediction

The aim of our research is to develop technologies and methods for data mining of electronic medical records (EHR). We use our approach to create predictive models of the total risk of cardiovascular diseases (CVD) in apparently healthy individuals before taking therapeutic measures. We have developed methods and technologies for extracting valid information from unstructured EHR data, first, patient examination protocols, taking into account the requirements for secure distributed storage and processing of medical information. The developed special services for visualizing the values of objective indicators allow us to determine the appropriate sets of predictors for predictive models for assessing the risk of CVD. Visualization services provide aggregated data for modeling the patient’s problem-oriented digital phenotype. In our case, this is an optimal data structure that combines key indicators for predicting the degree of CVD risk from the point of view of criteria (rules) adopted in cardiology with additional features. We determine these features based on the analysis of data extracted from a large array of EHR. The software implementation of the developed technologies allows not only assessing the risk of CVD, but also collecting data for further improving the accuracy of predictive models. The software product may be useful in preventive examinations and mass screenings.
Improving Speech Recognition Accuracy of Local POI Using Geographical Models

Nowadays voice search for points of interest (POI) is becoming increasingly popular. However, speech recognition for local POI names still remains a challenge due to multi-dialect and long-tailed distribution of POI names. This paper improves speech recognition accuracy for local POI from two aspects. Firstly, a geographic acoustic model (Geo-AM) is proposed. The proposed Geo-AM deals with multi-dialect problem using dialect-specific input feature and dialect-specific top layers. Secondly, a group of geo-specific language models (Geo-LMs) are integrated into our speech recognition system to improve recognition accuracy of long-tailed and homophone POI names. During decoding, a specific Geo-LM is selected on-demand according to the user's geographic location. Experiments show that the proposed Geo-AM achieves 6.5%~10.1% relative character error rate (CER) reduction on an accent test set and the proposed Geo-AM and Geo-LMs totally achieve over 18.7% relative CER reduction on a voice search task for Tencent Map.
An efficient approach for removing haze from single image using Gaussian pyramidal decomposition

Summary The visibility of the image is degraded by haze components which are affected in image during image acquisition process. The haze removal process can be categorized into haze reduction from single image and haze reduction from multiple images. The second case consumes more time and the complexity of algorithm is high. In order to overcome such limitations, this paper proposes an efficient methodology for haze reduction in single acquired image. In this paper, haze detection and removing methodology is proposed using Gaussian pyramidal decomposition. This proposed methodology is tested on both indoor and outdoor haze affected images. The performance of this proposed haze removal algorithm is analyzed with respect to average running time and peak signal to noise ratio, mean square error, mean absolute error, Entropy, and normalized histogram intersection coefficient.
A novel distributed deep learning training scheme based on distributed skip mesh list

Distributed large scale neural networks are widely used in complicated image recognition and natural language processing among several organizations, but it takes much maintenance cost for keeping the nodes (i.e, computation servers) performance and their network topology in a churn environment that nodes insertion/deletion occurs frequency. To reduce the cost in the environment, we proposed Distributed Skip Mesh List Architecture, which can provide high stability against node insertion/deletion and automatic node management to a distributed large scale neural network management. In the evaluation, we confirmed that it reduces the maintenance cost (e.g., transmission messages for managing nodes) with high stability.
Codes related to caps and the non-existence of some Griesmer codes

In this paper we consider the problem of class with parameters (q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> + 2q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + q + 2, q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + 2q + 2) in PG(3, q). Such arcs correspond to Griesmer [q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> + 2q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + q + 2, 4, q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> + q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2-</sup> q] <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> -codes. They are trivially obtained as the sum of a maximal cap in PG(3, q) and the whole geometry. We prove that for q = 5, 7 this is the only possible construction. This was known to be the case also for q = 4. For q = 3, there exists one further example. The characterization uses a divisibility result for plane blocking sets with parameters (q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , q-1) which is of independent interest.
Non-Functional Requirements for Blockchain: Challenges and New Directions

Abstract Blockchain is one of the breakout technologies of the 21st century and it is destined to rule the field of cybersecurity. Eventually, all applications and software will adopt blockchain for being the most secure technology ever existed. Unlike other fields of computer science, blockchain too has functional and non-functional requirements (NFRs). As it is an emerging technology, Blockchain’s non-functional requirements are yet to be well-defined. From requirements engineering perspectives, the NFRs for blockchain should be well-established. In this study, some of the major non-functional requirements of blockchain are thoroughly analyzed. And, challenges of NFRs for blockchain and new research directions have been proposed. Five non-functional requirements challenges are presented-lack of knowledge about NFRs for blockchain, the necessity of feasibility analysis, integration with software engineering, NFRs measurement metrics, and its formal language representation. Based on those challenges, four new research directions have also been proposed. If those challenges are dealt with properly, the world will surely march towards a safer future in terms of cybersecurity.
A Study on the Implementation Path of College English Classroom Culture Teaching Based on Big Data Analysis

Collecting and analyzing big data from college students in Ordos Institute of Technology, is an effective way to improve college English teaching at present. This paper deeply analyzes the present situation and existing problems of college English teaching, and probes into how to collect and analyze big data in college English teaching, how to set up the teaching goal of college English classroom culture, and how to construct the teaching mode, in order to promote the process of ideological elements integrated into college English teaching reform in our college.
Mobile Edge Computing Based Video Surveillance System Using Special Hardware and Virtualization Technology

Abstract As technology evolution, the solution for society security system is a hot research topic. Video surveillance is an important guarantee for society security. To meet various application scenarios, quick end-to-end (E2E) response is the key of video surveillance system. Mobile edge computing (MEC) could reduce E2E delivery delay by processing data nearby the data source. This paper introduces a MEC based solution that uses special hardware, graphics processing units (GPU), to accelerate graphic data processing. Taking the limited resource of edge scenario into consideration, virtualization technology is used to share computing resource on demand. The solution also proposes deployment policy basing on functional component’s requirement. Finally, this paper verifies the solution and puts forward recommendations.
An Answer Sorting Method Combining Multiple Neural Networks and Attentional Mechanisms

A deep learning model combining multiple neural networks and attentional mechanisms is proposed to solve the problem of answer ranking. The word vectors of the questions and candidate answers were sent to the Convolutional Neural Network for learning, which is used the Leaky Relu activation function, and the learning results were pieced together with four Attention items, and features in relation to the vocabulary and topic, and then input into the Bidirectional Gated Recurrent Units. After the output results were processed by Multi-layer Perception, the softmax classifier produced the final ranking results. Experimental results indicate satisfactory performance of the model on WikiQACorpus data set with an accuracy of 80.86%.
Contextual Soft Dropout Method in Training of Artificial Neural Networks

Research upon artificial neural networks has yielded regularization techniques in order to reduce overfitting, reduce weight increase rate, and in consequence improve model performance. One of the commonly used regularization technique is dropout. In order to make the technique more resistant to emerging interdependencies while learning, the dropout has been generalized to its improved version called Soft Dropout. In this paper we have proposed an extended version of Soft Dropout called Contextual Soft Dropout. This technique is dedicated to be used with Contextual Neural Networks and utilizing their multi-step aggregation functions. Experimental results have shown improvement in overfitting decrease and have achieved lower logloss values for used real-life and benchmark data sets.
The development of web based inquiry as online science inquiry environment

Abstract This paper studies a research in the design and development of web based inquiry as online science inquiry environment. The web based inquiry is constructed to help junior high school students in understanding everyday science phenomena with guided inquiry framework and various sources of scaffolding. Furthermore, it is aimed to support teacher in designing their inquiry virtual class for new normal studying era. This study used 4-D development consisting define, design, develop, and disseminate as research method. At the beginning, it is stated about inquiry theoretical framework and online science inquiry environment underpinning for designing the web based inquiry, describe the features and components of the system, and explain workflow of web based inquiry’s instruction. Web based inquiry went through expert judgement with positive feedback. Afterwards, web evaluation questionnaire is distributed to science junior high school teacher to underline their perception regarding web based inquiry. The result of web evaluation questionnaire gained over 80% positive response. Therefore, web based inquiry can be used for both teacher and students in learning science with appropriate content navigation and layout.
PrivGenDB: Efficient and privacy-preserving query executions over encrypted SNP-Phenotype database

Privacy and security issues limit the query executions over genomics datasets, notably single nucleotide polymorphisms (SNPs), raised by the sensitivity of this type of data. Therefore, it is important to ensure that executing queries on these datasets do not reveal sensitive information, such as the identity of the individuals and their genetic traits, to a data server. In this paper, we propose and present a novel model, we call PrivGenDB, to ensure the confidentiality of SNP-phenotype data while executing queries. The confidentiality in PrivGenDB is enabled by its system architecture and the search functionality provided by searchable symmetric encryption (SSE). To the best of our knowledge, PrivGenDB construction is the first SSE-based approach ensuring the confidentiality of SNP-phenotype data as the current SSE-based approaches for genomic data are limited only to substring search and range queries on a sequence of genomic data. Besides, a new data encoding mechanism is proposed and incorporated in the PrivGenDB model. This enables PrivGenDB to handle the dataset containing both genotype and phenotype and also support storing and managing other metadata, like gender and ethnicity, privately. Furthermore, different queries, namely Count, Boolean, Negation and k′-out-of-k match queries used for genomic data analysis, are supported and executed by PrivGenDB. The execution of these queries on genomic data in PrivGenDB is efficient and scalable for biomedical research and services. These are demonstrated by our analytical and empirical analysis presented in this paper. Specifically, our empirical studies on a dataset with 5000 entries (records) containing 1000 SNPs demonstrate that a count/Boolean query and a k′-out-of-k match query over 40 SNPs take approximately 4.3s and 86.4μs, respectively, outperforming the existing schemes.
Beyond Deep Event Prediction: Deep Event Understanding Based on Explainable Artificial Intelligence

Big data and machine learningMachine learning are essential ingredients in artificial intelligent-based applications and systems for decision making. However, intelligent-based approaches, such as neural networks and deep learningDeep learning, only aim to summarize and predict events extracted from raw data rather than to understand them. This latter feature is especially important for critical events that are unpredictable and complex with the potential to cause serious damage to the economy, society, and the environment. Good event understanding would help the government and enterprises manage an event so they could make the best plan to relieve or prevent a critical situation. This means that high-level knowledge must be supplied to support understanding in uncertain and difficult situations. This chapter describes Deep Event Understanding (DUE), a new perspective for machine learningMachine learning based on eXplainable Artificial IntelligenceExplainable artificial intelligence (XAI)XAI driven by the paradigm of Granular Computing (GrC)Granular computing. It semantically models knowledge by imitating human thinking which can be understood by decision-makers. DUE offers a level of understanding that is both argumentative and explainable for critical applications and systems. DUE aims to mimic human intelligence by generating real-time knowledge using scientific causal structures of human thinking. We begin this chapter by highlighting the limitations of current machine learningMachine learning models which are unable to achieve a satisfactory level of understanding in terms of human intelligence. The fundamental human learning process shows the current disadvantages of curve-fitting technology such as deep learningDeep learning. DUE architecture for critical systemsCritical system is proposed which employs a cycling process between the system and the environment. The desirable properties for DUE are introduced, along with its basic ability to mimic human-like intelligence from the viewpoint of computing technology in terms of critical thinking and contextual understanding. We describe a learning platform based on Causal Bayesian NetworksCausal Bayesian Networks (CBNs) that encodes critical thinking and contextual understanding based on machine-interpretable and understandable forms, which is ready to learn new experiences from big data. The outcome is a transparent DUE model that can be understood by domain experts, lay users, and machines, and which is manageable, maintainable, and shareable. Finally, we highlight research trends and future directions for DUE.
Semi-supervised Factorization Machines for Review-Aware Recommendation

Textual reviews, as a useful supplementary of the interaction data, has been widely used to enhance the performance of recommender systems, especially when the interaction data is sparse. However, existing solutions to review-aware recommendation only focus on learning more informative features from reviews, yet ignore the insufficient number of training examples, resulting in limited performance improvements. To this end, we propose a co-training style semi-supervised review-aware recommendation model, called Collaborative Factorization Machines (CoFM), to augment the training dataset as well as increase its informativeness. Our CoFM employs two FMs as base predictors, each of which labels unlabeled examples for its peer predictor in the learning process. Specifically, a user-leaded FM and an item-leaded FM are separately built using different reviews to increase the diversity between two predictors. Furthermore, to exploit unlabeled data safely, the labeling confidence is estimated through validating the influence of the labeling of unlabeled examples on the labeled ones. The final prediction is made by linearly blending the outputs of two predictors. Extensive experiments on three real-world benchmarks demonstrate the superiority of CoFM over several state-of-the-art review-aware and semi-supervised recommendation schemes.
An algorithm for Peruvian counterfeit Banknote Detection based on Digital Image Processing and SVM

In this work we propose an algorithm for Peruvian counterfeit banknotes detection.Our algorithm operates in banknotes with 50, 100 and 200 soles denominations that were manufactured from 2009 onwards.This algorithm offers an automatic diagnosis based on digital image processing and support vector machines (SVM).Current Peruvian counterfeit detection systems are specially designed to analyze relevant characteristics in dollars and euros.Then, some counterfeiters can fool these systems.We made our detection system robust because we focus on the image acquisition and the segmentation of intaglio marks engraved over the banknotes.After segmentation, we applied embossing and Sobel filters followed by an aperture morphological operation to obtain special characteristics that were then classified by an SVM.We have validated our methodology using real and fake banknotes from a dataset of 240 samples provided by Central Reserve Bank of Peru (BCRP).Our final identification accuracy was 96.5%.
Credit Card Fraud Detection Using Autoencoder-Based Deep Neural Networks

It is now customary to pay by credit cards due to the ease of purchase and the ability to keep customers' credit history. At the same time, credit card fraud emerges as a widely-concerned issue in the electronic payment sector, urging the development of fraud detection techniques. In this paper, I develop a credit card fraud detection model using an autoencoder-based deep neural network. To ensure accuracy and robustness, I train two models on normal transactions and fraud transactions respectively. After that, I combine these two individual models into a hybrid model, which is the major innovation of this creation. ROC score along with the AUC value is the evaluation method here. It turns out that the mean of the AUC of the hybrid model is 0.9577, which is between that of the two individual models and shows the ensured accuracy and increased stability.
Automatic Cyberbullying Detection on Twitter Using Bullying Expression Dictionary

Cyberbullying has become a serious problem with the spread of personal computers, smartphones and SNS. In this paper, for automatic cyberbullying detection on Twitter, we construct a bullying expression dictionary, which registers bullying words and their degrees related to bullying. The words registered in the dictionary are those that appear in the collected bullying-related tweets, and the bullying degrees attached to the words are calculated using SO-PMI. We also construct models to automatically classify bullying and non-bullying tweets by extracting multiple features including the bullying expression dictionary and combining them with multiple machine learning algorithms. We evaluate the classification performance of bullying and non-bullying tweets using the constructed models. The experimental results show that the bullying expression dictionary can contribute to cyberbullying detection in most of the machine learning algorithms and that the best model can obtain an evaluation of over 90%.
An Optical Character Recognition Technique for Devanagari Script Using Convolutional Neural Network and Unicode Encoding

This paper describes an optical character recognition technique to convert scanned Sanskrit text images scripted in Devanagari into digital documents. The segmentation mechanism, an adaptation from existing literature, identifies and separates upper and lower modifiers in a character. It also recognizes fused Devanagari letters. The segmented characters are fed to a convolutional neural network classifier which is trained upon a dataset with about 1.2 lakhs images belonging to 85 classes for the core part of a character. Each character from the segmentation phase is predicted and mapped to the respective Unicode representation. These Unicode values for characters are added to reconstruct the desired word. By keeping track of spaces between words and lines, a document can be reconstructed to an editable format.
New public blockchain protocol based on sharding and aggregate signatures

Existing blockchains, especially public blockchains, face the challenges of scalability which means the processing capacity will not get better with the addition of nodes, making it somewhat infeasible for mobile computing applications. Some improved technologies are known to speed up processing capacity by shrinking the consensus group, increasing the block capacity and/or shortening the block interval. Even these solutions are met with major problems such as storage limitations and weak security. To face the realistic application scenarios for blockchain technology in the mobile realm, we propose a new public blockchain designed based on sharding, aggregate signature and cryptographic sortition which we call SAC. In SAC, the transaction rate increases with the number of shards while the length of the consensus signature is a constant. Meanwhile, in SAC, the assignment of consensus representatives is controlled by a verifiable random function, which can effectively solve the problem of centralized consensus. In addition, this paper analyzes the performance of SAC to give adequate comparison with other sharding technologies while also giving a rational security analysis. Our experimental results clearly show the potential applicability of this novel blockchain protocol to in mobile computation.