Reviewerly/exHarmony huggingface dataset:
Blockchain - From Cryptocurrency to Vertical Industries - A Deep Shift

A technology that has shifted the entire paradigm of computer applications during the last decade is undoubtedly, the Blockchain technology. The purpose of inventing Blockchain was to develop a technology in which data is immutable, confidential and located on a de-centralized network. This technology was first used to build a cryptocurrency application, which is just one of many applications developed using Blockchain technology. Since, most of the emphasis has been on the money and finance related services, now the vertical industries have also started to implement Blockchain. This study contributes to review the opportunities of using Blockchain technology in vertical industries.
Firefly Algorithm and Its Applications in Engineering Optimization

Metaheuristic algorithms are optimization algorithms which attempt to enhance the degree of resolution of the solution space iteratively. This is performed by utilizing guided search methods along with some randomness properties. These algorithms are motivated by biological phenomena or the social behavior of the species. While the deterministic optimization methods depend on the nature of the optimization problem, the metaheuristic algorithms are generally problem independent. Due to their specific advantages over the classical methods, these algorithms have been used extensively in solving the different problems in the fields of science and engineering. One such metaheuristic algorithm is the firefly algorithm. It is inspired by the flashing behavior of fireflies and widely used for solving nonlinear- nonconvex optimization problems. This chapter describes the firefly algorithm and its recent modifications. The sensitivity of the parameters affecting the firefly algorithm along with the solution to optimization problems are discussed in this chapter.
Scientific paper classification using Convolutional Neural Networks

The Convolutional Neural Network (CNN), a class of artificial neural networks, has become dominant in various fields, including analysis and text processing. It designed to learn, automatically and adaptively, the spatial hierarchies of backscattered entities using multiple building blocks, such as convolutional layers, grouping layers, and fully connected layers. This article aims to present an approach based on CNN to classify scientific articles by their domains (7 different domains) from their abstracts, the process will base on several features extracted automatically from their summaries. The proposed approach has shown remarkable results for the automatic classification of text compared to other usual automatic learning algorithms.
Korean Grapheme Unit-based Speech Recognition Using Attention-CTC Ensemble Network

This study proposes an end-to-end speech recognition method based on the Attention-CTC ensemble network that uses Korean graphemes as recognition units. End-to-end speech recognition is a method that allows the processing of procedures that involved a number of modules, including the DNN-HMM-based acoustic model, the N-gram-based language model, and the WFST-based decoding network, with a single DNN network. To predict the outputs of the end-to-end model, this study utilizes grapheme-unit output structures. Building a network based on graphemes enables effective learning by reducing the number of output parameters to be predicted from 11,172 to 49. Towards this aim, this study designed an end-to-end model by combining the connectionist temporal classification (CTC), the DNN network structure primarily used in end-to-end learning, and the attention network model. The experiment resulted in a 10.5% syllable error rate.
Communication and technology theories from the South: the cases of China and India

This paper takes an alternative approach to understanding theory as description. New theoretical propositions and knowledge practices need to grow out of the comparisons between descriptions, especially comparisons between the Southern cases. Using China and India as two cases, this paper reviews the descriptions of communication technology in the two countries and compares the descriptions. Through such comparisons, the paper concludes that the communication technology studies on China and India provide three theoretical insights: firstly, the state-society relationship shapes communication technology; secondly, the increasing pluralization or hybridity of the cyberspace shapes how communication technology is used; and lastly, it is the quest for finding one’s self (or selves) in a Chinese/Indian modernity that could provide references to other contexts.
A Reinforcement-Learning Approach for Adaptive and Comfortable Assistive Robot Monitoring Behavior

Companion robots used in the field of elderly assistive care can be of great value in monitoring their everyday activities and well-being. However, in order to be accepted by the user, their behavior, while monitoring them, should not provide discomfort: robots must take into account the activity the user is performing and not be a distraction for them. In this paper, we propose a Reinforcement Learning approach to adaptively decide a monitoring distance and an approaching direction starting from an estimation of the current activity obtained by the use of a wearable device. Our goal is to improve user activity recognition performance without making the robot's presence uncomfortable for the monitored person. Results show that the proposed approach is promising for real scenario deployment, succeeding in accomplishing the task in more than 80%of episodes run.
Keynote Speech: Information processing hardware, physical reservoir computing and complex-valued neural networks

First we discuss the essence of neural networks, which are the bases of modern artificial intelligence (AI), to examine the relationship between the neural fundamental framework and the present hardware. Then, in this context, we review reservoir computing and complex-valued neural networks.
A Framework of Decentralized Electricity Market Based on The Collaborative Mechanism of Blockchain and Edge Computing

Distributed generations are expected to develop rapidly in the electric power system. As an essential infrastructure, the structure of electricity market moves to distributed or decentralized network. Without a central coordinator, transaction security and optimal dispatch will be a challenge. In this paper, blockchain and edge computing are used to be a potential solution of decentralized electricity market. A decentralized electricity pricing algorithm is installed in the onsite edge computing terminal, and then the transactions are implemented in blockchain. Case studies are presented to demonstrate the availability of pricing mechanism.
Online Semantic Subspace Learning with Siamese Network for UAV Tracking

In urban environment monitoring, visual tracking on unmanned aerial vehicles (UAVs) can produce more applications owing to the inherent advantages, but it also brings new challenges for existing visual tracking approaches (such as complex background clutters, rotation, fast motion, small objects, and realtime issues due to camera motion and viewpoint changes). Based on the Siamese network, tracking can be conducted efficiently in recent UAV datasets. Unfortunately, the learned convolutional neural network (CNN) features are not discriminative when identifying the target from the background/clutter, In particular for the distractor, and cannot capture the appearance variations temporally. Additionally, occlusion and disappearance are also reasons for tracking failure. In this paper, a semantic subspace module is designed to be integrated into the Siamese network tracker to encode the local fine-grained details of the target for UAV tracking. More specifically, the target’s semantic subspace is learned online to adapt to the target in the temporal domain. Additionally, the pixel-wise response of the semantic subspace can be used to detect occlusion and disappearance of the target, and this enables reasonable updating to relieve model drifting. Substantial experiments conducted on challenging UAV benchmarks illustrate that the proposed method can obtain competitive results in both accuracy and efficiency when they are applied to UAV videos.
RNTR-Net: A Robust Natural Text Recognition Network

In this work, a novel robust natural text recognition network (RNTR-Net) is proposed based on a combination of convolutional neural network (CNN) (for feature extraction) and a recurrent neural network (RNN) (for sequence recognition). The pipeline design comprises an improved block of residual learning combined with a general residual block to extract feature maps. Two bidirectional Long Short Term Memory (LSTM) networks are used for sequence recognition, and a transcription layer is used for decoding. The proposed network can handle text images suffering from distortion or other degradations. Compared with previous algorithms, we achieve superior results in general datasets, including the IIIT-5K, Street View Text and ICDAR datasets. Moreover, the performance of the presented network is either highly competitive or even state-of-the-art regarding the highly challenging SVT-Perspective and CUTE80 datasets. We obtain considerable performance of 84.7% and 62.6% on lexicon-free IIIT-5K and CUTE80 datasets, respectively. The experimental results demonstrate the effectiveness of our network.
Optimized Term Extraction Method Based on Computing Merged Partial C-Values

Assessing the completeness of a document collection, regarding terminological coverage of a domain of interest, is a complicated task that requires substantial computational resource and human effort. Automated term extraction (ATE) is an important step within this task in our OntoElect approach. It outputs the bags of terms extracted from incrementally enlarged partial document collections for measuring terminological saturation. Saturation is measured iteratively, using our $$ thd $$ measure of terminological distance between the two bags of terms. The bags of retained significant terms $$ T_{i} $$ and $$ T_{i + 1} $$ extracted at i-th and i + 1-st iterations are compared $$ (thd(T_{i} ,T_{i + 1} )) $$ until it is detected that $$ thd $$ went below the individual term significance threshold. The flaw of our conventional approach is that the sequence of input datasets is built by adding an increment of several documents to the previous dataset. Hence, the major part of the documents undergoes term extraction repeatedly, which is counter-productive. In this paper, we propose and prove the validity of the optimized pipeline based on the modified C-value method. It processes the disjoint partitions of a collection but not the incrementally enlarged datasets. It computes partial C-values and then merges these in the resulting bags of terms. We prove that the results of extraction are statistically the same for the conventional and optimized pipelines. We support this formal result by evaluation experiments to prove document collection and domain independence. By comparing the run times, we prove the efficiency of the optimized pipeline. We also prove experimentally that the optimized pipeline effectively scales up to process document collections of industrial size.
Machine Learning Based Early Detection System of Cardiac Arrest

Target-Most of the Cardiac Arrest (CA) cases are preventable because the CA patients usually had abnormal clinical signs or symptoms before their suffering from CA. In general, the appropriate steps of Cardiopulmonary resuscitation (CPR) for CA patients will increase the patients' survival rate and reduce the consequent medical expenses. Accordingly, we propose a system and the related methods for detecting CA before the CPR event occurred earlier and it is not only assisting physicians to early diagnose of CA and immediately warning but also improving the medical quality. Methods-In this study, the raw dataset is collected from the electronic health records (EHRs) of the adult patients (age ≥ 20 years) who visited emergency department (ED) and stayed in the emergency detention area for more than 6 hours during January 2014 to December 2015, and it is provided by National Taiwan University Hospital (NTUH). We perform the data preprocessing and cleaning for the dataset using a resampling technique to balance the data amount of CPR and Non-CPR patients of the dataset, and then we construct a sliding window and apply several classifiers for model training and reducing the possible overfitting problem. Additionally, we use the measures such as the Area Under the Receiver Operating Characteristic Curve (AUROC) and the Area Under the Precision-Recall Curve (AUPRC) to comparative evaluate the performance of our models built. Results-Our approach avoids the problems of dataset imbalance and possible overfitting effectively. The performance among classifiers selected show that the best one is random forest (RF) when CPR event happened, but the better ones are Logistic Regression and LSTM than the remaining classifiers and close to that of RF during 1 to 4 hours before the CPR time. Conclusion-We have the contribution in predicting CA and CPR event before it occurred around 3 to 3.5 hours in advance, and it is similar to that of the state-of-the-art such Early Warning Score (EWS) with around 3.5 hours. In addition, avoiding the problem of dataset imbalanced may effectively improve the accuracy of predicting CA as well. Accordingly, it helps to assist the emergency clinical physicians to achieve the hospital's quality management including the clinical or medical resources allocation.
BullyNet: Unmasking Cyberbullies on Social Networks

One of the most harmful consequences of social media is the rise of cyberbullying, which tends to be more sinister than traditional bullying, given that online records typically live on the Internet for quite a long time and are hard to control. In this article, we present a three-phase algorithm, called BullyNet, for detecting cyberbullies on Twitter social network. We exploit bullying tendencies by proposing a robust method for constructing a cyberbullying signed network (SN). We analyze tweets to determine their relation to cyberbullying while considering the context in which the tweets exist in order to optimize their bullying score. We also propose a centrality measure to detect cyberbullies from a cyberbullying SN and show that it outperforms other existing measures. We experiment on a data set of 5.6 million tweets, and our results show that the proposed approach can detect cyberbullies with high accuracy while being scalable with respect to the number of tweets.
Analysis of the Douban online review of the MCU: based on LDA topic model

Abstract With the rapid development of the Internet and the movie industry, online reviews have shown explosive growth. Online review data contains a lot of valuable information, through text mining techniques such as the LDA topic model to analyze this information, to help the movie creators to understand the public’s viewing needs, movie production team to reflect on the shortcomings in the production process, in order to promote the development of the movie industry to provide a reference. In this paper, the Douban online review data of the MCU are used as the research object, and the Chinese text is analyzed based on the LDA topic model. Firstly, data collection and preprocessing are carried out with various software. Then, the word cloud is used to visualize the core information in the online review data, and then the LDA topic model is used for a deeper semantic mining. The research results show that the rich and varied plot design, the audiovisual feast brought by advanced digital technology, the clever connection of bonus scene, the unique characters and actors and a super-hero’s commercial movie mode are closely related to the success of the MCU.
A Data-Efficient Training Model for Signal Integrity Analysis based on Transfer Learning

The signal integrity analysis is an essential part of electronic design, while direct high speed signal analysis becomes a time-consuming work. As machine learning exhibits high performance in communication fields in recent years, deep neural network(DNN) is thought to be a key tool to predict eye diagram metrics. However, DNN based signal integrity analysis faces two challenges: demands for amounts of labelled data and long training period. In this paper, we propose a signal integrity analysis model based on transfer learning. The model makes full use of a trained network and trains networks for various channel environments. To achieve the same predicting accuracy, 64% labelled data are utilized for training compared to DNN. The application of hyper-parameters in the neural network improves the prediction accuracy of the eye diagram width and height by 42.7% and 49.24% compared to current methods in few shot signal integrity analysis.
$$\mathsf {BackFlow}$$: Backward Context-Sensitive Flow Reconstruction of Taint Analysis Results

Taint analysis detects if data coming from a source, such as user input, flows into a sink, such as an SQL query, unsanitized (not properly escaped). Both static and dynamic taint analyses have been widely applied to detect injection vulnerabilities in real world software. A main drawback of static analysis is that it could produce false alarms. In addition, it is extremely time-consuming to manually explain the flow of tainted data from the results of the analysis, to understand why a specific warning was raised. This paper formalizes $$\mathsf {BackFlow}$$, a context-sensitive taint flow reconstructor that, starting from the results of a taint-analysis engine, reconstructs how tainted data flows inside the program and builds paths connecting sources to sinks. $$\mathsf {BackFlow}$$ has been implemented on Julia's static taint analysis. Experimental results on a set of standard benchmarks show that, when $$\mathsf {BackFlow}$$ produces a taint graph for an injection warning, then there is empirical evidence that such warning is a true alarm. Moreover $$\mathsf {BackFlow}$$ scales to real world programs.
Multi-level Authentication-Based Secure Aware Data Transaction on Cloud Using Cyclic Shift Transposition Algorithm

Cloud computing is used for the sake of storing, managing, and processing data on a remote server network hosted on the Internet, rather than a local server or a personal computer. While numerous data volume is saved in the cloud, the stored data within the cloud have some security issues. It is therefore essential that data stored in the cloud are secure. Therefore, in this paper, multi-level authentication-based secure aware data transaction on cloud is proposed. The proposed system consists of three modules, namely multi-level authentication, data security, and data retrieval. To avoid the unauthorized user login to the server, novel multi-level authentication is developed. In this model, data security is improved with the help of cyclic shift transposition algorithm. To avoid the real-time attack during the data transaction retrieval process, hash-based timestamp is used. In terms of time and memory, the proposed methodology is evaluated.
A Systematic Review of Feature Selection Techniques in Software Quality Prediction

Background: Feature selection techniques are important factors for improving machine learning models because they increase prediction accuracy and decrease the time to create a model. Recently, feature selection techniques have been employed on software quality prediction problems with different results and no clear indication of which techniques are frequently used.Objective: This study aims to conduct a systematic review of the application of feature selection techniques in software quality prediction and answers eight research questions.Method: The review evaluates 15 papers in 9 journals and 6 conference proceedings from 2007 to 2017 using the standard systematic literature review method.Results: The results obtained from this study reveal that the filter feature selection method was the most commonly used in the studies (60%) and RELIEF was the most employed among this method, and a limited number of studies employed an ensemble method. Several studies used public datasets available in the PROMISE software project repository (60%). Most studies focused on software defect prediction (classification problem) using area under curve (AUC) as a primary evaluation measure, whereas only two studies focused on software maintainability prediction (regression problem) using mean magnitude of relative error (MMRE) as a primary evaluation measure. All selected studies performed k-fold cross-validation to evaluate model accuracy. Individual prediction models were mostly employed and ensemble models appeared only in three studies. Naive Bayes was the most investigated among individual models, whereas Random forest was the most investigated among ensemble models.Conclusion: Feature selection techniques used by selected primary studies have a positive impact on the performance of the prediction models. Further, both ensemble feature selection method and ensemble models have the ability for increasing prediction accuracy over single methods or individual models and have reported improvement in the prediction accuracy; however, the application of these techniques in software quality prediction is still limited.
A comprehensive scheme for lithological mapping using Sentinel-2A and ASTER GDEM in weathered and vegetated coastal zone, Southern China

Abstract Lithological mapping in weathered and vegetated coastal zone is always a crucial but challenging issue for geological remote sensing. This study tested the applicability of Sentinel-2A and ASTER GDEM data for lithological mapping in the coastal zone of Shanwei district, southern China. Here, a comprehensive scheme for tuff and granite outcrops extraction and lithological classification was employed to process the remote sensing data: 1) Using the matched filtering with image-derived spectra as references, the weathered but representative outcrop-related patches were preliminarily extracted based on the fractal “DN-A” (digital number-area) algorithm; 2) The image masks of NDXI (NDVI, NDSI and MNDWI) in conjunction with spatial intersection, were conducted to minimize the influence of vegetation coverage; 3) Considering terrain features caused by differential weathering, slope analysis have allowed us to discriminate from tuff to granite outcrops; 4) A set of empirical verification criteria were established to assess the classification accuracy, which are exactly 82.4~83.2% of tuff and 91.3~92.3% of granite. The peak values of tuff and granite patch areas are 0–0.1 km 2 (51%) and 0–0.5 km 2 (60%), and 90% of patches are &lt; 0.6 km 2 of tuff and &lt; 3.5 km 2 of granite, respectively. The protolith of extracted weathered outcrops were further identified by several excavated trenches. This research has contributed a useful case study for remote-sensing lithological mapping in weathered and vegetated coastal zone, and the proposed approach should have a great possibility to be extrapolated for other similar cases.
Investigation of Image Edge Detection Techniques Based Flood Monitoring in Real-time

Nowadays, flooding is one kind of hazard that usually occurred all over the world, which brings enormous damage to human life and property. In order to decrease the harm of flooding, the image edge detection techniques have been used to monitoring the state of flood area in the real-time in this paper. The processing time of the method is a key point that should be considered, which related to the timeliness of the flood monitoring system. And the accuracy of the predicted flood area is also the standard that can be used for evaluation. The experimental results have been affected by the changing of the parameters of each algorithm and the method that has the highest accuracy and lowest processing time have been expected one. According to the results, Canny edge detection has the best performance, which has been considered for implementation on the real hardware in the future for the outdoor environment.