Reviewerly/exHarmony huggingface dataset:
Analysis of Node.js Application Performance Using MongoDB Drivers

At the last few years, the usage of NoSQL databases has increased, and consequently, the need for integrating with different programming languages. In that way, database drivers provide an API to perform database operations, which may impact on the performance of applications. In this article, we present a comparative study between two main drivers solutions to MongoDB in Node.js, through the evaluation of CRUD tests based on quantitative metrics (time execution, memory consumption, and CPU usage). Our results show which, under quantitative analysis, the MongoClient driver has presented a better performance than Mongoose driver in the considered scenarios, which may imply as the best alternative in the development of Node.js applications.
A Teacher-Student Learning Based Born-Again Training Approach to Improving Scene Text Detection Accuracy

With the recent success of convolutional neural network (CNN) based text detection approaches, designing better CNN-based text detection frameworks has become a major research focus to improve text detection accuracy. In this paper, instead of following this direction, we propose to use a born-again training strategy, which is based on teacher-student learning (TSL), to improve the accuracy of the state-of-the-art CNN-based text detectors. More specifically, given a well-trained CNN-based text detector, we take it as a teacher model and train from scratch a new student model with the same topology under the supervision of both the teacher model and ground-truth labels. Furthermore, we propose a new proposal-free multi-level feature mimicking approach to making multi-level convolutional feature maps be effectively mimicked in a unified manner. Experiments demonstrate that the student models trained by the proposed approach can achieve substantially better results than their teacher models and have better generalization abilities.
“Love Is as Complex as Math”: Metaphor Generation System for Social Chatbot

As the wide adoption of intelligent chatbot in human daily life, user demands for such systems evolve from basic task-solving conversations to more casual and friend-like communication. To meet the user needs and build emotional bond with users, it is essential for social chatbots to incorporate more human-like and advanced linguistic features. In this paper, the usage of a commonly used rhetorical device – metaphor – is investigated for social chatbot. Our work first designs a metaphor generation framework, which generates topic-aware and novel figurative sentences. Human annotators validate the novelty and properness of the generated metaphors. More importantly, we evaluate the effects of employing metaphors in human-chatbot conversations. Experiments indicate that our system effectively arouses user interests in communicating with our chatbot, resulting in significantly longer human-chatbot conversations.
An Enhancement to the Spatial Pyramid Matching for Image Classification and Retrieval

Spatial pyramid matching (SPM) is one of the widely used methods to incorporate spatial information into the image representation. Despite its effectiveness, the traditional SPM is not rotation invariant. A rotation invariant SPM has been proposed in the literature but it has many limitations regarding the effectiveness. In this paper, we investigate how to make SPM robust to rotation by addressing those limitations. In an SPM framework, an image is divided into an increasing number of partitions at different pyramid levels. In this paper, our main focus is on how to partition images in such a way that the resulting structure can deal with image-level rotations. To do that, we investigate three concentric ring partitioning schemes. Apart from image partitioning, another important component of the SPM framework is a weight function. To apportion the contribution of each pyramid level to the final matching between two images, the weight function is needed. In this paper, we propose a new weight function which is suitable for the rotation-invariant SPM structure. Experiments based on image classification and retrieval are performed on five image databases. The detailed result analysis shows that we are successful in enhancing the effectiveness of SPM for image classification and retrieval.
Testing Big Data Application

Today big data has become the basis of discussion for the organizations. The big task associated with big data stream is coping with its various challenges and performing the appropriate testing for the optimal analysis of the data which may benefit the processing of various activities, especially from a business perspective. Big data term follows the massive volume of data, (might be in units of petabytes or exabytes) exceeding the processing and analytical capacity of the conventional systems and thereby raising the need for analyzing and testing the big data before applications can be put into use. Testing such huge data coming from the various number of sources like the internet, smartphones, audios, videos, media, etc. is a challenge itself. The most favourable solution to test big data follows the automated/programmed approach. This paper outlines the big data characteristics, and various challenges associated with it followed by the approach, strategy, and proposed framework for testing big data applications.
Autonomous mobile soccer robot localization using particle filter through Omni-vision

"Where am I?" is the primary question, which is the representation of localization, that the mobile soccer robot must be answered. Dead reckoning is the most popular method used in a wheeled mobile robot. However, the increasing position error is the main topic of the dead reckoning method. Furthermore, in this paper, the localization of a mobile soccer robot using a particle filter through Omnivision is proposed. The sensor model and motion model of particle filter are also discussed, where the sensor model is obtained from segmentation and feature extraction of the soccer field landmark. The experimental result showed that the proposed method estimated the robot position accurately with 15% error.
Hand Gesture-Based On-Line Programming of Industrial Robot Manipulators

Robots are widely used in industrial manufacturing processes and play an important role in the enhancement of industrial organizations productivity. One of the major issues that engineers are facing is that, current programming methods are too time-consuming and they lack of intuitiveness use by human users. However, the latest advances in the field of sensors, let manufacturers to develop and produce devices that allow humans to interact with machines in a more intuitive way, reducing the need of additional complex software components, and hence, the required time to establish the aforementioned human-machine interactions. This research work presents an approach for gesture-based on-line programming of industrial robot manipulators. This is achieved by utilizing a combination of devices with a set of integrated, cost-effective visual and bending sensors, in order to precisely track the user's hand position and gestures at system run-time. This continuous tracking allows the robot manipulator to mimic the operator's hand motion. In addition, desired paths performed by a human with expertise on task execution, are translated into robot targets, composing a new robot path, and are stored for later use. Such path can be modified to fit into different robot manufacturers, programming language. Further steps of the presented approach will include the possibility of path optimization by the industrial manipulator itself.
Lecture as an Event in Postdigital Education: a Dyadic Teaching Diary

Abstract In this experimental text, we reflect in the form of a dyadic teaching diary upon a course we recently taught together on the ‘sociological imagination’. One of the main aims we set for the class was to ponder how the social scientific imagination could be enriched, and figure out ways to stress its social and political relevance. From the pedagogical point of view, we wanted to create a course which would disrupt the students’ oft-criticized routine of rote-learning, and in which they would learn to use digital platforms for the purpose of producing and sharing knowledge, meet each other, and have their social scientific thinking refreshed and perhaps even renewed. In pondering the weeks spent together with our students, we suggest that what gradually happened in our ‘postdigital classroom’ and outside of it was an Event—something surprising and unexpected that could not be controlled entirely.
UR: A User-Based Collaborative Filtering Recommendation System Based on Trust Mechanism and Time Weighting

The technology of personalized recommendation is aimed at studying the behaviors of users, analyzing what they may be interested in and recommending suitable items to them. In other words, the personalized recommendation is to better solve the contradiction between the requirements of users and the explosive information on the Internet. The userbased collaborative filtering recommendation is one of the most successful technology for recommendation system. The most significant step of user-based collaborative filtering recommendation is comprehensive user similarity calculation. However, most recommendation systems ignore the indispensability of trust mechanism of the users and the time weighted users rating attributes in user similarity calculation, which leads to the inaccurate recommendation. Based on these issues, this paper proposes an optimized user-based collaborative filtering recommendation systemcalled UR. UR not only validates the necessity of the trust mechanism of the users and the time weighted users rating in the comprehensive user similarity calculation, but also improves the recommendation accuracy
Resource Scheduling Algorithms for Cloud Computing Environment: A Literature Survey

Nowadays, resource scheduling in cloud environment is a challenging task as the number of customers increases for utilizing the cloud services. In this cloud environment, allocation of suitable resources to the corresponding VM depends on the QoS requirement of the specified applications. Researchers have developed so many resource scheduling algorithms. However, the service providers in cloud environment still find it difficult to choose the appropriate algorithm for their applications. This is due to the heterogeneity of resource types, interdependencies, uncertainty and dispersion of assets in the cloud environment. This paper reviews all the available load balancing algorithms in a nutshell.
Using Arabic Tweets to Understand Drug Selling Behaviors

Twitter is a popular platform for e-commerce in the Arab region—including the sale of illegal goods and services. Social media platforms present multiple opportunities to mine information about behaviors pertaining to both illicit and pharmaceutical drugs and likewise to legal prescription drugs sold without a prescription, i.e., illegally. Recognized as a public health risk, the sale and use of illegal drugs, counterfeit versions of legal drugs, and legal drugs sold without a prescription constitute a widespread problem that is reflected in and facilitated by social media. Twitter provides a crucial resource for monitoring legal and illegal drug sales in order to support the larger goal of finding ways to protect patient safety. We collected our dataset using Arabic keywords. We then categorized the data using four machine learning classifiers. Based on a comparison of the respective results, we assessed the accuracy of each classifier in predicting two important considerations in analysing the extent to which drugs are available on social media: references to drugs for sale and the legality/illegality of the drugs thus advertised. For predicting tweets selling drugs, Support Vector Machine, yielded the highest accuracy rate (96%), whereas for predicting the legality of the advertised drugs, the Naïve Bayes, classifier yielded the highest accuracy rate (85%).
Developing Cloud-Based Intelligent Touch Behavioral Authentication on Mobile Phones

Mobile devices especially mobile phones have become a major target for cyber-criminals, there is a great need to design appropriate authentication mechanisms to protect users’ private information. The majority of smartphones offers a touchscreen where users can perform various touch actions. Thus, touch behavioral authentication is considered to be an important way to complement the existing textual passwords. Most behavioral schemes usually adopt machine learning techniques to profile users’ touch behaviors; however, machine learning-based schemes suffer from unstable performance, which would greatly reduce the system usability, i.e., causing a high false rejection. In this chapter, we advocate the effectiveness of intelligent touch behavioral authentication, and propose a cloud-based scheme to further reduce the workload by offloading both classifier selection and behavioral modelling to a cloud. Our experimental results demonstrated that our scheme can greatly reduce the required workload to complete user authentication.
Recognition of Surrounding Environment for Electric Wheelchair Based on WideSeg

At present, the aging population is growing in Japan. Along with that, the expectation for the utilization of welfare equipment is increasing. Electric wheelchair, a convenient transportation tool, is popularized rapidly. On the other hand, accidents have occurred, and the dangers for driving are pointed out. Therefore, it needs to improve accident factors, reduce accidents and improve the convenience of electric wheelchair by automation. Environmental recognition is necessary for the development of autonomous electric wheelchair. Environmental recognition includes self-position estimation, recognition of sidewalks, crosswalks and traffic lights, moving object prediction, etc. In order to solve these various problems, this paper examines the segmentation of sidewalks, crosswalks and traffic lights. We develop the WideSeg that is one of semantic segmentation algorithms applying convolutional neural networks (CNN).
Analysis of Intended Viewing Area vs Estimated Saliency on Narrative Plot Structures in VR Film

In cinematic virtual reality film one of the primary challenges from a storytelling perceptive is that of leading the attention of the viewers to ensure that the narrative is understood as desired. Methods from traditional cinema have been applied to varying levels of success. This paper explores the use of a saliency convolutional neural network model and measures it's results against the intending viewing area as denoted by the creators and the ground truth as to where the viewers actually looked. This information could then be used to further increase the effectiveness of a director's ability to focus attention in cinematic VR.
Handwritten Words and Digits Recognition using Deep Learning Based Bag of Features Framework

Unconstrained handwriting text recognition is a stimulating field in the branch of pattern recognition. This field is still an open search due to the wide variability of human writing. Recent trends show a potential improvement of recognition by adoption a novel representation of extracted features. In the present paper, we propose a novel feature extraction model by learning a Bag of Features Framework for handwritten text recognition based on Deep Sparse Auto-Encoder. The Hidden Markov Models are then used for sequences modeling. For features learned quality evaluation, our proposed system was tested on two handwritten text datasets IFN/ENIT word images benchmark and MNIST handwritten digits. Our method achieves promising recognition on both datasets.
The KHC Seismic Station: The Birthplace of Broadband Seismology

Abstract Nearly half a century has passed since the first broadband seismometer was installed by A. Plešinger and his team. The principles behind this instrument, as well as its features and new possibilities regarding broadband data mining, have been reported within the scientific literature. This text is intended as a commemoration of this pioneering work and also serves as a historical reminder of the social and political circumstances surrounding the time period of broadband seismometer introduction that are not mentioned in original works but that played an important role during broadband seismometer development.
Comovement and Instability in Cryptocurrency Markets

We analyze the extent of comovement between daily price returns of nine major cryptocurrenciesfrom April 2013 to February 2021. We assess its evolution using bivariate and multivariate modelling approaches, and detect pronounced time variation. Generally, comovement is initially low and positive, but increases substantially between early 2017 and late 2018 and stays high until the end of the sample. We then adopt a right-tail version of the Augmented Dickey-Fuller unit root test to identify periods of mildly explosive behavior (statistical instability) in the Network Value to Transactions (NVT) ratio of seven cryptocurrencies. The NVT ratio is a novel measure of the dollar value of cryptocurrency transaction activity relative to network value. We show evidence of significant mild explosiveness in all seven cryptocurrencies. In 2018 and in the second half of 2019, several cryptocurrencies experience often simultaneous instability. Instability is a dominant feature of these markets.
RGB-to-TSDF: Direct TSDF Prediction from a Single RGB Image for Dense 3D Reconstruction

In this paper, we present a novel method to predict 3D TSDF voxels from a single image for dense 3D reconstruction. 3D reconstruction with RGB images has two inherent problems: scale ambiguity and sparse reconstruction. With the advent of deep learning, depth prediction from a single RGB image has addressed these problems. However, as the predicted depth is typically noisy, de-noising methods such as TSDF fusion should be adapted for the accurate scene reconstruction. To integrate the two-step processing of depth prediction and TSDF generation, we design an RGB-to-TSDF network to directly predict 3D TSDF voxels from a single RGB image. The TSDF using our network can be generated more efficiently in terms of time and accuracy than the TSDF converted from depth prediction. We also use the predicted TSDF for a more accurate and robust camera pose estimation to complete scene reconstruction. The global TSDF is updated from TSDF prediction and pose estimation, and thus dense isosurface can be extracted. In the experiments, we evaluate our TSDF prediction and camera pose estimation results against the conventional method.
Harmonious Sampling for Mobile Manipulation Planning

Mobile manipulation planning commonly adopts a decoupled approach that performs planning separately on the base and the manipulator. While this approach is fast, it can generate sub-optimal paths. Another direction is a coupled approach jointly adjusting the base and manipulator in a high-dimensional configuration space. This coupled approach addresses sub-optimality and incompleteness of the decoupled approach, but has not been widely used due to its excessive computational overhead. Given this trade-off space, we present a simple, yet effective mobile manipulation sampling method, harmonious sampling, to perform the coupled approach mainly in difficult regions, where we need to simultaneously maneuver the base and the manipulator. Our method identifies such difficult regions through a low-dimensional base space by utilizing a reachability map given the target end-effector pose and narrow passage detected by generalized Voronoi diagram. For the rest of simple regions, we perform sampling mainly on the base configurations with a predefined joint configuration, accelerating the planning process. We compare our method with the decoupled and coupled approaches in six different problems with varying difficulty. Our method shows meaningful improvements experimentally in terms of time to find an initial solution (up to 5.6 times faster) and final solution cost (up to 17% lower) over the decoupled approach, especially in difficult scenes with narrow space. We also demonstrate these benefits with a real, mobile Hubo robot.
Gaussian Mixture Model (GMM) Based Object Detection and Tracking using Dynamic Patch Estimation

In this paper, we have developed a Gaussian Mixture Model (GMM) based algorithm with dynamic patch estimation for real-time detection and tracking of a known object. This research work detects the object of interest, estimates its 3-D position using Extended Kalman Filter (EKF) and generates the control output to the quad-rotor to track the target. The proposed algorithm is capable of tracking the object with a high Frame Per Second (FPS). Rigorous experiments are carried out to demonstrate the efficacy of the proposed approach in outdoor environment.
Toward Achieving Formal Guarantees for Human-Aware Controllers in Human-Robot Interactions

With the primary objective of human-robot interaction being to support humans' goals, there exists a need to formally synthesize robot controllers that can provide the desired service. Synthesis techniques have the benefit of providing formal guarantees for specification satisfaction. There is potential to apply these techniques for devising robot controllers whose specifications are coupled with human needs. This paper explores the use of formal methods to construct human-aware robot controllers to support the productivity requirements of humans. We tackle these types of scenarios via human workload-informed models and reactive synthesis. This strategy allows us to synthesize controllers that fulfill formal specifications that are expressed as linear temporal logic formulas. We present a case study in which we reason about a work delivery and pickup task such that the robot increases worker productivity, but not stress induced by high work backlog. We demonstrate our controller using the Toyota HSR, a mobile manipulator robot. The results demonstrate the realization of a robust robot controller that is guaranteed to properly reason and react in collaborative tasks with human partners.
Urdu-Text: A Dataset and Benchmark for Urdu Text Detection and Recognition in Natural Scenes

Multi-lingual text in natural scene images conveys useful information and is a fundamental tool for tourists to interact with their environment. Multi-lingual text detection and recognition in natural scenes, therefore, has become a challenging problem for researchers in the last few years. Recently, a large-scale multi-lingual dataset for scene text detection and script identification is published by the ICDAR which, contains scene images with text in six different scripts including Arabic. This paper presents a novel dataset and benchmark for Urdu text in natural scenes. Currently, no dataset for Urdu text in natural scenes is publicly available. Urdu is a type of cursive language, which is derived from Arabic script and uses many similar alphabet characters. Therefore, the proposed dataset could be helpful for multi-lingual text detection, recognition and script identification. The aim of this dataset is to help the research community for algorithm development and evaluation of Urdu text in natural scenes. The Urdu-Text dataset contains 1400 complete scene images and 8200-segmented words. The images in the dataset contain a broad variety of text instances in multi-orientations with small and large font sizes. The dataset contains ground truths in the form of bounding boxes at the word level, the script of the text and the text-transcription. The performance of three deep neural networks is evaluated to measure the robustness of the Urdu-Text dataset.