Reviewerly/exHarmony huggingface dataset:
A Comparison of Open Source Web Crawlers for E-Commerce Websites

Web crawlers are important tools for retrieving data such as text or image from the internet. It is an automated program that can retrieve information from the websites. E-commerce websites are essential areas for crawler applications. Moreover, large-scale crawling involves many problems on the internet. Poor performing web crawlers can waste many resources for development and maintenance. Thus, choosing a suitable open source crawler becomes a huge challenge. This paper attempts to review the published previous studies on open source crawlers. The paper focuses on summarizing the performance evaluation methods of open source web crawlers, possible research trends and related research gaps. In addition, a proposed framework of the open source crawler evaluation was presented.
Location-based Privacy-preserving Techniques in Connected Environment: A Survey

With the development of networking technologies, the amount of the connected device has been dramatically increasing over the years. Privacy issues gradually have become a widely accepted obstacle that restrict many web-based applications. One of the major concerns in privacy-preserving is to protect individuals' location privacy. This survey investigates location-based privacy protection technologies and gathered a few representative recent studies addressing this issue. An analysis has been given in this survey in order to assess the existing solutions (e.g., advantages and disadvantages) and highlight potential research directions for future explorations.
Mobile Physiological Sensor Cloud System for Long-term Care

This world will soon enter its ageing and aged era. Many old people will be there on the earth. However, traditional long-term care (LTC) models, particularly for taking care of elderly, consume a huge amount of resources which have been invested and can be invested in current stage are terribly insufficient. To solve a part of this problem, in this study, we propose a Mobile Physiological Sensor Cloud System for Long-term Care (MPCLC), the function of which is collecting care-receiver's physiological data by using sensors. With the features of small in size, high convenience for use, and good immediacy of request response, it can partially solve the problems of LTC which are that the required labors are insufficient and the results of some traditional medical treatments are hard to track. After the data measured by using sensors are collected, the data are sent to the cloud for storage. Then the health reports will be calculated by the cloud, and delivered to care-givers and medical staffs for care reference under their requests. This can save the time that people go and come between hospitals and the places where care-receivers stay. One of the other key functions is to achieve the goal of preventing disease in advance.
Preference-Based Image Generation

Deep generative models are a set of promising methods, that are able to model complex data and generate new samples. In principle, they learn to map a random latent code sampled from a prior distribution into a high dimensional data space, such as image space. However, these models have limited utilities as the user has minimal control over what the network produces. Despite the success of some recent work in learning an interpretable latent code, the field still lacks a coherent framework to learn a fully interpretable latent code, without any random part for sample diversity. Consequently, it is generally hard, if not impossible, for a non-expert user to produce a desired image by tuning the random and interpretable parts of the latent code. In this paper, we introduce the Preference-Based Image Generation (PbIG), a new method to retrieve the corresponding latent code of the user's mental image. We propose to adopt preference-based reinforcement learning, which learns from a user's judgment of the generated images by a pre-trained generative model. Since the proposed method is completely decoupled from the training stage of the underlying generative models, it can easily be adopted by any method, such as GANs and VAEs. We evaluate the effectiveness of PbIG framework using a set of experiments on baseline datasets using a pretraind StackGAN++.
ReliveReality: Enabling Socially Reliving Experiences in Virtual Reality via a Single RGB camera

We present a new experience sharing method, ReliveReality, which transforms traditional photo/video memories into 3D reconstructed memories and allows users to relive past experiences through VR socially. ReliveReality utilizes deep learning-based computer vision techniques to reconstruct people in clothing, estimate multi-person 3D pose and reconstruct 3D environments with only a single RGB camera. Integrating with a networked multi-user VR environment, ReliveReality enables people to ‘enter’ into a past experience, move around and relive a memory from different perspectives in VR together. We discuss the technical implementation and implications of such techniques for privacy
Web Application Development using MVC-component-based approach

Component-Based Software Engineering (CBSE) is the concept of software development using existing components. However, the components themselves have many forms in the software. Some studies suggest that components are executable. In building an application, components are needed in different forms so that it can be used not only in an application but can also be used on a website. This study aims to produce another form of a component in the Model-View-Controller (MVC) based software model which can later be implemented in website development. In this case, the case is taken in a vehicle testing system application. The component model itself uses the CBSE approach by utilizing several UML diagrams in its design. First, use usecase to show the pattern of user interaction with the system being built. Second, activity diagrams to illustrate the flow of work activities that occur between actors and systems. Third, component diagrams to illustrate how the relationships between components. The model produced in this study is a component in a different form that can be placed in a library in web programming using the PHP language and in accordance with the CBSE concept.
Web Service Recommendation System using History and Quality of Service

Web service recommendation system is an alternative to achieve a better service discovery. Web service recommendation system using history has relatively low acceptance rate but web service recommendation system using quality of service can predict QoS values well. Therefore, this work proposes a solution to build a web service recommendation system using history and QoS. The built system has high accuracy, but the accuracy decreases as the system receives other inaccurate recommendation factors. Although the accuracy is able to decrease, the system has considered more recommendation factors. Therefore, this method can improve the accuracy of using history as recommendation factor by fusing it with another factors. As currently most web service recommendation systems use only one factor, proposed method can also be considered as a way to build a web service recommendation system using more than one factor.
Reconstruction of volcanic structures: examples from Kazakhstan and Egypt

Abstract The existing hypotheses and paradigms about the structure of the Earth’s crust and its parts, where igneous rocks are present, do not always show facial connections of rocks and explain the reasons for their formation, as well as the genetic connection of ore deposits. These issues can be solved with the help of paleovolcanic reconstruction of the territory. This paper explains the stages of formation of paleovolcanic structre in aid for their reconstruction. Such structures represent the main units of territories with existence of igneous rocks. Examples of reconstruction of certain territories of Kazakhstan and Egypt are given. These structures are tensely related to large metallic ore deposits of Mo, Cu, Zn, Au, Ag, Ni, Co, etc. These structures have various ages of formation and degree of development. Paleovolcanic structure has many similar features to recent well-preserved structures. Our model reset on the lithostatigraphic study of contributing units around oldest complexes and their geochronological sequence, spatial distribution and pattern fault systems and dykes. These researches have not only theoretical interest for science, but also practical. Reconstruction of such structures allows to allocate the most prospective areas for search the overlapped endogenous mineralization (porphyry copper-gold, copper-sulphide, poly-metallic deposits, etc.).
Deep Super-Resolution Imaging Technology: Toward Optical Super-Vision

Spatially variant optical blur is inevitable in real optical imaging systems with imperfect camera lenses since each object has a different depth in a real scene. Unfortunately, existing super-resolution imaging techniques may not achieve satisfactory performance on images acquired from practical optical imaging systems, where the optical blur is unknown. This is because a predefined global point spread function is adopted for every spatial coordinate without any optical blur estimation in the existing models. To address the challenging issue in recent video technologies, in this article, we propose an optimal super-resolution imaging network based on object-adaptive optical point spread function estimation. In technical, a new object sharpness consistency cost is suggested in the dark channel domain to estimate the point spread function, which is optimal to the super-resolution imaging. In addition, an object image sharpening network is proposed and adaptively combined with the point spread function estimation. The performance of the proposed network is also validated via experiments in terms of various quantitative metrics such as the peak signal-to-noise ratio, S3 index, and BRISQUE values.
Take a Look Around – The Impact of Decoupling Gaze and Travel-direction in Seated and Ground-based Virtual Reality Utilizing Torso-directed Steering

Leaning (the upper body) has several times been shown to be a suitable virtual travel technique when being seated; in both, flying as well as ground-based scenarios. The direction of the steering method most commonly used is gaze/head-directed. However, this does not allow to inspect the environment independently from the direction of movement. The change to torso-directed steering allows for the latter and additionally does not take anything from the natural character of the leaning metaphor. We empirically investigated the impact of this freedom in a ground-based scenario and complemented the conditions with a virtual body-directed method and then crossed all with device-based control conditions. In the conducted study (n = 25), we found the torso-directed methods objectively performed the best (traveled distance, completion time & number of collisions), and found torso-directed leaning subjectively rated the most usable one.
An efficient and scalable SPARK preprocessing methodology for Genome Wide Association Studies

The importance of the use of high-performance software frameworks to analyze omics data obtained by using High-Throughput (HT) essays is widely recognized. HT methodologies comprise microarrays, Genome-Wide Association Studies (GWAS), and Next Generation Sequencing (NGS), which provide a vast amount of data per a single experiment. Each HT vendor provides to the users only the software frameworks and the proprietary libraries for the annotation, and summarization of raw data. Consequently, the needs of algorithms for the preprocessing and analysis of omics data arise. GWAS aims to highlight the association between genetic variants and diseases by examining single nucleotide polymorphisms (SNPs), which differ in a statistically significant way between cases and controls. The effectiveness of GWAS analysis increases with the number of analyzed samples per single experiment. GWAS data analyzed through the use of statistical methods can detect associations among a single allelic variant and the clinical conditions of samples. To overcome these limitations, and to make it possible to discover multiple associations among allelic variants, it is possible to use Association Rules mining. Consequently, the need for the introduction of scalable Association Rule Mining (ARM) algorithms able to analyze GWAS data arises. Hence, the use of high-performance data analytics framework is needed. For this purpose, we propose a software framework called GARMS (GWAS Association Rule Mining in Spark) built on top of Apache Spark for the preprocessing, and mining of association rules from GWAS data sets. GARMS comprises a two steps analysis methodology: (i) in the first step, the GWAS data are preprocessed, along with the identification of the frequent itemsets; (ii) in the second step, frequent itemsets are employed to mine association rules without scanning the input data. We implemented our algorithm, and we tested it on some synthetic GWAS data sets. Preliminary results confirm that our method may extract relevant association rules from GWAS data reducing the computational time.
Experiencing and Navigating Virtual Reality without Sight (The all-seeING ears)

Navigating a public space or building can be challenging without sight. To facilitate understanding for the hazards visually impaired people face, the authors created a virtual reality environment modelled after the Ruhr-University’s ID building. The task in the simulation is simply to find your way through the building to a certain desired office. The virtual reality vision is darkened, so that all participants are effectively blinded. The participants can utilize various audio-based aids, like an AI agent to follow, a collision warning system, a semantic laser pointer and the haptic virtual white cane.
Multi-Authority Attribute-Based Encryption for Resource-Constrained Users in Edge Computing

Multi-authority attribute-based encryption (MA-ABE) is a promising technique to protect data privacy and achieve fine-grained access control in edge computing for Internet of Things (IoT). However, most of the existing MA-ABE schemes suffer from expensive computational cost in the encryption and decryption phases, which are not practical for resource constrained users in IoT. We propose a large-universe MA-CP-ABE scheme with online/offline encryption and outsourced decryption. In our scheme, most expensive encryption operations have been executed in the user's initialization phase by adding reusable ciphertext pool besides splitting the encryption algorithm to online encryption and offline encryption. Moreover, massive decryption operation are outsourced to the near edge server for reducing the computation overhead of decryption. The proposed scheme is proven statically secure under the q-DPBDHE2 assumption. The performance analysis results indicate that the proposed scheme is efficient and suitable for resource-constrained users in edge computing for IoT.
Do You Speak Holo? A Mixed Reality Application for Foreign Language Learning in Children with Language Disorders

In this paper we present Do You Speak Holo?, an application for Microsoft HoloLens co-designed with linguistics experts that aims to facilitate the learning process of a foreign language for children and adolescents with language disorders (LD). The application includes several educational activities, aimed at improving both vocabulary and morpho-syntactic aspects of the language through the interaction with virtual content immersed in the real world. The application includes a virtual assistant, which acts as a virtual teacher and guides the user step by step in the comprehension and execution of the activities.
A Comparison of Visual Attention Guiding Approaches for 360° Image-Based VR Tours

Mechanisms for guiding a user’s visual attention to a particular point of interest play a crucial role in areas such as collaborative VR and AR, cinematic VR, and automated or live guided tour experiences in xR-based education. The attention guiding mechanism serves as a communication tool that helps users find entities currently not visible in their view, referenced for instance by another user or in some accompanying audio commentary. We report on a user study in which we compared three different visual guiding mechanisms (arrow, butterfly guide, and radar) in the context of 360° image-based educational VR tour applications of real-world sites. A fourth condition with no guidance tool available was added as a baseline. We investigate the question: How do the different approaches compare in terms of target finding performance and participants’ assessments of the experiences. While all three mechanisms were perceived as improvements over the no-guidance condition and resulted in significantly improved target finding times, the arrow mechanism stands out as the most generally accepted and favored approach, whereas the other two (butterfly guide and radar) received a more polarized assessment due to their specific strengths and drawbacks.
CZ Investigator: Learning About Critical Zones Through a VR Serious Game

The critical zone (CZ) plays a pivotal role in the food-energy-water nexus, yet in its entirety, it is not well understood by society. Challenges range from imagining the invisible (from bedrock to soil) to understanding complex relations between the involved components. We have launched a transdisciplinary project driven by immersive technologies that allow for an extension of what physical reality offers society about the concept of CZ. We have developed a VR serious game that enables learners to have a concrete experience about what a CZ is, and how natural and human processes affect it.
A note on conditional expanders over prime fields

Let Fp be a prime field of order p, and A be a set in Fp with |A|≤p1∕2. In this note, we show that max{|A+A|,|f(A,A)|}≳|A|65+4305,where f(x,y) is a non-degenerate quadratic polynomial in Fp[x,y]. This improves a recent result given by Koh, Mojarrad, Pham, Valculescu (2018).
Develop of reconfigurable manufacturing plant

Abstract A scientific problem is how to develop automatically self-reconfigurable manufacturing plant. Self-reconfigurable manufacturing plant is a set of detailed descriptions in the level of plant models reconfigurable manufacturing systems (RMS) and RMS connection structure, which is sufficient to develop technically a plant. Primary components are described, which are necessary to create specialized system of a self-reconfigurable manufacturing plant automatic developing, which is done in the software. There is a functional scheme of manufacturing process in automatic mode for a device in a plant. There is a functional scheme of the self-reconfigurable manufacturing plant automatic developing route equipped with RMS.
Image Collection Summarization Method Based on Semantic Hierarchies

The size of internet image collections is increasing drastically. As a result, new techniques are required to facilitate users in browsing, navigation, and summarization of these large volume collections. Image collection summarization methods present users with a set of exemplar images as the most representative ones from the initial image collection. In this study, an image collection summarization technique was introduced according to semantic hierarchies among them. In the proposed approach, images were mapped to the nodes of a pre-defined domain ontology. In this way, a semantic hierarchical classifier was used, which finally mapped images to different nodes of the ontology. We made a compromise between the degree of freedom of the classifier and the goodness of the summarization method. The summarization was done using a group of high-level features that provided a semantic measurement of information in images. Experimental outcomes indicated that the introduced image collection summarization method outperformed the recent techniques for the summarization of image collections.
Quantum Secure Multiparty Computation with Symmetric Boolean Functions*

We propose a class of n -variable Boolean functions which can be used to implement quantum secure multiparty computation. We also give an implementation of a special quantum secure multiparty computation protocol. An advantage of our protocol is that only 1 qubit is needed to compute the n -tuple pairwise and function, which is more efficient comparing with previous protocols. We demonstrate our protocol on the IBM quantum cloud platform, with a probability of correct output as high as 94.63%. Therefore, our protocol presents a promising generalization in realization of various secure multipartite quantum tasks.
Constructing a Heterogeneous Training Dataset for Emotion Classification

Emotion classification deals with identifying emotions expressed within a text. Social media is generating a vast amount of emotion rich data in the form of tweets, status updates, blog posts etc. Tweets are a good representative of emotions a person usually expresses publicly. By analyzing the emotions in these tweets, one can get an idea of how a person feels about the subject they are referring to. Machine Learning (ML) techniques are widely used for analyzing emotions within the tweets. However, there are no balanced training datasets that can be used for training the ML classifiers. As a result, supervised classifiers demonstrate a poor performance with classifying emotions within texts particularly within the tweets. In addition to that, none of the available datasets are useful for training the classifiers to identify and classify emotions within the tweets. Therefore, in this paper we have proposed a novel approach for constructing a balanced heterogeneous training dataset for emotion classification of the tweets. Using the lexicon-based NRC classifier we have classified the textual instances in to four different emotions such as joyful, sad, angry and surprise. Using this as a training dataset we have trained six different machine learning models including the Multiclass Logistic Regression (MLR), Multinomial Naïve Bayes (MLB), Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). Our study reveals that this approach has the potentiality in boosting the performance of the supervised classifiers for emotion classification within the tweets.
An Adaptive Neural Network for Email Spam Classification

In this paper, we propose an adaptive learning rate algorithm for gradient descent method used in Artificial Neural Network (ANN). The proposed adaptive learning rate neural network algorithm makes use of an initial assigned learning rate and after a set of backpropagation rounds the learning rate gets revised to a learning rate which is equal to minimum learning rate plus average of minimum learning rate and maximum learning rate (these parameters are updated in each recursive calls). During the next recursive call of the algorithm, the revised learning rate is used, if the average total error decreases, or the previous learning rate is continued. This is done until the desired learning rate is achieved which results in optimal weight parameters with high classification accuracy. We have compared the proposed algorithms with existing heuristics approaches and found that the convergence rate is to be faster and the proposed algorithm returns the optimized weights with higher classification accuracy. We have also tested the effectiveness of the algorithm on spam email classification using multilayer neural networks and found that it performs better than the existing approach and less prone to overfitting problems. The proposed algorithm results in 99.12% accuracy for the email classification.