Reviewerly/exHarmony huggingface dataset:
Towards the Discovery of Influencers to Follow in Micro-Blogs (Twitter) by Detecting Topics in Posted Messages (Tweets)

Micro-blogs, such as Twitter, have become important tools to share opinions and information among users. Messages concerning any topic are daily posted. A message posted by a given user reaches all the users that decided to follow her/him. Some users post many messages, because they aim at being recognized as influencers, typically on specific topics. How a user can discover influencers concerned with her/his interest? Micro-blog apps and web sites lack a functionality to recommend users with influencers, on the basis of the content of posted messages. In this paper, we envision such a scenario and we identify the problem that constitutes the basic brick for developing a recommender of (possibly influencer) users: training a classification model by exploiting messages labeled with topical classes, so as this model can be used to classify unlabeled messages, to let the hidden topic they talk about emerge. Specifically, the paper reports the investigation activity we performed to demonstrate the suitability of our idea. To perform the investigation, we developed an investigation framework that exploits various patterns for extracting features from within messages (labeled with topical classes) in conjunction with the mostly-used classifiers for text classification problems. By means of the investigation framework, we were able to perform a large pool of experiments, that allowed us to evaluate all the combinations of feature patterns with classifiers. By means of a cost-benefit function called “Suitability”, that combines accuracy with execution time, we were able to demonstrate that a technique for discovering topics from within messages suitable for the application context is available.
Correction: Garcia-Gonzalez, D.; Rivero, D.; Fernandez-Blanco, E.; Luaces, M.R. A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors. Sensors 2020, 20, 2200

The authors wish to make the following corrections to this paper [...].
OSMWatchman: Learning How to Detect Vandalized Contributions in OSM Using a Random Forest Classifier

Though Volunteered Geographic Information (VGI) has the advantage of providing free open spatial data, it is prone to vandalism, which may heavily decrease the quality of these data. Therefore, detecting vandalism in VGI may constitute a first way of assessing the data in order to improve their quality. This article explores the ability of supervised machine learning approaches to detect vandalism in OpenStreetMap (OSM) in an automated way. For this purpose, our work includes the construction of a corpus of vandalism data, given that no OSM vandalism corpus is available so far. Then, we investigate the ability of random forest methods to detect vandalism on the created corpus. Experimental results show that random forest classifiers perform well in detecting vandalism in the same geographical regions that were used for training the model and has more issues with vandalism detection in “unfamiliar regions”.
Feature Extraction of Human Motion Video Based on Virtual Reality Technology

In virtual reality scenes, the premise of moving target recognition in video is accurate target segmentation and extraction of target low-level features. In order to distinguish moving targets, it is not enough to use the underlying features. Further extraction of structural features that reflect the target structure can improve the recognition and tracking of moving targets. In order to ensure the stability of the feature areas extracted from the three-channel most stable extremum region, this article proposes an improved algorithm of the three-channel most stable extremum region to improve the three-channel most stable extremum region. The algorithm can adaptively select the filters of each channel to filter the feature regions extracted from the three most stable extreme value regions. An action cycle is generally 30~50 frames, so it is faster and more advantageous to directly use the first 50 frames of video for processing. In this article, two feature representation methods of variance gait energy graph algorithm and image splitting algorithm are proposed. The variance energy graph algorithm significantly improves the recognition rate; image splitting enhances the robustness of behavior classification. This article proposes a feature representation algorithm of “distance from contour line to center line” to improve the recognition rate. By analyzing the feature extraction methods of principal component analysis, Fisher linear discriminant analysis and maximum divergence difference discriminant analysis, the main component discriminant analysis of row maximum divergence discriminant column and the two-dimensional two-dimensional maximum divergence discriminant analysis of row and column are proposed. This further enhances the ability to classify behaviors.
Twitter Sentiment Analysis using Aspect-based Bidirectional Gated Recurrent Unit with Self-Attention Mechanism

Nowadays, social media sites and applications such as Twitter, YouTube, Facebook and blogs gained much attention, because organizations use this huge information to monitor the user opinions.Therefore, the researchers focused on sentiment analysis to help organizations for better production.But, the existing studies concentrated only on document-level in the analysis, where the details of the sentiments are not considered.In order to perform a finegrained analysis, this research study uses the aspect-based sentiment analysis on Twitter data for finding the opinions of users.In general, a raw tweet contains stop words, Uniform Resource Locator (URL), emoji that are reduced in the stage of pre-processing.The polarities of pre-processed tweets are identified, then two important feature extraction techniques are used to extract the useful information.The specific sentiments with various aspects of services are extracted by using aspect-based feature extraction, which are given as an input for Bi-directional Gated Recurrent Unit (BGRU) for classification.The learning rate of BGRU is further improved by incorporating the self-attention layers.To test the efficiency of the proposed method, the experiments are conducted on real-time collected tweets in terms of accuracy, precision, recall and f-score.The results showed that the developed method achieved 80.4% accuracy, 81% precision, 80% of recall and f-score for 1000 dataset length when compared with traditional techniques: Long-short term Memory (LSTM) and standard GRU.
DA-Net: Pedestrian Detection Using Dense Connected Block and Attention Modules

Pedestrian detection plays an important role in some areas such as autonomous driving, but due to heavy occlusion and various scales, it is still challenging.In this article, we propose an improved pedestrian detection method called DA-Net based on the two-stage detector Feature Pyramid Network (FPN).DA-Net adds Dense Connected Block (DCB), a combination of channel-wise attention module (CWAM) and global attention module (GAM) to the network.FPN can produce features with various scales and semantic information, which is good for the detection of pedestrians on various scales.Due to many small-scale targets in pedestrian detection, we only regard the low layers with enough details of targets in FPN as prediction layers.After several DCBs to deepen the network, prediction layers in our network can encode richer semantic information of targets, which can make the location of a target more precisely.In order to highlight visible parts of occluded pedestrians and ignore occluded parts, CWAM weights each channel of features with different importance.GAM aggregates global information and long-range dependencies for small-scale and occluded targets.Thus, the combination of CWAM and GAM is not only beneficial for coping with occlusion problem in pedestrian detection, but also for gaining environmental information for small-scale targets.Evaluation results on CUHK and CityPersons datasets show that our proposed method achieves improved performance with log-average miss rate reduction of 9.6% on the CUHK dataset and 6.1% on the Heavy subset of CityPersons dataset compared with FPN.
Fuzzy Multi Criteria Decision Analysis Method for Assessing Security Design Tactics for Web Applications

Security and design tactics estimation of web application for ensuring the security, efficiency and design tactics of web applications is necessary.A survey conducted by the security research team, Micro Focus, of the USA reveals that 80% of the vulnerability defects occur due to the coding defect, validation causes 60% of the errors, 70% errors are due to encapsulation and path traversal.Such statistics call for a more efficacious design to enhance software security.The primary research goal of this study is to compute or evaluate the security threats of the software and web applications from the perspective of design tactics.Towards this intent, we have employed the methodology of Fuzzy Analytic Hierarchy Process (F-AHP) to evaluate the security factors or obtain the weight of different factors.The different design tactics of web application have also been selected according to the factors that affect the security.In this article, researchers have used a crossbreed technique of fuzzy based Multi Criteria Decision Method (MCDM) technique, i.e., F-AHP and Fuzzy Technique for Order of Preferences by Similarity to Ideal Solutions (F-TOPSIS) Technique.The results of the assessment of security will be helpful for developers or experts in designing the security tactics of software or web applications.We have also compared the results of classical and Fuzzy approach to determine the weight of alternatives or attributes and rank of the factors.This process is an effective and conclusive methodology for the developers working for more enhanced secure design tactics of software and web application design.
Generation of Schrödinger-cat states through photon-assisted Landau-Zener-Stückelberg interferometry

Schr\"odinger-cat states are useful for many applications, ranging from quantum-information processing to high-precision measurements. In this paper we propose a method for creating such cat states, based on photon-assisted Landau-Zener-St\"uckelberg interferometry in a hybrid system consisting of a qubit coupled to a photon cavity. We show that by initializing the qubit in one of its basis states, performing three consecutive sweeps of the qubit energy splitting across the 1-photon resonance, and finally projecting the qubit to the same basis state, the parity of the photon field can be purified to very high degree; when the initial photon state is a coherent state, the final state will then be very close to a Schr\"odinger-cat state. We present numerical simulations that confirm that our protocol could work with high fidelity ($\ensuremath{\sim}0.99$) for coherent states of reasonable size (${|\ensuremath{\alpha}|}^{2}\ensuremath{\sim}10$). Furthermore, we suggest that our protocol can also be used to transfer quantum information between the qubit and a superposition of orthogonal cat states in the cavity.
Cyber Bullying Detection and Classification using Multinomial Naïve Bayes and Fuzzy Logic

The advent of different social networking sites has enabled people to easily connect all over the world and share their interests.However, Social Networking Sites are providing opportunities for cyber bullying activities that poses significant threat to physical and mental health of the victims.Social media platforms like Facebook, Twitter, Instagram etc. are vulnerable to cyber bullying and incidents like these are very common now-a-days.A large number of victims may be saved from the impacts of cyber bullying if it can be detected and the criminals are identified.In this work, a machine learning based approach is proposed to detect cyber bullying activities from social network data.Multinomial Naïve Bayes classifier is used to classify the type of bullying.With training, the algorithm classifies cyber bullying as-Shaming, Sexual harassment and Racism.Experimental results show that the accuracy of the classifier for considered data set is 88.76%.Fuzzy rule sets are designed as well to specify the strength of different types of bullying.
Mining Software Repositories for Revision Age-Based Co-Change Probability Prediction

Changeability is an important aspect of software maintenance and helps in better planning of development and testing resources. Early detection of change-prone entities is beneficial in terms of both time and money and helps to estimate and meet deadlines reliably. Co-change prediction identifies the affected entities when implementing a change in the software system. Recent researches recommend the use of revision history for the identification of co-changed artifacts. However, very few studies are available for investigation of the effect of history size and age on prediction results. This manuscript studies the effect of age of change history on co-change prediction results in software applications by varying the weightage of change commits with time. ROC analysis is done to study the accuracy of the proposed approach, and the results indicate that the older change commits have lower significance in deriving the changeability pattern. The derived change impact set will be useful for software practitioners in change implementation and selective regression testing.
ContextBased MicroTraining: A Framework for Information Security Training

This paper address the emergent need for training measures designed to improve user behavior in regards to security. We do this by proposing a framework for information security training that has been developed for several years and over several projects. The result is the framework ContextBased MicroTraining (CBMT) which provides goals and guidelines for how to better implement information security training that supports the user in the situation where the user needs support. CBMT has been developed and tested for use in higher education as well as for the support of users during passwords creation. This paper presents version 1.0 of the framework with the latest refinements.
Movement Trajectory Recognition of Sign Language Based on Optimized Dynamic Time Warping

Movement trajectory recognition is the key link of sign language (SL) translation research, which directly affects the accuracy of SL translation results. A new method is proposed for the accurate recognition of movement trajectory. First, the gesture motion information collected should be converted into a fixed coordinate system by the coordinate transformation. The SL movement trajectory is reconstructed using the adaptive Simpson algorithm to maintain the originality and integrity of the trajectory. The algorithm is then extended to multidimensional time series by using Mahalanobis distance (MD). The activation function of generalized linear regression (GLR) is modified to optimize the dynamic time warping (DTW) algorithm, which ensures that the local shape characteristics are considered for the global amplitude characteristics and avoids the problem of abnormal matching in the process of trajectory recognition. Finally, the similarity measure method is used to calculate the distance between two warped trajectories, to judge whether they are classified to the same category. Experimental results show that this method is effective for the recognition of SL movement trajectory, and the accuracy of trajectory recognition is 86.25%. The difference ratio between the inter-class features and intra-class features of the movement trajectory is 20, and the generalization ability of the algorithm can be effectively improved.
Sentiment analysis of unstructured customer feedback for a retail bank

With the explosive growth of the Internet and social media, the communication model between an organisation and its customers has become increasingly complex. A problem arises due to the sheer volume of unstructured data that has to be processed for the purposes of studying and addressing customer feedback. This calls for the development of automated methods. Important objectives of such methods include the detection of the underlying sentiment of customer feedback, as well as the synthesis and presentation of this sentiment in meaningful clusters such as topics and geographical locations. In this paper, a case study is conducted in which unstructured customer reviews related to products and services of a South African retail bank are evaluated by means of sentiment analysis. After suitable preprocessing techniques are applied to the reviews, the process of developing suitable models (primarily within the realm of machine learning) for detecting sentiment with a high level of performance is described. Subsequently, model results are analysed, synthesised and visualised in order to extract valuable insight from the data. The findings of the study show that custom learning-based models significantly outperform both pre-trained and commercial tools in sentiment classification. Furthermore, the analysis approach is shown to yield actionable information that may inform decision making.
The Effectiveness of Interactive Visualization Techniques for Time Navigation of Dynamic Graphs on Large Displays

Dynamic networks can be challenging to analyze visually, especially if they span a large time range during which new nodes and edges can appear and disappear.Although it is straightforward to provide interfaces for visualization that represent multiple states of the network (i.e., multiple timeslices) either simultaneously (e.g., through small multiples) or interactively (e.g., through interactive animation), these interfaces might not support tasks in which disjoint timeslices need to be compared.Since these tasks are key for understanding the dynamic aspects of the network, understanding which interactive visualizations best support these tasks is important.We present the results of a series of laboratory experiments comparing two traditional approaches (small multiples and interactive animation), with a more recent approach based on interactive timeslicing.The tasks were performed on a large display through a touch interface.Participants completed 24 trials of three tasks with all techniques.The results show that interactive timeslicing brings benefit when comparing distant points in time, but less benefits when analyzing contiguous intervals of time.
Quantum dynamics under simultaneous and continuous measurement of noncommutative observables

We consider simultaneous and continuous measurement of two noncommutative observables of the system whose commutator is not necessarily a $c$-number. We revisit the Arthurs-Kelly model and generalize it to describe the simultaneous measurement of two arbitrary observables of the system. Using this generalized model, we continuously measure the system by following the scheme proposed by Scott and Milburn [Scott and Milburn, Phys. Rev. A 63, 042101 (2001)]. We find that the unconditioned master equation reduces to the Lindblad form in the continuous limit. In addition, we find that the master equation does not contain a cross term of these two measurements. Finally, we propose a scheme to prepare the state of a two-level system in an external field by feedback control based on the simultaneous, continuous measurement of the two observables.
The Usage of Books Containing Augmented Reality Technology in Preschool Education

The article analyses the importance of augmented reality technology in preschool education. It discloses augmented reality functions, digital tools and smart devices that supplement Lithuanian language books for preschool children, and the situation of their application in the process of preschool education. The results of the analyses show that books with augmented reality technology are perfectly suitable for developing the skills of cognition of the environment, exploration, perception, and expression of emotions and artistic expression. In addition, children learn to use software and other different digital tools. The results of the study of educators’ insights suggest that there is a whole range of ways to use books with augmented reality technology. The usage of books containing augmented reality technology provides opportunities to enrich the traditional educational methods and to make them more effective. These books supplement the educational process and encourage both - educators and children to develop the following skills: cognition of the environment, the concentration of attention, training of memory, enrichment of vocabulary, learning to read, creativity, learning sounds, ability to use software. According to the information provided by foreign researchers the most commonly used technologies in education are: mobile applications; augmented reality based on the motionsensing program; picture books with augmented reality technology; augmented reality game systems. The usage of books containing augmented reality for education opens up new learning opportunities.
Skeleton data pre-processing for human pose recognition using Neural Network

Automatic monitoring of daily living activities can greatly improve the possibility of living autonomously for frail individuals. Pose recognition based on skeleton tracking data is promising for identifying dangerous situations and trigger external intervention or other alarms, while avoiding privacy issues and the need for patient compliance. Here we present the benefits of pre-processing Kinect-recorded skeleton data to limit the several errors produced by the system when the subject is not in ideal tracking conditions. The accuracy of our two hidden layers MLP classifier improved from about 82% to over 92% in recognizing actors in four different poses: standing, sitting, lying and dangerous sitting.
Road Object Detection using Yolov3 and Kitti Dataset

Road objects (such as pedestrians and vehicles) detection is a very important step to enhance road safety and achieve autonomous driving.Many on-vehicle sensors, such as radars, lidars and ultrasonic sensors, are used to detect surrounding objects.However, cameras are widely used sensors for road objects detection for the rich information they provide and their inexpensive prices with compared to other sensors.Machine learning and computer vision algorithms are utilized to classify objects in the collected images and videos.There are many computer vision algorithms proposed for image and video object detection, e.g.logistic regression and SVM with feature extraction.However, Convolutional Neural Network (CNN) algorithms showed a high detection accuracy compared to other approaches.This research implements You Only Look Once (YOLO) algorithm that uses Draknet-53 CNN to detect four classes: pedestrians, vehicles, trucks and cyclists.The model is trained using Kitti images dataset which is collected from public roads using vehicle's front looking camera.The algorithm is tested, and detection results are presented.