Reviewerly/exHarmony huggingface dataset:
Exploring West African Folk Narrative Texts Using Machine Learning

This paper examines how machine learning (ML) and natural language processing (NLP) can be used to identify, analyze, and generate West African folk tales. Two corpora of West African and Western European folk tales are compiled and used in three experiments on cross-cultural folk tale analysis. In the text generation experiment, two types of deep learning text generators are built and trained on the West African corpus. We show that although the texts range between semantic and syntactic coherence, each of them contains West African features. The second experiment further examines the distinction between the West African and Western European folk tales by comparing the performance of an LSTM (acc. 0.79) with a BoW classifier (acc. 0.93), indicating that the two corpora can be clearly distinguished in terms of vocabulary. An interactive t-SNE visualization of a hybrid classifier (acc. 0.85) highlights the culture-specific words for both. The third experiment describes an ML analysis of narrative structures. Classifiers trained on parts of folk tales according to the three-act structure are quite capable of distinguishing these parts (acc. 0.78). Common n-grams extracted from these parts not only underline cross-cultural distinctions in narrative structures, but also show the overlap between verbal and written West African narratives.
Survey on Scene Classification techniques

Scene classification is emerging research areas in the field of computer vision, as it can be used in several applications such as surveillance, autonomous driving, robotics, and many more. Scene classification is to classify the scene as one of the categories predefined as a kitchen, coast, forest, living room, etc. This paper highlights the prevailing scene classification practices by summarizing the major categories of the scene classification available in the literature. Traditionally to classify the scene, researchers used local features such as Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF) or the global features like GIST from the image and classify using supervised learning method like Support Vector Machine (SVM). Nowadays deep learning based approaches are widely used as the approaches do not have to manually extract the features and can learn automatically. This paper discusses various traditional and deep learning based approaches for indoor and outdoor scene classification with their challenges. We also present analysis of state-of-the-art methods with their advantages and disadvantages for scene classification.
YOLO-based Threat Object Detection in X-ray Images

Manual detection of threat objects in an X-ray machine is a tedious task for the baggage inspectors in airports, train stations, and establishments. Objects inside the baggage seen by the X-ray machine are commonly occluded and difficult to recognize when rotated. Because of this, there is a high chance of missed detection, particularly during rush hour. As a solution, this paper presents a You Only Look Once (YOLO)based object detector for the automated detection of threat objects in an X-ray image. The study compared the performance between using transfer learning and training from scratch in an IEDXray dataset which composed of scanned Xray images of improvised explosive device (IED) replicas. The results of this research indicate that training YOLO from scratch beats transfer learning in quick detection of threat objects. Training from scratch achieved a mean average precision (mAP) of 45.89% in 416×416 image, 51.48% in 608×608 image, and 52.40% in a multi-scale image. On the other hand, using transfer learning achieved only an mAP of 29.54% while 29.17% mAP in a multi-scale image.
SAFETY RISK EVALUATIONS OF DEEP FOUNDATION CONSTRUCTION SCHEMES BASED ON IMBALANCED DATA SETS

Safety risk evaluations of deep foundation construction schemes are important to ensure safety. However, the amount of knowledge on these evaluations is large, and the historical data of deep foundation engineering is imbalanced. Some adverse factors influence the quality and efficiency of evaluations using traditional manual evaluation tools. Machine learning guarantees the quality of imbalanced data classifications. In this study, three strategies are proposed to improve the classification accuracy of imbalanced data sets. First, data set information redundancy is reduced using a binary particle swarm optimization algorithm. Then, a classification algorithm is modified using an Adaboost-enhanced support vector machine classifier. Finally, a new classification evaluation standard, namely, the area under the ROC curve, is adopted to ensure the classifier to be impartial to the minority. A transverse comparison experiment using multiple classification algorithms shows that the proposed integrated classification algorithm can overcome difficulties associated with correctly classifying minority samples in imbalanced data sets. The algorithm can also improve construction safety management evaluations, relieve the pressure from the lack of experienced experts accompanying rapid infrastructure construction, and facilitate knowledge reuse in the field of architecture, engineering, and construction.
Visual Imputation Analytics for Missing Time-Series Data in Bayesian Network

Bayesian network is derived from conditional probability and is useful in inferring the next state of the currently observed variables. If data are missed or corrupted during data collection or transfer, the characteristics of the original data may be distorted and biased. Therefore, predicted values from the Bayesian network designed with incomplete data are not reliable. Various techniques have been studied to resolve the imperfection in data using statistical techniques or machine learning, but since the complete data is unknown, there is no optimal way to impute missing values. In this paper, we present a visual analytics system that supports decision-making to impute missing values occurring in incomplete time series data. The visual analytics system allows data analysts to explore the cause of missing data in incomplete datasets. The system also enables us to compare the performance of a suitable imputation model with the Bayesian network accuracy and the Kolmogorov-Smirnov test. We evaluate how the visual analytics system supports the decision-making process for the data imputation through a use case.
Determinants of Cybercrime Originating within a Nation: A Cross-country Study

8Cybercrimes have an adverse impact on the reputation and economy of a nation. This paper investigates the factors that affect the frequency of cybercrime originating within a country. These factors were grouped into three categories, namely, economic capital, technological capital, and cybersecurity preparedness. On analyzing the data from 124 countries, it emerges that the economic capital and technological capital of a country are the primary factors that influence the frequency of cybercrime originating within it. Technological capital also partially mediates the relationship between economic capital and the frequency of cybercrime originating within the nation. Furthermore, the cybersecurity preparedness of a nation negatively moderates the relationship between technological capital and frequency of cybercrime originating within it. The findings have significant implications for policymakers at the national level and managers at the organizational level concerning cybersecurity preparedness. They should focus on both hard (legal, technical, organizational) and soft (training and co-operational) aspects of cybersecurity preparation to minimize the incidence of cybercrime within a nation.
Feature Selection for Unsupervised Bot Detection

In recent years, botnet becomes one of the biggest challenges that online social networks are facing. Due to the sudden increase in the botnet, the detection of bots become very crucial. Twitter is one of the most used platforms, targeted by the bots. In this research, we use clustering methods for the bot detection. In clustering, selection, of features are very challenging because some features are important for clustering, while others may hinder the clustering task. In our paper, we focus on the selection of features that distinguish bot users. After features selection, we use two clustering algorithms dbscan and k-mean for bots detection. We conclude that dbscan performs better on followers-count, friends-count, favorite-count, listed-count, retweet-count, reply-count, hashtag-count, mentions-count, by achieving 97.7% accuracy, 91% precision, 98% recall, and 94%, f-measure.
Investigation of the iCC Framework Performance for Solving Constrained LSGO Problems

Many modern real-valued optimization tasks use “black-box” (BB) models for evaluating objective functions and they are high-dimensional and constrained. Using common classifications, we can identify them as constrained large-scale global optimization (cLSGO) tasks. Today, the IEEE Congress of Evolutionary Computation provides a special session and several benchmarks for LSGO. At the same time, cLSGO problems are not well studied yet. The majority of modern optimization techniques demonstrate insufficient performance when confronted with cLSGO tasks. The effectiveness of evolution algorithms (EAs) in solving constrained low-dimensional optimization problems has been proven in many scientific papers and studies. Moreover, the cooperative coevolution (CC) framework has been successfully applied for EA used to solve LSGO problems. In this paper, a new approach for solving cLSGO has been proposed. This approach is based on CC and a method that increases the size of groups of variables at the decomposition stage (iCC) when solving cLSGO tasks. A new algorithm has been proposed, which combined the success-history based parameter adaptation for differential evolution (SHADE) optimizer, iCC, and the ε-constrained method (namely ε-iCC-SHADE). We investigated the performance of the ε-iCC-SHADE and compared it with the previously proposed ε-CC-SHADE algorithm on scalable problems from the IEEE CEC 2017 Competition on constrained real-parameter optimization.
Gaming Algorithmic Hate-Speech Detection: Stakes, Parties, and Moves

A recent strand of research considers how algorithmic systems are gamed in everyday encounters. We add to this literature with a study that uses the game metaphor to examine a project where different organizations came together to create and deploy a machine learning model to detect hate speech from political candidates’ social media messages during the Finnish 2017 municipal election. Using interviews and forum discussions as our primary research material, we illustrate how the unfolding game is played out on different levels in a multi-stakeholder situation, what roles different participants have in the game, and how strategies of gaming the model revolve around controlling the information available to it. We discuss strategies that different stakeholders planned or used to resist the model, and show how the game is not only played against the model itself, but also with those who have created it and those who oppose it. Our findings illustrate that while “gaming the system” is an important part of gaming with algorithms, these games have other levels where humans play against each other, rather than against technology. We also draw attention to how deploying a hate-speech detection algorithm can be understood as an effort to not only detect but also preempt unwanted behavior.
Pilot Communication Protocols for Group of Mobile Robots in USAR Scenarios

This paper describes process of development of communication protocol for group of mobile robots in Urban Search and Rescue (USAR) operations based on wireless technology and Robot Operating System framework. During this work cases of mobile robot's usage in USAR as well as modern worldwide researches in this area were studied and it was found out that one of the main issues with mobile robot usage in such situation is communication problem. Disasters typically destroy communication infrastructure and rescuers forced to use workarounds to be able to still use robots. Based on these studies goal for the paper has been set - to develop communication protocol that allows robots to communicate and exchange data with minimal human interference. To achieve this goal multiple types of communication technologies were analyzed and Bluetooth was chosen based on qualities most suited for this task. After that list of commands needed for USAR was combined and three of them (Follow, Stop, MoveAround) were chosen to be implemented during this research. Testing environment was created in simulator Gazebo with three TurtleBot robots to verify execution of developed command. Finally, commands and command handler were implemented and tested on robots in created testing environment by using trial scenarios.
Spaces for Participatory Design Innovation

This paper presents a model to guide the design of participatory design (PD) projects, which was generated through methodological reflection on a programme of digital health and care research. Building on wide use of the term spaces within PD literature and theory, the model comprises seven spaces that can be designed to support diverse stakeholders to engage in a PD process. The model encourages reflection on the capacity of participants to critically and creatively engage with the concepts being proposed, in order to design a process to scaffold participation. Aiming to support PD practitioners and researchers to identify the combination and sequence of hybrid spaces required to move participants and concepts towards resolved designs, the model guides the careful design of each space supported by examples of practice. The model is also designed to enable reflective practice and articulation of PD processes to support integration within multidisciplinary collaborations.
A Novel Framework using Apache Spark for Privacy Preservation of Healthcare Big Data

The privacy of sensitive data in healthcare big data should be protected so that it does not intrude the privacy of specific patient or transactions of health care organizations. Privacy protection is a major concern in big data, therefore, demanding resilient approaches for the safeguarding of customer privacy. The proposed novel framework spark employs K-anonymization and L-diversity to mask the personal sensitive information and Apache Spark to handle health care big data in a faster and effective way. Thus proposed approach make sure that shared data will not disclose the original data and segregation of sensitive data happens before it is a move to HDFS.
Recent Audio Steganography Trails and its Quality Measures

The term Steganography refers to the science that deals with hiding the secret messages (text or anything else) inside an original cover file of the text, audio, video or image. The need for modern methods is to keep the secret information and protected it with the increasing danger of outside hackers. Steganography seems to be a good option to increase information security by hiding them. This paper is a review of the past year's methods used in audio steganography. Audio steganography is a method to hide a secret message (sound or anything else) in an audio cover file, in varied ways. It is a proposal for future actions. These methods used different types of digital audio such as WAV, MP3 or AU. The result showed that most of the studies used LSB to embed secret messages, but with other encryption-system for strength LSB family. Others Modified LSB to produce an enhanced method. In general, most studies concentrate on common methods or their combination to develop new efficient methods. Most of the studies had a lack in all or some measures of invisibility and robustness. This concept came after authors' interesting in cryptography more than steganography. This study recommended maintaining all measures to enable future studies to determine which steganography method is most effective.
Efficient and Fast Traffic Congestion Classification Based on Video Dynamics and Deep Residual Network

Real-time implementation and robustness against illumination variation are two essential issues for traffic congestion classification systems, which are still challenging issues. This paper proposes an efficient automated system for traffic congestion classification based on compact image representation and deep residual networks. Specifically, the proposed system comprises three steps: video dynamics extraction, feature extraction, and classification. In the first step, we propose two approaches for modeling the dynamics of each video and produce a compact representation. In the first approach, we aggregate the optical flow in front direction, while in the second approach, we use a temporal pooling method to generate a dynamic image describing the input video. In the second step, we use a deep residual neural network to extract texture features from the compact representation of each video. In the third step, we build a classification model to discriminate between the classes of traffic congestion (low, medium, or high). We use the UCSD and NU1 traffic congestion datasets to assess the performance of the proposed method. The two datasets contain different illumination and shadow variations. The proposed method gives excellent results compared to state-of-the-art methods. It also can classify the input video in a short time (37 fps), and thus, we can use it with real-time applications.
Hybrid Security Framework for Blockchain Platforms

Computer and network security in the most interesting and challenging field in Computer Science. In Cyberspace, no one can guarantee the complete security of any computer system as technology is evolving day by day. Blockchain is an emerging technology and it is still evolving. The probability of a higher security threat rate has been seen in the case of immature technology. However, mature technologies are also not completely secure. One successful attack could cost millions of dollars to the victim organization and damage their public and financial reputation badly. A transaction can be performed anonymously in Blockchain. In many past reported security breaches, attackers are still unknown and untraceable. In this research article, researchers are proposing a more secure and reliable hybrid security framework for Blockchain platforms that can be used as a generalized reference model to counter various security threats in Blockchain platforms.The proposed hybrid security framework can be applied to secure any kind of Blockchain platform. This research could influence future research in the direction of security of Blockchain 1.0, Blockchain 2.0 and beyond.
Momentum methods for stochastic optimization over time-varying directed networks

Recently, decentralized optimization (DO) has received widespread attentions. Since the scale of DO is usually very large, research on acceleration has become a hotspot. However, the error caused by the global information deficiency for each agent becomes the key problem cumbering the existing elaborately designed accelerated methods from being applied to DO, directly. On the other hand, recent studies illustrate that a group of accelerated methods can be covered from a viewpoint of momentum. In this paper, we propose to follow this methodology to design accelerated algorithms and adapt them to DO over time-varying directed networks, of which the main benefit is that, because the proposed algorithms are derived from momentum, they not only avoid the design of elaborate iterative structures but also inherit the physical interpretability of momentum. Furthermore, we show that our proposed algorithms can achieve sharper convergence rates than their competitors under the same condition. In the end, experiments on a number of benchmark datasets validate well the competitiveness of our algorithms.
Discriminative Correlation Filter for Long-Time Tracking

Abstract Object tracking is a very important step in building an intelligent video monitoring system that can protect people’s lives and property. In recent years, although visual tracking has made great progress in terms of speed and accuracy, there are still few real-time high-precision tracking algorithms. Although discriminative correlation filters have excellent performance in tracking speed, there are deficiencies in handling fast motion. This leads to the inability to achieve long-term stable tracking results. The long-time tracking with discriminative correlation filter (LT-DCF) was proposed to solve these deficiencies. We use larger size detection image blocks and smaller size filters to increase the proportion of real samples to solve the boundary effects of fast motion. And we combine the histogram of oriented gradient (HOG) feature detection and scale-invariant feature transform (SIFT) key point detection to solve the obstacles caused by scale variations. The detector with deep feature flow is then incorporated into the tracker to detect key frames to improve tracking accuracy. This method has achieved more than 75% of the distance accuracy and 70% of the overlapping success rate on the VOT2015 and VOT2016 datasets, and the stable tracking video length can reach 6895 frames.
Support Vector Machine based Word Embedding and Feature Reduction for Sentiment Analysis-A Study

Sentiment analysis (SA), also called as opinion mining is the technique used to bring together the opinions of a specific entity or feature from reviews dataset. The opinions of other users help in performing the decision making process. This paper studies different methods that are aimed at performing sentiment analysis. These approaches vary from semantic based methods, machine learning, neural networks, and syntactical methods with each having its own strength. Although hybrid approach also exists, the main idea is to combine the strengths of two or more methods to increase the accuracy. A framework in which sentiment analysis is done by using the proposed word embedding and feature reduction techniques. Word embedding is a technique in which low-dimensional vector representation of words is provided. Feature reduction method employs a support vector machine (SVM) classifier. The framework will perform sentiment analysis of user opinions by using a machine learning approach and provides a recommendation system for the ease of decision making to users.