Reviewerly/exHarmony huggingface dataset:
FactQA: question answering over domain knowledge graph based on two-level query expansion

Purpose With the advent of the era of Big Data, the scale of knowledge graph (KG) in various domains is growing rapidly, which holds huge amount of knowledge surely benefiting the question answering (QA) research. However, the KG, which is always constituted of entities and relations, is structurally inconsistent with the natural language query. Thus, the QA system based on KG is still faced with difficulties. The purpose of this paper is to propose a method to answer the domain-specific questions based on KG, providing conveniences for the information query over domain KG. Design/methodology/approach The authors propose a method FactQA to answer the factual questions about specific domain. A series of logical rules are designed to transform the factual questions into the triples, in order to solve the structural inconsistency between the user’s question and the domain knowledge. Then, the query expansion strategies and filtering strategies are proposed from two levels (i.e. words and triples in the question). For matching the question with domain knowledge, not only the similarity values between the words in the question and the resources in the domain knowledge but also the tag information of these words is considered. And the tag information is obtained by parsing the question using Stanford CoreNLP. In this paper, the KG in metallic materials domain is used to illustrate the FactQA method. Findings The designed logical rules have time stability for transforming the factual questions into the triples. Additionally, after filtering the synonym expansion results of the words in the question, the expansion quality of the triple representation of the question is improved. The tag information of the words in the question is considered in the process of data matching, which could help to filter out the wrong matches. Originality/value Although the FactQA is proposed for domain-specific QA, it can also be applied to any other domain besides metallic materials domain. For a question that cannot be answered, FactQA would generate a new related question to answer, providing as much as possible the user with the information they probably need. The FactQA could facilitate the user’s information query based on the emerging KG.
Textual Question Answering for Semantic Parsing in Natural Language Processing

Question answering (QA) problem is one of the most frequent tasks in natural language processing (NLP) over language input. To understand the natural language, semantic parsing is an extensively used process. For deriving syntactic and semantic structures of texts, synchronous frameworks are being used frequently. With the aim of answering questions efficiently, in this paper, an approach by semantic parsing of texts with a view to leveraging semantic information is introduced. The proposed approach uses the method based on lambda calculus for semantic parsing to derive logical forms of sentences. The questions are analyzed by collecting significant features to find out correct answers from the facts. Unlike traditional approaches, this paper extends lambda calculus based dependency parser methodology for question analysis. This proposed approach for answering questions is based on generalized searching techniques. Proposed system achieves 95% accuracy for Yes/No questions with an overall 83% mean accuracy for the five tasks of bAbI-10k question answering dataset surpassing the existing approach by about 11%.
A Semi-Formal Requirement Modeling Pattern for Designing Industrial Cyber-Physical Systems

Requirement engineering is a crucial part of the engineering process. The traditional methods of requirement engineering are time-consuming and human-centered. A well-established software requirement description model needs to ensure the accuracy and integrity of the transformation and is also hoped to be scalable, versatile, and efficient in transformation and transmission. This paper presents a method of requirement engineering, including constricted nature language requirement input pattern, and the formalized requirement description JSON model. This method provides convenience for requirement modification and validation that can satisfy the real-time constraints of industrial cyber-physical systems.
Blur Feature Extraction Plus Automatic KNN Matting: A Novel Two Stage Blur Region Detection Method for Local Motion Blurred Images

To deal with the serious visual artifacts caused by the consistent restoration algorithm on local motion blurred images, a novel two stage blur region detection method including coarse location and region refinement for local blurred images is proposed in this paper. First, blur feature discriminant criteria in frequency domain and spatial domain is defined to generate blur feature image; then, binarization is used on the blur feature image to obtain the coarse blur region detection map; subsequently, morphological methods are used to process the clear and blur areas and the trimap is obtained. Finally, the refinement detection of blur region is achieved by combination of the trimap and automatic KNN matting algorithm. Experimental results show that the proposed algorithm can detect the local blurred region quickly and effectively, and it has outperformed other traditional methods in the application of blur region detection and image restoration as well.
Optimizing Query Times for Multiple Users Scenario of Differential Privacy

Differential privacy is the state-of-the-art for preserving privacy and differential privacy mechanism based on Laplace distribution with mean 0 is common practice.However, privacy budget is exhausted so quick that the number of queries is not enough.In this paper, a differential privacy mechanism is proposed to optimize the number of queries for application scenario of multiple users.We isolate different users by assigning various noise distribution with non-zero mean to different users.First, in terms of privacy guarantee, the proposed mechanism is better than common practice.Second, for the utility aspect, the accuracy of proposed mechanism is analyzed from the view of data distribution's distortion and the view of noise's absolute value.
User Modeling via Anomaly Detection Techniques for User Authentication

Anomaly detection is quickly becoming a very significant tool for a variety of applications such as intrusion detection, fraud detection, fault detection, system health monitoring, and event detection in IoT devices. An application that lacks a strong implementation for anomaly detection is user trait modeling. User trait models expose up-to-date representation of the user so that changes in their interests, their learning progress or interactions with the system are noticed and interpreted. The reason behind the lack of adoption in user trait modeling arises from the need for a continuous flow of high-volume data, that is not available in most cases, to achieve high-accuracy detection. This paper provides new insight into the anomaly detection techniques through Big Data utilization. With Big Data characteristics, i.e., volume, variety and velocity, anomaly detection techniques have become more suitable tools for user trait modeling. User traits will be modeled by creating a security user profile for each user. This profile is structured and developed to be a source for a strong real-time user authentication method. An ingenious implementation of three models; k-means, HMM, and auto-encoder neural network has been presented that automatically and accurately build a unique pattern of the users’ behavior. The implementation comprises four main steps: prediction of rare user actions, filter security potential actions, build/update a user profile, and generate a real-time (i.e. just in time) set of challenging questions. Real-world scenarios have been given showing the benefits of these challenging questions in building secure knowledge-based user authentication systems.
Chatbot Evaluation as Knowledge Application: a Case Study of PT ABC

ELISA chatbot acts as an automated answering machine to users' problem in PT ABC. Implemented in May 2018, it succeeded to provide a faster response time than offline customer service. However, it still has issues in delivering accurate and appropriate responses. One of the reasons is the lack of system control and evaluation. This paper aims to evaluate how ELISA chatbot, as a knowledge application, benefits the company using DeLone & McLean model of information system as a reference. A survey was conducted, and SEM-PLS was utilized to process the data. The result shows that information quality, service quality, and intention to use significantly affect user satisfaction in ELISA. Suggestions for ELISA improvement includes machine learning technology utilization and nonfunctional improvements.
Postdigital Possibilities: Operaismo, Co-research, and Educational Inquiry

Abstract There are parallels between the post-Marxist traditions of operaismo (workerism) and autonomism and emerging ideas about the ‘postdigital’. Operaist analyses and approaches, and particularly the work of Romano Alquati on co-research, have the potential to contribute to discourses as to what might be involved in postdigital inquiry in educational settings, and to better understand of critical data literacies. For such educational inquiry to evolve into a comprehensive strategy of ‘co-research’, it is argued that what is needed are models of teacher inquiry with the potential to challenge dominant rhetorics, to support emancipatory research and development, and to establish the postdigital as a counter-hegemonic educational programme.
Breast cancer classification using digital biopsy histopathology images through transfer learning

Abstract Breast cancer (BC) infection, which is peculiar to women, brings about the high rate of deaths among women in every part of the world. The early investigation of BC has minimized the severe effects of cancer as compared to the last stage diagnosis. Doctors for diagnostic tests usually suggest the medical imaging modalities like mammograms or biopsy histopathology (Hp) images. However, Hp image analysis gives doctors more confidence to diagnose BC as compared to mammograms. Many studies used Hp images to develop BC classification models to assist doctors in early BC diagnosis. However, these models lack better and reliable results in terms of reporting multiple performance evaluation metrics. Therefore, the goal of this study is to create a reliable, more accurate model that consumes minimum resources by using transfer learning based convolution neural network model. The proposed model uses the trained model after fine tuning, hence requires less number of images and can show better results on minimum resources. BreakHis dataset, which is available publicly has been employed in overall experiments in this research. BreakHis dataset is separated into training, testing, and validation for the experimentation. In addition, the dataset for training was augmented followed by stain normalization. By using the concept of transfer learning (TL), AleNext was retained after fine-tuning the last layer for binary classification like benign and malignant. Afterward, preprocessed images are fed into the TL based model for training. The model training was performed many times by changing the hyper-parameters randomly until the minimum validation loss was achieved. Now the trained model was used for feature extraction. The extracted features were further evaluated by using six ML classifiers (i.e. softmax, Decision tree, Naïve Bayes, Linear discriminant analysis, Support vector machine, k-nearest neighbor) through five performance measures such as precision, F-measure, accuracy, specificity, and sensitivity for experimental evaluation. The softmax has outperformed among all classifiers. Furthermore, to reduce the wrong prediction, a misclassification reducing (MR) algorithm was developed. After using the MR algorithm the proposed model produced better and reliable results. The observed accuracy, specificity, sensitivity, precision and F measure are 81.25%, 77.47%, 82.49%, 91.70%, and 86.80% respectively. These results show that the proposed TL based model along with misclassification reduction algorithm produced comparable results to the current baseline models. Hence, the expected model could serve as a second opinion for BC classification in any healthcare center.
A Novel User Membership Leakage Attack in Collaborative Deep Learning

Collaborative deep learning can provide high learning accuracy even participanted users' datasets are small. In the training process, users only share their locally obtained parameters, therefore it is believed that the privacy of users' original datasets can be protected. However, we present an attack approach against users' privacy in collaborative deep learning by utilizing Generative Adversarial Network (GAN) and Membership Inference. In this attack, an attacker builds a discriminator based on users' shared parameters and then trains a GAN network locally. The GAN can refactor the training records of the collaborative deep learning system. According to the generated records, the attacker uses the extent of model overfitting on an input and gets the membership of each group of records by the simplified Membership Inference attack. We evaluate the presented attack model over datasets of complex representations of handwritten digits (MINIST) and face images (CelebA). The results show that an attacker can easily generate the original training sets and classify them to obtain the membership between users' records and their identities in the collaborative deep learning.
Enhanced Genetic Algorithm for Single Document Extractive Summarization

In extractive summarization, summaries are generated by selecting the most salient sentences from the original text. The text summarization can be seen as a classification of sentences into two groups: in-summary/not-in-summary. Many approaches have been proposed to extract key sentences in which using Genetic Algorithms (GAs) has shown some promising results. In this paper, we propose an enhanced genetic algorithm in order to improve the quality of extractive text summarization. More concisely, we first evaluate the role of some sentence features and their contribution to improve the fitness function. We second investigate some crossover and mutation mechanisms in order to augment the accuracy of summarization as well as the performance of our model. The experiment has been conducted for the Daily Mail dataset to assess the proposed model and previous works. The empirical results show that our proposed GA gives better accuracy in comparison with TextRank and SummaRunNer, i.e., increasing the accuracy by 7.2% and 6.9% respectively.
Refining the Fusion of Pepper Robot and Estimated Depth Maps Method for Improved 3D Perception

As it is well known, some versions of the Pepper robot provide poor depth perception due to the lenses it has in front of the tridimensional sensor. In this paper, we present a method to improving that faulty 3D perception. Our proposal is based on a combination of the actual depth readings of Pepper and a deep learning-based monocular depth estimation. As shown, the combination of both of them provides a better 3D representation of the scene. In previous works we made an initial approximation of this fusion technique, but it had some drawbacks. In this paper we analyze the pros and cons of the Pepper readings, the monocular depth estimation method and our previous fusion method. Finally, we demonstrate that the proposed fusion method outperforms them all.
Bayesian Test of Normality Versus a Dirichlet Process Mixture Alternative

We propose a Bayesian test of normality for univariate or multivariate data against alternative nonparametric models characterized by Dirichlet process mixture distributions. The alternative models are based on the principles of embedding and predictive matching. They can be interpreted to offer random granulation of a normal distribution into a mixture of normals with mixture components occupying a smaller volume the farther they are from the distribution center. A scalar parametrization based on latent clustering is used to cover an entire spectrum of separation between the normal distributions and the alternative models. An efficient sequential importance sampler is developed to calculate Bayes factors. Simulations indicate the proposed test can detect non-normality without favoring the nonparametric alternative when normality holds.
Privacy Preserving Distributed Data Mining Based on Secure Multi-party Computation

Data mining is an important task to understand the valuable information for making right decision. There are numerous mature technologies for mining self-owned data of a party. However, how to perform distributed data mining to obtain information from data owned by multi-party without privacy leak is still a challenge. In recent years, secure multi-party computation (MPC) shows its potential capability for solving this problem. Meanwhile, there are a number of problems to be solved before utilizing in business environment. In this paper, we point out two unsupported tasks of MPC that are common in real-world. Towards this end, we design algorithms based on optimized matrix computation with one-hot encoding and LU decomposition to support these in MPC context requirements. In addition, we implement them based on a SPDZ protocol, a computation framework of MPC. The experimental evaluation results demonstrate that our design and implementation are feasible and effective for privacy preserving distributed data mining.
Combination of columnar transposition cipher caesar cipher and lempel ziv welch algorithm in image security and compression

Abstract Exchange of information through the medium of the internet allows information can be accessed by unauthorized parties. Techniques for securing information that is commonly used is Cryptography. This research will use a combination of two classic algorithms which are Columnar Transposition Cipher and a Caesar Cipher with a Linear Congruential Generator modification. Information in the form of images often has some of the same pixel value components. For saving the memory then the writers use the technique of compression with Lempel Ziv Welch algorithm, which is a dictionary-based compression algorithm. Stages of the process used in this study are that the pictures will be encrypted using the algorithm of Columnar Transposition Cipher, next step the file that has been encrypted will be encrypted with the Caesar Cipher algorithm using a combination of and Linear Congruential generators are modified. Then the image file that has been encrypted will be compressed using the second algorithm Lempel Ziv Welch. For the implementation of programming language C # systems used. The results of this study showed that the combination of Columnar Transposition Cipher and a Caesar Cipher with a Linear Congruential Generators could keep the confidentiality, integrity, and security of data. Lempel Ziv Welch algorithm produces the value of the average compression ratio (RC) 67.05%, in Compression Ratio (CR) aspect the value is 1.49, and in Space Saving (SS) parameter, the percentage is 32.93%.
Fun or frustration? How emotions shape user innovation processes

The paper explores how emotions influence innovation processes by users. User innovation research identifies an own need for a solution and/or enjoyment of experimenting with ideas as main drivers of innovation. The paper argues for a detailed view on the variety of emotions which contribute to user innovation processes and explores differences between user innovation processes that are primarily initiated in order to change an unsatisfactory situation and those initiated by enthusiastic engagement. The paper mainly draws on literature from emotional and economic geography. Theoretical starting point is a practice approach to creativity and innovation, emphasizing the importance of socio-material engagement and interaction for innovation. Empirically, the paper presents results from two innovation biographies. The pathways of an inhalation tool for children and the activity of geocaching have been reconstructed through time and space. The paper illustrates how emotional engagement affects user innovation processes and presents differences and similarities between the two innovations. The results demonstrate how both innovation paths were characterized by open search processes. However, while the process of the inhalation tool was focused on altering a specific situation, geocaching rather provided a purpose for ongoing tinkering with technology.
Sentiment analysis of commodity reviews based on multilayer LSTM network

With the rapid development of network technology and the popularization and application of e-commerce, comments with subjective sentiments on various mainstream e-commerce platforms play a crucial role in improving product quality. This paper takes the commodity comments on e-commerce websites as the research object, studies the sentimental tendency of the comment text, expresses the comment text based on the word2vec word vector model, and establishes the sentimental analysis model of the deep hierarchical network structure by adopting the multi-layer LSTM. Experiments show that the sentiment analysis method proposed in this paper is obviously superior to the traditional machine learning method and convolutional neural network method, and the accuracy rate reaches 92.23%, and model have a small time cost. This method can be applied to the comment sentiment analysis in the field of e-commerce and network social platforms.
Diverse Neural Photo Album Summarization

In this paper, we address the problem of learning to summarize personal photo albums. That is, given a photo album, we aim to select a small set of representative images from the album so that the extracted summary captures most of the story that is being told through the images. More specifically, we extend a recently proposed recurrent neural network based framework by employing a more effective way to represent images and, more importantly, adding a diversity term to the main objective. Our diversity term is based on the idea of jointly training a discriminator network to evaluate the diversity of the selected images. This alleviates the issue of selecting near-duplicate or semantically similar images, which is the primary shortcoming of the base approach. The experimental results show that our improved model produces better or comparable summaries, providing a good balance between quality and diversity.
Student’s Academic Performance Evaluation Method Using Fuzzy Logic System

For evaluating student's performance, the existing methods are not so much fair and satisfiable. The ranges of marks used for grading here are sometimes blessing and sometimes not. A method is proposed to evaluate performance based on Fuzzy Inference System (FIS). This system considers the student's continuous assessment which includes the attendances, total times he/she is spent in class and marks obtained in class test. This method increases the fairness and excellency of a student's assessment than existing methods. In existing FIS for grading the continuous assessment of student is not fuzzified where only the final exam papers mark is fuzzified. But, the proposed system fuzzified both the continuous assessment and exam paper's mark which actually increase more precise justification of student performances.